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PMC11696889 | This was a double‐blind, cross‐over study comparing Biotène and HydraSmile among patients with radiation‐induced xerostomia. Patients were recruited from UPMC's Head and Neck Cancer Survivorship Clinic between January 2021 and September 2022. Adult patients with subjective complaints of xerostomia were selected if they were previously diagnosed with squamous cell carcinoma (oral cavity, oropharynx, or larynx) and treated with radiotherapy (between 50 and 70 gray) at least 6 months prior to randomization. Exclusion criteria included any treatment for cancer in the last 6 months (Including surgery, radiation, and chemotherapy), recurrence of cancer, those with other medical conditions associated with xerostomia such as Sjogren's Syndrome, and those using pilocarpine or anticholinergic drugs. A project member explained the study to each participant, who read and signed an informed consent form. See Figure 1 for CONSORT flow diagram. This study was approved by the University of Pittsburgh Institution Review Board. An authorized third party repackaged the 2 products into opaque bottles labeled “A” or “B.” The contents of each type of spray bottle was not revealed to the research team or the study participants to preserve blinding. At the conclusion of the study, the research team was given the key revealing that Biotène was in bottle A and HydraSmile was in bottle B. Each patient was provided with both oral hydrating sprays (A and B) for use at home. The study was divided into 4 periods: 1 week with only water and no salivary substitute (washout period 1), followed by 2 weeks using one of the provided mouth sprays (mouth spray period 1), followed by 1 week with only water and no salivary substitute (washout period 2), followed by 2 weeks using the other provided mouth spray (mouth spray period 2). Study period length was adapted from previous randomized trials evaluating exogenous xerostomia products. 10 , 12 Computer‐generated simple randomization was used to assign patients to 1 of 2 groups: Patients were not permitted to use any other products to treat xerostomia, including chewing gum, hard candy, and lozenges for the entirety of the study. During washout weeks, participants were only permitted to use water for xerostomia relief. During mouth spray periods, participants were instructed to only use the sprays provided and water for xerostomia relief. Participants were permitted to use mouth sprays up to 4 times a day and 4 sprays with each use. Patients were sent a unique link via email to complete an online questionnaire at the end of each of the 4 study periods. The questionnaire included continuous variables derived from the 100 mm visual analog scale (VAS), the gold standard of symptomatic xerostomia evaluation. 10 , 11 , 12 , 13 , 14 Higher scores indicate better symptomatic control. At the end of the study, patients were asked “Overall, do you prefer mouth spray A, mouth spray B, or neither mouth spray?”. This analysis aimed to compare the relative treatment effect of HydraSmile versus Biotène, as well as evaluate each product's individual benefit compared to water. The primary outcome was change in overall xerostomia score with respect to baseline. The secondary outcomes were change in daytime xerostomia, sleep, speech, swallowing, and taste. This study followed a modified intention‐to‐treat design. Patients were required to report which product they used (product A or B) during each mouth spray period in the online questionnaire. During the analysis phase, participants who inadvertently used the products in the wrong order, were reassigned to the appropriate study group based on the protocol they completed. Assuming 1 − β = 0.9 and α = 0.05, a sample size of n = 96 was required to demonstrate a 5‐point change in 100 VAS score. Allowing for a dropout rate of approximately 10%, we aimed to recruit 110 patients. All statistical analyses were performed using STATA SE 17.0 for Mac OS. Descriptive statistics, including proportions, means, and standard deviations (SD), were used to compare demographic and clinical features between treatment groups. The primary and secondary outcomes were reported xerostomia scores derived from the 100‐mm VAS. Washout period scores were used as the baseline comparison for the mouth spray period that directly followed. Carryover effect was tested by unpaired t ‐test of the sum of outcomes after both treatments, with sequence as the grouping variable. 15 Period effect was tested by unpaired t ‐test of the difference in outcomes between Biotène and HydraSmile after both treatments, with sequence as the grouping variable. To evaluate the treatment effect of Biotène and HydraSmile, we used paired t ‐test to compare the outcome after treatment compared to the corresponding baseline measurements. To investigate the treatment effect of HydraSmile versus Biotène, we followed the recent recommendation for analysis of 2*2 cross‐over trials with 2 baseline measurements by Metcalfe and Mehrotra and implemented the analysis of covariance (ANCOVA) model to regress the difference in after‐treatment measurement between HydraSmile and Biotène over the difference of baseline between HydraSmile and Biotène. 16 , 17 The intercept term would be the treatment effect of HydraSmile compared to Biotène. In the exit survey, patients indicated which mouth spray (Biotène or HydraSmile) they preferred. A planned subgroup analysis was completed within each preference group to determine the effect of each mouth spray and the difference between them. Secondary end points were not adjusted for multiplicity, and therefore should be interpreted as exploratory hypothesis generating data. A total of 129 patients were enrolled in the study, of which 38 withdrew. Five patients withdrew after experiencing an adverse effect from HydraSmile (oral burning sensation and/or subjective lingual/labial swelling), 11 patients reported no longer having the capacity to participate due to social or medical factors, and 22 patients were lost to follow‐up. No patients experienced anaphylaxis from either product. The remaining 91 participants completed all intervention activities and were included in the final analysis (mean age 63.0 years [SD 9.7]; 85.7% male [n = 78]; 97.8% White [n = 89]). Thirteen patients who were randomized to Group 2 (Product B [HydraSmile], followed by Product A [Biotène]) inadvertently completed the Group 1 protocol (Product A, followed by Product B). These patients were re‐assigned to Group 1 per our modified intention‐to‐treat study design. The final analysis included 61 patients in Group 1 and 30 patients in Group 2 . The majority of patients were previously irradiated for cancer of the oropharynx (72.5% [n = 66]), while a smaller proportion were treated for cancer of the oral cavity (16.5% [n = 15]) or larynx (11% [n = 10]). The mean radiation dose received was 64.9gy (SD 6.2). See Table 1 for summary of demographics and clinical characteristics. Following the protocol outlined in the methods section, we found that there was no significant carryover effect or period effect for any of the 6 parameters evaluated ( Supplemental Table S1, available online ). At the conclusion of the study, there was no difference in overall treatment effect between HydraSmile and Biotène, with respect to baseline . Both products, however, were individually effective when compared to use of water alone. Participants achieved clinically significant improvements in overall xerostomia score with use of HydraSmile (mean difference 7.45, 95% CI 3.61‐11.29) and Biotène . Across our 5 secondary outcomes, the treatment effects of HydraSmile and Biotène were not statistically distinguishable . In comparison to use of water alone, participants using HydraSmile achieved statistically significant improvement in VAS score for daytime xerostomia (mean difference 4.52, 95% CI 0.96‐8.08) and clinically significant improvement in VAS score for swallow (mean difference 5.80, 95% CI 1.90‐9.70). With the use of Biotène, participants achieved clinically significant improvement in daytime xerostomia (mean difference 7.37, 95% CI 3.12‐11.63), sleep (mean difference 10.53, 95% CI 6.40‐14.66), speech (mean difference 8.73, 95% CI 4.25‐13.21), and swallow (mean difference 8.96, 95% CI 4.42‐13.50). Neither product allowed for improvement in taste compared to water . In our exit survey, 44% (n = 40) of patients reported a preference for Biotène, 50.5% (n = 46) preferred HydraSmile, and 5.5% (n = 5) had no preference. A subgroup analysis was conducted, in which participants were stratified by product preference. Within the Biotène preference cohort (n = 40), Biotène significantly improved overall xerostomia score (mean difference 9.80, 95% CI 3.66‐15.94, P = .003), while HydraSmile did not . There was no difference in treatment effect between HydraSmile and Biotène with respect to baseline . Within the HydraSmile preference cohort (n = 46), HydraSmile significantly improved overall xerostomia score (mean difference 9.37, 95% CI 4.09‐14.65, P < .001), while Biotène did not . HydraSmile displayed a greater improvement in overall xerostomia score, with respect to baseline, compared to Biotène . In this study, we found that Biotène and HydraSmile effectively improved symptoms of radiation‐induced xerostomia. While the treatment effects of Biotène and HydraSmile did not significantly differ, our exploratory analysis suggests that Biotène may provide a more comprehensive coverage of the subdomains evaluated. Ultimately, patient preference appeared to be the most important factor in predicting the effectiveness of a given product. Patients who preferred Biotène did not significantly benefit from HydraSmile, whereas those who preferred HydraSmile did not significantly benefit from Biotène. These data emphasize that patients with radiation‐induced xerostomia should be provided with multiple artificial saliva options to determine which works best for them. Multiple studies have explored the ways in which radiation‐induced xerostomia can reduce patient quality of life. 3 , 4 In addition to the lingering oral discomfort, these findings can be understood by considering how xerostomia broadly interferes with activities of daily life such as speech, taste, swallowing, and sleep. Several randomized trials have demonstrated that Biotène, as well as other artificial saliva substitutes, can effectively improve symptoms of xerostomia overall. 7 , 13 , 14 A few of these studies further delineated which components of xerostomia were improved with use of Biotène. Shahdad et al found that, in addition to overall xerostomia relief, Biotène improved swallowing and taste. The authors did not identify a significant improvement in chewing or speech with the use of Biotène. 10 In a similar study, Warde et al found that Biotène helped improve all domains evaluated, which included oral dryness, oral discomfort, sleep, speech, and swallowing. 11 In the current study, we found that in addition to improving overall oral dryness, Biotène significantly improved daytime xerostomia, sleep, speech, and swallowing. HydraSmile significantly improved overall oral dryness, daytime xerostomia, and swallowing. HydraSmile was found to provide a positive but nonxsignificant treatment effect for the remaining subdomains evaluated. Perhaps with a larger sample size, HydraSmile would provide a significant benefit with regard to sleep, speech, and taste. While not statistically significant, Biotène tended to outperform HydraSmile within the subdomains tested. In contrast, HydraSmile showed a nonsignificant trend towards outperforming Biotène with regard to overall symptomatic relief. Biotène may be more effective for patients who primarily suffer from disturbances in sleep and speech due to xerostomia, however, the current study does not demonstrate superiority of one product over the other. We found that 44% of patients preferred Biotène, 50.5% of patients preferred HydraSmile, and 5.5% of patients had no preference. Interestingly, patients who preferred Biotène did not significantly benefit from HydraSmile, whereas those who preferred HydraSmile did not significantly benefit from Biotène. Additionally, within the HydraSmile preference group, HydraSmile displayed a significantly greater treatment effect compared to Biotène. Currently, there are no other studies that evaluate how xerostomia product preference relates to effectiveness in the setting of xerostomia. While not quantified, many patients reported a preference based on product taste. This could have modified treatment effect by influencing a participant's willingness to use a given xerostomia spray. There are likely additional unmeasured interactions between product ingredients and patient clinical features that have also contributed to this finding. Overall, these results highlight that there is no easy way to predict whether Biotène or HydraSmile will work best for a given patient. If possible, patients should try multiple products to determine which is most effective for them. While lubricants and saliva substitutes have been shown to reduce symptoms of xerostomia, it has also been reported that these effects are generally short‐lived. 10 , 11 , 12 , 18 In a study published in 2021, Lung et al measured the mean duration of effect of Biotène spray to be 27 ± 25 min. 19 Gil‐Montoya et al in a systematic review previously noted that these types of products may not last long enough to improve quality of life meaningfully. 20 Therefore, rather than testing for the immediate effect of Biotène and HydraSmile after each use, we tailored our study to evaluate how routine use of each product influenced the overall symptomatic burden of xerostomia. Consequently, this study design likely underestimated the immediate treatment effect with each use. We found that both Biotène and HydraSmile provide longitudinal xerostomia relief in addition to immediate relief. We predict that use of these products can improve overall quality of life, however, this hypothesis will require testing in a future study. Five participants experienced mild adverse effects with use of HydraSmile. While none of the participants experienced anaphylaxis, these adverse reactions may have been allergy related. HydraSmile differs from Biotène in large part due to inclusion of several natural oils (avocado, peppermint, tea leaf, grapefruit, eucalyptus, wintergreen). This stands as another potential benefit of Biotène over HydraSmile. We recommend that patients avoid using HydraSmile if they have known allergies to any of these ingredients and discontinue use if they develop any sort of adverse reaction. This study is not without limitations. There may be a degree of attrition bias, given that 38 patients were unable to complete the study. Our sample size of 91 fell short of the 96 patients required to have 90% power to detect a 5‐point change in response. Overall, this increases our chance of type II error. Our modified intention‐to‐treat design, in which 13 patients from group 2 were reassigned after completing the group 1 protocol, may have also added bias to our analysis. Many participants found the product labeling (“A” and “B”) confusing and assumed that “A” was intended to be used during the first mouth spray period, and “B” was intended to be used during the second mouth spray period. Given that we found no evidence of a sequence or period effect in this cross‐over study, we feel any bias from this reassignment is minimal. While this study is much larger than similar studies (Warde et al, n = 28; Lopez‐Jornet et al, n = 30), our sample size was not sufficient to adjust for confounding variables in the final analysis. Additionally, we were unable to report objective measurements of salivary function to support our subjective survey data. Finally, the treatment effect of Biotène and HydraSmile was calculated in reference to use of water, which is known to improve oral dryness. 13 , 14 , 19 Therefore, the magnitude of the treatment effect may be underestimated, however, we would expect both products to be affected equally. In conclusion, we found that Biotène and HydraSmile effectively improved oral dryness among patients with radiation‐induced xerostomia. Direct comparison of the 2 products revealed a non‐significant difference in treatment effect across all domains evaluated. Therefore, this study did not find one product to be superior to the other. Through subgroup analysis we found that patients who preferred Biotène did not significantly benefit from HydraSmile, whereas those who preferred HydraSmile did not significantly benefit from Biotène. While Biotène and HydraSmile both have the potential to improve oral dryness, we recommend that patients try multiple products to determine which works best for them. | Study | biomedical | en | 0.999998 |
PMC11696981 | The complement cascade is an essential part of the innate immune system, comprising over 50 soluble and membrane-bound proteins that work together to destroy pathogens and maintain tissue homeostasis by removing dying cells ( 1 ). It involves three distinct pathways: the classical, alternative and lectin pathway. All rely on different molecules for initial cascade activation, yet they converge to a central step where the C3 convertase cleaves complement component C3 into the anaphylatoxin C3a and the opsonin (i)C3b. Invading microorganisms and dead cells become opsonized by (i)C3b, which enables phagocytic cells to recognize and internalize them. Depending on the nature of the dying cell, diverse molecules may facilitate clearance. For instance, apoptotic bodies unveil ‘eat me’ signals on their membranes, in particular phosphatidylserine, which is recognized by a multitude of phagocytic receptors on leukocytes ( 2 ). Complement also contributes to this removal via binding of C1q to the apoptotic cell, thereby opsonizing it and activating the complement pathway to facilitate clearance ( 3 , 4 ). Even though the general role of (i)C3b in phagocytosis is well-established, so far nearly all research on complement-mediated phagocytosis focused on microorganism clearance and efferocytosis (of apoptotic cells) ( 5 – 8 ). The removal of cellular corpses resulting from necrosis, referred to as necrotic cell debris, has been largely overlooked both in terms of mechanism description and physiological impact in vivo . Necrotic cell death diverges morphologically and immunologically from apoptosis due to plasma membrane rupture, the spilling of intracellular contents into the surrounding tissue, and the subsequent inflammatory response ( 9 ). These contents serve as damage-associated molecular patterns (DAMPs), which include ATP ( 10 ), high-mobility group box 1 ( 11 ), actin ( 12 ), mitochondria-derived molecules ( 13 ) and DNA ( 14 ), among others, and will interact with cognate pattern-recognition receptors (PRRs) on immune cells. For example, during drug-induced liver injury, widespread hepatocyte necrosis results in substantial DNA release from necrotic cells and causes intense TLR9-dependent inflammation ( 15 , 16 ). Other DAMPs such as histones and F-actin released from necrotic cells also contribute heavily to immune responses through Clec2d and Clec9a recognition, respectively ( 17 , 18 ). These highlight the importance of limiting the accumulation of debris/DAMPs in conditions where necrosis is prominent, such as drug-induced liver injury, atherosclerosis, stroke, severe trauma, and burn injuries. Phagocytosis is a cellular process of recognition and ingestion of particles larger than 0.5 µm, which promotes tissue homeostasis and elimination of microorganisms. Phagocytes recognize targets through specialized surface receptors, including non-opsonic receptors such as Dectin-1, Mincle, CD14 and CD36, which detect conserved molecular patterns, as well as various opsonic receptors. Complement receptors [e.g. CR1, CR3 (CD11b/CD18), CR4] are typical phagocytic receptors recognizing particles bound by complement opsonins ( 19 ). In general, complement has been implicated in the processing and removal of self-antigens, since the clearance of apoptotic cells is dependent on opsonization by C1q and C3 ( 20 , 21 ) and complement deficiencies increase the susceptibility to autoimmune disorders ( 22 ). Even though the role of complement in the clearance of apoptotic cells is clear, its contribution to the clearance of necrotic cells is poorly understood, with in vivo evidence lacking. Our group has recently shown that natural IgM and IgG antibodies are essential for the clearance of necrotic debris in vivo ( 23 ). Considering the substantial capacity of antibodies to initiate complement, the contribution of complement activation to necrotic cell debris clearance may be central. Therefore, we investigated complement activation in response to necrotic injury in mouse models of drug-induced liver injury and focal thermal injury (FTI) of the liver. We used intravital microscopy (IVM) to unveil the participation of complement in the clearance of necrotic debris in vivo and assessed its impact on the recovery from liver injury. 8-12 weeks old male and female C57BL/6J and C57BL/6NRj mice were purchased from Janvier Labs. Rag2 -/- mice (C57BL/6N-Rag2Tm1/CipheRj) were bred in specific pathogen-free (SPF) conditions at the Animal Facility of the Rega Institute (KU Leuven). C3 -/- mice (B6.129S4-C3tm1Crr/J) and Itgam -/- mice (B6.129S4-Itgamtm1Myd/J) were purchased from The Jackson Laboratory. Mice were housed in acrylic filtertop cages with an enriched environment (bedding, toys and small houses) and kept under a controlled light/dark cycle (12/12h) at 21°C with water and food provided ad libitum . All experiments were approved and performed following the guidelines of the Animal Ethics Committee from KU Leuven . Mice were starved for 15h and given a single oral gavage of 600 mg/kg APAP (Sigma-Aldrich) dissolved in warm PBS. Administration via oral gavage reflects the typical route of APAP-induced liver injury in patients. After 24, 48 or 72h, mice were sacrificed under anesthesia containing 80 mg/kg ketamine and 4 mg/kg xylazine, whereafter liver and blood were harvested. ALT in serum was determined with a kinetic enzymatic kit (Infinity, Thermo Fisher Scientific) according to the manufacturer’s instructions. Serum levels of mouse C3 were determined by a commercially available C3 ELISA kit according to the manufacturer’s instructions. Human neutrophils were purified from whole blood of healthy volunteers by immunomagnetic negative selection (EasySep™ Direct Human Neutrophil Isolation Kit, StemCell Technologies) according to the manufacturer’s instructions. Ethical permission for use of human blood-derived leukocytes was obtained with the ethical committee from the University Hospital Leuven . Mouse bone marrow neutrophils were extracted from femurs and tibias of C57BL/6J mice by flushing the bones with 5 ml cold RPMI-1640 medium using a 26-gauge needle. Cells were filtered through a 70 µm nylon strainer and further purified with the EasySep™ mouse neutrophil enrichment kit (StemCell Technologies), following the manufacturer’s instructions. Liver sections were stained with hematoxylin and eosin (H&E) and used to estimate hepatic necrosis via measurement of the necrotic area in the images. The livers were washed with 0.9% NaCl and fixed in 4% buffered formalin. Subsequently, the samples were dehydrated in ethanol solutions, bathed in xylol and included in histological paraffin blocks. Tissue sections of 5 μm were obtained using a microtome and stained with H&E. Sections were visualized using a BX41 optical microscope (Olympus) and images were obtained using the Moticam 2500 camera (Motic) and Motic Image Plus 2.0ML software. The left liver lobes of mice were harvested, embedded in Tissue-Tek O.C.T. Compound (Sakura Finetek Europe) and snap frozen in liquid nitrogen. 10 µm sections were cut using a Cryostat Microm CryoStar and subsequently fixed, permeabilized and blocked. Sections were incubated overnight at 4°C with 10 µg/ml polyclonal rabbit anti-human/mouse fibrin(ogen) (Dako), 5 µg/ml rat anti-mouse C3b/iC3b (clone 3/26, Hycult Biotec) and 5 µg/ml rabbit anti-mouse C1q (clone 4.8, Abcam). Secondary antibodies were added for 3h at RT: Alexa Fluor 647 donkey anti-rabbit, Rhodamine RED-X (RRX) donkey anti-mouse IgM, Alexa Fluor 488 donkey anti-rat, Alexa Fluor 560 donkey anti-rabbit (all at 10 µg/mL, Jackson ImmunoResearch). 10 µg/ml of Hoechst was added for 30 min at RT to stain nuclei. Finally, slides were mounted with ProLong Diamond Antifade Mountant. Images were captured using the Andor Dragonfly High-Speed Confocal Microscope (Oxford Instruments) or a Zeiss Axiovert 200M fluorescence microscope, and analyzed with FIJI. 8 images were acquired per liver with a 25X objective. Stained areas were selected using the threshold tool in FIJI, from which the percentage area of staining was determined. Pearson’s coefficient was calculated in FIJI using the JACoP plugin. Comparisons between WT and Rag2 -/- mice were normalized to the degree of injury [% of fibrin(ogen) labeling]. All images can be provided in different colors upon request. Mice were anaesthetized with a subcutaneous injection of 80 mg/kg ketamine and 4 mg/kg xylazine. For the experiments with APAP-induced liver injury, fluorescent antibodies (4 µg/mouse) and dyes (2 µl of a 10 mM Sytox Green solution; Thermo Fisher Scientific) were dissolved in 100 µl sterile PBS and injected intravenously 10 minutes before the surgery. The surgical procedure is described in detail in Marques et al. ( 24 ). For the FTI experiments, 1 mm 3 burns were made with a cauterizer and the injury site was then stained with 10 µl of pHrodo Red succinimidyl ester (SE) (4 µM; Thermo Fisher Scientific). The incision was stitched, and after 6h, mice were again anaesthetized with ketamine and xylazine to image the burn injury site. Images were taken every 30 sec for at least 30 min with the Dragonfly Spinning-Disk Confocal Microscope (Oxford Instruments) using the 25X objective. Quantification of phagocytosis was done in a blind manner by two individuals and counted manually. The percentage of Sytox Green labeling was determined from 2 mosaic images, each composed of 16 images per mouse, with FIJI software using thresholding. The % of CD11b + cells containing DNA was determined using Imaris software. Surfaces overlaying live cells and DNA debris were generated from 3D images and counted manually. Necrotic debris was generated from HepG2 cells by inducing mechanical disruption with a pellet mixer for 5 min in 0.1 M sodium bicarbonate (pH 8.5). The necrotic debris was labeled by adding 2 µl of 10 mM pHrodo Red SE (Thermo Fisher Scientific) solution per 10x10 6 cells. The debris was opsonized with 20% normal human serum, C1q-depleted serum (Complement Technology) or C3-depleted serum (Complement Technology) in PBS for 1h at 37°C. Opsonized debris was added to purified neutrophils in a 1:10 cell/debris ratio. Neutrophils were stimulated with 10 -7 M N-formyl-Met-Leu-Phe (fMLF; Sigma-Aldrich) for human neutrophils or 1 µM WKYMVM (Phoenix Pharmaceuticals) for mouse neutrophils, and labeled with 1 µM calcein AM viability dye (Invitrogen). Two 3D mosaics are captured per well, each comprising 9 overlapping images taken after 3h incubation at 37°C with the 25X objective of the Dragonfly Confocal Microscope (Oxford Instruments). Each condition was plated in duplicate and replicated at least 3 times. 3D reconstructions were generated using Imaris software. Additionally, with Imaris, surfaces were overlaid onto live cells and necrotic debris through thresholding, after which the volume of overlap was calculated in µm 3 . Liver lobes were surgically removed, put in MACS tubes with RPMI-1640 (Biowest Riverside) and minced with a gentleMACS Dissociator (Miltenyi Biotec). To this suspension, 2.5 mg collagenase D (Roche) and 1 mg DNAse I were added per liver for 1h at 37°C. The cell suspension was washed with PBS (300 g, 5 min, 4°C). Non-parenchymal cells were separated by density gradient centrifugation at 60 g for 3 min at 4°C. Supernatant was collected and filtered through a 70 µm nylon cell strainer. After centrifugation (300 g, 5 min, 4°C), ACK Lysing buffer (Gibco) was added for 10 min to the pellet to lyse red blood cells. 1x10 6 cells were collected in FACS tubes and washed with PBS (300 g, 5 min, 4°C). Zombie Aqua Fixable Viability dye (Biolegend) together with mouse FcR blocking Reagent (Miltenyi Biotec) were incubated for 15 min in the dark. Then, cells were washed with PBS supplemented with 0.5% bovine serum albumin (BSA) and 2 mM EDTA and the fluorescently labeled antibodies were incubated for 25 min at 4°C in the dark. After a final washing step, cells were read in a Fortessa X20 (BD Biosciences). Data was analyzed using FlowJo 10.8.1 software. HepG2 cells were mechanically lysed using a 22G syringe in PBS with 20 µg/ml RNAse A (Sigma-Aldrich) to generate RNA-free necrotic debris. The debris was incubated for 30 min at 37°C to allow enzyme activity. Then, the debris was washed twice with PBS containing 2 mM EDTA and 0.1% BSA, and centrifuged at 60g for 3 min to remove intact cells. The debris was opsonized with 20% serum or C3-depleted serum (Complement Technology) and then incubated for 1h at 37°C. Neutrophils from healthy donors were purified by immunomagnetic negative selection with an EasySep kit and stimulated with 10 -7 M fMLF. Cells and debris were co-incubated in a 6 well plate at a 1:10 cell/debris ratio and centrifuged at 300g for 5 min before being incubated for 3h at 37°C. Cells were harvested and total RNA was extracted by lysing the cells with β-mercaptoethanol and a Rneasy Plus Mini Kit (Qiagen) following the manufacturer’s instructions. After extraction, total RNA quality and quantity were determined using a Nanodrop. cDNA was obtained by reverse transcription using the high-capacity cDNA Reverse Transcriptase kit (Applied Biosystems). mRNA levels were analyzed by quantitative PCR using a TaqMan Gene Expression Master Mix (Applied Biosystems) and a 7500 Real-Time PCR System apparatus. Expression levels of genes of interest were normalized for the average RNA expression of three housekeeping genes (CDKN1A, 18S and GAPDH) using the 2 −ΔΔCT method ( 25 ). Data were analyzed using GraphPad Prism v9.3.1. All data are expressed as mean ± standard error of the mean (SEM). A Shapiro-Wilkinson test was performed to check for normality. Normally distributed data were analyzed with a Student’s t test or One-way ANOVA. Non-parametric data were analyzed with a Mann-Whitney test or Kruskal-Wallis test. Grubb’s test (extreme studentized deviate) was applied to determine significant outliers, which are identified as red dots in the graphs and removed from statistical analysis. A p-value equal or lower than 0.05 was considered significant. To assess complement activation at necrotic injury sites, a mouse model of paracetamol/acetaminophen (APAP)-induced liver injury was used, as it is characterized by extensive death of hepatocytes through necrosis ( 16 ). A sublethal dose of 600 mg/kg APAP was administered via oral gavage, causing significant liver damage as early as 12h after administration. In this acute model, hepatocellular necrosis was observed by elevated levels of serum alanine aminotransferase (ALT) , an enzyme primarily found in hepatocytes that serves as a biomarker for liver damage. Using histopathology, necrotic lesions were detected around the centrilobular veins, a typical pattern for APAP-induced injury, with the highest severity 12h after APAP administration . At 48h, the injury decreased significantly, as depicted by lower serum ALT levels and reduced necrotic areas . Fibrin deposition, known for its specific accumulation at necrotic sites after the activation of the coagulation cascade ( 26 ), was evaluated over time on liver cryosections to estimate the area of necrosis. Significant fibrin(ogen) staining was observed after 24h, whereafter it gradually decreased . A similar pattern was observed for IgM, with the highest deposition occurring after 24h and diminishing at later timepoints . Remarkably, C1q and (i)C3b deposition remained high up to 48h , indicating that the deposition of antibodies preceded complement activation through C1q binding and C3 cleavage. The staining in necrotic regions was not because of autofluorescence or unspecific labeling as confirmed in cryosections stained with secondary antibodies only . Pearson’s correlation coefficient between (i)C3b and fibrin was significantly higher in comparison to (i)C3b and intact cell nuclei in the liver . In addition, the other components of the classical complement pathway, IgM and C1q, also had high colocalization with (i)C3b . All this shows that complement proteins are deposited specifically at sites of necrotic injury in the liver. These data were supported by significantly lower C3 levels in the serum of APAP-treated mice , which confirm complement activation in response to injury. To investigate the contribution of C3 to the resolution of necrotic liver damage, C3 -/- mice were subjected to APAP overdose and evaluated at 2 timepoints: a) after 24h, to assess the peak of injury and b) after 48h, to observe the degree of tissue repair. No differences in serum ALT levels were observed after 24h, while significantly higher levels of ALT were found after 48h in C3 -/- mice compared to WT mice . In addition, fibrin staining of liver cryosections revealed no differences at the peak of injury, whereas more fibrin staining was found in C3 -/- mice at the later timepoint , indicating that C3 deficiency leads to larger, unresolving necrotic areas in the liver. During the liver regeneration phase, cellular proliferation can be estimated by the expression of Ki67 in liver cryosections. Using this approach, we observed a significant decrease in cell proliferation in C3 -/- mice 48h after APAP overdose , confirming that the absence of C3 impairs liver regeneration and recovery from injury. To directly assess whether C3 deficiency affects the amount of necrotic debris in injured tissues, we performed confocal IVM of mouse livers. Considering that DNA is abundantly released by necrotic hepatocytes ( 16 ) and based on the observed differences during the resolution phase , we chose to measure the amount of DNA exposed in the liver 48h after the APAP challenge using the membrane-impermeable DNA dye Sytox Green. Interestingly, WT mice presented minimal extracellular DNA in the liver at the 48h timepoint, which is consistent with the removal of necrotic debris and tissue recovery at that phase . Moreover, the vast majority of the fluorescent signal observed in the images of WT mice consisted of background fluorescence from healthy hepatocyte nuclei . In contrast, C3 -/- mice had significantly more extracellular DNA debris, demonstrating that these mice have a clear defect in the removal of necrotic debris from the liver . These results link poor recovery from liver injury in C3 -/- mice to impaired clearance of necrotic cell debris. To explore how debris persisted in injury sites, an analysis of the recruited leukocytes and their ability to take up debris was performed. The inflammatory response triggered by liver injury led to the recruitment of CD11b + leukocytes to necrotic areas identified by the extracellular DNA staining . These cells consisted primarily of inflammatory monocytes (CCR2 + ), neutrophils (Ly6G + ) and macrophages (F4/80 + ) . Of interest, CD11b, the α M subunit of the complement receptor CR3 (CD11b/CD18), which is known for its involvement in complement-mediated phagocytosis, was increased in neutrophils and monocytes during liver injury . Besides CD11b+ leukocytes, other immune cells (DCs, T and B cells) are present in lower percentages in the injured liver . However, their role in debris phagocytosis is less anticipated and therefore not examined in this study. We then inquired whether CD11b + cells were able to internalize extracellular DNA debris using IVM. Numerous CD11b + leukocytes were visualized deep within necrotic areas using Z-stacks, and multiple cells contained Sytox Green + particles . In total, 12% of the CD11b + cells contained DNA particles, with an average of 2 particles per cell . Internalization of DNA debris was confirmed upon intravenous administration of DNAse to remove the bulk of extracellular DNA in necrotic areas. DNA-positive vesicles in CD11b + leukocytes remained after DNAse injection, indicating that the DNA particles were located intracellularly, likely in phagosomes, which are not accessible to the circulating DNAse treatment . After observing impaired necrotic DNA removal in C3 -/- mice , flow cytometry was performed to quantify DNA uptake by leukocytes in WT and C3 -/- mice . Again, the membrane-impermeable DNA dye Sytox Green was injected intravenously 2h before sacrificing mice that received an APAP overdose 24h prior. This allowed sufficient time for the fluorescently-labeled necrotic DNA to be phagocytosed. We observed that a significantly lower percentage of neutrophils and macrophages were able to internalize DNA debris in the absence of C3, demonstrating that debris clearance depends at least partially on complement opsonization . Importantly, no differences were observed in the number of neutrophils (Ly6G + ) and macrophages (Ly6G - /Ly6C - /F4/80 + ) present in the injured liver between WT and C3 -/- mice , suggesting that impaired DNA removal observed in C3 -/- mice may be due to the reduced phagocytic capacity of these cells. In contrast, classical and non-classical monocytes did not require C3 to take up DNA debris in the injured liver , suggesting that different leukocyte populations utilize distinct mechanisms for debris clearance. However, the number of classical (Ly6G - /Ly6C + ) and non-classical monocytes (Ly6C - /CX 3 CR1 + ) recruited to the injured liver in C3 -/- mice was significantly reduced , showing that less monocytes reached the injured liver to perform debris phagocytosis. Overall, these data show that necrotic DNA debris is cleared by leukocytes in the liver and that neutrophils and macrophages require complement opsonization of debris for its uptake. Reduced clearance of DNA debris in the absence of C3 was observed in a model of acute liver injury induced by APAP overdose. To validate whether this finding applies to other types of injuries, we investigated debris clearance in a model of focal thermal injury (FTI) of the liver. In this model, necrotic lesions are induced locally with a hot needle, facilitating the observation of phagocytosis in vivo . This was challenging in the APAP model due to widespread necrosis throughout the liver causing an abundance of debris. The localized nature of the thermal injury allowed us to label necrotic debris by applying a droplet of the pH-sensitive dye pHRodo Red succinimidyl ester on top of the lesion. This dye binds covalently to proteins and exhibits increased fluorescence when the ingested material is processed in the acidic environment of a phagolysosome. Our previous work demonstrated that neutrophils predominated in the injured area 6h after FTI, with monocytes being attracted after 12h ( 23 ). The entire image of the FTI shows distinct burn injury zones, with neutrophils mostly accumulating around the injury core . Using IVM, neutrophils carrying pHRodo-labeled debris were observed crawling from the injury border into the necrotic core . Approximately 75% of neutrophils at the injury site had pHRodo-containing phagosomes, whereas less than 5% of neutrophils in healthy areas of the liver phagocytosed debris . This finding was confirmed by flow cytometry, which showed a significantly increased MFI of pHRodo in neutrophils and monocytes at the burn injury site compared to leukocytes in healthy areas . Similarly to the APAP model, complement proteins C1q and (i)C3b were specifically deposited on necrotic lesions 6h after FTI . Both components colocalized with each other and fibrin . Quantification of necrotic debris clearance by flow cytometry demonstrated a significant decrease in the percentage of neutrophils and non-classical monocytes phagocytosing debris in C3 -/- mice compared to WT . Interestingly, this phenomenon was not observed in classical monocytes nor macrophages . To be noted, the number of neutrophils migrating in the burn injury site , and the percentage of neutrophils, macrophages and non-classical monocytes attracted to the injured liver did not differ between WT and C3 -/- mice, while the percentage of classical monocytes was significantly reduced in C3 -/- mice . Due to the increase in CD11b + cells in response to liver injury and its known role in complement-mediated phagocytosis, the role of CR3 on debris uptake and liver resolution was investigated. In the FTI, using CD11b -/- mice, we found a significant decrease in debris clearance by neutrophils and non-classical monocytes , while no difference was observed in classical monocytes and macrophages . Moreover, the attracted phagocyte populations at the injury site were not affected by the deficiency in CD11b -/- , indicating that the defective clearance is not connected to inhibition of leukocyte recruitment . Conversely, no significant effect was observed on the progression of liver injury in CD11b -/- mice or in WT mice that received a CD11b blocking antibody, as evidenced by similar ALT values and fibrin staining after APAP overdose. These data show that in the FTI, neutrophils and monocytes migrate to the necrotic lesion to phagocytose necrotic debris, a process which depends on C3 and CD11b/CD18 for neutrophils and non-classical CX 3 CR1 + monocytes, whereas classical CCR2 + monocytes rely on other unidentified receptors. Investigating the factors driving debris phagocytosis in vivo is challenging due to the necessity of multiple knock-out strains and the technical limitations associated with observing cellular events in living mice. To overcome this, we developed an in vitro phagocytosis assay, enabling us to study the role of specific complement proteins in necrotic debris clearance. This approach also allowed us to verify our findings in human neutrophils and human necrotic debris. In this assay, necrosis was induced by mechanically disrupting HepG2 cells, whereafter sera lacking specific complement components were used to opsonize the debris. Images were taken with a confocal microscope 3h after combining the opsonized necrotic debris with human or mouse neutrophils. Importantly, the HepG2 cell debris itself did not contain detectable levels of C3/(i)C3b, as demonstrated by immunostaining HepG2 debris in vitro . However, when the debris came in contact with whole serum, it became clearly opsonized by C3/(i)C3b confirming the capacity of debris to induce complement activation. In vitro engulfment of pHRodo-labeled necrotic debris by live neutrophils was visualized by 3D reconstruction . Interestingly, the percentage of bone-marrow derived mouse neutrophils that performed phagocytosis did not differ when presented with debris opsonized with serum, serum lacking C3 (from C3 -/- mice) or lacking antibodies (from Rag2 -/- mice) . However, the volume of the necrotic debris internalized by neutrophils was significantly reduced when the debris was opsonized with serum lacking C3 . Likewise, freshly isolated neutrophils from healthy donors showed no difference in phagocytosis rates when debris was opsonized with serum, C1q-depleted serum or C3-depleted serum . Nevertheless, a significant decrease in the volume of debris uptake was observed when it was opsonized with serum lacking C3 . Latrunculin served as a positive control, as it inhibits phagocytosis globally by disrupting actin polymerization. These findings underscore the ability of both human and mouse neutrophils to internalize necrotic debris, while highlighting the role of complement on the amount of debris taken up through phagocytosis. To investigate the impact of debris phagocytosis on gene expression, qPCR was performed on human neutrophils incubated with necrotic debris from HepG2 cells. Neutrophils were exposed to pure unopsonized debris, debris opsonized with normal serum or with C3-depleted serum for 3 hours. The data were normalized to unopsonized debris in order to account for stimulation by DAMPs present in necrotic cell debris. Interestingly, uptake of serum-opsonized debris induced the upregulation of PTGS2 (encoding COX2) in neutrophils . COX2 is an enzyme with dual role in inflammation, catalyzing the production of pro-inflammatory prostaglandins from arachidonic acid, such as PGE2, but also participating in the synthesis of numerous pro-resolving lipid mediators ( 27 ). PTGS2 upregulation was reversed when the debris was opsonized with serum lacking C3, indicating a direct effect of complement on the upregulation of COX2, which was already observed for monocytes but not neutrophils ( 28 ). CXCR2 , coding for a major chemokine receptor in neutrophils that promotes both chemotaxis and reverse migration ( 29 , 30 ) was also upregulated by incubation with opsonized debris. Moreover, in the absence of C3, CXCR2 expression levels returned to baseline . In addition, incubation with opsonized debris led to the expression of other immunoregulatory and pro-resolving genes in neutrophils, namely, CXCR4 , encoding a chemokine receptor associated with homing of neutrophils to the bone marrow for apoptosis and removal ( 31 , 32 ); the immunoregulatory cytokine IL10 , and ANXA1 , encoding the protein annexin A1 that dampens leukocyte chemotaxis, respiratory burst and phagocytosis ( 33 ). The absence of C3 in the serum significantly reduced the expression of CXCR4 , IL10 or ANXA1 , indicating the essential role of C3 opsonization in the induction of pro-resolving genes in neutrophils. Moreover, alterations in gene expression in neutrophils are specific, since multiple genes were unaffected by stimulation with opsonized debris, including ALOX5, CYBB, CASP3, ARG1, FPR1 and FPR2 . Overall, the gene expression induced by the clearance of opsonized necrotic debris reflects a pro-resolving response in neutrophils, which is C3-dependent and plays a central in promoting tissue repair. Lastly, we investigated whether the classical complement pathway contributed to (i)C3b opsonization of necrotic debris. For this, Rag2 -/- mice, which lack mature T and B cells and therefore also antibodies, were subjected to the APAP overdose. Activation of the classical complement pathway requires the association of C1q with target-bound IgM or multiple IgGs, thus, this complement pathway cannot be activated in Rag2 -/- mice. First, the absence of IgM was confirmed by immunostaining, which revealed that IgM labeling in the injured Rag2 -/- liver was essentially absent . Interestingly, 24h after APAP overdose, liver cryosections showed significantly increased fibrin deposition in Rag2 -/- mice compared to WT mice . In our previous work, the absence of antibodies was shown to be responsible for a delayed liver resolution due to impaired necrotic debris clearance ( 23 ). Because the degree of necrotic injury also affects the degree of C1q and (i)C3b deposition, complement immunostaining was normalized to the area of fibrin staining. With this approach, we observed no difference in C1q binding to necrotic areas in Rag2 -/- mice compared to WT . Similarly, the mean fluorescence intensity (MFI) of C1q was not different between WT and Rag2 -/- mice, indicating that C1q binding does not rely on antibody opsonization of necrotic debris . However, the area of (i)C3b deposition was significantly decreased in Rag2 -/- mice, with also significantly lower MFI in (i)C3b-stained areas , even though it has been shown that Rag2 -/- mice have elevated C3 levels in blood ( 34 ). These data indicate that loss of IgM and IgG antibodies diminishes the level of complement activation on necrotic sites, even though C1q binding remains unaffected. These data also demonstrate that the classical complement pathway is activated in response to necrotic debris, leading to (i)C3b deposition in injury sites. We observed specific deposition of C1q and (i)C3b at necrotic lesions in the liver, in line with previous literature ( 35 – 37 ). We also demonstrated that in the absence of antibodies IgM and IgG, complement protein C1q still bound necrotic debris , likely due to its interaction with various ligands which include histones, DNA, C-reactive protein, pentraxin 3 and serum amyloid P component ( 4 , 38 – 40 ). Interestingly, the absence of antibodies significantly reduced C3b deposition, even though Rag2 -/- mice have elevated C3 levels compared to WT mice ( 34 ). This suggests that C3b deposition on necrotic lesions relies, at least partially, on the classical complement pathway, which is hampered in the absence of antibodies despite C1q presence. An in vitro study corroborated this, since adding C1q to sera lacking IgM and C1q did not affect C3 deposition on apoptotic cells ( 41 ). Of course, the activation of the alternative and lectin complement pathway in response to necrotic cells should not be overlooked, as properdin, a positive regulator of the complement system, has been proven to bind to necrotic cells and activate the alternative pathway ( 42 ). Also, mannose-binding lectin was shown to interact with apoptotic and necrotic cells and to facilitate uptake by macrophages in vitro ( 43 ). Realization that the classical pathway is activated reveals additional opportunities to ameliorate debris clearance. Patients with severe necrotic injuries might benefit from intravenous immunoglobulin (IVIG) supplementation and blood transfusions. The administered natural antibodies may bind necrotic debris, triggering C3 cleavage via the classical pathway, and aiding in the clearance of debris. Moreover, our previous work showed that the supplementation of natural antibodies directly enhanced Fc receptor-mediated phagocytosis, meaning that debris would be cleared via both complement- and Ab-mediated phagocytosis ( 23 ). Evaluating liver injury in C3 -/- mice following APAP overdose showed us a delayed recovery and the prolonged accumulation of necrotic debris . Roth et al. observed lower serum ALT levels in C3 -/- mice 6 and 12h post-APAP, possibly due to the administration of a lower dose of 300 mg/kg APAP intraperitoneally and the shorter evaluation time ( 35 ). However, other studies have similarly noted impaired liver regeneration in C3 -/- mice after toxic-injury induced by CCl 4 and partial hepatectomy ( 44 , 45 ). Although impaired liver resolution and debris accumulation in C3 -/- mice could be explained by impaired debris phagocytosis, other factors also contribute to this worsened phenotype. The absence of anaphylatoxins C3a and C5a impact liver regeneration by affecting hepatocyte priming and inhibiting neutrophil and monocyte chemotaxis ( 41 , 46 , 47 ). In addition to the decreased presence of monocytes at the injury site to perform debris phagocytosis, the reduced differentiation into monocyte-derived macrophages also contributes to delayed injury resolution, as observed in CCR2 -/- mice ( 48 , 49 ). The absence of the C3a/C3aR axis may also influence CCL2 expression in leukocytes, potentially affecting monocyte infiltration into the injured liver in C3 -/- mice. This is supported by studies showing that C3a upregulates CCL2 expression in human keratinocytes and mast cells ( 50 , 51 ). This emphasizes that liver resolution is a complex process involving multiple factors, with debris phagocytosis being just one of the contributing events. We showed that neutrophils, along with macrophages, rely on complement to phagocytose debris in a model of APAP-induced liver injury . This phenomenon is not limited to the liver, as similar uptake of debris occurred in the lungs of mice with acid-induced lung injury ( 52 ). Using confocal microscopy, we observed an average of two DNA-containing phagosomes per cell, consistent with previous findings of macrophages ingesting one or more small cytosolic particles from necrotic cells ( 53 ). Interestingly, cells that relied on complement for recruitment did not rely on it for phagocytosis, and vice versa , highlighting the existence of multiple pathways for leukocyte recruitment and debris clearance which may compensate each other. Moreover, in a model of FTI of the liver, the phagocytosis of pHRodo-labeled protein-rich necrotic debris was observed, complementing our findings and the one of Wang et al., where neutrophils engulfed nuclear debris ( 54 ). Our results showed that in this model phagocytosis in neutrophils and non-classical monocytes is complement-dependent , adding a mechanistic layer to the role of CX 3 CR1 + monocytes in sterile injury resolution. Classical monocytes (CCR2 hi , CX 3 CR1 lo ) surround the FTI site and transition into non-classical monocytes (CX 3 CR1 hi , CCR2 lo ) essential for injury repair, a process dependent on IL-10 and IL-4 ( 55 ). Human neutrophils exhibited a pro-resolving phenotype after phagocytosis of necrotic debris, marked by the gene upregulation of IL10, PTGS2, CXCR4, CXCR2 and ANXA1 . This gene expression shifts depend on the type of meal ingested, as illustrated in macrophages, where efferocytosis of apoptotic cells triggers an anti-inflammatory response ( 56 ). Research on efferocytosis revealed that the uptake of lipids from apoptotic bodies stimulates sterol receptors (PPARs and Liver X receptors), triggering an anti-inflammatory response via IL-10 and TGF-β production ( 57 , 58 ). Due to the lipid-rich nature of the debris, these insights could apply to necrotic cells as well. The upregulated expression of CXCR4 is associated with reverse migration of the neutrophils to the bone marrow, as similarly shown in vivo by Wang et al. ( 54 ). This process would in theory assist to alleviate the burden of dead cells to be cleared at the injury site when neutrophils undergo apoptosis. The opsonization of debris in the absence of C3 significantly impacted gene expression levels, reducing them to levels comparable to control. This suggests that the specific opsonization process, rather than phagocytosis itself, play a key role in regulating the expression of genes associated with resolution. The in vitro phagocytosis assay showed that both mouse and human neutrophils ingested necrotic debris, however, the phagocytosed volume was reduced in the absence of C1q and C3 . Opsonins affect hydrophobicity and surface charge, consequently influencing receptor interactions ( 59 ). This could potentially impact debris removal and explain the larger area of necrotic debris observed in C3 -/- mice 48h post-APAP . Also, mechanosensing of the target, which drives actin-based protrusions to mediate particle internalization, might be impaired, possibly due to the reduced stiffness/rigidity of the debris in the absence of C3 ( 60 ). In conclusion, our study demonstrates the crucial role complement proteins play in the opsonization and subsequent phagocytosis of necrotic debris. This mechanism was confirmed in both mouse and human neutrophils, irrespective of the nature of injury (chemical or thermal). This highlights a general complement-dependent pathway for debris clearance specifically for neutrophils. Consequently, individuals with complement deficiencies, whether it is due to genetic factors or auto-immune diseases, might exhibit impaired clearance of necrotic debris, causing prolonged inflammation and poor tissue regeneration. These individuals could potentially benefit from C3 or plasma supplementation to enhance debris clearance and fasten injury recovery. | Study | biomedical | en | 0.999997 |
PMC11697044 | A 68-year-old man was referred to the vascular surgery department for evaluation of an EIA aneurysm incidentally found on a screening magnetic resonance imaging after an elevated prostate-specific antigen on routine screening laboratory tests. Medical history was significant for type 2 diabetes mellitus, hypertension, hyperlipidemia, and coronary artery disease without a history of myocardial infarction or preventative intervention. He presented to the office and was evaluated for symptoms; he reported none. There was no history of trauma, infectious etiology, or prior vascular access. The patient denied any history of smoking, cycling or extreme sporting, or family history of aneurysms. Blood cultures were negative, and leukocytes were within normal limits. Computed tomography angiogram of the chest, abdomen, and pelvis with runoff revealed an isolated right sided saccular 2.6-cm EIA aneurysm above the inguinal ligament, with no extension proximally or distally . There was no aneurysmal or major atherosclerotic disease in the abdominal aorta or distal arterial vessels and there were no signs of disease in the contralateral iliac arteries. Based on discussion with the vascular surgery team, the patient was given the options of endovascular vs open surgery. Using shared decision-making, an endovascular approach was chosen to treat the isolated EIA aneurysm, and the patient provided consent. Fig 1 Preoperative computed tomography angiography showing an isolated aneurysm of the suprainguinal right external iliac artery ( EIA ). ( A ) Sagittal. ( B ) Axial. ( C ) Coronal with measurement. The operation was performed with the patient under general anesthesia. An 8F short 25 cm sheath (Terumo Medical Co, Tokyo, Japan) was placed percutaneously at the left femoral artery, and the Omni Flush Soft-Vu Angiographic Catheter (Angiodynamics, Latham, NY) was advanced and positioned into the infrarenal abdominal aorta. An aortogram was captured to visualize the iliac arteries with an oblique view to visualize the right hypogastric takeoff. A widely patent bilateral iliac system was visualized, and a large 2.6-m aneurysm was identified 4 cm above the femoral bifurcation. The Omni Flush over a floppy Glidewire (Terumo Medical Corp., Somerset, NJ) was advanced across the iliac bifurcation, beyond the aneurysm sac, and further down into the right superficial femoral artery. The short 8F sheath was exchanged for an 8F 45 cm Ansel Sheath (Cook Medical, Bloomington, IN) over a J-tipped Stiff Amplatz Wire (Boston Scientific, Natick, MA), and advanced to the mid-right EIA just proximal to the aneurysm. After heparinizing the patient and measuring the native vessel for optimal graft selection, the 9 mm × 10 cm Viabahn stent graft (W. L. Gore & Associates, Flagstaff, AZ) was ultimately selected and carefully deployed in a distal to proximal fashion. The entirety of the aneurysmal sac was covered while ensuring maintaining patency of the common femoral artery distally and hypogastric artery proximally. Final angiography demonstrated widely patent right EIA and widely patent femoral bifurcation with complete exclusion of the EIA aneurysm. Given that the stent graft appeared to have an excellent seal, it was decided not to post-dilate with balloon angioplasty. Fig 2 Intraoperative angiography. ( A ) Preoperative aortogram. ( B ) Preoperative iliac angiogram. ( C ) Completion angiogram after stent graft deployment. ( D ) Reconstructed three-dimensional image of 2-week follow-up computed tomography scan showing completely excluded aneurysm with patent stent graft in suprainguinal right external iliac artery ( EIA ). Common and internal iliac artery aneurysms have multifactorial pathogeneses that are nearly identical to that of abdominal aortic aneurysms, as seen by their histological similarities. The particular rarity of aneurysms involving the EIA can be attributed to the unique lamellar architecture and biomechanical properties of the external iliac arterial walls, particularly in the tunica media. 7 Distinct from the more proximal aortoiliac segments, external iliac arteries possess a more structured and layered lamellar architecture, as well as a higher elastin-to-collagen ratio, 8 which allows them to withstand higher hemodynamic stresses and accommodate higher pressures, thus reducing the susceptibility to wall weakening and aneurysmal dilation. Isolated iliac artery aneurysms, without any other identifiable aortoiliac or peripheral vascular disease, have been described in multiple investigations to be a rare pathology. Silver et al 9 in 1967 performed a chart review of patients with arterial aneurysms affecting the aortic or iliac artery systems and found 571 patients with abdominal aortic aneurysms and only 11 patients with isolated iliac artery aneurysms, a relative frequency of 1.9%. Later in 1983, McCready et al 10 reported the frequency of isolated iliac artery aneurysms of 0.9% and provided one of the only anatomical frequency distributions amongst isolated aneurysms of the iliac artery system: 90% of all isolated iliac artery aneurysms affect the common iliac artery solely, whereas <1% affect the EIA solely. Finally, in 1989 Brunkwall et al 5 performed the largest investigation on isolated iliac artery aneurysms, reporting 13 cases during the 15-year compilation of autopsy and operating records in Malmo Sweden, population 230,000. They found only one isolated EIA aneurysm in that same study. 5 Regardless of how rare they are, many investigations have demonstrated that isolated iliac artery aneurysm are associated with a high risk of rupture and mortality, with rates of rupture between 14% and 75%. 2 , 11 , 12 The high mortality rate is postulated to be due to the lack of inclusion of iliac artery aneurysms in a differential diagnosis of pelvic conditions, partly owing to their rarity. Also, owing to the nature and location of the pathology, they are difficult to detect on physical exam until they are at a size when they are at risk for a morbid rupture. 3 McCready et al described that 78% of the patients in their study presented asymptomatically with their iliac artery aneurysm, similar to the patient described in this case study. Few cases have been reported describing isolated aneurysms of the EIA ( Table ). The first case report in 1952 described a patient who presented symptomatically with abdominal and lower limb pain. Surgical exploration revealed an EIA aneurysm that had ruptured. 13 The next three case reports published between 1986 and 2009 described iliac artery aneurysms involving the EIA that were found histologically to be due to cystic medial necrosis. 14 , 15 , 16 More recently, in 2019 two cases were presented with a 65-year-old symptomatic patient with left lower limb edema and a 55-year-old symptomatic patient with intermittent left thigh pain associated with paresthesias, both ipsilateral to the isolated EIA aneurysm. 17 , 18 Finally in 2020, there was another case report, similar to Crivello's, describing a symptomatic isolated EIA aneurysm associated with cystic medial necrosis. 19 In the present report, we have presented the case of a 68-year-old man who was completely asymptomatic, with an incidental finding of an EIA aneurysm. Until the presentation of our case, a search of PubMed found only seven reported cases of isolated EIA aneurysm, none of which were completely asymptomatic or repaired endovascularly. Table Reported cases of isolated external iliac artery ( EIA ) aneurysm Author Age, years Sex Size, cm Year Priddle et al 13 29 Female 4.0 1952 Crivello et al 14 27 Male Not available 1986 Mohan et al 15 66 Male 11.0 1997 Kato et al 16 78 Female 4.0 2009 Van de Luijtgaarden et al 17 65 Male 3.5 2019 Hussain et al 18 55 Male 7.0 2019 Chatzantonis et al 19 51 Male 2.0 2020 Current case 68 Male 2.6 2023 With this case report, we hope to stimulate a discussion on when to intervene on these rare pathologies. Although we have guidelines, based on large sample studies, for when to fix common or internal iliac artery aneurysms, the rarity of isolated EIA aneurysms leaves vascular surgeons without clear directions on when to fix them, especially when they are asymptomatic. Of the different publications reporting sizes of isolated EIA aneurysms, we found that most were repaired between 2 and 4 cm, mostly in men approximately 60 years old. Until clearer, large sample studies are performed, we recommend early repair of these aneurysms (diameter ≥2 cm) owing to the risk of rupture or symptoms seen in prior publications. Additionally, we now have the availability of minimally invasive interventions, with endografts that are able to be surveilled postoperatively with ultrasound examination. | Clinical case | clinical | en | 0.999996 |
PMC11697048 | Colorectal cancer (CRC) is one of the most malignant diseases that easily metastasizes to important organs such as the liver, lung, and ovary. 1 Many strategies have been employed for clinical CRC therapy such as chemotherapy, targeted therapy, and immunotherapy. 2 , 3 , 4 Although chemotherapy is still the preferred strategy for CRC treatment, most patients only show a good response at the first treatment, and long-term administration is always not effective in reducing tumor recurrence. 5 Besides, the well-known acute toxicity also confines the application of chemotherapy in some patients. 6 The emergence of targeted drugs such as cetuximab has greatly reduced drug toxicity and improved the effectiveness of CRC therapy, but the frequent gene mutations such as P53 and KRAS in CRC cells always lead to clinical resistance to targeted drugs. 7 , 8 Immune checkpoint therapy has brought a breakthrough in cancer therapy, and the programmed cell death 1 (PD1) antibody has been approved for the treatment of CRCs with high levels of microsatellite instability. 3 However, CRCs with low levels of microsatellite instability exhibit a conventional morphology with minimal tumor-infiltrating lymphocytes, resulting in limited response rates among patients. 9 Therefore, there is an urgent need for the development of novel anti-tumor drugs that can effectively treat CRC patients with different gene statuses. In contrast to traditional targeted drugs, photodynamic therapy (PDT) destroys cancer cells immediately without considering their genetic status, making it a promising approach for treating various types of CRC. 10 PDT contains two individual non-toxic components, namely a laser device and a photosensitizer, 11 among which the laser device is used to generate a specific laser with an appropriate wavelength to activate the photosensitizer, 12 and the photosensitizer is used to target tumors and generates reactive oxygen species (ROS), especially singlet oxygen ( 1 O 2 ) upon laser irradiation to induce cell death. 13 PDT-induced cell death always promotes the release of tumor-associated antigens into the microenvironment, these released tumor-associated antigens can be presented by antigen-presenting cells to activate cytotoxic T cells for specific tumor killing. 14 Besides, several damage-associated molecular patterns related to immunogenic cell death (ICD) have been demonstrated, including the release of large amounts of ATP and high-mobility group box 1 (HMGB1) into the extracellular milieu, and the translocation of calreticulin (CRT) from the endoplasmic reticulum to the cell surface. 15 , 16 Despite being approved for clinical use 30 years ago, PDT is not widely utilized in cancer therapy due to the lack of suitable photosensitizers. Although first-generation photosensitizers such as hematoporphyrin and their derivatives show excellent photophysical and electrochemical properties, the self-quenching behavior, complex composition, and poor photochemical stability significantly impede their practical applications. 17 , 18 Second-generation photosensitizers such as chlorin E6 (Ce6) possess specific molecular structures, enhanced tumor-targeting capabilities, increased ROS production, and augmented cell-killing potential. 19 Ce6 is a degradation product of chlorophyll and generates numerous ROS upon the irradiation of 650–700 nm, and its derivatives such as talaporfin sodium and temoporfin have been approved for clinical cancer therapy. 20 , 21 The third-generation photosensitizers feature the highest targeting ability and suitable half-lives achieved through the conjugation of second-generation photosensitizers with targeting molecules such as antibodies, peptides, or nanoparticles. 22 Polypeptide is a kind of natural material with protein homology, good biocompatibility, and low toxicity. As a representative of the new generation of biological materials, polypeptide structure is simple and easy to synthesize, indicating a potential use in encapsulating and transporting small molecule drugs. 23 , 24 Gly-Phe-Phe-Tyr (GFFY) peptide was reported to possess the capability of self-assembling and was used for the development of different drugs or biomaterials. A highly sensitive aggregation-induced emission (AIE) fluorescent light-up probe TPE-GFFYK (DVEDEE-Ac) was designed based on the peptide GFFY, which can induce the ordered self-assembly of AIE luminogen (AIEgen), yielding close and tight intermolecular steric interactions to restrict the intramolecular motions of AIEgens for excellent signal output. 25 The naphthylacetic acid-modified D-enantiomeric GFFY (D-Nap-GFFY) can form a nanofiber hydrogel which is selectively taken up by antigen-presenting cells, and D-Nap-GFFY-encapsulated T317 (D-Nap-GFFY-T317) enhances dendritic cell maturation and infiltration into tumors, increases CD3 + /CD8 + cells in tumors, and inhibits tumor angiogenesis. 26 The naphthylacetic acid-modified GFFY (Nap-GFFY) also is a novel vaccine adjuvant, antigens can be easily incorporated into the hydrogel by a vortex or by gently shaking before injection, and the vaccines can stimulate strong CD8 + T-cell responses, which significantly inhibits tumor growth. 27 In addition, a naproxen acid-modified tetra peptide of GFFY (Npx-GFFY) hydrogels enhances the protection of the H7N9 vaccine and is a promising adjuvant for H7N9 vaccines against highly pathogenic H7N9 virus. 28 Besides, a previous study confirmed that the hydrogel formed by GFFY peptides has good stability in terms of both humoral immunity and anti-tumor cellular immunity. 29 The diameter of self-assembled particles formed by GFFY peptides varies depending on the coupling molecules used, but most macroparticles show a size of approximately 100 nm. 30 This size ensures that the macroparticles can target and penetrate tumor tissues through the enhanced permeability and retention (EPR) effect, a well-established mechanism by which macroparticles ranging from 100 nm to 800 nm in size enter solid tumors. 31 In this study, we have developed a third-generation photosensitizer, namely Ce6-GFFY, by combining the peptide GFFY with the Ce6 molecule. A series of experiments were conducted to investigate the functional mechanism of Ce6-GFFY in CRC therapy. Our findings indicate that Ce6-GFFY forms macroparticles, effectively targets and accumulates in tumor tissues, and induces significant ROS production in cancer cells upon the irradiation of a 660 nm laser. Additionally, Ce6-GFFY effectively inhibits the growth of both primary and metastatic tumors through the induction of ICD, demonstrating a promising application for the clinical treatment of CRC. HCT116 (human) and CT26 (mouse) CRC cell lines were purchased from ATCC (Rockville, MD, USA) and maintained in DMEM or 1640 medium at 37 °C in 5% CO 2 . The medium was supplemented with 10% fetal bovine serum (FBS; Thermo Fisher Scientific, Waltham, MA, USA). All cell lines were authenticated by short tandem repeat profiling and were tested for mycoplasma contamination. GFFY was synthesized by Synpeptide (Shanghai, China), chlorin e6 was purchased from Macklin Biochemical (Shanghai, China), 4′,6-diamidino-2-phenylindole and 2′,7′-dichlorodihydrofluorescein diacetate were purchased from Beyotime Biotechnology (Shanghai, China), propidium iodide and annexin V-FITC apoptosis detection kit (#BMS500FI-300) was purchased from Thermo Fisher Scientific (Waltham, MA, USA), and rabbit anti-CRT antibody was purchased from Abcam (Boston, MA). Ce6-GFFY was synthesized through a dehydration condensation reaction between the carboxyl of Ce6 and the amino group of GFFY, and purified using high-performance liquid chromatography to obtain the compound binding to only one GFFY peptide, then the compound was further identified using mass spectrometry. Ce6-GFFY was suspended in phosphate-buffered saline (PBS) (0.1 mg/mL) at room temperature for 10 min, then the particle size, zeta potential, and polydispersity index were measured by dynamic light scattering according to the manufacturer’s protocol. The morphological feature of Ce6-GFFY was examined using transmission electron microscopy (Tecnai Spirit T12) according to the manufacturer’s protocol. For stability examination, Ce6-GFFY macroparticles were incubated at 37 °C for 24 h, 48 h, 72 h, and 7 d, respectively, then the particle size and polydispersity index were detected by dynamic light scattering. Besides, Ce6-GFFY macroparticles were frozen at −80 °C for 1 h, and thawed at 37 °C, and then the particle size was examined by dynamic light scattering. Cell viability was measured using a CCK-8 cell counting kit (Beyotime Biotechnology, Shanghai, China). 7000 cells were seeded in 96-well plates and treated with drugs at various concentrations for 1 h and then treated with or without laser irradiation for 1 min (660 nm, 0.02 W/cm 2 ), followed by incubation at 37 °C for 24 h. After the addition of the CCK-8 reagent, the cells were continually incubated at 37 °C for 3 h before being detected by a microplate reader (TECAN, Victoria, Austria). For cell endocytosis detection, CT26 and HCT116 cells were treated with Ce6 molecules (5 μM) or Ce6-GFFY (5 μM) at 37 °C for 1 h. For ROS detection, cells were treated with Ce6 molecules (5 μM), GFFY peptide (5 μM), or Ce6-GFFY (5 μM) at 37 °C for 1 h and then treated with or without laser irradiation for 1 min (660 nm, 0.02 W/cm 2 ). Afterward, the cells were collected and stained with DCFH-DA at 37 °C for 20 min. For cell death analysis, cells were treated with Ce6 molecules, GFFY peptide, or Ce6-GFFY (CT26, 10 μM; HCT116, 5 μM) at 37 °C for 1 h and then treated with or without laser irradiation for 1 min (660 nm, 0.02 W/cm 2 ). After incubation at 37 °C for 24 h, the cells were collected and stained with propidium iodide (1:100) and annexin V-FITC (1:200). For examination of CRT expression, cells were treated with Ce6 molecules, GFFY peptide, or Ce6-GFFY (CT26, 10 μM; HCT116, 5 μM) at 37 °C for 1 h and then treated with or without laser irradiation for 1 min (660 nm, 0.02 W/cm 2 ). After incubation at 37 °C for 8 h, the cells were collected and blocked with 5% bull serum albumin for 10 min and then incubated with a primary anti-calreticulin antibody , followed by the incubation of an Alexa Fluor 488-conjugated secondary antibody . For tissues, primary and metastatic tumors were digested into single cells using the KeyGEN tissue dissociation kit (#KGA829, KeyGEN BioTECH) following standard protocol. Digested tumors were mashed through 40 μm filters into PBS and were centrifuged at 300 g and 4 °C for 5 min; the obtained cells were blocked with 5% bull serum albumin for 10 min and incubated with a surface antibody mixture at room temperature for 2 h. Antibodies against CD45 , CD3 , CD8a , CD11b , and Gr-1 were used. The above treated cells were determined by flow cytometry (Beckman–Coulter) and analyzed by FlowJo v.10.8.1 software. For cell endocytosis detection, CT26 and HCT116 cells were treated with Ce6 molecules (5 μM) or Ce6-GFFY (5 μM) at 37 °C for 1 h, and then the cells were fixed with 4% paraformaldehyde for 15 min and stained with DAPI for 20 min at room temperature. For ROS detection, cells were treated with Ce6 molecules (5 μM), GFFY peptide (5 μM), or Ce6-GFFY (5 μM) at 37 °C for 1 h and treated with or without laser irradiation for 1 min (660 nm, 0.02 W/cm 2 ); afterward, the cells were stained with DCFH-DA at 37 °C for 20 min. For examination of CRT expression, cells were treated with Ce6 molecules, GFFY peptide, or Ce6-GFFY (CT26, 10 μM; HCT116, 5 μM) at 37 °C for 1 h and treated with or without laser irradiation for 1 min (660 nm, 0.02 W/cm 2 ); afterward, the cells were incubated at 37 °C for 8 h. After being fixed using 4% paraformaldehyde for 10 min and blocked with 5% bull serum albumin at 4 °C overnight, the cells were incubated with a primary anti-calreticulin antibody , followed by the incubation with an Alexa Fluor 488-conjugated secondary antibody . Then, the cells were stained with DAPI at room temperature for 10 min. For tissues, paraffin-embedded samples were sectioned at 4 μm thickness. Antigen retrieval was performed by a pressure cooker (at 95 °C for 10 min) in citrate antigen retrieval solution . The sections were then blocked in PBS containing 2% goat serum albumin at room temperature for 1 h. Then, the sections were incubated in the mixture of two primary antibodies at 4 °C overnight. The following primary antibodies were used: rat anti-Gr-1 , mouse anti-Cytokeratin Pan , and rabbit anti-CD8 . The sections were washed with cold PBS and incubated with the mixture of two secondary antibodies raised in different species at room temperature in the dark for 2 h. The following secondary antibodies were used: Alexa Fluor 488 labeled anti-rabbit , Alexa Fluor 594 labeled anti-rat , Alexa Fluor 488 labeled anti-mouse , and Alexa Fluor 594 labeled anti-mouse . Then, sections were counter-stained with DAPI at room temperature for 10 min. The above treated samples were examined by laser confocal fluorescence microscopy and analyzed using Zeiss v.3.1 software. Four-to-six-week-old BALB/c mice and BALB/c nude mice were purchased from Guangdong Medical Laboratory Animal Center (Guangzhou, China). All mice were maintained under standard conditions and treated according to institutional guidelines for animal care. For primary tumor therapy, 2 × 10 5 CT26 cells were suspended in a 1:1 mixture of PBS and matrigel and subcutaneously injected into the flanks of the mice. When the volume of tumors reached 70 mm 3 , the mice were randomized into treatment and control groups. The treatment groups received tail intravenous injections of GFFY (2.5 mg/kg), Ce6 (2.5 mg/kg), or Ce6-GFFY (5 mg/kg), and the control group received PBS treatment, both groups were treated with once 660 nm laser irradiation for 8 min (1 min on, 1 min off; 4 cycles) on the tumor region at a power of 0.2 W/cm 2 . Tumor volume and mouse body weight were recorded every three days, and tumor tissues were collected and weighed at the end of treatment. Main organs such as the heart, liver, spleen, lung, and kidney were collected for pathological analysis and the blood was collected for blood routine examination. For metastatic tumor therapy, 2 × 10 5 CT26 cells were subcutaneously injected into the right flank of the mice (primary tumor), and 1 × 10 5 CT26 cells into the left flank (metastatic tumor). When the volume of primary tumors reached 200 mm 3 , the mice were randomized into treatment and control groups. The treatment groups received tail intravenous injections of Ce6-GFFY (5 mg/kg) and the control groups received the treatment of PBS; both groups were treated with once 660 nm laser irradiation for 8 min (1 min on, 1 min off; 4 cycles) on the primary tumor region at a power of 0.2 W/cm 2 . Tumor volumes were recorded every two days and tumor tissues were weighted and collected for further analysis such as immunofluorescence and flow cytometry detection at the end of treatment. For in vivo imaging, Ce6-GFFY (5 mg/kg) and Ce6 molecules (2.5 mg/kg) were injected into mice burdened with or without xenograft tumors through the tail vein (100 μL/mouse), and the fluorescence intensity of mice or main tissues such as brain, heart, liver, spleen, lung, kidney, intestine, and stomach was detected and analyzed with an IVIS spectrum imaging system (PerkinElmer, MA, USA). All animal experiments were approved by The Institutional Animal Care and Use Committee at Sun Yat-sen Cancer Center. Statistical analyses were performed using GraphPad Prism 8. Experiments were performed with 3 biological replicates, and the data from three independent experiments were presented as mean ± standard deviation and were compared using an unpaired t -test (groups ≤2) or ordinary one-way ANOVA (groups ≥3), and data with two independent variables was analyzed using two-way ANOVA. P < 0.05 was considered statistically significant . Ce6-GFFY was synthesized by coupling chlorin-e6 with peptide GFFY. To ensure a relative homogeneity of the Ce6-GFFY molecules used in subsequent experiments, we further performed high-performance liquid chromatography purification after the chemical synthesis reaction and obtained a compound binding to only one GFFY peptide . Besides, the Ce6-GFFY molecules were further confirmed using mass spectrometry . We also carried out the proton nuclear magnetic resonance analysis to identify the molecular feature of Ce6-GFFY. The nuclear magnetic resonance data successfully identified the distribution of 1 H on different functional groups, indicating that the molecular structure of Ce6-GFFY is relatively complex . However, the information provided by nuclear magnetic resonance is limited, making it difficult to determine the specific coupling site of the peptide GFFY on the Ce6 molecule. In fact, almost every photosensitizer developed based on Ce6 has encountered structural confirmation challenges. For example, talaporfin is a photosensitizer synthesized by coupling a single aspartic acid to the carboxyl group of Ce6 and has been approved for clinical use, but it was impossible to determine the coupling position of this amino acid for a long time. However, based on the chemical synthesis processes, we can make reasonable conclusions about the molecular structure of the photosensitizer. According to the previous studies, an anhydride will be firstly formed between the Ce6 15 2 and 13 1 carboxylic acid groups during the synthesis of Ce6-based photosensitizers, and this is more likely than a larger ring anhydride between the 17 3 and 15 2 acids. 32 This phenomenon has been verified in a wide variety of nucleophiles such as ethoxide, propylamine, isopropylamine, ethanolamine, p-tolylthiolate, phenoxide, isobutoxide, and benzyloxide; all of them yield the 15 2 -conjugates, with several of these structures being confirmed by single-crystal X-ray structures. 33 For talaporfin, the aspartic acid nitrogen atom undergoes nucleophilic attack upon the aliphatic side of the anhydride to produce the 15 2 conjugates and the structure has also been demonstrated using single-crystal X-ray diffraction. 34 In this study, the synthesis processes of Ce6-GFFY are the same as that of talaporfin, so the coupling position of the peptide GFFY is most likely to be on the 15 2 carboxylic acid group of Ce6 . Figure 1 Synthesis and characterization of Ce6-GFFY. (A) Schematic diagram of Ce6-GFFY synthesis. DCC, N, N′-dicyclohexylcarbodiimide; DMAP, 4-dimethylaminopyridine; CH 2 CL 2 , dichloromethane. (B) The size distribution of Ce6-GFFY macroparticles was analyzed using DLS. d, diameter; PDI, polydispersity index; DLS, dynamic light scattering. The data are representative of five independent experiments. (C) The Zeta potential of Ce6-GFFY macroparticles was analyzed using DLS. Blank, PBS. The data are representative of five independent experiments. (D) Ce6-GFFY macroparticle image was photographed by transmission electron microscopy. Scar bar, 100 nm. (E, F) Particle size (E) and polydispersity index (F) of Ce6-GFFY macroparticles incubated at 37 °C at different times as indicated was detected by DLS. The data are representative of five independent experiments. (G) Particle size of Ce6-GFFY incubated at room temperature (normal) or underwent −80 °C/37 °C freezing-thawing (Freeze-Melt) was detected by DLS. The data are representative of five independent experiments. Figure 1 Then, we examined the molecular characteristics of Ce6-GFFY from various perspectives. Dynamic light scattering analysis showed that Ce6-GFFY formed macroparticles when suspended in PBS, the average diameter of the polymers was 158.7 ± 2.8 nm , and the zeta potential was −23.1 ± 0.9 mV . We further confirmed the aggregation of Ce6-GFFY molecule using transmission electron microscopy, and the data showed that Ce6-GFFY formed irregular polymer with a uniform size distribution . Then, we explored the stability of Ce6-GFFY in different conditions. The Ce6-GFFY solution was incubated at 37 °C for different times, then the particle size and average polydispersity index were detected. Our results showed that there were almost no changes in the particle size during the incubation, even after seven days of incubation, indicating that Ce6-GFFY macroparticles had a high stability in the normal store and transport conditions . Moreover, the size of Ce6-GFFY macroparticles also remained stable after repeated freezing (−80 °C) and thawing (37 °C), which further identified the high stability of Ce6-GFFY macroparticles . Above all, Ce6-GFFY molecules form a uniform macroparticle aggregation in solution, and the particles remain stable under extreme conditions. Successfully entering cells through endocytosis is the prerequisite for photosensitizers to exert anti-tumor effects, thus we first focused on exploring the uptake of Ce6-GFFY by CRC cells. CRC cells derived from mouse (CT26) and human (HCT116) were treated with Ce6-GFFY or Ce6 molecules, respectively. The uptake of the agents was determined through confocal laser scanning microscopy due to the specific fluorescence produced by Ce6 molecules. Flow cytometry analysis showed that the Ce6-GFFY uptake of CT26 and HCT116 cells was much higher than Ce6 molecules, which enter the cells via free diffusion . Confocal laser scanning microscopy also demonstrated that cells treated with Ce6-GFFY had a noticeable aggregation of Ce6 fluorescence in contrast to cells treated with Ce6 molecules, indicating that Ce6-GFFY has an optimal cellular endocytic activity . Figure 2 Ce6-GFFY penetrates colorectal cancer cells and generates ROS. CT26 and HCT116 cells were treated with indicated agents at 37 °C for 1 h and treated with or without 660 nm laser irradiation for 1 min at a power of 0.02 W/cm 2 . The data are representative of three independent experiments. (A) Cells were treated with 5 μM Ce6-GFFY or Ce6 molecules and then subjected to flow cytometry determination and the Ce6 positive cells were analyzed. (B, C) Cells were treated with 5 μM Ce6-GFFY or Ce6 molecules; the cellular fluorescence was determined by confocal laser scanning microscopy (B) and the mean fluorescence intensity was analyzed (C). Red, Ce6; Blue, 4′,6-diamidino-2-phenylindole (DAPI); MFI, mean fluorescence intensity. Scale bar: 40 μm. (D) Cells were treated with agents as indicated and then treated with or without laser irradiation; the cellular ROS levels were detected using flow cytometry assays and the ROS positive cells were analyzed. ROS, reactive oxygen species. (E, F) 5 μM GFFY peptide, Ce6-GFFY, Ce6 molecules, or PBS treated cells with or without laser irradiation were stained with ROS probe DCFH-DA at 37 °C for 20 min; the cellular fluorescence was determined by confocal laser scanning microscopy (E) and the mean fluorescence intensity was analyzed (F). DCFH-DA, 2′,7′-dichlorodihydrofluorescein diacetate. “L” in “PBS + L”, “GFFY + L”, “Ce6+L”, “Ce6-GFFY + L”: laser irradiation. MFI, mean fluorescence intensity; green, DCFH-DA; BF, bright field. Scale bar in CT26: 40 μm; HCT116: 50 μm. Statistical analyses were performed using one-way ANOVA except for (C), which was performed using unpaired t -test; bars, standard deviation; ∗∗ P < 0.01; ∗∗∗ P < 0.001; ∗∗∗∗ P < 0.0001. Figure 2 Generally, PDT exerts its tumor cell-killing ability through ROS generated by photosensitizers under laser irradiation with specific wavelength. 35 To confirm the ability of Ce6-GFFY to generate ROS in tumor cells, we treated CT26 and HCT116 cells with Ce6-GFFY, and subsequently monitored the intracellular ROS levels using a molecular probe, namely 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA). Flow cytometry analysis revealed that Ce6-GFFY induced a higher level of ROS compared with the treatment with either GFFY peptides or Ce6 molecules alone upon laser irradiation, and minimal ROS generation was observed in cells treated with Ce6-GFFY without laser activation . Confocal laser scanning microscopy analysis also revealed that the Ce6-GFFY treated cells exhibited a substantial increase in ROS production upon 660 nm laser irradiation, whereas the levels of ROS were minimal in cells treated with GFFY peptides or Ce6 molecules, and negligible ROS generation was observed in non-irradiated cells . In summary, Ce6-GFFY macroparticles can effectively penetrate CRC cells and induce a substantial production of ROS. The cellular metabolism of ROS is tightly regulated, and excessive ROS production within a short time can result in cellular dysfunction and eventual cell death. To confirm the efficacy of ROS generated by Ce6-GFFY in suppressing CRC cells, we exposed CT26 and HCT116 cells treated with Ce6-GFFY to brief laser irradiation and assessed their proliferation status. Our results demonstrated that the ROS generated by a low concentration of Ce6-GFFY upon laser irradiation is sufficient to significantly impede the proliferation of CT26 (IC50 = 6.268 μM) and HCT116 (IC50 = 5.299 μM) cells . Figure 3 Ce6-GFFY suppresses the proliferation of colorectal cancer cells. CT26 and HCT116 cells were treated with indicated agents at 37 °C for 1 h and then treated with or without 660 nm laser irradiation for 1 min at a power of 0.02 W/cm 2 . The data are representative of three independent experiments. (A) Cells were incubated at 37 °C for 24 h after being treated with laser irradiation and the indicated dose of GFFY peptide, Ce6, and Ce6-GFFY, and then cell proliferation was determined using CCK-8 assays. The IC50 of Ce6-GFFY under laser irradiation was analyzed. IC50, 50 % inhibitory concentration. (B) Cells (CT26, 10 μM; HCT116, 5 μM) were stained with annexin V and propidium iodine dye, and then the ratio of dead cells was analyzed using flow cytometry. (C) The ratio of necrotic and apoptotic cells in the combined treatment of Ce6-GFFY and laser irradiation were analyzed. (D – F) After treated with the indicated agents (CT26, 10 μM; HCT116, 5 μM), cells were incubated at 37 °C for 8 h and stained with a CRT antibody, then the expression of CRT was analyzed using flow cytometry (D) and confocal laser scanning microscopy (E), and the CRT fluorescence was analyzed (F). CRT, calreticulin; green, CRT; blue, DAPI; MFI, mean fluorescence intensity. Scale bar, 40 μm “L” in “CT26+L”, “HCT116+L”, “PBS + L”, “GFFY + L”, “Ce6+L”, “Ce6-GFFY + L”: laser irradiation. Statistical analyses were performed using one-way ANOVA; bars, standard deviation; ∗∗ P < 0.01; ∗∗∗ P < 0.001; ∗∗∗∗ P < 0.0001. Figure 3 Next, we investigated the mechanisms underlying the inhibitory effects of Ce6-GFFY on tumor cell proliferation. Flow cytometry analysis was carried out using propidium iodide and annexin V staining to explore the status of Ce6-GFFY treated CT26 and HCT116 cells after brief laser irradiation. The results demonstrated that the combined use of Ce6-GFFY and laser irradiation induced a mortality rate of 73% in CT26 cells and 48% in HCT116 cells, while most cells in other groups remained viable . Further analysis revealed that part of the dead cells derived from the treatment of Ce6-GFFY and laser irradiation were necrotic (52% in CT26 cells and 21% in HCT116 cells) and apoptotic (21% in CT26 cells and 27% in HCT116 cells) . ROS-induced cell death always induces the alteration of damage-associated molecular patterns, which plays an important role in ICD. Damage-associated molecular patterns can be detected by hallmarks such as HMGB1, ATP, and surface-exposed CRT, 15 , 36 among which CRT is a classical hallmark that acts as an “eat-me” signal to stimulate dendritic cells maturation and promote T cell-mediated antitumor immunity. 37 , 38 Therefore, we further examined the expression of CRT in CRC cells induced by Ce6-GFFY. CT26 and HCT116 cells were treated with the combination of laser irradiation and Ce6-GFFY or other agents, and then the expression of CRT was evaluated through flow cytometry analysis using a CRT antibody. The results showed that the expression of CRT in Ce6-GFFY treated CT26 and HCT116 cells was much higher than other groups . Moreover, the immunofluorescence assays performed using confocal laser scanning microscopy further demonstrated that the CRT expression in Ce6-GFFY group was significantly up-regulated in both cells, while no significant changes were observed in other groups . These findings suggest that Ce6-GFFY can effectively induce ICD in CRC cells. In general, the combination of Ce6-GFFY and laser irradiation induces ICD in CRC cells, indicating a promising application of Ce6-GFFY for CRC therapy. Ce6-GFFY induced ICD indicates that Ce6-GFFY could be a potent anti-tumor drug candidate, we thus explored its metabolism and tumor-targeting ability in mice before investigating its potential therapeutic effect. The kinetics of Ce6-GFFY metabolism were determined in mice through tail vein injection. Living imaging analysis revealed that Ce6-GFFY exhibited a prolonged retention time in the mice for over 48 h, while Ce6 control molecules were almost cleared within 12 h after injection . The Ce6 fluorescence statistics indicate that the half-life of Ce6-GFFY was 10 h in mice, whereas that of Ce6 molecules was only 3 h, indicating that the macroparticles formed by Ce6-GFFY effectively extended the retention time of the drug in vivo . We also collected the main organs of mice treated with Ce6-GFFY for further imaging analysis and found that the Ce6-GFFY accumulation mainly occurred in the liver, stomach, and intestine, showing a typical metabolism process of protein drugs . Then, we explored the tumor-targeting ability of Ce6-GFFY in mice bearing CT26-derived tumors. Living imaging analysis showed that Ce6-GFFY accumulated rapidly in the tumor regions after tail vein injection . Remarkably, Ce6-GFFY exhibited stable aggregation in tumor tissues even after 24 h of administration, whereas it was almost entirely cleared from normal tissues except for the liver, which serves as a metabolic organ for large particles . In summary, previous studies and our data both demonstrate that macroparticles exhibit a prolonged half-life in vivo , thus enhancing the drug uptake by tumors and extending the therapeutic window of drugs. 39 , 40 Besides, the tumor targeting ability of Ce6-GFFY indicates that it is a promising agent for clinical CRC therapy. Figure 4 Pharmacokinetics and tumor-targeting ability of Ce6-GFFY. (A) Mice were treated with 2.5 mg/kg Ce6 control or 5 mg/kg Ce6-GFFY through tail vein injection, and the Ce6 luminescence was detected at the indicated time after the injection. n = 5. (B) The half-life of Ce6-GFFY and Ce6 molecules was analyzed based on the Ce6 luminescence changes. T 1/2 , half-live. (C) Main organs from mice in (A) (12 h) were collected for imaging. n = 3. (D) 2.5 mg/kg Ce6 control and 5 mg/kg Ce6-GFFY were injected into mice bearing CT26-derived tumors through tail vein, and the Ce6 fluorescence intensity was measured and analyzed at the indicated time after the injection ( n = 3). Red cycle, tumor region. (E) Main organs, along with the tumors from mice in (D) (24 h) were collected for imaging and analysis. n = 3. Figure 4 Considering the significant advantages of Ce6-GFFY in terms of metabolism and tumor targeting, we subsequently investigated its potential anti-tumor activity. A subcutaneous tumor mouse model was established using CT26 cells, and the mice were treated with Ce6-GFFY (5 mg/kg) once, followed by 8 min of laser irradiation 6 h after injection; tumor growth was assessed every three days . The data demonstrated that the combination of Ce6-GFFY and laser irradiation significantly inhibited tumor growth after a single treatment, while no significant change was observed in the groups treated with other agents combined with laser irradiation . Moreover, tumor growth curve statistic also confirmed the potent inhibition of tumor growth induced by the combined use of Ce6-GFFY and laser irradiation . Importantly, there was no decrease in mice weight during the treatment, indicating a minimal side effect . Figure 5 Ce6-GFFY prohibits colorectal cancer growth and has little side effects. Agents were injected through the tail vein of the CT26-derived subcutaneous tumor mice model, and the 660 nm, 0.2 W/cm 2 laser irradiation (1 min on, 1 min off; 4 cycles) was performed 6 h after the injection. Only a single dose was administered during the entire treatment cycle. (A) Schematic diagram of the PDT strategy. PDT, photodynamic therapy. (B – D) Mice were treated with PBS, GFFY (2.5 mg/kg), Ce6 (2.5 mg/kg), or Ce6-GFFY (5 mg/kg), and tumor tissues were collected (B) and weighed (C) after treatment, and tumor growth curve was analyzed during treatment (D). n = 4. (E) Mice body weight was analyzed during treatment. n = 4. (F) Pathological analysis of hearts, livers, spleens, lungs, and kidneys derived from the indicated agents treated mice using hematoxylin-eosin (H&E) staining. n = 4. “L” in “PBS + L”, “GFFY + L”, “Ce6+L”, “Ce6-GFFY + L”: laser irradiation. Scale bar, 200 μm. Statistical analyses were performed using two-way ANOVA; bars, standard deviation; n.s., not significant; ∗∗∗ P < 0.001; ∗∗∗∗ P < 0.0001. Figure 5 We further evaluated the toxicity of Ce6-GFFY in mice using pathologic analysis. At the end of the treatment, we performed the murine blood routine analysis, data showed that the combined administration of Ce6-GFFY and laser irradiation did not elicit any significant inflammatory responses . Besides, the molecular indices indicated that Ce6-GFFY did not exert an impact on the hepatic and renal function of mice . Histopathological analysis of organs such as heart, liver, spleen, lung, and kidney of Ce6-GFFY and laser irradiation co-treated mice showed that there was no apparent toxicity in mice . Therefore, our data demonstrate that the combined use of Ce6-GFFY and laser irradiation can effectively suppress CRC growth through a single treatment with no obvious side effects, indicating that Ce6-GFFY has good drug properties. Activating the anti-tumor immunity is an effective way to suppress cancer recurrence and metastasis. 41 , 42 Considering that our cellular-level results demonstrated that the combination of Ce6-GFFY and laser irradiation induced significant immunogenic cell death, and in vivo experiments confirmed the drug ability of Ce6-GFFY. Therefore, we conducted a comprehensive investigation to determine whether the PDT of Ce6-GFFY could enhance the anti-tumor immune responses in mice. We first constructed a mouse model using CT26 cells, which were transplanted subcutaneously in the left and right flanks of the BALB/c mouse, respectively, to mimic the primary and metastasis tumors. Then, the mouse was administered with Ce6-GFFY via tail vein injection, followed by laser irradiation on the primary tumor area while the metastatic tumor remained unirradiated . The data showed that the growth of primary tumors was significantly suppressed by the combined use of Ce6-GFFY and laser irradiation . Interestingly, the growth of metastasis tumors was also inhibited, even in the absence of irradiation . Tumor weight analysis further substantiated the inhibitory effect on both primary and metastatic tumor growth, thereby suggesting a potential induction of anti-tumor immunity through photodynamic treatment mediated by Ce6-GFFY . Figure 6 Ce6-GFFY activates anti-tumor immunity and suppresses metastatic tumor growth. Primary and metastasis tumor model was constructed by subcutaneously transplanting CT26 cells in the left (metastasis tumor) and right flanks (primary tumor) of BALB/c mouse. Then Ce6-GFFY (5 mg/kg) was injected through the tail vein of the mouse, and the 660 nm, 0.2 W/cm 2 laser irradiation (1 min on, 1 min off; 4 cycles) was performed on the primary tumor 6 h after the injection. Only a single dose was administered during the entire treatment cycle. (A) Schematic diagram of the mouse model construction and PDT strategy. (B) Primary tumor tissues were collected and tumor growth was analyzed after treatment. n = 6. (C) Metastasis tumor tissues were collected and tumor growth was analyzed after treatment. n = 6. (D) Primary and metastasis tumors were weighed and analyzed. n = 6. (E) Primary and metastasis tumors were collected and dispersed into single cells for flow cytometry analysis, and the amount of cytotoxic T cells (CD45 + CD3 + CD8 + ) and myeloid-derived suppressor cells (MDSCs, CD45 + CD11b + Gr-1 + ) were analyzed. n = 3. (F, G) IF staining for CD8 + T cells (CD8) and MDSCs (Gr-1) in primary and metastasis tumors (F), and the number of positive cells per mm 2 was analyzed (G). n = 3. IF, immunofluorescence. “(L)” in “Primary (+L)”: laser irradiation. Scale bar, 20 μm. The data are representative of three independent experiments. Statistical analyses were performed using unpaired t -test; bars, standard deviation; ∗ P < 0.05; ∗∗ P < 0.01; ∗∗∗ P < 0.001; ∗∗∗∗ P < 0.0001. Figure 6 Cytotoxic T cells eliminate tumor cells by recognizing tumor-associated antigens, and thus their extensive infiltration into tumor microenvironment is essential for the induction of anti-tumor immunity. 43 In addition, myeloid-derived suppressor cells exert immunosuppressive effects by producing arginase-1, inducible nitric oxide synthase, and other inhibitory substances, thereby playing an important role in reshaping the tumor immune microenvironment. 44 , 45 Therefore, we subsequently focus on exploring the changes of cytotoxic T cells and myeloid-derived suppressor cells in tumors with or without photodynamic treatment using Ce6-GFFY. The flow cytometry analysis demonstrated that the number of cytotoxic T cells (CD45 + CD3 + CD8 + ) was increased and myeloid-derived suppressor cells (CD45 + CD11b + Gr-1 + ) were decreased in both primary and metastasis tumors, despite only the primary tumor being subjected to laser irradiation . Moreover, immunofluorescence assays further confirmed that the cytotoxic T cells (CD8 + ) were accumulated whereas the number of myeloid-derived suppressor cells (Gr-1 + ) was decreased in both primary and metastasis tumors . Together, our results demonstrate that Ce6-GFFY is a promising agent in activating anti-tumor immunity and treating metastatic CRC. Currently, early CRC is usually treated with surgical excision, and the advanced CRC is treated with chemoradiotherapy, targeted therapy or immunotherapy based on the genetic status such as the RAS/BRAF mutation, microsatellite instability/deficient mismatch repair. 46 , 47 However, there are certain limitations to current therapeutic strategies, for example, only about 15 % of CRC patients had deficient mismatch repair with high levels of microsatellite instability, and the proportion of stage III and IV CRC patients is even lower at 11% and 5%, respectively, 48 among which only 30%–50% CRC patients are responsive to immunotherapy. 9 , 49 , 50 Therefore, targeted drugs developed in novel strategies are urgently needed. Unlike traditional targeted drugs, PDT requires a combination of drug and instrument (laser) to work. 51 PDT consists of three necessary elements: photosensitizer, laser, and oxygen, among which photosensitizer determines the tumor-targeting ability and therapeutic effect of PDT. 52 PDT is theoretically characterized by reduced toxicity and repaid effect compared with traditional drugs, which led to its FDA approval for clinical use 30 years ago. 11 However, the clinical application of PDT in cancer therapy on a large scale has been limited due to the lack of safe and effective photosensitizers. In this study, a novel photosensitizer Ce6-GFFY was synthesized through the conjugation of the photosensitive molecule Ce6 with the self-assembling peptide GFFY. 53 Ce6-GFFY forms stable macroparticles with a diameter of approximately 160 nm in solution, and our data demonstrate that these particles possess excellent targeting ability for CRC and exhibit potent anti-cancer effects while causing minimal side effects; the novel photosensitizer Ce6-GFFY developed in this study can induce rapid and efficient ICD of tumor cells under laser irradiation, and thus the systemic anti-tumor immune response will be activated after irradiating at a specific tumor site; and the tumors at various metastatic sites will be eliminated via immune-mediated killing. Different from the current anti-tumor drugs, Ce6-GFFY kills tumor cells via a physical manner that ignores the gene status of CRC, and thus it has a great potential in CRC patients, especially those who cannot be treated with any existing therapeutics, such as clinical drug resistance. 54 The tumor-targeting mechanism of Ce6-GFFY macroparticles remains uncertain; however, the EPR effect may be the underlying mechanism. The intervascular spaces in tumors contain pores ranging in size from 100 nm to 780 nm, which allow the infiltration of macroparticles. 55 Previous studies have shown that the EPR effect primarily occurs in solid tumors due to their disorganized and abnormal vasculature compared with healthy tissues, along with the impaired lymphatic clearance from the tumor stroma, thus facilitating the penetration and retention of macroparticles in tumors. 31 , 56 Besides, the shape, as well as the softness of macroparticles also have a potential impact on tumor accumulation through the EPR effect. 57 Some studies have shown that the EPR effect is more potent when the surface of macroparticles distributes a negative charge. 58 Our data demonstrated that Ce6-GFFY macroparticles have an irregular shape and a negative charge (derived from the Ce6 molecule) on the surface, indicating that Ce6-GFFY macroparticles have a good EPR effect, which makes it effective in tumor targeting and penetrating. Photosensitizer is activated by laser, the wavelength of which is also contained in sunlight. Therefore, patients need to avoid exposure to sunlight for several days after receiving PDT, which has had a certain effect on their everyday lives. 59 To address this issue, the half-life of photosensitizer needs to be suitable, a half-life of several hours of the photosensitizer seems to be suitable for the clinical application of PDT, as the patients can return to their normal lives within hours of the end of the treatment. In addition to the suitable half-life of photosensitizer, intra-tumoral injection is another effective way to reduce the side effects of PDT, the photosensitizer is injected into the tumor tissue through an endoscope or a drainage tube, then the optical fiber is guided to the tumor site where the drug was injected for laser irradiation. 60 Compared with intravenous injection, the dose of photosensitizer used for intra-tumoral injection is very low, and laser irradiation is performed within minutes of the injection, thus little normal tissues would be penetrated by the drug during the treatment, as well as little side effects would emerge to patients. Traditional PDT strategies seem to be more suitable for superficial tumors (such as skin cancer) than internal tumors. 61 However, benefiting from the improvement of tumor-targeting ability and half-life of novel photosensitizers, the interventional PDT will play an important role in the treatment of a variety of tumors in the future. Ce6-GFFY macroparticle has an ideal tumor-targeting ability and a half-life of about 10 h in mice, indicating that Ce6-GFFY is a promising agent for CRC therapy. In this study, we developed a novel photosensitizer termed Ce6-GFFY by covalently combining a photo-responsive Ce6 molecule with GFFY peptide. Ce6-GFFY forms stable macroparticles with an average size of 160 nm in solution, and these macroparticles have an ideal tumor-targeting ability and a suitable half-life in mice. Ce6-GFFY macroparticles induce ICD through ROS when treated with 660 nm laser irradiation. The combined use of Ce6-GFFY and laser irradiation significantly activates anti-tumor immunity by promoting the infiltration of cytotoxic T cells and prohibiting the accumulation of myeloid-derived suppressor cells in tumors, thus suppressing the growth of both primary and metastatic CRCs. Our data indicate that Ce6-GFFY is a promising agent for CRC therapy with little side effects. | Review | biomedical | en | 0.999997 |
PMC11697063 | Tumors are formed because of many reasons. For example, when cells in the cell cycle lose their regulation, control of cell proliferation is lost. Under normal circumstances, such cells are eliminated by the immune recognition function or may escape from immune cell monitoring by changing their surface antigens. Tumor cells compete with each other and eventually develop into malignant tumors, leading to cancer occurrence. 1 , 2 In addition to changing their surface antigen as mentioned above, tumor cells can affect immune cells or tissue components, thereby creating an environment conducive to tumor growth. 3 Such an environment with hypoxia, 4 poor nutrients, 5 high acidity, 6 and an immunosuppressive microenvironment 7 contains tumor cells as well as immune cells such as T cells, dendritic cells (DCs), macrophages, myeloid-derived suppressor cells (MDSCs), and regulatory T cells . Other non-immune cells and cytokines also occupy crucial positions in the tumor microenvironment (TME). 8 , 9 Figure 1 The UPS regulates the TME. (A) E1 ubiquitin-activating enzyme activates the carboxyl group of the C terminus of ubiquitin in an ATP-dependent manner through the formation of high-energy thioester bonds. Next, the ubiquitin molecule, which binds to E2, is moved to the targeting protein with the help of E3 ligase. Then, substrate protein labeled with ubiquitin enters into 26s proteasome for degradation, broken down into polypeptides and small-molecule amino acids. Deubiquitinase is to avoid degradation of substrate proteins by removing the ubiquitin tag from the substrate proteins. (B) UPS regulates tumor cells in the TME by regulating the levels of proteins related to the cell cycle, energy metabolism, and angiogenesis. (C) UPS regulates the anti-tumor immunity of T cells by regulating the protein levels of PD-1/PD-L1 and some inflammation-related cytokines. It also regulates the anti-tumor immunity of immune cells in the TME by regulating the maturation and anti-inflammatory presentation ability of DCs, the differentiation and their cytokines secretion of MDSCs, and the polarization of macrophages. (D) UPS modulates their role in tumor progression by regulating the lipid metabolism of adipocytes and the transformation and protein formation of CAFs. (E) UPS regulates tumor progression by regulating the levels of metalloproteinases and collagen in the TME. UPS, ubiquitin-proteasome system; TME, tumor microenvironment; PD-1/PD-L1, programmed death-1/ligand-1; DC, dendritic cell; PMN/M-MDSC, polymorphonuclear/monocytic-myeloid-derived suppressor cells; CAFs, cancer-associated fibroblasts; ICAM1, intercellular adhesion molecule 1. Fig. 1 Being a part of post-translational protein modification, ubiquitination is closely related to various physiological cellular activities, including regulation of protein transcription and interactions, DNA replication, cell growth response, immune responses, and signal transduction. 10 , 11 , 12 Ubiquitin modification is a reversible enzymatic cascade wherein ubiquitin ligases and deubiquitinating enzymes precisely regulate substrates. The ubiquitin molecule is a 76-amino acid-long protein, where adjacent amino acids directly form proteins through covalent binding. This molecule includes seven lysines (K6, K11, K27, K29, K33, K48, and K63). 13 Ubiquitin is modified through monoubiquitination and polyubiquitination. When a single ubiquitin molecule is added to a substrate's lysine residue, monoubiquitination occurs. In polyubiquitination, ubiquitin molecules are added to a single ubiquitin molecule to form polyubiquitin chains. 14 , 15 Polyubiquitin chains K48 and K11 mainly mediate proteasomal degradation. However, K63-linked polyubiquitination, which is typically less common in tumors, is usually not involved in proteasomal degradation but is associated with cellular signal assembly and transduction and repair of damaged cells. 16 , 17 The substrate protein is labeled with a ubiquitin molecule and then degraded in the 26s proteasome. The ubiquitin-proteasome system (UPS) includes three classes of ubiquitinates E1 ubiquitin activating enzymes (E1s), E2 ubiquitin binding enzymes (E2s), and E3 ubiquitin ligases (E3s). Based on the supply of ATP, E1 transfers activated ubiquitin molecules to E2. Bound ubiquitin molecules interact with E2 to transfer the ubiquitin molecules to E3. 18 In this process, E3 ubiquitin ligases play a substantial role. E3 ubiquitin ligases can be categorized into three families based on their structural characteristics and operational mechanism: RING (really interesting new gene) E3s, HECT (homologous to E6-AP carboxyl terminus) E3s, and RBR (RING-between-RING) E3s. 19 RING E3s transfer ubiquitin molecules from E2 to the lysine of the substrate, while HECT and RBR E3 ligases can catalyze the transfer of ubiquitin molecules from E2 to the cysteine of the substrate. 20 In deubiquitination, ubiquitin molecules are removed from the substrate using deubiquitinating enzymes. The deubiquitinase protein family removes ubiquitin molecules from substrate proteins by hydrolyzing the peptide or isopeptide bonds at the carboxyl-terminal end of the ubiquitin, which is opposite to the functions of E3 ubiquitin ligase. 21 Based on their sequence and structural domain characteristics, these deubiquitinating enzyme classes can be categorized into five families: UCH (ubiquitin carboxy-terminal hydrolases) family, USP/UBP (ubiquitin-specific protease and ubiquitin-binding protein) family, OTU (ovarian tumor proteases) family, MJD (Machado-Joseph domain) family, and JAMM (JAB1/MPN/MOV34) family. 22 Ubiquitination modification, as a critical post-translational protein modification , can regulate tumor progression by targeting various TME-related cells and proteins. Tumor cells undergo uncontrolled rapid proliferation and metastasis, which is a characteristic that distinguishes them from normal cells. The entry of cells into the cell cycle for mitosis in order to generate new cells requires the involvement of various cyclins/cyclin-dependent kinases to ensure normal cell proliferation. Ubiquitination, a post-transitional modification, is crucial for regulating the stability of various cyclins and cyclin-dependent kinases during cell cycle progression. Two important E3 ligases such anaphase-promoting complex/cyclosome (APC/C) and Skp1-Cul1-F-box (SCF) play crucial roles in regulating cell cycle proteins 23 , 24 . These two ubiquitin ligases are Cullin RING E3 ligase family members. APC/C is linked to the complex consisting of a scaffold Cullin-like protein APC2 and a coactivator subunit. This ubiquitin ligase regulates G1 phase cell activity by binding to coactivators cell division cycle 20 homolog and e-cadherin (CDH1) and subsequently regulates mitotic progression. 24 SCF contains an adaptor protein SKP1, scaffold protein CUL1, and a RING finger protein 1 (RBX1/RNF1) recruiting E2, which together address DNA damage in the cell cycle by binding to other ubiquitin ligases including FBXW7 (WD repeat domain containing 7), β-Trcp (β-transducin repeat-containing proteins), and SKP2. 25 Two ubiquitin ligases also interact. For example, the SCF/SKP 2 axis regulates APC/CDH1-mediated C-terminal binding protein interacting protein degradation to regulate p-RB in the G2 phase by inhibiting transcriptional gene responses of the E2F complex and regulating the stability of the cyclin/cyclin-dependent kinase inhibitor p27 by cooperating with SCF/SKP2 axis and APC/CDH1 to induce G2 retardance. 26 Thus, the imbalance in the expression of APC/C and SCF and SCF-associated ubiquitin ligases may affect the cell cycle, which thus affects cell proliferation and mediation of tumorigenesis. 27 , 28 For example, USP10 can up-regulate tumor development in esophageal squamous cell carcinoma by modifying cyclin Anillin in concert with the CDH1 of APC/C. 29 p53 is a key protein detected in the G1 phase and even in the whole cell cycle. It is a well-known tumor suppressor gene. 30 , 31 The majority of tumorigenesis is associated with p53 mutations. 32 Mouse double minute 2 (MDM2) functions as a classical ubiquitin ligase that regulates p53 protein degradation. MDM2 can undergo self-ubiquitination, but such ubiquitination is unstable and may cause aberrant p53 activation. 33 MDM4 (also known as MDMX) interacts with the MDM2 protein to ensure that p53 transcriptional activity is normal. 34 Furthermore, ubiquitin ligases such as tripartite motif-containing 28 (TRIM28), RNF2, and Cul4a can also promote p53 degradation by cooperating with MDM2. 35 , 36 , 37 , 38 Other ubiquitin-related enzymes such as TRIM31 can form a competitive relationship with MDM2 and prevent MDM2 from interacting with p53, leading to p53 activation in breast cancer. 39 E3 ligases such as TRIM24, TNF receptor-associated factor 6 (TRAF6), TRAF7, and C terminus of Hsc70-interacting protein (CHIP) maintain a low cellular p53 expression in the absence of signal activation of p53 genes 40 , 41 , 42 , 43 ( Table 1 ). Because of the special properties of the MDM2/p53 axis, most p53-related inhibitors, such as PROTAC, were developed based on this axis. 44 Other ubiquitin ligases are associated with the cell cycle such as TRIM21, USP1, and USP7 in cancer development. 45 , 46 , 47 Figure 2 UPS regulates tumor cells. (A) E3 ubiquitin ligases APC/C and SCF regulate the cell cycle. MDM2, as a classical ubiquitin ligase, regulates p53 to regulate the cell cycle, while TRIM2, another E3 ligase, competes with it. Other ubiquitin ligases such as Cul4a, TRIM28, and RNF2 also regulate MDM2 ubiquitin ligase, which indirectly regulates p53 protein stability. In addition, several ubiquitin ligases including CHIP, TRIM24, TRAF6, and TRAF7 can also regulate p53. (B) TRIM25, FBP1, RNF167, and TRIM22 regulate the mTOR signaling pathway by modulating PTEN, FBXW7, sestrin2, and RNF2, respectively, which ultimately regulates energy metabolism in tumor cells. (C, D) HIF-1α is regulated by different ubiquitin proteases under hypoxia and normoxia conditions respectively. APC/C, anaphase-promoting complex/cyclosome; CDC20, cell division cycle 20; SCF, Skp1-Cul1-F-box; CDH1, E-Cadherin; SKP, S-phase kinase-associated protein; TRIM, tripartite motif-containing; MDM, mouse double minute; RBX/RNF, RING finger protein; CRL, cullin-RING E3 ubiquitin ligase; USP, ubiquitin-specific protease; OTU, ovarian tumor proteases; mTOR, mechanistic target of rapamycin complex; FBXW, F-Box, and WD repeat domain containing; FBP1, fructose-bisphosphatase 1; UCH, ubiquitin carboxy-terminal hydrolases; VHL, Von Hippel-Lindau protein; HIF, hypoxia-inducible transcription factor; UBE2K, ubiquitin-conjugating enzyme E2K; MAEA, macrophage-erythroblast attacher; VEGF, vascular endothelial growth factor; DUB, deubiquitinase; TRAF, tumor necrosis factor receptor-associated factor. Fig. 2 Table 1 Summary of ubiquitination enzyme regulation of targeting proteins. Table 1 Enzyme Name Targets Cancer Animal models Reference E3 ligase APC/C G1 period / / 23 SCF DNA damage / / 24 E3 ligase FBXW7 SCF / / 25 β-Trcp / / SKP2 / / DUB USP10 ANLN Esophageal squamous cell carcinoma Human 29 E3 ligase MDM2 P53 Neuroblastoma Mice 33 MDM4 Neuroblastoma Mice 34 TRIM28 Melanoma / 36 TRIM31 Breast cancer Mice 39 RNF2 Ovarian tumor Mice 37 Cul4a Breast cancer/liver cancer Mice 38 TIRM24 Breast cancer Drosophila 40 TRAF6 Lung cancer Mice 41 TRAF7 Breast cancer / 42 CHIP Lung cancer Mice 43 E3 ligase TRIM25 PTEN Non-small cell lung cancer Mice 50 E3 ligase RNF43 p85 Colorectal cancer Human 51 DUB USP18 mTOR Ovarian cancer Human 52 E3 ligase RNF167 Sestrin2 Colorectal cancer Human 53 E3 ligase TRIM22 NRF2 Osteosarcoma Human 54 E3 ligase FBXW7 mTOR Nasopharyngeal carcinoma Human 25 E3 ligase Smurf1 VHL Many types of cancer / 60 DUB VHL (HIF-1α) normoxia Renal cancer Mice 62 USP8 Non-small-cell lung cancer Mice 61 USP9x Pancreatic cancer, gastric cancer Mice 60 UCH-L1 Ovarian cancer Mice 63 E3 ligase MDM2 (HIF-1α) Hypoxia Mesothelioma, ovarian cancer Mice 67 Based on the particularity of the TME, energy metabolism, including glucose metabolism, is mostly enhanced in tumor cells. 48 Mechanistic targets of rapamycin complex (mTOR) which consists of mTOR1 and mTOR2 mutation are involved in most cancer. 49 For example, TRIM25 down-regulates the activity of this protease through polyubiquitination modification of PTEN in non-small cell lung cancer, thus activating the PI3K/mTOR pathway to promote tumor development . Other ubiquitination modifications can also exert a tumor growth-promoting role by enhancing the mTOR signaling pathway. 50 Mutations in RNF43 G659fs are frequently found in colorectal cancer, and RNF43 G659fs mutations can bind to p85 and thus enhance p85 ubiquitination, leading to mTOR signaling activating. How p85 ubiquitination is regulated remains unclear. 51 The deubiquitination enzyme USP18 can also up-regulate ovarian cancer development in ovarian cancer by activating the AKT/mTOR signaling pathway through direct regulation of mTOR and AKT proteins. 52 In a few cancers, mTOR is also inhibited through ubiquitin modification. The E3 ligase RNF167 cooperates with STAM-binding-protein-like 1 to modify an amino acid sensor, sestrin2. This sensor transacts amino acid signals to mTOR1 and in turn, activates the mTOR signaling pathway. When sestrin2 ubiquitination increases, it inhibits mTOR signaling in colon cancer. 53 Other ubiquitin ligases, such as TRIM22, can accelerate nuclear factor erythroid 2-related factor 2 (NRF2) degradation and thus regulate mTOR signaling. In osteosarcoma, down-regulated TRIM22 expression led to increased stability of the NRF2 protein and inhibition of the mTOR-associated autophagy signaling pathway, thereby triggering cancer development. 54 Based on the important role of the mTOR signaling pathway, developing mTOR-related inhibitors seems extremely crucial. In nasopharyngeal carcinoma, fructose-1, 6-bisphosphatase 1 inhibits autologous ubiquitination of the E3 ligase FBXW7, thereby stabilizing this ubiquitin ligase, promoting FBXW7 to regulate mTOR protein ubiquitination, inhibiting the mTOR signaling pathway to inhibit glycolysis, and promoting radiation-induced apoptosis and DNA damage for tumor growth inhibition 55 ( Table 1 ). Because tumor cells require a large amount of nutrition from the TME, stromal cells in the TME would be nutrient-deficient, and the function of related stromal cells would be inhibited, thereby leading to tumor cell proliferation. 56 Rapidly proliferating tumor cells stimulate angiogenesis, but the uneven distribution of the new tumor vasculature results in the uneven distribution of oxygen, which makes the TME present a temporary or permanent hypoxic state. 57 Being a regulator, it can guide rapid tumor cell vascularization, offering oxygen and nutritional conditions for tumor cell growth and metastasis and enabling cancer cells to rapidly adapt to severe hypoxic conditions. 56 Along with hypoxia-inducible factor 1α (HIF-1α), HIF-2α, and HIF-3α are members of the HIF family. HIF-1α is the most sensitive to the oxygen content in the TME. 57 The HIF-1α content is low under normal oxygen conditions , which because this protein can be targeted through ubiquitination by E3 ubiquitin ligases such as Von Hippel Lindau (VHL) to mediate its degradation. This is a complex process involving other post-translational modifications. Hydroxylation-mediated changes in the two aerobic-dependent hydroxyprolines of HIF-1α indicate that HIF-1α can be modified through ubiquitination so that it enters the proteasomal degradation pathway. 58 Other E3 ligases such as Smad ubiquitylation regulatory factor 1 would also participate in regulating VHL stability under normal conditions. 59 Some deubiquitinases such as USP8, USP9X, and UCHL1 can participate in VHL-mediated ubiquitination modification of HIF-1α. 60 , 61 , 62 However, under hypoxia, VHL ubiquitination can no longer modify HIF-α , which leads to HIF1-α stabilization, thereby mediating vascularization activity and rapid tumor growth. 63 USP25 can regulate HIF-1α-associated transcription factors under severe hypoxia, regulating cancer development. 64 MDM2 can participate in the regulation of p53 protein stability. It can also directly ubiquitinate HIF-1α. According to some reports, MDM2, p53, and HIF-1α form a ternary complex, leading to MDM2 degradation in a p53-dependent manner 65 , 66 , 67 ( Table 1 ). Several ubiquitin-related enzymes also regulate HIF-2α protein stabilization; for example, in gliomas, USP33 modifies HIF-2α through deubiquitination to promote angiogenesis and cancer progression. 68 HIF may also regulate other activities of cancer cells in tumor development. For example, the ubiquitin-conjugating enzyme E2K could increase HIF expression in hepatocellular carcinoma, promoting tumor cell proliferation and migration. 69 HIF-1α can also be up-regulated by the E3 ligase macrophage-erythroblast attacher, leading to the proliferation of tumor cells and elevated migration capacity in glioblastoma. 70 CD4 + and CD8 + T cells can be differentiated into the corresponding T-helper (Th1, Th2, Th9, Th17) and regulatory T cells and cytotoxic T-lymphocytes respectively, under the stimulation of corresponding major histocompatibility complex (MHC) molecules. 71 , 72 T-helper, by recognizing MHC-II antigens on dendritic cells, secretes inflammation-related factors such as interleukin2 (IL-2) and interferon (IFN-γ). 73 Regulatory T cells, which can be differentiated from CD4 + , secrete IL-2 which regulates the homeostasis and function of natural killer cells. 74 PD-1, an inhibitory receptor of T cells, acts as a crucial checkpoint for immune escape. PD-1 and its ligands PD-L1 or PD-L2 play an extremely significant role in regulating tumor progression. 75 Aberrant ubiquitination and deubiquitination of this checkpoint affect checkpoint-mediated immune activity. 76 F-box only proteins 38 (FBXO38) is a PD-1-specific E3 ligase and mediates polyubiquitination of the K233 site on PD-1, thereby reducing PD-1 expression on the T cell surface and blocking PD-1/PD-L1 axis-mediated immunosuppression . In FBXO38 conditional knockout mice, PD-1 levels were elevated in tumor-infiltrating T cells, which resulted in more rapid tumor development in the mice. 77 Kelch like family member 22 (KLHL22), another E3 ligase of the BTB-CUL3-RBX1 complex, can specifically recognize the substrate and mediate ubiquitination. This ligase mediates PD-1 degradation before translocating to the T cell surface. A marked decrease in the level of this ubiquitin ligase in the tumor-infiltrating T cells led to PD-1 overaccumulation and T cell suppression. 78 USP12 also regulates PD-1 stabilization in cancer development. 79 MDM2, an E3 ubiquitin ligase of PD-1, can promote PD-1 degradation through ubiquitination of disaccharidased PD-1 and enhance the anti-tumor effect of T cells. 80 Along with the regulation of PD-1 in T cells, the ubiquitination system targets PD-1 in tumor-associated macrophages, thereby regulating overall tumor growth and development. In macrophages, the E3 ubiquitin ligase c-Cbl induces ubiquitination degradation by interacting with the PD-1 tail, thus ultimately improving the phagocytic ability of macrophages and exerting anti-tumor effects. 81 Tumor cells also regulate the ubiquitination of PD-L1, a PD-1 ligand, altering the expression of leucine-rich repeat kinase 2, ring finger protein 125, TRIM28, circ-0000512, USP22, OTUB1, etc . 82 , 83 , 84 , 85 , 86 , 87 ( Table 2 ). In summary, ubiquitination is crucial for regulating the PD-1/PD-L1 axis. Ubiquitination allows it to be combined with immune checkpoint inhibitors, such as PD-1/PD-L1, thereby improving patient response rates and treatment effects. Of note, although multiple studies have reported the regulatory effect of ubiquitination on the PD-1/PD-L1 protein level, it is not an isolated event but is closely related to other post-translational modifications. For example, MDM2 mainly promotes the ubiquitination of deglycosylated PD-1 to down-regulate its protein levels. 80 This suggests that studies should focus more on the overall concept of cells during research. In the future, researchers may concentrate more on the synergistic effects of ubiquitination and other post-translational modifications of proteins in order to improve the intervention efficiency. Figure 3 UPS regulates immune cells in the TME. DCs contact T cells via MHC to transmit antigen information. The dashed box proximal to DCs depicts the mechanism by which ubiquitination modulates DCs. Ubiquitin enzymes MARCH 1, UCH-L1, MARCH9, and HRD1 regulate MHC-I and MHC-Ⅱ respectively. The results of MARCH1 regulating MHC-Ⅱ may affect the stabilization of MHC-Ⅰ. Ubiquitin-editing enzyme A20, which exerts a deubiquitinating function, mediates the maturation of DCs by regulating NEMO in the NF-κB signaling pathway of DCs. UBR5 also mediated the antigen presentation function of DCs through the regulation of IFN-γ protein stability. PD-1/PD-L1 could be regulated by DUBs and E3 ubiquitin ligases such as USP22, OTUB1, and USP12. USP can regulate T cells by autophagy and NF-κB signaling which ultimately regulate their anti-tumor immune response of them. DCs, dendritic cells; TLR, Toll-like receptor; NF-κB, nuclear factor kappa B; NEMO, nuclear factor-kappa B essential modulator; MARCH, membrane-associated ring–CH–type finger 1; HRD, 3-hydroxy-3-methylglutaryl reductase degradation1; UBR, the ubiquitin-binding region; UCH, ubiquitin carboxy-terminal hydrolases; MHC-Ⅰ/Ⅱ, major histocompatibility complex Ⅰ/Ⅱ; IFN-γ, interferon-γ; USP, ubiquitin-specific protease; OTU, ovarian tumor proteases; RNF, RING finger protein 1; TRIM, tripartite motif-containing; LRRK, eucine-rich repeat kinase; FBOX, F-box-containing protein 38; KLHL, Kelch-like family member; MDM, mouse double minute; UBA, ubiquitin-like modifier activating enzyme. Fig. 3 Table 2 Summary of ubiquitination regulation of targeting proteins. Table 2 Enzyme Name Targets Cancer Animal models Reference E3 ligase FBXO38 PD-1 B16F10 melanoma Mice 78 KLHL22 PD-1 Cub cutaneous melanoma Mice 79 MDM2 PD-1 Colorectal cancer Mice 81 c-Cbl PD-1 (macrophages) Colorectal cancer Mice 82 DUB USP12 PD-1 Lung cancer Mice 80 E3 ligase RNF125 PD-1 Head and neck squamous cell carcinoma Mice 84 TRIM28 PD-1/TBK1 Gastric cancer Mice 86 DUB USP22 PD-L1 Pancreatic cancer Mice 88 OTUB1 Murine breast cancer Mice 87 DUB USP18 TAK1 / / 89 USP22 T cell Pancreatic cancer Mice 92 E1 activating enzyme UBA6 IκBα (T cells) Lupus Mice 93 E3 ligase UBR5 IFN-γ Triple-negative breast cancer Mice 104 MARCH1 MHC-II / Mice 97 MARCH9 MHC-I / Mice 98 HRD1 BLIMP-1 / Mice 99 DUB A20 NEMO Dendritic cells Mice 101 UCH-L1 MHC-I Listeria Mice 100 OTUD6A NLR3 114 E3 ligase Praja2 MFHAS1 Malignant fibrous histiocytoma Mice 109 Pellino-1 K63 of IRAK1 Melanoma Mice 110 FBXW7 c-Myc Lewis lung carcinoma cells Mice 112 ITCH Macrophages / Mice 111 TRIM24 Macrophages Breast cancer Mice 113 CRL4 CD47 Multiple myeloma Mice 120 UBR SHP-2 Many types of cancer / 121 DUB Mysm1 Macrophages / Mice 115 OTUD5 YAP Triple-negative breast cancer Mice 116 DUB USP12 p65 Colorectal cancer Mice 126 E3 ligase TRAF6 STAT3 of k63 Lung cancer Mice 127 Ubiquitination modification also regulates other T-cell functions. In the presence of androgens, the protein level of USP18, a deubiquitination enzyme, in T cells is up-regulated. This enzyme promotes transforming growth factor-beta (TGF-beta)-activated kinase 1 deubiquitination and inhibits TAK1 phosphorylation, and subsequent activation of the NF-κB signaling pathway, which ultimately induces the inhibition of the anti-tumor effect of T cells. 88 In addition, in a study of oral lichen planus, TRIM2, a ubiquitination ligase, also ubiquitinated NF-κB and activated its signaling pathway, ultimately up-regulating the inflammatory function of T cells. This suggested that targeting TRIM2 helps regulate the anti-tumor effect of T cells. 89 Moreover, E2–E3 ubiquitin ligases in T cells were disrupted in patients with renal metastatic cancer, which led to autophagy defects in circulating and tissue-resident CD8 + memory T cells and ultimately resulted in dysfunction and apoptosis. 90 In addition to directly affecting T cells, ubiquitination can indirectly affect T cell function through the regulation of ubiquitination in tumor cells. The decreased expression of the deubiquitinase USP22 in pancreatic cancer cells promoted the infiltration of natural killer and T cells, thereby enhancing the anti-tumor immune response of the TME. 91 Ubiquitination modification also regulates T cells to promote the differentiation of other cells. E1 ubiquitin-activating enzyme UBA6 increases p65 activation in the NF-κB signaling pathway of T cells by accelerating IκBα degradation. UBA6 regulates IFN-γ stability by modulating p65 of the NF-κB signaling pathway to promote Th1 and Tc1 cell differentiation 92 ( Table 2 ). Ubiquitination has a crucial regulatory role in the validation function and anti-tumor effect of T cells. It has a crucial impact on the survival of memory CD8 + T cells. However, the underlying mechanism remains unclear. If the regulatory action of ubiquitination-related enzymes on memory CD8 + T cells can be clearly studied, the findings may have a great effect on improving anti-tumor immunity. DCs are crucial for the immune system. They play a vital role in connecting innate and adaptive immunity. These cells can drive adaptive immunity through antigen presentation and regulate the activity of innate immune cells by secreting immunostimulatory cytokines. 93 MHC-I molecules load and present endogenous peptides to CD8 + T cells through different intracellular pathways. This is of great significance for the anti-tumor function of T cells. By contrast, MHC-II molecules load and present most exogenous peptides to CD4 + T cells. Furthermore, endogenous peptides in DCs can also be presented to CD8 + T cells using a cross-presentation approach. 94 Therefore, the antigen presentation function of DCs is of great significance for the anti-tumor immune response of immune cells in the TME. MARCH1 can mediate the ubiquitination of MHC-II molecules on the DC surface . This ubiquitin ligase regulates MHC II stability through ubiquitination at the tail of the MHC-II β chain. Then, the expression of MHC-II molecules and CD86 on the DC surface was regulated, thereby suppressing T-cell activation. 95 , 96 Moreover, MARCH1-mediated regulation of MHC-II affected the maturation of MHC-I stabilization. A specific relationship exists between MHC-I and MHC-II. MARCH1-mediated MHC-II ubiquitination affects the antigen presentation pathway of MHC-I. MHC-I expression was reduced in MARCH1-deficient DCs. MARCH1 does not directly regulate MHC-I. It is indirectly induced through MHC-II ubiquitination. 96 MARCH9, another ubiquitin ligase, regulates MHC-I ubiquitination. This transmembrane protein depends on lysine residues in the cytoplasmic tail for its ubiquitination function. MARCH9 plays a key role in regulating the entry of MHC-I into nucleosomes and MHC-I-mediated antigen presentation. 97 The E3 ligase 3-hydroxy-3-methylglutaryl reductase degradation 1 (HRD1) regulates ubiquitination modification of B lymphocyte-induced maturation protein 1, a transcription factor for MHC-II in DCs, thereby promoting MHC-II transcription and affecting CD4 + T cell activation in the inflammatory response. 98 UCH-L1 can regulate antigen cross-presentation pathway by promoting the recycling of MHC-I molecules in DCs. MHC-I at the cytoplasmic membrane or endoplasmic reticulum is recruited during antigen cross-presentation for phagosomal-cytoplasmic and vesicular cross-presentation pathways. Subsequently, some peptides derived from external pathogen molecules are loaded onto MHC-I in phagosomes and then shuttled to the plasma membrane for presentation and act as MHC-I/AG complexes. UCH-L1 deficient DCs present with reduced MHC recycling capacity. UCH-L1 deficient mice have a significantly reduced ability of antigen cross-presentation to cytotoxic T-lymphocytes in vivo and in vitro after infection with Listeria monocytogenes. 99 A20, a deubiquitinase targeting NEMO of DCs, up-regulates the maturation and cytokine production of DCs. A20 deficiency can lead to the development of autoimmune defects. 100 , 101 , 102 Ubiquitin ligase UBR5 does not directly target DCs in triple-negative breast cancer. However, IFN-γ expression increased in UBR5 knockout 4T1 tumor-bearing mice could enhance the antigen-presenting ability of DCs, promoting treatment and presentation of DCs to T cells, and triggering a specific immune response to a tumor to inhibit tumor growth 103 ( Table 2 ). In summary, ubiquitination significantly affects MHCI/II protein levels in DCs, which can subsequently affect the activation and anti-tumor function of T cells by impacting the antigen-presenting ability of DCs. Current research in this area is focused on exploring mechanisms, and gaps remain in how to intervene. Follow-up research is warranted to determine how to enhance the antigen-presenting ability of DCs and activate T cells by interfering with DC ubiquitination. Macrophages are among the most crucial cells in the tumor immune microenvironment. They can be roughly categorized into two polarization directions, M1 (anti-tumor macrophages) and M2 (pro-tumor macrophages). 104 , 105 , 106 However, in reality, the functions of macrophages are far from simple, and the macrophage population has strong heterogeneity and plasticity. In tumors, macrophages often tend to be M2-like macrophages. Therefore, targeting the elimination of M2-like macrophages or transforming them into M1-like macrophages is the main research direction in cancer treatment. Moreover, macrophages have a specialized and significant antigen-presenting function. In lung adenocarcinoma, under the action of microRNAs secreted by tumor cells, macrophages exhibit inhibition of the ubiquitination and degradation of misshapen-like kinase 1 through a series of pathway reactions , which ultimately activates the downstream c-Jun N-terminal kinase signaling pathway and polarizes the macrophages toward M2-like macrophages and thus promotes tumor progression. 107 The E3 ubiquitin ligase Praja2 catalyzes ubiquitination of the modified malignant fibrous histiocytoma amplified sequence 1 (MFHAS1). 108 This protein can activate JNK/p38 and NF-κB pathways to promote M1 macrophage polarization and inflammatory responses. 108 Pellino-1, an E3 ubiquitin ligase, regulates M1 macrophage polarization. However, new studies have demonstrated that Pellino-1 can inhibit IL-10-mediated M2 macrophage polarization by regulating k63 ubiquitination of IL-1 receptor-associated kinase 1 to activate signal transducer and activator of transcription 1 (STAT1) in response to IL-10 stimulation. 109 Some other E3 ligases such as FBXW7, itchy E3 ligase, and TRIM24 can also regulate macrophage polarization 110 , 111 , 112 ( Table 2 ). Figure 4 UPS regulates TAMs and CAFs in the TME. (A) Ubiquitination modification regulating macrophage polarization. Macrophages receiving different signals can be polarized into macrophages of M1 and M2. Ubiquitin enzymes that regulate macrophage polarization are shown in the figure. The polarization of tumor-associated macrophages can be regulated by UPS such as FBXW7, ITCH, and Mysm1. The ubiquitination enzymes CRL4 and UBR can regulate CD47/SIRPα to mediate tumor immune response in TAMs. (B) The transformation of normal fibrocytes to tumor-associated fibroblasts through regulation by snails can be regulated by UPS such as USP27X. CAFs could also transform into normal fibrocytes by regulation of CXCL12/CXCR4/CTGF. FBXW, F-Box and WD repeat domain containing; USP, ubiquitin-specific protease; TRIM, tripartite motif-containing; OTU, ovarian tumor proteases; CRL, cullin-RING E3 ubiquitin ligase; UBR, the ubiquitin-binding region; SIRP, CD47-signal-regulatory protein; EMT, endothelial-mesenchymal transition; TRAF, tumor necrosis factor receptor-associated factor; CAFs, cancer-associated fibroblasts; CXCL, C-X-C motif chemokine ligand; CTGF, connective tissue growth factor. Fig. 4 OTUD6A, a deubiquitination enzyme, in macrophages, can up-regulate NLRP3 protein levels through deubiquitination, which elevates IL-1β levels, ultimately enhancing the inflammatory function of macrophages. 113 Another deubiquitinating enzyme Myb-like, SWIRM, and MPN domains 1 (Mysm1) regulates macrophage survival and polarization. Mysm1-deficient macrophages produce more pro-inflammatory factors including IL-1β, TNFα, and iNOS, and sustained phosphorylation of AKT, a major PI3K target, can be detected. However, the exact mechanism of how Mysm1 regulates macrophage polarization remains unknown. 114 The deubiquitinase enzyme OTUD5 mediates YAP deubiquitination, thereby stabilizing the protein to promote M2 macrophage polarization. M2 macrophages with high YAP expression enhance the cellular invasive capacity of cancer cells, thereby improving the progression of triple-negative breast cancer 115 ( Table 2 ). Phagocytosis and antigen presentation are vital functions for macrophages to exert their anti-tumor effects. However, during interactions with macrophages, tumor cells often transmit the “don't eat me” signal to evade macrophage phagocytosis. For example, tumor cells can express CD47 to interact with SIRPα on the macrophage surface and mediate immune escape. 116 , 117 , 118 CD47 can be ubiquitinated by DDB1-CUL4A, which then blocks the CD47/SIRPα immune checkpoint and improves the anti-tumor immune response. 119 UBR also regulates the CD47/SIRPα axis during immune therapy 120 ( Table 2 ). In summary, ubiquitination is crucial for regulating macrophage polarization and function. This regulatory effect occurs in already existing tumors as well as in some precancerous lesions of tumors. 113 Thus, macrophage ubiquitination can not only serve as a target for anti-tumor therapy but also prevent tumor occurrence. MDSCs are an immature population of immune cells, which differentiate into DCs, macrophages, and neutrophils. 121 MDSCs secrete high NO, Arg1, iNOS, and ROS concentrations, which inhibit immune cells in the TME, especially T cells, promote tumor cell growth, and cause tumor immune escape. 122 Targeting MDSCs is likely to be a breakthrough therapy against tumors in the future. 123 MDSCs can be simply divided into two subgroups based on their surface marker: granular or polytype nucleoid (PMN-) and mononuclear (M−) MDSCs. The series of chemokines secreted by M-MDSCs can promote regulatory T-cell proliferation and differentiation to inhibit the immune microenvironment. 124 USP12 can regulate p65 deubiquitination in the NF-κB signaling pathway in MDSCs, thereby mediating PD-L1 and iNOS expression and the anti-tumor immune response of CD4 + T cells. At the same time, USP12 can affect INF-γ stability and reduce the anti-tumor immune capacity in the TME. 125 TRAF6, another member of the ubiquitin ligase family, modifies K63 polyubiquitination and STAT3 phosphorylation, thereby affecting MDSC differentiation. Examples of MDSC ubiquitination are few. More ubiquitination regulatory proteins will be identified in future studies. 126 Tumor-associated fibroblasts (CAFs) are the most abundant in stromal cells in tumors. They secrete cytokines and chemokines to enhance the proliferation and metastasis of malignant tumors. 127 CAFs have a wide range of sources. During the transition from normal fibroblasts to CAFs, snail plays a crucial role as a transcription factor regulating cell protein expression and cytokine secretion. 128 TRAF4, which is highly expressed in normal lung fibroblasts after radiotherapy , interacts with NADPH oxidase-2 (NOX2) and NOX4, thereby delaying lysosomal-dependent degradation. NOX2 and NOX4 localization in endosomes is stabilized and can activate the NF-κB signaling pathway in healthy cells of the lung, increasing ICAM1 secretion and non-small cell lung cancer invasion. 129 In invasive basal-like breast cancer cells, the ubiquitin editing enzyme A20 promotes tumor migration by modifying the monoubiquitination of three lysines in snails to promote transforming growth factor-β (TGF-β)-induced epithelial-mesenchymal transition in invasive basal-like breast cancer cells. 130 USP27X expression was positively correlated with snails. TGF-β-activated USP27X can serve as a deubiquitinating enzyme and stabilize snails, and the decreased USP27X expression leads to the inhibition of TGF-β-induced activation of epithelial-mesenchymal transition and fibroblasts. 128 Also, reports have proposed that activated CAFs are recovered to normal static fibroblasts by targeting signaling pathway downstream molecules, such as C-X-C motif chemokine ligand 12 (CXCL12), CXCR4, and anti-connective tissue growth factor (CTGF). 131 , 132 , 133 Therefore, these results all implied that targeting CAFs can be a future direction for tumor treatment ( Table 3 ). Targeting CAFs is of great significance in regulating the TME, especially that related to tumor invasion and metastasis. The expression of its related proteins may serve as both a target for subsequent research about anti-tumor therapy and an important indicator for judging tumor prognosis. The expression of CAF-related proteins may serve as both a target for subsequent research about anti-tumor therapy and a crucial indicator for judging tumor prognosis. Table 3 Summary of ubiquitination enzyme regulation of targeting proteins. Table 3 Enzyme Name Targets Cancer Animal models Reference E3 ligase TRAF4 NOX2/NOX4 Non-small-cell lung cancer Mice 130 A20 TGF-β Basal-like breast cancer Mice 131 DUB USP27x Snail Invasive basal-like breast cancer cells Mice 129 DUB USP18 ATGL Lung cancer cells Mice 136 DUB UCH-L1 COL1A1 Uterine leiomyoma 141 COL3A1 / DUB USP3 COL6A5 COL9A3 Gastric cancer / 140 E3 ligase HRD1 MMP2/9 Colon cancer / 153 MDM2 MMP9 Metastatic breast cancer / 151 TRIM13 MMP9 Clear-cell renal cell carcinoma / 152 FBXW2 MMP2/9 Lung cancer / 146 UCH-L1 MMP1 Brain glioma / 145 DUB OTUD7B TRAF3 Lung cancer Mice 154 USP15 MMP3 Non-small cell lung cancer Mice 149 Previous reports have only reported the link between adipocytes and obese patients, but new research has shown that some biomarkers in the adipose tissue of cancer patients can serve as an indicator of cancer characteristics, thereby suggesting a crucial link between tumor cells and adipocytes. 134 Although a substantial gap exists in the study of the role of ubiquitination in the interaction between adipocytes and tumor cells, the regulatory role of ubiquitination in lipid metabolism is clear. Ubiquitin-specific proteases, such as USP18, can promote the growth of lung cancer cells by inhibiting the degradation of adipose triglyceride lipase and promoting lipolysis and fatty acid oxidation 135 ( Table 3 ). Targeting ubiquitination to regulate adipocytes and lipid metabolism and ultimately exert anti-tumor effects may be the future research direction. Because the extracellular matrix (ECM) is rich in proteins such as collagens, matrix metalloproteins, and fibronectin. It maintains the overall environmental stability of the TME. 136 Some ubiquitin proteins can promote tumor proliferation by regulating the protein stability in the ECM and building a “highway” for the rapid migration of tumor cells. Collagen (COL) is the largest protein family in the ECM. As a major component involved in maintaining the ECM framework, collagen is a key player in maintaining ECM stability. 137 Most current studies on collagen have targeted cell fibrosis, but a few studies have reported ubiquitination-mediated regulation of collagen that promotes tumor cell migration. 138 COL9A3 and COL6A5 are members of the collagen family . The deubiquitination enzyme USP3, an essential mediator regulating oncogenic activity both in vitro and in vivo , can deubiquitinate COL9A3 and COL6A5 in gastric cancer cells. The elevated USP3 expression can affect the abundance of COL9A3 and COL6A5, thereby promoting tumor proliferation and migration of gastric cancer cells. 139 Moreover, according to a new report, UCH-L1 regulates cancer cell migration and contraction by regulating the stability of COL1A1 and COL3A1 proteins. 140 Figure 5 The UPS regulates the extracellular matrix in the TME. COLs and MMPs metalloproteinase are important proteins in ECM, which are regulated by ubiquitin enzymes during cancer progression. The level of COLs and MMPs can be regulated by UPS such as HCHL-L1, USP3, and mdm2. FBXW2 can regulate MMP2/9 protein stabilization through β-Trcp/FBXW2/SKP2 signaling and promote tumor cell proliferation. FBXW2 can also cause drug resistance during clinical treatment by modifying the ubiquitination of P65 protein. ECM, extracellular matrix; COL, collagen; MMP, matrix metalloproteinase; UCH, ubiquitin carboxy-terminal hydrolases; USP, ubiquitin-specific protease; OTU, ovarian tumor proteases; FBXW, F-Box and WD repeat domain containing; TRIM, tripartite motif-containing; SKP, S-phase kinase-associated protein; TRAF, tumor necrosis factor receptor-associated factor; SKP, S-phase kinase-associated protein. Fig. 5 The metalloproteinase family also occupies a large proportion of the ECM. This protease family can hydrolyze most proteins in the ECM, and even some cytokines and chemokines, thereby promoting tumor cell growth. 141 , 142 E3 ubiquitinase-regulated matrix metalloproteinases (MMPs) are found in most cancers. For example, RING E3 ubiquitin ligase and HECT ubiquitin ligase are involved in regulating MMP stability and thus affect tumor development. 143 One report for the first time identified MMP-1, UCHL1, and the 20s proteasome in patient plasma as markers for glioma. However, it could not clarify the specific regulatory relationship among MMP-1, UCHL1, and the 20s proteasome 144 . FBXW2, a RING E3 ubiquitin ligase, serves as a vital regulator in lung cancer. FBXW2 promotes MMP2, MMP7, and MMP9 expression by forming the β-Trcp/FBXW2/SKP2 axis with other ubiquitin ligases such as β-Trcp and SKP2. 145 , 146 , 147 The latest report proposes that FBXW2 overexpression in breast cancer leads to p65 ubiquitination, eliminating the effect of p65 resistance on paclitaxel use. 146 In other tumors such as non-small cell lung cancer, USP15 has been reported to be positively associated with MMP3. 148 In addition to regulating p53 protein stability, MDM2 also regulates MMP9 protein stability in ECM. There is an association between MDM2 expression in prostate cancer and the expression of MMP family proteins, especially MMP9, which promotes tumor cell migration by balancing pro-angiogenic mechanisms. 149 Moreover, MDM2 has also been shown to down-regulate the abundance of MMP3, MMP10, and MMP13, with a role in inhibiting the invasion of breast cancer cells. 150 TRIM13 can inhibit clear-cell renal cell carcinoma invasion by down-regulating MMP9 expression. 151 HRD1 promotes the proliferation and migration of colon cancer. The expression of this ubiquitin ligase was found to be higher in cancer cells than in other cells, and the expression of MMP2 and MMP9 was also elevated. However, the specific mechanism of how HRD1 regulates MMP2 and MMP9 is still unclear. 152 In addition, in lung cancer cells, LCL161 drugs could up-regulate the expression of MMP9 protein and thus induce cancer cell migration. OTUD7B inhibits the activation of NF-κ B by deubiquitinating TRAF3, which in turn promotes the transcription of MMP9, thereby exerting an inhibitory effect on the migration of lung cancer cells. 153 Although many inhibitors regulating ubiquitination have been screened out, very few drugs are truly applied for clinical therapeutic usage. MG132, a modified version of the first proteasome inhibitor, was widely investigated in most laboratories for proteasome inhibition. 154 Other proteasome inhibitors, such as bortezomib, carfilzomib, and ixazomib, were successively developed. They received FDA approval for clinical treatment, where the drugs exhibited good results in the treatment of various malignant tumors, especially multiple myelomas. 155 Bortezomib, the first proteasome inhibitor discovered, was developed and exploited in the clinical treatment of multiple solid tumors and hematology tumors. This inhibitor blocks the proteolytic function of the 26S proteasome complex by covalently binding to the β5 subunit of the 20s proteasome. 156 Clinically, bortezomib can be used alone or in combination with other chemotherapeutic drugs. For example, in the multiple myeloma clinical phase 2 experimental report, complete response/stringent complete response rate improved after treatment with the bortezomib-cyclophosphamide-dexamethasone combination. 157 The poor solubility of bortezomib owing to its chemical structure makes the clinical translation of this inhibitor difficult despite its excellent therapeutic efficacy. Second, due to the strong toxicity of bortezomib, patients experienced vomiting, nausea, poor mental state, and even abnormal perception symptoms during clinical trials. 158 , 159 , 160 , 161 Finally, the inhibitor may also lead to drug resistance because of the binding of bortezomib to the β5 subunit of the 20s proteasome, which thus inhibits the binding of the β5 subunit to other subunits. 162 Therefore, the development of relevant inhibitors based on bortezomib may be improved in future drug development. Subsequently, carfilzomib and ixazomib were also developed in 2012 and 2015, which were used to solve the problem of drug resistance arising during medication. Clinical data after the use of related inhibitors have also been reported. 163 Of note, E3 ubiquitin ligase is among the most crucial components of the UPS system, which guarantees the highly specific degradation of substrate proteins. Developing inhibitors targeting this ligase can maximize the drug's function. For example, MDM2-targeting-related inhibitors have been developed to block the binding of the MDM2 N-terminal domain to the peptide segment of p53. 164 Nutlin-3a and its derivatives play pivotal roles in inhibiting the growth of hematological malignancies, glioblastoma, and acute myelocytic leukemia cells because their structure is similar to that of p53 and allows competitive binding of MDM2 to p53. 165 , 166 Other inhibitors targeting MDM2 such as AMG-232 (KRT-232), APG-115, and Brigimadlin have also been reported recently. 167 , 168 , 169 PROTAC is used as a targeted UPS technology for regulating target protein degradation. The mechanism of this technology is not directly targeting E3 ubiquitin ligase, but by recruiting E3 ligase, one end connects to the target protein and the other end connects to E3 ubiquitin ligase, forming a ternary complex of target protein PROTAC-E3ligase, thereby achieving the degradation of the target protein. 170 This technology has the advantage of reducing drug resistance and toxicity. 171 It has good effects in treating various cancers. For example, in the treatment of triple-negative breast cancer, PROTAC targeting the MDM2-p53 axis can significantly improve the survival period of tumor-bearing mice. 172 Although multiple E3 ubiquitin ligases have been discovered, few ubiquitin ligases are targeted by PTROTAC. Such molecules only target classical proteins such as VHL and MDM2, 173 which means that there are still limitations in tumor treatment. We look forward to developing more types of ligases targeted by PROTAC in the future. The development of DUB inhibitors is another important target for cancer therapy. Some inhibitors are widespread and can target multiple types of DUB. For example, B-AP15 as a DUB inhibitor can address the problem of resistance arising during bortezomib treatment. It binds to the 26s proteasome to inhibit the function of the deubiquitinating enzymes USP14 and UCHL5. 174 Another inhibitor, VXL1570, also inhibits the functions of USP14 and UCHL5, which when used alone caused tumor reduction in Waldenstrom's macroglobulinemia tumor-bearing mice. Both the aforementioned inhibitors combined with bortezomib or ibrutinib could kill Waldenstrom's macroglobulinemia cancer cells. Because of the difference in the chemical structures of the two inhibitors, the water solubility of VXL1570 was better than that of BAP15, which resulted in a higher stability of VXL1570 in the patient's body. VXL1570 is approved for use in clinical trials. However, two patients with multiple myeloma developed severe exhalation insufficiency and diffuse pulmonary infiltration due to the severe toxicity and side effects of VXL1570. Thus, the clinical experiment was stopped when the patients died during phase I treatment despite the advantages of a broad spectrum of the inhibitors. 175 The development of high-specificity of inhibitors is the focus in tumors. 176 In the past few decades, the regulatory role of UPS in tumor progression has been extensively studied, especially to determine its impact on the biological behavior of tumor cells themselves and the shaping of the tumor immune microenvironment by tumor cells. Here, we retrospect the regulatory effects of USP on tumor cells, immune cells, stromal cells, and ECMs. This enhances our understanding of ubiquitination and provides a basis for further research on tumor occurrence and development and the development of ubiquitination-targeting anti-tumor drugs. In the study of UPS and tumors, numerous studies have reported the important role played by UPS in tumor cells, T cells, and tumor-related macrophages. However, most current research is limited to the effect of UPS on one cell type. The TME contains multiple cells, which often results in unpredictable other effects in the organism. Only considering its regulatory effects on one or more cells when developing targeted UPS drugs is not appropriate as other unpredictable effects are often observed during clinical treatment. UPS regulates multiple components of tumors and ultimately affects tumor progression. It regulates the cycle, energy metabolism, and protein molecule expression of tumor cells by regulating the ubiquitination and deubiquitination of target proteins. It also regulates the interaction between tumor cells and other cells as well as the function of immune cells and interstitial cells other than immune cells. However, a close synergistic relationship exists between ubiquitination regulation and other post-translational modifications. 80 Other post-translational modifications may play a regulatory role in protein ubiquitination. Moreover, the protein ubiquitination level can affect other post-translational modification processes. This suggests that attention must be paid to this point in future research. UPS-regulated targeting protein stability reported in some studies is only limited to changes in the protein level, but the specific mechanism remains unclear. Based on the characteristics of the UPS system, the development of related inhibitors such as PROTAC has become the recent research focus. 44 This type of inhibitor can hydrolyze proteins with the help of the UPS system, which causes the pathological protein to be tagged with ubiquitination, thereby achieving target protein degradation and tumor treatment. However, toxicity- and specificity-related concerns of these inhibitors need to be solved. In addition, this article only describes ubiquitination- and deubiquitination-associated enzymes. Some proteases also possess the function of ubiquitination modification. Such a modification is called ubiquitination-like modification. This type of modification also plays a pivotal role in regulating tumor development and needs to be explored. | Study | biomedical | en | 0.999996 |
PMC11697073 | The publication of the ImageNet classification with deep networks in 2012 1 marked a critical turning point for modern AI, particularly deep learning, and its significant impact on healthcare. This advancement enabled the use of medical data (text, images, etc.) with ground truth for tasks like clinical decision-making, diagnosis, prognosis, and patient management. Late 2022 witnessed another major leap forward with the introduction of large language models (LLMs) and large visual models (LVMs) to the public. 2 These models possess broader capabilities unlike specialized AI models designed for specific tasks. Building upon these developments, the recent proposal of Foundational Models 2 holds a great potential to further revolutionize healthcare. By combining LLMs and LVMs, these models (Foundational AI Models) are envisioned as highly trained and adaptable learning tools capable of understanding and working with different medical data, including text reports, images, clinical and pathological data, and even scientific literature. In other words, Foundational AI models are large-scale, general-purpose models trained on vast amounts of data that can be adapted to a wide variety of specific tasks. Unlike traditional AI models, which are designed for a single task or domain, foundational models learn broad patterns and representations from diverse data sources, allowing them to generalize across many different applications. 3 A key characteristic of foundational models is their lack of pre-programmed functionality for a specific task. This allows them to extract and learn broader and complex patterns/features and fundamental relations within the healthcare domain, making them flexible and adaptable. Consequently, they can be fine-tuned and applied to various tasks, ultimately serving as the foundation for building diverse, specialized AI tools within healthcare. Computer-aided diagnosis (CAD) systems are in our life for many decades now, and they primarily focus on detecting abnormalities in medical data to aid diagnosis. They rely on smaller and more specific datasets tailored to a particular disease or imaging modality. Often, they use rule-based, or machine learning algorithms (from pre-deep learning era) trained on labelled data for specific tasks (eg, detecting abnormalities in a specific type of scan). CAD has its roots in clinical practice, closely tied with the “Oslerian” strategy. As an example for disease-specific approach in clinical medicine, Dr William Osler contributed significantly by focusing on specific organs and their diseases. This “Oslerian” method led to categorizing conditions like diabetes into different types, aiding treatments decisions but potentially neglecting individual patient needs. 4 In addition to disease-specific approaches, medicine has come a long way since the days of Hippocrates, who emphasized treatment tailored to individual needs, much like the personalized medicine we strive for today. This focus on the whole person , rather than just the disease, has been a consistent thread throughout history, albeit with significant shifts along the way. For an evolved approach as an example, the common approach in healthcare before precision medicine was “one-size-fits-all” or standardized medicine. This model relied on treating patients based on generalized protocols and population-based averages, rather than individual patient characteristics. The shift toward personalized medicine emphasized the importance of tailoring medical treatments to individual patients based on their genetic, environmental, and lifestyle factors. This personalized approach aligns perfectly with the historical emphasis on treating the whole person and represents a significant leap forward from the disease-centric Oslerian approach. 5 Foundational models have its roots in this strategy as they aim to understand underlying disease processes and predict patient outcome. Foundational models employ deep learning architectures (more advanced than conventional machine learning strategies) to learn more complex, non-linear relationships within the data, enabling them to identify nuanced patterns and make predictions across various medical tasks. Furthermore, foundational models function as general-purpose models that can be adapted to various medical tasks via fine-tuning or prompting. To do so, these models need to be trained on massive and diverse datasets encompassing various medical data types (patient records, imaging scans, genetic information). Table 1 illustrates the major differences between classical CAD systems and foundational models. Lung nodule malignancy prediction : CAD systems have moderate-to-high success in predicting lung nodule malignancy in CT scans. Foundational AI models significantly improve these rates by being pre-trained on large amounts of unlabelled data, enabling them to learn more general and robust data representations. They can potentially uncover new biomarkers due to their capacity to integrate larger and multimodal datasets, whereas current CAD systems are limited. 3 Multi-modality data integration : Many CAD systems accept single-modality data and have limited capacity for combining multiple data types. Foundational AI models have the flexibility and power to combine imaging and non-imaging data such as clinical information, lab results, reports, and molecular data, offering a holistic understanding of a patient's condition. For instance, integrating a patient’s CT, MRI, and endoscopic ultrasound imaging with family history and laboratory data can, hypothetically, more accurately assess the risk of developing pancreatic cancer—a feat not achievable with conventional CAD systems. Both foundational AI models and modern medicine share a crucial understanding: single-faceted approaches have limitations. Just as doctors rely on diverse data points like blood tests, imaging, family history, Foundational AI models excel at processing and analysing varied data types such as genetic, environmental, behavioural, and more. 7 This comprehensive approach allows both disciplines to paint a more complete picture of the patient or the disease, leading to improved diagnosis, treatment, and even prevention. The similarities extend beyond data analysis. Both Foundational AI models and modern medicine strive for personalized care. By considering individual characteristics and circumstances, they aim to tailor interventions for maximum effectiveness and minimize side effects. This shift toward personalized medicine represents a significant paradigm shift, moving away from the “one-size-fits-all” mentality of the past. Most current AI models in medicine rely on limited, single-type data for risk predictions and diagnoses, making them imperfect yet still valuable. In contrast, foundational AI models go beyond these limitations. They can analyse multiple data types, including genetic, clinical, pathology, imaging, lifestyle, and information from other organs. This enables them to predict cancer risk with greater accuracy, tailor screening approaches to individual needs, and suggest personalized treatment options based on a comprehensive understanding of the patient. Furthermore, unlike the static nature of current models, foundational AI models can track changes over time using multi-modal data. This allows for both adaptive care plans and intervention, as well as facilitating collaborative decision-making through interaction with physicians. Taking an even more holistic approach, these models can incorporate socio-economic and environmental factors into their analysis, providing a more comprehensive picture of individual health. To mitigate data bias and ensure generalizability, it is imperative to curate diverse and representative training datasets. Collaboration across institutions and regions can facilitate the pooling of data, helping to overcome scarcity and promote inclusivity. Addressing interpretability challenges involves developing methods to elucidate the decision-making processes of complex models, fostering transparency and trust among clinicians and patients. Investing in computational resources and infrastructure is crucial to democratize access to advanced AI tools, preventing the widening of existing disparities. Additionally, establishing rigorous standards for model validation and encouraging a culture of thoroughness over speed in research publications will enhance the reliability of AI applications in medicine. | Review | biomedical | en | 0.999996 |
PMC11697094 | The skin accounts for approximately 16% of the body weight and is the largest organ of the body. 1 Once the skin barrier is damaged, the body initiates precise regulation of wound contraction, hemostasis, inflammation, angiogenesis, granulation tissue proliferation, and epithelial remodeling to promote wound healing. 2 , 3 Macrophages play a crucial role in the inflammatory response of wound tissue, and their active plasticity allows them to regulate tissue damage and repair functions, while macrophage-mediated inflammatory responses are closely associated with wound healing. 4 Macrophages polarized by environmental signals can be broadly classified into two main groups: classically activated macrophages with pro-inflammatory properties (M1), whose prototypical activating stimuli are interferon-gamma and lipopolysaccharide, and macrophages with anti-inflammatory and wound healing functions alternative to activation (M2), further subdivided into M2a (after exposure to interleukin (IL)-4 or IL-13), M2b (immune complexes in combination with IL-1β or lipopolysaccharide), and M2c (IL-10, transforming growth factor-beta (TGF-β) or glucocorticoids). 5 , 6 The phenotype of macrophages is influenced by the microenvironment of the wound and evolves during the healing process from a pro-inflammatory (M1) profile in the early stages to a less inflammatory pro-healing (M2) phenotype in the later stages. 7 M1 macrophages dominate in the early stages of wound healing and display phagocytic activity and the secretion of proinflammatory cytokines such as IL-1β, IL-6, IL-12, tumor necrosis factor-alpha (TNF-α), and oxidative metabolites to remove pathogens, tissue debris, and senescent cells from the wound surface. 8 In the middle to late stages, the M0 macrophage phenotype is reprogrammed to an anti-inflammatory M2 phenotype, secreting anti-inflammatory cytokines such as IL-4 and IL-10 to suppress the local inflammatory response and producing vascular endothelial growth factor (VEGF) to promote angiogenesis and stabilization. 9 Dysfunctional M0 macrophage polarization to M2 macrophage polarization, reduced M2 macrophage numbers, and diminished anti-inflammatory and angiogenic capacity are reasons why trauma results in the long-term persistence of nonhealing in the inflammatory phase. 10 Therefore, effective regulation of the polarization of M0 macrophages to M2-type macrophages, which exert anti-inflammatory effects and promote angiogenesis, will significantly improve wound healing. Mesenchymal stem cells (MSCs), an important endogenous cellular reservoir for tissue repair and regeneration, can effectively respond to inflammation and regulate macrophage reprogramming. 11 Currently, the ability of tissue engineering to promote wound healing has been investigated mainly through the secretion of paracrine growth factors, immune factors, chemokines, and extracellular vesicles by MSCs. 12 Recent studies have shown that extracellular vesicles produced by bone marrow-derived MSCs can contribute to tissue repair by promoting angiogenesis under a variety of pathological conditions, including skin wound healing, acute kidney injury, and myocardial infarction. In addition, they are widely used as drug delivery systems for cardiovascular diseases, neurodegenerative diseases, liver diseases, lung diseases, and kidney diseases. 13 , 14 , 15 Extracellular vesicles can be divided into three subgroups (exosomes, microvesicles, and apoptotic vesicles) and play a role in intercellular communication by transmitting complex signals. 16 Apoptotic bodies (ABs) are the largest extracellular vesicles, with a diameter of approximately 50–5000 nm, and are rich in DNA, microRNA, mRNA, proteins, and organelles. 17 After bone marrow-derived MSCs undergo apoptosis, macrophages rapidly respond to apoptotic signals, recognize and take up apoptotic vesicles within a short period, and trigger the polarization of M0 macrophages to the M2 phenotype, while M2 phenotype macrophages further enhance the function of fibroblasts and synergistically promote skin wound healing. 18 Therefore, the ABs of MSCs may serve as promising candidates for the development of cell-free therapies and provide new strategies for the treatment of cutaneous wounds. To further achieve the controlled release of key bioinformatic molecules to M0 macrophages within the wound surface and drive the polarization of M0 macrophages to M2 macrophages, the selection of suitable wound dressings to load therapeutic factors is a promising strategy. 19 Scaffolds serve as a means of restoring the morphology and function of diseased, damaged, and lost tissues by acting as an extracellular matrix for supporting cells and their fate and function. 20 Various natural and synthetic biopolymers can be used to fabricate such scaffolds, such as natural biomacromolecules, including silk fibroin, collagen, gelatin, chitosan, and hyaluronic acid, and synthetic biopolymers, including polyethylene glycol, polycaprolactone (PCL), polylactic acid-glycolic acid copolymer, and poly l -lactide. 21 Previous studies have shown that the loading of MSC-derived extracellular vesicles on heparin-modified 10.13039/100018919 PCL scaffolds inhibits thrombosis and calcification in the treatment of cardiovascular disease, thereby improving graft patency and enhancing endothelial and vascular smooth muscle regeneration while inducing M1 macrophage polarization to M2c macrophages. 22 MSC-exosomes loaded on 10.13039/100018919 PCL scaffolds modified with S-nitrosoglutathione reduce the expression of proinflammatory genes in treated macrophages and accelerate osteogenic differentiation in bone defects. 23 In our study, we prepared 10.13039/100018919 PCL scaffolds using an electrospinning technique that is thought to better mimic the physical structure of the extracellular matrix as well as the suitable mechanical properties for the delivery of apoptotic vesicles and wound dressings. 24 To investigate the specific regulatory mechanism of MSC involvement in apoptotic vesicles, we investigated the mechanism of action of MSC-AB-loaded 10.13039/100018919 PCL scaffolds in regulating macrophage polarization for wound healing in a mouse wound model, providing an experimental basis and theoretical rationale for the development of new drugs. Primary MSCs were derived from bone marrow-derived stem cells (BMSCs) harvested from C57BL/6 mice. Bone marrow was obtained from the femurs and tibias of C57BL/6 mice and was washed and filtered to form single-cell suspensions. Primary BMSCs were cultured in Dulbecco's modified Eagle medium (DMEM) (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin/streptomycin (Invitrogen, Carlsbad, CA, USA) at 37 °C in a 5% CO 2 cell culture incubator. The medium was changed every 2–3 days. The adherent cells were digested with 0.25% trypsin (MP Biomedicals, Irvine, CA, USA) and passaged in vitro , and third- and fourth-generation BMSCs were used for subsequent experiments. A mouse macrophage line (RAW264.7) and 293T cells were obtained from the CAS Cell Bank. The NIH-3T3 mouse fibroblasts used for the fibroblast scratch migration experiments were obtained from Procell Life Science & Technology Co., Ltd. (Wuhan, China). After BMSCs were cultured in serum-free DMEM for 24 h, staurosporine (0.5 μM) (MedChemExpress, NJ, USA) was added for 12 h to induce apoptosis of MSCs. The medium was then collected and centrifuged at 300 g for 10 min to remove cells and debris. After two repeated centrifugations, the supernatant was collected and further centrifuged at 3000 g for 30 min to concentrate the ABs into pellets, which were then resuspended in 1× phosphate buffer saline solution (PBS) and stored at −80 °C for subsequent experiments. Protein concentrations were measured using the BCA Protein Assay Kit (Beyotime Biotechnology, Shanghai, China). The purified ABs were characterized by western blotting using primary antibodies against caspase-3, CD9, CD63, GAPDH, and cleaved caspase-3 (rabbit mAb, Cell Signaling Technology, Boston, MA, USA). Dynamic light scattering analysis was performed using a Zetasizer Nano ZSE (Malvern Panalytical, Malvern, England, UK). The morphology of the ABs was observed by scanning electron microscopy (Hitachi, TKY, Japan). One gram of PCL solid particles was dissolved in 10 mL of dichloromethane to form a 10% (w/v) PCL/dichloromethane solution, which was stirred continuously at room temperature for 2 days until the solution became clear and transparent. The electrospun emulsion was drawn into a 10 mL syringe (with a 21G stainless steel flat-tipped dispensing needle) and placed on a microinjection pump (LongerPump, Baoding, China) for electrospinning, which was performed in a fume hood and collected from the holder via a homemade drum collector. The electrostatic spinning parameters were set as follows: injection rate of 1 mL/h, voltage of 15 kV, receiver speed of 150 rpm, and reception distance of 12–15 cm. The resulting fiber material was dried well in a fume hood and left at room temperature. PCL scaffolds were dried at room temperature for 1 day. All scaffolds were cut into 10 × 10 mm squares, fixed to the sample stage with double-sided carbon conductive adhesive, and examined by field emission scanning electron microscopy (Hitachi) after 40 s of gold spraying under vacuum with the acceleration voltage set to 10 kV. The scaffold diameters were statistically analyzed by ImageJ software (NIH, Bethesda, MD, USA) to characterize the PCL fiber scaffold morphology. The PCL scaffold material was also cut into 15 mm diameter circles to fit 24-well culture plates. The materials were sterilized using Co irradiation at a radiation dose of 10 kGy. After sterilization, the materials were fixed at the bottom of the 24-well plates, washed three times with PBS, and then incubated in PBS for 24 h. Subsequently, mouse bone marrow MSC ABs were inoculated at 50 μg/mL on the surface of the materials and incubated in a cell culture incubator at 37 °C for 12 h. The scaffold materials were incubated by scanning electron microscopy (Hitachi) to observe the morphology of the loaded BMSC-AB-PCL fibrous scaffold as a means of characterization. ABs of mouse bone marrow MSCs were inoculated at 50 μg/mL on the surface of PCL fibrous scaffolds and incubated in an incubator at 37 °C for 12 h. An equal amount of suspension was added to PBS and collected every 12 h. After the protein was measured with a BCA protein assay kit, an equal amount of the protein concentration was measured with a BCA protein assay kit (Beyotime Biotechnology), and then the protein was added to the scaffolds until the protein concentration was less than 5 μg/mL. According to the manufacturer's protocol, the cytoskeleton green fluorescent dye phalloidin (Thermo Fisher Scientific, Inc., Alexa Fluor 488, Invitrogen, USA) and exosome membrane red labeling dye (1′-dioctadecyl-3,3,3′,3′-tetramethylindole dicarbapenem, DiD) (Thermo Fisher Scientific) were used to label the purified ABs. ABs were incubated in 5 μg/mL DiD staining solution at 37 °C for 30 min, washed with PBS, and centrifuged at 3000 g for 30 min twice. The unattached dye was removed using an ultrafiltration tube (300 kDa, Sigma–Aldrich, Saint Louis, MO, USA). RAW264.7 cells were inoculated in 35 mm confocal dishes at a density of 1 × 10 6 and then cocultured with different concentrations of DiD-labeled ABs in a 37 °C incubator for 4 h and 6 h. After removal at different time points and fixation with 4% paraformaldehyde, cytoskeletal staining and nuclear staining were performed, and the cells were placed under a laser confocal microscope (Olympus SpinSR10, Shinjuku, TKY, Japan) for photographic observation. The cytotoxicity of PCL-ABs and PCL to RAW264.7 cells was evaluated according to the instructions of the cell counting kit-8 (CCK-8; Beyotime Biotechnology). Cells (1 × 10 4 cells/well) were first inoculated in 96-well plates and cultured overnight in DMEM. Next, BMSC-ABs (0, 5, 10, 15, 25, 50, 100, and 200 μg/mL) were added to the cells and incubated at 37 °C with 5% CO 2 for 24 h. Then, the cells were washed with PBS and incubated with 10% CCK-8 solution at 37 °C for 4 h. Finally, the cells were incubated for 4 h using an enzyme marker (Bio-Rad, Hercules, CA, USA) to measure the absorbance at 450 nm. Murine-derived RAW264.7 macrophages were treated with purified ABs, and after 0 h, 24 h, 48 h, and 72 h of culture, the cells were lysed, the proteins were extracted, the protein concentrations of the cells and ABs were determined by BCA protein assay, and the expression of the respective inflammatory factor proteins was detected by western blotting. Equal amounts of total proteins were separated by 4%–10% SDS‒PAGE and transferred to PVDF membranes, which were blocked with 5% skim milk at room temperature for 60 min and then incubated with primary antibodies (GAPDH, CD206, arginase-1, and VEGF) (rabbit mAb, Cell Signaling Technology) at 4 °C overnight. After washing with Tris-buffered saline with Tween® 20, the membranes were incubated with secondary horseradish peroxidase-coupled goat anti-rabbit IgG (Cell Signaling Technology) at room temperature for 2 h. The protein bands were visualized with enhanced chemiluminescence (Thermo Fisher Scientific). After treatment of the purified ABs with murine-derived RAW264.7 macrophages, the macrophages stained after 0 h, 24 h, 48 h, and 72 h of culture were analyzed by flow cytometry (Thermo Fisher Scientific). The cells were stained and analyzed using FITC-conjugated anti-mouse/human CD11b mAb (Blue Laser 488 nm), PE-conjugated anti-mouse CD86 mAb (Blue Laser 488 nm, Green Laser 532 nm/Yellow‒Green Laser 561 nm), Brilliant Violet 421-conjugated anti-mouse F4/80 mAb (Violet Laser 405 nm), and BrilliantViolet 650-conjugated anti-mouse CD206 mAb (Violet Laser 405 nm) (BioLegend, Diego, CA, USA) according to the manufacturer's instructions. All the data were analyzed using FlowJo software (Treestar Inc., Leonard Herzenberg, Palo Alto, CA, USA). Total RNA was extracted from BMSC-derived ABs using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). RNA extraction was followed by DNA digestion with DNaseI. RNA quality was determined using a NanodropTM OneC spectrophotometer (Thermo Fisher Scientific, Inc.) to determine the A260/A280 ratio. RNA integrity was confirmed by 1.5% agarose gel electrophoresis. The quality of the RNA was quantified with a Qubit 3.0 (Thermo Fisher Scientific, Inc.) using a QubitTM RNA wide range detection kit (Life Technologies). Strand RNA sequencing libraries were prepared using the Ribo-Off rRNA Depletion Kit (mouse) and the KC DigitalTM Strand mRNA Library Preparation Kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Library products corresponding to 200–500 bp were enriched, quantified, and finally sequenced on a NovaSeq 6000 sequencer (Illumina) using the PE150 model. The sequences of CCL-1 (C-C motif chemokine ligand 1) containing the wild-type (WT) or mutant (Mut) binding site of miR-21a-5p were designed and synthesized by GenePharma (Shanghai, China). 293 T and RAW264.7 cells were cotransfected with the corresponding plasmids and miR-21a-5p mimics/miR-NC or miR-21a-5p inhibitors/inh-NC with Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA). To construct a luciferase reporter gene vector containing the CCL-1 promoter, full-length CCL-1 promoters containing wild-type or mutant CCL-1 were cloned and inserted into pGL3-basic vectors (Genecreate, Wuhan, China) and subsequently cotransfected with or without the miRNA overexpression vector. After 48 h of incubation, the activities of firefly and renilla luciferase were measured using the dual luciferase reporter assay kit (Promega, Madison, WI, USA). The miR-21a-5p inhibitor and inhibitor-negative control (NC) (Shanghai Gene Pharma Co., Ltd., Shanghai, China) were used at a final concentration of 100 nM Lipofectamine 3000 (Invitrogen, Thermo Fisher Scientific, Inc.). The sequences of the inhibitor and negative control are shown in Table 1 . The same conditions were applied for each transfection experiment. After 12 h, the transfection was assessed under a fluorescence microscope, and further experiments were continued at 24 h. Table 1 Primer sequences for miR-21a-5p and the inhibitor. Table 1 DUplexName SenseSeq5'→3′ SenseSeq5'→3′ MW mmu-miR-21a-5p UAGCUUAUCAGACUGAUGUUGA (FAM)AACAUCAGUCUGAUAAGCUAUU 14512.99 mmu-miR-21a-5p inhibitors (Cy3) (mU) (mC) (mA) (mA) (mC) (mA) (mU) (mC) (mA) (mG) (mU) (mC) (mU) (mG) (mA) (mU) (mA) (mA) (mG) (mC) (mU) (mA) 7909.72 Inhibitors-NC (mC) (mA) (mG) (mU) (mA) (mC) (mU) (mU) (mU) (mU) (mG) (mU) (mG) (mU) (mA) (mG) (mU) (mA) (mC) (mA) (mA) 6953.66 After the transfection of the miR-21a-5p inhibitor and CCL-1 receptor antagonist into RAW264.7 macrophages, the expression of CD206, arginase-1, and CCL-1 mRNA was assessed by quantitative PCR at 24 h. Total RNA was isolated using TRIzol reagent (Life Technologies). According to the manufacturer's instructions (Promega, Madison, WI, USA), single-stranded cDNA was prepared from 1 μg of mRNA using reverse transcriptase with oligomeric dT primers and V-normalized to GAPDH mRNA levels, and the respective inflammatory factor gene expression was determined using the 2 −ΔΔCt method. The primer sequences for the RAW264.7 macrophages are shown in Table 2 . The effect of bone marrow MSC-derived apoptotic vesicles on the reprogramming of M0 macrophages was assessed by western blot analysis using anti-CD206, anti-arginase-1, and anti-CCL-1 primary antibodies and GAPDH as an internal reference at 48 h. Table 2 Primer sequences for quantitative reverse transcription PCR. Table 2 Primers Sequence (5'−3′) CD206-F CTCTGTTCAGCTATTGGACGC CD206-R TGGCACTCCCAAACATAATTTGA Arginase-1-F CGGCAGTGGCTTTAACCTTG Arginase-1-R TTCATGTGGCGCATTCACAG CCL-1-F GATGAGCCACCTTCCCATCC CCL-1-R TGACTGAGGTCTGTGAGCCT GAPDH-F ACTCTTCCACCTTCGATGCC GAPDH-R TGGGATAGGGCCTCTCTTGC The cells were cultured in serum-free DMEM for 24 h, after which 500 μL of medium was collected. The samples were processed using the Bio-Plex Mouse Cytokine 23-Plex Panel Array (Bio-Rad Laboratories, Hercules, CA, USA) and assayed using the Bio-Plex Protein Array System (Bio-Rad Laboratory) according to the manufacturer's instructions. Concentrations were calculated using the following equation: relative concentration = cytokine concentration ÷ total protein concentration. Additionally, the levels of the cytokines TNF-α, TGF-β, von Willebrand factor (vWF), and VEGF were measured using a mouse ELISA kit (BioVision, San Francisco Bay, CA, USA), standard curves were generated according to the manufacturer's instructions, and the concentrations of the factors were determined from the optical density data. NIH-3T3 mouse fibroblasts were inoculated in the lower layer of a Transwell plate (6-well plate, 0.4 μm, Jet Bio-Filtration, Guangzhou, China) and cultured to 100% confluence. RAW264.7 cells treated in the PBS group (M0), ABs group (M0 + ABs), PCL group (M0 + PCL), and PCL + ABs (M0 + PCL + ABs) groups were transferred to the upper layer of the Transwell plate at 30%–40% inoculation density, and when the cell density reached 100%, the old medium was removed, the cells were washed with PBS three times, and the medium was replaced with fresh complete culture medium. NIH-3T3 cells were scratched vertically along the diameter of the 6-well plate using a 200 μL pipette tip, and RAW264.7 cells treated as described above were placed on the upper layer of the Transwell plate for coculturing. After 12 h, the scratch area of each group was recorded, and the change in scratch area was calculated by ImageJ software (NIH, Bethesda, MD, USA). Twelve male (8-week-old) C57BL/6 mice (18–22 g) were purchased from the Animal Experiment Center of Chongqing Medical University (Chongqing, China). The mice were randomly divided into three groups, the PBS group, PCL group, and BMSC-AB-PCL group, with four mice in each group. After anesthesia by intraperitoneal injection of sodium pentobarbital (40 mg/kg), the mice were shaved and a full-thickness skin wound (0.8 cm in diameter) was produced on the back of each mouse. PCLs and PCLs loaded with BMSC-ABs were then placed over the wound surface of the mice. Images of the wounds were taken on days 0, 2, 4, 6, 8, and 10. Changes in wound size were analyzed using Image-Pro Plus software (Media Cybernetics, Rockville, MD, USA). ABs were fluorescently labeled according to the manufacturer's reagent instructions (Cyanine7 NHS Ester Cy7 NHS, MKBio, Shanghai, China). In vivo fluorescence analysis was performed on the trabeculae of C57BL/6 mice. ABs (10 μg/10 μL) or PBS was injected into the subcutaneous tissue of mice ( n = 3), and the fluorescence intensity was measured by an in vivo imaging system (AniView100, BLT, Guangzhou, China). The fluorescence intensity of the region (ROI) was quantified using AniView software (BLT). Mice were sacrificed by intraperitoneal injection of 150 mg/kg sodium pentobarbital on day 10 after the establishment of the mouse model of trauma. Mouse tissues were collected to observe pathological changes. Tissues from the wound site were collected, fixed in 10% paraformaldehyde, and embedded in paraffin. Pathological changes in the tissues were examined using an hematoxylin-eosin staining kit (Solarbio, Beijing, China). Semiquantitative analysis of hematoxylin-eosin staining was based on the number of follicles and granulation tissue (scores: 0–4; higher scores indicate greater numbers), inflammatory infiltration (scores: 0–2; higher scores indicate less infiltration), and neovascularization (scores: 0–4; higher scores indicate more vascularity). An overall score was calculated, with higher scores indicating better wound recovery. In addition, collagen deposition in the tissue was measured using a Masson trichrome staining kit (Solarbio) and quantified using ImageJ software. Neovascularization was further examined by immunohistochemical staining with a CD31 (Abcam, Cambridge, England, UK) antibody. All the quantitative analyses were performed independently by three pathologists in random unknown groups. We obtained primary bone marrow MSCs from the femurs and tibias of 6-to-8-week-old male C57BL/6 mice, and after induction of the cells with staurosporine in culture for 24 h, apoptosis was detected by flow cytometry, which revealed a 99.94% apoptosis rate in the control group . MSC-derived ABs were collected by differential gradient centrifugation . The collected ABs showed a typical vesicle structure under scanning electron microscopy, and dynamic light scattering analysis revealed a particle size of approximately 2.314 μm . The zeta potential of the ABs was −17.8 mV, indicating that they were relatively stable . In addition, western blotting confirmed that the ABs in the collected pellets all expressed the same membrane markers (CD9 and CD63) as the source cells and had high levels of cleaved cysteine protease-3 . These results indicate the successful collection of apoptotic vesicles while inducing apoptosis. Figure 1 Acquisition and identification of bone mesenchymal stem cell-derived apoptotic bodies (BMSC-ABs) and preparation and characterization of polycaprolactone (PCL) fiber scaffolds. (A) Acquisition of BMSC-ABs. (B) Identification of apoptotic cells by flow cytometry. (C) Morphological images of ABs. Left: scanning electron microscopy (SEM) images of ABs (scale bar, 2 μm); right: particle size of ABs measured by dynamic light scattering (DLS). (D) Zeta potential of the ABs. (E) Flow chart of PCL scaffolds prepared by electrostatic filament mimicking technique. (F) SEM image of an electrostatic filament mimicking the PCL scaffold. Scale bar, 20 μm. (G) SEM images of electrostatic filament-mimicking PCL scaffolds loaded with ABs. Red arrows indicate sites representing ABs. (H) Identification of ABs by western blot. (I) Release rate of AB-loaded PCL scaffolds. Figure 1 Next, we prepared PCL scaffolds by electrostatic filament mimicry and observed them by scanning electron microscopy . The extracted MSC-ABs were loaded on the PCL scaffold, and subsequent scanning electron microscopy showed that the ABs were successfully adsorbed on the scaffold surface . To determine the release rate of the AB-loaded PCL scaffolds, the ABs were placed in PBS and DMEM, and the suspensions were collected every 12 h. The protein concentration was detected by BCA, and the results showed that the release rate tended to stabilize after 24 h, and the release amount in DMEM was slightly greater than that in PBS . These results indicate that the electrostatic filament-mimetic preparation of PCL scaffolds can better deliver and release apoptotic vesicles. Moreover, we evaluated the efficiency of macrophage uptake of apoptotic vesicles from bone marrow-derived MSCs by incubating macrophages with PCL scaffolds of fluorescent dye-labeled apoptotic vesicles at concentrations ranging from 0 to 100 μg/mL for 4 or 6 h . Confocal microscopy revealed that the attachment and internalization of apoptotic vesicles from MSCs increased in a dose- and time-dependent manner, with the internalization of apoptotic vesicles by macrophages stabilizing at an apoptotic vesicle concentration of 50 μg/mL for 6 h . The survival rate of RAW264.7 cells incubated with PCL and PCL + ABs was maintained at approximately 90%, as determined by CCK-8 assays . Therefore, 50 μg/mL was chosen as the optimal concentration of apoptotic vesicles to determine whether reprogramming of the macrophage phenotype was possible, and the rate was maximized at 6 h. Figure 2 Polycaprolactone-loaded bone mesenchymal stem cell-derived apoptotic bodies (PCL-BMSC-ABs) guided in vitro polarization of M0 to M2 macrophages (Mϕs). (A) Immunostaining of 0–100 μg/mL PCL-BMSC-ABs incubated for 4 h. Green: cytoplasm; blue: nucleus; red: ABs. (B) Immunostaining of 0–100 μg/mL PCL-ABs incubated for 6 h. Green: cytoplasm; blue: nucleus; red: ABs. (C) Relative fluorescence intensity of 0–100 μg/mL PCL-BMSC-ABs incubated for 4 h and 6 h n = 3; ∗∗ P < 0.01. (D) RAW264.7 cell viability after incubation with PCL-ABs. (E) Western blot analysis of Mϕs reprogrammed with the M2 phenotype treated with 50 μg/mL PCL-ABs. (F) Relative grayscale values of western blot analysis of 50 μg/mL PCL-BMSC-ABs Mϕs reprogrammed with the M2 phenotype. n = 3; ∗∗ P < 0.01, ∗ P < 0.05. (G) Flow cytometry comparison of CD206-positive M1 Mϕs and Mϕs incubated with 50 μg/mL PCL-BMSC-ABs for different periods. Figure 2 To test whether BMSC-ABs could induce M0 macrophages to polarize to an anti-inflammatory M2 phenotype, we first incubated RAW264.7 cells with 50 μg/mL PCL-BMSC-ABs for 0 h, 24 h, 48 h, and 72 h. Western blot analysis revealed increased expression of arginase-1 and CD206, indicating that BMSC-ABs enhanced the transformation of M0 macrophages to an anti-inflammatory M2 phenotype and that VEGF expression also increased, indicating that BMSC-ABs promoted angiogenesis . Notably, the expression of arginase-1, VEGF, and CD206 was significantly increased in RAW264.7 cells incubated with BMSC-ABs and remained stable at 48 h . To accurately quantify the extent of M0 polarization to M2 polarization, we used flow cytometry to compare the CD206 positivity of RAW264.7 cells incubated with 50 μg/mL BMSC-ABs for different durations . Flow cytometry analysis revealed that the percentage of M0 macrophages that reprogrammed into M2 macrophages reached 58.3% after incubation with BMSC-ABs for 24 h, increased significantly to 67.6% after 48 h, and stabilized at 69.2% after 72 h . Extracellular vesicles can regulate gene expression at the posttranscriptional level by delivering miRNAs, thereby affecting the function of recipient cells. We analyzed and quantified the expression of miRNAs in BMSC-ABs. A total of 353 known miRNAs were identified via miRNA sequencing analysis of RNA purified from BMSC-ABs. Next, the top 50 known miRNAs detected in BMSC-ABs were sorted based on total read counts . After the miRNAs were predicted to target genes, miR-21a-5p was predicted to bind to the target gene CCL-1 by dual-luciferase experiments . After transfection, colocalization of the fluorescently labeled miR-21a-5p inhibitor and macrophages was detected by fluorescence microscopy . BMSC-ABs were added to M0 that had been transfected with miR-21a-5p inhibitors, and CCL-1 receptor blockers were added to M0 that had been transfected with miR-21a-5p inhibitors, and the transcript levels of CD206, arginase-1, and CCL-1 were assessed at 48 h. The effect of stem cell-derived ABs on the programming of M0 was assessed by western blot analysis after 48 h. The western blot results revealed a significant increase in M2-specific proteins detected in the BMSC-AB and miR-21a-5p inhibitor NC groups compared with the control, miR-21a-5p inhibitor, and CCL-1 receptor blocker groups , indicating that miR-21a-5p in BMSC-ABs significantly affected macrophage function via CCL-1. The western blot results were consistent with the quantitative PCR results . Figure 3 miRNA sequencing analysis of bone mesenchymal stem cell-derived apoptotic bodies (BMSC-ABs)-loaded polycaprolactone (PCL) scaffolds driving the molecular reprogramming of macrophages (Mϕs) to M2-Mϕs. (A) The top 50 known miRNAs detected in BMSC-ABs. (B) miRNA-mRNA regulatory network. (C) The binding of miR-21a-5p to the target gene CCL-1 in 293T cells validated by dual luciferase assay. (D) Colocalization of miR-21a-5p with Mϕs. (E) Fluorescence intensity analysis. n = 3; ∗ P < 0.05. (F) Western blot analysis of the effect of the miR-21a-5p inhibitor on the reprogramming of BMSC-ABs to Mϕs. (G) Relative grayscale values of the western blot analysis of miR-21a-5p inhibitor's effect on the reprogramming of BMSC-ABs that drive Mϕs to M2-Mϕs. n = 3; ∗∗∗ P < 0.001. (H) Quantitative PCR analysis of miR-21a-5p inhibitor's effect on the reprogramming of BMSC-AB-driven Mϕs to M2-Mϕs. n = 3; ∗∗∗ P < 0.001. Figure 3 Although the above findings suggest that BMSC-ABs can drive the transition of M0 to M2 phenotype via miRNA , the ability of programmed M2 macrophages to produce anti-inflammatory cytokines and promote fibroblasts is unknown. We evaluated changes in the secretion of anti-inflammatory and pro-inflammatory cytokines by programmed M2 macrophages and activated M0 macrophages in serum-free medium and then further analyzed the effect of programmed M2 macrophages on fibroblast migration . A Bio-Plex protein array was used to analyze the levels of certain cytokines and chemokines in the supernatants of various cell types. In the M0 and M0+PCL groups, the levels of anti-inflammatory cytokines (IL-4, IL-10, CCL-1, and TGF-β) and vascular indicators (VEGF and vWF) were significantly lower than those in the M0+Abs and M0+PCL + ABs groups, and the expression levels of pro-inflammatory factors in each group (IL-1β, IL-6, and TNF-α) were not significantly different . Interestingly, there was a significant difference in the anti-inflammatory cytokine IL-10 and the angiogenic indicator vWF between the M0+ABs and M0+PCL + ABs groups, suggesting that the PCL material may have a synergistic role in the transition of M0 to M2 phenotype. Different macrophage populations were cocultured with fibroblasts in transwell chambers for 24 h , and differences in fibroblast migration were observed by inverted microscopy at 4× every 12 h . The results showed that fibroblasts reached satisfactory migration capacity within 24 h. The M0+PCL + Abs and M0+ABs groups demonstrated significantly enhanced fibroblast migration compared with the M0 and M0+PCL groups . These results further support the idea that BMSC-ABs can program M0 to M2 and may promote wound healing. Figure 4 In vitro anti-inflammatory and pro-fibroblast migration effects of reprogrammed polycaprolactone (PCL)-loaded M2 macrophages (RM2). (A) Schematic diagram of the scratch assay. (B–F) Levels of cytokines and chemokines in the supernatants of various cell types. n = 3; ∗∗∗ P < 0.001, ∗∗ P < 0.01. (G) Differences in fibroblast migration at different time points (with 4 × microscopy). (H) Migration distance of fibroblasts at different time points. n = 3; ∗∗ P < 0.01. Figure 4 Before exploring the effect of PCL-loaded BMSC-ABs on wound progression, we performed real-time fluorescence imaging analysis of the in vivo distribution of PCL-loaded BMSC-ABs. The fluorescence signal of Cy7-N-hydroxysuccinimide (NHS)-labeled ABs was clearly maintained after trauma for 2 days and gradually decreased over time. On day 2 after coinjection, the signal decreased to less than 10% of the initial value. Substantial programming was found at 48 h during in vitro coincubation . This result suggested that locally injected ABs will have sufficient time to reprogram M0 to M2. Thus, our data suggest that local macrophage programming can be achieved every two days of local treatment. Figure 5 In vivo biodistribution of bone mesenchymal stem cell-derived apoptotic bodies (BMSC-ABs). (A) Real-time imaging of Cy7-N-hydroxysuccinimide (NHS)-labeled ABs. Fifty micrograms of apoptotic vesicles suspended in 20 μL of phosphate buffer saline solution were injected into the tissue near the wound site through subcutaneous injection for real-time observation. (B) Observation of trauma-related changes in mice at different time points. (C) Wound size changes in mice at different time points. (D) Kidney function indicators. (E) Liver function indicators. (F) Histopathological sections of various tissues and organs. Figure 5 To investigate the effect of BMSC-ABs on trauma-related inflammation and angiogenesis, we generated a 0.8 cm diameter trauma model in the skin of anesthetized mice on the back via a trauma punch and covered the trauma with PCLs loaded with BMSC-ABs and a separate PCL. Wound healing assays revealed that the PCL-BMSC-ABs promoted wound healing , and there was no significant difference in wound healing between the PCL and PBS groups . Moreover, the liver and kidney function indices and histopathological sections of the mice were not significantly different between the groups ( P > 0.05) . We also examined the histological changes in the wounds. The results of hematoxylin-eosin staining and Masson staining showed that the wounds in the PCL-ABs treatment group exhibited a significant trend toward healing, inflammatory cell regression, and collagen fiber formation ( P < 0.05), while those in the control group (PBS injection) and the PCL treatment group covered with PCL alone showed a significant trend toward delayed wound healing compared with those in the PCL-ABs group and increased inflammatory cells, and there was no significant difference between the two groups ( P > 0.05) . Based on these histological findings, we further evaluated the distribution of traumatic macrophage types by immunohistochemistry staining of the traumatic tissue. The percentage of arginase-positive macrophages significantly increased ( p < 0.01) in the PCL-ABs treatment group , whereas the percentage of INOS (inducible nitric oxide synthase)-positive macrophages decreased ( P < 0.01) , indicating that ABs can promote the conversion of macrophages from the M0 phenotype to the M2 phenotype. Interestingly, the percentage of CD31-positive cells increased in the PCL-AB treatment group ( P < 0.05) , suggesting that ABs may promote neovascularization. The above results further demonstrate that BMSC-derived apoptotic vesicles can reduce inflammatory infiltration by programming M0 macrophages into M2 macrophages, thereby preventing or reducing delayed wound healing and thus exerting anti-inflammatory and angiogenic effects in mice. Figure 6 Effect of bone mesenchymal stem cell-derived apoptotic bodies (BMSC-AB)-loaded polycaprolactone (PCL) scaffolds on wound healing. (A) Hematoxylin-eosin staining maps of each group. (B) Degree of healing shown by hematoxylin-eosin staining. ∗∗ P < 0.01. (C) Masson staining of each group. (D) Masson staining of each group relative to the average optical density (AOD). Differences between the PCL group and the PCL + AB group. ∗∗ P < 0.01, ∗ P < 0.05. (E) Immunohistochemical analysis of the expression of the trabecular cell marker inducible nitric oxide synthase (INOS). (F) Immunohistochemical analysis of the expression of the traumatic cell marker CD31. (G) Immunohistochemical analysis of the traumatic cell marker arginase-1. (H) Quantitative analysis of traumatic cell markers. Differences between the PCL + ABs group and each other group. ∗∗ P < 0.01. Figure 6 The skin is the body's first line of defense and has the essential function of repelling pathogens and preventing mechanical, chemical and physical damage, which, when damaged, can also lead to infection and necrosis, as well as other serious local and systemic consequences. 25 Persistent skin inflammation can lead to the onset and progression of chronic inflammatory diseases, resulting in delayed wound healing. 26 Therefore, there is an urgent need for novel effective strategies for the treatment of skin injuries to improve the healing process and repair the skin barrier. 27 An imbalance in macrophage number and function is one of the important causes of the long-term persistence of trauma in the inflammatory phase. 28 A decrease in the number of M2 macrophages leads to a significant increase in the levels of the traumatic local inflammatory cytokines TNF-α and IL-6 and a decrease in the level of the anti-inflammatory cytokine IL-10. 29 , 30 Given the high plasticity among macrophage phenotypes, restoring the normalization of macrophage phenotype number and function by directly reprogramming M0 macrophages to M2 macrophages may be an effective strategy for treating traumatic inflammation and accelerating wound healing. The field of exosome research has attracted renewed interest due to the discovery of tubular RNAs, including mRNAs and miRNAs, in exosomes. 31 Previous studies have shown that exosomes secreted by human MSCs accelerate wound healing by reducing the number of neutrophils, inhibiting macrophage recruitment to the site of injury, promoting M2 macrophage polarization, angiogenesis, and collagen deposition, and modulating the inflammatory response. 32 However, the limitations of obtaining the number and function of exosomes under different experimental conditions, the highly variable relative ratio of cell-released exosomes to other small extracellular vesicles, the difficulty in controlling exosome production and release, and the nonspecific recognition of target cells still hamper clinical wound repair applications. 33 , 34 , 35 In recent years, apoptotic vesicles have been found to be effective in overcoming the limitations of exosome application as a product of programmed apoptosis. 36 Phosphatidylserine (PtdSer, PS) and annexin-V (Anxin-V) can be transferred to the surface of the vesicle envelope during apoptosis, where they act as signals to trigger phagocyte recognition and uptake and accurate delivery of apoptotic vesicles to their target cells for apoptosis-mediated cell reprogramming. 37 During the acute inflammatory phase of wound healing, most of the removal of decayed and damaged cells is carried out by macrophages and neutrophils through phagocytosis and in the absence of an inflammatory response during this process. Apoptotic vesicles promote the efficient removal of apoptotic material by peripheral phagocytes and mediate the transfer of biomolecules, including miRNAs and proteins, between cells to aid intercellular communication. 38 , 39 , 40 There is evidence that mononuclear phagocytes respond to apoptotic cells by releasing anti-inflammatory factors, including IL-10 and TGF-β1 and that apoptotic vesicles can break down apoptotic cells into smaller fragments to facilitate the removal of apoptotic debris and intercellular communication. 41 , 42 , 43 Extensive apoptosis of exogenous MSCs in a short period, down-regulation of the expression of the proinflammatory cytokines IL-6 and TNF-α, and up-regulation of the anti-inflammatory cytokine IL-10 in the wound area accelerate wound healing. 44 Liu et al reported that ABs derived from bone marrow MSCs triggered the polarization of macrophages toward the M2 phenotype, which could enhance the migration and proliferation of fibroblasts. 18 However, the molecular mechanisms by which stem cell-derived ABs act have not been elucidated. In this context, we extracted MSC-derived ABs and programmed them to verify the effect of ABs on wound healing trends, inflammatory cell regression, and collagen fiber formation in a mouse skin wound healing model. In this study, PCL fiber scaffolds prepared by electrostatic spinning were used to deliver apoptotic vesicles to the trauma site. PCL, a biodegradable, biocompatible and FDA-approved polymer, is widely used in the fields of tissue regeneration and drug delivery because it better mimics the physical structure of the extracellular matrix and has suitable mechanical properties. 45 , 46 Electrostatic spinning can provide nanoscale extracellular matrix mimetic structures with a high specific surface area and high porosity, which overcomes the disadvantages of conventional nanofibrous scaffolds due to their smaller fiber size and pore size, dense fiber structure, and lower porosity, resulting in low cell infiltration. More importantly, the porosity of PCL allows nutrient exchange between the inner and outer sides of the trauma surface, which is more compatible with the needs of trauma healing. 47 Therefore, we loaded MSC-ABs onto PCL fiber scaffolds in this study. PCL scaffolds have good biocompatibility, and BMSC-derived apoptotic vesicle-loaded PCL scaffolds could prevent or reduce delayed wound healing by reprogramming M0 macrophages into M2 macrophages and reducing inflammatory infiltration, which in turn exerted anti-inflammatory and angiogenic effects in mice. However, the exact underlying mechanism needs to be further investigated. miRNAs are a class of small noncoding RNAs approximately 22 nt long that are widely found in plants and animals and mainly function by inhibiting and regulating the translation of target genes. 48 The genome of an organism can encode thousands of miRNAs, which target approximately 60% of protein-coding genes, and regulate gene expression translation by binding to target mRNAs, leading to inactivation or activation of the latter and participating in important biological processes in life, such as cell proliferation, differentiation, apoptosis, growth and development of the organism, and regulation of the immune response to pathogen infection. 49 Up-regulation of miR-21-3p, miR-126-5p, and miR-31-5p and down-regulation of the genes miR-99b and miR-146a were associated with wound healing. 50 The ratio of M2 macrophages to M1 macrophages was positively correlated with miR-21. In the early stages of inflammation, pri-miR-21 dominates and has pro-inflammatory effects. Conversely, during the repair phase of inflammation, mature miR-21 exerts anti-inflammatory effects and converts macrophages to M2 macrophages, which exhibit low inflammatory levels and an immunosuppressed state, leading to persistent inflammation. 51 , 52 Previous studies have shown that M2-ABs can complete phenotypic reprogramming from the M1 phenotype to the M2 phenotype by targeting M1-mediated delivery of miR-21a-5p. 53 Similarly, we also found that miR-21a-5p expression was high according to transcriptome sequencing, leading us to hypothesize that miR-21a-5p could also drive Mϕ reprogramming to M2-Mϕ. We found that miR-21a-5p is a key molecule for wound healing and that its reduced expression may be one of the pathogenic molecular mechanisms of impaired M0 to M2 conversion; inhibition of miR-21a-5p expression in MSC apoptotic vesicles silences the target gene CCL-1 and inhibits M0 to M2 conversion. In this study, we validated that MSC-ABs deliver miR-21a-5p to promote the conversion of M0 to M2 macrophages, which in turn exerts anti-inflammatory and pro-angiogenic effects on M2 macrophages, providing theoretical support for an in-depth study of the pathological mechanisms of wound healing. In this study, we isolated and characterized BMSC-derived ABs and explored their regulatory mechanisms in macrophage programming, and we selected mouse trauma as a disease model. We found that the delivery of miR-21a-5p to the ABs of BMSC-programmed macrophages to M2 macrophages, which target the CCL-1 gene, promoted angiogenesis by secreting the anti-inflammatory cytokines IL-4, IL-10, CCL-1, and TGF-β and the angiogenesis-related factors VEGF and vWF to alter the local inflammatory environment. We found that BMSC treatment effectively promoted wound healing and attenuated the development of early wound inflammation. After the dorsal wounds of mice were covered with PCL fiber scaffolds loaded with apoptotic vesicles from BMSCs, the ABs from the BMSCs significantly accelerated the time to wound healing and promoted the formation of blood vessels, indicating that the use of PCL fiber scaffolds can act synergistically with the ABs. Although this study suggested that ABs from BMSCs are effective at preventing delayed wound healing in mice, the mechanisms by which these improvements in wound healing occur have not been fully determined. Therefore, further study of the additional molecular composition and mechanisms by which BMSCs act as ABs is necessary. This study demonstrates for the first time that apoptotic vesicles derived from bone marrow MSCs can induce macrophage M2 polarization and promote skin wound healing by targeting the CCL-1 gene through the presence of mmu-miR-21a-5p. Through electrostatic spinning technology, we prepared PCL composite fiber materials to construct MSC-AB-released carrier scaffolds and targeted the delivery of miR-21a-5p through local slow-release MSC-ABs to drive macrophage M0 to M2 programming to exert its dual effect of inflammation regulation and angiogenesis and then synergistically promote wound healing. In this study, stem cell-derived apoptotic vesicles, cell signaling required for macrophage programming, and PCL scaffolds were used to investigate the immunopathogenic mechanism of wound healing and new therapeutic targets, providing a promising therapeutic strategy and an experimental basis and theoretical rationale for various diseases associated with an imbalance of pro- and anti-inflammatory immune responses. | Review | biomedical | en | 0.999997 |
PMC11697111 | Stem cells (SCs) are capable of self-renewal and can exhibit multipotency. Under specific conditions, SCs can differentiate into various functional cell types and have the potential to regenerate various tissues and organs. The history of stem cell research dates back to the late 19th century, when scientists began to focus on cells with differentiation and developmental potential. In 1868, the famous German biologist Ernst Haeckel first developed the concept of undifferentiated cells, which was the earliest concept related to SCs. The 1963 publications by Ernest A. McCulloch and James E. Till marked the beginning of modern stem cell research . The use of SCs or their derivatives to repair diseased or damaged tissues overcomes the limitations of conventional clinical treatments and introduces new possibilities for regenerative medicine and the treatment of other human diseases . Organoids are 3D cultures derived from SCs that are capable of mimicking the spatial structure and physiological characteristics of organs in vitro . Compared with traditional cell cultures, organoids comprise diverse cell types that go beyond making simple physical connections, fostering more complex intercellular communication processes, including interaction, induction, feedback, and collaboration, allowing the organoid to more accurately simulate tissue structure and function . Novel advanced biomanufacturing technologies offer the opportunity to design complex cell niches with specific geometries and architectures that influence the spatiotemporal behavior of stem/progenitor cells . With continued advances in organoid technology, researchers have successfully cultivated various organoids, including organoids derived from the brain , kidney , stomach , liver , lung , mammary gland , and pancreas . These organoids serve as invaluable tools for in vitro studies of organ development , basic research , drug discovery , and regenerative medicine . The skin, the largest organ in the human body, contains various tissue structures, including the epidermis, dermis, subcutaneous tissue, and appendages. The composition of skin tissue enables it to play a variety of important roles, such as roles in physical protection, temperature regulation, immune defense, secretion, and excretion, as the first barrier through which the body resists infection and injury . Efficient wound repair is crucial for maintaining homeostasis, and research in this field has received increasing attention . The idea of using a skin culture system as an in vitro substitute for skin was first proposed by Rheinwatd et al. in 1975 . Those authors pioneered the development of a self-organizing strategy for squamous epithelium generation, which involved serial cocultivation of primary human keratocytes and irradiated 3T3 mouse fibroblasts. This breakthrough paved the way for in vitro culture of self-assembled skin tissue. The emergence and rapid development of skin organoids have brought new opportunities in skin wound healing research, mainly for the following reasons. First, compared with the traditional full-thickness skin model, skin organoids more accurately replicate the in vivo development process. These organoids can self-organize and differentiate directionally into different cell types, aligning more closely with the structure and function of native tissues. Furthermore, skin organoids can produce skin appendages, such as hair follicles and sebaceous glands, which are absent in traditional skin models , providing a biological environment that closely resembles real skin. Therefore, skin organoids are ideal in vitro models for studying the complex process of wound healing. Second, skin organoids have important applications in regenerative medicine. Owing to their ability to simulate the structure and function of real skin, skin organoids are innovative tools for treating skin injuries, burns, and other skin conditions . Transplanting skin organoids into the wound site can promote the regeneration and repair of damaged skin . In addition, skin organoids can be used as platforms for drug screening and toxicology studies . By testing drugs or compounds on organoids, their effects on the skin and potential side effects can be predicted. This approach can improve the efficiency of drug development and reduce risks in clinical trials . In recent years, skin organoids constructed in vitro have been widely used in skin development research , skin pathology research , and drug screening . Hong et al. comprehensively summarized the milestones in skin organoid generation and discussed the diverse applications of skin organoids, including their relevance in developmental biology, disease modelling, regenerative medicine, and personalized medicine. This review focuses on the construction and application of skin organoids in wound healing, elaborates on the construction process, and discusses the evolving role of skin organoids in wound healing research. Cells are the fundamental building blocks of an organism, and their proper function is the cornerstone of effective tissue repair and regeneration . Skin organoids are predominantly composed of SCs , including adult SCs (ASCs) and pluripotent SCs (PSCs) ( Table 1 ). Many organoids associated with the epidermis, sweat glands, and hair follicles are derived from ASCs . PSCs replicate in skin tissue systems in vitro through induced differentiation. This approach enables the simulation of the skin and its associated organoids, enhancing the understanding of the complex interactions between different cell types and molecular signaling pathways during development and homeostasis . In human skin, various types of ASCs play pivotal roles, including epidermal SCs (EpSCs), dermal SCs (DSCs), and hair follicle SCs (HFSCs). These SCs collaboratively contribute to the development and composition of diverse skin cell lineages, which form the skin. EpSCs are precursors to a wide array of epidermal cells that originate from the embryonic ectoderm and are capable of bidirectional differentiation. Boonekamp et al. established an organoid culture system that enables mouse EpSCs to continually expand and differentiate for an extended period of up to 6 months. DSCs, also known as dermal mesenchymal SCs, undergo differentiation into fibroblasts under specific conditions, and they then stimulate the synthesis and secretion of vital components such as collagen and elastin . Su et al. successfully aggregated DSCs with embryonic stem cells (ESCs) to form hair follicle-like organoids. This innovative approach promoted hair follicle formation both in vitro and in vivo via WNT pathway activation. HFSCs function as crucial tissue signal centers within the skin, generating rich signal outputs during all stages of adult skin homeostasis. HFSCs play a vital role in regulating the organization and function of skin niches . Chen et al. pioneered the construction of a nanoscale biomimetic extracellular matrix tailored for individual HFSCs. This development facilitated the stable expansion of HFSCs while preserving their essential SC properties. These properties thus markedly influence the outcomes of skin tissue regeneration. ESCs are pluripotent, self-renewing cells derived from undifferentiated cells originating from preimplantation embryos. Signaling molecules can promote the self-renewal of ESCs and induce the derivation of PSCs. Koehler et al. established 3D mouse ESC cultures to generate a new in vitro model of sensory epithelial differentiation in the inner ear to obtain a deeper understanding of inner ear development and disorders. Lee et al. progressively modulated the transforming growth factor β (TGF-β) and fibroblast growth factor (FGF) signaling pathways, co-induced the aggregation of cranial epithelial cells and neural ridge cells into spheres, constructed an organoid culture system capable of generating complex skin directly from human ESCs and successfully used it for skin reconstruction in vivo . Furthermore, a 3D mouse ESC culture was developed to spontaneously produce new hair follicles that mimic their normal counterparts . Induced PSCs (iPSCs) are a category of PSCs with the capacity for infinite self-renewal and proliferation and can differentiate into mature cells of the ectoderm, mesoderm, and endoderm . iPSCs can be generated from somatic cells, including fibroblasts, keratinocytes, and blood cells, through a process known as reprogramming . This approach overcomes the immunological concerns associated with ESCs. Furthermore, iPSCs can differentiate into diverse cell types, including keratinocytes and fibroblasts , providing a rich source of cellular components for constructing skin organoids. Yang et al. enriched keratinocytes in culture dishes and transfected them with lentivirus encoding transcription factors to obtain epidermal cells and generate iPSCs. Kim et al. reported that iPSCs derived from human cord blood mononuclear cells exhibited high pluripotency, normal karyotypes, and the ability to differentiate into all three blastoderm layers. Keratinocytes and fibroblasts derived from these iPSCs presented characteristics similar to those of primary cell lines. Sahet et al. employed a coculture approach in which iPSC-derived fibroblasts and keratinocytes were utilized to produce 3D skin equivalents. Itoh et al. generated iPSCs from fibroblasts and directed their differentiation into keratinocytes, resulting in the production of functional 3D skin equivalents. Abbas et al. generated skin organoids from human iPSCs derived from human skin fibroblasts or placental CD34+ cells, produced complex skin organoids with skin layers and pigmented hair follicles, and successfully developed sebaceous glands, tactile receptive Merkel cells, and secretory sweat glands. Organoid construction often requires the regulation of physical signals. Hydrogels mimic the in vivo environment through their unique physical, chemical, and biological properties, providing essential signaling support for skin cell growth and differentiation . In terms of physical properties, the 3D cross-linked polymer network of the hydrogel provides cells with a 3D scaffold similar to the structure of the extracellular matrix in vivo . This 3D environment facilitates the appropriate arrangement and interaction of cells in space to mimic complex tissue structures in vivo . Second, the mechanical properties of hydrogels (such as hardness and elasticity) can be adjusted by changing their degree of cross-linking, polymer concentration, and other parameters. This flexibility allows researchers to precisely control the mechanical environment according to different organoid needs, mimic the mechanical properties of different tissues in the body, and provide a growth space for cells that is similar to that in vivo . In terms of biochemical characteristics, hydrogels can promote interactions between cells and biochemical signaling; affect cell morphology, growth rate, and differentiation direction; prevent excessive proliferation and migration; maintain structural stability and organoid function; and facilitate organoid reproduction in vitro , thereby increasing the complexity and functionality of the tissue . When choosing hydrogels, factors such as chemical composition, cross-linking degree, elastic modulus and other factors should be considered . During the preparation process, the properties of a hydrogel can be regulated by changing its type, concentration, and cross-linking conditions . Hydrogels with different biochemical properties and physical properties can simulate different in vivo environments, thereby regulating the growth and differentiation of cells and promoting the formation and maturation of skin organoids . Hydrogels can also serve as carriers for drugs or growth factors to promote cell growth and differentiation and accelerate skin organoid formation. One team designed a microfluidic device to produce an asymmetric gradient of differentiation factors in a spindle hydrogel to improve the spatial organization of dermal and epidermal cells, promoting keratinocyte differentiation and hair follicle formation in skin organoids . The epidermal layer of the skin produces hair and glands. This layer is primarily composed of keratinocytes, which aid in thermoregulation and barrier formation. The dermis, underneath the epidermis, houses an array of structures, including blood vessels and nerves. Dermal fibroblasts within this layer are prolific producers of extracellular matrix components, such as collagen and elastic fibers. These elements provide skin with its well-known elasticity and facilitate the initiation and circulation of hair follicles . The subcutaneous fat lying beneath the dermis serves as an energy reservoir within subcutaneous tissues. Moreover, the skin contains sweat glands, sebaceous glands, and hair follicles derived from the epidermal and dermal layers. A variety of epidermal organoids, as well as organoids containing skin appendages, such as hair follicle and sebaceous and sweat gland organoids, have been constructed for scientific research and clinical treatment . Boonekamp et al. cultured epidermal keratinocytes extracted from the dorsal skin surface of mice to obtain mouse epidermal organoids for long-term expansion and differentiation, contributing to the study of epidermal homeostasis in vitro . Xie et al. constructed mouse primary epidermal organoids, which presented stratified histological and morphological features resembling those of the epidermis. These organoids closely simulate their native tissues at the transcriptomic and proteomic levels, which is valuable for skin infection modelling and drug screening. Wiener et al. seeded cells obtained from microdissected interfollicular epidermis within a basement membrane extract to generate epidermal organoids. This method shows promise as an in vitro model for exploring epidermal structure, function, and dysfunction. Wang et al. harnessed freshly isolated human protodermal cells to establish 3D cultures of human primary epidermal organoids, which served as an effective model for studying dermatophyton infections. In their most recent study, Kwak et al. developed multipotent stem cell-derived epidermal organoids, which produce efficient extracellular vesicles for skin regeneration and contribute to target cell proliferation, migration, and angiogenesis, showing promise as therapeutic tools for wound healing in vivo . Hair follicles are sac-like structures located within the dermis and subcutaneous tissue that are responsible for hair growth. The hair follicle has two main parts: the upper part, which includes the infundibulum and isthmus, and the lower part, which includes the bulb and suprabulbar region. Gupta et al. constructed in vitro 3D organoid models encapsulated in sericin hydrogels containing human hair dermal papilla cells, hair follicle keratinocytes, and SCs. These models exhibited structural features akin to those of natural hair follicles, mimicking cell–cell interactions and a hypoxic environment. Ramovs et al. successfully produced hair-skin organoids from two human iPSC lines and thoroughly characterized epidermal junctions via immunofluorescence and transmission electron microscopy. Weber et al. combined neonatal foreskin keratinocytes with scalp dermal cells and successfully established hair peg-like structures in vitro that expressed appropriate epidermal and dermal markers. This method serves as a platform for optimizing the engineering of human hair follicles for transplantation. Veraitch et al. reported that ectodermal precursor cells derived from human iPSCs were able to communicate with hair-induced dermal cells, ultimately promoting hair follicle formation. Marinho et al. developed a technique to construct hair follicle organoids in vitro by combining multiple cell types. When these organoids were transplanted into the skin, structural fusion and hair bud generation occurred. Kim et al. developed solar UV-exposed skin organoids derived from human iPSCs, which effectively recapitulated several symptoms of photodamage, including skin barrier disruption, extracellular matrix degradation and the inflammatory response. The sebaceous gland is a notable skin appendage that is widely distributed across the body, except for the hands and feet. Secreted sebum combines with sweat to create a lipid membrane that is crucial for skin protection. Feldman et al. used Blimp1+ cells isolated from adult mice to replicate sebaceous gland lineage expression and homeostasis dynamics in vitro . The authors successfully established sebaceous gland organoids, which serve as valuable tools for drug screening and investigations into sebaceous gland homeostasis, function, and pathology. Wang et al. demonstrated that functional hair follicles and sebaceous glands could be reconstituted by transplanting a combination of culture-expanded ESCs and skin-derived progenitors from mice and adult humans. Sweat glands secrete sweat, excrete waste, maintain body temperature, and originate from epidermal progenitor cells, similar to other skin components. The regenerative ability of sweat glands after full-thickness injury is limited, and the repair and regeneration of the sweat gland structure and function after severe burns remain major challenges in clinical treatment . Diao et al. embedded sweat gland epithelial cells in matrix glue in the dermis of the paw pads of adult mice, maintaining SC characteristics to enable differentiation into sweat glands or epidermal cells, effectively integrating sweat glands into tissues, and successfully establishing mouse sweat gland organoids. Sun et al. reprogrammed human epidermal keratinocytes to differentiate into a lineage of sweat gland cells capable of sweat gland regeneration. Yuan et al. generated replicable spheres of sweat gland cells from adipose mesenchymal SCs and formed blood vessels with dermal microvascular endothelial cells, successfully simulated the morphogenesis of vascularized glands in vitro , and reported the precise anatomical relationships and interactions between sweat gland cells and the surrounding vascular niche. Skin damage from surgery, trauma, or burns has considerable physical and psychological effects on patients . Skin organoids offer a promising avenue for overcoming the challenges posed by hard-to-heal wounds and the permanent loss of skin appendages. Currently, a variety of biological structures and clinical strategies are being employed to harness the potential of skin organoids to address clinical issues related to wound healing . At present, the organoids used in skin wound healing are derived predominantly from iPSCs. Takagi et al. generated 3D human skin organoids from iPSCs, incorporating accessory organs such as hair follicles and sebaceous glands that exhibited full functionality after transplantation into nude mice, effectively integrating with surrounding host tissues, including the epidermis, arrector pili muscles, and nerve fibers. Ma et al. established epithelial and mesenchymal organoid models derived from human induced pluripotent SCs, which enhanced epidermal stem cell activity, promoted sweat gland and blood vessel regeneration and provided new therapeutic options for skin lesions and functional defects. Lee et al. developed complex skin organoids from human PSCs, which comprised a layered epidermis, a fat-enriched dermis, and pigmented hair follicles complete with sebaceous glands. When these skin organoids were transplanted into nude mice, the epidermal layer was oriented to create hair follicles, which resulted in the formation of planar hair-bearing skin. This innovation holds promise for skin reconstruction in patients with burns or trauma. Ebner–Peking et al. differentiated human tissue-derived iPSCs into endothelial cells (ECs), fibroblasts, and keratinocytes to generate a cell suspension, which promoted full-thickness wound healing in mice in vivo . Diao et al. embedded epithelial cells derived from sweat glands in the dermis of the paw pads of adult mice using Matrigel, forming sweat gland organoids that retained SC characteristics. These organoids had the capacity to differentiate into sweat gland cells or epidermal cells, and in vivo experiments confirmed that such organoids could enhance skin wound healing and sweat gland regeneration. Thai et al. co-cultured ECs and mesenchymal SCs (MSCs) to form EC-MSC spheres encapsulated within hydrogels that promoted wound healing in an in vitro full-thickness skin burn model. Organoids constructed from reprogrammed skin cells can also be used in wound healing research. Sun et al. employed reprogrammed human epidermal keratinocytes to construct regenerative sweat gland organoids. These organoids were subsequently transplanted into a skin injury mouse model, resulting in the successful development of fully functional sweat glands. For refractory diabetic wounds, Choudhury et al. creatively transdifferentiated chemokine receptor allogenic mesenchymal SCs overexpressing Cxcr2 into keratinocyte-like cells in 2D and 3D cell culture. After these organoids were transplanted into a diabetic mouse wound healing model, epithelialization of the epidermal layer and endothelialization of the dermal layer significantly increased, notably increasing the wound closure rate. To date, numerous studies have employed skin cells as seed cells for 3D printing to form skin organoids, which have been applied in wound healing research. Cubo et al. utilized keratinocytes and fibroblasts as seed cells for 3D printing skin tissue. By using histological and immunohistochemical analyses both in vitro and in vivo , the authors demonstrated that the printed skin closely resembled normal human skin in terms of structure and function. This printed skin could mimic various physiological properties of human skin, holding promise for future applications in scientific and clinical research on skin wound healing. In another study, six primary human skin cell types were used to bioprint a three-layer skin construct comprising the epidermis, dermis, and hypodermis. The bioprinted skin organoids were transplanted into full-thickness skin injury models of mice and pigs, where they achieved full integration and regenerated skin, promoted skin neovascularization and extracellular matrix remodeling, and accelerated wound healing . Moreover, 3D printing can control the spatial arrangement of cells in skin organoids to facilitate skin reconstruction. Pappalardo et al. employed 3D-printed skin tissue for skin reconstruction, successfully replicating biophysical interactions and cellular/extracellular tissue dynamics in human skin. Compared with traditional hydrogel skin organoid transplantation, this approach results in superior mechanical resistance and angiogenesis potential. It can effectively replace full-thickness wounds with minimal sutures and reduce surgery duration. Abaci et al. harnessed 3D printing technology to control the spatial arrangement of cells within a bioengineered human skin structure. The authors initiated dermal cell formation by controlling the autoaggregation of globules within a physiologically relevant extracellular matrix, which facilitated epidermal–mesenchymal interactions. This innovative approach led to hair follicle formation in vitro , offering new possibilities for research into hair follicle regeneration following scalp trauma. In addition, 3D printing of organoids via laser-assisted bioprinting (LaBP) technology and digital light processing (DLP) technology has been applied in skin wound healing research. For example, LaBP technology was employed to 3D print fibroblasts and keratinocytes onto a stable matrix, forming a fully cellular skin substitute. In vivo experiments confirmed that this skin substitute, when grafted onto full-thickness skin wounds, accelerated the formation of new blood vessels and promoted the healing of dorsal skin wounds in mice . DLP-based 3D printing technology can enable the precise positioning of clusters of human skin fibroblasts and human umbilical vein ECs with high cell viability. This technology facilitates the generation of functional living skin (FLS) that can be easily implanted into wound sites to promote neovascularization and skin regeneration . FLS mimics the physiological structure of natural skin and displays robust mechanical and bioadhesive properties. The combination of skin organoids with novel biomaterials also provides new approaches for skin wound healing. Huang et al. and Yao et al. used alginate/gelatine hydrogels as bioinks for 3D printing of the extracellular matrix to simulate the regenerative microenvironment, spatially integrate a variety of biophysical and biochemical cues for cell regulation, promote the transformation of epithelial progenitor cells and mesenchymal SCs into functional sweat glands, and promote sweat gland tissue recovery in mice. Kang et al. constructed a multilayer composite scaffold with epidermal and dermal structures using gelatine/alginate gel. After the scaffold was transplanted into full-thickness wounds in nude mice, it exhibited good cytocompatibility, increased the proliferation ability of dermal papillary cells (DPCs), promoted the formation of self-aggregating DPC spheres, and initiated cuticle–mesenchymal interactions, promoting the formation of hair follicles. Two types of polymer mesh were physically strengthened and integrated into a type I collagen hydrogel to generate a novel dermoepidermal skin substitute; when this platform was transplanted into rats, it uniformly developed into a well-layered epidermis and formed a well-vascularized dermal component . Zhao et al. prepared an alginate–gelatine composite hydrogel bioink with platelet-rich plasma (PRP) integration. The inclusion of PRP not only improved the extracellular matrix synthesis, but also regulated the vascularization of vascular Ecs and macrophage polarization in a paracrine manner. This approach accelerated high-quality wound healing in a rat dorsal full-thickness wound model, demonstrating the remarkable feasibility of 3D bioprinting combined with a PRP-functionalized bioink for expediting wound healing. Bacakova et al. developed a bilayer skin structure composed of collagen hydrogels enhanced with a nanofiber poly(L-lactic acid) membrane of preseeded fibroblasts, which could promote fibroblast adhesion, proliferation and migration to collagen hydrogels. In addition, this construct induced keratinocytes to form the basal and upper layers of cells with high mitotic activity, which could be used for cases of full-thickness skin damage. Guo et al. cross-linked recombinant human collagen (rHC) and transglutaminase to prepare rHC hydrogels and embedded fibroblasts to develop a new tissue-engineered skin equivalent with good biocompatibility, which can promote fibroblast migration and the secretion of a variety of growth factors. This construct has been shown to significantly promote skin wound repair in a full-thickness skin defect mouse model. The TGF-β and FGF signaling pathways are the main regulators of skin cell induction, fate determination, migration, and differentiation . The addition of basic FGF-2 stents can promote neovascularization in the dermis, thus further enhancing the repair of full-thickness skin defects . Lee et al. reported that complex skin organoids derived from human pluripotent SCs gradually regulate TGF-β and FGF and that transplantation of these skin organoids into the hairy skin of nude mice results in the formation of smooth hairy skin. Currently, WNER (Wnt-3a, Noggin, EGF and R-Spondins) is the classic cytokine culture protocol used in organoid culture because fluctuations in the levels of these four factors are relevant for almost all organoid culture experiments. Other studies have shown that the addition of other small molecules, such as CHIR99021 and valproic acid, to ENR (epidermal cell growth factor (EGF), Noggin, and R-Spondins) can induce specific differentiation of SCs . The study of Kageyama et al. confirmed that adding oxytocin to hair follicle organoids upregulated expression of the growth factor VEGF-A and promoted the growth of hair- and nail-like buds. EGF, FGF, TGF-β, and other growth factors have been applied in wound treatment and in the culture of skin organoids. The use of appropriate concentrations of these growth factors is expected to promote cell proliferation and differentiation, improve wound healing speed and quality, and reduce scar formation and infection risk. The currently established skin organoids follow a natural developmental pathway involving the directional differentiation of SCs to replicate the structure and function of in vivo tissue. These organoids play an increasingly vital role in research on skin development, skin disease pathology, and drug screening. Wound healing approaches using skin organoids have the following advantages. First, skin organoids can be generated in vitro to simulate the wound healing process, accelerating drug screening and therapy development. Second, skin organoids derived from patients themselves have a high degree of personalization, which enables them to better simulate the wound environment of patients and improve the precision and effectiveness of treatment. Third, performing drug screening and treatment development in vitro is safe and reduces risk and uncertainty in clinical trials to improve treatment safety. As in vitro models, skin organoids have the ability to simulate skin structure and function, providing an important platform for in-depth studies of skin development, disease mechanisms and drug screening. However, their generation process is relatively complex and time-consuming, with concerns related to standardization and diversification, which are major challenges in current research and related applications. Nonetheless, with continuous advances in technology, we can expect to overcome these limitations in the future to better leverage the potential of skin organoids in wound healing and other fields. One feature worth noting is that organoid cultures lack consistency, making standardized production difficult to achieve. The inconsistency of organoid cultures can be attributed to challenges related to strictly controlling the source, state, and culture conditions of cells , which hinders their clinical applicability. Therefore, in the preliminary research phase, extensive clinical, genetic, and morphological data must be integrated to construct a more stable and clinically suitable organoid model . Researchers and enterprises should strengthen the collaboration between medicine and industry and integrate and innovate existing technology by developing new bioactive materials and establishing standardized processes, standards, and quality control methods. This would enhance the stability, biocompatibility, and degradability of skin organoids and reduce the risks associated with clinical use, making these organoids more widely applicable . In addition, angiogenesis plays a crucial role in wound healing , and vascularized organoids can recreate the interaction between the parenchyma and blood vessels, restoring a realistic skin environment . The incorporation of angiogenesis-related factors into skin organoids via advanced biotechnology can regulate biological signal transmission and accelerate blood vessel formation . Furthermore, microfluidic systems simulating blood vessels have been employed to increase blood vessel formation and perfusion in skin organoids . In a recent study, one team developed a microfluidic platform that connects the vascular network to organoids and improves the growth and maturation of 3D vascular organoids produced with human-induced pluripotent SCs . Wang et al. constructed a 3D vascular fluidized organ chip based on open microfluidic control, providing a method for realizing in vitro construction of vascularized organoid models. This method can be further applied to combine organoids with vascularized organ chips to culture vascularized organoids and resolve the challenge of vascularization in organoid culture. Another important challenge related to skin organoids is the lack of an immune system. The skin is an important part of the body’s immune defense system and has the ability to recognize and resist invasion by foreign pathogens. However, the currently established skin organoids lack the immune components required to adequately recapitulate human skin biology and disease complexity, limiting their ability to simulate real skin function fully. Currently, Bouffi et al. have deciphered human gut–immune crosstalk during development and developed organoids containing immune cells by transplanting intestinal organoids under the kidney envelope of mice with a humanized immune system. Another research team has jointly developed functional macrophages in human colonic organoids derived from multipotent SCs. These macrophages regulate cytokine secretion in response to proinflammatory and anti-inflammatory signals, perform phagocytosis, and respond to pathogenic bacteria . These developments provide completely new ideas for the construction of skin organoids that simulate the immune response in the skin. Finally, the clinical translation of organoids raises considerable ethical concerns. Compared with some cell types that have been widely used in clinical practice (such as red blood cells and platelets), there are more ethical concerns about the safety, efficacy and long-term impact of skin organoids in clinical applications because of their unique regenerative potential and undifferentiated state. Moreover, the utilization of organoids derived from patient-specific ASCs or iPSCs for drug testing can be a valuable way to tailor treatments to individual patients. However, such patient-specific trials are costly and offer limited benefits, preventing them from passing cost–benefit evaluations during ethical review. From a technical safety standpoint, organoid transplantation involves invasive surgery, and the uncontrolled development of SCs may pose substantial risks, making predictions based on animal models challenging. The International Society for Stem Cell Research has issued guidelines for human SC research and clinical translation . It is crucial to carefully study and assess potential ethical issues in research on and applications using organoids; improve the corresponding laws, regulations, and scientific research ethics guidelines; and standardize the research and application of these organoids. This proactive approach will contribute to the responsible and ethical development of the SC field. The future focus of research on skin organoids mainly includes CRISPR-mediated gene-editing technology, microfluidic organoid chip technology, 3D printing technology, high-throughput automation technology based on artificial intelligence (AI), and organoid sample bank establishment . Targeting key endogenous genes via CRISPR technology and increasing their expression may contribute to skin wound repair. In 2020, Artegiani et al. achieved fast and efficient knock-in of human organoids via the nonhomology-dependent CRISPR-Cas9 technology CRISPR-HOT (CRISPR-Cas9-mediated homology-independent organoid transgenesis), providing a vital platform for endogenous knock-in in human organoids. Dekkers et al modelled breast cancer via CRISPR-Cas9-mediated engineering of human breast organoids. Michels et al developed a platform for pooled CRISPR–Cas9 screening in human colon organoids, which was helpful for screening for tumor suppressors both in vitro and in vivo . Mircetic et al pioneered the use of negative selection-based CRISPR screening for patient-derived organoids and identified a cohort of patients who may benefit from gene-targeting therapy. In the field of skin organoid models, Dabelsteen et al used CRISPR-Cas9 gene targeting to generate a library of 3D organotypic skin tissues that selectively differ in their capacity to produce glycan structures on the main types of N- and O-linked glycoproteins and glycolipids. Engineering solutions based on microfluidic and 3D printing technology can resolve issues related to the difficulty of molding organoids, the short modelling and molding time, and small sample sizes, thereby enabling the transition of skin organoids from research and development to commercial application as standardized clinical tools. Organ chips based on microfluidic technology can replicate and regulate multiple microenvironments within microfluidic devices. They offer advantages in terms of the controllability and standardization of modelling, enabling the construction of more complex skin models . 3D printing technology can not only support the long-term growth of cells under laboratory conditions but also simulate the mechanical properties of real organs, providing strong technical support for the in vitro culture of skin organoids. AI high-throughput automation can be applied to sample quality control and standardization of the culture and use process, improving the success rate, optimizing and reducing the time associated with manual procedures, and facilitating clinical application. First, image analysis technology combined with deep learning can more accurately capture the microstructure and changes of organoids, improve the ability to identify changes in their morphology and growth, provide accurate data support for experiments, and reduce time and costs . Second, omics data from organoids provide new tools for the resolution of cell development and disease mechanisms . In drug screening, a key application of organoids, AI enables real-time monitoring of drug activity, which enhances screening accuracy and efficiency . In the future, AI is expected to play a greater role in the study of skin organoids, accelerating their clinical translation and the development of precision treatment. The establishment of biobanks is conducive to the cultivation and maintenance of organoid models, collaborative scientific research among researchers, and the transformation of scientific research results into market applications. By establishing a large-scale library of organoid samples, many experimental materials can be generated to provide accurate and reliable data support for experiments . In addition, diverse samples can simulate the physiological state of the skin in different populations and under different healing conditions, providing a more comprehensive reference for drug development and wound treatment . Skin organoids are emerging as promising models and treatment strategies for skin wound healing, offering novel avenues for scientific research and clinical interventions. With ongoing advances in technologies, such as 3D printing, culture systems for skin organoids are continually maturing, evolving from simple in vitro cultures to complex systems encompassing the epidermis, dermis, and appendages. These systems can facilitate skin cell regeneration and help establish a microenvironment conducive to skin wound healing. However, skin organoid technology currently has several limitations, and related research has yet to comprehensively meet clinical use requirements. With the continuous refinement of skin organoid culture systems, translation from basic research to clinical applications can be expected soon. This approach will enable functional repair and regeneration of wounded skin, ultimately benefiting a substantial number of patients with skin burns and trauma. | Review | biomedical | en | 0.999997 |
PMC11697123 | Liver transplantation is an effective treatment for end-stage liver disease. 1 However, ischemia-reperfusion injury (IRI) is a common and unavoidable surgical complication of liver transplantation, which can result in impaired liver function and even post-transplant liver failure. 2 Previous studies have shown that the inflammatory cascade response and cytokines play vital roles in the mechanism of hepatic IRI, and metabolic stress following metabolic homeostasis disruption in the liver influences the pathogenesis and pathological process of hepatic IRI by stimulating reactive oxygen species overproduction and sterile inflammation. 3 , 4 However, the intrahepatic inflammatory microenvironment and metabolite changes caused by IRI are still undefined; thus, the characteristic variations in the transcription and metabolite levels in the early, intermediate, and late phases of hepatic IRI require further research. Transcriptomics technology is used as a medical tool to explore the underlying mechanisms in IRI research. For example, through transcriptomics, hepatic metabolic remodeling, including lipid/fatty acid and 5-aminolevulinate (5-ALA) metabolisms, has shown its significance in IRI and could be a targeted therapeutic intervention. 5 Tripartite motif-containing 27 (TRIM27), a critical mediator of inflammation, has been revealed by transcriptomics to negatively regulate inflammation via suppressing the NF-κB and MAPK signaling pathways during hepatic IRI and is expected to be a promising treatment to attenuate hepatic IRI. 6 Moreover, metabolomic technology has also been employed to study the mechanism of hepatic IRI. Metabolomics was used to investigate the impact of glucose metabolism-related genes on hepatic IRI and showed that insulin-induced gene 2 (INSIG2) could reduce hepatic IRI by triggering the downstream pentose phosphate pathway to reprogram glucose metabolism. 7 A recent study applied metabolomics to demonstrate that oxidized lipid metabolites markedly increased during hepatic IRI and lipid peroxidation, partially caused by nicotinamide adenine dinucleotide deprivation, and could aggravate hepatic IRI. 8 Here, we established mouse models of liver IRI and investigated the characteristic alterations of transcriptome and metabolome levels of mouse liver in the early, intermediate, and late phases of IRI with transcriptomics and metabolomics. Additionally, we explored the effects of these changes on hepatic IRI during different periods. Finally, our study offers a novel perspective for exploring the occurrence and development of hepatic IRI by combining transcriptomics and metabolomics analyses. Following anesthesia, the mice underwent laparotomy, and the blood vessels of the left and middle liver lobes were clipped with a vascular clamp to form 70% warm ischemia of the liver. After ischemia for 1 h, the clamp was removed and kept for reperfusion for 12, 24, and 48 h 9 , 10 . Mice in the Sham group were subjected to the same procedure, but the blood vessels were not clipped. All 32 mouse liver samples were obtained and divided into the Sham, I1R12, I1R24, and I1R48 groups ( n = 8 per group). After the mice were sacrificed, liver samples were sectioned and fixed with paraformaldehyde. Subsequently, the liver sections were dehydrated using a gradient series of alcohol and embedded in paraffin wax. Liver sections of mice from the four groups were stained with hematoxylin and eosin and then observed under a 200X or 400X light microscope. Blood samples were collected from the four groups of mice, and an ELISA kit (Nanjing Jiancheng, Nanjing, China) was used to measure the serum concentrations of aspartate aminotransferase and alanine aminotransferase. IRI in mouse liver samples was evaluated by calculating Suzuki’s score in a blinded manner. 11 Mouse liver tissue samples were preserved at −80 °C until mRNA was extracted. Furthermore, quantitative real-time PCR with cDNA as a template and β-actin as an internal reference was conducted to analyze the relative mRNA expression of several biomarkers we selected. The primer sequences are listed in Table 1 . Table 1 The sequences. Table 1 The primer The sequence (5′-3′) The sequence (3′-5′) PKG1 CCACAGAAGGCTGGTGGATT GTCTGCAACTTTAGCGCCTC GcK CCCAGTCGTTGACTCTGGTAG CTTCTGAGCCTTCTGGGGTG LDHA AACTTGGCGCTCTACTTGCT GGACTTTGAATCTTTTGAGACCTTG PI3K CCACCTCTTTGCCCTGAT TCGGTTCTTTCCCGTTAG AKT1 CCGCCTGATCAAGTTCTCCT GATGATCCATGCGGGGCTT 4E-BP1 ACTCACCTGTGGCCAAAACA TTGTGACTCTTCACCGCCTG ALDOA AACCCAGCTGAATAGGCTGC CATGGGTCACCTTGCCTGG TIMP-1 AGCCTGGAGGCAGTGATTTC GGCCATCATGGTATCTGCTCT STAT3 TACACCAAGCAGCAGCTGAA TACGGGGCAGCACTACCT PLA2 AACACCTCCGCTAAGAACCC GCAGCCGTAGAAGCCATAGT PLAAT3 GGAGAAAAGGAGCCAGGGG GCTTGGGTTCTGGTATGGGT AML12 cells were purchased from Procell (Wuhan, China) and cultured in DMEM/F12 (Gibco, USA) with 10% fetal bovine serum (Procell), 40 ng/mL dexamethasone and 0.5% insulin-transferrin-selenium . The AML12 cells were incubated in a tri-gas incubator to hypoxia for 12 h (94% N 2 , 5% CO 2 , and 1% O 2 ) and transferred to 5% CO 2 typical incubator for reoxygenation for 12, 24, and 48 h in medium without fetal bovine serum. The proteins of the tissues and cells were extracted by lysis buffer and the protein concentration was measured using a BCA protein assay kit. The proteins were separated by SDS-PAGE and transferred to PVDF membranes, which then were blocked with NcmBlot blocking buffer for 30 min. The membranes were incubated overnight at 4 °C with primary antibodies and 1 h at room temperature with horseradish peroxidase-conjugated goat-anti-rabbit or goat-anti-mouse antibodies. The blots were visualized by the FUSION Solo S system. The primary antibodies were used in this study included PGK1 , GCK , LDHA , PI3K , AKT1 , 4EBP1 , ALDOA , TIMP1 , STAT3 , P-AKT , P-PI3K , mTOR , β-actin . ELISA assays (RUIXIN, China) were used to calculate the concentration of prostaglandin F1 alpha (PGF1α) and the free fatty acid content in the cell culture supernatant of mouse liver tissue samples according to the manufacturer’s instructions. The hydroxyproline content of mouse liver tissue samples was detected by a hydroxyproline content assay kit according to the manufacturer’s instructions. Differentially expressed genes (DEGs) between two samples were identified by calculating the expression level based on the transcripts per million. Furthermore, we quantified gene abundances using RNA-seq by expectation maximization. 12 Differential expression analysis was conducted via DESeq2. 13 Significant DEGs were identified depending on the criteria |log 2 fold change| ≥ 1 and false discovery rate <0.05. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted by Goatools and Python SciPy, respectively, for function and pathway enrichment analysis of DEGs. The above data were analyzed on the Majorbio Cloud Platform. The analysis of mouse liver samples with liquid chromatography coupled with mass spectrometry was conducted on a Thermo UHPLC-Q Exactive HF-X system. After the addition of 6 mm diameter grinding beads, 50 mg of solid samples were ground and then centrifuged at 13,000 g and 4 °C for 15 min. The supernatant was transferred for analysis with liquid chromatography coupled with mass spectrometry. Progenesis QI software pretreated the raw data. The metabolites were identified by the Human Metabolome Database (HMDB), Metlin, and Majorbio databases. The data matrix from the search database was uploaded to the Majorbio Cloud platform for analysis. Significant DEMs were identified based on variable importance in projection >1 and P < 0.05. DEMs were sorted into the corresponding biochemical pathways via the KEGG pathway enrichment analysis. Metabolic compound identification was performed using the HMDB and KEGG compound databases. Simultaneously, integrated pathway analysis was conducted using the iPath database version 3. All data were analyzed using SPSS 22.0 software and presented as mean ± standard error of the mean. The study employed the student’s t -test to analyze significant differences between two groups, while one-way analysis of variance (ANOVA) was used for the analysis among three or more groups. Statistical significance was determined at P values < 0.05. In each group, liver tissue samples were stained with hematoxylin and eosin . Hepatic IRI extent was assessed by Suzuki’s score . The degree of liver damage increased significantly with the extension of post-reperfusion time, and the serum concentrations of aspartate aminotransferase and alanine aminotransferase corroborated this result, peaking at 24 h of reperfusion . To investigate the characteristic changes in liver IRI over time, we collected mouse liver tissue samples from the I1R12, I1R24, I1R48, and Sham groups, metabolomics and transcriptomics analyses were performed, and the findings were validated by quantitative real-time PCR . The transcriptome and metabolome quality control results indicated that the data were fit to be used for further analysis . Figure 1 Establishment and validation of the hepatic ischemia-reperfusion injury model. (A) Hematoxylin-eosin staining of liver tissue samples of the Sham, I1R12, I1R24, and I1R48 groups (magnification, × 200/ × 400; scale bar, 100 mm). (B) Serum concentrations of aspartate aminotransferase (AST) and alanine aminotransferase (ALT). (C) Suzuki’s scores of the Sham, I1R12, I1R24, and I1R48 groups. (D) Schematic of the research process. n = 8; ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001; ns, no significance. I1R12, ischemia for 1 h and reperfusion for 12 h; I1R24, ischemia for 1 h and reperfusion for 24 h; I1R48, ischemia for 1 h and reperfusion for 48 h. Figure 1 The co-expressed and specifically expressed genes among the four groups were indicated in a Venn diagram . Principal component analysis and correlation heatmaps revealed significant between-group differences , proving the rationality of liver tissue samples. Through transcriptome data analysis, based on the criteria |log 2 fold change| ≥ 1 and P < 0.05, 2203 DEGs ( Table S1A ) were identified in the I1R12 group versus the Sham group. Furthermore, 2353 DEGs ( Table S1B ) were identified in the I1R24 group versus the Sham group, and 4146 DEGs ( Table S1C ) were identified in the I1R48 group versus the Sham group . The corresponding heatmaps and volcano plots are shown in detail in Figure 3 C and D. Based on the above DEG data, the Venn diagram showing the co-expressed genes of three comparison groups yielded 1115 DEGs . Figure 2 Correlation analysis of liver tissue samples. (A) Venn diagram analysis of the genes among the Sham, I1R12, I1R24, and I1R48 groups. (B) Principal component analysis of the Sham, I1R12, I1R24, and I1R48 groups. (C) Correlation heatmap of the Sham and IR groups. IR, ischemia and reperfusion; I1R12, ischemia for 1 h and reperfusion for 12 h; I1R24, ischemia for 1 h and reperfusion for 24 h; I1R48, ischemia for 1 h and reperfusion for 48 h. Figure 2 Figure 3 Hepatic ischemia-reperfusion injury involves transcriptional reprogramming. (A) Venn analysis of DEGs in the Sham/I1R12 groups, Sham/I1R24 groups, and Sham/I1R48 groups. (B) Histogram of the DEG number of the Sham/I1R12 groups, Sham/I1R24 groups, and Sham/I1R48 groups. (C) Hierarchical clustering heatmap of DEGs in the Sham/I1R12 groups, Sham/I1R24 groups, and Sham/I1R48 groups. (D) Volcano plots of DEGs in the Sham/I1R12 groups, Sham/I1R24 groups, and Sham/I1R48 groups. Blue denotes down-regulated genes, and red represents up-regulated genes. I1R12, ischemia for 1 h and reperfusion for 12 h; I1R24, ischemia for 1 h and reperfusion for 24 h; I1R48, ischemia for 1 h and reperfusion for 48 h; DEG, differentially expressed gene. Figure 3 GO function ( Tables S2A–C ) and KEGG pathway enrichment ( Tables S3A–C ) analyses were performed to analyze the characteristic changes in biological functions and involved pathways in the early, intermediate, and late phases of IRI. In the early phase of hepatic IRI, KEGG analysis revealed four significant enrichment pathways: glycolysis/gluconeogenesis, galactose metabolism, biosynthesis of unsaturated fatty acids, and pentose and glucuronate interconversions, whereas lipid biosynthetic process and response to oxidative stress processes were enriched in GO analysis . Thus, glucose and carbohydrate metabolism were characteristic changes in the early phase of IRI. Regarding the intermediate phase of hepatic IRI, GO enrichment analysis was enriched in the cellular lipid metabolic process and acute inflammatory response. In contrast, KEGG analysis was enriched in the glycolysis/gluconeogenesis and insulin resistance pathways, and PI3K-AKT, HIF-1, and adipocytokine signaling pathways , involving glucose and lipid metabolism and activation of the inflammatory pathway. In addition, KEGG analysis was enriched in the fatty acid degradation, fatty acid elongation, and linoleic acid metabolism pathways in the late phase of IRI, and GO enrichment analysis was enriched in lipid catabolic, neutral lipid metabolic, fatty acid metabolic, lipid biosynthetic, and triglyceride metabolic processes . This result demonstrated that lipid metabolism was the main characteristic change during the late phase of IRI. Figure 4 GO function and KEGG pathway enrichment analyses of the DEGs. (A) GO and KEGG enrichment analyses of the Sham and I1R12 groups. (B) GO and KEGG enrichment analyses of the Sham and I1R24 groups. (C) GO and KEGG enrichment analyses of the Sham and I1R48 groups. All GO function and KEGG pathway enrichment analyses of the DEGs revealed the top 20 functional terms and pathways. DEG, differentially expressed gene; KEGG, Kyoto encyclopedia of genes and genomes; GO, Gene Ontology; I1R12, ischemia for 1 h and reperfusion for 12 h; I1R24, ischemia for 1 h and reperfusion for 24 h; I1R48, ischemia for 1 h and reperfusion for 48 h; DEG, differentially expressed gene. Figure 4 To validate the results of GO and KEGG analyses, western blotting and quantitative real-time PCR were applied to measure the relative mRNA and protein expression levels of several biomarker-related pathways selected from KEGG analysis. Compared with the Sham group, the relative mRNA expression of PGK1 and LDHA increased, while the mRNA expression of GCK decreased, indicating up-regulation of the glycolysis pathway. The relative mRNA expression levels of PI3K, AKT1, 4EBP1, ALDOA, TIMP-1, and STAT3 were elevated, suggesting the up-regulation of the HIF-1 and PI3K-AKT pathways. In addition, the increased expression of PLA2 and PLAAT3 indicated up-regulation of the linoleic acid metabolism pathway . At the same time, we confirmed that the protein expression of PGK1, LDHA, PI3K, AKT, 4EBP1, ALDOA, TNP-1 and STAT3 in the liver tissue were increased, which were consistent with the results of quantitative real-time PCR . Though hypoxia/reoxygenation model in vitro , we used LY294002 (PI3K inhibitor) to confirm the activation of PI3K/AKT/mTOR pathway and found obviously increased protein expression of phosphorylation of PI3K (p-PI3K) and phosphorylation of AKT (p-AKT) in the hypoxia/reoxygenation-treated group. Meanwhile, the group treated with hypoxia/reoxygenation and LY294002 showed a reduction of the expression of p-PI3K and p-AKT, indicating that LY294002 suppressed the PI3K/AKT/mTOR pathway successfully . These results were consistent with the GO and KEGG analyses of transcriptomics. Figure 5 The relative mRNA and protein expression levels of 11 biomarkers in the Sham and IRI groups. (A) The relative mRNA levels of 11 biomarkers in the Sham and IRI groups measured by quantitative real-time PCR. (B, C) The protein levels of 11 biomarkers detected by western blotting. n = 4; ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001. IRI, ischemia-reperfusion injury. Figure 5 Figure 6 The activation of PI3K/AKT/mTOR pathway and hepatic ischemia-reperfusion injury involves the metabolic reprogramming. (A, B) The protein levels of P13K/AKT/mTOR pathway detected by western blotting. (C) Principal component analysis of the Sham, I1R12, I1R24, and I1R48 groups in the positive and negative ionization modes. (D) Correlation heatmaps of the Sham, I1R12, I1R24, and I1R48 groups in the positive and negative ionization modes. ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001. I1R12, ischemia for 1 h and reperfusion for 12 h; I1R24, ischemia for 1 h and reperfusion for 24 h; I1R48, ischemia for 1 h and reperfusion for 48 h; DEG, differentially expressed gene. Figure 6 Principal component analysis and correlation heatmaps of the positive and negative ionization modes showed apparent separation differences between the samples of each group . Principal component analysis plots showed that the separation between the I1R48 and Sham groups was the most obvious. Furthermore, based on the criteria (variable importance in projection >1, and P < 0.05), 358 DEMs (137 up-regulated, 221 down-regulated) ( Table S4A ) were identified in the I1R12 and Sham groups. A total of 339 DEMs (142 up-regulated, 197 down-regulated) ( Table S4B ) were identified in the I1R24 and Sham groups, and 367 DEMs (99 up-regulated, 268 down-regulated) ( Table S4C ) in the I1R48 and Sham groups. The corresponding volcano plots are shown in Figure 7 B. The Venn diagram for analyzing the co-expressed DEMs among the three groups yielded 151 metabolites , and we selected two metabolites for validation. We determined the concentration of cell culture supernatant PGF1α was decreased and the hydroxyproline content of mouse liver tissue samples was increased following the extent of reperfusion, consistent with metabolomics results . Figure 7 Hepatic ischemia-reperfusion injury involves the metabolic reprogramming. (A) Venn diagram (left) and histogram (right) of DEMs in the Sham/I1R12 groups, Sham/I1R24 groups, and Sham/I1R48 groups. (B) Positive and negative ionization modes volcano plots of DEMs in the Sham/I1R12 groups, Sham/I1R24 groups, and Sham/I1R48 groups. The blue dots denote uptake of metabolites, and the red dots indicate release of metabolites. (C) The level of PGF1α in cell culture media measured by ELISA. (D) The level of hydroxyproline in mouse liver samples measured with hydroxyproline content assay kit. n = 3; ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001. I1R12, ischemia for 1 h and reperfusion for 12 h; I1R24, ischemia for 1 h and reperfusion for 24 h; I1R48, ischemia for 1 h and reperfusion for 48 h; DEG, differentially expressed gene; DEM, differentially expressed metabolite. Figure 7 To analyze the differences in metabolites between groups, we performed clustering heatmaps of the I1R12, I1R24, and I1R48 groups versus the Sham group . Clear separations were demonstrated in the heatmaps, showing that the mouse liver reperfusion models underwent significant metabolic recombination following ischemia and reperfusion, consistent with the principal component analysis and correlated heatmaps. Subsequently, KEGG analysis of the DEMs was conducted ( Tables S5A–C ). Analysis of metabolites showed that the following metabolic pathways were enriched in the I1R12 and Sham groups: arachidonic acid, glycerophospholipid, and ether lipid metabolism . In the I1R24 and Sham groups, the significantly differential metabolic pathways were linoleic acid metabolism, glycerophospholipid metabolism, regulation of lipolysis in adipocytes, sphingolipid signaling pathway, and glucagon signaling pathway . In the I1R48 and Sham groups, KEGG analysis was enriched in arachidonic acid metabolism, PPAR signaling pathway, alpha-linolenic acid metabolism, and biosynthesis of unsaturated fatty acids . Meanwhile, we detected the free fatty acid content of mouse liver tissue samples and showed that the free fatty acid levels in the I1R12 and I1R24 groups were significantly decreased, indicating the existence of lipid metabolism disorder. Our findings demonstrated that the primary metabolic characteristics of lipid metabolism were altered in the early, intermediate, and late phases of IRI. Figure 8 Hierarchical clustering heatmap and KEGG pathway enrichment analysis of the DEMs. (A) Hierarchical clustering heatmap of DEMs in the Sham and IR groups. (B) KEGG analysis of the DEMs in the Sham and I1R12 groups. (C) KEGG analysis of the DEMs in the Sham and I1R24 groups. (D) KEGG analysis of the DEMs in the Sham and I1R48 groups. (E) The level of free fatty acid (FFA) measured by ELISA. All KEGG pathway enrichment analyses revealed the top 20 pathways. n = 3; ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001. DEM, differentially expressed metabolite; KEGG, Kyoto encyclopedia of genes and genomes; IR, ischemia and reperfusion; I1R12, ischemia for 1 h and reperfusion for 12 h; I1R24, ischemia for 1 h and reperfusion for 24 h; I1R48, ischemia for 1 h and reperfusion for 48 h. Figure 8 To analyze the specific components of DEMs, 358 DEMs in the I1R12 and Sham groups were assigned to the HMDB database; 330 metabolites were classified into 10 HMDB superclasses and 21 HMDB subclasses; 156 metabolites were included in the “lipids and lipid-like molecules” superclass, and 77 metabolites were included in the “others” subclass, which were the first class in superclass and subclass, respectively . Similarly, 339 DEMs in the I1R24 and Sham groups were assigned to the HMDB database; 313 metabolites were classified into 11 superclasses and 21 subclasses; “lipids and lipid-like molecules” superclass contained 153 metabolites, and the “others” subclass contained 82 metabolites, both of which are the first class . In the I1R48 and Sham groups, 367 DEMs were assigned to the HMDB database; 338 metabolites were classified into 12 superclasses and 21 subclasses; 185 metabolites were included in the first superclass, “lipids and lipid-like molecules”, and 70 metabolites were included in the first subclass, “others” . It was evident from the results of the HMDB database that the proportion of “lipids and lipid-like molecules” increased with prolonged reperfusion time, indicating that the lipid metabolism was significantly altered. In addition, the KEGG compound classification results indicated that the number of fatty acids increased markedly with an increase in reperfusion time , consistent with the HMDB database results and KEGG analysis. Notably, our findings showed that most DEMs in the three groups were linked to lipid metabolism, and the integrated pathway analysis also proved that . Figure 9 The identified metabolites were classified based on the HMDB and KEGG compound databases. (A) Pie chart of the identified metabolites based on the HMDB database. (B) Histogram of the identified metabolites based on the KEGG compound database. (C) Integrated pathway analysis of DEMs in the Sham/I1R24 groups and Sham/I1R48 groups. The rectangle circled by red line indicated that most DEMs were linked to lipid metabolism. DEM, differentially expressed metabolite; I1R24, ischemia for 1 h and reperfusion for 24 h; I1R48, ischemia for 1 h and reperfusion for 48 h. Figure 9 Liver IRI is an unavoidable consequence of liver transplantation and partial hepatectomy that involves multiple pathological mechanisms. 14 , 15 Most liver IRI research focuses on the inflammatory response and cell death; however, metabolism and detoxification are important functions of the liver. Thus, a study suggested ischemia-reperfusion primarily disrupts metabolic homeostasis, followed by an inflammatory response and hepatic damage. 5 Previous studies have indicated that glucolipid metabolism regulated by INSIG2, and lipid metabolic reprogramming, including arachidonate 12-lipoxygenase (ALOX12) and its downstream metabolites, influence hepatic IRI through the release of damage-associated molecular patterns (DAMPs) and oxidative stress. 7 , 16 However, alterations in signaling pathways and metabolic profiles in the early, intermediate, and late phases of hepatic IRI remain undefined. Transcriptomics and metabolomics have been employed in medical research to investigate the mechanisms underlying various disorders. 17 In a recent study, transcriptomics was used to provide a deeper explanation of the molecular mechanism of IRI. 18 Additionally, metabolomics is to reveal certain pathophysiological processes by detecting the level of changes in metabolites in organisms and as an effective method has been used in research on the molecular mechanisms of IRI in metabolic remodeling. 8 This study investigated the pathogenesis of hepatic IRI from a new perspective by combining transcriptomics and metabolomics in the early, intermediate, and late phases of hepatic IRI. In the initial phase of hepatic IRI, ischemia leads to an insufficient oxygen supply to hepatic cells and impairs them via exposure to glucose consumption, pH changes, and ATP depletion, resulting in disturbances in cellular metabolism and inflammation. 3 , 19 Meanwhile, glycolysis is a major energy source, and accelerated glycolysis and ATP depletion increase the accumulation of acidic metabolites, impairing signaling interactions, cellular homeostasis, and hepatocytes, and triggering mitochondrial dysfunction and inflammatory responses. 7 , 20 In this study, KEGG analysis showed that the glycolysis/gluconeogenesis pathway was altered exclusively during the early phase of IRI when glycolytic flux increased to satisfy the energy requirement in the state of hypoxia. The outcomes of our study illustrated that glycose metabolism reprogramming is critical in the early phase of IRI and could be a metabolic intervention treatment to reduce the subsequent inflammatory response. Several studies have reported that glycolysis interference treatments significantly inhibited glycolysis and the release of inflammatory cytokines, improving the acidic microenvironment and acidosis and attenuating hepatic cellular damage. 21 , 22 , 23 Liver IRI involves two interconnected stages: local ischemia injury and reperfusion injury caused by sterile inflammation. 19 The findings of this study confirmed that in the intermediate phase of IRI, inflammatory responses were triggered and became more intense. The intermediate phase of IRI is characterized by inflammatory disorder, triggered by the overproduction of reactive oxygen species and the release of DAMPs and pro-inflammatory cytokines, aggravating apoptosis and hepatocyte damage. 24 , 25 Based on the KEGG analysis of the intermediate phase, this study found that inflammation-related signaling pathways such as PI3K-AKT and HIF-1 were markedly regulated, taking part in anti-inflammatory and adaptive hypoxia responses during IRI, providing a potential therapeutic intervention in regulating anaerobic glycolysis and inflammatory response to improve IRI. 26 , 27 , 28 Our experiments in vitro cell model of hypoxia/reoxygenation also indicated that the activation of the PI3K/AKT pathway during IRI was obviously suppressed by PI3K inhibitors. Meanwhile, some studies have demonstrated the P13K-AKT pathway has the potential to serve as a therapeutic intervention target to mitigate IRI by reducing reactive oxygen species production and pro-apoptotic signals. 29 , 30 Moreover, KEGG analysis, compound classification, and integrated pathway analysis found that lipid metabolism remodeling was the characteristic alteration in the late phase of IRI. The liver is an important organ for lipid metabolism, and essential fatty acids play a vital role in hepatic IRI; for example, lipids are one of the main targets of reactive oxygen species in oxidative stress, contributing to IRI through the concentration of fatty acids and lipid peroxidation, forming cytotoxic lipid aldehydes and lipid hydroperoxides. 31 Previous studies have reported that lipid metabolic disorders during IRI induce oxidative stress, inflammation, apoptosis, and ferroptosis by modulating interrelated transduction signaling pathways and suppressing antioxidant capacity, which could aggravate lipid metabolic reprogramming. 32 , 33 Meanwhile, previous clinical research demonstrated that lipid biosynthesis was the major change during IRI, severe steatosis was associated with a higher incidence of graft failure after liver transplantation, and some metabolites had the potential to be biomarkers of lipid-related damage of IRI. 24 , 34 These findings and our results indicate that lipid metabolic reprogramming plays a key role in hepatic IRI and aggravates IRI. Consequently, we present a new perspective on IRI therapeutic intervention: intervening in the major metabolic reprogramming at each stage through clinical means could effectively control the subsequent inflammatory response, and even predict the prognosis of liver transplantation through the concentrations of mainly different metabolites at each stage. It has been reported that regulating lipid metabolism response and mediators could ameliorate the pathological damage from ischemia-reperfusion by reducing mitochondrial damage and liver macrophage pyroptosis. 5 , 35 , 36 This study illustrated the importance of metabolic reprogramming in hepatic IRI and its potential as a therapeutic intervention target. In summary, by combining transcriptomics and metabolomics, our study first revealed characteristic changes in signaling pathways and metabolism in the early, intermediate, and late phases of hepatic IRI. Lipid metabolism, precisely regulated by the liver through biochemical, signaling, and cellular pathways, plays a non-negligible role in the occurrence and development of hepatic IRI. This represents a potential therapeutic intervention to treat hepatic IRI and strengthens the understanding of the pathogenesis and pathological process of IRI and its molecular mechanism. | Study | biomedical | en | 0.999996 |
PMC11697147 | If a woman becomes pregnant as early as she giving birth, then it can lead to low birth weight, a doubling of the chances of premature birth, and a 60% increase in the risk of infant mortality for babies born less than 24 months after a previous birth ( 8 ). Review of literature showed that marital status, secondary and above level of education, maternal age, longer birth interval after delivery, ever used contraceptive methods, menses resumption, starting sex, antenatal care follow-up, postnatal care, knowledge about family planning, and discussion with husband ( 4 , 9 – 13 ) were found to be determinants of postpartum contraceptive utilization. Research in Ethiopia shows that 47% of pregnancies occur within 24 months of the previous birth ( 14 ), having the highest maternal mortality rate in Sub-Saharan Africa at 412 per 100,000 live births ( 15 ). Furthermore, there is a high demand for postpartum family planning (PPFP), with rates as high as 86% within the first 5 months after childbirth, decreasing to 76% within the first year. Additionally, 81% of women do not use PPFP because they are unaware that they can conceive within a year after giving birth ( 16 ). To improve postpartum contraceptive use, provision of health education, counseling about the importance of FP, access to various family planning methods are paramount factors ( 17 ). There are significant differences in Ethiopia regarding awareness and use of modern contraceptives. Nearly all married women in Addis Ababa know at least one contraceptive method, compared to only 67% in the Somali region. Urban women have a higher adoption rate (48%) than rural women (38%). The public sector provides 87% of modern contraceptives, while the private sector offers 12%. Usage rates are highest in Addis Ababa (48%) and Amhara (50%), and lowest in Somali (3%) and Afar (13%) ( 18 , 19 ). This data suggested that there are no standard approaches to helping Ethiopian women who wish to utilize family planning per their needs. The 2016 EDHS data shows that 25% of women use modern family planning six months postpartum, with significant differences based on delivery location: 18% for home births and 43% for facility births ( 20 ). These disparities may result from variations in access to healthcare and demographic factors influencing childbirth location and contraceptive use. The report's inconsistencies hinder clinical decision-making. Despite previous studies and reviews ( 21 – 25 ), policymakers and providers still lack a comprehensive overview of the evidence on factors influencing postpartum family planning demand. This study aims to present a comprehensive overview of systematic reviews (SRs) to consolidate the current evidence concerning the uptake of modern postpartum family planning (PPFP) and contributing factors among women women in the postpartum periods. A preliminary search was carried out in PROSPERO to examine the current research landscape and mitigate the duplication risk. At that point, no analogous studies were discovered. Subsequently, a research protocol was formulated and registered in PROSPERO . The search terms were integrated using Boolean operators “OR” and “AND”. The following MeSH terms or keywords were applied in the online database: postpartum OR post-delivery OR parturition OR puerperium OR immediate postpartum OR extended postpartum AND prevalence OR magnitude OR proportion AND use OR utilization OR intention OR unmet need OR barrier AND predictors OR contraception OR contraceptive OR family planning OR modern contraceptives OR modern postpartum family planning OR modern family planning AND Ethiopia AND systematic review ( Supplementary Table S1 ). The Meta-analysis of Observational Studies guideline (MOOSE) ( 26 , 27 ). These methodologies include detailed checklists with 35 elements to guide the execution and documentation of observational studies at high risk of bias and confounding, particularly in evaluating retrospective data. The systematic reviews (SRs) and meta-analyses concerning the adoption of modern contraceptive postpartum family planning (PPFP) per the PRISMA guidelines ( 28 ) was used . An extensive literature review was performed utilizing four major electronic databases, such as MEDLINE/PubMed, Cochrane, Web of Science and Science Direct covering the period from June 15, 2024 to July 15, 2024. A set of inclusion and exclusion criteria was established to identify all pertinent systematic reviews: (i) population: studies focusing on postpartum mothers, (ii) outcome: adoption of modern postpartum family planning (PPFP) and its determining factors, (iii) language: all published studies in English, (iv) study design: systematic reviews and meta-analyses, (v) geographical area: research conducted exclusively in Ethiopia. The exclusion criteria for reviews were determined based on the following factors: the presence of similar duplicate reviews published across multiple journals, articles that did not report the intended outcome (i.e., incomplete quantitative data), and reviews that failed to present a clear research question, search strategy, or a defined methodology for article selection. The PICOS framework highlights four critical components: population, intervention, comparison, and outcome. Its primary aim is to identify and evaluate the clinical aspects of evidence throughout the systematic review process. The PICOS components in this study were delineated as follows: Population: postpartum women in Ethiopia; Intervention: postpartum family planning; Comparison: No adoption of modern PPFP; Outcomes: overall prevalence of modern contraceptive uptake among postpartum women and the factors associated with it in Ethiopia; Study design: systematic reviews or meta-analyses. Each study in the analysis underwent a thorough evaluation using the AMSTAR tool, which consists of 11 questions to assess methodological and evidential integrity. Quality was rated on a scale of 0–11, with scores indicating high 8–10, medium 4–7, or low quality <3 ( 29 ). Date from included studies was extracted using standardized extraction tool developed in an Excel spreadsheet and labeled as follows. For each SRM, the following information was extracted: (1) Identification data (first author's last name and publication year), (2) Review aim, (3) Prevalence or proportion of uptake of postpartum family planning, (4) Risk factors, (5) Odds ratio or relative risk with 95% confidence intervals for the risk factors, (6) Number of primary studies included within each SRM study and their respective design type, (7) Total number of sample size included, (8) Publication bias assessment methods and scores, (9) Quality assessment methods and scores, (10) Data synthesis methods (random or fixed-effects model), and (11) The authors' main conclusion of the SRM study (12). TEG and SA independently extracted information on study characteristics and key findings from each review as stated above. In cases of disagreement, additional input was sought from a third and fourth author LLF and ATA, respectively. The study did not employ a specific search strategy, and there were no limitations regarding publication years. Records were organized using Endnote version 8. Data synthesis in the included SRM studies involved both qualitative and quantitative methods. Multiple estimates for modern PPFP prevalence and associated factors were presented as a range, with an aggregated estimate calculated using STATA version 17. Study heterogeneity was assessed using I 2 and Cochran Q statistics ( 30 ). A random effects model with a 95% CI was used to determine the pooled prevalence of modern PPFP utilization. Due to the inadequate number of studies incorporated in this umbrella review, we did not appraise publication bias. To successfully assess the publication bias, at least 10 studies is required ( 31 ). Stata version 17.0 software was used for analyses. The included studies that fulfilled the specified criteria are presented in Figure 1 . A total of 145 articles were initially gathered from four distinct databases. PubMed/Midline generated 58 research articles, Cochrane generated 41 articles, Science Direct generated 12, and Web of science Identified 34 records through reference list review ( 34 ). Subsequently, after eliminating 92 duplicate entries, we were left with 53 records. A review of the titles and abstracts led to the exclusion of 31 articles. The remaining 22 articles were then assessed for eligibility. Ultimately, 17 articles were excluded for several reasons; five were inconsistent with the outcome of study, six were conducted outside of the study area, five had a methodological differences and one was not related to postpartum women. In the end, 5 studies ( 21 – 25 ) were found to meet the eligibility requirements for inclusion. This umbrella review encompasses five systematic review and meta-analysis ( 21 – 25 ), which were derived from observational primary studies. These primary studies consisted of 3 cohort studies, 2 case-control studies, and 77 cross sectional studies, amounting to a total of 82 studies. The collective sample size across these studies involved 44,276 postpartum but one study included women of reproductive age group, pregnant and post-partum ( 23 ) The number of primary studies varied per SRM, ranging from 12 ( 23 ) to 19 ( 24 ). Additionally, the sample size per meta-analysis exhibited variability, spanning from 4,367 ( 23 ) to 11,932 ( 24 ). Two SRM studies were published in the year 2020 ( 24 , 25 ) and one each were published in 2021 ( 23 ), 2022 ( 22 ) and 2023 ( 21 ). These studies comprehensively examined both the prevalence and determinants of postpartum family planning uptake. Based on the included SRMA the prevalence of postpartum family planning were ranged from 21.04, (13.08, 29.00), I 2 = 98.43% ( 21 ) to 48.11 (36.96, 59.27), I 2 = 99.4 ( 25 ). These statistics highlight the diversity in prevalence rates across the studies. General characteristics of the systematic review and meta-analyses studies are presented ( Table 1 ). From umbrella review of five SRM studies the pooled prevalence of PPFP utilization was 36.41% (95% CI, 24.78, 48.03) with the heterogeneity index ( I 2 = 99.9%, P < 0.000) showing substantial heterogeneity. Therefore, we have used the random effect model to resolve the issue of heterogeneity among the included reviews Figure 2 . The subgroup analysis focusing on sample size revealed that the sample greater than 10,000 had the highest prevalence of use of postpartum family planning 46.44% (95% CI, 44.81, 48.08), whereas sample size less than 10,000 had the lowest prevalence 21.28% (95% CI, 20.50, 22.05) Figure 3 . We conducted a thorough investigation into the origins of heterogeneity by employing a leave-one-out sensitivity analysis. This analysis demonstrated that the removal of each study from the overall assessment did not significantly impact the estimated average prevalence. The average prevalence consistently fell within the 95% confidence interval of the overall average prevalence calculated when all studies were included. Therefore, no single study exerted a notable influence on the average prevalence. Furthermore, the sensitivity analysis indicated that the exclusion of each study individually yielded an average prevalence of 36.41%, accompanied by a 95% confidence interval ranging from 24.78 to 40.03, as illustrated in Figure 4 . Considerable variability was noted among the studies included in the meta-analysis. To investigate the origins of this variability, we performed a meta-regression analysis utilizing sample size, which indicated a significant influence on the observed differences in PPFP uptake, as illustrated in Table 3 . Four SRs included in this umbrella review analyzed family planning counseling ( 21 – 24 ) and the finding revealed that postpartum women who counseled about family planning were 4.12 times more likely to utilize family planning methods than their counterpart (AOR: 4.12, 95% CI: 2.89, 4.71). Moreover, the studies did not reveal any heterogeneity in the results ( I 2 = 0.0%, P = 0.874) Figure 5 . Three out of 5 SRs focused on the couple discussion about family planning during postpartum period ( 21 – 23 ). Postpartum women who had discussed contraception with their partners during the postpartum period were 3.06 times more likely to utilize modern contraceptive methods than their counterparts (AOR: 3.06, 95% CI: 1.42, 5.60). Furthermore, the studies did not showed any heterogeneity in the results ( I 2 = 4.7%, P = 0.369) Figure 6 . Furthermore, the statistical significance of having post natal follow up among postpartum mothers' regarding PPFP utilization was analyzed using three studies ( 23 – 25 ). Women who had PNC follow up were almost four times more likely to use PPFP methods than those women who had no post natal follow up(AOR: 3.48, 95% CI: 2.60, 4.83). Also, the studies did not showed any heterogeneity in the results ( I 2 = 0.0%, P = 0.822) Figure 7 . Postpartum family planning (PPFP) plays a crucial role in decreasing high fertility rates among both those who wish to space their children and those who aim to limit family size. It enhances maternal and child health by mitigating the risks associated with unintended short inter-pregnancy intervals and unsafe abortions ( 2 ). Currently, approximately 222 million women worldwide experience an unmet need for family planning services ( 32 ). Addressing unmet needs in family planning and mitigating the risks associated with closely spaced pregnancies can be achieved through the utilization of postpartum contraceptives ( 33 , 34 ). Conversely, postpartum women experience amenorrhea for different durations, influenced by their breastfeeding habits. For instance, women who do not engage in breastfeeding may conceive within 45 days following childbirth, while those who do not exclusively breastfeed may also become pregnant before the return of menstruation, leading to a range of pregnancy-related complications ( 35 ). To date, five SRM reports have been published concerning the utilization of PPFP in Ethiopia. These SRM studies are generally regarded as providing robust evidence for decision-making in health programs. Nevertheless, as the volume of individual reviews increases, it may become challenging for individuals seeking information ( 36 ). Consequently, this umbrella review was conducted to synthesize the findings from the five SRM studies on PPFP utilization into a comprehensive document. Additionally, several factors, including family planning counseling, couple discussions, and postnatal follow up, were recognized as statistically significant. The comprehensive review of the five selected systematic review and meta-analysis studies regarding the use of postpartum family planning (PPFP) in Ethiopia produced a summary estimate of 36.41% (95% CI: 24.78, 48.03). This result stands in contrast to findings from studies in Bangladesh at 62.4% ( 37 ), Kenya at 86.3% ( 38 ), Rwanda at 51.1% ( 39 ), Zambia at 45.9% ( 40 ), and a systematic review and meta-analysis conducted in low- and middle-income countries, which reported a rate of 41.2% ( 41 ). The observed discrepancy may stem from cultural factors, alongside the significant unmet demand for family planning services in Ethiopia ( 42 ). Therefore, postpartum family planning (PPFP) services must be provided immediately after childbirth. It's also essential to improve access to basic health facilities in the county and ensure various family planning options, especially PPFP services. Another potential explanation could be the lack of male participation in family planning initiatives within a society where male dominance prevails, as is the case in Ethiopia, where men often wish to have more children than their female partners ( 43 ). Additionally, there is a lack of policies that promote male involvement in family planning practices. This includes the absence of support for initiatives aimed at male engagement, social and behavioral change strategies, guidance on collaborative decision-making with partners, and the execution of a holistic approach to male participation in family planning services. Moreover, the discrepancies noted may stem from differences in sample sizes, geographical study locations, and the execution of governmental policies. Our research offers a thorough evaluation of the PPFP across the nation, in contrast to the aforementioned studies, which were confined to specific regions within the country. The findings, however, surpassed the postpartum contraceptive prevalence indicated in the Ethiopian Demographic and Health Survey (EDHS) by 23% ( 44 ). This discrepancy may be linked to the EDHS survey's methodology, which covers extensive geographical regions, including hard-to-reach areas. This could result in an underreporting of postpartum family planning (PPFP) usage, particularly among postpartum women living in rural locations with a history of home deliveries, who may lack access to maternal health services. In addition to this, the results observed exceeded the rates documented in studies carried out in Ethiopia, which reported a figure of 21.04% ( 21 ). This variation may be attributed to factors such as the previously mentioned finding being derived from a single systematic review and meta-analysis (SRMA) that specifically concentrated on immediate postpartum family planning utilization among postpartum women in Ethiopia. Conversely, the current study encompassed five SRMA studies and addressed both immediate and extended postpartum family planning uptake among postpartum mothers. The umbrella review revealed that postpartum women who engaged in discussions about contraception with their partners during the postpartum period were 3.06 times more likely to adopt modern contraceptive methods compared to those who did not. This observation is corroborated by findings from other studies conducted in Nigeria ( 45 ) and Congo ( 46 ). This umbrella review revealed that postpartum women who received counseling on family planning were 4.12 times more likely to adopt family planning methods compared to those who did not receive such counseling. This conclusion is corroborated by research conducted in India, Nepal, Sri Lanka, and Tanzania ( 47 ). Women who participate in family planning counseling may gain a more comprehensive understanding of the various family planning methods, including their advantages and disadvantages. This increased awareness of birth spacing through contraceptive use following childbirth can improve their decision-making abilities regarding postpartum family planning and encourage them to utilize contraceptives. The analysis indicated that women who participated in postnatal care (PNC) follow-up were nearly four times more inclined to adopt postpartum family planning (PPFP) methods compared to those who did not engage in postnatal follow-up. This observation is corroborated by research conducted in Kenya and Zambia ( 40 ). It is suggested that women attending PNC appointments during the postpartum phase likely receive comprehensive guidance on the significance of postpartum family planning. Consequently, they may exhibit a greater motivation to implement the methods they choose. This umbrella review of systematic reviews and meta-analyses exhibits significant strengths, such as the application of varied search strategies, a thorough evaluation of methodological quality, compliance with the PRISMA 2020 extension guidelines, and the execution of a funnel test. To our knowledge, no extensive assessment in the form of an umbrella review has been performed regarding postpartum family planning utilization in Ethiopia, despite the existence of numerous empirical studies and specific systematic reviews and meta-analyses. However, this review is not without its limitations; it exclusively includes articles published in English which could introduce a potential bias by excluding studies published in other languages and is constrained by a limited number of studies. Additionally, despite significant attempts to tackle the issue, heterogeneity remained evident among the studies included, suggesting that there were discrepancies in methodologies or populations that were not entirely resolved. One additional limitation is that one of the systematic reviews included women of reproductive age, pregnant women, and postpartum women ( 24 ), which may have introduced biases. The substantial variation in sample sizes across studies (ranging from 4,367 to 11,932) and the considerable variation in prevalence, lack of analysis regarding regional variations within Ethiopia, and the absence of analysis regarding specific contraceptive methods preferred and cost-effectiveness considerations limits the practical utility of the findings were additional limitations. Moreover, the absence of comparable reviews from other countries further hampers our ability to reach significant conclusions, as these were primary studies. To overcome these limitations and enhance the depth of future research, it is recommended to adopt a more inclusive methodology. In particular, the integration of interventional studies into the research framework is suggested, as this could significantly strengthen the overall validity of the findings and lead to a more thorough and nuanced comprehension of the topic at hand. The overall prevalence of postpartum family planning (PPFP) was determined to be 36.41%, highlighting a significant gap that requires attention. Contributing factors to this situation include the availability of family planning counseling, discussions between couples, and postnatal follow-up care. These results emphasize the necessity for focused interventions aimed at increasing the utilization of PPFP services, as well as improving postnatal follow-up, family planning counseling, and couple discussions. Consequently, our findings strongly recommend that special consideration be given to mothers. Furthermore, policymakers in the health sector, along with promoters, non-governmental organizations, community organizations, and other relevant stakeholders, should initiate educational programs on family planning that emphasize the health advantages of postpartum contraceptive use, particularly in preventing unintended pregnancies and prolonging inter-pregnancy intervals. Healthcare providers should advocate for breastfeeding and introduce the lactational amenorrhea method (LAM) alongside other immediate postpartum contraceptive options. It is also essential for contraceptive programs to involve men in the promotion and uptake of family planning services. This research provides current and succinct evidence regarding the uptake of postpartum family planning (PPFP) in Ethiopia. It serves as a valuable resource for program developers and implementers across various sectors, including government, non-governmental organizations, bilateral and multilateral agencies, the private sector, as well as charitable and civic institutions that aim to deliver standardized family planning services in Ethiopia. The findings underscore the importance of preventing closely spaced pregnancies, which allows families to better support their children, invest in their education, enhance child health, and enable women at risk of pregnancy-related complications to space and delay pregnancies. Furthermore, the study highlights the essential role of male involvement, both from a programmatic perspective and as a means to achieve gender equity in reproductive rights and responsibilities. It stresses the necessity of providing health education and family planning counseling services that guarantee full, free, and informed choices while maintaining privacy and confidentiality, which are critical for ensuring the quality of family planning services. Additionally, this study identifies key determinants influencing the uptake of PPFP and proposes strategies to address these issues in Ethiopia, such as enhancing family planning counseling, encouraging couple discussions, and ensuring postnatal follow-up, particularly by promoting male participation in family planning to improve communication between couples regarding fertility and family planning, thereby ensuring that decisions reflect the needs and preferences of both partners. | Review | biomedical | en | 0.999997 |
PMC11697150 | The rumen is a digestive organ unique to ruminants. It has a distinctive microbial fermentation system that can efficiently convert macromolecular substances from the diet, such as lignin, cellulose, and non-protein nitrogen, into nutrients that are easier for the host to utilize . Early research focused on the structure of the complex microbial community in the rumen and its impact on nutrient usage from feeds of different compositions. These microorganisms metabolize dietary compounds and produce volatile fatty acids (VFAs), amino acids and other essential nutrients, thereby providing energy for the host body and maintaining the optimal functioning of the rumen . The hindgut of ruminants was previously considered the endpoint of digestion; however, in-depth research on the fungal microbiota of ruminants has shown that the fungi in the rectum produce a large number of lignin-degrading enzymes that can ferment unused lignin. These enzymes effectively decomposed the structural polymers of plant cell walls, thereby improving the absorption and utilization rate of their nutrients . Compared with the traditional measurement of feed conversion ratio (FCR), residual feed intake (RFI) is considered to be a more accurate and flexible assessment method. RFI refers to the difference between the actual feed intake of an individual animal and the expected feed intake based on its body size and production performance. Low RFI indicates that individual animals consume less feed than predicted and therefore have a lower environmental pollution capacity, without affecting individual body weight, daily weight gain, or body shape. In studies on the microbiota of ruminants with different RFIs, Herd and Arthur and Paz et al. reported that rumen fermentation patterns and the microbial composition of the ruminant gut accounted for 19%-20% of the variation in RFI. Ellison et al. found that six types of rumen microorganisms were highly correlated with actual feed efficiency in ewes. Liu et al. showed that the abundance of Firmicutes and Bacteroidetes in the intestines of Angus cattle with low RFI was significantly higher than that of cattle with high RFI. Elolimy et al. found that the abundance of Bacteroidota in rectal feces of Holstein cattle was higher in low-RFI cattle than in high-RFI cattle. Both Firmicutes and Bacteroidetes are the most abundant bacterial groups in the digestive tract of ruminants and play a major role in fiber fermentation in the diet. Previous studies have shown that the gastrointestinal microbiota of ruminants affects RFI expression, which is one of the important indicators of feed efficiency type. Digestion and absorption in ruminants are processes that involve a number of interconnected steps in the gastrointestinal tract (GIT). For our experiments, we chose Dexin fine-wool meat sheep of the same age and under the same feeding conditions. To systematically examine the influence of RFI on the digestive tract as a whole, the type and abundance of microorganisms in the rumen, ileum and rectum were determined to represent the overall GIT of the sheep under different feed efficiencies. Before the test, the pen was thoroughly disinfected and the sheep entering the pen were marked with numbered ear tags. Before the start of the pilot test period, the experimental animals were dewormed using a combination of intramuscular injection and feeding of anthelminthics. Sheep in individual cages were fed twice a day at 10:00 and 18:00, with free access to food and water, ensuring that the amount of leftover feed for each sheep was >15% per day. During the experiment, three test sheep were eliminated due to disease, and five test rams were selected for breeding. The average daily weight gain, ADGi = (FBWi – IBWi)/N was also calculated. FBWi (final body weight) is the weight of individual i at the end of the trial, IBWi (initial body weight) is the initial weight of individual i , and N is the number of trial days. The formula for average metabolic body weight is MBWi = [1/2 × (FBWi + IBWi)] 0.75, where β0 is the regression intercept, β1 and β2 are fixed values, and ei is the RFI of sheep i . Based on the mean and standard deviation of the RFI, the test sheep were divided into a high RFI (H-RFI) group (RFI > mean + 0.5SD) with 11 sheep, a medium RFI (M-RFI) group (mean + 0.5SD < RFI < mean – 0.5SD) with 18 sheep, and a low RFI (L-RFI) group (RFI < mean – 0.5SD) with 13 sheep . On the 90 th day of the experimental period, six lambs from each group were selected to collect feces, twice a day for 3 days. The samples were placed in plastic bags and stored at −20°C until used. On the 100 th day of the experimental period, the animals were humanely slaughtered before feeding. Six Dexin male lambs were randomly selected from each group and 10 ml of solid digesta from the rumen and the ileum solid digesta were taken, transferred to cryopreservation tubes, and stored in liquid nitrogen. During the collection process, there was no chyme or feces in the rectum of some slaughtered sheep, so only the rectal feces of three lambs from each group were collected and stored in liquid nitrogen. The rumen chyme, ileum chyme and rectal feces were sent to the Xinjiang Morgan Biotechnology Co., Ltd. for sequencing analysis of the bacterial and fungal microbiota. Apparent digestibility was determined according to a published method using hydrochloric acid-insoluble ash content of feces and feeds as a readout : Ammoniacal nitrogen was determined using indophenol blue colorimetry according to a published method . Concentrations of volatile fatty acids were determined by gas chromatography. Briefly, VFAs were separated on a 2 m glass column (3 mm i.d.) using a Fisons HRGC MEGA 2 Series model 8560 chromatograph (Fison Instruments, Glasgow, UK) equipped with a flame ionization detector (Fison Instruments, Glasgow, UK). The chromatographic column was 10% SP-1000 + 1% H3PO4, the chromatographic column was 100/120 ChromosorbWAW (Tehnokroma Analitica SA, Sant Cugat del Valles, Spain), and the carrier gas was nitrogen. The injector and detector temperatures were 200°C, and the column temperature was 155°C. The internal standard used was 2-ethylbutyric acid (Sigma Aldrich, Taufkirchen, Germany). GC was used to determine the concentration of acetic acid, propionic acid, butyric acid, and valeric acid in the rumen. For determining the genus and species of microbiota in the rumen, genomic DNA rumen fluid sample was extracted by the cetyltrimethylammonium bromide (CTAB) method. DNA concentration and purity was determined by 0.8% agarose gel electrophoresis, and the DNA was diluted to 1 ng/μL for use. PCR amplification was performed using the V3 region-specific primers of 16S rDNA, the extracted DNA, a fungal ITS1 primer pair (F - 5′CTTGGTCATTTAGAGGAGTAA3 and R - 5′GCTGGTTTCTTTCATATCGATTGCB), and the appropriate combination of PCR reagents and polymerase for amplification. The PCR products were run on an agarose gel and isolated with an OmegaDNA purification kit (Omega Corporation, USA). The purified PCR products were collected and sequenced on an IlluminaNovaSeq. PCR amplification, paired-end sequencing on the 6,000 platform, Illumina HiSeq sequencing, and analysis of the results were performed by the Beijing NuoHe Bioinformation Technology Co., Ltd. Amplicon qiime2 cloud platform . As shown in Table 2 , the daily feed intake and RFI in the H-RFI group were significantly higher than those in the L-RFI and the M-RFI groups ( P < 0.01). The F/G was significantly higher than that of the L-RFI and M-RFI groups ( P < 0.05), and there was no significant difference in the daily weight gain and mid-term metabolic weight of the sheep ( P < 0.05). As shown in Figure 2A , the apparent digestibility of dry matter in the L-RFI group was extremely significantly higher than the H-RFI group ( P < 0.01) and significantly higher than the M-RFI group ( P < 0.05), the M-RFI group was extremely significantly higher than the H-RFI group( P < 0.01), and the apparent digestibility of crude protein in the L-RFI group was significantly higher than M-RFI group ( P < 0.05), and the apparent digestibility of neutral detergent fiber the L-RFI group was significantly higher than the M-RFI group and the H-RFI group ( P < 0.05), the M-RFI group was extremely significantly higher than the H-RFI group( P < 0.01). There was no significant difference in ammonia nitrogen and volatile fatty acids between lambs with different RFI ( P > 0.05) . RFI and DMI were significantly negatively correlated with dry matter digestibility (r = −0.765, −0.546) and significantly positively correlated with propionic acid (r = 0.518, 0.500). ADG was significantly positively correlated with isobutyric acid (r = 0.578) . As shown in Supplementary Figure S1A , in the analysis of rumen chyme samples of Dexin lambs with different RFIs, 4,160 bacterial OTUs were found in the L-RFI group, 1,359 OTUs in the M-RFI, and 3,872 OTUs in the H-RFI. Among them, there were 845 OTUs in common in the three groups, 1,078 OTUs in common in L-RFI and M-RFI, 1,698 OTUs in common in L-RFI and H-RFI, and 1,081 OTUs in common in H-RFI and M-RFI. With respect to the fungi, 775 OTUs were found in L-RFI, 934 OTUs were found in M-RFI, and 747 OTUs were found in H-RFI . Among them, L-RFI and H-RFI had 97 OTUs in common, L-RFI and M-RFI had 115, M-RFI and H-RFI had 102, and among all three groups there were 63 OTUs in common. As shown in Supplementary Tables S1 , S2 , there were no significant differences in Chao1, Shannon, and Simpson indices of bacteria and fungi in rumen digesta among male Dexin lambs with different RFIs ( P > 0.05). As shown in Supplementary Figures S2A , B , the PCoA plots of rumen digesta of male Dexin lambs with different RFIs partially overlapped without obvious separation, indicating that the differences in microbial communities between and within rumen fluid sample groups were small. As shown in Figure 3A and Table 3 , at the phylum level, Firmicutes, Bacteroidia , and Proteobacteria were the dominant bacterial groups in rumen fluid, The Bacteroidota bacteria in the L-RFI group were significantly lower than those in the M-RFI group( P < 0.05), while there were no significant differences in the other bacteria ( P > 0.05). At the genus level, Escherichia-Shigella, Prevotella _7 and Methanobrevibacter were the dominant bacterial genera in rumen fluid, and there was no significant difference among the top ten bacterial genera ( P > 0.05) . At the fungal phylum level, Ascomycota, Basidiomycota , and Mortierellomycota were the dominant phyla, and there was no significant difference among the top ten fungal phyla groups ( P > 0.05) . At the fungal genus level, Cladosporium, Fusarium , and Debaryomyces were the dominant genera, and there was no significant difference among the top ten fungal genera ( P > 0.05) . As shown in Figure 4 , there were seven species with LDA difference >4.0. The results show that the genera with the greatest impact on RFI on community structure are p__Proteobacteria and g__Roseburia in H-RFI , and o__Bacteroidales, c__Bacteroidia, o__Oscillospirales, p__Bacteroidota , and f__Eubacterium__coprostanoligenes_group in M-RFI. No differences were detected in fungi and L-RFI group. It can be seen from Figures 5A , C that the bacteria identified in the different RFI groups are mainly involved in membrane transport, gene translation, carbohydrate metabolism, energy production, and amino acid metabolism. The main differences in fungi in rumen digesta occurred in the L-RFI group and included wood-digesting saprophytes, soil saprophytes, plant pathogens and endophytic plant pathogens. The H-RFI group contained unclassified saprophytes, while the M-RFI samples mainly included animal pathogens, unclassified, and undefined saprophytes . As shown in Supplementary Figure S3A , 1,781 OTUs were found in L-RFI, 1,195 in M-RFI, and 1,454 in H-RFI among the bacteria identified in ileum digesta samples of male Dexin lambs with different RFIs. Among them, there were 401 OTUs in common among all three groups, 578 OTUs in common between L-RFI and M-RFI, 652 OTUs in common between L-RFI and H-RFI, and 533 OTUs in common between H-RFI and M-RFI. With respect to the fungi, 496 OTUs were found in L-RFI, 354 OTUs were found in M-RFI, and 448 OTUs were found in H-RFI . Among them, L-RFI and H-RFI had 107 OTUs in common, L-RFI and M-RFI had 102, M-RFI and H-RFI had 103, and among all three groups there were 80 OTUs in common. As shown in Supplementary Table S3 , in the ileal digesta samples of male Dexin lambs with different RFIs, the bacterial there were no significant differences in Chao1, Simpson, and Simpson indices between any of the groups ( P > 0.05). There were no significant differences in the Chao1, Shannon, and Simpson indices among the fungal groups ( P > 0.05) ( Supplementary Table S4 ). As shown in Supplementary Figures S4A , B , the PCA plots of ileal chyme partially overlapped without obvious separation, indicating that the microbial communities in the ileal chyme samples were less diverse between and within groups. At the phylum level, Firmicutes, Bacteroidota , and Proteobacteria were the dominant phyla in ileal chyme, The Campylobacterota bacteria in the L-RFI group were significantly higher than those in the H-RFI group ( P < 0.05), while there were no significant differences in the other bacteria ( P > 0.05) . At the genus level, Escherichia-Shigella, Bacteroides , and Erysipelatoclostridium were the main dominant genera in the ileal chyme . For the Methanobrevibacter genus, L-RFI was significantly higher than M-RFI and H-RFI ( P < 0.05), and there were no significant differences in the remaining genera ( P > 0.05). At the fungal phylum level, the dominant phyla in ileal chyme samples were Ascomycota, Basidiomycota and Mortierellomycota , and there was no significant difference among the top ten dominant phyla ( P > 0.05) . At the fungal genus level, Geotrichum, Penicillium , and Cladosporium were the dominant genera, and there was no significant difference among the top ten dominant genera ( P > 0.05) . As shown in Figures 7A , B , there are three species with LDA values >4, indicating that the bacterial genera that have a greater impact on ileal chyme due to residual feed intake are f-Family-Xi in M-RFI , f_Anaerovoracaceae and g_Christensenellaceae_R_7_group in L-RFI. No differences were detected in fungi and H-RFI group. The differentially expressed functions of ileal digesta bacteria from different RFI groups were mainly membrane transport, carbohydrate metabolism, and amino acid metabolism . Among the ileal chyme fungi, the functional annotations of L-RFI and H-RFI fungi were quite different, mainly concentrated in plant pathogens, undefined animal pathogens, endophytic plant pathogens and undefined saprophytes . As shown in Supplementary Figure S5A , 979 OTUs were found in the samples of rectal feces from L-RFI lambs, 870 from M-RFI, and 1,454 from H-RFI. There were 293 OTUs in common among the three groups, 404 OTUs in common between L-RFI and M-RFI, 442 OTUs in common between L-RFI and H-RFI, and 425 OTUs in common between H-RFI and M-RFI. A total of 500 OTUs were found in the fungi of the rectal fecal samples from L-RFI, 1,170 OTUs from M-RFI, and 1,246 OTUs from H-RFI, of which 31 OTUs were found in common in all three groups, 101 OTUs in common in L-RFI and M-RFI, 99 OTUs in common in L-RFI and H-RFI, and 72 OTUs in common in H-RFI and M-RFI . As shown in Supplementary Tables S5 , S6 , there were no significant differences in the Chao1, Shannon, and Simpson indices observed in any of the groups of rectal fecal samples ( P > 0.05). As shown in Figure 9A and Table 11 , at the phylum level, Euryarchaeota, Bacteroidia , and Firmicutes were the main bacterial groups in rectal feces. The Uryarchaeota phylum in L-RFI was extremely significantly higher than that in M-RFI and H-RFI ( P < 0.01), the Actinobacteriota phylum in L-RFI and H-RFI was significantly higher than that in M-RFI ( P < 0.05), the Patescibacteria phylum in H-RFI was significantly higher than that in M-RFI and L-RFI ( P < 0.05), but there were no significant differences in other phyla ( P > 0.05). At the genus level, Achromobacter, Chryseobacterium , and Methanobrevibacter were the dominant bacterial genera in rectal feces . The abundance of Methanobrevibacter in L-RFI was significantly higher than in M-RFI and H-RFI ( P < 0.05), and there were no significant differences in the other genera ( P > 0.05). The dominant rectal fecal fungi at the phylum level were Ascomycota, Basidiomycota and Mortierellomycota , and there were no significant differences among the three groups in the top ten dominant phyla . At the genus level for rectal fecal fungi, Penicillium, Acaulium , and Scopulariopsis were the dominant genera . The levels of Aspergillus and Ustilago genera in the L-RFI group were significantly higher than those in the other two groups ( P < 0.05), but those in the “other” group were significantly lower than those in the two RFI groups ( P < 0.05). As shown in Figures 10A , B , the expression differences in the microbial functions of rectal fecal bacteria are mainly concentrated in membrane transport, energy production, carbohydrate and amino acid metabolism. The rumen is the main site for ruminants to receive feed, water and saliva. It is a humid environment with a favorable temperature of 36–40°C, which makes it an excellent place for microbial growth and reproduction . A large number of studies have shown that the various microbial communities in the rumen work synergistically to convert substances such as cellulose and hemicellulose into volatile fatty acids, and convert the nitrogen produced by dietary degradation into microbial proteins that are absorbed and utilized by the host . 1 The apparent digestibility of dry matter, crude protein, neutral detergent fiber and other insdicators can accurately mirror a sheep's ability to digest and absorb nutrients . In a study on the effect of RFI on apparent digestibility, Bonilha et al. found that the digestibility of neutral detergent fiber and total digestible nutrients in Neruda cattle in the H-RFI group was significantly lower than in the L-RFI group. The research results of Arce-Recinos et al. on growing beef cattle showed that the digestibility of dry matter, crude protein, neutral detergent fiber and acid detergent fiber of L-RFI cattle was 4%−5% higher than that of H-RFI cattle, similar to the results in this study. The apparent digestibility of dry matter, crude protein, neutral detergent fiber and acid detergent fiber of L-RFI male Dexin lambs was higher than in the H-RFI group, indicating that apparent digestibility may be one of the reasons why some sheep show higher feed efficiency. In this experimental study, no difference in rumen volatile fatty acids was found among the three groups of sheep, which is similar to the findings of two studies by Arce-Recinos et al. and Zhang et al. . This may be due to the fact that the feed substrates were exactly the same, making it difficult to establish a difference in the volatile fatty acid content. In the OTU results of this experiment, the fungal and bacterial microbiota detected in the L-RFI group were greater than those in the M-RFI and H-RFI groups, suggesting that an enrichment of beneficial microbes could enable individuals to have higher environmental adaptability and stress resistance. There were no significant differences in the Chao1, Shannon, and Simpson indices of bacteria and fungi in the rumen digesta, ileum digesta, and rectal feces samples among the three groups. The PCoA plots did not show significant differences in the microorganisms from the three groups of sheep, which is consistent with the results of Pinnell et al. on the rumen of Holstein cows. This may be because the RFI only affects individual microbial types and not the overall microbial community. Ruminants rely on the abundant microbial communities in their digestive tract to digest feed and convert it into nutrients that are easily absorbed. The most abundant gut bacteria are from the Firmicutes and Bacteroidota phyla . Recent progress in microbial research indicates that Bacteroidota can produce large amount of glycoside hydrolases, which can effectively degrade nutrients such as cellulose, pectin and starch, and plant polysaccharides in the rumen . Firmicutes are mainly polysaccharide-degrading microorganisms in the rumen, but recent studies indicate that Firmicutes can produce biotin, playing a major role in cellulose degradation, VFA production, and metabolism . In this study, at the bacterial phylum level, the dominant phyla of bacteria in rumen digesta, ileum digesta and rectal feces were Firmicutes, Bacteroidota , and Proteobacteria . The rumen bacteria results were consistent with the results of Liu et al. on the rumen microbiota of Hu sheep with different RFIs. The rumen of healthy ruminants is characterized by the dominance of anaerobic bacteria of Firmicutes and Bacteroidota . The results of ileal digesta were consistent with those of Elolimy et al. , with Firmicutes and Bacteroidota as the main bacterial communities, and the abundance of Firmicutes and Bacteroidota in the L-RFI group was higher than that in the H-RFI group. The rectal feces results were similar to those of Elolimy , with the abundance of Bacteroidota in cattle with L-RFI being higher than that in cattle with H-RFI. At the bacterial genus level, the genera with greatest abundance in rumen chyme were Bacteroides, Rikenellaceae_RC9_gut_group , and Prevotella _7. Among them, the abundance of Bacteroides in the L-RFI group was higher than that in the H-RFI group, and the abundance of Rikenellaceae_RC9_gut_group was lower than that in the H-RFI group. The Bacteroides genus has been shown to effectively degrade plant cell wall polysaccharides and improve fiber utilization . The specific role of the Rikenellaceae _RC9_gut_group genus is still unclear, and it has so far only been shown to be related to butyrate and propionate metabolism . In the ileal digesta, Escherichia-Shigella, Bacteroides , and Turicibacter were the main genera. The abundance of Bacteroides in L-RFI lambs was higher than that in the H-RFI group, while the abundance of Escherichia-Shigella was lower than that in the H-RFI group. Escherichia-Shigella is a harmful bacterium that may cause bacterial dysentery . Methanobrevibacter is a methanogen. Although it was not the main genus in terms of abundance in the ileal digesta, the data showed that its numbers in the L-RFI group were significantly higher than in the H-RFI group. This was contrary to the results of Mia , which showed that the ileal digesta of L-RFI male Dexin lambs contained more methanogens. In our results, the abundance of methanogens in the three parts of the L-RFI group was higher than that in the H-RFI group. This may be a result of the sheep production model and feed type. However, recent microbiological research indicates that methanogens are of great significance to the early intestinal microbial colonization of ruminants. They can effectively reduce the H 2 produced by the fermentation and decomposition of plant fibers in the digestive tract, reduce the hydrogen partial pressure, and improve the body's hydrogen nutrition pathway . However, the mechanistic details and regulatory pathways need to be verified by subsequent studies. In rectal feces, Achromobacter, Chryseobacterium , and Methanobrevibacter were the main genera. The abundance of Methanobrevibacter in the L-RFI group was significantly higher than that in the H-RFI group, while the abundance of Achromobacter and Chryseobacterium was lower than in the H-RFI group. Achromobacter is a conditionally pathogenic bacterium that can cause urinary tract infections under certain conditions . Chryseobacterium has a strong ability to digest collagen and can cause disease in the body . Through the abundance analysis of bacterial microbiota under the three RFI conditions, we can conclude that L-RFI sheep achieve higher digestion efficiency of feed nutrients by having a greater abundance of Firmicutes and Bacteroidetes in the digestive tract. This echoes the results of apparent digestibility in this study, indicating that L-RFI sheep have a higher efficiency of decomposition and absorption of feed nutrients through an enriched population of microorganisms. The increased abundance of pathogenic bacteria detected in the H-RFI group may be a result of differences in their digestive tract microbiota, which makes them less adaptable and resistant than the L-RFI sheep, which is consistent with our OTU results. There are a large number of anaerobic fungi in the GIT of ruminants. Previous studies have generally concluded that these fungi exist in the animal body in the form of zoospores, which produce highly active, fiber-degrading enzymes, and play a major role in the digestion of fibrous plant material . It is increasingly recognized that fungi can optimize rumen fermentation, enhance nutrient availability, and promote intestinal health. Anaerobic fungi degrade plant cell walls through both enzymatic reactions and physical means targeting fibers that are difficult for bacteria to degrade . In addition to fiber degradation in the rumen, 5% to 10% of carbohydrate degradation occurs in the hindgut, indicating that fiber degradation by anaerobic fungi can occur over the entire digestive tract . It has been proven that Basidiomycota and Ascomycota can produce mycelial hyphae, which can penetrate the silica cuticle produced on the surface of forage through stomata and damaged parts of the dermis, thereby promoting more efficient digestion of plant fiber . Basidiomycota and Ascomycota are aerobic fungi with the highest relative abundance, based on sampling of rumen chyme. Within 2 h of eating, the oxygen concentration in the rumen is sufficient for the survival of Ascomycota. As aerobic fungi multiply, the oxygen content gradually decreases. This process causes the rumen to turn into an anaerobic environment, and anaerobic fungi and bacteria begin to multiply in large numbers . Mortierellomycota is a type of saprophytic fungus that is widely distributed in the digestive tract of ruminants and has the ability to efficiently decompose lignin . In this experiment, at the phylum level, the main fungal phyla in rumen and ileum digesta were Basidiomycota, Ascomycota and Mortierellomycota, among which Basidiomycota and Mortierellomycota were more abundant in L-RFI sheep, and Ascomycota was more abundant in the H-RFI group. At the genus level, Cladosporium, Fusarium , and Debaryomyces were the dominant genera in rumen digesta. Current microbiological studies have shown that Cladosporium fungi can produce lipases, proteases, urease, and chitinase, which can help the host digest corn-rich diets . Fusarium can produce biomass that is broken down and transformed by the animal rumen; however, in vivo research on the effects of Fusarium on nutrient digestibility and rumen function is lacking and the details of the specific mechanism of action are still unclear . Debaryomyces has emerged as a potentially valuable probiotic. Its cell wall and the polyamines it produces have been shown to stimulate immunity, regulate the microbiome, and improve digestive function . However, the abundance of Debaryomyces in the H-RFI group was higher than that in the L-RFI group. At the genus level in the ileal digesta, the dominant fungal genera were Geotrichum, Penicillium , and Cladosporium . Geotrichum are believed to have the potential to be useful probiotics, which can increase the production of acetic acid and propionic acid and the ratio of propionic acid to acetic acid in ruminants; their effectiveness is higher than that of traditional brewer's yeast . The cellulase produced by Penicillium can effectively increase the content of oleic acid, linoleic acid and linolenic acid in milk, and can also affect the fat yield and unsaturated fatty acid content of milk . In the results of this study, although there was no significant difference in the abundance of Geotrichum in the ileal digesta among the three groups, the abundance in the L-RFI group was 21% higher than that in the H-RFI group, which may be related to the improvement in feed efficiency. Aspergillus can secrete cellulase and protease to improve the digestibility of the feed . Ustilago plays a role in lignin degradation and participates in the fermentation of feed . In our experiments, Aspergillus and Ustilago were significantly more abundant in the L-RFI group than in the other two groups, suggesting that Aspergillus may play a major role in improving feed efficiency. LefSE is a high-dimensional biomarker mining tool based on the LDA algorithm, which is used to identify significantly characterized microorganisms . In the results of this experiment, when LDS > 4.0, the characteristic bacteria were only found in the rumen and ileum digesta. Among them, the g__ Roseburia genus found in the H-RFI group in rumen chyme is believed to affect the core populations and produce ketones that affect host development . P_Proteobacteria may parasitize the phylum Proteobacteria in rumen fluid and rumen epithelium, and Proteobacteria may oxidize ammonia and methane on the surface of the rumen epithelium . In the ileal chyme, the f_Anaerovoracaceae bacteria found in the L-RFI group can utilize a variety of types of organic matter as carbon sources and participate in the fermentation of plant polysaccharides in the GIT . The g_Christensenellaceae_R_7_group is currently thought to be a new type of probiotic that can effectively improve the growth performance and meat quality of ruminants. It plays an important role in the degradation of carbohydrates and amino acids into acetate and ammonia, respectively . The LEfSE results suggest that there are differences in some probiotics in the digestive tracts of sheep in the L- and H-RFI groups, which may be the main reason for the differences in feed utilization efficiency. The Proteobacteria found in the rumen chyme will compete for H ions with methanogens in the digestive tract, which may be one of the reasons for the differences in the abundance of methanogens among the three groups. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis is a software module that builds a manually curated pathway graph representing the current knowledge on biological networks under defined conditions in a specific organism. The pathway diagrams are graphical representations of the networks of interacting molecules responsible for specific cellular functions . In our study, the rumen chyme KEGG diagram showed that the differential metabolic pathways between L-RFI and H-RFI were mainly concentrated in Cellular Process (L-RFI), Metabolism (L-RFI), and Environmental Information Processing (H-RFI). Among these, the Cellular Process pathway is mainly concentrated in the Transport and Catabolism pathway, the Metabolism pathway is mainly related to carbohydrate metabolism, amino acid metabolism and energy production pathways, and the Environmental Information Processing metabolic pathway was mainly concentrated in the Membrane Transport pathway. This is similar to the research results of Zhang et al. and Zhou et al. . The ileal chyme KEGG map shows that the pathway differences are mainly Cellular Process (L-RFI), Metabolism (L-RFI), Genetic Information Processing (L-RFI), and Environmental Information Processing (H-RFI). Among these, the Cellular-Process pathway was mainly concentrated in cell motility, the Genetic Information Processing pathway was mainly concentrated in replication and repair, the Metabolism pathway was mainly concentrated in carbohydrate metabolism, amino acid metabolism and energy production, and the Environmental Information Processing metabolic pathway was mainly concentrated in membrane transport. In the rectal fecal KEGG map, only the Environmental Information Processing pathway and the Human Diseases pathway were overexpressed in the H-RFI group. The Environmental Information Processing pathway was mainly concentrated in the membrane transport pathway, and the Human Diseases pathway was mainly concentrated in the endocrine system pathway. This is quite different from the research results of Elolimy et al. on Holstein cattle, which may be related to the breed, production mode and gender. Overall, the KEGG analysis indicated that the overexpression in the L-RFI group was mainly concentrated in carbohydrate metabolism, amino acid metabolism and energy production, which may be related to the high abundance of Bacteroidetes and Firmicutes in the microbiota of L-RFI sheep, which could effectively improve the conversion and absorption of nutrients such as cellulose and amino acids. In the LEfSE results, some differentially expressed microbial metabolites had the function of promoting carbohydrate metabolism and amino acid decomposition and conversion, which may be related to the differences in KEGG metabolic pathways. In the KEGG diagram of fungi from the rumen and ileum digesta, the main differences observed in the fungi in rumen fluid were wood saprophytes (L-RFI), soil saprophytes (L-RFI), plant pathogens and endophytic plant pathogens (L-RFI), and unclassified saprophytes (H-RFI). The functions of ileal chyme fungi are mainly concentrated in undefined (H-RFI), endophytic plant pathogens (H-RFI) and animal pathogens (H-RFI). Given the current limitations in the KEGG functional annotation of fungi, the specific metabolic pathways should be further explored. | Study | biomedical | en | 0.999994 |
PMC11697165 | Over 800 000 older people are currently living with dementia in the UK and millions more worldwide. Within 25 years, this number is projected to double, with an average 45% increase in those aged 65 and over. 1 , 2 Dementia-related costs are substantial, estimated at £26.3 billion annually in the UK, 3 , 4 and around $355 billion across the USA for long-term health and care services. 5 Recent trials of novel monoclonal antibody immunotherapy against amyloid deposition have demonstrated the potential to slow disease progression. 6 , 7 However, these trials recruited participants experiencing very early cognitive impairment with eligibility confirmed by PET imaging or cerebrospinal fluid sampling, neither of which are routinely offered in clinical practice. Even in those with positive amyloid imaging, only a minority would be likely to meet trial eligibility criteria. 8 If these drugs gain regulatory approval, there will be unprecedented pressures on already constrained diagnostic resources. As such, there is an urgent need to target imaging in pre-symptomatic individuals with the highest probability of future dementia. Routinely collected electronic health record (EHR) data contain relevant information on dementia risk factors, such as those identified by the Lancet Commission. 9 Studies suggest that signs of cognitive impairment and progressive neurodegeneration can occur up to 9 years prior to diagnosis. 10 Data-driven studies have the potential to refine our understanding of future dementia risk, analogous to the approach adopted for proactive cardiovascular risk screening to target interventions such as lipid-lowering therapy. 11-13 Aside from novel therapies, personalized brain health estimates could influence better risk factor control through supported individual lifestyle modifications. Several studies have used machine learning approaches to predict future dementia, but these frequently use selective populations such as those undergoing specialized imaging, 14 , 15 or where symptom concerns have already prompted memory clinic referral, 16 , 17 or where detailed genetic profiling has been completed. 18 , 19 While such approaches may have value in supporting efficient dementia diagnosis, they are not appropriate for pre-symptomatic risk identification across the whole population. Some EHR data models have recently been published. 20 , 21 While these present an advancement in this field, limitations are still apparent, either in the observation periods or the lack of relevant linked data for socioeconomic status, lifestyle risk factors, and relevant frailty markers. Our aim was to evaluate the utility of machine learning models developed using comprehensive linked routine primary and secondary care data to predict future dementia diagnosis. We report risk estimates of incident dementia at 5, 10 and 13 years across a large, unrestricted adult population in Scotland. In this longitudinal retrospective cohort study, we included all older adults (50–102 years old) registered with a research-linked general practice in a regional health board in South East Scotland. This includes 90% of all general practices and covers a population of approximately 900 000 people of all ages. Only individuals who were alive without a recorded diagnosis of dementia in either the primary or secondary care electronic patient record systems on 1st April 2009 were eligible. Patient follow-up continued until 1st April 2023. Individuals with prior outpatient old age psychiatry clinic attendance for dementia assessment and diagnosis, or any of 111 primary care codes related to ‘memory and cognitive problems’ were excluded. We defined an observation window from 1st April 2009 to 1st April 2010, excluding individuals who were diagnosed with dementia or died in this period. A summary of the data flows in this study is presented in Fig. 1 . Common comorbidities were defined using HDR UK CALIBER phenotype codelists 22 for the presence of relevant codes acquired prior to or during the observation window in either GP records (Read version 2), or hospital (ICD-10 codes) using the Scottish Morbidity Records (SMR). We included information from outpatient clinic attendances (SMR00), acute inpatient episodes (SMR01) and acute mental health admissions (SMR04). We used the Scottish Index of Multiple Deprivation (SIMD) to stratify individuals across quintiles of relative socioeconomic deprivation. 23 The cause of any deaths were identified from ICD-10 coded certification using linkage with the National Records of Scotland. Medication history was collected using a 6-month lookback window for the 50 most prescribed medications within the Scottish Prescribing Information System (Public Health Scotland), which contains records of all non-hospital dispensed prescriptions. Laboratory data for common haematology and biochemistry tests requested from either community or hospital settings were extracted from the local EHR system (TrakCare, InterSystems, MA, USA). Where completed in primary or secondary care, we extracted relevant coded records of lifestyle risk factors [alcohol, body mass index (BMI), smoking status] and measures of blood pressure and lung function (spirometry measures). The models also included two routinely recorded risk scores from primary care, representing cumulative deficit scores of frailty [electronic Frailty Index (eFI)] 24 and 10-year cardiovascular disease risk (ASSIGN score). 25 The eFI was modified to exclude prior reported memory or cognitive problems and was therefore calculated as a modified eFI using 35 deficits. The last valid record within the observation window was used for model development in all cases. The primary outcome was defined via a new dementia code in either primary, secondary or death records within 5, 10 or 13-year prediction windows starting from the index prediction date of 1st April 2010. We used the HDR UK CALIBER dementia phenotype, 22 combining all dementia subtype codes. We additionally performed a sensitivity analysis for the more specific outcome of an Alzheimer's Disease-related Dementia (ADRD) diagnosis. All-cause mortality was a secondary outcome. We undertook both a data-driven and a clinically supervised approach, creating two model variations per outcome. In the data-driven approach, we used the complete set of available routine data, totalling 219 continuous and 92 categorical variables for training . Further details on these variables, the use of thresholds and temporal criteria are described in Supplementary Table 1 . Details regarding patient follow-up, including observation and prediction windows across the three dementia outcomes are described in Fig. 2 . In the clinically supervised approach, candidate features were selected using clinical input (by author A.A.). This included modifiable and non-modifiable risk factors known to impact dementia risk in the literature, with clinical relevance for early dementia screening. 9 The complete list of 22 curated features included age, sex, SIMD (quintiles of multiple deprivation estimated across seven resource or income-based domains), 26 alcohol and smoking history, BMI, modified eFI (EHR-based cumulative markers of frailty status) 24 and ASSIGN (cardiovascular disease risk score incorporating SIMD) 25 risk scores, selected blood tests (HDL/LDL/total cholesterol, triglycerides, glycosylated haemoglobin [HbA1c]) and long-term conditions from primary and secondary health records (atrial fibrillation, hearing loss, heart failure, ischaemic heart disease, hypertension, stroke, peripheral vascular disease, diabetes, obesity, alcohol and substance misuse). The model hyperparameters were fine-tuned using a cross-validated grid search strategy, targeting the 13-year outcomes ( Supplementary Table 2 ). Models were developed using Python version 3.10.12, using the ‘xgboost’ package (version 2.0.3) for training and ‘scikit-learn’ (version 1.3.2) for evaluation and calibration procedures. We employed additional data cleaning to remove sparse features with <1% completeness within the observation window. We removed samples containing any outlier measurements (<0.5 or >99.5 percentile of the dataset). Correlated features were removed when Pearson’s correlation coefficient was over 0.9, prioritizing the retention of continuous variables over defined categorical or temporal variables of the same nature. We used an established ensemble model with gradient-boosted trees (XGBoost) to develop the dementia incidence and all-cause mortality models. 27 We additionally tested performance on any future dementia diagnosis using other linear and non-linear estimators (logistic regression, naïve Bayes, decision trees and random forests). We performed a random stratified split (70% for training and 30% for validation), balancing for age, dementia incidence and all-cause mortality rates between the two sets. At the evaluation stage, we measured the receiver operating characteristic area-under-the-curve ( ROC-AUC ) and the precision-recall area-under-the-curve ( PR-AUC ). We measured the positive predictive value ( PPV ), negative predictive value ( NPV ), Sensitivity and Specificity through thresholding based on the maximum F1-Score achieved for the positive class per outcome. In the presence of high class imbalance between dementia and dementia-free cases, ROC-AUC can produce falsely elevated estimates, undermining the impact of the PPV score. Meanwhile, the PR-AUC provides a relative measure of trade-off between PPV and Sensitivity compared to baseline dementia risk. A higher score than the baseline disease prevalence is treated as ‘better than random choice’. We employed a post hoc calibration technique using cubic splines to normalize the probability distribution and re-evaluate the classifier. 28 At the calibration stage, a further 30% of the training set was held out to fit the spline model and re-calibrate the probability scores within the internal validation set. The evaluation measures were reported post-calibration. A stratified 10-fold cross-validation strategy was used as an additional procedure to validate both the PR-AUC and ROC-AUC on random data partitions. The 95% confidence intervals (95% CI) for these values were generated using the DeLong method, optimized for large sample sizes using linearithmic weights. 29 Stratified analyses were conducted, evaluating potential imbalances in classification performance across age groups and SIMD quintiles. Baseline differences in characteristics from the observation window between patients with and without a future dementia diagnosis are reported using the clinically supervised feature set. Continuous variables were measured using the Kruskal–Wallis test (for non-normal distributions), while categorical variables were reported using Pearson’s chi-squared test, where significance was assumed at P < 0.001. An unadjusted multivariate Cox Proportional Hazards model was used to test the significance of the clinically supervised features in relation to the timing of diagnosis. We ranked the top predictors in both the clinically supervised and data-driven models using the SHAP framework (Shapley Additive eXplanations) 30 and the calibrated probability scores. The estimated patient-level Shapley values on the internal validation set were summarized in density plots, highlighting the observations contributing to increased (red) and decreased risk (blue). To perform risk stratification, we used quantile-based discretization on the validation set's sorted and calibrated probability scores to generate 10 equally sized risk groups. The response rate (% observed dementia diagnoses) and the age distribution across each model subset were then reported. The cohort included 144 113 individuals, of whom 11 143 (8%) developed dementia during the 13-year prediction window. Baseline differences between the people who did and did not develop dementia are shown in Table 1 . Those individuals who developed dementia were older at baseline [75 (69–80) versus 60 (55–69) years, P < 0.001] and more likely to be female (62% versus 51%, P < 0.001). The distribution of socioeconomic deprivation was similar between groups, but records were more complete in people with a diagnosis. In those who went on to develop dementia, rates of recorded smoking, high alcohol consumption and BMI measures were lower within the observation window when compared to those who remained free from dementia. However, the ASSIGN and modified eFI risk scores were higher, although the ASSIGN score was only recorded in 3% of all participants. In most clinically curated variables, completeness rates were higher in the dementia group in the early study years, but lower closer to the end of follow-up . All clinically supervised comorbidities were more frequently observed in the group who developed dementia, except for prior alcohol or substance misuse. By the end of the study period, all-cause mortality rates were significantly higher in those who developed dementia (70% versus 24% in non-dementia cases, P < 0.001). Median observation time until death or end of study period was 125 (84–156) months in those who developed dementia, compared to 156 (156–156) months in those who did not ( P < 0.001). However, in patients who died ( n = 40 074), the observation period was longer in those with future dementia diagnosis [102 (68–128) versus 81 (40, 120) months without dementia, P < 0.001]. The rate of newly coded diagnoses in the routine data fluctuated over the years, with a notable drop following the COVID-19 pandemic in 2020 and a slower recovery in subsequent years . Most participants (76% mean) had their index diagnosis coded within primary care , although this could follow correspondence from a specialist outpatient clinic making the diagnosis. The mean age at diagnosis was 82 ± 7 years, and most dementia diagnoses were made in the 80–89 years old group after a median period of 78 (40–114) months from the index prediction date. There was non-specific subtype coding in 48% of dementia diagnoses, while 36% were ADRD-coded . The linear Cox regression model (excluding age) suggested that modified eFI >0.05 (the equivalent of two or more non-cognitive deficits) had the highest positive association with the timing of diagnosis, and most curated risk factors significantly contributed to a future diagnosis . After performing a stratified random split to generate the training ( n = 101 286) and validation ( n = 43 409) sets, the samples were fully balanced by age group, mortality and dementia incidence rates ( Supplementary Table 3 ). The incidence at 5-, 10- and 13-year prediction windows was 3, 6 and 8% for any dementia diagnosis, 1, 2 and 3% for ADRD, and 9, 20 and 28% for all-cause mortality, respectively. Table 2 shows the performance of both the data-driven and clinically supervised models, estimated after calibration. Overall, the data-driven model performed marginally better than the model that used a clinically supervised subset of features. For the data-driven model, the ROC-AUC scores had similar discrimination for dementia [0.89 (0.88–0.89) at 5 years, 0.87 (0.86–0.87) at 10 years and 0.85 (0.84–0.85) at 13 years] and mortality [0.89 (0.89–0.90) at 5 years, 0.89 (0.88–0.89) at 10 years and 0.88 (0.88–0.89) at 13 years]. Using the PR-AUC and F1-score thresholded scores ( PPV , NPV , Sensitivity and Specificity ) to assess discrimination in detected cases, we observed that model performance improved as the prediction windows widened for both dementia [ PR-AUC of 0.18 (0.13–0.23) at 5 years, 0.28 (0.24–0.32) at 10 years and 0.30 (0.26–0.34) at 13 years] and all-cause mortality [ PR-AUC of 0.55 (0.51–0.59) at 5 years, 0.73 (0.70, 0.75) at 10 years and 0.79 (0.77, 0.81) at 13 years]. In this case, precision was limited among the dementia incidence models (0.14, 0.26 and 0.30 versus 0.54, 0.65 and 0.70 in all-cause mortality models at 5, 10 and 13 years, respectively). The NPV was more robust, consistent with the relatively low dementia incidence (0.99, 0.97 and 0.96 at 5, 10 and 13 years, respectively). Sensitivity for all-cause death improved with the longer prediction window (0.54, 0.67 and 0.72 at 5, 10 and 13 years, respectively) but worsened for dementia (0.76, 0.58 and 0.53 at 5, 10 and 13 years, respectively), while the opposite trend was apparent for specificity (0.93, 0.89 and 0.86 for all-cause death versus 0.85, 0.89 and 0.89 for dementia incidence at 5, 10 and 13 years, respectively). The clinically supervised models for dementia incidence had marginally lower PR-AUC (0.17, 0.27 and 0.29 at 5, 10 and 13 years, respectively) compared to the data-driven models. Whilst the PPV was worse (0.13, 0.25 and 0.28 at 5, 10 and 13 years, respectively), the NPV (0.99, 0.97 and 0.96 at 5, 10 and 13 years, respectively) and sensitivity were improved (0.76, 0.62 and 0.55 at 5, 10 and 13 years, respectively). There were 4162 ADRD-specific diagnoses within the full prediction window, representing 37% of the total diagnoses of dementia. In this cohort, baseline characteristics were similar, with slight variation in some cardiovascular risk factors ( Supplementary Table 4 ). In the ADRD-specific sensitivity analysis, performance was poorer than for any dementia diagnosis at all prediction windows ( Supplementary Table 5 ). Using the clinically supervised model, the PR-AUCs were 0.05, 0.09 and 0.10 for ADRD compared to 0.17, 0.27 and 0.29 for any dementia at 5, 10 and 13 years, respectively. PR curves comparing performance for any incident dementia, ADRD and all-cause mortality are shown in Fig. 3 . ROC curves visualized across the same model sub-types showed similar discrimination . The 10-fold cross-validated ROC and PR curves indicated comparable performance to the internal validation sets for outcomes of future dementia . Model calibration improved precision by correcting for over-estimation of risk in the baseline models, with the effects of the spline calibration in adjusting the output probability scores becoming more pronounced as the prediction windows increased . Model performance over other common supervised ML classifiers is shown in Supplementary Fig. 7 , demonstrating optimal ROC-AUC and PR-AUC using the XGBoost model compared to other approaches tested. More diagnoses of dementia were made in individuals from the least deprived SIMD groups, and this relationship was stable over time and by sex . To investigate underlying model bias, the performance of the clinically supervised model for any incident dementia at 13 years was stratified by age and SIMD groups ( Table 3 ). Precision for any dementia diagnosis was notably lower in the smaller number of younger-onset cases ( PR-AUC of 0.025 [0.015–0.035], PPV of 0.047 [0.029–0.075] in those below 60 versus PR-AUC of 0.366 [0.332–0.400], PPV of 0.320 [0.302–0.332] in those between 80 and 89). The group >90 years old achieved a lower PR-AUC score (0.296 [0.196–0.397]) than the 80- to 89-year-old group, but had a better balance between sensitivity (0.731 [0.623–0.817]) and specificity (0.603 [0.553–0.650]). The PR curves were notably more stable across the individual deprivation quintiles . We used the SHAP framework to examine the top 20 ranked predictors of future dementia diagnosis in both the clinically supervised and data-driven models . In all instances, age (older), deprivation status (least deprived) and eFI (higher frailty) were among the top features associated with increased risk of dementia. Within the data-driven models, a range of variables were linked to a higher likelihood of a future diagnosis, including a higher number of long-term conditions, number of prescriptions, depression, hearing loss, epilepsy, stroke, smoking history, elevated calcium, glucose and cholesterol, and clinic reviews within neurology and geriatric medicine services. On the other hand, a documented increased cardiovascular risk (ASSIGN score ≥20), poorer lung function, higher blood pressure, high BMI, use of anti-hypertensive drugs and abnormal urea blood results appeared protective. Within the clinically supervised subsets, the variation in SHAP scores was more pronounced. Prior hearing loss, alcohol or substance misuse, stroke, obesity, peripheral vascular disease as well as smoking were associated with increased risk of future diagnosis. A history of hypertension appeared protective in 5-year outcomes but was associated with increased risk of dementia incidence over longer periods. On the other hand, heart failure was protective over 10 and 13 years, but this may be biased by survival rates in these patients. After risk stratification, both clinically supervised and data-driven models had predictions that reached an incidence rate above 30% at 13 years in the highest risk decile, compared to a whole-population incidence of 8% . Over 40% of this group were in their 70s at the time of index risk prediction. Conversely, 13-year dementia risk was <1% in the lowest three prediction deciles, although these predominantly consisted of individuals in their 50s at the time of risk prediction. We have extensively evaluated the diagnostic quality of a machine learning prediction model for long-term dementia risk developed from entirely routinely collected data. We demonstrate moderately capable prediction for diagnoses up to 13 years later, which could inform further testing or risk factor surveillance in those at the extreme of predicted risk. There was marginally improved PR with a high variable count data-driven approach using XGBoost, but at the expense of rule-out performance compared to a clinically supervised model restricted to 22 variables. Precision was consistent across quintiles of socioeconomic deprivation, but detection of younger-onset dementia cases earlier than 70 years old was notably limited, and performance fell markedly when restricted to more specific ADRD-coded diagnoses. Early detection of dementia is a major societal challenge, but population-level screening using routinely collected data to identify high-risk subgroups may improve the targeting of resource-intensive dementia investigations. Our study has important strengths. We used a large, population-level dataset with integrated primary and secondary care health data to maximize ascertainment of risk factors and dementia diagnoses over 13 years of follow-up. We were conservative in identifying a model development cohort at low risk of established cognitive issues by exclusion criteria using hundreds of codes and clinic attendances suitable for identifying pre-diagnostic dementia. In contrast to many reports of machine learning models for risk prediction, we have presented performance beyond basic discrimination and calibration measures flattered by relatively low outcome incidence, using PR to demonstrate the clear challenge of confident prediction for this complex condition. We have also shown a direct comparison between a data-driven approach and clinically supervised selection, suggesting in this case that the latter provides similar performance with parsimony and, therefore, greater potential for transferability to other EHR systems. The challenge of managing dementia-related disorders across an ageing population cannot be overstated. The anticipated increase in the number of individuals living with dementia by 2050 is likely to be in the region of 166%. 31 EHR data have led to a surge of studies covering large integrated population-level data to understand disparity, adverse effects and outcomes in those affected by dementia and related conditions. 32-34 These data contain important long-term markers of health to understand disease progression. Highlighting the cumulative effects of these markers and ranking their contribution to a potential diagnosis can be used to improve the prioritization of public health measures in middle-aged populations. However, this knowledge often lacks understanding of individualized risk, which is essential in an era of precision medicine to drive shared treatment decisions between an individual and their clinician. The great potential of data-driven prediction is coming closer to realization with larger EHR datasets that are crucially more granular in detail to understand heterogeneity of risk, using ever-advancing machine learning methods. The predictive quality and precision of our models for dementia improved as the prediction windows lengthened, highlighting the long-term cumulative effect of many risk factors associated with dementia pathology. Inevitably in an observational study, coding of disease or lifestyle factors reflects engagement with health services and is at risk of ascertainment bias. Despite this, our final model achieved sufficient precision-recall in the highest decile of risk to identify individuals with a 1 in 3 risk of a dementia diagnosis within 13 years, compared to baseline population risk of 1 in 13. While it might be expected that many of these individuals would be of advanced age, over 40% were under 80 years old. So, although the PPV was generally limited, in the context of a general population, high-risk stratification could still provide substantial public benefits. These may include better health and care resource utilization and improved targeting of pharmacological treatments for primary prevention. 35 , 36 The length of the prediction window is relevant for modern Alzheimer’s dementia immunotherapy, where confirmation of amyloid deposition and treatment are needed many years prior to established cognitive symptoms. 6 , 7 Population screening using predictive models of future risk might offer a more equitable strategy for determining eligibility for PET-imaging or novel therapies, in contrast to the potential for access favouring those with financial means or sufficient healthcare literacy. Here, we must acknowledge some critical challenges with the use of routinely collected data in model development. We have shown higher rates of dementia diagnoses in those with the least socioeconomic deprivation, which is in sharp contrast to selected cohort studies such as those in UK Biobank, the Whitehall II study of UK civil servants and Finnish cohorts. 37 , 38 Our data are likely to reflect stronger health-seeking behaviours for early cognitive decline in more advantaged populations, but the competing risk of earlier death in those from more disadvantaged backgrounds must also be considered. Further, the stratified analysis of our prediction model showed stable predictive performance across deprivation groups, suggesting potential utility even if under-representative in higher deprivation groups. Interestingly, the added value of SIMD measures is well recognized in the prediction of long-term cardiovascular events using the ASSIGN score. 25 Ultimately, our data reflect the true known population burden of dementia within a National Health Service in the UK where access to testing or diagnosis is not limited by ability to pay or requirement for insurance. Cohort studies have their own issues with representative inclusion, so caution must be taken against over-interpretation in this area. The lower likelihood of obtaining a dementia diagnosis in people from poorer backgrounds is a challenge for all healthcare systems but contributes to the argument for population screening and proactive targeting if and when more effective treatments for earlier dementia are available. Even without novel immunotherapies there are likely to be other societal advantages to system-wide recognition of early cognitive decline, to maximize access to appropriate health and social care service support and benefits where needed. While various clinical models have been developed in the past, the evidence suggests that there is no single best prognostic model for dementia prediction. Only a small proportion of such models have been externally validated. One example is the Cardiovascular Risk Factors, Ageing and Dementia model, which showed low discrimination power for prediction of incident dementia, with a ROC-AUC of 0.71 (95% CI 0.66–0.76). 39 , 40 Additionally, models that include cognitive testing as a predictor tend to have higher ROC-AUC scores (>0.75) compared to those that do not. 41 However, this renders them unsuitable for pre-symptomatic screening. This limitation is also present in most ML studies. Some EHR-based studies have demonstrated exceptional performance with ROC scores of 0.89 and above for long-term predictions, but target only patients with memory clinical referrals. 17 Other existing data studies achieved ROC-AUCs over 0.80 at 6 years prior to diagnosis using propensity-matched cohorts. 42 However, this approach may limit generalizability, as it typically discards many control cases that could contain important risk indicators. Through our approach, we opted for evaluating an unbiased sample of community-dwelling adults using the PR-AUC, which effectively measures discrimination when sample sizes are imbalanced. Thus, the variability in study design settings and reported outcomes makes it difficult to establish a clinical or data-driven performance baseline for comparison against our models. The estimated SHAP values indicated a wide range of routine data points associated with dementia risk. While a lot of model assumptions regarding modifiable risk factors were consistent with reports from the literature 9 (e.g. smoking, alcohol consumption, hearing loss, stroke and epilepsy), there were also some contradictions (e.g. heart failure being protective of long-term diagnoses and hypertension being protective of short-term incidence). Although these may be partially explained by underreporting in EHR data or competing risks of death from acute conditions, there may also be a causal effect stemming from better control measures provided for high-risk individuals. 43 , 44 Nonetheless, frailty and lifestyle risk factors (age, eFI, SIMD, blood pressure, smoking and BMI) unsurprisingly headlined the summary plots. In these cases, frailty in dementia was associated with a high number of ageing-related health deficits and signs of lower BMI and blood pressure. We also acknowledge several limitations of our study. Firstly, our resources did not allow patient and public engagement to be incorporated into the study design. Although we have been robust in cross-validation, our models lack external dataset validation. However, our clinically supervised model of 22 variables has a high transferability potential to achieve this. Due to the underreporting of PR-AUC in dementia studies, and lack of established prediction baselines, we could not compare performance against similar studies in the literature. We utilized phenotype code lists to derive confirmed clinical diagnoses of dementia from GP and hospital coding. Although the accuracy of EHR-based phenotype definitions is typically high, 45 there is also a possibility that these are underreported across the general population. Furthermore, our analysis highlighted difficulties in developing models for specific ADRD diagnoses that were clearly under-reported in our data. ADRD represents the most common dementia sub-type, but only around a third of dementia cases in our cohort reflected this, with a high proportion of unspecified dementia codes used particularly in primary care. Our relatively low numbers of younger-onset dementia diagnoses limited the validity of prediction in this group. This was partially due to the data collection procedure, as we defined a fixed cutoff of individuals aged 50 and above on 1st April 2009, with no additional entries after this date. This further prevented training and validation on shorter follow-ups beyond the first study year, as it would limit applicability for younger individuals. Future models could incorporate competing risk components to better account for the potential overestimation of dementia risk in our models for people at simultaneously higher risk of earlier non-dementia-related death. This approach has been integrated into newer cardiovascular risk models such as SCORE2. 46 Strategies to improve primary prevention for dementia are essential to mitigate the challenges of an ageing population. We have demonstrated that gradient-boosting (XGBoost) machine learning prediction models, based entirely on routinely collected health data, can provide moderately capable prediction for high-risk individuals many years prior to dementia diagnosis, including when using a parsimonious clinically supervised model with high transferability. Personalized estimates of future dementia risk could influence risk factor modification, access to clinical trials and help target brain imaging required for novel immunotherapy treatments in selected individuals with pre-symptomatic disease. | Review | biomedical | en | 0.999995 |
PMC11697169 | We recruited participants from a prospective cohort study investigating maternal immunizations in low- and high-risk pregnancies at the University of Washington (UW). Inclusion criteria were ability to obtain informed consent, singleton pregnancy, and availability of paired maternal-cord blood samples. Exclusion criteria were multiple pregnancy (eg, twin), known fetal or neonatal genetic anomaly, or small-for-gestational-age birth weight infants (<10th percentile for gestational age per Olsen growth curves) . This study was reviewed and received ethics approval through the UW Human Subjects Division. All participants provided written informed consent. Clinical health, immunization, race and ethnicity, and insurance data were abstracted from electronic medical records and linked Washington State Immunization Registry data as previously described . We considered insurance status categories as public, private, Tricare (military), federal, or other. We calculated body mass index using maternal weight at the time of delivery. We categorized pregestational diabetes to participants with type 1 or 2 diabetes mellitus and defined chronic hypertension as participants diagnosed with hypertension before 20 weeks’ gestational age. We defined preeclampsia with or without severe features, chronic hypertension with superimposed preeclampsia, or eclampsia based on American College of Obstetricians and Gynecologists' definitions . We categorized autoimmune or inflammatory conditions as participants with conditions including systemic lupus erythematous or Crohn disease, respectively. We considered participants as being on immunosuppressing medications if they received long-term corticosteroids, biologics, or other immunosuppressants during pregnancy ( Table 1 ). We categorized birth quarter by dividing a year into 4 sections: January to March, April to June, July to September, and October to December. We defined low birth weight deliveries as infants born weighing <2500 g. Maternal blood samples were collected within 72 hours of delivery and cord blood samples at delivery. Blood samples were centrifuged at 1800 rpm for 20 minutes and sera stored at −80 °C. Maternal total IgG testing was performed by the UW Immunology Clinical Laboratory on maternal and cord serum samples using an Optilite analyzer with standard reagents. Maternal and cord sera were tested in parallel on the same day for IgG against RSV preF, IAV A/Hong Kong/4801/2014 HA (H3), and A/Michigan/45/2015 HA (H1) with an electrochemiluminescence immunoassay (Meso Scale Discovery [MSD]). Information regarding seasonal influenza vaccine strains and match to MSD antigens is presented in the supplement ( Supplementary Table 1 ) . Serum samples were diluted 1:5000 and 1:25 000 and processed according to the manufacturer's protocol . Quantification of specimen antibody (arbitrary units per milliliter) was determined by plotting assay outputs onto the log-transformed standard curve generated from serially diluted calibrators. We defined efficient maternal antibody transfer as a cord to maternal (cord:maternal) antibody ratio >1. Baseline demographic and pregnancy characteristics were described, and these variables were compared by t tests, chi-square tests, and Fisher exact tests for comparisons with small numbers. We categorized pregnancies into those with preterm deliveries (gestational age <37 weeks) and those with full-term deliveries (≥37 weeks). We evaluated the relationship between preterm birth (PTB) and maternal and cord RSV and IAV IgG levels using Wilcoxon rank sum tests. We assessed this relationship using log-transformed maternal and cord RSV and IAV IgG levels with linear regression. Similar analyses were performed for untransformed ratios of infant to maternal RSV and IAV IgG, which we tested with t tests and linear regression. We assessed the correlation between infant and maternal RSV and IAV IgG for the entire sample and across PTB status. We evaluated the relationship between participants with and without influenza vaccination during pregnancy and maternal and cord IAV IgG levels using Wilcoxon rank sum tests stratified by birth status. We also compared cord:maternal IAV untransformed IgG ratios between pairs with and without maternal influenza vaccination using t tests. We assessed statistical differences of maternal IgG concentrations, cord IgG concentrations, and cord:maternal IgG transfer ratio between maternal vaccinations that occurred >6 and <6 months before, >3 and <3 months before, and >1 and <1 month before delivery using t tests. Covariates were selected a priori and based on significant associations between the exposure of PTB and the outcomes of RSV and IAV cord IgG. The first minimally adjusted linear regression model included the annual quarter of the infant's birth date. A second minimally adjusted linear regression model included annual birth quarter and insurance status. We performed statistical analyses using SAS software version 9.4 (SAS Institute Inc) and considered a 2-sided P < .05 to be statistically significant . We followed STROBE guidelines (Strengthening the Reporting of Observational Studies in Epidemiology) . Between June 2018 and July 2021, 115 maternal-infant pairs met inclusion criteria. Most births (69.6%) occurred in 2020 to 2021. Of 115 infants, 29 (25.2%) were born preterm. Demographic and baseline medical information was similar among preterm and full-term pregnancies ( Table 1 ), with the exception of insurance status, preeclampsia, and receipt of Tdap vaccine (tetanus, diphtheria, acellular pertussis) during pregnancy. A large proportion of participants in this cohort were vaccinated with the annual influenza vaccine (72.2%) and received the Tdap vaccine (94.8%) during pregnancy. Birth dates in this cohort were relatively evenly distributed across birth quarters, with the greatest number of infants born in quarter 3 (33.9%) and the fewest born in quarter 2 (19.1%; Table 2 ). As expected, birth weight had a significantly lower median in preterm infants ( P < .001). A small subset of infants was born low birth weight (14.8%) and admitted to the neonatal intensive care unit (21.1%). Higher maternal and cord IgG antibody levels were seen for RSV, IAV-H3, and IAV-H1 in full-term as compared with preterm infants. The median transfer ratio of IgG was highest for RSV in the total cohort and the FTB group, while the median transfer ratio was highest in the PTB group for IAV-H3. When participants were stratified by maternal influenza vaccination and birth status, the highest median maternal and cord IgG concentrations were in the vaccinated FTB group and the lowest in the unvaccinated PTB group for IAV-H3 and IAV-H1 ( Table 3 ). Maternal and cord antibody concentrations were highest in the influenza-vaccinated group for IAV-H3 and IAV-H1 regardless of PTB status . The median IAV-H3 IgG transfer ratio was highest in the unvaccinated group for PTB and FTB, but the difference was not significant. The differences in maternal and cord IgG concentrations between vaccinated and unvaccinated mothers were not significantly different by birth year or when vaccination was stratified by <6, <3, or <1 month prior to delivery (not shown). When PTB and FTB were compared without stratification by maternal vaccination, median maternal IgG level, cord IgG level, and transfer ratio were significantly lower in the PTB group for all specific antibodies, including RSV anti-preF IgG cord concentration and IgG transfer ratio and IAV-H3 IgG cord concentration and IgG transfer ratio . Maternal and cord concentrations were moderately correlated with each other for RSV, IAV-H3, and IAV-H1 . The correlations for full-term infants and their mothers were similar for RSV and higher for IAV-H3 and IAV-H1 as compared with the correlations in the total sample . The correlation remained significant and increased for RSV and IAV-H1 in the PTB group but decreased and was not significant for IAV-H3 . As expected, PTB was not significantly associated with log-transformed maternal IgG concentrations for any of the virus-specific antibodies in either the null model or when adjusted for birth quarter ( Table 5 ). PTB was associated with significant decreases in log-transformed cord IgG RSV and IAV antibody concentrations before and after adjustment; PTB was also significantly associated with a substantial decrease in cord:maternal IgG transfer ratios for RSV and IAV-H3 (47% and 34% decrease, respectively). Results for a further adjusted model including insurance status produced similar results to the adjusted models presented in Table 5 with slight decreases in precision; therefore, these results were not reported. We studied the transfer of maternal to infant RSV and influenza IgG antibodies and documented using standard and novel immunoassays to demonstrate not only the efficient transfer of these antibodies in preterm as well as full-term infants but also the fact that preterm infants can benefit from influenza immunization during pregnancy. We found that maternal and cord antibody concentrations were well correlated for RSV, IAV-H3, and IAV-H1. Importantly, we observed efficient transplacental transfer of RSV, IAV-H3, and IAV-H1 IgG antibodies in preterm as well as full-term infants. When we further stratified our analyses by maternal influenza vaccination, maternal and cord antibody concentrations were highest for IAV-H3 and IAV-H1 in the vaccinated groups regardless of gestational age category at delivery. However, cord antibody concentrations and cord:maternal IgG transfer ratios were significantly lower in the PTB group for RSV and IAV-H3. Associations between cord concentration and PTB as well as less efficient maternal IgG transfer ratios and PTB were significant ( P ≤ .05) for RSV and IAV-H3. This demonstrates that influenza vaccination during pregnancy has the potential to enhance transplacental IgG transfer even for preterm infants, which is important given high rates of preterm delivery globally and increased risks of morbidity and mortality in preterm infants. In our study, the median cord:maternal transfer ratio was very high: 1.64 for RSV, 1.50 for IAV-H3, and 1.49 for IAV-H1. These findings are similar to other studies investigating transplacental antibody transfer for common respiratory viruses, including RSV [ 19–21 ]. For example, Albrecht et al calculated an average maternal-infant antibody transfer ratio of 1.5 for IAV but did not investigate differences between H3 and H1. A study of 57 full-term mother-infant pairs from Seattle calculated cord:maternal antibody transfer ratios of 1.15 for RSV, 1.22 for IAV-H3, and 1.38 for IAV-H1 . While differences in effect size might be due to sampling site, vaccine exposure, and IgG detection methods, our findings confirm that RSV and influenza specific antibodies are transferred very efficiently across the placenta and in similar ratios. We found that cord:maternal antibody transfer ratios were lower in pregnancies with preterm infants, a finding that is well established in the literature for multiple virus-specific antibodies . More recently, in studies including Alaska Native and Seattle-based mother-infant pairs by Chu et al and an Australian indigenous population by Homaira et al , the authors found significantly lower cord:maternal antibody transfer ratios in preterm as compared with full-term infants for RSV but did not see significant differences between groups for IAV-H3 or H1. The differences observed for IAV may be affected by the timing of predominant subtype circulation, sampling site, respiratory virus season of study, and infection history. For IAV-H3 and IAV-H1, we found higher maternal and cord antibody concentrations in vaccinated individuals and their infants regardless of PTB status, with the largest discrepancies produced in the PTB group for IAV-H1. Zhong et al investigated the difference in H1N1 and H3N2 IgG concentrations in pregnant individuals and infants at birth and found significantly higher concentrations in those persons vaccinated during pregnancy and their infants for H1N1 but saw no significant differences in antibody concentration for H3N2. However, they limited their H3N2 analysis to the 2013–2014 and 2014–2015 influenza seasons, which in turn limited the sample size and power to detect significant differences. It is important to consider that due to antigenic drift, antigens included in the annual influenza vaccine are often changed, and we used the same target strains for all specimens regardless of year for the MSD assay in this study. For example, the strains used in the MSD assay, A/Hong Kong/4801/2014 HA (H3) and A/Michigan/45/2015 HA (H1), matched the 2017–2018 season vaccine strains and the H1 2018–2019 season vaccine strain but did not match vaccine strains for subsequent seasons ( Supplementary Table 1 ) . This is an important distinction because the assay targets may not fully represent the true protection elicited by vaccination in each season. In turn, the MSD output gives a representation of partial or nonspecific protection elicited from vaccination after the 2017–2018 season. Although these results may not provide a true representation of the protection conferred from vaccination, it is reasonable to assume that vaccination may have boosted antibodies toward the MSD H3 and H1 targets, which could represent a cross-reactive correlate of protection and be considered a correlate of season-specific protection. Strengths of our study include a moderately sized multiyear cohort with an adequate representation of pregnancies with preterm infants. This allowed us to investigate PTB as a risk factor for less efficient cord:maternal antibody transfer across multiple respiratory virus seasons. We also collected reliable vaccination data utilizing the electronic medical record linked to the Washington State Immunization Registry, providing access to reliable vaccination data. This study supports previous data showing that maternal influenza immunizations increase maternal and cord H3N2 and H1N1 IgG concentrations. This evidence has been primarily presented in clinical trials with little information known about this association in observational settings. This study helps fill this knowledge gap by presenting increased maternal and cord IAV IgG concentrations in an observational setting. Furthermore, laboratory methods used in this study utilized MSD, a novel, previously validated, high-throughput assay that requires a minimal amount of sera, limiting the time and resources needed to obtain meaningful results . Our study was limited by the fact that we were unable to document prior infection with influenza or RSV. Since prior infection may influence antibody concentrations more strongly in unvaccinated people, this may be especially relevant to our RSV analysis where all participants were unvaccinated . Also, we did not follow participants and their infants for infections after birth . This gap points toward future work that should be done to understand how vaccination timing and IgG concentrations in the mother and infant affect subsequent infection risk. These data can be used to make recommendations about the optimization of maternal influenza vaccination to potentially include protection of preterm infants. There is a possible detection issue from mismatched seasonal influenza vaccine strains and the influenza MSD antigens for the 2018–2019, 2019–2020, and 2020–2021 influenza seasons . Although the vaccine strains and MSD antigens are different in these seasons, the comparisons are internally controlled for by assessing cord:maternal transfer ratio. This study period coincided with COVID-19 mitigation measures, which could have affected the generalizability of the last season of the study. However, upon further investigation there were not large differences in the distributions of maternal factors, birth outcomes, maternal IgG concentrations, cord IgG concentrations, or cord:maternal IgG transfer ratios between the periods before and after COVID-19 mitigation measures. Last, we were unable to investigate biological variation or antibody function in this study. Therefore, we cannot draw any conclusions to whether biologic variability influences antibody concentrations or how functional the measured antibodies were. Our results demonstrate that cord antibody concentrations are higher in vaccinated individuals in full-term and preterm infants, illustrating that maternal influenza vaccination is an important mitigation technique to boost infant antibody concentration and potentially decrease the risk of infection in infants, particularly infants born preterm. Additionally, we saw cord:maternal transfer ratios >1 for RSV and influenza in unvaccinated pregnancies, indicating that efficient antibody transfer can occur following natural infection. Infants born preterm are more likely to have lower cord antibody concentrations and less efficient maternal antibody transfer for RSV and IAV-H3, putting them at a greater risk for infection after birth. Our study also acts as a baseline for comparison with data after maternal RSV vaccine uptake increases and potential assistance in validating correlates of protection against RSV infection in the future. | Study | biomedical | en | 0.999997 |
PMC11697185 | Human childbirth has been strongly shaped by evolution. During parturition, the human fetus undergoes a uniquely complex rotational descent through the pelvis . In response to this evolutionary selective pressure, humans have developed a reliance on cooperative and social birthing practices, a phenomenon termed “obligate midwifery” by Trevathan . While some evidence of social birth exists in other primates (e.g. [ 3–5 ],), the near universality of this practice and the active role taken by birth attendants is believed to be a distinctive trait of our species. Trevathan postulated that social birth assistance is a product of natural selection, with individuals who sought aid having higher fitness. Consequently, birth support offered by kin and community members, particularly those with prior childbirth experiences, has become a consistent feature across diverse cultures . Birth assistance carries profound implications during medical emergencies, such as cases where the umbilical cord is entangled around the newborn’s head , but this assistance extends beyond such critical situations. Other key types of support include informational support, advocacy, and emotional support . Emotional support during labor, specifically support that fosters comfort and encouragement and makes the recipient feel loved and respected , has been shown to trigger the release of oxytocin, a hormone that orchestrates uterine contractions during labor and initiates successful breastfeeding . Physical touch from birth attendants also stimulates oxytocin release . Mirroring its effects in non-parturient contexts, oxytocin fosters positive mood, reduces stress, and, crucially during labor, mitigates perceived pain . Emotional support therefore exerts notable biological impacts on the labor and birthing process. For instance, a Cochrane review demonstrated that emotional support during labor corresponds to a reduced likelihood of negative childbirth sentiments, decreased use of intrapartum analgesia, shorter labor duration, and a lower chance of cesarean or instrumental vaginal births . The biomedicalization of childbirth, which emphasizes a technocratic and medicalized approach to birth , has disrupted the availability of emotional support in labor. This biomedical model, which often deprioritizes emotional support and traditional birth practices, had particularly devastating effects during the COVID-19 pandemic. In an effort to control infection, many hospitals in the USA implemented strict policies that prohibited or severely limited the presence of preferred labor support persons, such as partners, mothers, or doulas . In some cases, individuals were forced to give birth without any emotional support persons due to hospital-imposed restrictions, support person infection, travel restrictions, or childcare needs . Since these barriers to emotional support were somewhat randomly applied across birth locations and timeframes, the pandemic offers insight into how an evolutionary mismatch in the distinctly human reliance on emotional support during labor may impact perceived childbirth stress. Here, we evaluate how giving birth alone, the number of emotional support persons, the absence of specific preferred persons, and the perceived availability of one’s medical provider are associated with perceived childbirth stress among individuals who gave birth during the COVID-19 pandemic in the USA, Data come from the COVID-19 and Reproductive Effects (CARE) study, which has been extensively described elsewhere . In brief, this was an online convenience sample survey of pregnant people aged 18 years and older living in the USA. This study was approved by the Dartmouth Committee for the Protection of Human Subjects and all participants provided informed consent. Participants were recruited through study announcements posted on social media platforms (Facebook, Twitter) and distributed via email to contacts working in maternity care and public health. The first survey, administered prenatally, was launched on 17 April 2020 using the Research Electronic Data Capture (REDCap) platform . During the prenatal survey, participants provided their anticipated due date. Individuals who consented to be re-contacted were sent a follow-up survey to ask about their birth experience. The invitation for this postnatal survey was sent four weeks after their listed due date. Data for this analysis come from the prenatal and postnatal data collection waves. One thousand seven hundred and ninety-two participants from the pregnancy survey had complete data for the study variables and agreed to be contacted again. Of those, 1120 completed the postnatal survey (62.9%). Participants who completed the follow-up survey were more likely to have higher education but did not significantly differ from those who did not complete the follow-up survey in relation to age, self-identified race, prenatal depression, or previous birth. Complete data for all the study variables were available for 1082 participants. We first evaluated the characteristics of emotional support during delivery for our participants using descriptive statistics. We then ran six separate linear multivariate regression models to evaluate our hypothesis of whether emotional support predicted perceived childbirth stress. The first three variables were giving birth alone (dichotomous), number of emotional support persons (ordinal), and perceived emotional availability of providers (dichotomous). The next three models evaluated whether a mismatch in a desired partner, parent, or doula presence predicted perceived childbirth stress, with each of those variables analyzed dichotomously. For all models, we evaluated multicollinearity (all variance inflation factors < 1.09), linearity, normality, and homoscedasticity to ensure all assumptions for linear regression were met. We set alpha at P < .05. Beta coefficients, 95% confidence intervals, P -values, and adjusted R 2 values are reported for all models. We compared our longitudinally collected data on missing support persons with similar data collected cross-sectionally. During the postpartum survey we asked, “Was there anyone you wanted in the delivery room who was not there?” (Yes/No). If participants answered yes, we asked who was missing (options: partner, mother, father, sibling, a friend, mother or father-in-law, a doula, other). Finally, we asked “Was anyone able to attend the labor and delivery virtually (over a video chat or phone)?.” We used this to assess whether virtual labor support was associated with childbirth stress, or whether virtual support attenuated any of the associations with missing support persons and childbirth stress. This allowed us to evaluate whether in-person emotional support is particularly important for alleviating childbirth stress. Sample characteristics are described in Table 1 . The mean maternal age was 31.8 years (SD = 4.0; range = 18–47). Most participants had one support person at delivery (89.6%, N = 969); 1.9% ( N = 21) had no support persons, 7.3% ( N = 79) had two support persons, and 1.2% ( N = 13) had three or more. Thirty-four percent of participants ( N = 373) reported in the postpartum interview that there was someone they wanted at delivery who could not attend. Six percent ( N = 72) of participants had at least one person attend the labor and delivery virtually, with those who were missing support persons being more likely to receive this form of support (12.6% [ N = 47] of participants missing support persons vs 3.5% [ N = 25] of those not missing a support person). 92.7% of participants who provided a reason for a missing support person ( N = 346) said that at least one of the reasons that their missing support person(s) could not attend was due to hospital restrictions (See Table 2 for all listed reasons). Fourteen percent of participants reported that they perceived their provider as busy, worried, stressed, or limiting their time with them during labor. During the pregnancy interview, 0 participants said that they would want “no one” to support them in labor and delivery in the absence of restrictions. 98.9% of participants stated that they would want a partner with them at delivery, followed by 31.1% ( N = 337) who stated that they would want a parent, and 18.9% ( N = 204) who stated they would want a doula. After delivery, 87.3% of participants reported that their partner attended their birth, while mothers were present at 2.4% ( N = 29) of births, fathers at 0.2% ( N = 2) of births, and doulas at 4.5% ( N = 54). Of the 34% of participants who said that they wished someone else had been in the delivery room with them, mothers were the most frequently missed (45.5% of the subset of 373 participants). Full model results are provided in Supplementary Tables 1 – 2 . Nulliparity was associated with significantly higher childbirth stress ( B = 8.4–9.1 across models, all P < .001). Education was associated with significantly higher childbirth stress, with a more advanced degree associated with significantly higher reported stress than those without a college degree ( B = 7.6–8.0 across models, all P =< .002). Self-identified race was not associated with perceived birth stress in adjusted models. Cesarean section delivery ( B = 12–14 across models, all P < .001) and other labor and delivery complications ( B = 17–18 across models, all P < 0.001) were also associated with significantly higher childbirth stress. We found that five out of six emotional support variables were significantly associated with childbirth stress in the expected direction in both unadjusted and adjusted models ( Table 3 ). Specifically, there was a significant linear relationship between the number of support persons and perceived birth stress . The quadratic ( B = 2.8, P = .6) and cubic ( B = −3.6, P = .2) contrasts were not significant, suggesting that a linear trend sufficiently captures the relationship. Individuals who gave birth alone had significantly higher childbirth stress ( B = 15.7, P < .001). Individuals who experienced a mismatch in partner (12.5, P = .008) or doula support ( B = 5.2, P = .021) reported significantly higher childbirth stress. Parent mismatch was unrelated to childbirth stress ( B = 0.85, P = .6). Finally, individuals who said that their provider seemed busy, worried, or stressed during delivery had significantly higher childbirth stress ( B = 16.0, P < .001). Cross-sectional data generally supported the longitudinal trends for the mismatch variables described above. Individuals who said at the postpartum visit that they were missing a desired support person because of the pandemic reported significantly more childbirth stress ( Supplementary Table 3 , B = 7.0, P < .001). Individuals who said that they wished their partner ( B = 14, P = 0.004) or doula ( B = 8.5, P = 0.004) had been able to attend the birth and also reported significantly more childbirth stress ( Supplementary Table 4 ). In contrast to the longitudinal analysis, participants who reported that they wished their mother had been able to attend the birth reported significantly higher childbirth stress ( B = 5.2, P = .021). Humans are characterized by their sociality in all aspects of reproduction, including childbirth. Pelvic changes across human evolutionary history that caused rotational birth could have placed a selective advantage on seeking assistance at delivery . It has been hypothesized that the occiput anterior position of human birth (i.e. most babies are born facing away from the parent, toward the back) makes it difficult for birthing individuals to catch their babies or to remove the umbilical cord from around the baby’s neck . Given the importance of birth attendants for maternal and newborn health, it has been argued that the powerful emotions around labor and birth, such as fear or excitement, could encourage support-seeking behaviors, ultimately enhancing reproductive success . We were therefore interested in understanding whether the evolutionary mismatch resulting from the COVID-19 pandemic—which represented a rapid and somewhat randomly distributed disruption to emotional support persons in labor—would be associated with variation in perceived childbirth stress. We found that five of the six emotional support variables that we tested, including wanting but not having a partner and doula present at delivery, were associated with childbirth stress, even when adjusting for maternal sociodemographic factors and labor and delivery complications. The effect sizes were also substantial, being similar or even greater than those observed for cesarean section and clinical complications. These findings are consistent with the hypothesis that natural selection has shaped a preference for birth attendants to provide in-person emotional support in labor to reduce stress and anxiety . These findings align with and extend previous work. For instance, Preis et al . found the absence of an emotional support person during labor was associated with decreased birth satisfaction among US-based individuals during the pandemic. Our study advances this knowledge in several key ways. First, we demonstrate that virtual support failed to mitigate the increased stress associated with missing in-person support, highlighting the irreplaceable nature of physical presence during birth. Second, we identified a previously unreported linear relationship between the number of support people and perceived stress, where each additional support person was associated with lower stress levels. These findings have important implications beyond pandemic contexts, as many hospitals routinely limit support persons to one or two visitors . Given that institutional policies often restrict the number of support persons without clear evidence for these limitations, future research should aim to replicate these findings, with particular attention to potential differences in characteristics among participants who do and do not prefer multiple support persons. One possible interpretation of our results is that individuals who are more anxious generally are more likely to perceive their births as more stressful and to say that they needed more support in labor, irrespective of the amount of support that was received. While this is possible, the temporal separation between four of our six measures—capturing desired support persons during pregnancy and actual support presence during delivery—helps mitigate concerns about reverse causality. Notably, zero participants expressed a preference during pregnancy for giving birth alone. Therefore, all cases of participants giving birth alone represent an undesired mismatch between preferred and actual support. Similarly, mismatches between desired and actual presence for partners, parents, and doulas were identified by comparing pre-birth preferences to delivery room presence. This prospective study design, combined with the finding that no participants initially desired to give birth alone, strengthens our interpretation that the support person's absence contributed to increased childbirth stress, rather than stress levels influencing retrospective wishes about support. An additional novel aspect of our study was the evaluation of the perceived attentiveness of the care provider in relation to reported childbirth stress. Fourteen percent of participants perceived their provider as busy or distracted or said that they perceived their provider as limiting their time in the room with them, which could indicate less availability for emotional support. This measure was associated with significantly higher perceived childbirth stress, with a slightly greater magnitude of effect on childbirth stress than cesarean section delivery. While never directly assessed previously, these findings are consistent with the finding that continuous care from a known provider is associated with a more positive birth experience . Such findings have been used to advocate for continuity of care maternity models that are found in cultural contexts such as New Zealand (i.e. in which the same provider meets with the pregnant individual at all stages of prenatal and postpartum care, fostering the development of a trusting relationship), but which are absent from most other cultural contexts, including the USA . While our findings support the hypothesis that emotional support during labor has been shaped by natural selection in response to the challenges of human childbirth, an alternative explanation is that the sociality of humans more broadly underlies the desire for emotional support during labor. This alternative view finds some support in studies of three captive and one wild bonobo birth, where researchers observed that females remained in close proximity to the parturient female and demonstrated emotional engagement and supportive behaviors . If similar patterns are observed in more individuals, it could suggest that the evolutionary origins of “midwifery” may predate the specific “obligation” for support that arose from the more difficult, rotational birth process that emerged during hominin evolution. Childbirth is orchestrated by a complex, changing array of interacting hormones—a process shaped by evolution and closely tied to the mental state and emotions of the birthing individual. These physiological effects therefore offer insights into the mechanisms by which emotional support during labor shapes both parental and infant biology and survival, with implications for human evolution. Key hormones involved in this process include oxytocin, epinephrine (adrenaline), and endorphins. Oxytocin causes uterine contractions while also generating calming and analgesic effects during labor and promoting immediate bonding between parent and infant upon delivery . Epinephrine, in contrast, may slow or even reverse labor progress in some cases. This hormone plays a central role in the evolved “fight-or-flight” response and may have enhanced survival during human evolution by slowing or stopping labor in response to perceived danger—especially during early stages . Parallel responses have been observed in experimental studies of other mammals disturbed or stressed during labor, such as mares or cows . Thus, in busy and unfamiliar birth settings laboring individuals may experience elevated epinephrine levels that stall labor progress, even in the absence of direct danger . Conversely, the presence of trusted support people and care providers may promote feelings of calm and safety, thereby facilitating labor progression and reducing the risk of interventions often implemented in cases of “prolonged” labor . Finally, endorphins are endogenous opioids that provide some relief throughout labor, including by potentially altering the birthing person’s state of consciousness to help manage labor-related pain and stress . Endorphins have been linked with feelings of euphoria and reward following delivery and subsequent enhanced parent–infant bonding . The positive effects of endorphins are greatest when individuals feel secure, supported, and are not frightened . Despite the strengths of this manuscript—including uniquely considering the effects of specific support person absence and the influence of the provider on childbirth stress—there are several limitations. First, the survey only asked about the perceived availability of a “provider” generally, and we were, therefore, unable to account for varying levels of support experienced by participants with more than one provider during delivery. Second, we adjusted for both cesarean section and labor and delivery complications, the latter of which is broad and therefore includes complications that vary greatly in terms of clinical significance. We did this because it was the most conservative approach in our analysis. However, future work may choose to evaluate more specific clinical complications in their models. It is also unclear whether or how the order of questioning about perceived stress in the questionnaire could have influenced the results. In addition, due to the use of convenience sampling, these data are not nationally representative. Online surveys may result in biased samples for various reasons, including that they are limited to individuals with internet access, who learn about the survey, who are interested in the topic, and who have the ability to complete it . Thus, white, highly educated individuals are overrepresented in the CARE study sample compared to the US birthing population as a whole . This is consistent with other online surveys conducted during the pandemic, with the shift to online data collection during lockdown resulting in biased samples and non-random attrition in many longitudinal studies [ 35–37 ]. The inability to collect data from a nationally representative sample has implications for interpreting study results. While universally beneficial, emotional support in labor is potentially even more important for individuals with elevated risks of adverse birth outcomes, including racialized minorities, individuals with public health insurance, and the uninsured . Specifically, research suggests that doulas in these contexts can enhance health literacy, social support, and quality of care received, in large part by acting as experienced advocates for birthing individuals who are more likely to experience medical mistreatment and encounter inattentive providers . The non-representative nature of the CARE dataset precludes analyses rigorously testing the hypothesized benefits of emotional support during labor in these population sub-groups. More work is therefore needed to assess the impact of emotional support across more representative and diverse samples. We found that the absence of any emotional support person during labor—and particularly missing support from a partner, doula, or healthcare provider—was associated with significantly higher perceived childbirth stress. Receiving virtual support did not attenuate these effects. These results align with the hypothesis that human evolution has specifically shaped the need for physical, in-person emotional support during labor. This interpretation is bolstered by previous research suggesting that the evolutionary mismatch of inadequate support during labor increases the risk of cesarean delivery . Given the high rates of cesarean delivery and poor maternal-infant health outcomes in many parts of the world, strategies to improve birth experiences and outcomes are urgently needed. Addressing this evolutionary mismatch by prioritizing adequate emotional support during labor could be a low-risk, low-cost intervention to enhance delivery experiences and outcomes, even outside of public health emergencies like the COVID-19 pandemic. Prioritizing this essential element of the birth process has the potential to yield substantial benefits for mothers, infants, and families. | Study | biomedical | en | 0.999997 |
PMC11697191 | Parkinson's disease (PD) is the second most prevalent neurodegenerative disease worldwide, affecting millions of individuals. It is characterized by the selective and progressive loss of dopaminergic neurons in the midbrain, leading to motor dysfunction symptoms, including bradykinesia, tremor, rigidity, and postural instability. The neuropathological hallmark of PD is the formation of Lewy bodies and Lewy neurites, primarily composed of α-synuclein (α-Syn). Despite the precise etiology of the disease remaining elusive, mounting evidence suggests that targeting α-Syn and mitochondria as a therapeutic approach to inhibit or slow down the progression of PD holds promise. 1 , 2 The protein α-Syn was initially associated with PD in 1997 upon the identification of point mutations in the SNCA (synuclein alpha) gene in familial PD cases. 3 Genome-wide association studies have further implicated SNCA as a major gene linked to sporadic PD. 4 An increasing body of evidence suggests that the accumulation and aggregation of α-Syn play a crucial role in the pathogenesis of PD by disrupting various subcellular functions, including autophagic and mitochondrial dysfunction. 5 , 6 Hence, facilitating the clearance and degradation of α-Syn may represent a promising therapeutic approach for treating PD. Research has shown that α-Syn can be degraded through the ubiquitin-proteasome system and the autophagy/lysosomal pathway. 7 Further studies have indicated that under normal conditions, α-Syn is predominantly degraded via the ubiquitin-proteasome system. However, elevated levels of α-Syn activate the autophagy/lysosomal pathway, emphasizing the critical role of autophagy in α-Syn degradation under pathological conditions. 8 Therefore, it is crucial to activate autophagy and facilitate the degradation of α-Syn in PD. Mitochondrial dysfunction is another major pathological mechanism of PD. 9 PD-associated mitochondrial dysfunction can arise from various causes, such as impaired mitophagy, compromised mitochondrial biogenesis, abnormalities in fission and fusion processes, and deficiencies in electron transport chain complexes. 10 Numerous mutations in genes associated with PD have been confirmed to be linked to mitochondrial dysfunction, including PRKN (Parkin RBR E3 ubiquitin protein ligase), PINK1 (PTEN induced kinase 1), LRRK2 (leucine-rich repeat kinase 2), and DJ-1 (encoded by PARK7). 11 The Pink1-Parkin axis is widely acknowledged as the most extensively studied mitophagy pathway. 12 Mutations in PRKN lead to inhibition of Parkin activity, thereby causing impairment in mitophagy. 13 Recent studies have also found that the crucial transcription factors PGC1-α (peroxisome proliferator-activated receptor-gamma coactivator-1 alpha) and TFAM (transcription factor A, mitochondrial), which regulate mitochondrial biogenesis, are down-regulated in PD patients, 14 , 15 providing evidence of impaired mitochondrial biogenesis in PD. Furthermore, mitochondrial dysfunction exacerbates the generation of reactive oxygen species and the release of cytochrome c, while decreasing ATP levels, ultimately leading to neuronal death. 16 Thus, promoting the clearance of damaged mitochondria and facilitating the generation of new mitochondria are crucial in PD. Transcription factor binding to IGHM enhancer 3 (TFE3), a well-established regulator of autophagy, positively modulates the autophagy/lysosomal pathway by up-regulating genes associated with autophagy. 17 Recent reports have validated that TFE3 activation enhances autophagy, exerting neuroprotective effects in models of spinal cord injury and Alzheimer's disease. 18 , 19 , 20 Moreover, our recent findings demonstrated that TFE3 activation enhances autophagy, providing protective effects in the MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced PD model. 21 However, whether TFE3 activation can promote α-Syn degradation in dopaminergic neurons remains unclear. Additionally, an expanding body of research has revealed the significant role of TFE3 in metabolic regulation, particularly in mitochondrial metabolism. 22 A recent study indicates that the PRCC-TFE3 fusion protein enhances cell survival and proliferation through the induction of mitophagy and mitochondrial biogenesis in translocation renal cell carcinoma. 23 Nevertheless, it remains to be determined whether TFE3 can regulate mitochondrial autophagy and biogenesis in dopaminergic neurons. Therefore, in this study, we further investigated whether TFE3 exerted neuroprotective effects in PD by regulating α-Syn and mitochondria. AAV-hSyn-3xFlag (AAV-Flag), AAV-TH-Prkn (AAV-Parkin), and AAV-hSyn-SNCA-3xFlag (AAV-α-Syn) were generated and packaged by BrainVTA (Wuhan, China). AAV-TH-Tfe3 (AAV-TFE3) and AAV-TH-EGFP (AAV-EGFP) were generated and packaged by OBiO Technology (Shanghai, China). For AAV viral injection, mice were anesthetized with 3% isoflurane and subsequently secured in a stereotaxic instrument (RWD Life Science Co., Shenzhen, China). Anesthesia was consistently upheld at 1.5% isoflurane administered through a nose tip integrated into the stereotactic frame. Injections were performed using a 10 μL syringe (Hamilton, Switzerland) coupled with a 33-Ga needle (Hamilton) and facilitated by a microsyringe pump (KD Scientific, Massachusetts, USA). A unilateral injection into the substantia nigra (SN) was performed at a rate of 0.1 μL/min, delivering 1 μL of either AAV-Flag (1.0 × 10 12 vg/mL), AAV-α-Syn (1.0 × 10 12 vg/mL), a mixture of AAV-α-Syn (1.0 × 10 12 vg/mL)/AAV-TFE3 (2.0 × 10 12 vg/mL), AAV-EGFP (2.0 × 10 12 vg/mL), AAV-TFE3 (2.0 × 10 12 vg/mL), and a mixture of AAV-α-Syn (1.0 × 10 12 vg/mL)/AAV-Parkin (1.0 × 10 12 vg/mL). The coordinates representing distance (mm) from the bregma were as follows: anteroposterior −2.9, mediolateral +1.3, and dorsoventral −4.35. After the injection, the needle was left in position for a minimum of 5 min to mitigate retrograde flow along the needle track. After surgery, the mice were gently warmed using a heating pad until they regained consciousness. For real-time PCR and Western blotting, mice were sacrificed by cervical dislocation. Subsequently, the brains were rapidly extracted and rinsed with ice-cold phosphate buffer saline solution (PBS). The SN and striatum (STR) tissue was promptly dissected on ice and preserved at −80 °C until further experiments. For immunofluorescence and immunohistochemistry analyses, mice were anesthetized with urethane (1.5 g/kg, intraperitoneal injection) and subjected to intracardial perfusion with 20 mL of ice-cold PBS, followed by 50 mL of cold 4% paraformaldehyde. After perfusion, mouse brains were removed, post-fixed overnight in 4% paraformaldehyde at 4 °C, and subsequently immersed in 20% and 30% sucrose solutions. The tissues were then embedded in optimal cutting temperature and sectioned into 20 μm-thickness cryosections for immunofluorescence and 40 μm-thickness cryosections for immunohistochemistry. Cryo-coronal sections (40 μm) spanning the entire midbrain and STR were systematically collected. Initially, selected sections were permeabilized in 0.3% Triton-X in PBS at room temperature for 30 min and treated with 3% hydrogen peroxide in PBS at room temperature for an additional 30 min to quench endogenous peroxidase activity. Subsequently, the sections were blocked with a blocking buffer in PBS at room temperature for 1 h to minimize non-specific staining. Following this, sections were incubated overnight at 4 °C with anti-TH diluted in 3% bovine serum albumin in PBS. Visualization was achieved using the VECTASTAIN® Elite® ABC-HRP Kit and the ImmPACT® DAB Substrate Kit , following the manufacturer's protocol. Stained sections were mounted onto slides, coverslipped, and subsequently imaged using an optical microscope (Slide Scan System SQS-40 P, Shenzhen Shengqiang Technology, China). Free-floating 20 μm-thick sections were rinsed in PBS and then incubated in a blocking solution at room temperature for 1 h. Primary antibodies, including TH , TFE3 , α-Syn , p-α-Syn Ser129 , Lamp1 (lysosomal associated membrane protein 1; 1:500, #1D4B–C, DSHB), p62 , LC3 , Parkin , Tom20 , VDAC1 , PGC1-α , and TFAM were diluted in 1% bovine serum albumin in 1× TBST (0.3% Triton X-100) and applied to the sections overnight at 4 °C. Following three washes in PBS, the sections were incubated with secondary antibodies (Thermo Fisher, Massachusetts, USA) conjugated with Alexa 488, Alexa 555, or Alexa 647 at room temperature for 1 h. Finally, the sections were visualized using a confocal laser scanning microscope (A1, Nikon, Tokyo, Japan), and immunofluorescence results were analyzed using ImageJ software. The rotarod test, a well-established method for evaluating motor deficits of rodents in neurodegenerative disease models, was performed as previously described. 24 In brief, all mice were trained on the rotarod for two consecutive days at a consistent speed of 10 rpm for 60 s. Subsequently, on the following day, the mice were tested on a rod with a gradual acceleration from 4 to 40 rpm over a 5-min duration. The latency time to fall from the rod was recorded, with a maximum observation time of 5 min. RNA was extracted from the SN tissue using a TRIZOL kit , and its concentration was determined spectrophotometrically (NANODROP, Thermo). PrimeScript™RT Reagent Kit with gDNA Eraser (RR047A, TakaRa, Japan) was employed to synthesize cDNA, which was then amplified using KAPA SYBR® FAST qPCR Master Mix (2 X ) Kit with specific primers for real-time PCR analysis. All reactions were conducted using the Light Cycler 480 System (CFX96, Bio-Rad, California, USA). The primer sequences utilized were as follows: Tfe3 : forward, 5′-ATCTCTGTGATTGGCGTGTCT-3′, reverse, 5′-GAACCTTGAGTACCTCCCTGG-3′; Prkn : forward, 5′-TGGAAAGCTCCGAGTTCAGT-3′, reverse, 5′-CCTTGTCTGAGGTTGGGTGT-3′; Ppargc1 : forward, 5′-AAGGTCCCCAGGCAGTAGAT-3′, reverse, 5′-GGCTGTAGGGTGACCTTGAA-3′; Actb : forward, 5′-GGCTGTATTCCCCTCCATCG-3′, reverse, 5′-CCAGTTGGTAACAATGCCATGT-3′. Dissected ventral midbrain and STR tissues from mice were homogenized and lysed in Laemmli buffer (50 μL/mg tissue) composed of Tris·Cl (62.5 mM, pH 6.8), SDS (2%, w/v), bromophenol blue (0.005%, w/v), glycerol (10%, v/v), and DTT (8 mg/mL). The lysates were then boiled at 95–100 °C for 5 min 25 μg of protein from each sample were loaded onto SDS-PAGE gels and subsequently transferred to PVDF membranes (Millipore, Darmstadt, Germany). Following the transfer, membranes were blocked with 5% skim milk at room temperature for 1 h and then incubated at 4 °C overnight with the following primary antibodies: TFE3 , α-Syn , p-α-Syn Ser129 , Lamp1 (1:500, #1D4B–C, DSHB), LC3 , p62 , Tom20 , PGC1-α , Parkin , TFAM , and β-actin . After washing, membranes were incubated with the appropriate horseradish peroxidase-conjugated secondary antibodies . All blots were visualized using ECL chemiluminescence , and the results were analyzed using ImageJ software. The quantification of TH-positive cells in brain sections exhibiting typical SN morphology was performed as previously described. 25 Briefly, TH-positive neurons were manually counted at four-section intervals across the entire extent of the SN using bright-field microscopy and ImageJ software. To assess changes in TH-positive neuron numbers, the counts from AAV-Flag-injected mice (control) were set to 100%, and the counts from other groups were expressed as a percentage relative to this control. The optical density of striatal TH-positive fibers in the mouse dorsolateral STR was quantified using ImageJ software. The optical density of the corpus callosum served as background and was subtracted from each measurement in the STR. The optical density in the experimental group was then normalized to the value obtained from the control group. All analyses were performed blinded to the treatments. Statistical analyses were performed using GraphPad Prism version 8.0 (GraphPad Software). The data were presented as mean ± standard error of the mean. Comparisons between two groups were conducted using a two-tailed student's t -test. Multiple group comparisons were assessed through a one-way ANOVA followed by Tukey's post-hoc tests. Statistical significance was established at a probability value of P < 0.05 for all analyses. To investigate the neuroprotective effects of TFE3 and elucidate specific mechanisms in α-Syn pathology, we utilized AAV viral vectors to overexpress human wild-type α-Syn in the SN of mice, establishing an AAV-α-Syn model . Initially, we evaluated whether α-Syn and TFE3 were successfully overexpressed in nigral dopaminergic neurons following stereotaxic nigral injection of either AAV-α-Syn or AAV-TFE3. Immunofluorescent staining confirmed robust expression of α-Syn and TFE3 in dopaminergic neurons on the injected side one month after AAV delivery . Figure 1 TFE3 overexpression attenuates α-Syn toxicity in a mouse model of Parkinson's disease. (A) Schematic diagram of AAV virus stereotactic injection targeting the SN in mice. (B) Representative immunofluorescent staining of α-Syn and TFE3 in dopaminergic neurons of mice injected with AAV-α-Syn and AAV-TFE3. Scale bars, 500 μm. (C) Representative image of TH immunostaining in the SN and STR of mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/TFE3. Scale bars, 200 μm for SN and 500 μm for STR. (D, E) Quantitative analysis of TH-positive cells in the SN (D) and TH-positive terminals in the STR (E). n = 7 mice per group. (F) Assessment of motor function using the accelerating rotarod test, depicting latency to fall time (s) for mice in different experimental groups. n = 16 or 17 mice per group. The data were presented as mean ± standard error of the mean. Statistical significance was determined using one-way analysis of ANOVA followed by Tukey's multiple comparisons test. ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗∗ P < 0.0001. TFE3, transcription factor binding to IGHM enhancer 3; α-Syn, α-synuclein; AAV, adeno-associated virus; SN, substantia nigra; TH, tyrosine hydroxylase; STR, striatum. Figure 1 We then investigated whether TFE3 overexpression conferred neuroprotection in the AAV-α-Syn model. Naive mice were stereotaxically injected into the SN with different AAV vectors and subsequently categorized into three groups: AAV-Flag-injected group (F), AAV-α-Syn-injected group (α), and AAV-α-Syn and AAV-TFE3 co-injected group (α+T). For immunohistochemical analysis and behavioral tests, mice were sacrificed three months after virus injection. We systematically analyzed dopaminergic neuron numbers on the injected side after unilateral injection of AAV. Consistent with previous reports, 26 administration of AAV-α-Syn caused a 46.1% loss of dopaminergic neurons compared with mice injected with AAV-Flag . Co-administration of AAV-α-Syn with AAV-TFE3 prevented α-Syn-induced degeneration of dopaminergic neurons, resulting in only a 1.2% reduction in the number of dopaminergic neurons compared with AAV-Flag-injected mice . To assess whether the total preservation of dopaminergic cell bodies corresponded with the maintenance of dopaminergic terminals in the STR, we quantified the optical density of TH staining in the STR. Consistent with the results in the SN, the administration of AAV-α-Syn resulted in a 55.8% reduction in the optical density of the STR compared with mice injected with AAV-Flag . However, co-administration of AAV-α-Syn with AAV-TFE3 only led to a 3.9% decrease . To determine whether TFE3 expression not only preserved the integrity of nigral dopaminergic neurons but also maintained their function after α-Syn intoxication, rotarod tests were performed. The results revealed that the administration of AAV-α-Syn had a significantly shorter latency to fall from the accelerated rod compared with AAV-Flag-injected mice, and co-administration of AAV-α-Syn with AAV-TFE3 significantly increased retention time on the rotarod . These findings demonstrate that TFE3 overexpression reduces neurodegeneration and associated motor function deficits in the AAV-α-Syn model of PD. Next, we explored the specific mechanisms underlying TFE3's neuroprotective effects in the AAV-α-Syn model. Autophagic defects can enhance the accumulation of α-Syn, which in turn further inhibits autophagy. 27 Therefore, restoring α-Syn-mediated autophagic dysfunction is especially crucial. Consistent with our previous findings, 21 our new results indicate a notable up-regulation of the TFE3 mRNA and protein levels one month after AAV-TFE3 injection , concomitant with the induction of lysosomal marker Lamp1, autophagy receptor p62, and autophagosome marker LC3 in the SN , confirming an enhancement of autophagic flux by overexpression of TFE3. Figure 2 TFE3 overexpression rescues autophagy defects of dopaminergic neurons in the AAV-α-Syn model. (A) Quantitative reverse-transcription PCR analysis of Tfe3 mRNA in ventral midbrain homogenates from mice injected with AAV-EGFP and AAV-TFE3. n = 4 mice per group. (B, D) Representative western blots for TFE3, Lamp1, p62, and LC3 in ventral midbrain homogenates from mice injected with AAV-EGFP and AAV-TFE3. (C, E–G) Quantification of Western blot bands corresponding to TFE3 (C), Lamp1 (E), p62 (F), and LC3 (G) normalized to β-actin. n = 6 mice per group. The data were presented as mean ± standard error of the mean. Statistical significance was determined using a two-tailed student's t -test. ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗∗ P < 0.0001. (H, J, L) Representative immunofluorescent staining of Lamp1 (H), p62 (J), and LC3 (L) in dopaminergic neurons of mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/TFE3. n = 4 or 5 mice per group. Scale bars, 10 μm. (I, K, M) Quantitative analysis of the fluorescence results shown in (H), (J), and (L). The data were presented as mean ± standard error of the mean. Statistical significance was determined using one-way analysis of ANOVA followed by Tukey's multiple comparisons test. ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001, ∗∗∗∗ P < 0.0001. TFE3, transcription factor binding to IGHM enhancer 3; α-Syn, α-synuclein; AAV, adeno-associated virus; Lamp1, lysosomal associated membrane protein 1; LC3, microtubule-associated protein light chain 3. Figure 2 Subsequently, we investigated whether TFE3 overexpression could ameliorate autophagic dysfunction in dopaminergic neurons within the AAV-α-Syn model. Our results indicated that the overexpression of α-Syn for three months significantly down-regulated Lamp1 in dopaminergic neurons compared with AAV-Flag injected mice , suggesting a reduction in lysosomal abundance. Co-injection of AAV-α-Syn with AAV-TFE3 completely restored Lamp1 levels, indicating that TFE3 overexpression reverse lysosomal depletion in the AAV-α-Syn model . Concurrently, α-Syn overexpression significantly increased p62 levels and resulted in the formation of numerous p62-positive puncta in dopaminergic neurons compared with the AAV-Flag injected group , indicating the accumulation of autophagic substrates. Remarkably, co-administration of AAV-α-Syn with AAV-TFE3 also induced p62 up-regulation but significantly reduced the number of p62-positive puncta . Furthermore, α-Syn overexpression resulted in a down-regulation of LC3, indicative of a decrease in the number of autophagosomes . Co-administration of AAV-α-Syn with AAV-TFE3 reversed the LC3 down-regulation in the AAV-α-Syn model, restoring the formation of autophagosomes . Taken together, these findings collectively demonstrate that TFE3 overexpression reverses α-Syn-induced autophagic dysfunction in dopaminergic neurons. Activating autophagy has been demonstrated to promote the degradation of α-Syn. For instance, AAV-mediated overexpression of TFEB, BECN1 (beclin 1), ATG7 (autophagy-related 7), and other factors has been shown to facilitate α-Syn degradation, suggesting therapeutic implications for modulating autophagy in α-Syn-related pathologies. 28 , 29 , 30 Our results have already demonstrated that TFE3 overexpression restores autophagy in the AAV-α-Syn model. Therefore, we sought to explore whether activating TFE3 could promote the degradation of α-Syn. Immunofluorescence and Western blot analyses were performed three months after viral injection. The AAV-Flag group showed no α-Syn staining, while AAV-α-Syn-injected mice displayed pronounced α-Syn staining in the SN . The results under high magnification reveal a strong expression of α-Syn in dopaminergic neurons following AAV-α-Syn injection . However, co-administration of AAV-α-Syn with AAV-TFE3 reduced α-Syn protein levels in dopaminergic neurons . This result was further confirmed by Western blot analysis . These results confirm that TFE3 overexpression promotes α-Syn degradation in the AAV-α-Syn model. Figure 3 TFE3 overexpression promotes α-Syn degradation and inhibits α-Syn propagation in the AAV-α-Syn model. (A, C) Immunofluorescence (A) and Western blot (C) analysis for α-Syn expression in dopaminergic neurons of the SN or ventral midbrain homogenates from mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/TFE3. n = 4 mice per group. Scale bars, 100 μm for low magnification and 10 μm for high magnification. (B) Quantitative analysis of the fluorescence results shown in (A). (D) Quantification of Western blot bands corresponding to α-Syn normalized to β-actin. n = 4 mice per group. (E, G) Immunofluorescence (E) and Western blot (G) analysis for p-α-Syn expression in dopaminergic neurons of the SN or ventral midbrain homogenates from mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/TFE3. n = 4 mice per group. Scale bars, 100 μm for low magnification and 10 μm for high magnification. (F) Quantitative analysis of the fluorescence results shown in (E). (H) Quantification of Western blot bands corresponding to p-α-Syn normalized to β-actin. n = 4 mice per group. (I, K) Immunofluorescence (I) and Western blot (K) analysis for α-Syn expression in the STR from mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/TFE3. n = 4 mice per group. Scale bars, 500 μm. ( J ) Quantitative analysis of the fluorescence results shown in (I). (L) Quantification of Western blot bands corresponding to α-Syn normalized to β-actin. n = 4 mice per group. The data were presented as mean ± standard error of the mean. Statistical significance was determined using one-way analysis of ANOVA followed by Tukey's multiple comparisons test. ∗∗∗ P < 0.001, ∗∗∗∗ P < 0.0001. TFE3, transcription factor binding to IGHM enhancer 3; α-Syn, α-synuclein; AAV, adeno-associated virus; SN, substantia nigra; STR, striatum. Figure 3 Additionally, the study shows around 90% of the α-Syn found in Lewy bodies undergoes phosphorylation at serine 129. In contrast, the normal brain exhibits phosphorylation at this residue in only 4% or less of the total α-Syn. 31 Thus, phosphorylation of α-Syn at the serine 129 residue (p-α-Syn) correlates with pathological developments in PD and promotes fibril formation and insoluble aggregation. 32 Reducing such aggregation has long been proposed as a therapeutic strategy for PD. In our results, the expression pattern of p-α-Syn was not entirely consistent with that of α-Syn, revealing a predominant co-staining with dopaminergic neurons in AAV-α-Syn-injected mice . This suggests that α-Syn is more prone to aggregate in dopaminergic neurons and exert neurotoxicity. However, co-injection of AAV-α-Syn with AAV-TFE3 nearly eliminated p-α-Syn staining in dopaminergic neurons . Similarly, this result was further validated by Western blot analysis . These results indicate that TFE3 overexpression reduces α-Syn aggregation in the AAV-α-Syn model. α-Syn is known to act as a prion-like protein and exhibits well-established spreading characteristics. 33 Therefore, we investigated whether TFE3 overexpression could inhibit α-Syn propagation. Our results showed that α-Syn expression was detected in the STR and cortex on the side ipsilateral to the viral injection , confirming its propagation. Additionally, co-injection of AAV-α-Syn with AAV-TFE3 significantly reduced α-Syn levels in both the STR and cortex . Western blot analysis of the STR further confirmed these findings . These results confirm that TFE3 overexpression also inhibits α-Syn propagation. Autophagic dysfunction can impede the clearance of damaged mitochondria, ultimately leading to cell death. Research has shown that overexpression of A53T human α-Syn in transgenic mice induces extensive abnormalities in mitochondrial macroautophagy. Subsequently, genetic deletion of either Parkin or PINK1 in mice overexpressing A53T α-Syn further significantly exacerbates mitochondrial inclusions and reduces mitochondrial mass, 34 providing evidence that PINK1/Parkin-mediated mitophagy is essential for the effective autophagic elimination of impaired mitochondria in dopaminergic neurons. A recent study has also reported that the PRCC-TFE3 fusion mediates Parkin-dependent mitophagy in translocation renal cell carcinoma. 23 The overexpression of Parkin has been demonstrated to promote mitophagy in dopaminergic neurons. 35 Therefore, to address whether TFE3 regulated mitochondrial autophagy in dopaminergic neurons, we first examined whether TFE3 regulated Parkin. Immunofluorescence results showed that AAV-mediated TFE3 overexpression significantly increased Parkin protein levels in dopaminergic neurons , and this finding was further confirmed by Western blot analysis . Furthermore, reverse-transcription PCR results revealed that TFE3 overexpression up-regulated Prkn mRNA levels , suggesting that TFE3 can trans-regulate Parkin in dopaminergic neurons. Further investigation in the AAV-α-Syn model demonstrates that the overexpression of α-Syn results in a reduction of Parkin protein levels . Notably, co-administration of AAV-α-Syn with AAV-TFE3 significantly restores the Parkin protein levels , implying that TFE3 overexpression can enhance mitophagy. Figure 4 TFE3 overexpression transcriptionally up-regulates Parkin, promoting the removal of accumulated mitochondria in the AAV-α-Syn model. (A, C) Immunofluorescence (A) and Western blot (C) analysis for Parkin in dopaminergic neurons of the SN or ventral midbrain homogenates from mice injected with AAV-EGFP and AAV-TFE3. Immunofluorescence: n = 6 mice per group. Scale bars, 50 μm. (B) Quantitative analysis of the fluorescence results shown in (A). (D) Quantification of Western blot bands corresponding to Parkin normalized to β-actin. n = 6 mice per group. (E) Quantitative reverse-transcription PCR analysis of Prkn mRNA in ventral midbrain from mice injected with AAV-EGFP and AAV-TFE3. n = 4 mice per group. The data were presented as mean ± standard error of the mean. Statistical significance was determined using a two-tailed student's t -test. ∗ P < 0.05, ∗∗∗∗ P < 0.0001. (F) Western blot analysis for Parkin expression in ventral midbrain homogenates from mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/TFE3. (G) Quantification of Western blot bands corresponding to Parkin normalized to β-actin. n = 4 mice per group. The data were presented as mean ± standard error of the mean. Statistical significance was determined using one-way analysis of ANOVA followed by Tukey's multiple comparisons test. ∗∗∗ P < 0.001, ∗∗∗∗ P < 0.0001. (H, I) Immunofluorescence analysis for Tom20 (H) and VDAC1 (I) in dopaminergic neurons of the SN from mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/TFE3. n = 3–5 mice per group. Scale bars, 10 μm. (J, K) Immunofluorescence analysis for Tom20 (J) and VDAC1 (K) in dopaminergic neurons of the SN from mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/Parkin. n = 4 mice per group. Scale bars, 10 μm. TFE3, transcription factor binding to IGHM enhancer 3; α-Syn, α-synuclein; AAV, adeno-associated virus; SN, substantia nigra; Parkin, Parkin RBR E3 ubiquitin protein ligase; Tom22, outer mitochondrial membrane protein; VDAC1, voltage-dependent anion channel 1. Figure 4 Subsequently, we observed the specific impact of TFE3 on mitophagy in the AAV-α-Syn model. Tom20 is often used as a marker for mitochondria. Immunofluorescence analysis revealed significantly increased Tom20 inclusions in dopaminergic neurons of mice overexpressing α-Syn, indicating the accumulation of damaged mitochondria . However, co-administration of AAV-α-Syn with AAV-TFE3 resulted in the complete elimination of Tom20 inclusions , demonstrating that activating TFE3 could promote the clearance of accumulated mitochondria. Similar results were further validated by VDAC1, an outer membrane protein of mitochondria . To further confirm that Parkin could mediate the clearance of mitochondrial inclusions in the α-Syn overexpression model, we co-injected AAV-α-Syn with AAV-Parkin (α+P) into the SN. The results showed that Parkin overexpression also significantly reduced Tom20 and VDAC1 inclusions . Taken together, these findings suggest that TFE3 overexpression promotes the clearance of accumulated mitochondria by transcriptionally up-regulating Parkin. Recent research suggests that α-Syn not only directly damages mitochondria and impedes their degradation but also suppresses mitochondrial biogenesis in certain cellular models. 36 , 37 Simultaneously, a recent report demonstrates that PRCC-TFE3 fusion can regulate mitochondrial biogenesis in translocation renal cell carcinoma. 23 Moreover, previous research has shown that in muscle, TFE3 directly regulates PGC-1α, 38 a co-transcriptional factor and master regulator of mitochondrial biogenesis. 39 The activation of PGC1-α has also been demonstrated to promote mitochondrial biogenesis in PD models, thereby exerting neuroprotective effects. 40 Therefore, we first examined whether TFE3 could regulate PGC-1α in dopaminergic neurons. The immunofluorescence results confirm a significant up-regulation of PGC-1α protein levels in dopaminergic neurons of mice overexpressing TFE3 compared with those injected with AAV-EGFP . This result was further validated by Western blot analysis . Additionally, reverse-transcription PCR results demonstrated that TFE3 overexpression up-regulated Ppargc1a mRNA levels , suggesting that TFE3 transcriptionally up-regulates PGC1-α in dopaminergic neurons. Then, we examined TFAM, identified as a transcription factor for mitochondrial DNA, which is recognized to be crucial for the maintenance of mitochondrial DNA. 41 Both immunofluorescence and Western blot results confirmed that overexpression of TFE3 significantly promoted the up-regulation of TFAM in dopaminergic neurons . Concurrently, we also observed a significant increase in Tom20 expression in dopaminergic neurons overexpressing TFE3 . These results demonstrate that activation of TFE3 could enhance mitochondrial biogenesis in dopaminergic neurons. Recent research has indicated impaired mitochondrial biogenesis in both PD patients and PD models. 42 Next, we observed the impact of TFE3 on mitochondrial biogenesis in the AAV-α-Syn model. Our results revealed that overexpression of α-Syn led to down-regulation of PGC1-α and TFAM, indicating impaired mitochondrial biogenesis . However, co-administration of AAV-α-Syn with AAV-TFE3 significantly increased the expression of PGC1-α, TFAM, and Tom20 , demonstrating that activation of TFE3 could promote mitochondrial biogenesis in the AAV-α-Syn model. Figure 5 TFE3 overexpression reversed the impairment of mitochondrial biogenesis in the AAV-α-Syn model. (A, C) Immunofluorescence (A) and Western blot (C) analysis for PGC1-α in dopaminergic neurons of the SN or ventral midbrain homogenates from mice injected with AAV-EGFP and AAV-TFE3. Immunofluorescence: n = 6 mice per group. Scale bars, 50 μm. (B) Quantitative analysis of the fluorescence results shown in (A). (D) Quantification of Western blot bands corresponding to PGC1-α normalized to β-actin. n = 6 mice per group. (E) Quantitative reverse-transcription PCR analysis of Ppargc1a mRNA in ventral midbrain from mice injected with AAV-EGFP and AAV-TFE3. n = 5 or 6 mice per group. (F, H) Immunofluorescence (F) and Western blot (H) analysis for TFAM in dopaminergic neurons of the SN or ventral midbrain homogenates from mice injected with AAV-EGFP and AAV-TFE3. Immunofluorescence: n = 6 mice per group. Scale bars, 50 μm. (G) Quantitative analysis of the fluorescence results shown in (F). (I) Quantification of Western blot bands corresponding to TFAM normalized to β-actin. n = 6 mice per group. (J, L) Immunofluorescence (J) and Western blot (L) analysis for Tom20 in dopaminergic neurons of the SN or ventral midbrain homogenates from mice injected with AAV-EGFP and AAV-TFE3. Immunofluorescence: n = 6 mice per group. Scale bars, 50 μm. (K) Quantitative analysis of the fluorescence results shown in (J). (M) Quantification of Western blot bands corresponding to Tom20 normalized to β-actin. n = 6 mice per group. The data were presented as mean ± standard error of the mean. Statistical significance was determined using a two-tailed student's t -test. ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001, ∗∗∗∗ P < 0.0001. (N) Western blot analysis for PGC1-α, Tom20, and TFAM expression in ventral midbrain homogenates from mice injected with AAV-Flag, AAV-α-Syn, and AAV-α-Syn/TFE3. (O – Q) Quantification of Western blot bands corresponding to PGC1-α (O), Tom20 (P), and TFAM (Q) normalized to β-actin. n = 4 mice per group. The data were presented as mean ± standard error of the mean. Statistical significance was determined using one-way analysis of ANOVA followed by Tukey's multiple comparisons test. ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗∗ P < 0.0001; ns, not significant. TFE3, transcription factor binding to IGHM enhancer 3; α-Syn, α-synuclein; AAV, adeno-associated virus; SN, substantia nigra; Tom20, outer mitochondrial membrane protein; PGC1-α, peroxisome proliferator-activated receptor-gamma coactivator-1 alpha; TFAM, transcription factor A. Figure 5 α-Syn plays a central role in PD pathology. Consequently, employing the AAV virus to express α-Syn in rodents has become a popular tool for modeling PD. This model proves valuable in exploring potential therapeutics targeting α-Syn and its associated pathology. 26 Autophagy is crucial for maintaining the homeostasis and survival of dopaminergic neurons. 43 In recent years, autophagy impairment has been well-established in PD. 44 In this study, our results also demonstrate that overexpression of α-Syn leads to autophagic dysfunction in dopaminergic neurons. However, overexpression of TFE3 fully restores the autophagy of dopaminergic neurons. Our recent work has confirmed that knocking down TFE3 in dopaminergic neurons causes autophagy dysfunction, indicating that TFE3 is crucial for maintaining autophagy within these neurons. 21 Additionally, a previous study has shown that α-Syn can interact with TFEB, sequestering it in the cytoplasm and inhibiting its activity. 28 As TFE3 and TFEB belong to the same family with structural similarities, it is plausible that the partial inhibition of autophagy by α-Syn may originate from the suppression of the transcriptional activity of both TFE3 and TFEB. Notably, TFE3 overexpression also increased p62 protein levels, which is often associated with impaired autophagic degradation. 45 TFE3 has been shown to transcriptionally regulate autophagy-lysosome-related genes, including p62. 20 Our previous work and other studies confirm that TFE3 overexpression leads to elevated p62 protein levels. 21 , 46 Additionally, TFE3 overexpression typically up-regulates other autophagy-related proteins, such as LC3, LAMP1, and cathepsin D, indicating an overall increase in autophagy flux. Consequently, in the α-Syn model, TFE3 overexpression raises p62 protein levels while reducing p62 puncta, thereby enhancing the degradation of autophagic substrates. An increasing body of evidence indicates that enhancing autophagy can facilitate the clearance of α-Syn. 47 Our results demonstrate that activating TFE3 significantly reduces α-Syn protein levels in the AAV-α-Syn model. The degradation of autophagic substrates requires prior ubiquitination, and α-Syn has been confirmed as a substrate for the E3 ligase Parkin 48 . Moreover, activation of Parkin has been shown to enhance the autophagic degradation of α-Syn. 49 Notably, we have also observed that TFE3 overexpression promotes the up-regulation of Parkin, suggesting that TFE3 may facilitate the degradation of α-Syn through the Parkin-mediated autophagic pathway. Additionally, our findings indicate that TFE3 significantly reduces the phosphorylation levels of α-Syn, which implies a decrease in α-Syn aggregation in the AAV-α-Syn model. Furthermore, TFE3 overexpression appears to eliminate phosphorylated α-Syn compared with total α-Syn, suggesting that TFE3-mediated autophagy may more effectively promote the degradation of aggregated α-Syn. Moreover, we observed that TFE3 overexpression inhibits the spread of α-Syn to other brain regions, such as the STR and cortex. Research has shown that cellular stressors like serum deprivation, proteasomal or lysosomal inhibition, and hydrogen peroxide stimulate the vesicular translocation and subsequent release of α-Syn. 50 In addition to regulating the autophagy/lysosomal pathway, TFE3 has been confirmed to up-regulate anti-oxidation proteins, including SOD1 (superoxide dismutase 1) and HO-1 (heme oxygenase-1). 51 Therefore, TFE3 overexpression may inhibit α-Syn propagation by influencing lysosomal function and oxidative stress. Mitochondrial dysfunction has been confirmed in PD patients. 52 Compromised mitophagy in PD impedes the effective elimination of impaired mitochondria, thereby exacerbating the neurotoxicity linked to mitochondrial dysfunction. 53 Recent investigations have also validated the neuroprotective effects of Celastrol and Morin in PD models by activating mitophagy. 54 , 55 Our results indicate that TFE3 can transcriptionally up-regulate Parkin, which can ubiquitinate mitochondrial surface substrates for degradation by autophagy. 56 Additionally, our findings show that overexpression of TFE3 reversed the down-regulation of Parkin in the AAV-α-Syn model and eliminated the accumulation of mitochondria. Furthermore, overexpression of Parkin in the AAV-α-Syn model also promotes the clearance of mitochondrial inclusions. These results suggest that TFE3 may enhance mitophagy by up-regulating Parkin, but is perhaps distinct from Parkin-mediated mitophagy solely. TFE3 may, on one hand, up-regulate Parkin for the ubiquitination of damaged mitochondria, and on the other hand, enhance the autophagy/lysosomal pathway, thereby synergistically promoting the clearance of damaged mitochondria. Therefore, this may enhance the efficiency of mitophagy. Clearing damaged mitochondria necessitates the generation of new mitochondria to sustain energy supply. Our results demonstrate that overexpression of TFE3 also enhances mitochondrial biogenesis. Specifically, our findings reveal that TFE3 overexpression transcriptionally up-regulates PGC1-α, which is recognized as a master regulator of mitochondrial biogenesis. 39 Recent studies have shown that Parkin can promote the degradation of the PGC1-α inhibitors ZNF746 (zinc finger protein 746) and PARIS (Parkin-interacting substrate) in cellular models, thereby up-regulating the expression of PGC1-α. 57 , 58 Since we have also found the up-regulation of Parkin by TFE3 overexpression, the increase in PGC1-α may partly result from the Parkin/ZNF746 and PARIS/PGC-1α axis. Additionally, we observed the up-regulation of TFAM and Tom20 upon TFE3 overexpression, further supporting the increase in mitochondrial biogenesis. Enhancing mitochondrial biogenesis has been considered a focal point in the development of novel therapeutic approaches for treating PD. 59 Recent research has also demonstrated that promoting mitochondrial biogenesis exerts neuroprotective effects in PD models. 60 , 61 Furthermore, our study reveals that overexpression of α-Syn leads to decreased levels of PGC1-α and TFAM, with no significant change in Tom20 levels, likely due to impaired mitophagy and mitochondrial accumulation. In contrast, overexpression of TFE3 significantly increases PGC1-α, TFAM, and Tom20 in the AAV-α-Syn model, restoring mitochondrial biogenesis and preserving mitochondrial function. These findings deepen our understanding of TFE3's role in regulating mitochondrial homeostasis. Consistent with findings from the MPTP mode, 21 we observed that TFE3 overexpression in the AAV-α-Syn model provided nearly fully protected dopaminergic neurons. This suggests that the neuroprotective effects exerted by TFE3 in PD may be multifaceted. In this study, we report that TFE3 exerts neuroprotective effects by regulating autophagy to facilitate the degradation of aggregated α-Syn and damaged mitochondria, as well as promoting mitochondrial biogenesis. In a spinal cord injury model, TFE3 has been reported to inhibit oxidative stress by transcriptionally regulating anti-oxidant proteins and to suppress pyroptosis and necroptosis, or alleviate endoplasmic reticulum stress through the augmentation of autophagy. 19 , 20 Recent investigations have also shown that TFE3 enhances autophagy, promoting the degradation of NLRP3 (NLR family pyrin domain containing 3), thereby inhibiting neuroinflammation in Alzheimer's disease models. 18 Therefore, further research is needed to explore additional neuroprotective mechanisms of TFE3 in PD. These findings contribute to our understanding of the diverse roles of TFE3 in neuroprotection. While TFE3 is more abundant in the central nervous system than TFEB, 62 , 63 the literature on TFE3 in the field of neuroscience remains limited. TFEB has been extensively implicated in various neurodegenerative diseases, such as Alzheimer's disease and PD, leading to the development of numerous agonists aimed at activating TFEB to exert neuroprotective effects. 64 , 65 Our current study provides additional support for the neuroprotective role of TFE3 in PD. As TFE3 belongs to the same family as TFEB, sharing many structural and functional similarities, the higher abundance of TFE3 suggests that the exploration of TFE3 agonists or dual-target agonists for TFE3 and TFEB may offer a more promising therapeutic avenue. In conclusion, our findings elucidate the potential neuroprotective effects of TFE3 in PD . Our results show that TFE3 overexpression enhances autophagy, promoting the degradation of α-Syn and thereby reducing α-Syn aggregation in the AAV-α-Syn model. Additionally, we present the first evidence that TFE3 regulates the mitochondrial metabolism of dopaminergic neurons in the AAV-α-Syn model by up-regulating Parkin to promote mitochondrial autophagy and increasing levels of PGC1-α and TFAM to enhance mitochondrial biogenesis. These results not only expand the scope of TFE3 applications in α-synucleinopathy-based PD models but also further underscore TFE3 as a promising therapeutic target for PD. Figure 6 A schematic illustration depicting the presumed mechanism of TFE3 in Parkinson's disease. Increased α-Syn in Parkinson's disease leads to dysfunction in autophagy and mitochondrial impairment, exacerbating the accumulation of α-Syn and damaged mitochondria, ultimately resulting in neuronal death. Conversely, activation of TFE3 enhances autophagic flux, Parkin, and PGC1-α, thereby facilitating the clearance of aggregated α-Syn and accumulated mitochondria, as well as promoting mitochondrial biogenesis, ultimately fostering neuronal survival. TFE3, transcription factor binding to IGHM enhancer 3; α-Syn, α-synuclein; PGC1-α, peroxisome proliferator-activated receptor-gamma coactivator-1 alpha. Figure 6 | Review | biomedical | en | 0.999997 |
PMC11697192 | Maintenance of genomic integrity is vital for both evolutionary fitness and individual health. Cells have evolved protective DNA mechanisms, while it is prone to mutations from internal and external insults. On the one hand, mutations act in recombination and DNA repair to preserve genome diversity and integrity; on the other hand, mutations are associated with aging, tumors, immune disease, etc . 1 , 2 For most, if not all, of these mechanisms, nucleases are required to cleave DNA phosphodiester bonds in a controlled and accurate manner. A wide variety of nucleases have been discovered and characterized based on their subunit constitution, cofactor demands, and DNA cleavage modes that can be divided into exonucleases and endonucleases, which participate in multiple pathways such as DNA replication, mismatch repair (MMR), and DNA degradation . 3 , 4 Figure 1 Functions of DNA nucleases. (A) DNA endonuclease recognition specific sites. (B) Mismatch repair. (C) DNA replication. Fig. 1 DNA exonucleases contain 3′–5′ or 5′–3′ exonuclease activities and flap endonuclease activities in maintaining genome stability that remove a deoxyribonucleoside monophosphate from the end of one strand of DNA. DNA endonucleases are enzymes that can hydrolyze the phosphate diester bond in the molecular chain to generate oligonucleotides in the nucleic acid hydrolase, corresponding to the exonucleases. 5 A critical difference between exonucleases and endonucleases is that endonucleases can be combined with associated DNA substrates, whereas most exonucleases bind in a non-sequence-specific manner. 6 Their functions are involved in removing mismatched, modified, fragmented, and normal base-paired nucleotides, which is crucial in the subsequent steps of DNA synthesis. Double-strand breaks (DSBs) in DNA are detrimental to genome integrity and cell survival. 7 Commonly, non-homologous end joining (NHEJ) or homologous recombination (HR) are the two main repair methods for DSBs. 8 HR is more accurate than NHEJ because a homologous DNA sequence, usually the identical sister chromatid, is utilized as a repair template in HR. 9 HR and NHEJ depend on the nature of the DNA ends and cell cycle phase. The nucleolytic degradation of DNA ends, defined as DNA end resection, plays a pivotal role in DSB repair, which can serve as the substrate for the HR machinery . Figure 2 Functions of DNA nucleases in DNA repair. Fig. 2 In addition to their role in DNA damage repair, endonucleases and exonucleases also play important roles in immunity. The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway serves as a key pathway for innate immunity and can be activated by sensing exogenous or endogenous DNA. 10 However, when endo/exonuclease activity is dysregulated leading to massive accumulation or excessive cleavage of double-stranded DNA (dsDNA), the pathway can also become dysregulated and cause certain diseases. In some autoimmune diseases, there is often a decrease in exonuclease or endonuclease activity. Examples include rheumatoid arthritis, Aicardi-Goutières syndrome, familial chilblain-like lupus, etc ., which will result in dsDNA accumulation. 11 , 12 , 13 The dsDNA will be recognized by cGAS and cyclic GMP-AMP synthesis. Cyclic GMP-AMP acts as a second messenger to activate the innate immune response via STING. 14 However, in the treatment of some tumors, the rational use of inhibitors to inhibit the cleavage activity of endo/exonucleases can cause genomic instability in tumor cells, thereby activating the cGAS-STING pathway and enhancing the efficacy of immunotherapy. Besides, exonucleases and endonucleases regulate the growth and development of immune cells. Exonucleases such as MRE11 regulate the lifespan of T cells by maintaining telomere length, while the endonuclease CtIP is essential for the development and proliferation of B cells. 11 , 15 This review summarizes the various functions of several important nucleic acid exonucleases and nucleic acid endonucleases and discusses their various roles in DNA damage response and immunity. Exonucleases are evolutionarily highly conserved and may be divided into groups based on sequence and function. The well-known exonucleases including their origin, mechanism, and their relationship with diseases are elaborated ( Table 1 ). Table 1 Exonucleases functions and associated diseases. Table 1 Name Polarity DNA Function Disease Clinical feature Ref. MRE11 3′–5′ DS Recombination Ataxia-telangiectasia-like syndrome; Nijmegen breakage syndrome Progressive cerebellar degeneration; increased cancer incidence, cell cycle checkpoint defects, and ionizing radiation sensitivity. 18 , 113 , 114 EXO1 5′–3′ DS Repair Tumor; immune deficiency Tumor suppression. 115 WRN 3′–5′ DS Repair; telomeres Werner syndrome Premature aging. 116 , 117 TREX1 3′–5′ SS/DS removal/proofreading? AGS syndrome Upregulated type I interferon. 64 , 118 DS: double stranded DNA; SS: single stranded DNA; AGS syndrome: Aicardi Goutières syndrome. MRE11 was first identified as a meiotic recombination-related gene in Saccharomyces cerevisiae in 1993, and its atomic resolution was first glimpsed from P. furiosus in 2001, which is responsible for the recognition, repair, and signaling of DSBs in eukaryotes. 16 , 17 , 18 It is the fundamental component of the MRE11/RAD50/NBS1 (MRN) complex and exhibits dual 3′–5′ exonuclease and endonuclease activity. MRE11 can detect DNA DSBs and activate ataxia telangiectasia-mutated kinase while initiating HR repair. 19 , 20 MRE11 and Sae2 cleave DSB ends to generate an intermediate, which is then cleaved further by EXO1 to form a mobile single-stranded DNA (ssDNA) substrate for Rad51. 21 To investigate the mechanism of HR promotion by MRE11, Shibata's team designed MRE11 endonuclease and exonuclease inhibitors. Using the inhibitors, it was discovered that both MRE11 endonuclease and exonuclease are required for HR, with endonuclease activity starting cleavage and supporting HR repair and exonuclease acting downstream. 22 MRE11 activity is essential for DNA damage repair and in the pursuit of genomic stability. The MRE11 C-terminus contains two DNA-binding domains and a glycine-arginine-rich structural domain involved in the regulation of nucleic acid endonuclease and exonuclease activities. PIH1D1, a subunit of the R2TP complex of the heat shock protein 90 co-chaperone, binds to the C-terminus of MRE11 to regulate its stability. Therefore, when the MRE11 C-terminus is mutated, the instability of MRE11 will lead to a decrease in the level of MRN complexes. 23 The mutation of MRE11 is associated with immune diseases such as ataxia-telangiectasia-like disease and cancers. 24 , 25 , 26 The N-terminus of MRE11 contains a nuclease structural domain essential for HR, and N-terminal mutations lead to structural and functional defects in MRE11 and have effects in the MRE11/NBS1/RAD50 complex. Patients with ataxia telangiectasia-like disease and MRE11 mutations show cerebellar ataxia. Their cells are unable to activate ataxia telangiectasia-mutated kinase and can therefore prevent DNA damage repair, leading to chromosomal mutations, increased susceptibility to radiotherapy, and immune checkpoint defects. 27 , 28 , 29 MRE11 can directly or indirectly participate in the activation of immune pathways or regulate DNA damage response. In the cytoplasm, MRE11 acts as a DNA damage receptor that directly recognizes dsDNA, facilitates its translocation into the Golgi by interacting with the stimulator of STING, and directly promotes the activation of the cGAS-STING innate immune pathway. 30 In addition, UFMylation modification of MRE11 could promote DSB repair by enhancing the phosphorylation and activation of ataxia telangiectasia-mutated kinase. This helps to maintain normal cellular mitosis and chromosome stability. 31 Meanwhile, in a study on zebrafish, it was shown that UFMylated deletion of MRE11 could shorten telomere length and accelerate aging in zebrafish. This interesting finding provides us with clues to further explore MRE11. 32 MRE11 is not only present in the nucleus, and cytoplasm, but is also localized in the mitochondria. 33 As a protector of mitochondria, MRE11 ensures mitochondrial energy production and blocks caspase-1 activation to inhibit mitochondrial stress-induced inflammatory vesicle activation. At the same time, MRE11 reduces T cell pyroptosis and regulates T cell lifespan. 11 Rheumatoid arthritis patients have lower levels of MRE11, which shortens T-cell lifespan; this condition can be reversed by overexpressing MRE11. This may be related to the protective effect of MRE11 on telomeres. 34 However, in Fanconi anemia patients, due to mutations in the Fanconi anemia proteins that protect nascent mitochondrial DNA, MRE11, which acts as a mitochondrial protector, will over-cleave nascent mitochondrial DNA and release it into the cytoplasm. This will activate the cGAS-STING pathway via signal transducer and activator of transcription 1. 35 , 36 Therefore, how to utilize MRE11 to protect mitochondrial DNA is particularly important in various immune diseases. In normal cells, excessive cleavage of MRE11 activates immune pathways and causes autoimmune diseases. However, in cancer cells subjected to radiotherapy, MRE11 is recruited to the damage site to cleave damaged dsDNA to produce ssDNA for HR repair. p97, a hexameric ATPase of the AAA family, can bind to and remove MRE11, preventing its over-cleavage. 37 However, when p97 is inactivated, MRE11 will cleave excessively to produce large amounts of ssDNA, transforming HR repair into rad52-mediated single-strand annealing. This will enhance the sensitivity of cancer cells to radiotherapy. 38 Therefore, the over-cleavage of MRE11 is a double-edged sword, and its rational utilization will likely be a potential target for cancer therapy. Overall, MRE11 plays an important role as a nuclease with dual endonuclease/exonuclease activity in both activation of immune pathways and DNA damage repair. In the cytoplasm, MRE11 directly binds to dsDNA, activates the cGAS-STING pathway, and induces interferon (IFN)-1 production. In chromatin and mitochondria, MRE11 cleaves damaged DNA to promote HR repair. Of course, this is based on moderation. When its cleavage is out of control, MRE11 can cause a range of autoimmune diseases. However, in radiation-treated cancer cells, excessive MRE11 cleavage instead increases radiation sensitivity. Therefore, utilizing the nuclease activity of MRE11 may provide a good idea for future disease treatment. Exonuclease 1 (EXO1) is a gene encoding a multifunctional 5′–3′ exonuclease found in Saccharomyces cerevisiae , which plays a role in MMR by interacting with MMR genes such as MSH2 and MLH1. 39 Studies reported that EXO1 facilitates the modulation of cell cycle checkpoints, the maintenance of replication forks, and the post-replication DNA repair pathways, which are required for the solution of DNA replication arrest or blockage associated with replication stress and replication forks. 40 In MMR, MSH dimer recruits downstream factors such as EXO1, PCNA, and MLH protein. Among them, EXO1 is mainly responsible for cleaving mismatched bases, with replication protein A and HMGB1 playing a supporting role. Meanwhile, RFC and PCNA promote pol δ to fill the gap created by the cleavage. Finally, the MLH1 protein binds to EXO1 to terminate the cleavage. 41 , 42 MMR-deficient tumors (dMMR) are unable to degrade EXO1 due to the lack of the MLH1 protein. dsDNA is therefore excessively cleaved, leading to the accumulation of large amounts of ssDNA. Meanwhile, replication protein A can bind to ssDNA, preventing the cleavage of ssDNA by EXO1. dMMR tumors are also characterized by a lack of MLH1 protein, which is unable to degrade EXO1. However, due to the unrestricted cleavage by EXO1, replication protein A is soon depleted. 43 The additional unprotected ssDNA produced is further cleaved and leaks into the cytoplasm, leading to activation of the cGAS-STING innate immune pathway, which results in enhanced effects of immune checkpoint therapy. 44 However, there are still some dMMR tumors that do not benefit from immunotherapy, such as metastatic colorectal cancers with the microsatellite instability (MSI) phenotype and melanoma. Their common feature is that they have Janus kinase 1 or Janus kinase 2 mutations, which may increase the resistance of the tumor to immunotherapy. 45 , 46 In addition, because dMMR tumors are highly mutagenic, they may even introduce mutations into the cGAS-STING pathway. In conclusion, EXO1, as the nucleic acid exonuclease mainly responsible for cleavage in MMR, was utilized in some dMMR tumors for dsDNA hyperexcision to serve as an enhanced immune checkpoint therapy through activation of the cGAS-STING immune pathway. It provides ideas for further exploration of immunotherapy in association with DNA hyperexcision. WRN is a RecQ family member with 3′–5′ exonuclease and 3′–5′ helicase activities and plays important roles in stalling forks, counteracting replication stress, maintaining genome stabilization, and slowing cellular senescence. 47 Mutations in WRN lead to Werner syndrome, a type of autosomal recessive disorder being recognized as premature senility. 48 The cause of premature aging in Werner syndrome patients may be due to the ability of WRN to regulate the transcription of NMNAT1, a key enzyme in NAD + biosynthesis. In Werner syndrome patients, WRN deficiency leads to impaired transcription of the enzyme, resulting in NAD + depletion, which leads to accelerated aging. 49 Werner syndrome is currently incurable, and some emerging therapies such as mammalian targets of rapamycin inhibitors are still being explored. 50 , 51 MSI tumors are WRN-dependent, and WRN is a synthetic lethal target for MSI tumors. Loss of WRN induces DSBs in MSI cancers and selectively promotes apoptosis and cell cycle arrest. Since WRN has both helicase and exonuclease activities. To determine which enzyme is acting, WRN mutants with inactivated helicase or inactivated exonuclease were constructed to validate the sgRNA targeting WRN exon-intron junctions (WRN EIJ sgRNA), finally, the helicase domain was determined being in action. 52 , 53 In addition, in MSI tumors, there is a type of short repeat mutation called “genomic scar”. These “genomic scar” are folds formed by large expansions of TA nucleotide sequence repeats that depend on the deconvolving enzyme activity of the WRN for deconvolution. Therefore, when WRN is inactivated, the “genomic scar” will be cleaved by MUS81 endonuclease, resulting in cancer cell death. 54 Therefore, WRN exonuclease may be a promising target for the treatment of MSI tumors. Similarly, in BACA2-deficient breast cancer cells, WRN helicase protects against the over-degradation of stalled forks in BRCA2-deficient cancer cells by inhibiting the activity of MRE11 and EXO1 nuclease on the degenerating forks. When WRN nuclease activity is inhibited, MRE11 will cleave unprotected forks, generating mus81-dependent DSBs, while increasing NHEJ and chromosomal instability, leading to cancer cell death. 55 It also has the potential to further stimulate the host response to mediate tumor transition from cold to hot tumors by increasing the cGAS-STING-dependent type I IFN response, thereby increasing the efficiency of the immune response. 56 , 57 In conclusion, WRN exonuclease can interact with exonucleases MRE11 and EXO1 and endonuclease MUS81. By inhibiting the deconjugating enzyme activity of WRN in certain tumors, these exonucleases and endonucleases are activated, triggering an innate immune response while enhancing the efficacy of immunotherapy. TREX1 is a 3′–5′ nucleic acid exonuclease expressed mainly in the cytoplasm of mammalian cells, which is capable of cleaving ssDNA and dsDNA. 58 TREX1 is a relatively small dimeric protein that efficiently cleaves the 3′ end. The TREX1 sequence has an ExoIII motif variant (ExoIIIε), which is closely related to the ε subunit of EXO1. 59 TREX1 prevents the accumulation of dsDNA as an autoantigen to induce autoimmune diseases by cleaving it. 60 Many autoimmune diseases are caused when TREX1 is mutated, such as Aicardi–Goutières syndrome, familial chilblain-like lupus, systemic lupus erythematosus, and leukodystrophy-related retinopathy. 12 , 13 , 61 A common feature of these diseases is the reduced 3′–5′ nucleic acid exonuclease activity of the mutant TREX1. Intracytoplasmic accumulation of dsDNA and ssDNA, as pathogen-associated molecular patterns, causes autoimmune reactions. 62 , 63 , 64 In these diseases, Aicardi-Goutières syndrome is caused by the accumulation of a large amount of damaged DNA in the cytoplasm caused by TREX1 mutation, which strongly triggers the cGAS-STING pathway, resulting in systemic autoimmunity. 65 Among them, cyclic GMP-AMP synthase acts as a DNA receptor in the cytoplasm and binds to DNA to form the cGAS-DNA complex. Through a phase separation mechanism, TREX1 is restricted to the periphery of phase-separated droplets, and its exonuclease activity is inhibited. In contrast, Aicardi-Goutières syndrome patients with TREX1 mutations have increased permeability to the interior of the droplet, allowing it to enter the droplet, and the phase separation mechanism is disrupted. 66 cGAS synthesizes cyclic GMP-AMP, which activates the cGAS-STING pathway and produces large amounts of IFNs and inflammatory factors. 63 , 67 TREX1 is a radiation-driven upstream regulator of anti-tumor immunity that guides patient radiation dose selection. Radiotherapy can enhance the immunogenicity of tumors by activating immune signaling to fight tumors, however, when the radiation dose reaches 12–18 Gy or more, TREX1 can be induced to degrade DNA accumulated in the cytoplasm after radiation, weakening its immunogenicity. Conversely, when TREX1 is not induced, the cGAS-STING pathway is activated and recruits BATF3-dependent dendritic cells that activate anti-cancer CD8 + T cells to mediate systemic tumor immunity. Consequently, finetuning the dose of radiotherapy to modulate tumor expression of TREX1 is a potential target for improving therapeutic efficacy. 68 In addition, repeated doses in radiotherapy are also important to enhance tumor immunogenicity, and the IFN-β production of 8GyX3-treated cancer cells was significantly higher than that of 8Gy single-dose-treated cancer cells. 69 TREX1 localizes to the endoplasmic reticulum in the cytoplasm. The endoplasmic reticulum enters the ruptured micronucleus and enables TREX1 to play a key role in degrading damaged DNA in the micronucleus. Mutation of TREX1 in autoimmune diseases, dissociating TREX1 from the endoplasmic reticulum, disrupts the localization of TREX1 in micronuclei, reduces micronucleus-damaged DNA degradation, and enhances cGAS activation. Thus, the immobilization of TREX1 on the endoplasmic reticulum is the basis for preventing autoimmune diseases. 70 When the nuclear envelope is damaged, TREX1 undergoes nuclear ectopic translocation into the nucleus, causing TREX1-dependent DNA damage. This causes cellular senescence in normal cells. 71 In contrast, it promotes tumor invasion in tumor cells. 72 This phenomenon may often occur in cancer, where the nuclear membrane is squeezed and ruptured because the cancer cells are more crowded. As a result, inhibition of TREX1 may be a potential target to stop cancer invasion and inhibit its further development. Endonucleases can hydrolyze the phosphodiester bond inside the molecular chain to generate oligonucleotides, corresponding to exonucleases. During DNA replication, it plays a role in maintaining gene stability by cutting double strands. In addition, some endonucleases can also combine with exonucleases to facilitate the cleavage of exonucleases ( Table 2 ). Table 2 Endonuclease functions and associated diseases. Table 2 Name Polarity DNA Function Disease Clinical feature Ref. CtIP 5′–3′ DS G1/S transition Tumor Dual role in tumors 119 FEN1 5′–3′ DS DNA metabolism; telomeres Tumor Promoted 120 MUS81/SLX4/EME1 ∖ DS DNA interstrand cross-linking repair; medullary development Anemia Fanconi anemia; bone marrow failure; cancer predisposition 121 RAG1/RAG2 5′–3′ DS NHEJ; lymphocyte development Omenn syndrome SCID; erythrodermia; hepatosplenomegaly; lymphadenopathy; alopecia. 104 DS: double stranded DNA; SS: single stranded DNA. SCID: severe combined immunodeficiency. CtIP, an endonuclease capable of excising damaged DNA 5′ overhangs, was first isolated in 1998 by a yeast two-hybrid screening assay and it is a 125-kDa protein, which interacts with the oncogenic transcriptional corepressor CtBP. 73 Yun and Hiom et al suggested that the interaction of BRCA1 with CtIP is required for CtIP-mediated DNA end resection and tumor suppression. They constructed chicken DT40 cells with CtIP S327 mutation resulting in loss of CtIP-BRCA1 interaction and found that HR repair was inhibited. 74 However, in 2010, Nakamura et al clarified that the chicken CtIP S332A protein could effectively promote DSB repair through interaction with BRCA1 in an HR-independent manner. 75 In 2013, Reczek et al constructed CtIP-S326A mutant mice and showed that HR repair was not affected. 76 Furthermore, in 2014, Polato et al used a mouse model expressing S327A mutant CtIP which suggests that loss of CtIP-BRCA1 interaction does not significantly affect the maintenance of genomic stability. 77 The above findings suggest that CtIP-BRCA1 interaction may not be necessary for dsDNA end resection and tumor suppression in mammals. In yeast, MRE11 is involved in DSB cleavage together with Sae2. CtIP is homologous to Sae2 and also acts as a cofactor for DSB cleavage by MRE11. 78 CtIP interacts with the MRN complex, promotes MRN to perform 5′–3′ excision of the broken DNA ends, converts the DSB ends into 3′ ssDNA overhangs, which can inhibit NHEJ, and is a necessary intermediate to promote HR repair. 9 , 79 The FHA and BRCT domains of NBS1 in MRN can sense CtIP phosphorylation and activate MRN endonuclease activity when CtIP is extensively phosphorylated. T847 (the phosphorylation site of cyclin-dependent kinase) in CtIP is an important site for phosphorylation, and the absence of phosphorylation at this site could severely impair the binding of dsDNA to MRN. 80 In addition, the study found that MRN also has cleavage activity when combined with CtIP in the absence of NBS1, but the efficiency is much lower than the cleavage ability of MRN holocomplex when combined with CtIP. 81 These results suggest that the MRN endonuclease activity is restricted and the activity is fully activated in the presence of both NBS1 and phosphorylated CtIP. 82 Terminal excision performed by CtIP generates 3′ ssDNA, which promotes immune checkpoint activation and arrests the cell cycle in the S–G2 phase for DNA damage repair. 83 In addition, the terminal excision and DNA repair effects of CtIP affect B-cell development and proliferation. Phosphorylation of CtIP at T847 is essential for B-cell development and class-switching recombination, and loss of T847 phosphorylation leads to accumulation of replication intermediates and loss of cell viability. 15 In summary, CtIP was initially found to interact with the oncogenes CtBP and BRCA1. It can also act as a cofactor for MRE11, activate ATR-dependent checkpoints by enhancing the endonuclease capacity of MRE11, and promote HR repair, as well as the development and proliferation of B cells. Harrington et al first purified flap endonuclease 1 (FEN1) in 1994. FEN1, as a DNA structure-specific endonuclease, has 5′–3′ endonuclease activity and can specifically recognize the 5′ unannealed single strand of dsDNA (flap), and make an incision at the bottom of the flap. 84 FEN1 can process Okazaki fragments for long-patch base excision repair, so it contributes to DNA replication fidelity and maintains genome stability. 85 FEN1 is recruited to the telomeres to maintain telomere stability during DNA replication, and loss of FEN1 results in γH2AX accumulation and lagging-strand sister telomere loss. The interaction of FEN1 with WRN and the telomere-binding protein TRF2 is required for the activity of FEN1 at telomeres. 86 FEN1 is a classic lagging endonuclease, however, in addition to maintaining lagging telomere stability, FEN1 can also limit the telomere fragility of the leading strand. The study from Daniel et al showed for the first time that FEN1 can also cleave a flap structure similar to Okazaki fragment substrates in the leading strand. The absence of FEN1 activity results in replication stress and DNA damage. 87 Collectively, FEN1 is a key endonuclease for genome stability. FEN1 is also involved in mitochondrial DNA metabolism. In the mitochondria of non-apoptotic immune cells, FEN1 cleaves oxidized mitochondrial DNA and releases its small fragments (<650 bp) into the cytoplasm, where it binds to NLRP3 and triggers NLRP3 inflammation body assembly and activation of its inflammatory pathways. 88 Another target of cytoplasmic oxidized mitochondrial DNA fragments is cGAS, which activates the cGAS-STING pathway and promotes the production of type I IFN, which further amplifies the inflammatory response. 89 , 90 , 91 We can conclude that inhibiting the cleavage activity of mitochondrial FEN1 endonuclease may serve as a target for the treatment of inflammatory diseases. FEN1 has been widely recognized as a tumor suppressor in previous studies, and FEN1 haplo-deficient mice allow the accumulation of replication intermediates leading to genomic instability, which promotes rapid tumor development. 92 In contrast, Zheng et al speculated that FEN1 expression is required for cancer growth and proliferation and promotes cancer development. 93 Several recent studies have found that FEN1 is highly expressed in a variety of cancers and is positively correlated with tumor proliferation rate, tumor size, lymph node metastasis, and degree of differentiation. 94 , 95 In addition, Wang et al found that in oral squamous cell carcinoma, inhibition of FEN1 could cause up-regulation of IFN-γ and activation of JAK/STAT signaling pathway, resulting in reduced expression of programmed cell death ligand 1 to play an immunomodulatory role. 96 Thus, inhibition of FEN1 in some cancers may be a potential target for their treatment. Methyl methanesulfonate and ultraviolet-sensitive gene 81 (MUS81), a fission yeast protein related to the XPF subunit of ERCC1-XPF endonuclease, together with EME1 and SLX4, forms an endonuclease complex that cleaves Holliday junctions. 97 , 98 Holliday junctions are four-way DNA intermediates formed during DNA replication or DNA damage, and their cleavage facilitates the maintenance of chromosome stability. 99 Therefore, the MUS81-EME1-SLX4 complex plays an important role in DNA repair and cell cycle regulation. Meanwhile, MUS81-EME1 acts as a conformation-specific nucleic acid endonuclease, which is normally recruited by SLX4, and is phosphorylated by cyclin-dependent kinases to form a stable complex in the G2–S phase, resulting in an intact endonuclease activity. 100 The endonuclease action of MUS81-EME1 inhibits long interspersed element-1 reverse transcription. When SLX is inhibited, long interspersed element-1 transcription is increased, leading to an increase in dsDNA and proinflammatory factors in the cytoplasm, which activates the innate immune cGAS-STING pathway. 101 In addition, MUS81-EME1 also enables G2/M phase blockade, helping HIV-infected cells to evade sensing by the innate immune system. The HIV accessory protein Vpr interacts with the SLX4 protein and prevents the triggering of the cGAS-STING pathway by recruiting VPRBP and PLK1 to activate the endonuclease activity of MUS81-EME1, which cleaves viral DNA. 102 Similarly, the exonuclease TREX1, which cleaves viral DNA via exonuclease activity, prevents IFN-1 production in HIV-infected cells. 103 , 104 MUS81-EME1 acts as an oncogene and enhances the immune response in cancer cells. In prostate cancer cells, MUS81-EME1 acts as an endonuclease, causing fragmentation of genomic DNA, and leading to the accumulation of intracytoplasmic dsDNA. 105 This is recognized by intracytoplasmic DNA receptors and activates the cGAS-STING pathway to produce IFN-1, which enhances the immune response of phagocytes and T cells against prostate cancer cells. 106 In addition, MUS81-EME1 can serve as a potential target to enhance the efficacy of cancer immunotherapy. In gastric cancer, MUS81-EME1 disrupts β-TRCP-induced ubiquitination and increases the expression of WEE1, which acts as a DNA-damage checkpoint kinase and inhibits the activation of the intrinsic immune cGAS-STING pathway. Therefore, in gastric cancer, WEE1 inhibitors are used to enhance the efficacy of immunotherapy. Meanwhile, inhibition of MUS81-EME1 was able to increase WEE1 ubiquitination, which led to a further decrease in WEE1 levels and further enhanced the efficacy of immunotherapy. 107 Overall, utilizing the endonuclease activity of MUS81-EME1 could shed light on the future treatment of the disease. RAG1 and RAG2 are specific endonucleases that form a complex to initiate the V(D)J recombination process. 108 The production of T and B cell-specific receptors is dependent on V(D)J recombination of RAG1 and RAG2. 109 The expression of RAG1 and RAG2 endows early T and B cells with adaptability to repair DSBs. 110 The RAG1 protein functions as a catalytic active member of the RAG complex and cleaves dsDNA through a catalytic core. The C-terminal region of RAG2 binds to DNA bending cofactors (HMGB1 or HMGB2) to assist RAG1 in cleaving dsDNA. 111 Then, the RAG complex remains bound to the DNA ends in the cleavage complex, preventing abnormal recombination. 112 RAG1 and RAG2 are essential for the early development of T and B lymphoid immune cells. RAG1 and RAG2 mutation or deficiency lead to impaired V(D)J recombination and blocked B cell and T cell differentiation, and are associated with many types of immunodeficiency diseases, 113 such as severe combined immunodeficiency (including T and B cell deficiency), Omenn syndrome, leaky severe combined immunodeficiency (production of small amounts of functional T cells, B cells, and immunoglobulins in the body and no clinical features of tumor (osteosarcoma)), and combined immunodeficiency with granuloma or autoimmunity. 114 The rearrangement of the RAG1 and RAG2 genes is labile, resulting in potentially oncogenic DNA. BH3-only protein is a protein in the Bcl-2 family with only one Bcl-2 homologous region, which is the promoter of apoptosis and is capable of inducing cell apoptosis. 115 , 116 In these potentially oncogenic cells, BIM deficiency accelerates the development of lymphomas in p53-deficient mice, a process that relies on RAG1/RAG2-mediated rearrangement of antigen receptor genes. 117 Accordingly, the rearrangement of RAG1 and RAG2 genes is of great significance for the regulation of the immune system's function and the maintenance of genome stability. In conclusion, the cleavage of dsDNA and ssDNA by endo/exonucleases plays an important role in DNA damage repair, maintenance of genome stability, and regulation of the innate immune cGAS-STING pathway. Furthermore, these endonucleases and exonucleases have interactions with each other. Exonuclease MRE11 can cleave broken DNA through its 3′–5′ nuclease activity, while the endonuclease CtIP interacts with MRE11 to facilitate its cleavage by converting the DSB end into the 3′ ssDNA overhangs. This 3′ ssDNA is recognized by the MMR proteins MSH2-MSH3 and recruits the exonuclease EXO1 to perform 5′–3′ cleavage, thereby facilitating HR repair. 21 , 118 , 119 However, in BRCA2-deficient tumors, the exonuclease activities of MRE11 and EXO1 are inhibited by WRN helicase and exonuclease activities. WRN exonuclease replaces BRCA2 to protect the stalled fork from degradation. 55 When WRN is inhibited, stalled replication forks are cleaved by MRE11 and EXO1 and further degraded by MUS81 nucleic acid endonuclease. This leads to genomic instability in BRCA2-deficient tumor cells, resulting in increased tumor cell death. 120 , 121 At the same time, the nuclease may be a double-edged sword. When these nuclease activities are properly regulated, they enable timely cleavage of damaged DNA and DNA damage repair and inhibit the activation of innate immune pathways. When nuclease activity is uncontrolled, large amounts of dsDNA are cleaved, which are recognized by DNA receptors and activate the cGAS-STING pathway, thereby triggering autoimmune diseases. However, excessive cleavage by nuclease is not always harmful. In cancer cells, the use of nuclease over-cleavage can increase the efficacy of immunotherapy and sensitivity to radiation therapy for cancer. Therefore, rational utilization of these nucleases will be a therapeutic target for cancer and autoimmune diseases. In this review, we discussed in detail the cleavage activities of major nucleic acid endonucleases/exonucleases, their interactions with each other, the roles they play in DNA damage, and their effects in autoimmune diseases and tumors through activation of immune pathways . To date, many nucleases remain to be characterized. Therefore, how to fulfill the role of nucleases in DNA damage repair and immunity and provide effective treatment for clinical patients may become a top priority for future research. Figure 3 DNA nucleases in the immune response. Fig. 3 | Review | biomedical | en | 0.999997 |
PMC11697194 | Articular cartilage is a specialized connective tissue located on the surface of the synovial joint and plays an important role in lubrication and weight-bearing. 1 With aging, progressive degeneration of articular cartilage leads to joint pain and dysfunction, namely osteoarthritis (OA). OA is the most common type of chronic musculoskeletal disease which is characterized by degeneration of articular cartilage, fibrosis of articular cartilage, formation of osteophyte, inflammation of synovium, and loss of mobility. OA has affected 7% of the global population, or more than 500 million people worldwide. 2 , 3 Clinically, the knee joint is the most common site of OA, followed by the hand and hip joints. 4 Furthermore, the global prevalence of OA is higher in women and increases with age, with 10% of men and 18% of women over 60 years old being affected. 5 However, there are no effective therapies except for joint replacement in the late stage of OA, because the molecular mechanisms underlying the progression of OA remain largely unknown. Chondrocyte is considered the only cell type in cartilage, which secretes growth factors and enzymes to regulate extracellular matrix synthesis. 6 , 7 Chondrocytes are derived from mesenchymal stromal cells which differentiate into chondroprogenitors and then into chondrocytes. 8 , 9 After chondrogenesis, chondrocytes remain as resting cells to form articular cartilage or exhibit a life cycle of proliferation, maturation, hypertrophy, and apoptosis. 10 , 11 The degeneration of articular cartilage prompts the release of cytokines from damaged cartilage, thus triggering synovial fibrosis. 12 , 13 Fibrosis is thought to be a prominent and consequential hallmark of OA, which includes fibrosis of synovial and generation of fibrocartilage. 12 Although it is well known that cartilage is composed of chondrocytes, the cell heterogeneity of chondrocytes in human articular cartilage is not well defined. Single-cell sequencing, in particular single-cell RNA sequencing (scRNA-seq), is a powerful tool to study cell heterogeneity, which has identified various cell types and provided insights into physiological and pathological processes of diseases. 14 , 15 , 16 , 17 , 18 Recently, several studies used scRNA-seq to explore the cell heterogeneity of chondrocytes in cartilage from OA or other joint disease patients. 19 , 20 , 21 , 22 , 23 Ji et al identified seven chondrocyte subsets in human OA cartilage, including proliferative chondrocytes (ProC), prehypertrophic chondrocytes (PreHTC), and hypertrophic chondrocytes (HTC). Furthermore, they identified chondrocyte subsets and their specific genes and found a potential transition among ProC, PreHTC, and HTC. 19 Sun et al 20 constructed a chondrocyte atlas in the healthy and degenerated meniscus, in which most chondrocyte subsets were consistent with that reported in Ji et al. 19 Whereas Fu et al 22 constructed a chondrocyte atlas and named chondrocyte subsets based on their significant enriched gene ontology (GO). Lv et al 23 identified ferroptotic chondrocytes based on molecular characteristics and their markers in OA patients. This study also found that TRPV1 protected chondrocytes from ferroptosis and could be an anti-ferroptotic target. Swahn et al 24 found a senescent chondrocyte subset with ZEB1 as the main regulator that promoted OA in cartilage and meniscus. Although these studies identified chondrocyte subsets in human cartilage, these results are not well consistent, and dynamic processes of chondrocyte subsets in the progression of OA are not clear. In this study, we performed scRNA-seq on chondrocytes from cartilage to better elucidate the cell heterogeneity of chondrocytes in human healthy cartilage and OA cartilage. We identified chondrocyte subsets using pre-defined markers and constructed a single-cell transcriptomics atlas of cartilage chondrocytes. The trajectory analysis was used to infer the potential transition and dynamics among chondrocyte subsets. We further compared the single-cell landscape between healthy cartilage and OA cartilage to reveal the distinct landscape of OA cartilage. These results offer a better understanding of the chondrocyte heterogeneity and provide a deeper insight into the pathogenetic mechanisms of OA. Human joint cartilage tissues were collected from Shenzhen Second People's Hospital. The healthy donor signed informed consent approved by the Institutional Review Board (IRB) of Shenzhen Second People's Hospital . The cartilage was isolated from knee joints of the healthy human donor and OA patients and cultured following previous studies. 9 , 19 In brief, cartilage was immediately put in physiological saline containing heparin anticoagulant at 4 °C after collection, which was further processed within 6 hours. Then the cartilage was cut into pieces (1 mm 3 ) and digested with 0.2% collagenase in high-glucose Dulbecco's modified Eagle's medium (Gibco, Australian) containing 10% fetal bovine serum (Gibco, Australian) and 10 μg/L basic fibroblast growth factor (Gibco, Australian). Following overnight incubation at 37 °C with 5% CO 2 , cells were collected by centrifugation, washed twice, resuspended in high-glucose Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum and 10 μg/L basic fibroblast growth factor, plated in a culture flask, and allowed to attach for three days. Nonadherent cells were removed after a seven-day culture and the medium was replaced. Medium replacement was carried out every 72 hours until the cells reached an 80% confluent layer. Cells were harvested with 0.25% (w/v) trypsin plus 0.02% (w/v) EDTA (Hyclone, USA) and subcultured at a density of 1000 cells/cm 2 . Chondrocytes were isolated from the cultured cells and subjected to fluorescence-activated cell sorting using the BD FACSAria II instrument (BD Biosciences) to eliminate nonviable cells. scRNA-seq was conducted using the 10X genomics platform. Chromium Single Cell 3'Gel Bead and Library Kit were used following protocol. Each channel accommodated approximately 15,000 cells. Sequencing libraries were subsequently loaded on the Illumina NovaSeq 6000 platform using paired-end kits. We further obtained scRNA-seq data of articular cartilage from Swahn et al , 24 namely Sw_data. The raw data were processed following our previous studies. 9 , 18 , 25 In detail, the raw sequencing data was transformed into FASTQ format using the Illumina bcl2fastq software. To align the reads and demultiplex the barcodes, we employed Cell Ranger V2.2.0 from 10X Genomics, aligning the reads to the hg38 reference genome. The resulting digital gene expression matrices underwent preprocessing and filtering using the R packages scran and scater. 26 Cells surpassing the expression threshold of 4000 genes (potentially indicating doublets), falling below 200 expressed genes (suggesting low-quality libraries), or exhibiting mitochondrial unique molecular index counts exceeding 10% (possibly indicative of cell fragments and debris) were excluded from subsequent analysis. Additionally, we utilized Scrublet 27 to identify potential doublets, calculating a doublet score for each cell and determining the threshold based on the default parameters of the bimodal distribution. We set the expected doublet rate at 0.08, and cells predicted to be doublets or with a doubletScore parameter exceeding 0.25 were removed from consideration. After implementing rigorous quality control measures, the healthy cartilage retained a total of 13,363 cells, while OA#1 and OA#2 retained 8808 cells and 12,770 cells, respectively. After quality control of Sw_data, six normal cartilage samples retained 8505, 7183, 3601, 6519, 6243, and 7214 cells, while six OA samples retained 4389, 4458, 7060, 5362, 4944, and 5468 cells, respectively. Seurat 28 package was used for performing scRNA-seq data analysis, including data integration, normalization, dimension reduction, and cell clustering, following our previous studies. 9 , 18 , 25 We implemented a gene-wise scaling approach to set the mean and variance of each gene across cells to 0 and 1, respectively, thus preventing highly expressed genes from dominating subsequent analyses. The scaled expression data was then employed to identify highly variable genes, which were subsequently utilized for dimension reduction. The UMAP algorithm was applied for the visualization of the scRNA-seq data. 29 We assigned annotations to each cell cluster based on the highly expressed genes specific to that particular population, as well as the established marker genes unique to each population. By employing the Wilcoxon Rank-Sum test, we compared the gene expressions within each investigated cluster to those of the remaining clusters. Genes exhibiting significantly higher expression levels within the investigated cluster were identified as cluster-specific genes. Furthermore, we performed the Wilcoxon Rank-Sum test to determine the differentially expressed genes between any two clusters. To ascertain statistical significance, a minimum log2(fold change) threshold of 0.25 and an adjusted P -value of 0.01 were applied. Metascape was applied for the investigation of biological process enrichment. 30 To investigate the intricate network of cellular communication, we employed the CellChat package (version 1.6.1) for ligand–receptor interaction analysis. 31 Leveraging the extensive ligand–receptor pair data available in CellChatDB, we evaluated the potential interactions among the different cell populations. Specifically, we focused on the datasets pertaining to “secreted signaling”, “ECM–receptor”, and “cell–cell contact” interactions. These selected datasets provided valuable insights into the intricate cell communication occurring between each cluster. We also used CellPhoneDB 32 and iTalk 33 to infer cell–cell interaction between chondrocyte subsets. BAM files aligned using the Cell Ranger pipelines were initially sorted using SAMtools. 34 Next, the Velocyto pipeline was used to count spliced and un-spliced reads and generate loom files. 35 To compute gene-specific velocities, we utilized the scVelo Python package. 36 Additionally, the projection clustered with metabolic genes was embedded with the velocity streams predicted by scVelo with the loom files. Finally, plots for the ratio of spliced and un-spliced, for the velocity and the expression of various individual genes were generated based on the velocity calculated by scVelo. To verify the robustness of our findings, we employed additional developmental trajectory inference algorithms, specifically partition-based graph abstraction (PAGA). 37 For PAGA analysis, pseudotime was calculated using scanpy v1.4.3. Briefly, we followed the pipeline integrated into scVelo, employing the same projection generated by scVelo. We performed the prediction using the scv.pl.paga function in scVelo, setting the basis parameter as UMAP, the size as 50, the alpha as 0.3, the min_edge_width as 2, and the node_size_scale as 1.5. We also used monocle2 38 to infer the trajectory of chondrocytes of Sw_data. The programming languages R and Python were employed for all statistical analyses and data visualizations. Wilcoxon Rank Sum test was used to identify the differentially expressed genes between two cell clusters. Bonferroni correction was applied for multiple testing. To reveal the cell heterogeneity of human chondrocytes, we conducted scRNA-seq on chondrocytes from healthy human knee cartilage. We obtained single-cell transcriptomes from 13,363 cells, with a median number of 10,606 detected unique molecular indexes and an average of 2753 detected genes per cell after quality control . Unsupervised clustering of the chondrocytes resulted in a total of nine cell clusters . We annotated each cluster according to cluster-specific genes: (i) homeostatic chondrocytes (HomC) ( DDIT3 , ATF3 , and GDF15 ), (ii) proliferative chondrocytes (ProC) ( BHLHE41 , CCL20 , and DUSP6 ), (iii) prehypertrophic chondrocytes (PreHTC) ( IL11 , MMP3 , and CXCL3 ), (iv) hypertrophic chondrocytes-1 (HTC-1) ( FMOD , EBF1 , ADAMTS5 , ELL2 , and NEAT1 ), (v) hypertrophic chondrocytes-2 (HTC-2) ( FMOD , EBF1 , OLFM2 , PDGFRB , and SCG2 ), (vi) prefibrochondrocytes (PreFC) ( PTX3 , TAGLN , and SPARC ), (vii) proliferate fibrochondrocytes (ProFC) , (viii) fibrochondrocytes (FC) ( MYLK , ACTA2 , and CTGF ), and (ix) regulatory chondrocytes (RegC) ( CFH , LUM , and DCN ) . Among all chondrocyte subsets, PreHTC and PreFC were abundant and accounted for 22% and 19% of the total cells, respectively; while HomC and RegC were relatively rare and accounted for 3% and 4% of the total cells, respectively . We found ProFC expressed cell cycle genes including STMN1 , KIAA0101 , and MCM3 . Further analysis showed that ProFC were mainly in the S phase of the cell cycle . Meanwhile, ProFC-specific genes enriched in the cell cycle, DNA replication, cell activation, and collagen formation , strongly supporting that ProFC is in an active phase of the cell cycle. The GO terms enriched in the specifically expressed genes of each chondrocyte subset were consistent with its identity inferred by marker genes . Figure 1 A single-cell transcriptomic atlas of chondrocytes in healthy human cartilage. (A) UMAP visualization of the 13,363 chondrocytes from healthy human cartilage. Color represents the chondrocyte subset. (B) UMAP visualization of the expression of representative marker genes for each chondrocyte subset. (C) The heatmap of chondrocyte subset-specific genes. (D) Cell–cell communication between chondrocyte subsets was analyzed by CellChat. The width and color of the line represent the strength of cell–cell interaction and signaling source, respectively. (E) Gene ontology (GO) enrichment of RegC-specific genes. HomC, homeostatic chondrocytes; PreHTC, prehypertrophic chondrocytes; ProC, proliferate chondrocytes; HTC, hypertrophic chondrocytes; ProFC, proliferate fibrochondrocytes; preFC, prefibrochondrocytes; FC, fibrochondrocytes; RegC, regulatory chondrocytes. Figure 1 We analyzed the crosstalk of ligand–receptor pairs to understand the cell-cell communication between chondrocyte subsets. We found HomC had the lowest self-interactions among all chondrocyte subsets based on three cell–cell interaction analysis methods . In particular, HomC sent out a few cell–cell interaction signals . These results potentially indicate that HomC is relatively resting and isolated. RegC has one of the strongest inter-subset interactions and self-interactions among all chondrocyte subsets . The GO enrichment analysis showed that RegC-specific genes were enriched in extracellular matrix organization, regulation of cellular component movement, regulation of cell motility, collagen formation, cellular responses to stimuli, connective tissue development, etc . . These results indicated that RegC played an important role in shaping cartilage microenvironment and regulation of chondrocyte movement and activation. We used CellChat to identify cell–cell interaction signaling among chondrocyte subsets and the most significant cell–cell interaction signaling pathways included the COLLAGEN signaling pathway, FN1 signaling pathway, THBS signaling pathway, LAMININ signaling pathway, TENASCIN signaling pathway, and HSPG signaling pathway . These cell–cell interaction signaling pathways showed distinct patterns, indicating each pathway has its own feature and story. Taking the COLLAGEN signaling pathway as an example, FC displayed the strongest interaction with the other cell clusters, which indicates that the collagen metabolism in FC was more active than the other cell types . We found two HTC subpopulations in human cartilage , and it is interesting to investigate the similarities and differences between the two HTC subpopulations. Although both HTC subpopulations highly expressed chondrocyte hypertrophic specific genes , we identified total 241 HTC-1-specific genes and 616 HTC-2-specific genes ; ADAMTS5 39 , 40 and FGF2 , 41 , 42 which are associated with chondrocyte hypertrophy and cartilage degeneration, were expressed higher in HTC-1, while COL1A1 19 , 20 and BGN , 43 , 44 which are associated with fibrocartilage formation and collagen fibril organization, were expressed higher in HTC-2 . Moreover, GO enrichment analysis suggested that HTC-1-specific genes were enriched in the regulation of apoptosis, cellular responses to stress, and programmed cell death; while HTC-2-specific genes were enriched in skeletal system development, collagen fibril organization, and ossification , indicating the two HTC subpopulations have quite different functions. Figure 2 The different features of the two HTC populations. (A) Highlighting of the two HTC subpopulations on the UMAP plot of chondrocytes. (B) The heatmap of the expression level of differentially expressed genes (DEGs) between HTC-1 and HTC-2. (C) The violin plots showing the expression levels of representative DEGs between HTC-1 and HTC-2. (D) Gene ontology (GO) enrichment analysis of HTC-1-specific genes and HTC-2-specific genes. (E) Gene set enrichment analysis (GSEA) showed apoptosis and programmed cell death were associated with HTC-1-specific genes. (F) GSEA showed collagen fibril organization and ossification were associated with HTC-2-specific genes. HTC, hypertrophic chondrocytes. Figure 2 RNA velocity exploited the relative abundance of nascent (unspliced) and mature (spliced) mRNA to infer trajectory direction during dynamic processes. 35 , 36 We calculated RNA velocity in each cell to infer the trajectories of chondrocytes using PAGA. We identified two main trajectories (trajectory #1: ProC → preHTC → HTC-2 → PreFC → FC, and trajectory #2: ProC → preHTC → HTC-1), which shared the starting point . The trajectories inferred by scVelo and monocle3 were similar to those inferred by PAGA . Interestingly, the expression of MMP3 decreased along the pseudotime , consistent with recent reports that MMP3 expressed in early chondrocyte development. 45 , 46 The expression of COL1A1 increased along the pseudotime , consistent with recent reports that COL1A1 expressed in late chondrocyte development. 19 , 20 Figure 3 The pseudotime trajectories of chondrocytes and trajectory-associated genes. (A) The pseudotime trajectories of chondrocytes inferred by PAGA. (B) The expression of MMP3 along pseudotime. (C) The expression of COL1A1 along pseudotime. (D) Pseudotime score of ProC, PreHTC, HTC-2, PreFC, and FC in trajectory #1. (E) Pseudotime score of ProC, PreHTC, and HTC-1 in trajectory #2. (F) Trajectory #1 showed the progression of ProC, PreHTC, HTC-2, PreFC, and FC. (G) The dynamic gene expression along trajectory #1. (H) Trajectory #2 showed the progression of ProC, PreHTC, and HTC-1. (I) The dynamic gene expression along trajectory #2. PreHTC, prehypertrophic chondrocytes; ProC, proliferate chondrocytes; HTC, hypertrophic chondrocytes; preFC, prefibrochondrocytes; FC, fibrochondrocytes. Figure 3 We found that the pseudotime scores increased along either trajectory #1 or trajectory #2 . Trajectory #1, starting from ProC and ending up with FC, showed a process of chondrocyte proliferation, hypertrophy, and fibrosis , which was consistent with previous reports 19 , 47 and Sw_data inferred by monocle2 . We identified hundreds of trajectory-coordinated genes with expression gradually changing along trajectory #1 that differentiated into FC . For example, NGF , 48 ITM2B , 49 and RUNX1 , 50 being reported associated with chondrocyte differentiation and proliferation, were highly expressed at the beginning of the trajectory . While THBS1 , 51 COL1A2 , 52 and GREM1 , 53 being reported associated with chondrocyte fibrosis, were highly expressed at the end of the trajectory . Trajectory #2 is the process of chondrocyte development, degradation, and apoptosis , which has not been reported at the single-cell level. The genes associated with chondrocyte degradation and apoptosis, such as NFIA , 54 SERPINE1 , 55 and CAP2 , 56 were highly expressed in the later stage of trajectory #2 . Although it is reported that precisely regulated apoptosis plays an important role in the homeostasis of cartilage degradation in vitro , 47 , 57 , 58 the trajectory of HTC apoptosis provides novel insight into the process of chondrocyte apoptosis and cartilage degradation. We conducted a comparative analysis of the single-cell landscape of chondrocytes between healthy cartilage and OA cartilage . After quality control, we had a total of 34,941 single-cell transcriptomes, comprising 13,363 cells from healthy individual and 21,578 cells from OA patients ( Table S1 ). We identified ten chondrocyte subsets , nine of which were consistent with that in our constructed single-cell atlas . PreHTC and PreFC were abundant, comprising 28% and 15% of the total cells, respectively, while ProFC and HomC were relatively scarce, accounting for only 1% and 2% of the total cells, respectively . Notably, we discovered a new chondrocyte subset, termed ProFC-2 that specifically expressed CCNB1 and MYLK . Remarkably, ProFC-2 was exclusively present in OA cartilage, while the other clusters contained cells from both the healthy individual and OA patients . The expression of cluster-specific genes showed there were some genes expressed differently between healthy chondrocytes and OA chondrocytes . Furthermore, the proportions of PreFC, RegC, ProFC, and HTC-1 in OA patients were increased compared with those in the healthy individual, whereas the proportions of HomC, ProC, PreHTC, and HTC-2 in OA patients were decreased compared with those in the healthy individual . In particular, PreFC and HTC-1 were almost dominant by cells from OA patients, while HomC and ProC were almost dominant by cells from healthy individual , essentially consistent with independent analyses of Sw_data . Figure 4 Comparison of the landscape of chondrocytes between healthy cartilage and OA cartilage. (A) UMAP visualization of 34,941 chondrocytes in healthy and OA cartilage. (B) UMAP visualization of chondrocytes in healthy cartilage (left) and OA cartilage (right). (C) Comparison of the expression of chondrocyte subset-specific genes between healthy cartilage and OA cartilage. Dot size and color intensity represent the fraction of cells expressing the genes and the average expression level, respectively. (D) Cell compositions of chondrocytes in healthy and OA cartilage. (E) The bar plot displaying the cell compositions of each chondrocyte subset based on cell sources. OA, osteoarthritis. Figure 4 Both ProFC and ProFC-2 highly expressed cell cycle genes including TOP2A and STMN1 . Gene set enrichment analysis of ProFC-specific genes and ProFC-2-specific genes revealed that both cell subsets enriched in the mitotic process , indicating that both ProFC and ProFC-2 are in an active state of cell proliferation. A total of 178 ProFC-specific genes and 329 ProFC-2-specific genes were identified by differential analysis . ProFC-specific genes included GINS2 , HELLS , and MCM3 , while ProFC-2-specific genes included CENPA , CDKN3 , and AURKA . GO enrichment analysis suggested that ProFC were enriched in extracellular matrix organization, skeletal system development, and cell cycle; while ProFC-2-specific genes were enriched in cytokine signaling, inflammatory response, and cellular responses to stimuli . Therefore, ProFC-2 might contribute to OA via inflammation since inflammation is thought to be associated with the development of OA. 59 We identified three significantly expanded chondrocyte subpopulations in OA cartilage, namely ProFC, ProFC-2, and HTC-1. First, the proportion of ProFC in OA cartilage was significantly higher than that in healthy cartilage , indicating the increase of ProFC may be associated with or contribute to the occurrence and development of OA. Differential analysis of ProFC between healthy and OA cartilage identified 321 OA-specific genes . These OA-specific genes include CEMIP , 60 , 61 ACAN , 62 and HMOX1 63 which are associated with chondrocyte inflammation, degradation, or fibrosis. We also identified 437 healthy specific genes ( Table S4 ) including BDNF , IGFBP2 , and WNT5A . GO enrichment analysis of OA cartilage-specific genes in ProFC enriched in extracellular matrix organization, collagen fibril organization, and ossification, while healthy cartilage-specific genes in ProFC enriched in the cellular response to cytokine stimulus, cell activation, and cell population proliferation , indicating that ProFC in OA cartilage have increased extracellular matrix and collagen than in healthy cartilage. Intriguingly, ProFC-2 represented a small cell population predominantly in OA cartilage , implying that ProFC-2 have a unique effect on the occurrence and development of OA. Figure 5 Expansion of ProFC, ProFC-2, and HTC-1 in OA cartilage and change of gene expression. (A) Highlighting of ProFC on UMAP plot of chondrocytes in healthy cartilage (left) and OA cartilage (right). (B) Highlighting of ProFC-2 on UMAP plot of chondrocytes in healthy cartilage (left) and OA cartilage (right). (C) The proportions of ProFC and ProFC-2 in healthy cartilage and OA cartilage. (D) The heatmap of differentially expressed genes (DEGs) between healthy cartilage and OA cartilage in ProFC. (E) Enrichment analysis of healthy specific genes and OA-specific genes in ProFC. (F) Highlighting of HTC-1 on UMAP plot in healthy cartilage (left) and OA cartilage (right). (G) The proportion of HTC-1 in healthy and OA cartilage. (H) The heatmap of the expression level of DEGs between healthy and OA cartilage in HTC-1. (I) Enrichment analysis of healthy specific genes and OA-specific genes in HTC-1. OA, osteoarthritis; HTC, hypertrophic chondrocytes; ProFC, proliferate fibrochondrocytes. Figure 5 The proportion of HTC-1 in OA cartilage was significantly higher than that in healthy cartilage , which was consistent with the result of Sw_data . Differential analysis of HTC-1 between healthy and OA cartilage identified 230 OA-specific genes ( Table S4 ) including CEMIP , SFRP4 , and CXCL12 . We also identified 333 healthy specific genes ( Table S4 ) including IGFBP2 , WISP3 , and IFIT3 . GO enrichment analysis of OA cartilage-specific genes in HTC-1 enriched in vasculature development, degradation of the extracellular matrix, and ossification, while healthy cartilage-specific genes in HTC-1 enriched in cellular response to stress, proteasome degradation, and regulation of apoptosis , implying that HTC-1 might be stimulated into apoptosis via degradation of the extracellular matrix, and the increase of HTC-1 might trigger OA. Although we found several chondrocyte subsets expanded in OA cartilage, the proportion of HomC in OA cartilage was significantly lower than that in healthy cartilage , which was consistent with independent analyses of Sw_data . HomC have been reported for their protective role in preventing cartilage degeneration and exhibit high expression of human circadian clock rhythm genes, 19 and its decrease may indicate weaker regulation in OA cartilage. Differential analysis of HomC between healthy and OA cartilages identified 454 OA-specific genes ( Table S4 ) including COL1A1 and BGN . We also identified 850 healthy specific genes ( Table S4 ) including WARS and ISG20 . GO enrichment analysis showed healthy specific genes in HomC enriched in cellular response to protein processing, immune system function, and maintenance of cellular homeostasis, indicative of their regulatory effect on cartilage homeostasis . However, OA-specific genes in HomC enriched in skeletal system development, degradation of the extracellular matrix, and ossification, implying their potential involvement in OA progression and pathological remodeling of the joint . These results indicated that HomC in OA cartilage decreased in number and were dysfunctional. Figure 6 Reduction of HomC in OA cartilage and change of gene expression. (A) Highlighting of HomC on UMAP plot in healthy cartilage (left) and OA cartilage (right). (B) The proportion of HomC in healthy cartilage and OA cartilage. (C) The dotplot of healthy specific genes and OA-specific genes in HomC. (D) The violin plot of representative OA-specific genes. (E) The violin plot of representative healthy specific genes. (F) Enrichment analysis of healthy specific genes and OA-specific genes in HomC. OA, osteoarthritis; HomC, homeostatic chondrocytes. Figure 6 Here, we employed scRNA-seq to construct a single-cell transcriptomic atlas of chondrocytes in healthy human cartilage. We identified two HTC subpopulations with distinct functions and disparate terminal fates, namely HTC-1 and HTC-2. HTC-2 is involved in skeletal system development, which differentiate into PreFC and then FC. It is worth noting that HTC-1 specifically expresses genes related to apoptosis and programmed cell death and is the terminal of chondrocyte apoptosis trajectory at single-cell resolution. Importantly, we observed the expansion of the HTC-1 population in the cartilage of OA patients compared with the healthy individual. Compared with healthy cartilage, the OA-specific genes of HTC-1 showed weaker cellular response to stress and regulation of apoptosis, and are more likely to participate in vasculature development, degradation of the extracellular matrix, and ossification. These significant findings offer compelling clues indicating that an increased presence of HTC-1 and decreased chondrocyte apoptosis play pivotal roles in the pathogenesis of OA. It is reported that the change in chondrocyte subpopulations and the cellular states may contribute to the occurrence of OA. 19 , 64 Notably, the population size of ProFC in OA cartilage has significantly expanded compared with healthy cartilage. ProFC highly expressed cell cycle genes and played an important role in extracellular matrix organization, collagen formation, and collagen fibril organization. Compared with ProFC in healthy cartilage, cellular response to cytokine stimulus and angiogenesis signals decreased in OA cartilage, while extracellular matrix organization, collagen fibril organization, and ossification increased in OA cartilage, indicating the dysfunction of ProFC. Interestingly, not only ProFC has significantly expanded, but also a new subset, namely ProFC-2 showed up in OA cartilage. Different from ProFC, ProFC-2 showed increased cytokine signaling, inflammatory response, and cellular responses to stimuli. Thus ProFC-2 is an OA cartilage-specific subpopulation and may contribute to the development of OA via inflammation. HomC is known for its protective effects against cartilage degeneration and its pronounced expression of human circadian clock rhythm genes. 19 Here, we successfully identified well-defined gene markers for HomC including ATF3 , DDIT3 , and GDF15 , 21 , 65 all of which have been linked to collagen synthesis, chondrocyte proliferation, and chondrocyte differentiation. Interestingly, our results showed that HomC in OA cartilage was significantly lower than those in healthy cartilage, providing an interesting insight into the molecular mechanism of OA. In summary, this study provided a single-cell transcriptomic atlas of chondrocytes in healthy cartilage. In particular, we identified a novel THC subset, namely HTC-1, that specifically expressed genes associated with cell apoptosis and programmed cell death. We identified two main trajectories of chondrocytes, one of which differentiates into FC, while the other terminates in apoptosis. A comparison of chondrocyte subsets between healthy cartilage and OA cartilage showed that ProFC and HTC-1 populations expanded in OA patients, whereas the HomC population decreased. Interestingly, we also discovered an OA-specific ProFC subset, namely ProFC-2, which showed enhanced cytokine signaling and inflammatory response. Therefore, ProFC-2 may contribute to the development of OA via inflammation signaling. In short, our study promotes a better understanding of chondrocyte heterogeneity in articular cartilage and also provides a new insight into the mechanisms underlying the progression of OA. | Review | biomedical | en | 0.999997 |
PMC11697247 | In the last few years, extensive research has been conducted on CDEs. Some of these studies include a geometric method in any domain that is based on meromorphic functions , a topological explanation of certain CDE solutions involving multi-valued coefficients , the complex oscillation of some linear CDE , the growth estimates of linear CDE , analytic functions in the complex plane: the polynomial and rational approximations , , the linear differential equations’ meromorphic solutions , [ p , q ] -order linear differential equations in the complex plane , a higher-order periodic linear differential equation problem , on complex domain the solution of IVP for retarded differential equations , an analytic method for the non-linear CDEs , the solution growth of algebraic systems of nonlinear CDEs , and also on meromorphic solutions and entire solutions by julia limiting directions and some function spaces solutions for the nonlinear CDEs have been studied in , , , , . The following equation is a representation of the generalized m th order CDE with complex variable coefficients. (1) ∑ k = 0 m Q k ( z ) f ( k ) ( z ) = h ( z ) ; m ∈ N where z is the complex variable, Q k ( z ) and h ( z ) are the analytic functions in the following rectangular domain D of the complex plane C D = { z ∈ C , z = x + i y , i = − 1 ; a ≤ x ≤ b , c ≤ y ≤ d ; a , b , c , d ∈ R } . Eq. (1) is a general CDE written down in derivative form given , , , , , , , with the following mixed conditions (2) ∑ k = 0 m − 1 ∑ l = 0 L [ b r k f ( k ) ( ξ l ) + c r k f ( k ) ( z 0 ) ] = λ r ; L ∈ N and r = 0 , 1 , 2 , ⋯ , ( m − 1 ) . where b r k , c r k , and λ r are suitable real or complex constants; ξ l , z 0 ∈ D . One of the most renowned weighted residual numerical methods for solving differential equations is the Galerkin method, which performs a significant role in the solution of differential equations numerically. For instance, the Galerkin method of Wavelet, Chebyshev, Taylor, Petrov, Legendre, Hermite, Bernstein, Exponential B-splines, and Bernoulli has been used to solve differential equations , , , , , , , integral and integro-differential equations , Volterra integro-differential equations , Fredholm integro-differential equations , eigenvalue problems , delay differential equations , , , Burger’s differential equations , KdV equations , nonlinear partial differential equations , , , and perturbed partial differential equations . It has come to our attention that the previous research has not applied the Galerkin method for the numerical solution of CDEs. Since there is a research gap in this field, we presented a new technique for solving CDEs called the Taylor Galerkin Method (TGM). TGM uses Taylor series expansions for discretization, resulting in higher-order precision in temporal integration. This is especially useful for applications needing precise temporal resolution. TGM may successfully address nonlinear differential equations by adding Taylor expansions, which better represent nonlinear term behavior. While TGM can achieve high accuracy, it may incur additional computational costs due to the necessity for higher-order derivatives and the complexity of the numerical implementation. The collocation method is directly evaluating the governing equations at specific collocation points. The method can have lower computational overhead and cost for certain problems, especially when using fewer basis functions. However, this method can struggle with problems that involve sharp gradients or discontinuities, as the collocation points may not adequately capture local behavior. Poorly chosen points can lead to suboptimal convergence and inaccuracies. It can produce non-unique solutions, particularly when dealing with ill-posed problems or insufficiently defined basis functions. Both methods may encounter difficulties with boundary conditions, particularly for complicated geometries or where exact enforcement is required. However, TGM can be applied to a variety of spatial discretization techniques, including both structured and unstructured domains, increasing its application to a wide range of issues. When dealing with complex shapes or domains, the TGM uses finite element discretization, which is highly flexible in terms of geometry. The domain (shape) is subdivided into smaller elements, and the method solves the problem over these elements using basis functions that are defined locally on each element. In this method, boundary conditions must be incorporated into both the time-stepping scheme (via the Taylor expansion) and the spatial discretization (Galerkin method). The objective we set in the present research is to obtain an approximate solution f ˜ N ( z ) of equation (1) and subject to the condition Eq. (2) , and want to express in terms of N degree Taylor polynomial in the following form; at the point z = z 0 , (3) f ˜ N ( z ) = ∑ j = 0 N a j ( z − z 0 ) j j ! ; z , z 0 ∈ D and N ≥ m . where the unidentified Taylor coefficients a j , j = 0 , 1 , 2 , ⋯ , N are to be determined. We will find out the unknown coefficients a j of the Eq. (3) by the proposed approach TGM. Since the proposed method is based on a Taylor series expansion, it may inherently handle higher-order derivatives more naturally than some other methods. This could lead to increased accuracy when approximating solutions that involve high spatial or temporal gradients. Computational complexity for the present method can grow rapidly, especially for multi-dimensional problems or when higher-order derivatives need to be accounted for. This could lead to a significant increase in memory allocation and high process time requirements, making TGM less efficient for very large-scale or real-time applications. At first, we consider the approximate solution f ˜ N ( z ) and its k th derivative f ˜ N ( k ) ( z ) in the following form , , (4) f ˜ N ( z ) ≡ f ˜ N ( 0 ) ( z ) = ∑ j = 0 N a j ( z − z 0 ) j j ! = ∑ j = 0 N a j θ j ( z ) and (5) f ˜ N ( k ) ( z ) ≡ d k [ f ˜ N ( z ) ] d z k = d k d z k [ ∑ j = 0 N a j θ j ( z ) ] = ∑ j = 0 N a j d k d z k [ θ j ( z ) ] ; k ∈ N . where θ j ( z ) = ( z − z 0 ) j j ! are considering the basis function for TGM. We first gather every term in the CDE (1) on the left side to get the residual function , . Now we substitute the relation (5) into the Eq. (1) , then we obtain the corresponding residual function of f ˜ N ( z ) as follows, (6) R ( f ˜ N ) = ∑ k = 0 m Q k ( z ) f ˜ N ( k ) ( z ) − h ( z ) ⇒ R ( f ˜ N ) = ∑ k = 0 m Q k ( z ) [ ∑ j = 0 N a j d k d z k [ θ j ( z ) ] ] − h ( z ) . We have to do to get a weighted residual is multiply the integral over the residual’s domain D by a weighting function w ( z ) , i . e . ∫ D R ( f ˜ N ) w ( z ) d z . By choosing ( N + 1 ) weight functions, w i ( z ) for i = 0 , 1 , 2 , ⋯ , N ; and to find out the ( N + 1 ) unknown coefficients a j of Eq. (3) , we have to solve ( N + 1 ) equations that result from putting these ( N + 1 ) weighted residuals to zero. The ( N + 1 ) weighted residual for w i ( z ) is defined as follows (7) R i ( f ˜ N ) ≡ ∫ D R ( f N ˜ ) w i ( z ) d z for i = 0 , 1 , ⋯ , N . Since the weighted residual method requires , , , R i ( f ˜ N ) = 0 for i = 0 , 1 , ⋯ , N . That implies (8) ∫ D R ( f N ˜ ) w i ( z ) d z = 0 for i = 0 , 1 , ⋯ , N . The main task of the TGM is to match the weight functions to the basis functions of the approximate solution f ˜ N ( z ) . That is, (9) w i ( z ) = θ i ( z ) for i = 0 , 1 , ⋯ , N . Now, by substituting the Eq. (6) and (9) into the Eq. (8) , then the Galerkin weighted residual equations or simply the Galerkin equations are , (10) ∫ D [ ( ∑ k = 0 m Q k ( z ) ( ∑ j = 0 N a j d k d z k [ θ j ( z ) ] ) − h ( z ) ) θ i ( z ) ] d z = 0 ⇒ ∫ D [ ∑ k = 0 m Q k ( z ) ( ∑ j = 0 N a j d k d z k [ θ j ( z ) ] ) θ i ( z ) ] d z = ∫ D h ( z ) θ i ( z ) d z ⇒ ∑ j = 0 N a j [ ∫ D ( ∑ k = 0 m Q k ( z ) ( d k d z k [ θ j ( z ) ] ) θ i ( z ) ) d z ] = ∫ D h ( z ) θ i ( z ) d z for i = 0 , 1 , ⋯ , N . The Eq. (10) can be written in conventional matrix form as follows, (11) [ K ] { A } = { C } where (12) K = [ k i , j ] ; k i , j = ∫ D [ ∑ k = 0 m Q k ( z ) ( d k d z k [ θ j ( z ) ] ) θ i ( z ) ] d z for i , j = 0 , 1 , ⋯ , N and (13) C = [ c i ] T ; c i = ∫ D h ( z ) θ i ( z ) d z for i = 0 , 1 , ⋯ , N and (14) A = [ a j ] T for j = 0 , 1 , 2 , ⋯ , N . The appropriate matrix configuration for the mixed conditions (2) can be obtained in the following manner , , (15) ∑ k = 0 m − 1 ∑ l = 0 L [ b r k d k d z k ( ∑ j = 0 N a j θ j ( ξ l ) ) + c r k d k d z k ( ∑ j = 0 N a j θ j ( z 0 ) ) ] = λ r ⇒ ∑ j = 0 N a j [ ∑ k = 0 m − 1 ∑ l = 0 L ( b r k d k θ j ( ξ l ) d z k + c r k d k θ j ( z 0 ) d z k ) ] = λ r for r = 0 , 1 , 2 , ⋯ , ( m − 1 ) . The Eq. (15) can be written in conventional matrix form as follows, (16) [ U ] { A } = { λ r } where (17) U = [ u r , j ] ; u r , j = ∑ k = 0 m − 1 ∑ l = 0 L ( b r k d k d z k θ j ( ξ l ) + c r k d k d z k θ j ( z 0 ) ) for r = 0 , 1 , ⋯ , ( m − 1 ) ; j = 0 , 1 , ⋯ , N . Thus, it is possible to determine the unidentified Taylor coefficients a j ; j = 0 , 1 , 2 , ⋯ , N , associated with the equivalent solution of the problem (1) , which is composed of Eq. (11) and conditions (16) , by swapping the m row matrices (19) out the last m rows of the augmented matrix (18) . We have the new augmented matrix form as follows , , (20) [ K * : C * ] = [ k 0 , 0 k 0 , 1 ⋯ k 0 , N : c 0 k 1 , 0 k 1 , 1 ⋯ k 1 , N : c 1 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ k N − m , 0 k N − m , 1 ⋯ k N − m , N : c N − m u 0 , 0 u 0 , 1 ⋯ u 0 , N : λ 0 ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ u m − 1 , 0 u m − 1 , 1 ⋯ u m − 1 , N : λ m − 1 ] or, the equivalent matrix equation (21) [ K * ] { A } = { C * } . If det K * ≠ 0 , we can rewrite the Eq. (21) as (22) { A } = [ K * ] − 1 { C * } and it is unique to determining the column matrix A which is the unknown coefficient of the Taylor polynomial (3) . Therefore, there exists a unique solution to the m th order linear CDE with variable coefficients under the given conditions. For nonlinear CDE, we construct a nonlinear system of equations with undetermined Taylor coefficients. We can solve this nonlinear system of equations numerically by well-known iterative techniques such as Newton’s Method, Levenberg-Marquardt, and Broyden’s Method , . For a better approximation, we have to increase the degree of N polynomial (3) . In this section, by using the residual function of the m th order CDE provided by Eq. (1) , we estimate the error for the proposed method. Next, we demonstrate how to use this estimation to improve the approximate solution of the equation, known as the corrected solution. Finally, the Taylor theorem is utilized to determine an error bound on the corrected solution’s error , , . Let us consider the residual function of Eq. (1) as follows: (23) R ( z ) = ∑ k = 0 m Q k ( z ) f ( k ) ( z ) − h ( z ) = 0 . Now substitute the approximate solution f ˜ N ( z ) in place of f ( z ) to the Eq. (23) , we get (24) R N ( z ) = ∑ k = 0 m Q k ( z ) f ˜ N ( k ) ( z ) − h ( z ) as the residual function of f ˜ N ( z ) . Subtracting Eq. (23) from Eq. (24) , we obtain (25) ∑ k = 0 m Q k ( z ) E N ( k ) ( z ) = − R N ( z ) which is just as Eq. (1) with non-homogeneous term − R N ( z ) instead of h ( z ) and f ( z ) − f N ˜ ( z ) is restored by E N ( z ) . Since the approximate solution f ˜ N ( z ) also assure the mixed condition (2) , we obtain the corresponding homogeneous condition (26) ∑ k = 0 m − 1 ∑ l = 0 L [ b r k E N ( k ) ( ξ l ) + c r k E N ( k ) ( z 0 ) ] = 0 ; r = 0 , 1 , 2 , ⋯ , ( m − 1 ) . This is the mixed condition of the Eq. (25) . Now using the solution method described in Method details: Phase 1 and Method details: Phase 2 section to obtain an approximation solution E N , M ( z ) to Eq. (25) , where M is any positive integer. Lastly, we apply this approximation to obtain the approximate corrected solution for Eq. (1) , which is given by (27) f ˜ N , M ( z ) = f ˜ N ( z ) + E N , M ( z ) where the actual error of f ˜ N , M ( z ) is given by f ( z ) − f ˜ N , M ( z ) . In the following theorem, the truncation error of the Taylor expansion for the exact solution of Eq. (1) is used to evaluate the error bound for the approximate solution f ˜ N ( z ) . Theorem 1 Let f ˜ N ( z ) be the approximate solution and f ( z ) be the exact solutions of Eq. (1) . If f ( z ) has ( N + 1 ) times continuous derivative, then the error bound for the absolute error is given by | f ( z ) − f ˜ N ( z ) | ≤ | R N T ( z ) | + | f N T ( z ) − f ˜ N ( z ) | . Where f N T ( z ) denotes the N t h degree Taylor polynomial of f ( z ) around the point z = z 0 ∈ D and R N T ( z ) represents its Cauchy form remainder term . Proof The Taylor series can be rewritten with reminder term of f ( z ) around the point z 0 ∈ D as f ( z ) = ∑ j = 0 N ( z − z 0 ) j j ! f ( j ) ( z 0 ) + R N T ( z ) . Where R N T ( z ) = 1 2 π i ∮ γ ( z − z 0 t − z 0 ) ( N + 1 ) f ( t ) t − z d t is the Cauchy form reminder term of the Taylor expansion of f ( z ) and this contour integral is evaluated around the circle γ which centered at z 0 , such that γ ⊂ D . Consequently, R N T ( z ) = f ( z ) − f N T ( z ) . By using this in conjunction with the triangle inequality, we get | f ( z ) − f ˜ N ( z ) | = | f ( z ) − f ˜ N ( z ) + f N T ( z ) − f N T ( z ) | ≤ | f ( z ) − f N T ( z ) | + | f N T ( z ) − f ˜ N ( z ) | = | R N T ( z ) | + | f N T ( z ) − f ˜ N ( x ) | . As a result, we have located an upper bound for the absolute error based on the Taylor truncation error of the exact solution. □ This section will demonstrate the numerical solution of three linear and two nonlinear CDEs by applying the proposed method. Nonlinear CDEs often involve terms like products of the unknown solution or its derivatives, which complicate both the iterative solution and error correction. The Newton-Raphson or Picard iteration methods are used to decouple these nonlinear terms at each iteration. All results are presented numerically, along with the exact solution and comparison. Since the N degree polynomial (3) is an approximate solution of Eq. (1) , when the approximation solutions f ˜ N ( z ) and exact solution f ( z ) are substituted in the following equation, we can evaluate the absolute errors E N ( z ) at the subsequent particular points within the specified domain; that is, for z = z j ∈ D , (28) E N ( z j ) = | f N ˜ ( z j ) − f ( z j ) | . The absolute error E N ( z ) diminishes when N grows to a significant size. We can also evaluate the maximum absolute error L ∞ n o r m as follows: (29) L ∞ n o r m = max [ E N ( z j ) ] . Example 1 Let us examine the second-order non-homogeneous CDE that is linear and has variable coefficients , , (30) f ″ ( z ) + z f ( z ) = e z + z e z ; z ∈ C . Where m = 2 , Q 0 ( z ) = z , Q 1 ( z ) = 0 , Q 2 ( z ) = 1 , h ( z ) = e z + z e z and subject to the initial conditions are (31) f ( 0 ) = 1 , f ′ ( 0 ) = 1 . The corresponding transcendental entire solution of Eq. (30) is f ( z ) = e z and now consider an approximate solution f ˜ 5 ( z ) by the N = 5 degree Taylor polynomial at z 0 = 0 in the following form (32) f ˜ 5 ( z ) = ∑ j = 0 5 a j z j j ! . Thus, we have θ j ( z ) = z j j ! for j = 0 , 1 , ⋯ , 5 and θ i ( z ) = z i i ! for i = 0 , 1 , ⋯ , 5 Assume the Galerkin integral domain D = { z ∈ C , z = x + i y , i = − 1 ; − 1 ≤ x ≤ 1 , − 1 ≤ y ≤ 1 } . From Eq. (18) , we obtain the augmented matrix by using Eq. (12) and (13) as follows: [ K : C ] = [ 0 − 4 3 + 4 i 3 2 + 2 i − 4 15 − 4 i 15 − 2 3 + 2 i 3 2 105 − 2 i 105 : − 0.3103 + 3.6453 i − 4 3 + 4 i 3 0 − 4 5 − 4 i 5 − 4 3 + 4 i 3 2 21 − 2 i 21 − 4 15 − 4 i 15 : − 3.6136 + 1.4915 i 0 − 4 5 − 4 i 5 − 2 3 + 2 i 3 4 21 − 4 i 21 − 2 5 − 2 i 5 2 135 + 2 i 135 : − 1.6120 − 0.7535 i − 4 15 − 4 i 15 0 4 21 − 4 i 21 − 4 15 − 4 i 15 2 81 + 2 i 81 4 63 − 4 i 63 : − 0.2513 − 0.7562 i 0 2 21 − 2 i 21 − 1 15 − i 15 2 81 + 2 i 81 1 21 − i 21 − 1 495 + i 495 : 0.1046 − 0.1764 i 2 105 − 2 i 105 0 2 235 + 2 i 235 2 105 − 2 i 105 − 1 495 + i 495 2 405 + 2 i 405 : 0.0553 − 0.0161 i ] . From Eq. (20) , we obtain the new augmented matrix form by applying the initial condition as follows: [ K * : C * ] = [ 0 − 4 3 + 4 i 3 2 + 2 i − 4 15 − 4 i 15 − 2 3 + 2 i 3 2 105 − 2 i 105 : − 0.3103 + 3.6453 i − 4 3 + 4 i 3 0 − 4 5 − 4 i 5 − 4 3 + 4 i 3 2 21 − 2 i 21 − 4 15 − 4 i 15 : − 3.6136 + 1.4915 i 0 − 4 5 − 4 i 5 − 2 3 + 2 i 3 4 21 − 4 i 21 − 2 5 − 2 i 5 2 135 + 2 i 135 : − 1.6120 − 0.7535 i − 4 15 − 4 i 15 0 4 21 − 4 i 21 − 4 15 − 4 i 15 2 81 + 2 i 81 4 63 − 4 i 63 : − 0.2513 − 0.7562 i 1 0 0 0 0 0 : 1 0 1 0 0 0 0 : 1 ] . Here, det ( K * ) ≠ 0 and so by solving the linear system of equations [ K * ] { A } = { C * } , the unknown Taylor coefficients a j become [ a 0 a 1 a 2 a 3 a 4 a 5 ] = [ 1.000000000000000 + 0.000000000000000 i 1.000000000000000 + 0.000000000000000 i 1.014201842129353 + 0.001179180289414 i 1.008159146912473 − 0.001763403006139 i 0.990894060694918 + 0.141332871646214 i 0.995534591695822 + 0.111337779580632 i ] . Therefore, the approximate solution (32) of Eq. (30) is For N = 5 , 9 the tabular comparison and for N = 5 the graphical comparison, the absolute error produced by the present method is compared with the outcomes generated by the Taylor Collocation method and the Bessel Collocation method are shown in Table 1 and in Fig. 2 for ℜ e part, and in Table 2 and in Fig. 3 for ℑ m part. Example 2 Let us examine the second-order non-homogeneous CDE that is linear and has variable coefficients , , (33) f ″ ( z ) + z f ′ ( z ) + 2 z f ( z ) = 2 z sin z + z cos z − sin z ; z ∈ C . Where m = 2 , Q 0 ( z ) = 2 z , Q 1 ( z ) = z , Q 2 ( z ) = 1 , h ( z ) = 2 z sin z + z cos z − sin z and subject to the initial conditions are (34) f ( 0 ) = 0 , f ′ ( 0 ) = 1 . The corresponding transcendental entire solution of Eq. (33) is f ( z ) = sin z . Assume the Galerkin integral domain D = { z ∈ C , z = x + i y , i = − 1 ; − 1 ≤ x ≤ 1 , − 1 ≤ y ≤ 1 } . For N = 5 , by applying the proposed method discussed in Method details: Phase 1 and Method details: Phase 2 section, we obtain the approximate solution of the problem (33) is Table 1 Absolute error E N ( z ) analysis of Example 1 ( ℜ e part) for N = 5 , 9 . Table 1 z j Taylor Collocation E 5 ( z j ) ( ℜ e part) Bessel Collocation E 5 ( z j ) ( ℜ e part) Present method E 5 ( z j ) ( ℜ e part) − 1.00 − 1.00 i 1.5455878 × 10 − 1 4.56461167157 × 10 − 2 1.55825712363 × 10 − 4 − 0.60 − 0.60 i 4.306944 × 10 − 2 4.69890942146 × 10 − 3 2.92740621909 × 10 − 5 − 0.20 − 0.10 i 7.56259 × 10 − 3 3.53588372881 × 10 − 6 1.70147084216 × 10 − 4 − 0.20 − 0.20 i 2.021649 × 10 − 3 5.28547892122 × 10 − 5 2.88995538694 × 10 − 5 − 0.10 − 0.20 i 6.3395028 × 10 − 3 1.17953217994 × 10 − 5 2.07165233691 × 10 − 4 − 0.10 + 0.20 i 6.3395028 × 10 − 3 1.17953217994 × 10 − 5 1.88735059177 × 10 − 4 − 0.10 − 0.10 i 2.685866 × 10 − 4 4.08981352739 × 10 − 6 9.54656886026 × 10 − 6 − 0.10 + 0.10 i 2.685866 × 10 − 4 4.08981352739 × 10 − 6 1.52868641185 × 10 − 5 0.00 + 0.00 i 0 0 0 0.10 + 0.10 i 3.00956 × 10 − 4 5.34405858898 × 10 − 7 1.37335135544 × 10 − 5 0.10 − 0.10 i 3.00956 × 10 − 4 5.34405858898 × 10 − 7 8.60026504344 × 10 − 6 0.10 − 0.20 i 9.461532 × 10 − 3 5.89808309592 × 10 − 6 2.18213126268 × 10 − 4 0.10 + 0.20 i 9.461532 × 10 − 3 5.89808309592 × 10 − 6 2.37584226249 × 10 − 4 0.20 + 0.20 i 2.545070 × 10 − 3 4.02982871539 × 10 − 6 6.05804000660 × 10 − 5 0.20 + 0.10 i 8.12515 × 10 − 3 3.01135500091 × 10 − 6 1.81330834974 × 10 − 4 0.60 + 0.60 i 8.494471 × 10 − 2 1.04420502024 × 10 − 4 4.39688872244 × 10 − 4 1.00 + 1.00 i 4.774621 × 10 − 1 1.08853442660 × 10 − 2 3.93946395961 × 10 − 5 L ∞ n o r m → 4.774621 × 10 − 1 4.56461167157 × 10 − 2 4.39688872244 × 10 − 4 z j Taylor Collocation E 9 ( z j ) ( ℜ e part) Bessel Collocation E 9 ( z j ) ( ℜ e part) Present method E 9 ( z j ) ( ℜ e part) − 1.00 − 1.00 i 1.108733182 × 10 − 1 3.76537324165 × 10 − 4 2.57859638130 × 10 − 9 − 0.60 − 0.60 i 1.45548425 × 10 − 2 6.06258539787 × 10 − 6 7.58469130873 × 10 − 9 − 0.20 − 0.10 i 3.294138 × 10 − 4 2.45158049416 × 10 − 9 2.26350818464 × 10 − 8 − 0.20 − 0.20 i 1.061492 × 10 − 4 4.87937790172 × 10 − 9 9.44547400322 × 10 − 10 − 0.10 − 0.20 i 3.063314 × 10 − 4 1.05705222352 × 10 − 9 2.49267239421 × 10 − 8 − 0.10 + 0.20 i 3.063314 × 10 − 4 1.05705244557 × 10 − 9 3.61462878580 × 10 − 8 − 0.10 − 0.10 i 1.9201 × 10 − 6 3.5108893570 × 10 − 10 6.30457370627 × 10 − 10 − 0.10 + 0.10 i 1.9201 × 10 − 6 3.5108893570 × 10 − 10 1.51172321082 × 10 − 9 0.00 + 0.00 i 0 0 0 0.10 + 0.10 i 3.429 × 10 − 5 1.4785728197 × 10 − 11 1.29170784506 × 10 − 9 0.10 − 0.10 i 3.429 × 10 − 5 1.4785728197 × 10 − 11 6.39311490701 × 10 − 10 0.10 − 0.20 i 4.95029 × 10 − 4 1.2836420815 × 10 − 10 4.06109024591 × 10 − 8 0.10 + 0.20 i 4.95029 × 10 − 4 1.2836420815 × 10 − 10 2.88975497779 × 10 − 8 0.20 + 0.20 i 4.11766 × 10 − 4 1.7463808177 × 10 − 11 5.50078312939 × 10 − 9 0.20 + 0.10 i 3.58652 × 10 − 4 3.4350300381 × 10 − 11 2.34756393087 × 10 − 8 0.60 + 0.60 i 2.7396252 × 10 − 2 1.5724670343 × 10 − 7 1.08960406759 × 10 − 8 1.00 + 1.00 i 2.12823672 × 10 − 1 1.8848182780 × 10 − 5 7.60426705103 × 10 − 10 L ∞ n o r m → 2.12823672 × 10 − 1 3.76537324165 × 10 − 4 4.06109024591 × 10 − 8 Fig. 2 Visual depiction of the absolute error analysis of Example 1 for N = 5 ( ℜ e part). Fig. 2 Table 2 Absolute error E N ( z ) analysis of Example 1 ( ℑ m part) for N = 5 , 9 . Table 2 z j Taylor Collocation E 5 ( z j ) ( ℑ m part) Bessel Collocation E 5 ( z j ) ( ℑ m part) Present method E 5 ( z j ) ( ℑ m part) − 1.00 − 1.00 i 2.6405612 × 10 − 1 3.20625005945 × 10 − 2 4.61134911108 × 10 − 4 − 0.60 − 0.60 i 1.296049635 × 10 − 1 1.93852368188 × 10 − 3 2.09682236332 × 10 − 3 − 0.20 − 0.10 i 9.025824 × 10 − 3 2.46106053944 × 10 − 5 2.82627329290 × 10 − 4 − 0.20 − 0.20 i 1.8727366 × 10 − 2 7.97723721643 × 10 − 6 5.05755573585 × 10 − 4 − 0.10 − 0.20 i 1.0687015 × 10 − 2 1.50108726619 × 10 − 5 2.62174108542 × 10 − 4 − 0.10 + 0.20 i 1.0687015 × 10 − 2 1.50108726619 × 10 − 5 3.13020553920 × 10 − 4 − 0.10 − 0.10 i 4.964118 × 10 − 3 1.80962075498 × 10 − 6 1.36401933341 × 10 − 4 − 0.10 + 0.10 i 4.964118 × 10 − 3 1.80962075498 × 10 − 6 1.42214405880 × 10 − 4 0.00 + 0.00 i 0 0 0 0.10 + 0.10 i 5.532334 × 10 − 3 1.831842975447 × 10 − 6 1.42946035745 × 10 − 4 0.10 − 0.10 i 5.532334 × 10 − 3 1.831842975447 × 10 − 6 1.46555754649 × 10 − 4 0.10 − 0.20 i 1.011738 × 10 − 2 6.199232248094 × 10 − 6 3.00371756593 × 10 − 4 0.10 + 0.20 i 1.011738 × 10 − 2 6.199232248094 × 10 − 6 2.63978549581 × 10 − 4 0.20 + 0.20 i 2.325711 × 10 − 2 9.399457632647 × 10 − 6 5.56436485635 × 10 − 4 0.20 + 0.10 i 1.2161465 × 10 − 2 3.156056383443 × 10 − 6 3.10634103809 × 10 − 4 0.60 + 0.60 i 2.4723143 × 10 − 1 9.018303218719 × 10 − 4 3.05961727471 × 10 − 3 1.00 + 1.00 i 7.63417273 × 10 − 1 9.857912120237 × 10 − 3 3.03618060646 × 10 − 3 L ∞ n o r m → 7.63417273 × 10 − 1 3.20625005945 × 10 − 2 3.05961727471 × 10 − 3 z j Taylor Collocation E 9 ( z j ) ( ℑ m part) Bessel Collocation E 9 ( z j ) ( ℑ m part) Present method E 9 ( z j ) ( ℑ m part) − 1.00 − 1.00 i 4.026018 × 10 − 2 9.03074377008 × 10 − 5 1.88617719668 × 10 − 9 − 0.60 − 0.60 i 7.679373 × 10 − 3 5.48651788112 × 10 − 6 1.46971560696 × 10 − 8 − 0.20 − 0.10 i 4.924492 × 10 − 4 2.79223615062 × 10 − 9 3.99808868758 × 10 − 8 − 0.20 − 0.20 i 8.613902 × 10 − 4 7.48862591382 × 10 − 9 6.44273901376 × 10 − 8 − 0.10 − 0.20 i 4.202928 × 10 − 4 1.40807795978 × 10 − 9 3.95198737399 × 10 − 8 − 0.10 + 0.20 i 4.202928 × 10 − 4 1.40807795978 × 10 − 9 4.78513258255 × 10 − 8 − 0.10 − 0.10 i 2.3079652 × 10 − 4 8.6068374649 × 10 − 11 1.98854405035 × 10 − 8 − 0.10 + 0.10 i 2.3079652 × 10 − 4 8.6068374649 × 10 − 11 2.25097428782 × 10 − 8 0.00 + 0.00 i 0 0 0 0.10 + 0.10 i 2.656783 × 10 − 4 3.0673394380 × 10 − 11 2.06909033500 × 10 − 8 0.10 − 0.10 i 2.656783 × 10 − 4 3.0673394380 × 10 − 11 2.32382756256 × 10 − 8 0.10 − 0.20 i 3.784386 × 10 − 4 8.5109169711 × 10 − 11 4.63237345777 × 10 − 8 0.10 + 0.20 i 3.784386 × 10 − 4 8.5109169711 × 10 − 11 3.94731838006 × 10 − 8 0.20 + 0.20 i 1.124509 × 10 − 3 1.5459328261 × 10 − 11 7.01385834213 × 10 − 8 0.20 + 0.10 i 6.947382 × 10 − 4 1.4565446071 × 10 − 11 4.38067448607 × 10 − 8 0.60 + 0.60 i 1.0193823 × 10 − 2 6.6743245152 × 10 − 8 6.10843735118 × 10 − 8 1.00 + 1.00 i 9.404928 × 10 − 3 4.4066271108 × 10 − 5 1.21583262896 × 10 − 7 L ∞ n o r m → 4.026018 × 10 − 2 9.03074377008 × 10 − 5 1.21583262896 × 10 − 7 Fig. 3 Visual depiction of the absolute error analysis of Example 1 for N = 5 ( ℑ m part). Fig. 3 For N = 5 , 9 the tabular comparison and for N = 5 the graphical comparison, the absolute error produced by the present method is compared with the outcomes generated by the Taylor Collocation method and the Bessel Collocation method are shown in Table 3 and in Fig. 4 for ℜ e part, and in Table 4 and in Fig. 5 for ℑ m part. From the above discussion, the tables and the figures claim more approximation accuracy of the proposed method than the mentioned methods. Example 3 Let us examine the fourth-order non-homogeneous CDE that is linear and has variable coefficients , (35) f ″ ″ ( z ) − 2 z f ″ ( z ) + z f ( z ) = 24 + 19 z + 2 z 2 − 29 z 3 + z 5 z ∈ C . Where m = 4 , Q 0 ( z ) = z , Q 1 ( z ) = 0 , Q 2 ( z ) = − 2 z , Q 3 ( z ) = 0 , Q 4 ( z ) = 1 , h ( z ) = 24 + 19 z + 2 z 2 − 29 z 3 + z 5 and subject to the conditions are (36) f ( 0 ) = − 1 , f ′ ( 0 ) = 2 , f ( 1 ) = − 3 , f ′ ( 1 ) = − 4 . The corresponding exact solution is a fourth-degree polynomial that is f ( z ) = − 1 + 2 z − 5 z 2 + z 4 . Assume the Galerkin integral domain D = { z ∈ C , z = x + i y , i = − 1 ; − 1 ≤ x ≤ 1 , − 1 ≤ y ≤ 1 } . Table 3 Absolute error E N ( z ) analysis of Example 2 ( ℜ e part) for N = 5 , 9 . Table 3 z j Taylor Collocation E 5 ( z j ) ( ℜ e part) Bessel Collocation E 5 ( z j ) ( ℜ e part) Present method E 5 ( z j ) ( ℜ e part) − 1.00 − 1.00 i 2.4326713 × 10 − 1 5.89986041904 × 10 − 2 2.85970519951 × 10 − 4 − 0.60 − 0.60 i 5.740202 × 10 − 2 8.45000949478 × 10 − 3 2.10298845644 × 10 − 4 − 0.20 − 0.10 i 2.81797 × 10 − 4 1.64846642128 × 10 − 4 7.01455471542 × 10 − 6 − 0.20 − 0.20 i 2.212166 × 10 − 3 1.90988842115 × 10 − 4 9.74400536351 × 10 − 6 − 0.10 − 0.20 i 1.532345 × 10 − 3 2.54330433371 × 10 − 4 1.00394364354 × 10 − 5 − 0.10 + 0.20 i 1.532345 × 10 − 3 2.54330433371 × 10 − 4 1.66588489879 × 10 − 5 − 0.10 − 0.10 i 2.775133 × 10 − 4 2.06556950317 × 10 − 5 4.54004234077 × 10 − 7 − 0.10 + 0.10 i 2.775133 × 10 − 4 2.06556950319 × 10 − 5 4.89361311986 × 10 − 6 0.00 + 0.00 i 0 8.5541519885 × 10 − 13 0 0.10 + 0.10 i 2.775107 × 10 − 4 1.4879698500 × 10 − 5 3.72562526995 × 10 − 6 0.10 − 0.10 i 2.775107 × 10 − 4 1.4879698500 × 10 − 5 1.48814842361 × 10 − 6 0.10 − 0.20 i 1.530695 × 10 − 3 6.2325482521 × 10 − 5 1.20027715801 × 10 − 5 0.10 + 0.20 i 1.530695 × 10 − 3 6.2325482521 × 10 − 5 1.92300221186 × 10 − 5 0.20 + 0.20 i 2.212125 × 10 − 3 9.8572923268 × 10 − 5 2.20274275447 × 10 − 5 0.20 + 0.10 i 2.80156 × 10 − 4 1.3159328847 × 10 − 4 3.08519534006 × 10 − 6 0.60 + 0.60 i 5.739872 × 10 − 2 9.6432020506 × 10 − 4 2.43755696897 × 10 − 4 1.00 + 1.00 i 2.432416 × 10 − 1 1.2386559787 × 10 − 3 4.93934953302 × 10 − 5 L ∞ n o r m → 2.4326713 × 10 − 1 5.89986041904 × 10 − 2 2.85970519951 × 10 − 4 z j Taylor Collocation E 9 ( z j ) ( ℜ e part) Bessel Collocation E 9 ( z j ) ( ℜ e part) Present method E 9 ( z j ) ( ℜ e part) − 1.00 − 1.00 i 1.2078499 × 10 − 2 4.115916475067 × 10 − 5 1.74967622014 × 10 − 8 − 0.60 − 0.60 i 2.484785 × 10 − 3 9.820587112296 × 10 − 6 1.20111606850 × 10 − 8 − 0.20 − 0.10 i 1.1912 × 10 − 5 4.429130137650 × 10 − 8 4.51597524796 × 10 − 10 − 0.20 − 0.20 i 8.97123 × 10 − 5 1.804838386798 × 10 − 7 2.22042272375 × 10 − 9 − 0.10 − 0.20 i 6.22013 × 10 − 5 1.093696364723 × 10 − 7 1.95549871291 × 10 − 9 − 0.10 + 0.20 i 6.22013 × 10 − 5 1.093696364723 × 10 − 7 2.23261008978 × 10 − 9 − 0.10 − 0.10 i 1.11861 × 10 − 5 1.616685911531 × 10 − 8 3.17901463180 × 10 − 10 − 0.10 + 0.10 i 1.11861 × 10 − 5 1.616685925409 × 10 − 8 4.41483525557 × 10 − 10 0.00 + 0.00 i 0 6.49920795016 × 10 − 13 0 0.10 + 0.10 i 1.11835 × 10 − 5 7.01703836702 × 10 − 9 3.25059744464 × 10 − 10 0.10 − 0.10 i 1.11835 × 10 − 5 7.01703836702 × 10 − 9 4.33283141427 × 10 − 10 0.10 − 0.20 i 6.05515 × 10 − 5 2.45374184859 × 10 − 9 2.13889327312 × 10 − 9 0.10 + 0.20 i 6.05515 × 10 − 5 2.45374230656 × 10 − 9 1.94741138691 × 10 − 9 0.20 + 0.20 i 8.96715 × 10 − 5 3.44256884110 × 10 − 8 2.24201153179 × 10 − 9 0.20 + 0.10 i 1.0271 × 10 − 5 3.54173038119 × 10 − 8 4.14418753795 × 10 − 10 0.60 + 0.60 i 2.481483 × 10 − 3 1.98254734962 × 10 − 7 1.10180060964 × 10 − 8 1.00 + 1.00 i 1.2053017 × 10 − 2 4.60677713531 × 10 − 7 1.20032742658 × 10 − 9 L ∞ n o r m → 1.2078499 × 10 − 2 4.115916475067 × 10 − 5 1.74967622014 × 10 − 8 Fig. 4 Visual depiction of the absolute error analysis of Example 2 for N = 5 ( ℜ e part). Fig. 4 Table 4 Absolute error E N ( z ) analysis of Example 2 ( ℑ m part) for N = 5 , 9 . Table 4 z j Taylor Collocation E 5 ( z j ) ( ℑ m part) Bessel Collocation E 5 ( z j ) ( ℑ m part) Present method E 5 ( z j ) ( ℑ m part) − 1.00 − 1.00 i 3.09312 × 10 − 1 1.85893527304 × 10 − 2 4.53596288293 × 10 − 4 − 0.60 − 0.60 i 6.25585 × 10 − 2 6.68868421674 × 10 − 3 3.55780327721 × 10 − 4 − 0.20 − 0.10 i 1.52585 × 10 − 3 3.19933894691 × 10 − 4 9.73150983529 × 10 − 6 − 0.20 − 0.20 i 2.23548 × 10 − 3 5.46524065895 × 10 − 4 2.04215934919 × 10 − 5 − 0.10 − 0.20 i 2.73608 × 10 − 4 1.68336224113 × 10 − 4 1.21921823707 × 10 − 6 − 0.10 + 0.20 i 2.73608 × 10 − 4 1.68336224113 × 10 − 4 1.01332516060 × 10 − 5 − 0.10 − 0.10 i 2.78721 × 10 − 4 1.19731980447 × 10 − 4 2.29471931160 × 10 − 6 − 0.10 + 0.10 i 2.78721 × 10 − 4 1.19731980447 × 10 − 4 1.38653178968 × 10 − 6 0.00 + 0.00 i 0 2.21437262029 × 10 − 28 0 0.10 + 0.10 i 2.77624 × 10 − 4 8.46333017160 × 10 − 5 3.92533850752 × 10 − 6 0.10 − 0.10 i 2.77624 × 10 − 4 8.46333017160 × 10 − 5 2.93506904779 × 10 − 6 0.10 − 0.20 i 2.75787 × 10 − 4 2.05738371287 × 10 − 4 1.61657156734 × 10 − 6 0.10 + 0.20 i 2.75787 × 10 − 4 2.05738371287 × 10 − 4 1.36086931283 × 10 − 7 0.20 + 0.20 i 2.2311 × 10 − 3 2.70937062759 × 10 − 4 2.74365619024 × 10 − 5 0.20 + 0.10 i 1.52364 × 10 − 3 1.23452638561 × 10 − 4 1.75887713028 × 10 − 5 0.60 + 0.60 i 6.2519 × 10 − 2 6.68465941159 × 10 − 4 4.66194239589 × 10 − 4 1.00 + 1.00 i 3.09202 × 10 − 2 1.84717548590 × 10 − 3 1.02296380001 × 10 − 3 L ∞ n o r m → 3.09312 × 10 − 1 1.85893527304 × 10 − 2 1.02296380001 × 10 − 3 z j Taylor Collocation E 9 ( z j ) ( ℑ m part) Bessel Collocation E 9 ( z j ) ( ℑ m part) Present method E 9 ( z j ) ( ℑ m part) − 1.00 − 1.00 i 1.03425 × 10 − 2 2.48152172653 × 10 − 4 1.85987290531 × 10 − 8 − 0.60 − 0.60 i 2.36461 × 10 − 3 9.64545246917 × 10 − 6 1.70240876063 × 10 − 8 − 0.20 − 0.10 i 6.26632 × 10 − 5 1.61341194057 × 10 − 7 1.91601385659 × 10 − 9 − 0.20 − 0.20 i 9.13166 × 10 − 5 1.90064818800 × 10 − 7 2.85283863781 × 10 − 9 − 0.10 − 0.20 i 1.0171 × 10 − 5 2.27097510518 × 10 − 8 3.21724716929 × 10 − 11 − 0.10 + 0.20 i 1.0171 × 10 − 5 2.27097510518 × 10 − 8 7.67970593828 × 10 − 10 − 0.10 − 0.10 i 1.17155 × 10 − 5 4.33176848557 × 10 − 8 4.15969033987 × 10 − 10 − 0.10 + 0.10 i 1.17155 × 10 − 5 4.33176844949 × 10 − 8 3.71587874864 × 10 − 10 0.00 + 0.00 i 0 4.44685976728 × 10 − 27 0 0.10 + 0.10 i 1.06186 × 10 − 5 2.27537023805 × 10 − 8 3.87344639278 × 10 − 10 0.10 − 0.10 i 1.06186 × 10 − 5 2.27537023805 × 10 − 8 3.33844698485 × 10 − 10 0.10 − 0.20 i 1.23496 × 10 − 5 5.64710605222 × 10 − 8 8.27722774918 × 10 − 10 0.10 + 0.20 i 1.23496 × 10 − 5 5.64710598283 × 10 − 8 3.43136409355 × 10 − 11 0.20 + 0.20 i 8.6929 × 10 − 5 5.89471798040 × 10 − 8 2.79208080708 × 10 − 9 0.20 + 0.10 i 6.04542 × 10 − 5 2.75819321549 × 10 − 8 1.86596570940 × 10 − 9 0.60 + 0.60 i 2.31512 × 10 − 3 1.56472432677 × 10 − 7 2.10176396896 × 10 − 8 1.00 + 1.00 i 1.02328 × 10 − 2 2.25562201583 × 10 − 7 4.69233857440 × 10 − 8 L ∞ n o r m → 1.03425 × 10 − 2 2.48152172653 × 10 − 4 4.69233857440 × 10 − 8 Fig. 5 Visual depiction of the absolute error analysis of Example 2 for N = 5 ( ℑ m part). Fig. 5 For N = 4 , by applying the proposed method discussed in Method details: Phase 1 and Method details: Phase 2 section, we obtain the required augmented matrix as follows: [ K * : C * ] = [ 0 − 4 3 + 4 i 3 0 12 5 − 44 i 15 2 + 2 i : 163 3 + 152 i 3 1 0 0 0 0 : − 1.00 0 1 0 0 0 : 2.00 1 1 1 2 1 6 1 24 : − 3.00 0 1 1 1 2 1 6 : − 4.00 ] . By solving the above matrix for unknown coefficients, then the coefficients a j become a 0 = − 1.00 , a 1 = 2.00 , a 2 = − 10.00 , a 3 = 0.00 , and a 4 = 24.00 . Therefore, the approximate solution of the Eq. (35) becomes f ˜ 4 ( z ) = − 1.00 + 2.00 z − 5.00 z 2 + z 4 = f ( z ) . Example 3 demonstrates how the approximate outcomes generated by the proposed method match perfectly the exact solution of the CDE if the exact solution of the CDE is in the N degree or less than N degree polynomial form. Example 4 Let us examine the second-order non-linear non-homogeneous CDE , (37) f ″ + ( e z − e − z ) ( f ′ ) 2 − ( e z + 1 ) f = 1 ; z ∈ C where subject to the initial conditions are (38) f ( 0 ) = − 1 / 2 , f ′ ( 0 ) = 1 / 4 . Now consider an approximate solution f ˜ 3 ( z ) by the N = 3 degree Taylor polynomial at z 0 = 0 in the following form (39) f ˜ 3 ( z ) = ∑ j = 0 3 a j z j j ! . Thus, we have θ j ( z ) = z j j ! for j = 0 , 1 , 2 , 3 and θ i ( z ) = z i i ! for i = 0 , 1 , 2 , 3 . By solving the system of nonlinear Eq. (41) , the unknown Taylor coefficients a j become a 0 = − 0.500 − 1.722344475 × 10 − 16 i . a 1 = 0.2500 + 8.978526104 × 10 − 16 i , a 2 = 0.4501617132 + 0.2833277909 i , and a 3 = 0.7341905712 − 0.2539949629 i . For N = 3 , 4 , 5 the absolute error E N ( z ) generated by the present method are shown in Table 5 and in Fig. 6 for ℜ e part, and in Table 6 and in Fig. 7 for ℑ m part. Example 5 Let us examine the second-order non-linear non-homogeneous CDE , (42) f 3 f ′ + ( f ′ ) 3 + f f ″ + 3 f 2 ( f ″ ) 2 = 64 e 4 z + 8 e 3 z + 4 e 2 z ; z ∈ C where subject to the initial conditions are (43) f ( 0 ) = 2 , f ′ ( 0 ) = 2 . Table 5 Absolute error E N ( z ) analysis of Example 4 ( ℜ e part) for N = 3 , 4 , 5 . Table 5 z j Absolute error E N ( z ) analysis ( ℜ e part) E 3 ( z j ) ( ℜ e part) E 4 ( z j ) ( ℜ e part) E 5 ( z j ) ( ℜ e part) -1.00-1.00i 9.12210175 × 10 − 2 4.31795313 × 10 − 1 1.94703639 × 10 − 4 -0.90-0.90i 8.80103073 × 10 − 2 4.08792338 × 10 − 1 1.70246213 × 10 − 4 -0.80-0.80i 8.10764674 × 10 − 2 3.61981716 × 10 − 1 1.58242201 × 10 − 4 -0.70-0.70i 7.11610504 × 10 − 2 3.01161415 × 10 − 1 1.43632273 × 10 − 4 -0.60-0.60i 5.91154570 × 10 − 2 2.34654142 × 10 − 1 1.19582451 × 10 − 4 -0.50-0.50i 4.58883571 × 10 − 2 1.69319919 × 10 − 1 8.75903101 × 10 − 5 -0.40-0.40i 3.25096110 × 10 − 2 1.10572163 × 10 − 1 5.40924242 × 10 − 5 -0.30-0.30i 2.00733527 × 10 − 2 6.23946022 × 10 − 2 2.62331769 × 10 − 5 -0.20-0.20i 9.72194476 × 10 − 3 2.73573224 × 10 − 2 8.50546983 × 10 − 6 -0.10-0.10i 2.63162954 × 10 − 3 6.63111334 × 10 − 3 1.08762437 × 10 − 6 0.00+0.00i 0.00000 0.00000 0.00000 0.10+0.10i 3.03492628 × 10 − 3 5.87231509 × 10 − 3 1.28267091 × 10 − 6 0.20+0.20i 1.29442785 × 10 − 2 2.12909765 × 10 − 2 9.42271958 × 10 − 6 0.30+0.30i 3.09256497 × 10 − 2 4.19437644 × 10 − 2 2.88109773 × 10 − 5 0.40+0.40i 5.81552821 × 10 − 2 6.21743947 × 10 − 2 5.99544406 × 10 − 5 0.50+0.50i 9.57755383 × 10 − 2 7.49950482 × 10 − 2 9.93196530 × 10 − 5 0.60+0.60i 1.44880552 × 10 − 1 7.21007195 × 10 − 2 1.40995803 × 10 − 4 0.70+0.70i 2.06500185 × 10 − 1 4.38852617 × 10 − 2 1.80054017 × 10 − 4 0.80+0.80i 2.81583105 × 10 − 1 2.05416962 × 10 − 2 2.16778545 × 10 − 4 0.90+0.90i 3.70980714 × 10 − 1 1.33338671 × 10 − 1 2.60059321 × 10 − 4 1.00+1.00i 4.75434564 × 10 − 1 3.07919747 × 10 − 1 3.27285753 × 10 − 4 L ∞ n o r m → 4.75434564 × 10 − 1 4.31795313 × 10 − 1 3.27285753 × 10 − 4 Fig. 6 Visual depiction of the absolute error functions with the present method of Example 4 for N = 3 , 4 , 5 ( ℜ e part). Fig. 6 Table 6 Absolute error E N ( z ) analysis of Example 4 ( ℑ m part) for N = 3 , 4 , 5 . Table 6 z j Absolute error E N ( z ) analysis ( ℑ m part) E 3 ( z j ) ( ℑ m part) E 4 ( z j ) ( ℑ m part) E 5 ( z j ) ( ℑ m part) -1.00-1.00i 7.27147628 × 10 − 2 5.58946462 × 10 − 1 7.43492113 × 10 − 4 -0.90-0.90i 9.01205576 × 10 − 2 3.82972581 × 10 − 1 6.96983381 × 10 − 4 -0.80-0.80i 9.57821070 × 10 − 2 2.49380915 × 10 − 1 6.54712150 × 10 − 4 -0.70-0.70i 9.20556334 × 10 − 2 1.51678245 × 10 − 1 5.70944575 × 10 − 4 -0.60-0.60i 8.13112453 × 10 − 2 8.36991376 × 10 − 2 4.47654187 × 10 − 4 -0.50-0.50i 6.59110889 × 10 − 2 3.96277885 × 10 − 2 3.08486537 × 10 − 4 -0.40-0.40i 4.81954325 × 10 − 2 1.40119408 × 10 − 2 1.80656303 × 10 − 4 -0.30-0.30i 3.04760098 × 10 − 2 1.76954077 × 10 − 3 8.41035673 × 10 − 5 -0.20-0.20i 1.50353289 × 10 − 2 1.81057099 × 10 − 3 2.66155254 × 10 − 5 -0.10-0.10i 4.13047212 × 10 − 3 1.07245975 × 10 − 3 3.43966990 × 10 − 6 0.00+0.00i 1.72234447 × 10 − 16 6.49808461 × 10 − 17 1.18191659 × 10 − 18 0.10+0.10i 4.87276214 × 10 − 3 2.22568680 × 10 − 3 3.47874091 × 10 − 6 0.20+0.20i 2.09776082 × 10 − 2 1.10403465 × 10 − 2 2.64474268 × 10 − 5 0.30+0.30i 5.05530986 × 10 − 2 2.94028477 × 10 − 2 8.25089107 × 10 − 5 0.40+0.40i 9.58563157 × 10 − 2 5.99489123 × 10 − 2 1.74793787 × 10 − 4 0.50+0.50i 1.59169768 × 10 − 1 1.04998019 × 10 − 1 2.93244181 × 10 − 4 0.60+0.60i 2.42805188 × 10 − 1 1.66557203 × 10 − 1 4.15000569 × 10 − 4 0.70+0.70i 3.49102846 × 10 − 1 2.46320368 × 10 − 1 5.09280062 × 10 − 4 0.80+0.80i 4.80424886 × 10 − 1 3.45661622 × 10 − 1 5.48220135 × 10 − 4 0.90+0.90i 6.39141418 × 10 − 1 4.65621372 × 10 − 1 5.24981520 × 10 − 4 1.00+1.00i 8.27608664 × 10 − 1 6.06884467 × 10 − 1 4.79783556 × 10 − 4 L ∞ n o r m → 8.27608664 × 10 − 1 6.06884467 × 10 − 1 7.43492113 × 10 − 4 Fig. 7 Visual depiction of the absolute error functions with the present method of Example 4 for N = 3 , 4 , 5 ( ℑ m part). Fig. 7 Now for N = 3 , 5 , 7 the absolute error E N ( z ) generated by the present method are shown in Table 7 and in Fig. 8 for ℜ e part, and in Table 8 and in Fig. 9 for ℑ m part. Table 7 Absolute error E N ( z ) analysis of Example 5 ( ℜ e part) for N = 3 , 5 , 7 . Table 7 z j Absolute error E N ( z ) analysis ( ℜ e part) E 3 ( z j ) ( ℜ e part) E 5 ( z j ) ( ℜ e part) E 7 ( z j ) ( ℜ e part) -1.00-1.00i 4.04211484 × 10 − 1 5.46973308 × 10 − 2 8.17365239 × 10 − 4 -0.90-0.90i 2.43011014 × 10 − 1 5.13235575 × 10 − 2 7.10864263 × 10 − 4 -0.80-0.80i 1.27495547 × 10 − 1 4.47224516 × 10 − 2 5.91143240 × 10 − 4 -0.70-0.70i 5.00375561 × 10 − 2 3.64450535 × 10 − 2 4.52687224 × 10 − 4 -0.60-0.60i 3.26081238 × 10 − 3 2.77220908 × 10 − 2 3.11176527 × 10 − 4 -0.50-0.50i 1.99062140 × 10 − 2 1.94813171 × 10 − 2 1.86293963 × 10 − 4 -0.40-0.40i 2.61718651 × 10 − 2 1.23699396 × 10 − 2 9.21692603 × 10 − 5 -0.30-0.30i 2.18178852 × 10 − 2 6.78196884 × 10 − 3 3.35881162 × 10 − 5 -0.20-0.20i 1.26314005 × 10 − 2 2.89017748 × 10 − 3 6.23900354 × 10 − 6 -0.10-0.10i 3.83277158 × 10 − 3 6.82186967 × 10 − 4 5.58258273 × 10 − 7 0.00+0.00i 0.000000 0.0000000 0.0000000 0.10+0.10i 4.99059643 × 10 − 3 5.82071389 × 10 − 4 3.95317573 × 10 − 6 0.20+0.20i 2.18620755 × 10 − 2 2.10675218 × 10 − 3 1.99906117 × 10 − 5 0.30+0.30i 5.27925309 × 10 − 2 4.23565379 × 10 − 3 4.98925287 × 10 − 5 0.40+0.40i 9.90030631 × 10 − 2 6.65515886 × 10 − 3 8.98798595 × 10 − 5 0.50+0.50i 1.60684186 × 10 − 1 9.11395386 × 10 − 3 1.32493224 × 10 − 4 0.60+0.60i 2.36928720 × 10 − 1 1.14540761 × 10 − 2 1.69648901 × 10 − 4 0.70+0.70i 3.25674085 × 10 − 1 1.36325560 × 10 − 2 1.95991325 × 10 − 4 0.80+0.80i 4.23657367 × 10 − 1 1.57302960 × 10 − 2 2.11223200 × 10 − 4 0.90+0.90i 5.26386952 × 10 − 1 1.79443689 × 10 − 2 2.19632988 × 10 − 4 1.00+1.00i 6.28135051 × 10 − 1 2.05594357 × 10 − 2 2.24560538 × 10 − 4 L ∞ n o r m → 6.28135051 × 10 − 1 5.46973308 × 10 − 2 8.17365239 × 10 − 4 Fig. 8 Visual depiction of the absolute error functions with the present method of Example 5 for N = 3 , 5 , 7 ( ℜ e part). Fig. 8 Table 8 Absolute error E N ( z ) analysis of Example 5 ( ℑ m part) for N = 3 , 5 , 7 . Table 8 z j Absolute error E N ( z ) analysis ( ℑ m part) E 3 ( z j ) ( ℑ m part) E 5 ( z j ) ( ℑ m part) E 7 ( z j ) ( ℑ m part) -1.00-1.00i 1.03611560 × 10 0 6.01045938 × 10 − 2 1.41528255 × 10 − 4 -0.90-0.90i 8.06736405 × 10 − 1 4.54161578 × 10 − 2 2.00690619 × 10 − 4 -0.80-0.80i 6.12934359 × 10 − 1 3.20963001 × 10 − 2 2.70031006 × 10 − 4 -0.70-0.70i 4.51415193 × 10 − 1 2.09823648 × 10 − 2 3.04581051 × 10 − 4 -0.60-0.60i 3.19141665 × 10 − 1 1.24304057 × 10 − 2 2.93049274 × 10 − 4 -0.50-0.50i 2.13331451 × 10 − 1 6.42316081 × 10 − 3 2.43897582 × 10 − 4 -0.40-0.40i 1.31448920 × 10 − 1 2.67192486 × 10 − 3 1.74573442 × 10 − 4 -0.30-0.30i 7.11896499 × 10 − 2 7.11149655 × 10 − 4 1.03758386 × 10 − 4 -0.20-0.20i 3.04564010 × 10 − 2 1.54952933 × 10 − 5 4.63952780 × 10 − 5 -0.10-0.10i 7.32518528 × 10 − 3 7.89612721 × 10 − 5 1.11648744 × 10 − 5 0.00+0.00i 1.35958319 × 10 − 16 1.36716890 × 10 − 17 1.92160585 × 10 − 14 0.10+0.10i 6.75470769 × 10 − 3 1.97194680 × 10 − 4 9.15710610 × 10 − 6 0.20+0.20i 2.58605042 × 10 − 2 9.49403187 × 10 − 4 3.13146474 × 10 − 5 0.30+0.30i 5.54973870 × 10 − 2 2.37420107 × 10 − 3 5.81407275 × 10 − 5 0.40+0.40i 9.36480383 × 10 − 2 4.42390989 × 10 − 3 8.27745943 × 10 − 5 0.50+0.50i 1.37972574 × 10 − 1 6.90075890 × 10 − 3 1.01704221 × 10 − 4 0.60+0.60i 1.85662695 × 10 − 1 9.49264756 × 10 − 3 1.15602408 × 10 − 4 0.70+0.70i 2.33273893 × 10 − 1 1.18308464 × 10 − 2 1.28814242 × 10 − 4 0.80+0.80i 2.76534578 × 10 − 1 1.35707846 × 10 − 2 1.47377576 × 10 − 4 0.90+0.90i 3.10131223 × 10 − 1 1.44968166 × 10 − 2 1.75714220 × 10 − 4 1.00+1.00i 3.27468967 × 10 − 1 1.46515281 × 10 − 2 2.12461960 × 10 − 4 L ∞ n o r m → 1.03611560 × 10 0 6.01045938 × 10 − 2 3.04581051 × 10 − 4 Fig. 9 Visual depiction of the absolute error functions with the present method of Example 5 for N = 3 , 5 , 7 ( ℑ m part). Fig. 9 To solve the high-order linear and nonlinear CDEs analytically, is a challenging task. To solve numerically, we provide the TGM in a rectangular domain, and based on the Taylor polynomials. A fascinating feature of the suggested method is its ability to yield precise results in instances when the linear CDE has an exact solution that is represented by a polynomial of degree N or less than N . For the linear CDE, the tabular and graphical comparisons reveal that the method we suggested is more accurate and stable than the existing Collocation method. For the nonlinear CDE, our proposed method goes to the accurate solution when N is sufficiently large enough. Those reveal the validity of our proposed method but it comes with greater computational complexity due to the need to compute higher-order terms. | Study | biomedical | en | 0.999997 |
PMC11697261 | Industrial production of AA is dominated by thermocatalytic processes, including carbonylation of methanol and oxidation of acetaldehyde and hydrocarbons. 3 Different chemo-catalytic methods for the conversion of CO 2 into AA have been proposed, using either H 2 , 5 methanol + H 2 , 6 methane, 7 or electricity 8 to drive CO 2 reduction. An interesting alternative to the chemical methods is based on biotechnology and involves the use of microorganisms or enzymes to catalyze that conversion. It is generally accepted that biotechnological methods show some advantageous properties when compared with chemical ones, such as ambient temperature and pressure operation, which reduces energy costs, and a high selectivity and specificity, which avoids byproduct generation. The main biological process for the conversion of CO 2 into AA is carried out by autotrophic acetogenic bacteria and involves the Wood–Ljungdahl pathway. 9 The main electron donors used by acetogens to drive reduction of CO 2 are H 2 and CO, but a wide range of organic compounds can also be used. 9 Alternatively, electrons can also be supplied by electricity, through the so-called microbial electrosynthesis, 10 or light, by means of organic semiconductor-bacteria biohybrid photosynthetic systems. 11 Unfortunately, the industrial implementation of a biotechnological process for the conversion of CO 2 into AA is currently a great challenge. The slow growth and low productivity of acetogens under autotrophic conditions, resulting from metabolic energy limitations, and the low solubility of gaseous substrates are important hurdles to overcome. 12 A key factor to consider regarding AA and any other chemical's production processes, in general, is the need of recovering and purifying them through the so-called downstream processing. The aim of downstream processing is the efficient, reproducible, and safe recovery of the targeted product to the required specification (biological activity, purity, etc. ), while maximizing recovery yield and minimizing costs. Product separation and purification from bioprocess media is often a complex task accounting for a significant share of the process costs (around 50–70%) of the total production cost can be attributed to it, 13 mainly due to the low concentrations of the target molecules in the production media and the complexity of it. This is of special relevance for CO 2 (gas)-derived products, that usually are present at much lower concentrations than, for example, their sugar-derived counterparts, so increasing downstream complexity and costs. 14 Therefore, the development of efficient and cost-effective downstream processes for product recovery and purification is a mandatory need for industrial feasibility and accordingly, efficient, and non-energy intensive downstream technologies are preferred. Recovery of AA from fermentation media has several key challenges, derived from its high solubility in aqueous media and its relatively low concentration. The concentration of AA in typical fermentation broths may vary over a wide range but is generally less than 10% by weight. 15 Therefore, its recovery in pure form involves separation from a large quantity of water. Different methods have been used for AA separation from fermentation broths, including distillation (simple, reactive, azeotropic, extractive), extraction or reactive extraction, supercritical fluid extraction, precipitation, crystallization, adsorption, ion-exchange, electrodialysis/electrodeionization, pressure-driven membrane methods, and pervaporation. 16,17 Distillation and precipitation are the most conventional industrial methods, but they are neither economically nor environmentally feasible at the low concentrations found in fermentation broths. Furthermore, the presence of various ions (phosphate, chloride, sulfate, proteins) in significant amounts must be considered when designing a downstream separation process, since they could strongly interfere with the purification of AA. Ion exchange (IEX) is found among the non-energy intensive technologies often used in downstream processing. IEX is a separation technique where insoluble polymers having different positively or negatively functional groups, called IEX resins, are used. These resins, normally present in the form of porous microbeads, membranes, or granules, have the potential to bind the ions of opposite charge. IEX, a separation process that do not require high power input, is widely used in bioseparations, in the recovery of organic acids, including AA, from aqueous fermentation media. 16–18 Very often these IEX separation processes claim to address the recovery and purification of AA from diluted solutions but, when they talk about “diluted solutions”, they are referring to AA concentrations in the range 1–10 g L −1 , that is, one to two orders of magnitude higher than the concentrations usually available in CO 2 -derived bioprocesses, which gives an idea of the extreme difficulty of the task. In addition, purification of diluted carboxylic acids from bioprocess media using separation technologies based on electrical charge, such as IEX, poses a great challenge, not only due to the extremely low concentration of the target product, but also due to the complex composition of the media. Many of the different chemicals present in these media, mainly inorganic salts and low molecular weight charged organic compounds, often in concentrations higher than those of the carboxylic acid to be purified, can potentially interfere, by competition, with its purification, and would result in a lower recovery yield and a product having higher levels of impurities. In the framework of the Horizon Europe Photo2Fuel project ( https://www.photo2fuel.eu/ ), an artificial photosynthesis process for the conversion of CO 2 into AA using a hybrid system of non-photosynthetic bacteria and organic photosensitisers is addressed, with sunlight as the only energy source. 11 As the effluents obtained are characterized by the extremely low concentrations of AA, a suitable downstream processing should be developed not only to efficiently recover and purify the acid, but also to concentrate it. In this paper, such a downstream process is presented, involving the use of mixed bed IEX resins. The process is carried out in two steps: a first step to remove the contaminating mineral anions from the medium (demineralization), while AA remains in solution, and a second step to recover and concentrate AA from the mineral anions-free medium. AA separation and purification experiments were carried out starting from DPM medium, a model solution with the following composition: 0.1 g L −1 AA, 0.4 g L −1 NaCl, 0.64 g L −1 K 2 HPO 4 , 1.5 g L −1 KH 2 PO 4 , 0.4 g L −1 NH 4 Cl, 0.33 g L −1 MgSO 4 ·7H 2 O, 0.05 g L −1 CaCl 2 , 0.25 g L −1 KCl and 2.5 g L −1 NaHCO 3 . This model solution was based on defined photosynthesis medium (the actual DPM medium), 11 the medium where artificial photosynthesis would be performed, but only containing its main inorganic salts. The amount of AA supplemented to this medium reflects the target concentration expected to be reached upon artificial photosynthesis. The minor components of the original DPM medium, trace mineral mix and Wolfe's vitamin mix, were omitted as they were considered not relevant for the different separation procedures to be applied. Although this model solution was slightly different from the original DPM medium this name was maintained in this work. Concentration of AA (acetate) and inorganic anions (chloride, sulfate, and phosphate) was quantified by ion chromatography, using a Metrohm 930 Compact IC Flex ion chromatograph equipped with a conductivity detector. Anion separation was carried out in sequential suppressor mode on a Metrosep A Supp 19 – 250/4.0 analytical column connected in series with a Metrosep A Supp 19 Guard/4.0 precolumn. A gradient elution with the eluents A (4 mM Na 2 CO 3 ) and B (20 mM Na 2 CO 3 ) was used in the chromatographic separation as follows (flow rate, 0.7 mL min −1 ): eluent 100% A was initially held for 15 min, then this proportion was reduced to 20% in 25 min while that of B was increased from 0 to 80% and held for 10 min; finally, the proportion of B was reduced to zero while that of A increased to 100% in the next 0.1 min and held for 10 min. A solution of 500 mM H 2 SO 4 /100 mM oxalic acid/20% acetone was used as the regenerant. Column temperature was set at 35 °C and sample volume was 20 μL. DPM medium, the medium from which AA is to be purified, without being a very complex medium, contains a mixture of salts in the form of bicarbonates, phosphates, sulfates, and chlorides. And, actually, AA is in minority with respect to the mineral anions: 100 mg L −1 AA, 1816 mg L −1 bicarbonate, 129 mg L −1 sulfate, 659 mg L −1 chloride and 1411 mg L −1 phosphate. As these inorganic anions could presumably interfere with the purification of AA through IEX 19 a demineralization pretreatment of DPM medium was considered to be required to remove them and improve the subsequent purification of AA. Demineralization of DPM medium was first addressed by IEX, using the Amberlite MB20 resin, a mixed bed resin containing both a strong acid cation exchange resin and a strong base anion exchange resin, supplied in the H and OH forms, respectively. This resin would allow the removal of both cations and anions in only one step. The chemical forms of the resin mean that cations in solution would be exchanged by protons (H + ) in the resin and anions in solution would be exchanged by hydroxyl anions (OH − ) in the resin. So, cation and anion binding to the resin would result in acidification and alkalinization of the solution, respectively. If the number of cation and anion equivalents bound to the resin are the same, so will the H + and OH − ions released, which would neutralize each other, and the pH of the solution should not be altered. The key to demineralize the DPM medium with the IEX resin without, at the same time, also removing the AA, was to adjust medium to an acidic pH value well below its p K a , so that AA (a weak acid) is undissociated and, therefore, uncharged, while mineral anions remain still charged. This would consequently allow mineral anions to bind to the resin, but not AA, which would remain free in solution. Such kind of demineralization process was previously proposed for the purification of lactic acid from fermentation broths. 20 According to its p K a (4.76), 99.45% of AA would be undissociated at pH 2.5, so this pH was selected to perform the demineralization tests. Mineral anions would remain charged at this pH according to their p K a values: phosphoric acid p K a1 2.12, sulfuric acid p K a2 1.92, and hydrochloric acid p K a −6.3. A very relevant difference would occur for carbonic acid (p K a1 6.35) however, as will be explained at the end of this section. Consequently, HCl-acidified DPM medium (pH 2.5) was treated, in batch, with the Amberlite MB20 resin at different resin to medium ratios ranging from 10 to 200 g of resin per L of DPM medium. Results are shown in Fig. 1 . Two parameters, pH and conductivity at equilibrium, were directly measured to evaluate the effect of the treatments. pH remained practically unchanged around the initial pH of acidified DPM medium for ratios up to 75 g L −1 . At higher ratios medium pH increased, to around 4.1 at 100 g L −1 and near 9 at 200 g L −1 . As previously explained, if the same number of equivalents of cations and anions are bound to the resin, it was expected that the pH of the medium remained unchanged because the released H + and OH − ions would neutralize each other. Therefore, such pH increase would be the result of an unbalanced binding of cations and anions to Amberlite MB20, being higher that of the latter. According to the manufacturer, the percent volume of the anion exchange resin in Amberlite MB20 exceeds that of the cation exchange resin (62–56% vs. 38–44%). So, when the cation binding sites of the resin are saturated, more anions can still be bound, which would result in a net alkalinization of the solution. Conductivity of DPM medium strongly decreased from its initial value of 9.5 mS cm −1 to virtually zero (60 μS cm −1 ) by increasing resin to medium ratio to 100 g L −1 . This decrease was quite linear and would reflect the removal of charged ions from the solution. So, apparently, the resin was very efficient in removing the salts. When the salt composition of DPM medium after IEX resin treatment was analyzed several conclusions could be extracted. First, the efficient removal of all the anions suggested by conductivity measurements was confirmed. All the anions, including acetate, were totally removed from DPM medium at 100 g L −1 resin. At a slightly lower resin to medium ratio, 75 g L −1 , 97% of sulfate, 96% of chloride and 80% of phosphate were removed, while 86% of AA remained in solution. Second, the binding selectivity sequence of the anions to the resin, which reflects the affinity of the resin to them, was sulfate > chloride > phosphate (dihydrogen) > acetate, which agreed with the data reported in literature, 21 so suggesting that this treatment could be very suitable to carry out demineralization of DPM medium because among the anions present the affinity of the resin for acetate (as free AA) was the lowest one. However, as explained above, when the resin to medium ratio was higher than 100 g L −1 , all the acetate was removed from the solution, which likely was a result of the parallel pH increase observed. The pH increase approaching and surpassing the p K a of AA would displace equilibrium to the formation of the dissociated and charged acetate form, which could bind to the resin. So, it was very important that the pH of the medium during treatment with the IEX resin is maintained as far as possible in the acidic side from the p K a of AA to avoid its removal. Regarding the mineral anions, sulfate and chloride were almost totally removed at 75 g L −1 resin (97 and 96%, respectively). However, note that this value for the removal of chloride is related to an initial concentration of 2464 mg L −1 , considerably higher than that contained in the original DPM medium (659 mg L −1 ), which is explained by the addition of HCl used to acidify DPM medium to the starting pH of 2.5. So, if the original chloride concentration is considered, the final 96 mg L −1 attained after resin treatment would represent a lower actual removal of 86%. The most reluctant mineral anion to be removed was phosphate, remaining still 20% in solution at 75 g L −1 resin. By slightly increasing the ratio to 80 g L −1 , the removal of phosphate increased to 88%, but then the AA remaining in solution decreased to 64% of the initial value, a loss that was considered unacceptable. The reason under the incomplete removal of phosphate is likely related to the different species found at equilibrium at the pH used (2.5). At this pH, the main species present in solution would be the monovalent dihydrogen phosphate anion (p K a1 2.14), but around 30% of the undissociated and, therefore, uncharged form would be also present, and this later form could not bind to the resin. Finally, some comments about bicarbonate, the most important mineral anion, in concentration terms, found in DPM medium. The ion chromatography method used to quantify the anions did not allow quantification of bicarbonate because the eluent used was Na 2 CO 3 . So, no direct information regarding this anion was available. However, we can suppose with a high degree of accuracy what happens with it. At the pH of the original DPM medium, around 7.1, bicarbonate is the main species found in solution (p K a1 6.35), also appearing a fraction of undissociated carbonic acid. When pH is acidified to 2.5 before IEX resin treatment equilibrium would be totally shifted to the formation of carbonic acid, which, in turn, would decompose to CO 2 and be released from solution as a gas. Therefore, it was expected that at the initial pH of the IEX resin treatment most of the bicarbonate anions would have been removed. In conclusion, it appeared that the strategy used to demineralize DPM medium, without hardly affecting AA, using the mixed bed IEX resin Amberlite MB20 at a ratio of resin to medium of 75 g L −1 at acidic pH would be quite successful. The previous experiment, involving the treatment of DPM medium with the mixed bed IEX resin Amberlite MB20 at pH 2.5, allowed the almost complete removal of sulfate (and probably bicarbonate) anions, and most of chloride, still remaining around 86% of AA in solution. The problem was that more than 20% of phosphate still remained in the medium. So, a new strategy was planned to improve the demineralization extent, involving a calcium-treatment of DPM medium. It is known that phosphate forms very insoluble salts with calcium. Therefore, treatment of phosphate-containing solutions with Ca 2+ was expected to result in the precipitation of different calcium phosphate salts, so removing this anion from solution. The best option of calcium source was considered to be CaO (calcium oxide or quicklime), which is converted into Ca(OH) 2 upon dissolution in water, because its use would have a double benefit. First, no extra anions would be added to the medium, avoiding the need to remove them later. And second, medium pH would become very alkaline, so favouring not only phosphate precipitation, but also bicarbonate removal, because at that pH values equilibrium would be displaced to the formation of carbonate anion (p K a2 10.32), which would precipitate as the very insoluble CaCO 3 salt. Consequently, an amount of CaO sufficient to achieve 60 mM Ca 2+ was added to the DPM medium and was let stirring overnight. This concentration of calcium is in excess from the bicarbonate and phosphate content of the medium, around 30 and 15 mM, respectively. From the solubility data of both calcium salts it was expected that calcium carbonate was precipitated first, and then calcium phosphate. Following CaO addition a dense white precipitate appeared, rising the solution pH from an initial value of 7.14 to 12.40, and also increasing its conductivity from 6.24 to 9.92 mS cm −1 . The medium was then filtered to remove the precipitate, resulting in a clear filtrate with no trace of phosphate . Regarding bicarbonate, although it could not be quantified as explained before, it was also expected to be completely absent from the calcium-treated solution. Around half of sulfate, precipitated as gypsum, was also removed with the calcium treatment. The other anions, chloride and acetate, remained unchanged in solution. So, once achieved the complete removal of phosphate, the most difficult mineral anion to be removed with the Amberlite MB20 resin at acidic pH, experiments of demineralization with this resin could be resumed. As explained before, the best pH to demineralize the medium with this resin, affecting AA as little as possible, was an acidic pH well below its p K a value (4.76). So, the calcium-treated medium had to be acidified from its very alkaline pH of 12.40 to around 2.5 or less. One possibility to get such a strong acidification was to add strong mineral acids ( e.g. , HCl or H 2 SO 4 ), but this would result in an increase in the mineral anion content of the solution, which would complicate further treatment with the IEX resin. The most feasible alternative was the use of a strong acid cation exchange resin in the H form. 20 Treatment of the calcium-precipitated DPM medium (DPM-Ca medium) with this type of resin would allow to remove the cations originally present in it and the excess of calcium likely still remaining after precipitation. And most importantly, the binding of these cations to the resin would be coupled to the release of an equivalent amount, in terms of charge, of protons, so resulting in a pH decrease of the solution. According to these assumptions, DPM-Ca medium was treated with the strong acid cation exchange resin Amberlite IR-120 at a resin to medium ratio of 25 g L −1 , a ratio sufficient to decrease the solution pH and conductivity to 2.30 and 3.72, respectively . This treatment, as expected, did not affect to the concentration of anions still present in solution, which remained unchanged , but strongly decreased the concentration of cations (results not shown). Regarding the anions, in the very unlikely event that there was still some trace of bicarbonate remaining in solution after calcium-precipitation, the strong acidification of the medium would have completed its removal, as it would have been released as CO 2 gas. Finally, this resin-acidified DPM-Ca medium was treated with the mixed bed IEX resin Amberlite MB20 to remove the remaining mineral anions (sulfate and chloride). When the resin to medium ratio was 20 g L −1 the pH increased from 2.30 to 3.15 and the conductivity decreased from 3.72 to 0.44 mS cm −1 . Under these conditions, while the AA concentration in solution was still kept at 91 mg L −1 (from the 100 mg L −1 in the original DPM medium), sulfate was completely removed, and chloride concentration decreased to 63 mg L −1 (from the initial 659 mg L −1 ) . So, as a result of the combined treatments (calcium precipitation, acidification with the Amberlite IR-120 resin and treatment with the IEX resin Amberlite MB20) an almost complete demineralization of the DPM medium was achieved, with total removal of phosphate and sulfate (and likely bicarbonate) and 90% removal of chloride, while 91% of AA still remaining in solution. A comparative summary of the results obtained with both demineralization treatments, with Amberlite MB20 alone and with the above combined treatment, is shown in Table 2 . Results are shown in terms of concentration remaining in solution for four of the five main anions present in DPM medium. The values for the fifth anion, bicarbonate, are not shown because it could not be quantified by the ionic chromatography method used, but are expected to be zero or near zero according to the known behaviour of this anion under the different conditions applied to the medium (calcium-precipitation at alkaline pH and/or strong acidification). Although both treatments were very efficient in demineralizing DPM medium, but still maintaining most of the AA in solution, the combined treatment was somewhat better ( Table 2 ). According to these results of the demineralization process of the DPM medium, a new synthetic simplified solution was prepared to be used in the next steps to purify AA. This synthetic simplified solution, called Demineralized-DPM (DM-DPM) medium, had the following composition: 91 mg L −1 AA and 63 mg L −1 chloride (as NaCl, 104 mg L −1 ), pH 3.15. Once DPM medium was almost completely demineralized, obtaining the DM-DPM medium as explained in the previous section, the recovery of AA from such an extremely diluted solution using IEX resins was addressed. For that, initially four different anion exchange resins were tested, covering all the types available in the market: two weak base anion , one strong base anion (Amberlite IRN78) and one mixed bed strong acid cation and base anion (Amberlite MB20) resins. The resins were used as received, without any prior conditioning. DM-DPM medium was treated in batch with increasing concentrations of the single resins, ranging from 0 to 10 g L −1 , and their capacity to remove AA and chloride from the solution was determined. In addition, changes in pH and conductivity were also recorded. The results of such assays are shown in Fig. 3 . First, weak base anion resins Amberlite IRA-67 and Lewatit VP OC 1065 had a very similar behaviour. Initially, by increasing the concentrations of the resins up to 2 g L −1 , AA was increasingly removed from the starting 100 mg L −1 , reaching its lowest concentration in the medium, around 35 mg L −1 . From that point no additional AA was removed from the solution despite the increase in resin concentration to 10 g L −1 . A similar effect was observed for chloride, that reached a minimum concentration of around 10 mg L −1 at 2 g L −1 resin. Regarding pH, it continuously increased in line with resin concentration from the initial value of 3.15 to around 7.00 at 10 g L −1 resin. Conductivity, in turn, strongly decreased to less than 100 μS cm −1 at the lowest concentration of resins assayed, stabilizing thereafter. The reason behind the limited removal of AA by the weak base anion exchange resins can be probably found in the alkalinization of the medium resulting from the anion exchange activity of the resins, which increased pH to values higher than the p K a of AA, so that its acid–base equilibrium was shifted to the formation of the charged acetate anion. And these weak base anion exchange resins, supplied in free base form, are known to only bind carboxylic acids as charge-neutral units (either through hydrogen bonding or via proton transfer) to maintain the charge neutrality of the adsorbent phase. 19,21 In other words, they can only bind undissociated carboxylic acids, hence the importance of the pH being below the p K a of the acid for a proper removal of it. Moreover, it should be noted that the mere presence of the resins in pure water caused a strong alkalinization to pH close to 9.0 (results not shown), which would be a consequence of the behavior as a weak base of their functional groups (free amines). Amberlite IRN78 strong base anion exchange resin was more efficient for anions removal than the weak base resins, achieving values higher than 90 and 98% for AA and chloride, respectively, using resin concentrations higher than 3.5 g L −1 . Medium pH rapidly rose to very alkaline values, higher than 10.0. As the functional group of the resin is in the OH form, the binding of anions results in the equivalent release of hydroxyl groups, the source of the alkalinization observed. However, unlike what happened with the weak base resins, in this case that alkalinization hardly affected to the extent of the anion binding. The functional group of this resin is trimethylammonium, so that it only binds dissociated, negatively charged, carboxylic acids, which are mainly found at alkaline pH values, when pH > p K a . The comparison between weak and strong base anion exchange resins showed a higher AA removal capacity for the strong base resins, in agreement with other results found in the literature. 16 However, the opposite trend has also been reported, i.e. , better performance of weak base resins compared to strong base ones. 22 This discrepancy can probably be attributed to the different counter-ion present in the strong base anion exchange resins used in those studies. While in ref. 16 and the present work the resins were in the OH form, in ref. 22 they were in the Cl form. And it has been reported that the nature of the counter-ion in the resin influences strongly the exchange equilibrium, suggesting that the OH counter-ion is more easily displaced by the carboxylate anions than the Cl anion. 23 Finally, Amberlite MB20, a mixed bed strong acid cation and base anion resin, behaved similarly to Amberlite IRN78, but with some relevant differences. The removal of AA and chloride was significantly lower with MB20 than with IRN78 at resin concentrations lower than 5 g L −1 , but from this point on the removal was virtually complete with the former, while with the latter it was near but never reached. This difference could be explained by the fact that MB20 is a mixed bed resin, where only about half of it is a base anion resin. Therefore, at the same concentration of resin, the binding capacity of anions by MB20 would be lower (half, approximately) than by IRN78. This means that a two-fold concentration of MB20 would be needed, with respect to IRN78, to get the same result. However, although this explanation might be correct for chloride removal , it does not appear to be correct for AA . The key for this discrepancy would be in the different pH evolution observed with both resins. As explained above, the use of the IRN78 resin resulted in a strong alkalinization of the medium upon anion binding. With the MB20 resin, however, the binding of the anions did not entail such strong pH increase, but it was better controlled . For resin concentrations lower than 5 g L −1 , pH was maintained at values lower than the p K a of AA, so that the acid was mainly in its undissociated uncharged form, which would not bind to the resin. Only from 5 g L −1 of resin the pH rose above the p K a of the acid and, consequently, the concentration of the dissociated charged acetate anion, the species that actually binds to the resin, increased. The pH buffering capacity of the MB20 resin would result from the concerted activity of the base anion and acid cation resins present in it, so that the simultaneous binding of anions and cations would release hydroxyl groups and protons, respectively, that would neutralize each other. Therefore, the binding of AA, a weak acid, would depend on pH, which controls the proportion of acetate available to bind to the resin. On the contrary, as HCl is a strong acid, it is always completely dissociated and available to bind (chloride) independently of the pH. It was previously mentioned that the removal of AA and chloride with the IRN78 resin was near to be complete but was never reached. This effect could result from the strong alkalinization induced upon anion binding, which means that the concentration of OH − anions in the medium increased to such an extent that ultimately could compete for binding sites with the other anions. With the MB20 resin, as pH was better controlled, the concentration of OH − anions would be very low and would not mean a real competition for the other anions, which could be removed completely. An analysis of the graph in detail allowed to differentiate between two scenarios. In the first one , occurring at a resin concentration of 2 g L −1 , the concentration of chloride was decreased from 67 mg L −1 (the concentration in DM-DPM medium) to 7.3 mg L −1 , that is, it was decreased by 89% or, in other words, only 11% of the original chloride remained in solution. Meanwhile, AA concentration only decreased from 99 to 91 mg L −1 , remaining in solution 92% of the initial acid. This means that at that resin concentration most of chloride was removed from DM-DPM medium, while most of AA remained in solution. So, in this scenario a “cleanup” of DM-DPM medium occurred, selectively removing chloride. As a result, the solution was relatively enriched in AA, so that its purity increased. In the second scenario , occurring at a resin concentration of 5 g L −1 , both AA and chloride were totally removed from DM-DPM medium, so that it could be the starting point for alternative purification strategies where, following this first step, AA would be selectively released from the resin to separate it from the remaining chloride. (d) Enrichment factor (EF): ratio of the concentrations (in mg L −1 ) of AA to the rest of anions in a sample with respect to its ratio in DPM medium. A summary of the performance of the AA purification process following scenario 1 of the treatment of DM-DPM medium with the Amberlite MB20 resin in batch is shown in Table 3 . Therefore, after the treatment of DM-DPM medium with the Amberlite MB20 resin according to the conditions of scenario 1, a solution containing 83.7% of the AA present in the original DPM medium was obtained, with a purity of 92.6%, which represents a 38.6-fold purity increase and a 513-fold enrichment. In all the previous experiments, DM-DPM medium was treated with the Amberlite MB20 resin in batch. This operation mode involved the addition of a certain amount of resin to the medium, mixing for a sufficient contact time to achieve anions-resin binding equilibrium, and separation of resin and liquid fractions. There is an alternative operation mode where the medium is passed through the resin packed in a column, which might allow a better separation of AA and chloride. The resin binds chloride with higher affinity than AA, but under batch mode some AA is still removed, probably as a result of the pH increase observed. It would be possible that under column mode these pH changes could be better controlled, thus improving AA separation. So, that column mode was tested aiming to selectively remove chloride from DM-DPM medium. As medium pH was acidic (2.95), a pH value where AA is undissociated and, therefore, uncharged, it was expected that it was unable to bind to the resin, while chloride, negatively charged, could do it. Therefore, the eluate could contain AA at the original concentration and be free of chloride. Once the resin had reached its maximum chloride binding capacity, it would begin eluting from the column. A column containing 0.65 g of Amberlite MB20, with a bed volume (BV) of 1 mL was prepared and the DM-DPM medium was passed through it with a flow rate of 1 mL min −1 , equivalent to 60 BV per h. Fractions of 25 mL (25 BV) of the eluate were taken in the course of the experiment and characterized for the pH, conductivity and AA and chloride content. Results are shown in Fig. 5 . Initially, the eluate was almost free of AA. After a few BV, its concentration in the eluate started to increase, reaching a maximum value close to 200 mg L −1 , double than in the feeding DM-DPM medium, at around 250 BV. Then, the AA concentration decreased to reach finally at about 400 BV the concentration present in the feeding solution and being unchanged thereafter. Most of chloride was, in turn, removed by the resin in the first 250 BV, maintaining a concentration in the eluate below 10 mg L −1 , and then increased slowly to reach its feeding concentration by 400 BV. From that number of BV, the concentration of both anions in the eluate was exactly the same as in the feed, so reaching the breakthrough point. The selective removal of chloride with respect to AA depends on two factors, the intrinsic affinity of the resin for them and the pH. As the affinity of the resin for chloride is higher than for acetate, chloride can displace acetate anions bound to the resin. So, as the liquid front moves through the column, when it finds free binding sites, both chloride and acetate can be bound. However, when the liquid behind the front finds that the binding sites are occupied, acetate can not bind and continues its way to the next free sites, but chloride can displace the acetate previously bound to the resin. As a result, the liquid front would be depleted in chloride and enriched in acetate, which explains the elution pattern showing an overshooting of acetate after the breakthrough of chloride. 24 In addition, pH also plays a relevant role in this process. The pH of the first fraction of eluate increased abruptly from the initial pH of the DM-DPM medium (2.95) to 4.7 and then decreased slowly in the next fractions, as the feed passed through the column, to finally reach the pH value of the feed. As repeatedly explained previously, the charged acetate fraction depends on the pH of the medium, so that the higher the pH the more acetate will be present. This means that at the initial BV, when the pH is higher, more acetate molecules are available for binding. Later, as the pH decreased, most of AA molecules would be undissociated (uncharged) and unable to bind to the resin. Therefore, this pH effect would enhance the displacement of acetate by chloride. If all the fractions eluted to the breakthrough point of chloride are pooled, the resulting solution would contain 92% of the AA fed to the column and one third of the chloride, so obtaining a solution enriched in AA with respect to the DM-DPM medium, but the purification parameters ( Table 3 ) would not improve the results obtained in batch mode with the same resin. The reason behind the worse performance obtained in column compared to batch might be related to the influence of the flow rate of the liquid through the column on the binding of the anions to the resin. The “contact time” between the anions and the binding sites in the column would decrease at higher flow rates, surpassing its kinetic capabilities, so being more difficult to reach equilibrium and likely negatively affecting anion separation. 25 So, a low flow rate would be preferred. In the column experiment the residence time was 1 min, so that the “contact time” was quite short. A lower flow rate could also be applied, but the time required to pass the liquid would be extremely high. For example, in the column system used in this study, it would be necessary 500 min (more than 8 h) to pass 500 mL of liquid. If the flow rate is reduced by half, the time would be increased the double, to 1000 min (more than 16 h), which would be operationally unpractical. Under batch mode, conversely, the “contact time” is higher, high enough to allow equilibrium to be reached, and independent of the liquid volume to be treated. Another factor that could be involved in the lower performance achieved under column mode could be the nature of the mixed bed resin. This kind of IEX resin contains a mixture of strong acid cation and base anion exchange resins and, according to the manufacturer, the densities of both resins are quite different, being lower that of the latter. This means that during the resin bed formation in the column some degree of separation of the resins could have occurred, resulting in an uneven distribution of both types of resin, with the base anion exchange resin enriched towards the top of the column and vice versa . And this uneven distribution of the resins could locally affect both the binding of ions and pH, which are closely linked, thus affecting the beneficial pH-buffering effect of the mixed bed resin and resulting in a lower separation performance than expected if the resins had been distributed homogeneously throughout the column. Under batch mode, however, this phenomenon would not occur and the separation achieved with the mixed bed resin would be better. Although the purification of AA under the conditions of scenario 1 was considerably improved, particularly under batch mode, the acid still remained in solution at a very diluted concentration, even lower than in the original DPM medium. Accordingly, it would be necessary to apply additional treatments to fully recover and concentrate AA, which would reduce again the recovery yield and make the process unfeasible. So, a different approach was required to further improve purification, and this is where scenario 2 appears. The previous experiments showed a better performance for the Amberlite MB20 resin in batch mode than in column mode. So, an AA recovery and purification strategy based on the scenario 2 described in Section 3.2.2 under batch mode was assessed. The idea was to first remove AA and chloride totally from DM-DPM medium with Amberlite MB20 in batch (5 g L −1 ) and then selectively elute AA using a small volume of a diluted solution of sulfuric acid. Elution was carried out in batch, by successively applying small volumes of the eluent, so that it would be a step-elution. There were several reasons to apply such kind of step-elution with sulfuric acid. First, considering the affinity order of the resin for the anions (sulfate > chloride > acetate), it was expected that sulfate eluent would first displace acetate from the resin and later chloride. Second, the acidic pH of the sulfuric acid solution would shift the AA/acetate equilibrium to the formation of undissociated AA, which would enhance its release from the resin binding sites. Third, the step-elution under batch mode would allow to reach the binding equilibrium of all the anionic species involved by simply extending the “contact time” sufficiently (a “contact time” of 30 min was found to be enough to reach equilibrium). Fourth, the step-elution would allow precise control of the extent of the acetate displacement and elution, allowing the elution to be finished when chloride or sulfate began to appear in the eluate. And fifth, the use of small volumes of eluent would allow to obtain a more concentrated solution of AA in the eluate compared with that in the feeding. A 500 mL solution of DM-DPM medium was treated with Amberlite MB20 at a rate of 5 g L −1 at room temperature for 2 h with gentle stirring to remove completely AA and chloride. Then, the anion-loaded resin was separated from the anion-depleted liquid by filtration. The anion-loaded resin was finally step-eluted with 20 mM H 2 SO 4 applied in eight 5 mL steps. Each elution step involved the addition of 5 mL of the eluent to the resin, stirring for 30 min, and separation of resin and liquid by filtration. The results of this process are shown in Fig. 6 . The anion-loaded resin was then step-eluted. In the first elution fraction (1) no anions were detected, which suggest that sulfate anions had bound to free binding-sites still present in the resin. Thereafter, in the next four elution steps (2–5), the AA concentration in the elution fractions steadily increased, reaching a maximum value as high as nearly 2200 mg L −1 in the step 5, that is, more than 20 times more concentrated than in DM-DPM medium. Chloride, in turn, was hardly detected in these fractions, with concentrations lower than 15 mg L −1 in all of them, and sulfate was totally undetectable. From step 6, AA concentration began to decrease and, at the same time, concentration of chloride, first, and sulfate, later, increased. If fractions 2 to 7 (2–7) are pooled the resulting solution would contain 1520 mg L −1 of AA and only 42 mg L −1 of chloride, with a recovery yield for AA of 90.3%. This means that AA would have been concentrated by around 15 times, while chloride levels would be 37% lower than in the original DM-DPM medium, so having considerably improved its purity. Instead, if those that are pooled are fractions 2 to 6 (2–6), the AA concentration would be the same, 1520 mg L −1 , and that of chloride lower, 12 mg L −1 , that is, a greater purity would be obtained, but with a lower recovery yield of 75%. The increase in the concentration of AA in the pooled elution fractions compared to that in the original DM-DPM medium results from the strong decrease in the volume of the solutions, from 500 mL to 30 or 25 mL for pooled fractions 2–7 or 2–6, respectively. The purification parameters of this process are shown in Table 4 . The AA purity of the pooled fractions 2–7 and 2–6 would be 96.9 and 99.2%, respectively, so clearly improving the values obtained in the previous processes, involving treatments with the same resin (scenario 1) under batch or column modes. Moreover, as a result of the purity improvement, the enrichment factor shot up to values as high as 1256 and 5086, respectively. The aggregate recovery yield was the only parameter with lower data: slightly lower, but not significantly different, for the pooled fractions 2–7 (82.2 vs. 83.7%), and 18% lower for pooled fractions 2–6. Furthermore, apart from the better results regarding purity and enrichment, the purification process described in this section had an additional and very relevant benefit: the final AA solution was concentrated by 15–16 times compared to the original DPM medium, while in the other two processes its concentration was around 10% lower. Therefore, further concentration of AA to industry-demanding levels using conventional technologies, preferably non-energy intensive technologies such as liquid–liquid reactive extraction 26 or IEX resins again, would be easier by applying this process. A scheme of the whole recovery and purification process proposed in this work is presented in Fig. 7 . In this paper, a case study dealing with the technical feasibility of a downstream process for the recovery and purification of AA from extremely diluted solutions (100 mg L −1 or 0.01% w/w) containing contaminating inorganic salts is presented. The process is based on two successive steps using of IEX resins, that is, a non-energy intensive separation technology. The first step, demineralization, involved a combined treatment of calcium precipitation, acidification with the Amberlite IR-120 resin and treatment with the mixed bed Amberlite MB20 resin, which allowed the total removal of phosphate and sulfate (and likely bicarbonate) and 90% removal of chloride, while still remaining 91% of AA in solution. The demineralized medium resulting from this first step was, in the second step, treated again with the mixed bed Amberlite MB20 resin in batch to remove all AA and chloride remaining in solution and, finally, the anion-loaded resin was step-eluted with a low volume of diluted H 2 SO 4 to selectively elute AA. The recovery yield and purity of AA in the final solution obtained showed an inverse relationship depending on the number of eluted fractions pooled. The greater the number of fractions pooled (2–7 vs. 2–6), the greater the recovery yield (82.2 vs. 68.5%) but the lower the purity (96.9 vs. 99.2%). In any case, the values of both parameters appear to be good, especially considering the final solution of AA obtained, which was 15-fold more concentrated than the original medium . Two issues should be highlighted to support the novelty of this work. On the one hand, the vast majority of downstream processes dealing with the recovery of AA, or carboxylic acids in general, from fermentation media are applied to solutions with concentrations of AA, at least, one to two orders of magnitude higher than the concentration available in this work. On the other hand, a mixed bed ion exchange resin is used in this work to both demineralize the AA solution and purify it, instead of the commonly used single strong or weak base anion exchange resins. As far as we know there are no reports in the literature addressing the recovery and purification of AA (or other short-medium chain length fatty acids) either from extremely diluted solutions nor using mixed bed ion exchange resins. It is worth mentioning that although the experimentation has been done with synthetic solutions the results can be fully extrapolated to real samples such as broths resulting from CO 2 fermentation processes to AA, characterized by the very low content of the acid. The microbial biomass present in the broth would be easily removed by microfiltration or centrifugation, and the macromolecular compounds contained in the clarified broth by ultrafiltration. The resulting broth would mainly contain AA and the inorganic salts, so it would be very similar to the DPM synthetic medium used in this work. Other compounds potentially present in the broth, such as trace elements and vitamins, would be at so low concentrations that would hardly interfere with the purification process. | Study | biomedical | en | 0.999998 |
PMC11697287 | However, compared to the traditional binary sentiment analysis, the multi-label sentiment analysis task faces challenges such as data sparsity, class imbalance, and difficulty in modeling emotional semantics. To this end, researchers have proposed various multi-label text emotion classification models based on statistics, machine learning, and deep learning techniques. For example, sentiment analysis models based on emotional dictionaries identify the emotional categories of texts by matching the retrieved words in the emotional dictionary. Text emotion dictionary models based on Naive Bayes and support vector machines use statistical learning methods to analyze and model word frequency statistical features to recognize the probability of text emotions. With the widespread application of deep learning in the field of natural language understanding , deep learning text emotion recognition models represented by recurrent neural networks (RNN) and large-scale pre-trained models (pre-trained model) have made significant progress in the identification of specific text emotion categories by relying on the powerful capabilities of deep learning in semantic representation modeling. To efficiently mine and utilize semantic correlation between emotions to enhance multi-label sentiment analysis, in this study, we propose an emotion correlation-enhanced sentiment analysis model (ECO-SAM). Inspired by the widely used self-attention mechanism for language modeling and the basic emotion theory, we first design a novel attention-based emotion correlation modeling module that could automatically learn the semantic correlation between emotions from data and obtain correlation-enhanced emotion embedding representation. Next, we transform the multi-label sentiment analysis problem into an information retrieval problem, which aims to find the most suitable emotions from the emotion candidate list for a given query text. Then, we design an emotion-matching module that uses neural networks to learn the matching function between emotion and text embedding from data. Finally, we demonstrate the effectiveness of ECO-SAM via extensive experiments on two public sentiment analysis datasets. The experiment results unveil that the ECO-SAM obtains the precision score increasing by 13.33% at most, the recall score increasing by 3.69% at most, and the F1 score increasing by 8.44% at most. Meanwhile, the modeled sentiment semantics are interpretable. The basic emotion theory was proposed by American psychologist Ekman . The theory believes that humans have six basic emotions: happiness, sadness, fear, anger, surprise, and disgust. These basic emotions are considered to be universally present across cultures and species. Based on the basic emotion theory, Ekman found some universality of emotional expressions through observing the facial expressions of people in different cultures. Izard expanded the basic emotion theory, discussing the relationship between basic emotions and the relationship between emotion and cognition. The study proposed a model of the emotional system, describing the relationships between basic emotions and how they interact and regulate each other. For example, the author pointed out that there is a close relationship between “anger” and “disgust,” while “happiness” and “sadness” have an antagonistic relationship. Russell proposed the circular emotion theory, which expanded the basic emotion theory and emphasized the construction and subjective experience of emotions, implying the idea of modeling the association between emotions. Cowen and Keltner explored how people describe and distinguish different emotional experiences in self-reports. The study found more fine-grained emotional experiences compared to the basic emotion theory, expanding the understanding of emotions and breaking through the traditional concept of basic emotions. It shows that emotions are complex and diverse and can be described and captured through multiple discrete emotion categories and continuous gradients. The deep learning-based method is also a supervised approach, training neural network classification models on text data with emotion labels, and utilizing the strong fitting ability of neural networks to accurately predict text emotion categories. For example, Grandjean et al. proposed a sentiment analysis model based on convolutional neural networks, where the dual convolutional layer structure can extract features from sentences of any length. Ji et al. proposed a sentiment analysis model based on deep belief networks, solving the problem of sparse text features. With the rise of large language models (LLMs) , the pre-trained LLM-based methods have emerged in sentiment analysis and achieved excellent performance on large-scale datasets. For instance, Valderrama et al. used the BERT model to obtain more complete text semantic representations, thereby more accurately predicting text emotion categories. Sailunaz et al. compared the sentiment analysis capabilities of various large language models in the research on user behaviors of spreading others’ privacy information on social networks. Gao et al. proposed to use prompt learning to enhance the classification performance of pre-trained models when the data volume is relatively small. In the multi-modal emotion recognition scenario, Zhu et al. proposed a sentiment analysis model based on improved ResNet to analyze and improve the accuracy of image emotion classification. Currently, deep learning models play a pivotal role in accurate sentiment analysis. As shown in Table 1 , we count and list current state-of-the-art sentiment analysis methods based on previous research. The attention-based emotion correlation modeling module uses the self-attention mechanism to model the semantic correlation of emotions, thereby addressing the lack of research on emotion correlation in existing studies. Specifically, the self-attention mechanism adopts the query-key-value (QKV) pattern. Each emotion in the framework has a trainable query vector, key vector, and value vector . First, for a target emotion, its query vector is obtained, and the cosine similarity between the query vector and the key vector of each other emotion is calculated. The similarity with each other emotion reflects the semantic dependence of the target emotion, i.e., the extent to which the semantic representation of the target emotion depends on that particular emotion. Then, the feature vector containing the emotion correlation of the target emotion is calculated. This vector is the weighted average of the inherent feature vectors (value vectors) of each emotion, with the weights being the calculated semantic dependence. Finally, Pearson’s correlation coefficient between the feature vectors containing emotion correlations is calculated and the emotion correlation matrix is output. where W = O ⊤ Λ O is the eigenvalue decomposition of the semantic matching matrix W ∈ ℝ D × D . The above eigenvalue decomposition transformation implies that this neural network prediction process is equivalent to applying the same linear transformation to the text semantic vector and the emotion-semantic vector and then taking the element-wise weighted average, with the weights being the eigenvectors. The training process of the neural network is equivalent to optimizing the linear transformation and the eigenvectors, so that the predicted probability of text emotion is close to the true data label. This experiment compares the proposed multi-label sentiment analysis model, ECO-SAM, with various baseline text emotion prediction models using the public Weibo dataset. The goal is to verify the accuracy of the ECO-SAM in sentiment analysis and its ability to model emotion feature correlations. For the experimental datasets, this study used two publicly available datasets: NLPCC2014 and GoEmotions . This module takes three inputs for emotion k: the feature inherent vector, query vector, and the corresponding key vectors for all emotions. Each text contains up to two emotions. The GoEmotions dataset consists of 58,000 text data from the English forum Reddit, with the original data containing 27 fine-grained emotion categories. Based on the basic emotion theory, we screened out the 7 emotions consistent with the NLPCC2014 dataset as well as the neutral case as the target of sentiment analysis and selected 32,445 valid samples. Next, we split each dataset into training, validation, and test sets in the ratio of 70%:10%:20%, respectively. This section uses the NLPCC2014 dataset as an example to analyze the ability of the ECO-SAM to model emotional semantic similarity. The ECO-SAM text emotion prediction model improves the accuracy of text emotion prediction by modeling the correlation between emotion features through the attention-based emotion modeling module. This experimental stage mainly focuses on the modeling results of the emotional feature correlation in the ECO-SAM. In the ECO-SAM, emotional features are represented as e k a t t , where k represents the emotion category sequence number. For any two emotions k1 and k2, this experiment uses Pearson’s correlation coefficient of the emotion features as the measure of emotion feature correlation, denoted as Corr k 1 k 2 . This correlation coefficient ranges between −1 and 1. When Corr k 1 k 2 > 0 , the two emotion features are positively correlated (similar); when Corr k 1 k 2 ≈ 0 , the two emotion features are uncorrelated (independent); when Corr k 1 k 2 < 0 , the two emotion features are negatively correlated (semantically opposite). The results of the emotion feature correlation calculation are shown in the following figure, which includes seven emotions: anger, disgust, fear, happiness, like, sadness, and surprise. The brighter the color of each square in the figure, the greater the correlation value, and the stronger the association between the two emotions. According to Figure 2 , the three emotions most strongly associated with each emotion are as follows: The above results show that different types of emotions, due to their semantic differences, either exhibit strong correlations or are mutually independent of each other. Some emotions, due to the consistency of their semantics, often exhibit a relatively strong clustering feature. For example, “anger” and “disgust” are both negative emotions, and their semantic correlation reaches 0.99. They also have relatively strong correlations with “fear,” indicating that the above four emotions are similar in semantic connotation, which is consistent with people’s intuition. At the same time, “happiness” and “like” have a relatively strong correlation, indicating that the two intuitively positive emotions also have similar semantic connotations. In addition, “surprise” has a relatively high semantic similarity with positive emotions such as “happiness,” as well as with negative emotions such as “fear.” This suggests that “surprise” as an emotion that an individual perceives due to sudden changes tends to be neutral. In other words, “surprise” can coexist with positive emotions (such as “pleasant surprise”) and also with negative emotions (such as “horrifying surprise”). | Study | other | en | 0.999997 |
PMC11697288 | Magnesium plays a crucial role in the body’s functions. It is involved in over 600 enzymatic reactions that regulate the functioning of the heart, blood vessels, neurons, muscles, and other organs and systems ( 1 ). Most of the magnesium is found in bones and soft tissues, with only 1% in the blood ( 2 ). Therefore, serum magnesium levels correlate poorly with total body magnesium levels or concentrations in specific tissues ( 3 ). Serum magnesium concentrations slightly depend on a child’s age and range from 0.70 to 0.95 mmol/L in children older than 5 months ( 4 , 5 ), and serum levels below 0.7 mmol/L are defined as hypomagnesemia ( 2 ). Symptoms of magnesium deficiency are non-specific and may mask signs of other nutrient deficiencies or non-specific symptoms of chronic diseases ( 6 ). Common causes of magnesium deficiency include insufficient dietary intake, impaired absorption in the gastrointestinal tract, kidney dysfunction, medications (diuretics, calcineurin inhibitors, and certain antibiotics), and genetic factors ( 2 ). Insufficient dietary intake is one of the most common factors of hypomagnesemia in children. Recommended magnesium intake varies by age and sex ( 7 ) and ranges from 75 mg in children aged 7–12 months to 410 mg in boys and 360 mg in girls aged 14–18 years ( 8 ). Several studies have shown insufficient dietary magnesium intake in adult patients in Europe and North America ( 7 , 9 ). Data on magnesium intake from food in the pediatric population are limited, though insufficient dietary intake is noted, particularly in adolescents ( 10 ). Numerous studies have demonstrated the impact of hypomagnesemia on the development of various metabolic disorders, including insulin resistance and diabetes mellitus (DM) ( 11 ). The frequency of hypomagnesemia ranges from 13.5% to 47.7% in patients with type 2 DM ( 12 ). On the other hand, high magnesium intake has been shown to prevent chronic metabolic complications ( 11 ). The positive effects of magnesium in diabetes include improved glucose and insulin metabolism, reduced chronic low-grade inflammation, protection of cells from oxidative stress and damage, improved lipid profile, enhanced endothelium-dependent vasodilation, and neuropathy prevention ( 2 , 11 , 13 ). The aim of our study was to determine dietary magnesium intake, serum magnesium concentration in children with type 1 DM, and their impact on the clinical course of DM. This case-control study included 50 children with type 1 DM (cases) and 67 healthy children (control) aged 6–17 years. The children with DM were examined during hospitalization in the endocrinology department of Ternopil regional children’s hospital, Ukraine. The control group children were examined during routine preventive check-ups at the outpatient department of city and regional children’s hospital in Ternopil, Ukraine. The study was conducted in the spring and autumn of 2021. Inclusion criteria for the control group were the absence of chronic diseases, acute illnesses, and medication intake, along with informed consent from the children and/or their parents to participate in the study. Inclusion criteria for the DM group were a confirmed diagnosis of DM. Exclusion criteria for this group included the presence of other chronic diseases, kidney dysfunction, acute illnesses, and refusal of children and/or their parents to participate in the study. Using a questionnaire based on a magnesium content database in food products ( 10 ), the average amount and sources of magnesium intake were determined. The total weekly magnesium intake and the average daily intake from food were calculated and compared with national and international recommendations for daily nutrient requirements in children ( 8 , 10 , 15 ). All children underwent comprehensive clinical examinations, including anthropometric measurements [weight, height, and body mass index (BMI)]. The level of glycemic control was determined based on glycated hemoglobin (HbA1c) levels. According to the ISPAD Clinical Practice Consensus Guidelines 2022 ( 16 ), HbA1c levels below 7% were considered optimal glycemic control, while levels above 7% indicated poor glycemic control. Additionally, serum magnesium, calcium, and phosphorus concentration were measured. Blood samples were taken via venipuncture from the elbow vein using disposable “Vacutainer” systems on an empty stomach. Quantitative determination of magnesium, calcium, and phosphorus was performed using ELISA kits from Assay Kit Elabscience, USA, by a colorimetric method. All measurements were conducted in the same laboratory for all participants. Statistical analysis was conducted using the STATISTICA 10.0 statistical package and Microsoft Excel 2003. For normally distributed samples, mean values ( m ) and standard deviation (SD) were calculated. The data were processed using variation statistics methods. Student’s t -test was used to compare mean values. For non-normally distributed samples, data were presented as medians and interquartile ranges (IQR) [25%–75%]. Mann–Whitney U -test was used to compare indicators in two independent groups. Frequency indicators in the observation groups were compared using the χ 2 test and Yates’ corrected χ 2 test. Odds ratios (ORs) and 95% confidence intervals were determined to explore the influence of potential risk factors. Only statistically significant features were used for this analysis. Correlation analysis was performed by calculating Spearman’s rank correlation coefficient. Differences were considered significant at p < 0.05. Baseline characteristics of observed children with type I DM (cases) and healthy children (control) are presented in Table 1 . Boys predominated among children with DM (62%), while there was no significant gender difference among healthy children. There was no significant difference in place of residence among patients with DM, and most parents (80%) had secondary education . In the group of healthy children, urban residents predominated with high significance, and higher education was observed in 52.2% of parents. There was no significant difference in age and BMI between the groups of children with DM and healthy children. Calcium and phosphorus levels did not differ between the groups. The average duration of DM in children was 4.95 ± 4.38 years, ranging from 1 week to 14 years. The average HbA1c level in children with type I DM was 8.83 ± 2.77%, ranging from 5.5% to 15.8%. Optimal glycemic control was observed in 31.6% of patients, while poor control was noted in 68.4%, among which 10 (26.3%) patients had newly diagnosed diabetes. Specific symptoms of DM, such as polyuria and polydipsia, were present in 11 (22.0%) children at the time of the examination, mostly in those with newly diagnosed or poorly controlled DM. The frequency of non-specific symptoms in children with DM and healthy children is shown in Figure 1 . Among the non-specific complaints in children with DM, irritability (34%), muscle spasms (30%), headache (28%), dizziness (16%), and muscle weakness (16%) were most commonly reported. Healthy children significantly less often reported these non-specific symptoms, with headache being the most common – 9 (13.4%), sleep disturbances – 9 (13.4%), and irritability – 7 (10.4%). Children with DM more frequently reported irritability, muscle spasms, headache, dizziness, and muscle weakness compared to healthy children ( p = 0.002; p < 0.001; p = 0.005; p < 0.001; p = 0.013, respectively). The daily dietary magnesium intake and serum concentrations in patients with type 1 DM and healthy children is shown in Table 2 . The median values of dietary magnesium intake did not differ between the group of children with DM and healthy children. The percentage of children with DM whose magnesium intake was below the recommended age norms was 1.34 times higher than the corresponding percentage of healthy children, although the difference was not statistically significant ( p = 0.201). Serum magnesium concentration in healthy children was higher than that in children with DM ( p = 0.011) , although the proportion of children with hypomagnesemia did not differ between the two groups (14.0% and 11.9%, respectively). Based on the serum magnesium concentration, children in both groups were divided into two subgroups: those with normal magnesium concentration and those with hypomagnesemia ( Table 3 ). Baseline characteristics and clinical indicators were determined according to the magnesium concentration in patients with DM and in healthy children. No effect of gender on magnesium status was found in either group. However, hypomagnesemia was more frequently observed in children from rural areas in both groups: 85.7% in children with DM and 62.5% in healthy children ( p = 0.054 and p = 0.010, respectively). Living in a rural area influence on hypomagnesemia . Parental education did not affect the magnesium status in either group. In the group of patients with DM, the mean age of the children did not differ depending on the magnesium status, but the mean age of healthy patients with hypomagnesemia was higher than that of children with normal magnesium levels ( p = 0.027). Accordingly, similar trends were observed for BMI, which was higher in children with hypomagnesemia than in patients with normal serum magnesium concentration, but the difference was statistically significant only in the group of healthy children ( p = 0.031). However, there was no difference in BMI percentiles between groups with hypomagnesemia and normal magnesium concentration in both groups. The mean duration of DM did not differ between children with hypomagnesemia and those with normal magnesium concentration. The mean HbA1c level was somewhat higher in patients with hypomagnesemia, but the difference was not statistically significant ( p = 0.313). There was no significant correlation between HbA1c levels and magnesium concentration in children with DM . However, all children with hypomagnesemia had poor DM control compared to 61.3% of patients with normal magnesium concentration ( p = 0.047). The mean magnesium concentration in children with optimal glycemic control was significantly higher than in children with poor control (0.96 ± 0.09 vs. 0.78 ± 0.14 mmol/L, p = 0.001) . Additionally, there was an inverse correlation between serum magnesium levels and glycemic control . The median value of daily magnesium intake in children with DM was higher in those with normal blood magnesium concentration, but the difference was not statistically significant ( p = 0.131). In children with DM and hypomagnesemia, significant decreases in serum calcium and phosphorus concentrations were observed ( p = 0.008 and p = 0.017, respectively). In healthy children, changes in phosphorus and calcium levels due to hypomagnesemia were not significant ( p > 0.05). Comparing the frequency of non-specific symptoms in children with DM depending on magnesium status, found that headache and attention disorders were significantly more frequent in patients with hypomagnesemia (71.4% vs. 20.9%, p = 0.006; 28.6% vs. 4.7%, p = 0.031, respectively). Additionally, OR was determined for significant indicators. Hypomagnesemia was found to influence the occurrence of headache [OR – 9.4444; 95% CI ; p = 0.014] and attention disorders [OR – 8.2000; 95% CI ; p = 0.057]. In the group of healthy children, no difference in the frequency of symptoms was observed between children with normal magnesium concentrations and those with hypomagnesemia. This study aimed to assess dietary magnesium intake and serum magnesium concentration in children with DM compared to healthy peers aged 6–17 years, focusing on the implications of hypomagnesemia on glycemic control and its relationship with calcium and phosphorus levels. No significant difference was found in dietary magnesium intake between children with DM and healthy children. Studies on magnesium intake are quite limited, especially in children. According to the National Health and Nutrition Examination Survey (NHANES) for 2013–2016, 48% of Americans of various ages consume less magnesium from food and beverages than needed ( 10 ). The study also showed low magnesium dietary intake in adolescents. Another study indicated that 66% of adult non-users of dietary supplements had inadequate mineral intakes ( 17 ). Our study also revealed more frequent inadequate magnesium dietary intake in adolescents, both healthy and with DM (70% and 61.8%, respectively). The inadequate magnesium intake observed, especially in adolescents, is concerning given the increased dietary needs during this growth phase ( 8 ). Previous studies on dietary magnesium intake in patients with DM mainly focused on adults with type 2 DM ( 16 , 17 ). Overall, 23.5% of patients with type 2 DM had inadequate magnesium intake ( 18 ). A meta-analysis demonstrated an inverse association between magnesium intake and the risk of type 2 diabetes ( 19 ). The lower serum magnesium concentration in children with DM compared to healthy children is clinically significant ( p = 0.011), suggesting that magnesium deficiency may contribute to complications associated with diabetes ( 20 ). Researchers suggest that there may be an association between impaired antioxidant protection and magnesium deficiency in children with type 1 DM ( 21 , 22 ). The frequency of hypomagnesemia in children with DM was 14% and did not significantly differ from that in healthy patients ( p > 0.05). Other studies reported a hypomagnesemia frequency of 3.4% in children with type 1 DM, also not significantly different from healthy children ( 23 ). Some researchers indicate that about 10% of hospitalized patients have magnesium deficiency ( 5 ). Hypomagnesemia was more frequently observed in rural residents, both in healthy children and in patients with DM, probably due to potential dietary access differences. The OR indicated that living in rural areas may be a risk factor for hypomagnesemia. Separate studies have shown the impact of low magnesium and potassium intake in rural areas on the development of type 2 DM ( 24 ). Hypomagnesemia was more common in children with DM with poor glycemic control, as demonstrated in other studies ( 13 , 22 , 25 , 26 ). Less than a third of patients had optimal glycemic control, while the rest had poor control, consistent with the results of our previous study with a larger number of patients ( 27 , 28 ). The average serum magnesium concentration in children with optimal glycemic control was significantly higher than in children with poor control ( p = 0.001). A negative correlation between serum magnesium levels and glycemic control was also established . This negative correlation indicates that hypomagnesemia may exacerbate glycemic dysregulation in children with DM. Other researchers suggest that hypomagnesemia in adult DM patients is due to insulin resistance, a sign of type 2 diabetes ( 29 ). The authors also did not note a correlation between HbA1c levels and magnesium concentration, which was also demonstrated in our study. Similar trends of hypomagnesemia affecting glycemic control were noted in adults with type 2 DM ( 18 , 30 ). However, another study showed a negative correlation with HbA1c % in children with type 1 DM ( 22 ). In children with DM, hypomagnesemia affected serum calcium and phosphorus levels. Lower serum calcium and phosphorus levels in children with DM and hypomagnesemia ( p = 0.008 and p = 0.017, respectively) highlight the potential risk for compromised bone health in this population. Such changes were not observed in healthy children. Another study showed that serum magnesium concentration positively correlated with calcium and phosphorus levels ( 13 ). Magnesium is involved in the transport of potassium and calcium ions and maintains their levels in the blood ( 1 ). Electrolyte imbalance due to hypomagnesemia was most pronounced in patients with DM. Hypomagnesemia results in decreased levels of parathyroid hormone and vitamin D3, which can affect calcium-phosphorus metabolism and impair bone resorption ( 31 ). Magnesium influences bone cell growth and formation and its strength ( 15 ). The role of hypomagnesemia in the development of osteoporosis is also well-established ( 32 ). The symptoms of hypomagnesemia are not specific and may be associated with the underlying disease and other deficiency states, including hypocalcemia ( 14 , 33 ). While muscle cramps and headaches are common symptoms, their persistence in children with DM may indicate underlying metabolic disturbances that could impact overall health and quality of life ( 32 ). We collected symptoms that may be associated with hypomagnesemia . In children with DM and hypomagnesemia, headaches, and attention disorders were more common. Although these symptoms are multifactorial, the OR indicated that hypomagnesemia in children with DM could contribute to headaches and tended to affect attention disorder symptoms. These patterns were observed only in children with DM. Overall, it is suggested that symptomatic magnesium deficiency due to low dietary intake in healthy individuals is rare since the kidneys limit the excretion of the mineral in case of its deficiency ( 32 ). However, insulin resistance and/or type 2 diabetes increase magnesium excretion in the urine. Magnesium loss is considered a secondary cause of poor glycemic control and high glucose concentrations in the kidneys, which increase urine output ( 15 ). Nonetheless, other studies showed that increased magnesium intake reduced the risk of developing DM ( 19 ) and improved glycemic control in DM patients ( 34 , 35 ). While this study provides valuable insights into magnesium intake among the Ukrainian pediatric population, the small sample size and single-center design limit the generalizability of our findings. However, the study allowed us to identify certain patterns. Conducting a multicenter study involving more patients and more indicators will help identify other effects of hypomagnesemia on the course of DM in children. Mean serum magnesium concentration in patients with type 1 DM was lower than in healthy children, although there was no difference in dairy magnesium intake. Hypomagnesemia was more frequently observed in rural children, both those with type1 DM and healthy ones and was associated with poor glycemic control in children with DM. Additionally, children with type 1 DM and hypomagnesemia had lower serum calcium and phosphorus levels and more frequent symptoms such as headaches and attention deficits. These findings underscore the need for routine screening of magnesium levels in children with DM, particularly those in rural areas, to prevent potential complications associated with hypomagnesemia. Further research is needed to explore the other impact of hypomagnesemia on the clinical course of DM in children. | Study | biomedical | en | 0.999996 |
PMC11697290 | Suicide prevention interventions can reduce suicide deaths and behaviors ( 5 ), and numerous brief interventions exist to support people experiencing suicide-related distress ( 6 ). One intervention that has been gaining popularity in both clinical and community settings is the Safety Planning Intervention (SPI; 7). The SPI involves developing a personalised list of coping and personal support strategies for use during the onset or worsening of suicide-related distress, typically through six components: a) recognising individual warning signs for an impending suicidal crisis; b) identifying and employing internal coping strategies; c) using social supports to distract from suicidal thoughts; d) contacting trusted family or friends to help address the crisis; e) contacting specific mental health services; f) eliminating or mitigating use of lethal means ( 7 ). Although widely used with US military veterans, the flexibility of the SPI has been demonstrated through its application across diverse age groups ( 8 , 9 ), settings ( 10 ), and with varied populations including refugees ( 11 ), autistic people ( 12 ) and individuals recently incarcerated ( 13 ). The SPI has also been incorporated within or alongside wider therapeutic approaches, such as motivational interviewing ( 14 ). Traditionally completed in hard-copy format, the SPI has more recently been adapted to various digital versions (e.g., 15,16) which can be used in clinical settings or accessed by the public without clinical support. Two recent systematic reviews ( 17 , 18 ) and one meta-analysis ( 19 ) have explored the effectiveness of the SPI and safety planning type interventions. Through narrative synthesis of results, two of the reviews (n = 20 studies, 17; n = 22 studies, 18) concluded that this intervention contributes to reductions in suicidal ideation and behaviour, as well as suicide-related outcomes, such as depression and hopelessness, and improvements in service use and treatment outcomes. While the meta-analysis of six safety planning type studies ( 19 ) also found reduced suicidal behaviour among intervention participants compared to treatment as usual, this study found no evidence for effectiveness on suicidal ideation. Thus, despite the difference in findings related to ideation, current evidence generally supports the efficacy of the SPI in improving people’s coping capacities and safety, with benefits particularly pronounced for reductions in suicidal behavior. However, less emphasis has been dedicated to understanding the underlying processes by which people using the SPI derive benefits ( 20 ). While there is emerging evidence linking the quality and personalisation of safety plans to reduced suicidal behaviour and hospitalisations ( 16 , 21 ), these mechanisms have been quantitatively assessed, rather than qualitatively described from the perspective of those who have used a safety plan. Contemporary thinking recognizes the critical role that lived and living experience plays in suicide prevention research yet there has been limited integration of lived experience in the development of existing suicide prevention interventions ( 22 ). Incorporating lived and living experience understandings into all stages of suicide prevention research is essential for ensuring that suicide prevention strategies meet the needs of those they have been designed for. Moreover, a personalized understanding of peoples’ experiences of using the SPI is needed to inform clinical practice, policy, and future research to enhance the effectiveness of the SPI and ultimately reduce the incidence of suicide and suicide-related distress. This review aims to complement quantitative reviews and meta-analysis ( 17 – 19 ) by synthesizing the existing qualitative, peer-reviewed evidence regarding the experiences of diverse stakeholders (consumers, support persons, and clinicians) involved in the SPI. These stakeholder experiences include but are not limited to: what is perceived as helpful and unhelpful about safety planning; what processes facilitate positive effects; the collaborative process regarding how the safety plan is developed, used, accessed, and revised; as well as the perceived impact of the safety plan on suicide-related outcomes and other well-being indicators. This systematic review followed the PRISMA 2020 guidelines ( 23 ) and was conducted according to the Joanna Briggs Institute (JBI) methodology for systematic reviews of qualitative evidence ( 24 ). The review protocol was pre-registered with PROSPERO . The search strategy was developed by MF, based on a previous safety planning systematic review ( 17 ), and refined in consultation with an academic librarian. We conducted searches on 28 November 2023 in seven databases: Embase, Emcare, MEDLINE and PsycInfo, in the Ovid platform; as well as CINAHL, Scopus and Web of Science. The final search strategy was broad, including terms for safety planning and suicide. Additional terms were trialed (e.g., for participant groups and study designs), however these restricted results and were excluded from the final strategy. We limited results to English language and a publication date range of 2000 to present. See Supplementary Data Sheet 1 for the search strategies used in each database. Reference lists of included articles were pearled in duplicate (MF, EO, KR) for potentially relevant studies. Eligibility criteria included: published in English language; qualitative in design (or mixed-methods, but where qualitative data were able to be extracted); participants of any age who had direct involvement in safety planning (including consumers, support persons, service providers, clinicians, etc.) in any setting (e.g. emergency department, inpatient, outpatient, community, online, school, etc.); and where it was clear that safety planning was based on the Stanley and Brown ( 7 ) version. Studies could include the SPI as a standalone intervention, or as part of a wider intervention approach. Studies were excluded if they: were not published in English; were not primary research; were not qualitative in design (either purely quantitative or where qualitative method and data could not be extracted); participants had no direct involvement in safety planning; or where the type of safety planning intervention was irrelevant or unclear (i.e., no reference to Stanley and Brown, and/or no definition or description of safety planning procedures). We custom-built an electronic survey (LimeSurvey, Hamburg, Germany) to extract key information from the included studies, including: aim; study location and setting; study design; participant characteristics (sample size, population description, age, sex); SPI details (delivery modality, format, other intervention components if relevant); methods of data collection and analysis. Reviewers (MF, EO, KR) extracted data independently, in duplicate. Where necessary, we discussed and consulted the original papers until consensus was reached. As part of the data extraction phase, and to facilitate the meta-aggregation process, we read and re-read included studies in duplicate (MF, EO, KR) to extract individual findings (i.e., authors’ analytic interpretative statements of qualitative data) and accompanying illustrations (i.e., verbatim participant quotation that exemplifies the finding). Any verbatim analytic statement was eligible to be extracted as a finding, provided an accompanying illustration was available. Where an accompanying illustration was not available, the finding was not included in this review. As per JBI guidelines ( 24 ), we (independently and in duplicate) assigned finding and illustration pairings a credibility rating: unequivocal (i.e., illustration supports the finding beyond reasonable doubt and therefore not open to challenge), credible (i.e., illustration lacks clear association with the finding and is therefore open to challenge) or not supported (i.e., illustration does not support the finding). Risk of bias assessment was conducted for each eligible study independently by three reviewers (MF, EO, KR) using the JBI Checklist for Qualitative Research ( 25 ). In this 10-item tool, each item is rated as: yes, no, unclear, or not applicable. We resolved discrepancies via discussion, re-checking the papers together, and discussion with a fourth author (NP) as required. As per recent guidelines for ensuring review results represent the best available evidence ( 26 ) eligible studies were included if they satisfied at least six criteria on the appraisal tool. Qualitative findings were pooled via meta-aggregation ( 24 ). Findings, illustrations, and credibility data were exported and printed for repeated reviewing in hard copy and for discussion in duplicate by two authors (EO, KR). Using butchers paper, we manually grouped the printed findings into categories based on our discussions. We first placed findings into categories based on similarity of meaning. Second, we combined similar categories into ‘synthesized findings’, referring to indicatory statements that convey the whole, inclusive meaning of a collection of categories, and which can be used to develop policy and practice recommendations. We then transferred these hard copy synthesized findings back to an Excel spreadsheet for discussion with the wider team. Following team discussion, we prioritized these synthesized findings into conceptual order for presentation in the manuscript. As per JBI guidelines, we used the ConQual approach ( 27 ) to establish confidence in each synthesized finding. ConQual argues that confidence in a meta-synthesized finding is determined by the dependability and credibility of the studies and individual findings that comprise it. Confidence ratings range from high, moderate, low, to very low. By default, qualitative studies are initially given a ‘high’ confidence rating, which can be downgraded based on dependability and credibility. Dependability is determined based on performance of each study on items 2-4 and 6-7 of the JBI Checklist for Qualitative Research, with the overall confidence level unchanging if the majority of individual findings are from studies with 4-5 ‘yes’ responses, downgraded one level for majority 2-3 ‘yes’ responses, and downgraded two levels for majority 0-1 ‘yes’ responses. For credibility, where a synthesized finding contains only unequivocal individual findings, no downgrading penalty is applied; however, confidence is downgraded one level if the synthesized finding comprises a mix of unequivocal and credible individual findings. The overarching qualitative methodology guiding this review was an interpretivist approach, which recognizes subjectivity and reflexivity ( 28 ). This approach makes the perspectives and positioning of the authors explicit, ensuring that the impact of researcher lenses on the synthesis and examination of results is transparent. While the components of the SPI should be universal, we acknowledge our positioning in the Australian context, which is associated with a unique set of cultural factors and policy frameworks that influence SPI practices and implementation. It is also important to acknowledge the authors’ backgrounds. Collectively, the research team brings expertise across lived experience, clinical practice, and research. EO is a postdoctoral researcher with expertise in behavioral science and mental health. KR is an experienced mental health nurse and doctoral level health psychologist working in research and education. NP is a professorial level mental health nurse expert and leader in suicide prevention research and education. ML is a Lived Experience academic. AP is a PhD researcher in health and medical sciences and Expert by Experience with the SPI. J-AR is a mental health nurse expert in clinical and senior management. SP is an experienced mental health nurse. MF is a senior suicide prevention researcher. Database searching yielded 1862 results, reduced to 588 after removal of duplicates. Results were screened at the title/abstract level, leaving 60 eligible for full-text screening. One additional article was identified via a correction that appeared in the search results. No further articles were identified through reference list pearling. Twelve eligible studies were critically appraised; two ( 15 , 29 ) were excluded by the minimum risk of bias threshold, leaving ten studies for inclusion. See Included studies were published between 2015 and 2023 and primarily conducted in the United States (n =7). Results for this review are based on data from n = 243 participants (note: this relates to the total number of participants from eligible phases of the included studies). The mean sample size was 24 (range, n=12-50). Across all studies, participants included n = 113 clinicians/staff (n=5 studies), n = 103 adults (including 95 veterans, n=4 studies; and 8 general population, n=1 study), n = 20 adolescents (n=2 studies), and n = 7 support persons (n=2 studies). Eight studies included both female and male participants, two did not report any gender data, and none reported data on other gender identities. Study settings included combined inpatient and outpatient (n=4), outpatient only (n=4), emergency department (n=1), and community services (n=1), with six studies relating to the context of veterans. Six studies were purely qualitative ( 10 , 30 – 34 ), one was mixed methods ( 35 ), and while a further three identified as qualitative they also included some minor quantitative aspects (e.g., quantitative measures to collect participant clinical information, 36, 37; or quantification of time spent creating safety plans, 38) but were not considered mixed methods. Most studies (n=8) collected qualitative data via semi-structured individual interviews but focus groups (n=1) and open-ended survey items (n=1) were also used. Studies analyzed qualitative data using thematic analysis (n=4), content analysis (n=2), interpretive phenomenological analysis (n=1), and matrix analysis (n=1). Two studies did not clearly report an analytic method. There was substantial variability across studies in SPI features, and its role in suicide prevention and mental health care. Studies discussed versions of the SPI including additional components such as text-message and/or telephone follow-up support ( 31 , 35 ), and the inclusion of support persons ( 36 ). Most studies (n=9) used or discussed the SPI as one component of care, alongside other psychological interventions (e.g., individualized, outpatient psychotherapy). The specific format of initial construction, ongoing access, or both, was often unclear. Only three studies described a specific SPI format, including a traditional hard-copy format ( 33 ), a mobile phone app-based version ( 30 ), and either hard copy or electronic versions ( 38 ). There was also a lack of detailed reporting regarding delivery modality, with four studies ( 30 , 31 , 33 , 38 ) clearly indicating in-person creation of the SPI, and one describing a group-based SPI delivered online via telehealth ( 37 ). Eight studies described who the SPI was co-created with – working with a clinician was the most frequent approach ( 10 , 30 , 31 , 33 , 34 , 38 ), with one study describing construction with a study counselor ( 35 ), and another describing a collaborative creation process with other SPI users in a group format ( 37 ). See Table 1 for full characteristics of included studies. Included studies performed well on critical appraisal items related to congruity between research methodology and study methods, as well as ethical research conduct and appropriateness of study conclusions. However, guiding philosophical perspectives were largely unreported, with only one study mentioning this ( 32 ), and studies did not consistently meet criteria for reflexivity, with only one study ( 32 ) locating the researchers culturally or theoretically, and two studies ( 10 , 32 ) discussing the influence of the researcher on the research and vice-versa. See Table 2 for study-level critical appraisal results. Five findings were located from two studies ( 31 , 38 ) describing stakeholders’ perspectives on the utility of the SPI. The SPI is deemed an acceptable and even essential intervention by clinicians working with suicidal veterans ( 31 , 38 ). Clinicians view the SPI as a useful addition to their repertoire, noting that its structured nature can help to facilitate conversations regarding consumers’ emotional states, early warning signs and risk factors ( 38 ). Despite initial skepticism about the SPI ( 31 ), clinicians describe it as a tool they rely on in everyday practice. For example, one emergency department clinician shared that the SPI assists in engaging individuals with emerging suicidality prior to the onset of suicidal behaviors: Five findings from four studies ( 10 , 33 , 34 , 38 ) described stakeholder skepticism about the utility of the SPI. Clinicians were unsure of the SPI’s effectiveness, both in general and in times of crisis ( 38 ). Clinicians also described their experiences with consumers who decline to engage in safety planning at all, perhaps due to stigma attached to suicide-related phenomena ( 10 ). Some consumers expressed doubt that any intervention could deter a person with suicidal intent ( 34 ). Other consumers doubted the helpfulness of SPI strategies, especially whilst experiencing severe neurovegetative symptoms ( 33 ). Finally, one consumer shared the perspective that the SPI was unnecessary: Featuring rich data from the perspectives of consumers, clinicians and support persons, this qualitative systematic review provides unique insights regarding the practices and processes perceived to impact on consumers’ experiences with the SPI. Through meta-aggregation, four synthesized findings were produced, with the results indicating that the SPI is a beneficial intervention, enhanced through person-centered collaboration and the involvement of supportive others. However, several perceived limitations impact on perceived acceptability and efficacy, which must be considered by organizations and clinicians involved in service delivery. These findings add an important lived experience lens to SPI literature, complementing previous quantitative studies and reviews of SPI efficacy. Consumers, clinicians, and support persons viewed the SPI as broadly acceptable and beneficial for reducing consumers’ suicide risk. These qualitative data concur with previous findings ( 39 ), wherein 95% of veterans endorsed the SPI as both acceptable and helpful. In addition, clinicians in the present review perceived SPI practices to be helpful in reducing suicide risk during consumers’ transition from inpatient to home or community settings. This is an important finding, as risk of suicide may be most acute following discharge from psychiatric hospitalization, particularly for those with active suicidal ideation, perceived hopelessness, and history of suicidal behavior ( 40 ). Overall, the efficacy of the SPI in helping consumers to reduce suicidal ideation and behavior is supported by both quantitative systematic reviews ( 17 – 19 ) and by the experiences and perspectives synthesized in the present review. People involved in the SPI also perceived a range of specific benefits that may help to explain the effectiveness of SPI practices. First, person-centered safety planning was seen to facilitate greater consumer autonomy, giving individuals a greater sense of ownership over their own health care. Consumers and clinicians also described how SPI practices helped to increase consumers’ sense of hope by internalizing and valuing their existing reasons for living. The amplification of reasons for living is an important protective mechanism, with reasons for living associated with reduced suicidal ideation and suicide attempts ( 41 ). In the present results, reasons for living often included loved ones such as children, partners, family, and friends. As such, greater identification of reasons for living appeared to intersect with an improved sense of connection with supportive others. This fundamental need for connection was maximized when support persons were involved in consumers’ safety planning. Similarly, ongoing engagement with SPI practices supported individuals’ self-efficacy in recognizing early warning signs and engaging self-regulatory coping strategies to interrupt the trajectory of escalating distress. This latter result aligns with recent evidence for growth in suicide-related coping as a key predictor of reduced suicidal ideation during an SPI intervention ( 16 ). In sum, the lived experience data synthesized in this review broadly align with some of the psychological mechanisms of effect for the SPI as theorized by Rogers et al. ( 20 ). Specifically, these findings add support to Rogers et al.’s ( 20 ) suggestions that the SPI promotes autonomy among users, both in initial plan creation and in their choices surrounding whether, when and how to use the plan to keep themselves safe; encourages connection with others (including healthcare services, and friends, family and community), which is a known protective factors against suicide; and builds competence through encouraging individuals to identify personalized support strategies and to practice using these to build confidence over time. Clinicians and consumers strongly recommended a collaborative, person-centered approach to constructing and using the SPI over time. This approach refers to clinicians and consumers working together, sharing decision making and having a balance of power, to develop plans that address the consumer’s unique needs and circumstances ( 42 ). Unlike a crisis risk assessment process, which can imply a mechanistic and alienating experience of safety planning, collaborative and person-centered approaches allow a normalizing space for consumers to feel supported and to have voice in exploring suicide-related feelings. Recent quantitative evidence suggests that stronger therapeutic alliance established early in psychotherapy is a key predictor of reductions in suicidal ideation and behavior ( 43 ) and this review supports those findings from many consumers using safety plans. Collaborative and person-centered interactions were viewed as essential for helping people in distress to understand and process difficult emotional states, to find meaningful connection with others, and for using their strengths and supports to cope in the future. Most mental health professionals would recognize the importance of person-centered therapeutic engagement. However, our results highlight a range of organizational barriers impairing clinicians’ ability to use the SPI according to these core principles. Time constraints were the primary barrier impacting clinicians’ perceived ability to conduct person-centered safety planning. Thus, without sufficient organizational support, the SPI may be more likely to be delivered instrumentally with a focus on risk mitigation, rather than in a person-centered and collaborative way. Consumers reported experiences of ‘tunnel vision’ or an inability to consider SPI coping strategies, while enduring acute distress. This finding converges with the understanding that the ability to engage cognitive and/or behavioral self-regulatory coping strategies is diminished during heightened periods of crisis ( 44 ). This perceived limitation of SPI utilization further highlights the importance of appropriate and effective methods to work with consumers in deciding to restrict access to lethal means. At an individual level, clinicians and consumers can work collaboratively to make changes to living environments to restrict access to high lethality means should they experience acute and unbearable distress. This part of the planning process should focus on means identified by the consumer that feature in suicidal ideation. Appropriate involvement of support persons may be particularly beneficial in maintaining safe environments and reducing the help seeking burden placed on consumers. In the present results, the SPI was disregarded as unhelpful by some consumers and clinicians. Similar uncertainty regarding the SPI has recently been documented in a quantitative study, with clinicians doubtful of the effectiveness of safety planning in reducing risk of suicidal behavior ( 45 ). As noted by an included study ( 31 ), this hesitancy suggests a need for prior education and training about the efficacy, usability, and acceptability of the SPI. Consumers’ fear of disclosure was another barrier to SPI engagement identified in the present results ( 10 ). Self-stigma and fear of stigmatized responses to disclosure can deter consumers from seeking help for suicide-related concerns ( 46 ), and consumers also report fears of disempowerment from treatment orders under mental health Acts ( 47 ). Similar worries may also deter individuals from engaging with interventions such as the SPI. The four synthesized findings in this review suggest specific recommendations for practice, policy, and future research. For practice, it is recommended that the SPI is developed via a person-centered and compassionate collaboration, where clinicians are afforded sufficient time (minimum 30 minutes) to develop authentic therapeutic rapport for the person to express their suicidal experiences. Further, to address the transient nature of suicidal thoughts and maximize effectiveness of the safety plan, the SPI should be viewed as a living document that is shared with others (support persons, care providers) and revised regularly. Given that involving support persons appears to enhance the SPI, practitioners should genuinely explore this involvement during the initial safety plan co-construction and at review appointments. Supportive others should receive SPI education with assistance from the clinician and guidance from the consumer regarding how to best provide support. Further research is required to address gaps in our understanding of the SPI and how best to support the people who use it. First, the specific processes which assist consumers to reduce suicidal ideation and behavior require further examination. Our findings indicate that SPI practices may enhance consumers’ connection, autonomy, and competence: three of the processes of SPI effect proposed by Rogers et al. ( 20 ). Further mixed-methods research is required to investigate causal pathways from specific SPI strategy-use to improved suicide and wellbeing-related outcomes via theorized processes of effect. Greater integration of diverse user experiences is required to inform future SPI adaptations that meet the needs of the specific consumer groups for whom they are designed. In the current review, over half of the included papers related to veterans, their support persons and/or people who work with them. There has been little to no focus on the experiences of safety planning from other priority groups known to experience high rates of suicidality, such as LGBTQIA+ communities ( 48 ). Finally, our results reveal a common perception whereby states of acute and severe distress temporarily impair peoples’ capacity to engage in safety planning behaviors. This perceived barrier should be explored in more depth using rigorous qualitative approaches. Research has begun to illuminate the temporal dynamics of suicidal states, often using digital technologies to monitor suicidal distress in real-time ( 49 ). Lived experience research will be crucial to develop a greater understanding of how consumers experience the fluctuating and dynamic nature of suicidal states, as well as the relationship between current distress severity and specific SPI strategy use. Such understandings may assist consumers, support persons, clinicians, and researchers to adapt SPI practices to mitigate the onset and worsening of distress, and to improve safety during peak distress. Our search strategy, study selection procedures and meta-aggregation approach were systematic and thorough. In the JBI approach, findings can only be extracted if accompanied by an illustrative participant quotation. Whilst methodologically rigorous, this may have excluded relevant qualitative data if reported in a different format. There is also substantial scope for improvement in the methodological quality of studies in this area. In the present review, the dependability of included studies was limited due to inconsistent reporting of reflexivity details and guiding methodological frameworks. Three of the four synthesized findings were also downgraded due to a mix of unequivocal and credible findings, resulting in “low” overall confidence ratings. To enhance confidence in future qualitative findings, studies should follow best-practice guidelines for reporting qualitative research. Further, some studies lacked SPI details, such as format and delivery modality. We did not attempt to contact the authors of these papers to seek confirmation of these details. Doing so may have improved the generalizability of findings. However, we do not believe these details to be crucial to the results, as the findings relate more to overall experiences with the SPI, rather than specific features (with the exception that we had one finding category related to digital modalities). Finally, although one included study indicated a mental health lived experience academic as part of the authorship team ( 10 ), none of the included studies explicitly indicate involvement or consultation with people with lived experience of suicidality and/or safety planning in designing or conducting the studies. More high-quality qualitative studies of consumer, support person and clinician perspectives, conceived and conducted collaboratively with people with lived experience of suicidality and safety planning, would advance our understanding of peoples’ experiences of using SPI practices. While there is scope for improving the methodological quality of future qualitative SPI research and a need to better understand the causal pathways between SPI use and suicide-related outcomes, the findings from this review indicate that SPI practices are regarded positively from the qualitative perspectives of consumers, support persons and clinicians. This complements what is known about SPI effectiveness from quantitative research, and indicates that the SPI is perceived as acceptable and beneficial, and can be an important strategy to support people experiencing suicide-related distress. Use of the SPI could be strengthened by ensuring that services have sufficient time and resources (including training) for staff to engage in safety planning, as well as pathways for support persons to be involved, and strategies to ensure the SPI is tailored to individual consumer needs. Continuing to prioritize diverse lived experience perspectives of this suicide prevention approach is critical to ensuring that the SPI meets the needs of those using it. | Review | biomedical | en | 0.999998 |
PMC11697295 | Hydrazine (N 2 H 4 ) has long been widely used in many fields as a useful diamine with strong basic, reducing, and nucleophilic properties. Hydrazine can be used as a reducing agent, 1,2 high-energy rocket propellant, 3 precursor of pharmaceuticals, 4 insecticide, 5 and a raw material for industrial products such as polymers and carbon dioxide sorbents. 6,7 However, despite its usefulness, hydrazine is highly toxic and is known to be hepatotoxic, neurotoxic, and mutagenic. 8–10 Its use poses a risk of exposure to the environment during various stages of the manufacturing process. In humans, endogenous aminoacylase may also induce hydrolysis of hydrazine-containing drugs such as isoniazid, releasing hydrazine and/or acetylated hydrazine as a toxic metabolite. 11 Given the hazardous nature of hydrazine, the sensitive and selective detection of hydrazine in environmental and biological samples is an important issue. A chemiluminescent probe, 12 fluorescent probes, 13–15 and other analytical methods 16–19 have been developed to detect hydrazine selectively. Among them, fluorescent probes with sufficient solubility, suitable lipophilicity, and negligible toxicity are convenient for assessing hydrazine exposure in living cells, but faster probes with better sensitivity and selectivity are necessary for real applications. Therefore, there has been ongoing development of fluorescent probes that operate on novel detection mechanisms. We have designed a novel β-ketoester-type fluorescent probe platform (OB-MU1) for detecting hydrazine. The putative mechanism for hydrazine detection is shown in Scheme 1 . First, the ketone moiety of OB-MU1 reacts with hydrazine to form a hydrazone (1). Subsequently, the amine moiety of the hydrazone reacts with the ester in an intramolecular nucleophilic attack to the ester, releasing 5-methyl-2,4-dihydro-3 H -pyrazol-3-one (2), whereas the fluorophore 4-methylumbelliferone (3) is released and exhibits a fluorescent response. We expected that the combination of the strong nucleophilicity 20 and the adjacent positioning of the two primary amine moieties within the same molecule could be used to distinguish hydrazine from other bisnucleophiles, including ethylenediamine and hydroxylamine. In addition, strong endogenous mononucleophiles such as ammonia and hydrogen sulfide do not undergo such ring closure reactions due to the unfavorable four-membered ring formation. Levulinic acid ester-type probes 21–25 have intrinsically fluorogenic reactivity to mononucleophiles by the formation of a five-membered ring as well as to hydrazine by the formation of a six-membered ring. The detection mechanism of OB-MU1 is similar to that of levulinic acid ester-type probes, but our strategy is characterized by five-membered ring formation upon hydrazine detection, giving our probes an advantage. 26 Another potential research gap with levulinic acid ester-type probes is that it also reacted with sulfite (SO 3 2− ) to form 2-methyl-5-oxotetrahydrofuran-2-sulfonate by the formation of a five-membered ring and the fluorescent product as seen in resorufin levulinate, 27 which could complicate hydrazine detection. On the contrary, such a sulfite reaction is unlikely in the case of β-ketoester-type fluorescent probes because unfavorable four-membered ring formation is required for the transformation. First, we attempted to synthesize OB-MU1 by the condensation reaction of 4-methylumbelliferone (3) and 3-oxobutanoic acid (4a), but the desired ester OB-MU1 was not obtained. We attribute this unsuccessful reaction to the reactivity of the active methylene, and condensation with 2,2-disubstituted 3-oxobutanoic acids 4b and 4c gave the desired β-ketoesters OB-MU2 and OB-MU3 ( Scheme 2 ). The reactions of OB-MU2 and OB-MU3 (50 μM) with hydrazine (1 mM, 20 eq.) were monitored by UV-vis spectroscopy in 50 mM HEPES buffer (pH 7.4). Upon addition of hydrazine, the absorption of the OB-MUs around 270 nm, which is considered to be the maximum absorption of β-ketoesters, decreased in a time-dependent manner. Concomitantly, the absorption peak at around 310 nm, which is considered to be the maximum absorption of esterified 3, shifted to 320 nm, the maximum absorption wavelength of free 3. OB-MU2 (apparent k 2 = 27 M −1 min −1 ) reacted with hydrazine more slowly than OB-MU3 (apparent k 2 = 147 M −1 min −1 ) . 28,29 The reaction of OB-MU2 was not complete even after 1 hour, whereas OB-MU3 reacted faster and nearly reached completion by 30 min. Considering that the cyclopropyl moiety is less bulky than the dimethyl moiety, these differences in reaction rates are inconsistent with the Thorpe–Ingold effect for the cyclization reaction; therefore, hydrazone formation with the OB-MUs and hydrazine is likely the rate-determining step. The OB-MUs were subsequently evaluated by fluorescence spectroscopy: the reaction of 10 μM OB-MU derivative with 200 μM (20 eq.) hydrazine was observed by measuring the fluorescence spectrum of 3 . Fluorescence spectroscopy also revealed a slower reaction of OB-MU2 than OB-MU3 with hydrazine, similar to the UV-vis results. These results indicate that OB-MU3 is a better fluorogenic probe for detecting hydrazine in aqueous environments. As the OB-MU3 probe reacted successfully with hydrazine, further studies were conducted to evaluate its selectivity and other properties. The fluorescence intensity was proportional to the concentration of hydrazine , indicating that OB-MU3 is a quantitative probe under these conditions. The detection limit of OB-MU3 was found to be 95.3 nM . The pH-dependence of the reaction of OB-MU3 with hydrazine was also examined from pH 3 to 10. We observed that under biological conditions (pH 6–8), an increase in pH led to a faster reaction and higher signal in the presence of hydrazine (open circles), whereas nonspecific reactions (closed circles) are still negligible . These data imply that the pH dependence can be simply derived from the p K a of the 3-phenol domain (p K a ∼ 7.8), whereas hydrazine and hydrazone intermediates both have sufficient nucleophilicity when the pH is above 5, according to the p K a s of their conjugated acids. Above pH 8, the fluorogenic reaction by non-specific alkaline hydrolysis competes with the intended reaction with hydrazine. Further examination of the selectivity of OB-MU3 toward different test substances showed that the fluorescence response to various amines, amino acids, reducing substances, and metal ions was weak . As anticipated, the better selectivity of OB-MU3 for hydrazine compared to other nucleophiles including sulfite (#12) is thought to be due to the formation of a five-membered ring with two adjacent nucleophilic amines. To confirm the fluorogenic reaction mechanism of OB-MU3 with hydrazine, we evaluated the product formed from the reaction when hydrazine was added to OB-MU3 on a 1 mmol scale. The addition of 10 equivalents of hydrazine dihydrochloride to OB-MU3 in an aqueous acetonitrile solution yielded 3 in quantities corresponding to OB-MU3 consumption, as well as formation of what is presumably the cyclized product 5 which likely reacts with HCl to form 6 ( Scheme 3 ). These results suggest that the reaction of OB-MU3 with hydrazine proceeded as we expected in Scheme 1 . The plausible reaction mechanism of fluorogenic OB-MU3 with hydrazine is shown in Scheme 4 . First, the ketone moiety of OB-MU3 reacts with hydrazine to form a hydrazone (7). 7 exists as either the E -7 or the Z -7 isomer. Subsequently, the amine moiety of Z -7 reacts with the ester in an intramolecular nucleophilic attack to the ester, releasing 5, while the fluorophore 3 is released as the phenolate and exhibits a fluorescent response. We also state here that 6 with reactive alkyl halide may appear toxic similar to the HaloTag due to its possible modification of intracellular nucleophiles such as glutathione. However, generation of potentially toxic 6 under physiological conditions is unlikely because the chloride ion concentration is low (5 to 60 mM) 30 compared to the concentrations found in our experiment (2.0 M) which comes from the use of the stable dihydrochloride form of hydrazine. Finally, we evaluated the ability of OB-MU3 to detect hydrazine in live-cell imaging using fluorescence microscopy. After HeLa cells were treated with 20 μM OB-MU3 and washed with Hanks' balanced salt solution (HBSS)(+), they were exposed to HBSS(+) containing 600 μM hydrazine . Fluorescence imaging results showed that after treatment with hydrazine, an OB-MU3-derived signal was observed throughout the cell with a fluorescence increase of ca. 7-fold . Therefore, OB-MU3 is a hydrazine probe based on a novel cyclization reaction that can visualize exogenous hydrazine in live cells. Finally, we confirmed that up to 50 μM of OB-MU3 exhibited minimal acute cell toxicity and it is thought that OB-MU3 exerts a negligible influence on cells at low concentrations (20 μM). We plan to exchange 3 with a long-wavelength fluorophore like TokyoGreen for further biological applications and/or imaging within specific organelles using the same synthesis scheme as that used for OB-MU3. In summary, we have developed novel fluorescent probes bearing the β-ketoester structure, OB-MU2 and OB-MU3, for the detection of hydrazine. Based on UV-vis and fluorescence spectroscopic measurements, the cyclopropyl moiety of OB-MU3 accelerates the response to hydrazine compared to the dimethyl structure of OB-MU2. OB-MU3 also exhibited a fluorogenic response under aqueous conditions containing 1% organic solvent (acetonitrile) at physiological pH, with up to a 54-fold increase in fluorescence. In addition, OB-MU3 demonstrated a very strong hydrazine-selective response, showing little reaction to many nucleophiles and reducing agents, with hydroxylamine and hydrogen sulfide as notable examples. Moreover, OB-MU3 can visualize intracellular hydrazine. Coumarin-based OB-MU3, which has short excitation and emission wavelengths, has drawbacks for bioimaging applications such as limited imaging depth and interference from the autofluorescence from biological substances. 31 The moderate signal-to-noise ratio of OB-MU3 was caused by low intracellular retention of released 3. Therefore, the signal-to-noise ratio can be improved by replacing 3 with a hydrophilic fluorophore with better intracellular retention. In the future, β-ketoester structures will be combined with long-wavelength fluorescent dyes that are more suited than 7-hydroxycoumarin for bioimaging applications to develop probes that can similarly detect intracellular hydrazine with high sensitivity. All reactions were monitored by thin-layer chromatography with E. Merck silica gel 60 F 254 pre-coated plates (0.25 mm) and were visualized by UV (254 nm). IR spectra were obtained on a PerkinElmer Spectrum One. 1 H NMR and 13 C NMR spectra were recorded on a JEOL ECZ400S spectrometer ( 1 H: 400 MHz, 13 C: 100 MHz) instrument. Chemical shifts are reported in ppm relative to the carbons of deuterated solvents (CDCl 3 : 77.0 ppm, DMSO- d 6 : 39.5 for 13 C) or the internal standard tetramethylsilane (CDCl 3 and DMSO- d 6 : 0.00 ppm for 1 H). The mass spectra were measured on a Thermo Fisher Scientific LTQ Orbitrap Discovery. Melting points were determined with a Yanaco micro melting point apparatus MP-J3. Yields refer to isolated yields of compounds greater than 95% purity as determined by 1 H NMR analysis. All new products were characterized by 1 H NMR, 13 C NMR, IR, and HRMS. UV-vis spectroscopy was recorded by Cary 8454 (Agilent). Fluorescence spectroscopy was recorded by Duetta (HORIBA) and SpectraMax iD5 multiplate reader (Molecular Devices). To a solution of 2,2-dimethyl-3-oxobutanoic acid 32 (4b, 260 mg, 2.00 mmol) in CH 2 Cl 2 (4.0 mL), 4-methylumbelliferone (3, 177 mg, 1.00 mmol), 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDCI·HCl, 560 mg, 2.92 mmol), and 4-dimethylaminopyridine (DMAP, 13.0 mg, 0.106 mmol) were added and stirred for 14 hours at room temperature. Reaction mixture was quenched by adding H 2 O and extracted with CHCl 3 . The aqueous layer was extracted with CHCl 3 twice. The combined organic layers were dried over anhydrous Na 2 SO 4 and filtered and solvents were removed under reduced pressure. The residue was purified by column chromatography (SiO 2 , CHCl 3 ) to obtain OB-MU2 (162 mg, 56%) as a colorless solid. Rf: (CHCl 3 /MeOH = 30 : 1): 0.63. Mp: 90–91 °C. 1 H NMR (400 MHz, CDCl 3 ) δ : 7.62 (d, J = 8.0 Hz, 1H), 7.11 (d, J = 2.5 Hz, 1H), 7.06 (dd, J = 8.0, 2.5 Hz, 1H), 6.29 (d, J = 1.5 Hz, 1H), 2.44 (d, J = 1.5 Hz, 3H), 2.31 (s, 3H), 1.55 (s, 6H). 13 C NMR (100 MHz, CDCl 3 ) δ : 205.1, 171.5, 160.2, 154.0, 152.8, 151.8, 125.5, 117.9, 117.6, 114.5, 110.0, 56.1, 25.5, 21.7, 18.6. IR (KBr) 3073, 2976, 1761, 1726, 1709, 1616 cm −1 . HRMS (ESI) m / z calcd for [C 16 H 16 O 5 + Na + ] 311.0890, found 311.0884. To a solution of 1-acetylcyclopropane-1-carboxylic acid 33 (4c, 609 mg, 4.75 mmol) in CH 2 Cl 2 (20 mL), 3 (351 mg, 1.99 mmol), EDCI·HCl (933 mg, 4.87 mmol), and DMAP (16.8 mg, 0.138 mmol) were added and stirred for 19 hours at room temperature. Reaction mixture was quenched by adding H 2 O and extracted with CHCl 3 . The aqueous layer was extracted with CHCl 3 twice. The combined organic layers were dried over anhydrous Na 2 SO 4 and filtered and solvents were removed under reduced pressure. The residue was purified by column chromatography (SiO 2 , CHCl 3 ) to obtain OB-MU3 (364 mg, 64%) as a colorless solid. Rf: (CHCl 3 /MeOH = 30 : 1): 0.59. Mp: 129–130 °C. 1 H NMR (400 MHz, CDCl 3 ) δ : 7.63 (d, J = 8.5 Hz, 1H), 7.12 (d, J = 2.5 Hz, 1H), 7.07 (dd, J = 8.5, 2.5 Hz, 1H), 6.30 (d, J = 1.5 Hz, 1H), 2.58 (s, 3H), 2.45 (d, J = 1.5 Hz, 3H), 1.78–1.70 (m, 4H). 13 C NMR (100 MHz, CDCl 3 ) δ : 201.8, 169.2, 160.3, 154.2, 152.5, 151.8, 125.5, 118.1, 117.8, 114.7, 110.3, 34.9, 29.9, 20.4, 18.7. IR (KBr) 3077, 1752, 1730, 1700, 1612 cm −1 . HRMS (ESI) m / z calcd for [C 16 H 14 O 5 + H + ] 287.0914, found 287.0911. To a solution of OB-MU3 (282 mg, 0.985 mmol) in acetonitrile (5 mL) and sodium phosphate buffer (100 mM, pH = 7.5, 5 mL), hydrazine dihydrochloride (1.04 g, 9.91 mmol) was added and stirred for 24 hours at room temperature. Reaction mixture was diluted with EtOAc and washed with aqueous NaHCO 3 . The aqueous layer was extracted with EtOAc twice. The combined organic layers were dried over Na 2 SO 4 and filtered and solvents were removed under reduced pressure. The residue was purified by column chromatography (SiO 2 , CHCl 3 /MeOH = 10 : 1 to 4 : 1) to give the unreacted OB-MU3 (78.4 mg, 28%), concomitant with fluorescent 3 (125.5 mg, 72%) as a colorless solid, 5 (19.1 mg, 15%) as a colorless solid, and 6 (22.1 mg, 14%) as a colorless solid. Rf: (CHCl 3 /MeOH = 10 : 1): 0.58. Mp: 136–138 °C (lit. 140–141 °C). 34 1 H NMR (400 MHz, DMSO- d 6 ) δ : 11.1 (s, 1H), 1.80 (s, 3H), 1.70 (q, J = 4.0 Hz, 2H), 1.35 (q, J = 4.0 Hz, 2H). 13 C NMR (100 MHz, DMSO- d 6 ) δ : 176.1, 159.3, 31.4, 17.0 (2C), 12.3. HRMS (ESI) m / z calcd for [C 6 H 8 ON 2 + Na + ] 147.0523, found 147.0523. Rf: (CHCl 3 /MeOH = 10 : 1): 0.23. Mp: 167–168 °C (lit. 170–171 °C). 34 1 H NMR (400 MHz, DMSO- d 6 ) δ : 10.44 (br s, 1H), 3.57 (t, J = 7.5 Hz, 2H), 2.63 (t, J = 7.5 Hz, 2H), 2.06 (s, 3H). 13 C NMR (100 MHz, DMSO- d 6 ) δ : 159.7, 137.6, 97.2, 44.5, 25.6, 9.9. HRMS (ESI) m / z calcd for [C 6 H 9 ON 2 35 Cl + H + ] 161.0476, found 161.0477, calcd for [C 6 H 9 ON 2 37 Cl + H + ] 163.0447, found 163.0447. To a quartz cuvette, 2.94 mL of 50 mM HEPES buffer (pH 7.4), 30 μL of OB-MU2 or OB-MU3 (5 mM solution in acetonitrile, final conc. 50 μM), and 30 μL hydrazine (100 mM solution in H 2 O, final conc. 1 mM) were added and incubated for each time (0, 1, 3, 5, 10, 15, 30, 45, and 60 min) at 25 °C. After incubation, UV-vis spectra were measured using a Cary 8454 spectrophotometer . All kinetics analyses were conducted with the following eqn (1) and (2) according to the literatures, 28,29 1 2 [P] t = [U] 0 − [U] t * Δ 0 = [V] 0 − [U] 0 , [V] 0 = 20[U] 0 , k 2 : second order rate constant [M −1 min −1 ], U: hydrazine probe (OB-MU2 or OB-MU3), V: hydrazine, P: product (4-methylumbelliferone) The estimated absorbance ( A 365–375 : average of absorbance at 365–375 nm) was calculated with [U] t and [P] t given by eqn (1) and (2) , respectively, and the absorption constants (average of extinction coefficients at 365–375 nm). The apparent k 2 was obtained by least squares curve fitting between the measured and estimated absorbance with scanning of k 2 value in eqn (1) . To a quartz cuvette, 2.94 mL of 50 mM HEPES buffer (pH 7.4), 30 μL of OB-MU2 or OB-MU 3 (1 mM solution in acetonitrile, final conc. 10 μM), and 30 μL hydrazine (20 mM solution in H 2 O, final conc. 200 μM) were added and incubated for each time (0, 1, 3, 5, 10, 15, 30, 45, and 60 min) at 25 °C. After incubation, fluorescence spectra were measured using a Duetta fluorescence spectrometer ( λ ex : 323 nm, λ em : 370–570 nm, 25 °C). To a quartz cuvette, 2.94 mL of 50 mM HEPES buffer (pH 7.4), 30 μL of OB-MU3 (1 mM solution in acetonitrile, final conc. 10 μM), and 30 μL hydrazine (0, 1, 2, 3, 5, 7, 10, or 15 mM solution in H 2 O, final conc. 0, 10, 20, 30, 50, 70, 100, 150 μM) were added and incubated for 30 min at 25 °C. After incubation, fluorescence spectra were measured using a Duetta fluorescence spectrometer ( λ ex : 323 nm, λ em : 370–570 nm, 25 °C). Additionally, using the following equation DL = K × Sb 1 / S ; the detection limit (DL) of OB-MU3 (10 μM) by fluorescence ( λ ex : 323 nm, λ em : 447 nm, 25 °C) for hydrazine (serial dilution from 10 μM) in 50 mM HEPES buffer (pH 7.4, 1% acetonitrile) was calculated, where K = 3; Sb 1 is the standard deviation of the blank solution; and S is the slope of the calibration curve. Sb 1 = 0.01292, S = 0.4067, ∴DL = 95.3 nM. To each well of a flat bottom black 96-well plate , 196 μL of buffer (Mcllvaine buffer for pH 3.0, 4.0, 5.0, 6.0, 7.0, and 8.0; 100 mM sodium borate buffer for pH 9.0 and 10.0), 2 μL of OB-MU3 (1 mM solution in acetonitrile, final conc. 10 μM), and 2 μL of hydrazine (0 or 20 mM solution in H 2 O, final conc. 0 or 200 μM) were added, followed by incubation for 30 min at 25 °C. After incubation, fluorescence intensity was measured by SpectraMax iD5 multiplate reader ( λ ex : 323 nm, λ em : 447 nm, 25 °C). To each well of a flat bottom black 96-well plate , 196 μL of 50 mM HEPES buffer (pH 7.4), 2 μL of OB-MU3 (1 mM in acetonitrile, final conc. 10 μM), and 2 μL of each analyte (for 1: H 2 O, 2: ammonia, 3: NH 2 OH·HCl, 4: ethylenediamine, 5: Na 2 S, 6: aniline, 7: methylamine, 8: piperidine, 9: p -tolylhydrazine, 10: lysine, 11: glycine, 12: Na 2 SO 3 , 13: Na 2 S 2 O 3 , 14: CuBr, 15: CuBr 2 , 16: ZnSO 4 , 17: FeSO 4 , 18: FeCl 3 , 19: MnCl 2 , 20: NiCl 2 , 21: CoCl 2 , 22: N 2 H 4 ·2HCl, 20 mM in H 2 O, final conc. 200 μM) were added and incubated for 20 and 30 min at 25 °C. After incubation, fluorescence intensity was measured using a SpectraMax iD5 multiplate reader ( λ ex : 323 nm, λ em : 447 nm, 25 °C). HeLa cells were cultured in Dulbecco's modified Eagle's medium supplemented with 5% fetal bovine serum (FBS; Sigma Lot No. S.15N348), 50 μg per mL kanamycin sulfate (Meiji Seika Pharma Co.), 50 U per mL penicillin G potassium (Meiji Seika Pharma Co.), and 50 μg per mL streptomycin sulfate (Meiji Seika Pharma Co.) at 37 °C under a humidified atmosphere of 5% CO 2 in air. Cell passages from subconfluent cultures were performed once a week using a trypsin–ethylenediaminetetraacetic acid (EDTA) solution . For fluorescence bioimaging, cells (5.0 × 10 4 cells per mL) were cultured in 500 μL DMEM for 24 hours in each compartment with a 35 mm glass-bottomed dish . After washing twice with 500 μL HBSS(+), the cells were incubated with phenylmethylsulfonyl fluoride (PMSF, 2 mM) in 250 μL HBSS(+) and OB-MU3 (40 μM) in 250 μL HBSS(+) for 30 min. After washing once with 500 μL HBSS(+), the cells were treated with 0 or 600 μM hydrazine dihydrochloride in 500 μL HBSS(+) for 30 min, followed by observation of the cells using an AxioObserver 7 inverted microscope (Carl Zeiss AG) equipped with 20× (N.A. 0.8) objective lens, Colibri7 LED illumination system, and Prime BSI sCMOS camera (Teledyne Photometrics) under differential interreference contrast (DIC) and fluorescent mode (fluorescence channel: λ ex = 370–400 nm, λ em = 410–440 nm). We also confirmed that no fluorescence was observed without the OB-MU3 probe for this channel with or without hydrazine dihydrochloride (data not shown). All treatments were conducted in CO 2 incubator (37 °C, 5% CO 2 , humidified atmosphere). PMSF was purchased from FUJIFILM Wako Pure Chemical Co. HBSS(+) was purchased from FUJIFILM Wako Pure Chemical Co. or Nacalai Tesque Inc. . Cells (1.0 × 10 5 cells per mL) were cultured in 100 μL DMEM with 5% FBS for 24 hours in each well . After removal of the medium via aspiration and washing with PBS(−) (100 μL), the cells were incubated in HBSS(+) containing different concentrations [OB-MU3] = 0, 20, and 50 μM for 1 hour. After removal of the solution, the cells were incubated in 100 μL DMEM with 5% FBS containing 10% WST-8 cell counting solution . After 4 hours of treatment, absorbance was measured at 450 nm (Abs 450 ) to quantify the metabolite water-soluble formazan and at 650 nm (Abs 650 ) to measure background absorbance using a SpectraMax iD5 multiplate reader. Cell viability was calculated from the mean values of four wells using the following equation: Abs = Abs 450 − Abs 650 . | Study | biomedical | en | 0.999999 |
PMC11697296 | Parkinson's disease (PD), a common neurodegenerative disease in the elderly, is mainly caused by the lack of dopamine related to midbrain neurons. This deficiency leads to the impairment of motor functions, significantly affecting the quality of life for patients. 1 Levodopa ( l -3,4-dihydroxyphenylalanine, l -Dopa), a precursor drug for dopamine, can enter the brain through the blood–brain barrier and be converted into dopamine by dopamine-decarboxylase to supplement dopamine in the brain, thus alleviating the symptoms. l -Dopa is a commonly used clinical drug for the treatment of PD. 2 However, long-term use of l -Dopa can increase the concentration of l -Dopa in the body, and excessive l -Dopa will lead to many side effects, such as bradykinesia, muscle stiffness, and tremors. 3 Therefore, it is critical to monitor the concentration of l -Dopa in patients taking this drug to improve the curative effect. To date, some traditional analytical methods for l -Dopa detection have been reported, such as high-performance liquid chromatography (HPLC), capillary electrophoresis (CE), spectrophotometric and electrochemical methods. 4,5 These methods achieve effective detection of l -Dopa, but they have various disadvantages, including high costs, complex operation procedures, long analysis times, and easy interference. In recent years, fluorescence sensing has developed rapidly in detection of various types of targets due to its superiority of enhanced selectivity, high sensitivity, quick response, low cost, and independence from expensive instruments. 6 Therefore, the construction of a fluorescence sensor for l -Dopa detection is a reasonable and ideal solution to overcome the limitations of traditional methods and meet clinical requirements. Carbon dots (CDs) are a novel type of fluorescent carbon nanoparticles and have attracted much attention because of low toxicity, superior biocompatibility, high chemical stability, easy surface functionalization and minimal photo bleaching. 7 CDs have been successfully applied in various applications, including fluorescence sensing, bioimaging, drug delivery and photocatalysis, of which fluorescence sensing is a key part. 8,9 CDs-based fluorescence sensors mainly rely on the enhancement or quenching of fluorescence after their reaction with analytes. 10 Surface functional groups play a major role in the reaction between carbon dots and detection objects. Hence, various CDs have been prepared in many studies by changing the carbon source or surface modification. 11,12 These CDs have been applied in constructing fluorescence sensors for diverse analytes, including metal ions, pesticides, antibiotics and so on. 13–15 Among them, there are few reports published on the development of CDs-based l -Dopa sensors. Therefore, there is an urgency to explore new carbon sources or preparation methods for producing CDs with high quantum yield and fluorescence properties for l -Dopa detection. Pandanus amaryllifolius Roxb. is a tropical green plant common in Hainan province of China that is rich in vitamins, proteins, chlorophyll, nucleic acids, and other nutrients. Due to its low price and availability, it is an excellent precursor for preparing carbon dots. In this study, Pandanus amaryllifolius Roxb., was applied as the green carbon source for preparing CDs. A nitrogen-rich chemical (ethylenediamine, EDA) was deliberately introduced to the hydrothermal reaction system to regulate the properties of CDs. CDs prepared with (NPCDs) and without (PCDs) EDA were comparatively analyzed. It was found that a higher quantum yield and a more sensitive fluorescence response to l -Dopa were achieved when using NPCDs. After optimizing the preparation and detection conditions, a fluorescence sensor for l -Dopa was developed using NPCDs with the limit of detection of 0.05 μM. This sensor was successfully used to detect l -Dopa in fetal bovine serum samples with excellent precisions (RSD ≤ 2.99%) and recoveries of 88.50–99.71%. In summary, this work enriches the types of CDs and provides an innovative idea for the regulation of the properties of CDs derived from biomass carbon sources. An effective method for monitoring l -Dopa was presented, demonstrating substantial potential for clinical applications. Transmission electron microscopy (TEM) images were observed on a JEM 2100F microscope (JEOL, Japan). Raman spectra were obtained by HR evolution (Horiba, France). X-ray photoelectron spectroscopy (XPS) was performed using a ESCALAB 250Xi spectrometer (Thermo Scientific, USA). Fourier Transform Infrared Spectroscopy (FTIR) spectra were recorded by a Thermo Field IS5 spectrometer (Thermo, USA). X-ray diffraction (XRD) spectra were obtained by a SmartLab-9 kW X-ray diffractometer (Rigaku, Japan). Fluorescence lifetimes were evaluated with the help of a FLS1000 fluorescence spectrometer (UK). UV-vis absorption spectra were recorded by a UV-2600 spectrophotometer (Shimadzu, Japan). Fluorescence intensities and spectra were recorded by a FL-4700 fluorescence spectrophotometer (Shimadzu, Japan). Zeta potential was measured by a ZSU310 nano-particle potential analyzer (Malvern, UK). Elemental analysis of carbon source was performed using a UNICUBE CHNSO element analyzer (Elementar, Germany). Quantum yield (QY) was measured and calculated according to previous work. 16 Pandanus amaryllifolius Roxb. were washed, dried in the oven and ground into powder. 0.5 g of the powder, 10 mL of ultrapure water and 50 μL of ethylenediamine were mixed evenly in a 25 mL Teflon lined high-pressure reactor, and then heated at 200 °C for 8 h. After naturally cooled to room temperature, a 0.22 μm membrane was used to filter the products and thus obtain the filtrate, known as NPCDs. The filtrate was further dialyzed against ultrapure water through a dialysis bag for 24 h with renewing the water every 4 h to remove the small molecules. PCDs were prepared using the same method, but without the addition of EDA in the reaction system. The stock solution was prepared by dissolving l -Dopa in ultrapure water. Standard working solutions of l -Dopa with different concentrations (0.1–100 μM) were prepared by diluting the reserve solution with ultrapure water. 4 mL of working solution or samples was added to a 5 mL centrifuge tube and adjusted to pH 11 using NaOH solution. 200 μL of NPCDs solution was added. After standing for 30 minutes, the fluorescence intensity (FL intensity) of the mixture was measured at excitation and emission wavelengths of 365 nm and 440 nm, respectively. The fluorescence quenching efficiency ( F 0 / F ) was calculated, where F and F 0 represent the FL intensity of NPCDs/PCDs solution with and without l -Dopa, respectively. The limit of detection (LOD) was calculated by 3 σ / k , where σ is the standard deviation of the intercept and k is the slope of the regression equation. In order to investigate the selectivity, NPCDs solution was added to the solution containing a series of ions (10 μM, Mg 2+ , K + , SO 4 2− , Na + , CH 3 COO − , Cl − ), small molecules (10 μM, Gly, l -Ala, VC, l -Cys, l -Ser, l -Thr, l -Leu, l -Glu, l -Phe, l -Tyr, urea, β-CD) or DA (1 μM), and FL intensity was measured. The above chemicals were separately added to l -Dopa solution (10 μM) to investigate the anti-interference ability for l -Dopa detection of the sensor. The precision and accuracy experiments were conducted with three concentration levels (5 μM, 20 μM, and 60 μM). Three analysis batches over a three-day period were conducted. Precision was assessed by calculating the variation coefficient of l -Dopa samples at each concentration level. The deviation between the measured concentration and the actual concentration of l -Dopa samples was calculated for accuracy evaluation. Standard working solutions of l -Dopa in FBS at different concentrations (5–100 μM) were prepared by diluting the stock solution with FBS. Samples were measured following the same procedure described in section l -Dopa detection. FBS samples spiked with l -Dopa at low (10 μM), medium (20 μM) and high (60 μM) concentrations were prepared and analyzed with the sensor. The recovery was calculated according to eqn (1) , 1 Recovery (%) = ( C measured / C added ) × 100% where C measured represents the l -Dopa concentration calculated through the linear regression equation of the method, and C added is the actual l -Dopa concentration. In this work, NPCDs were prepared by a simple one-step hydrothermal method using Pandanus amaryllifolius Roxb. as the carbon source for the first time. In contrast to other reported CDs with only biomass as the precursor, EDA was introduced to the reaction system for nitrogen doping to regulate the properties of CDs. 17 The preparation conditions were optimized, including the addition amount of EDA, reaction temperature and time to increase the QY of NPCDs (QY NPCDs ). As shown in Fig. S1a, † QY NPCDs first increased and then decreased slightly with the increase of the amount of EDA. Similar to the preparation of CDs with chemical sources, N doping also resulted in improved quantum yields of CDs with biomass as carbon precursors. 18 With the optimal doping amount of EDA (50 μL), QY NPCDs was 8.19%, which was 2.66 times higher than that without EDA doping (3.08%). To optimize the reaction temperature, the preparation was carried out at 140 °C, 160 °C, 180 °C and 200 °C. As can be seen in Fig. S1b, † QY NPCDs gradually increased when increasing temperature. According to previous reports, the possible fluorescence mechanisms of CDs include quantum effect, edge structure effect, surface defect states and crosslink-enhanced emission, etc. 19,20 It is possible that as the temperature increase, the degree of carbonization increase, resulting in smaller particle sizes of CDs or more surface light emitting functional groups. This phenomenon may contribute to the increase of QY. Considering convenience and safety of operation, the performances at higher temperature were not investigated, and subsequent experiments were conducted at 200 °C. According to Fig. S1c, † the formation of CDs should be basically complete after 8 hours of reaction. In summary, the optimal reaction conditions for the preparation of NPCDs are as follows: 50 μL EDA, 200 °C, and the reaction time of 8 h. Under the optimal condition, QY NPCDs can reach 8.19%, which is higher than many reported CDs synthesized with biomass carbon sources. 18,21,22 Three batches of Pandanus amaryllifolius Roxb. leaves were purchased and used to prepare NPCDs following the same method. The reproducibility was assessed by comparing quantum yield, UV-vis spectra, fluorescence spectra, and response to l -Dopa of the NPCDs across the batches. As shown in the Table S1 and Fig. S2, † the QYs of the three batches of NPCDs exhibited minimal differences, with their spectra showing a high level of consistency and all displaying a fluorescence response to l -Dopa. These findings indicated a high degree of reproducibility in NPCDs. The synthesis yields of CDs were calculated. The yield of NPCDs (1.27 ± 0.02%) was approximately 5 times greater than that of PCDs (0.27 ± 0.03%). This result indicated that nitrogen doping may also enhance the yield of carbon dots. NPCDs and PCDs were compared to assess the impact of nitrogen doping on CDs properties. TEM images of NPCDs and PCDs revealed their predominantly spherical shapes with good dispersion. The diameters ranged from 1.15 nm to 3.55 nm for NPCDs and 1.45 nm to 3.55 nm for PCDs, with average sizes of 2.41 ± 0.03 nm and 2.23 ± 0.04 nm, respectively . According to these results, nitrogen doping appeared to increase the particle size of CDs and lead to a less uniform size distribution. HRTEM images showed lattice spacings of 0.21 nm for NPCDs and PCDs. XRD patterns of both NPCDs and PCDs displayed diffraction peaks at approximately 29° , indicative of graphite lattice spacing (002). 18 Both HRTEM and XRD results confirmed the graphite-like structures of NPCDs and PCDs. Raman spectra in Fig. S3 † revealed D and G bands for NPCDs and PCDs, associated with sp 2 graphitic carbon structure (ordered arrangement) and sp 3 hybrid carbon structure (disordered arrangement), respectively. 23 The calculated intensity ratios ( I D / I G ) of NPCDs and PCDs were 1.51 and 1.32, suggesting more defects presence in NPCDs compared to PCDs. 23 FTIR and XPS were applied to explore the elements components and surface functional groups of NPCDs and PCDs. As observed in Fig. 1d , the FTIR spectra of NPCDs and PCDs exhibited similar absorption peaks at 3455 cm −1 , 2920 cm −1 , 1639 cm −1 and 1056 cm −1 , corresponding to N–H/O–H, C–H, C <svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="13.200000pt" height="16.000000pt" viewBox="0 0 13.200000 16.000000" preserveAspectRatio="xMidYMid meet"><metadata> Created by potrace 1.16, written by Peter Selinger 2001-2019 </metadata><g transform="translate scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg> O, C–O, respectively. 24,25 A notable difference was the stronger strength of C O in NPCDs compared to PCDs, indicating a higher concentration of oxygen-containing groups on NPCDs. XPS survey spectra of NPCDs and PCDs revealed their predominant elements of carbon (285.0 eV), oxygen (532.3 eV), and nitrogen (400.2 eV) . The proportion of each element was calculated (Table S2 † ). The O/N content ratio in NPCDs was higher than in PCDs, while the C content ratio showed the opposite trend. Each element was further analyzed by high-resolution XPS. As illustrated in Fig. 1f , the high-resolution C 1s spectra of NPCDs/PCDs can be deconvoluted into three peaks at around 284.8 eV, 286.2 eV and 288.5 eV, which were attributed to C–C, C–O/C–N, and C O, respectively. 24 Compared to PCDs, there were more C O and less C–O/C–N on NPCDs. In the high-resolution O 1s spectra of NPCDs/PCDs , the fitted peaks at 531.5 eV and 532.5 eV were related to the C O and C–O–H/C–O–C groups, respectively. 25 The content of C O on the surface of NPCDs was obviously higher than that of PCDs. This was consistent with FTIR analysis. The high-resolution N 1s spectra of NPCDs/PCDs showed two peaks at around 400.1 eV and 402.3 eV, confirming the existence of nitrogen in the forms of pyrrole N and N–H . 25 Overall, the FTIR and XPS analyses confirmed the presence of many hydrophilic groups such as hydroxy, carboxyl and amino groups on the surface of the prepared NPCDs and PCDs, ensuring their water solubility and modifiability. Nitrogen doping was identified as a factor influencing the elemental and functional group composition, potentially explaining the observed differences in QY. The UV-vis spectra of NPCDs and PCDs showed characteristic absorption peaks at 335 nm and 280 nm, attributed to n–π* transitions of C O and π–π* transitions of C C. 26 There is a large difference between the UV-vis spectra of the two CDs due to the different composition of functional groups on each CDs. From the photos , both NPCDs and PCDs appeared pale yellow in aqueous solutions under daylight and exhibited blue fluorescence under UV light ( λ = 365 nm). PCDs and NPCDs demonstrated a wide range of wavelengths for excitation and emission . Besides, the fluorescence emission spectra of NPCDs and PCDs at different excitation wavelengths were examined. According to Fig. 2c and d , as the excitation wavelength increased from 300 nm to 420 nm, the fluorescence emission peaks of NPCDs and PCDs exhibited a redshift, with the fluorescence intensity initially increasing and then gradually decreasing. Both NPCDs and PCDs demonstrated excitation-dependent luminescence properties, likely due to variations in particle sizes and surface functional group compositions. 27 The maximum excitation and emission wavelengths were used for subsequent fluorescence intensity measurements. The impact of pH on the fluorescence of NPCDs and PCDs was investigated. As shown in Fig. S4a, † the FL intensity of NPCDs and PCDs was found to be high within a pH range of 4–11, with weak fluorescence observed in extreme acidic or alkaline conditions. In strong acid environments, there may be more free carboxyl groups on the surface of CDs, forming additional hydrogen bonds. 28 A certain degree of aggregation of NPCDs and PCDs may result in reduced fluorescence. 28 A strong alkaline environment will result in carboxyl group deprotonation and destroy the functional group of NPCDs and PCDs, leading to decreased fluorescence. 29 NaCl was used to examine the effect of ionic strength on the FL intensity of NPCDs and PCDs. As can be seen in Fig. S4b, † the FL intensity of NPCDs and PCDs exhibited remarkable stability in the presence of 1.0 M NaCl, indicating excellent ionic strength stability. Moreover, prolonged exposure to 365 nm UV irradiation for 100 minutes did not significantly alter the FL intensity of NPCDs and PCDs , highlighting their exceptional photo-stability. These findings suggest that NPCDs are a highly stable and promising material for constructing fluorescence sensors. NPCDs and PCDs were tested simultaneously to compare their sensing performance. The key sensing conditions, including pH and reaction time, were optimized to maximize detection sensitivity. It can be seen in Fig. 3a that the acidity and alkalinity of the solution had a significant influence on quenching efficiency. Notably, the fluorescence of NPCDs/PCDs was hardly quenched by l -Dopa at pH 1–8 or 13–14, with the highest quenching efficiency observed at pH = 11, which was subsequently used in the following sensing experiments. Furthermore, as depicted in Fig. 3b , the fluorescence of NPCDs and PCDs exhibited rapid quenching, reaching equilibrium after 20 minutes and 5 minutes, respectively. To streamline operations, both sensing reactions were incubated for 20 minutes. NPCDs and PCDs were incorporated to samples with different concentrations of l -Dopa, and their fluorescence spectra were recorded. The FL intensity of NPCDs/PCDs gradually decreased as the concentration of l -Dopa increased, while the maximum emission wavelengths remained constant, as depicted in Fig. 3c and d . A higher quenching efficiency was noted for NPCDs at equivalent l -Dopa concentrations, suggesting that NPCDs may exhibit greater sensitivity to l -Dopa. To assess their sensing capabilities, calibration curves were constructed by plotting the quenching efficiency ( F 0 / F ) against the l -Dopa concentration. As shown in Fig. 3e , the linear relationships were observed in the l -Dopa concentration range of 0.1–100 μM for NPCDs and 10–100 μM for PCDs . The limits of detection (LOD) of the sensors based on NPCDs and PCDs were calculated to be 0.05 μM and 1.54 μM, respectively. NPCDs-based sensor demonstrated a lower LOD and broader calibration range. These findings suggest that N doping can enhance the sensor's sensitivity for l -Dopa detection, in addition to the QY. When compared to previously reported CDs-based sensors for l -Dopa detection ( Table 1 ), this approach exhibits superior sensitivity and a wider linear range. The NPCDs-based fluorescence sensor was systematically validated. A total of three standard curves were measured on separate days. All standard curves demonstrated a correlation coefficient ( R 2 ) exceeding 0.99, indicating exceptional linearity. The parameters of the weighted linear regression equations were given in Table S3. † The RSD values for slope and intercept were 1.92% and 1.03%, respectively, indicating superior repeatability of the sensor. The accuracy and precision tests were conducted at three concentrations of l -Dopa (5 μM, 20 μM and 60 μM). For each concentration, three replicates were performed over three days. Table S4 † displays the results, showing that the intra-day and inter-day accuracy fell within the range of 92.11% to 104.27%. Intra-day and inter-day precision were both below 8.89%. These results indicated that the method is accurate, reliable, and reproducible. There are a variety of chemicals such as ions (Mg 2+ , K + , SO 4 2− , Na + , CH 3 COO − , Cl − ), amino acids (Gly, l -Ala, l -Cys, l -Ser, l -Thr, l -Leu, l -Glu, l -Phe, l -Tyr), and other organic compounds (urea, β-CD, DA, VC) in serum that can be present alongside the target compound, l -Dopa. 30–33 Consequently, it is essential to assess the possible interference caused by these substances. For selectivity validation, the fluorescence of NPCDs was measured in the presence of various organic compounds or ions, respectively. The quenching efficiency ( F 0 / F ) was calculated based on the FL intensity without ( F 0 ) and with ( F ) each substance. According to Fig. 4 (blue bar), only l -Dopa significantly quenched fluorescence of NPCDs, demonstrating the good selectivity of the sensor. When DA was introduced into the sensing system at a concentration of 1 μM, DA could partially quench the fluorescence of NPCDs, which may be through a similar quenching mechanism due to its similar catechol structure with l -Dopa. As we know, DA concentration in normal people's blood is below 130 pM. 34 In contrast, the concentration of l -Dopa in the blood of patients following treatment may reach levels between 2.54–8.11 μM, 35 which is more than 100 times higher than that of DA. DA therefore has a negligible effect on l -Dopa sensing in serum. The interference of coexisting substances was further investigated by adding different substances to the l -Dopa standard solution. The samples were then analyzed via the same procedure. As seen in Fig. 4 (pink bar), there is little effect of other coexistence chemicals on the quenching efficiency of NPCDs by l -Dopa, suggesting a good anti-interference ability of the sensor. The composition of FBS and human serum is similar, as both contain a variety of plasma proteins, polypeptides, fats, carbohydrates, growth factors, hormones, and inorganic substances. This similarity makes FBS a suitable substitute for blood plasma in method validation, a practice commonly utilized in previous researches and also implemented in this study. 36–38 Standard samples were prepared by spiking l -Dopa in FBS matrix. By following the same detection process, the relationship between quenching efficiency and the concentration of l -Dopa was explored. As illustrated in Fig. 3f , a decent linearity was observed within the concentration range of 5 to 100 μM, with a correlation coefficient of 0.9970, indicating that the influence of the matrix on the detection was negligible. A spike recovery test was carried out at three concentrations. The calculated recoveries (ranging from 88.50% to 99.71%) and RSDs (ranging from 2.08% to 2.99%) further verified the accuracy of the method in real sample detection (Table S5 † ). To explore the sensing mechanism, the Stern–Volmer equation, UV absorption spectra, fluorescence spectra, fluorescence lifetime decay curves and Zeta potential were exploited. According to Fig. 3e , the fluorescence quenching of NPCDs by l -Dopa fitted well to the Stern–Volmer equation ( F 0 / F = 1 + K SV [ l -Dopa]), and K SV (Stern–Volmer constant) was calculated to be 1.72 × 10 4 M −1 . The fluorescence quenching may be through static or dynamic quenching mechanisms. 39,40 UV absorption spectra of NPCDs with and without l -Dopa were examined at pH 11. In Fig. 5a , after adding l -Dopa, the absorption peaks of NPCDs did not shift and no new peaks appeared, suggesting that static quenching might not appear. 41 Fluorescence decay curves of NPCDs with and without l -Dopa (20 μM) were measured. After fitting with the double exponential function, the average lifetimes without and with l -Dopa were calculated to be 5.51 ns and 5.50 ns, respectively . The basically unchanged fluorescence lifetime of NPCDs demonstrated that there was no dynamic quenching process and photo-induced electron transfer (PET). 42 The absorption of l -Dopa and l -Dopa of pH 11 and fluorescence spectra of NPCDs were compared for further investigation . When the pH was adjusted to 11, the absorption of l -Dopa increased obviously in the wavelength range of 280–600 nm. This stems from its oxidation to dopaquinone under an alkaline condition. 30 In this situation, there is a large overlap between the excitation/emission spectra of NPCDs and the absorption of l -Dopa. Thus, the quenching may occur through Förster resonance energy transfer (FRET) and internal filter effect (IFE) mechanisms. Since no change in lifetime of NPCDs was observed, the FRET mechanism was ruled out. 42 In addition, the zeta potential of NPCDs at pH = 11 was measured to be −1.54 mV, and it changed to −36.92 mV when l -Dopa was added, further proving that l -Dopa was oxidized . Overall, the quenching effect of l -Dopa on the fluorescence of NPCDs may be mediated primarily by IFE after its oxidation into dopaquinone. In this research, a new biomass material ( Pandanus amaryllifolius Roxb.) was utilized as a sustainable carbon source for the preparation of carbon dots (CDs). Through comparative analysis, it was found that when CDs were prepared with EDA doping (NPCDs), a higher quantum yield and a more sensitive fluorescence response to l -Dopa were achieved. By optimizing the preparation and detection conditions, a fluorescence sensor for l -Dopa detection based on NPCDs was developed, achieving a limit of detection (LOD) of 0.05 μM. The sensor was successfully applied for detecting l -Dopa in fetal bovine serum samples with promising outcomes. Compared to existing CDs-based l -Dopa sensors, the sensor in this study offers advantages such as simple preparation, high selectivity, strong anti-interference capability, and enhanced sensitivity. The findings suggest that the NPCDs-based sensor has potential for monitoring l -Dopa in clinical settings. | Review | biomedical | en | 0.999995 |
PMC11697332 | Chirality, a distinct characteristic of objects that cannot be perfectly aligned with their mirror image, is present in various aspects of nature. For example, the standard form of the DNA double helix always twists in a right-handed manner, while snails exhibit left–right asymmetry both internally and externally. 1 , 2 A large number of naturally occurring molecules, such as proteins, enzymes, amino acids, carbohydrates, etc., are chiral and contain at least one stereogenic center in the structure, typically tetrahedral (sp 3 -hybridized) carbons with four different substituents, 3 and the two nonsuperimposable mirror-image forms of chiral molecules are called enantiomers. 4 A review from 2003 states that approximately 50% of the pharmaceuticals marketed and used in medical treatment are chiral compounds, and 88% among them are administered as racemates. 5 Different enantiomers of a chiral compound generally possess identical physical and chemical properties in an achiral environment, but they may exhibit significant variations in biological activities. For example, the ( S , S )-(+)-enantiomer of ethambutol is utilized for treating tuberculosis, while the ( R , R )-(−)-enantiomermay lead to blindness. 6 Nowadays, regulatory authorities require independent pharmacological tests for each enantiomer as well as their combined effects, and only the therapeutically active isomer can be used in a marketed drug product, 7 consequently, stereochemistry and chiral resolution are of paramount importance in the pharmaceutical industry. The 2001 Nobel Prize in Chemistry was awarded to three scientists for their work in the development of asymmetric synthesis using chiral catalysts in the production of single enantiomer drugs or chemicals. 8 In spite of the rapid development of asymmetric synthesis in recent years, there are still numerous chiral compounds synthesized as racemates, and then separated by a suitable physical separation approach. 9 In industry, two main categories of techniques are often applied for chiral resolution. Diastereomeric salt formation and enzymatic or kinetic resolution are two classical technologies, and the modern approach is the use of preparative high-performance liquid chromatography. 10 − 12 The main restrictions of the above methods is that sometimes they are impractical and uneconomical. Cocrystallization, the process of producing cocrystals, i.e., crystals with two or more molecular species in a specific stoichiometric ratio within a crystal lattice, has gained increasing attention recently as a feasible strategy to achieve chiral separation. 13 − 15 This process enables the formation of new crystalline materials involving two chiral molecules, leading to changes in its physical and physicochemical properties. 16 This approach involves two possible scenarios when both cocrystallizing components are chiral: (i) the chiral coformer only forms an enantiospecific crystal with one enantiomer of the target compound or (ii) the chiral coformer can form a diastereomeric cocrystal pair with each enantiomer of the target compound. Structural modifications in the supramolecular assembly in enantiospecific cocrystals or diastereomeric cocrystal pairs lead to changes in the crystal lattice energy and related physical and physicochemical properties, enabling separation . Therefore, both possible outcomes can be used to develop a chiral resolution process. The application of achieving chiral resolution through enantiospecific cocrystal formation in solution was first introduced by Leyssens’s group in 2012, 14 and developed to include a dual-drug chiral resolution process 17 and the use of ionic cocrystals. 16 , 18 They initially demonstrated that only the S -enantiomer of 2-(2-oxopyrrolidin-1-yl) butanamide, which exhibits nootropic activity and is marketed under the name levetiracetam, can cocrystallize with S -mandelic acid, while the R -enantiomer cannot form a cocrystal with S -mandelic acid, leading to 70% of the S -enantiomer separated from the racemic mixture in a single cocrystallization step. Diastereomeric cocrystal systems have been less extensively studied in comparison to enantiospecific systems. Höpfl and colleagues reported a diastereomeric cocrystal pair of R / S -praziquantel with l -malic acid, and the chiral separation was enabled by phase-decomposition of the R -praziquantel- l -malic acid cocrystal due to the different aqueous solubilities of the diastereomeric cocrystals. 19 l -Proline was proven to form diastereomeric cocrystals with both R - and S -enantiomers of mandelic acid in different stoichiometric ratios, hence, the chiral separation can be attained by simply altering the stoichiometry of the two constituents. 20 Mandelic acid is a widely used compound for forming enantiospecific or diastereomeric cocrystals. The literature and Cambridge Structural Database (CSD) search indicate that approximately 40 cocrystals/salts incorporating mandelic acid with another chiral compound have been documented ( Table S11 ). Somewhat surprisingly, no cocrystals involving mandelamide, the amide derivative of mandelic acid, have been reported or deposited in the CSD, 21 even though it is an important drug precursor. 22 In this work, the crystal structure of racemic [(±) - MDM], enantiopure mandelamide ( S -MDM) and enantioenriched MDM (94 S : 6 R ) were identified, and the potential of S -MDM as a chiral resolution agent via cocrystallization was considered. Two diastereomeric cocrystal pairs of S -MDM with both R - and S -enantiomers of mandelic acid (MDA) and proline (Pro) were obtained by both liquid-assisted grinding and slow evaporation, and fully characterized by thermal analysis, X-ray techniques, and FT-IR spectroscopy. To further investigate the diastereomeric behavior of S -MDM with the chiral coformers, detailed analyses of crystal structures, motifs and Hirshfeld surfaces were performed. LAG experiments were performed by placing a physical mixture of S -MDM with each coformer in a 5 mL stainless steel grinding jar along with a 2.5 mm stainless steel grinding ball. After the addition of 30 μL of ethyl acetate the mixture was ground using a Retsch MM400 Mixer mill at a rate of 30 Hz for 30 min. The products obtained were analyzed by powder X-ray diffraction (PXRD). A 1:1 molar ratio of S -MDM: coformer was used in all cases. After single crystal analysis, a 1:2 molar ratio of S -MDM with l -Pro was used. 20.5 mg of synthesized (±)-MDM was dissolved in 10 mL of THF by heating. Colorless plate-like crystals were obtained by slowly evaporating the filtered solution at room temperature for 3–5 d. 20.4 mg of the commercial S -MDM was dissolved in 10 mL of the solvent mixture THF and toluene (1:1, v/v) by heating. Colorless plate-like crystals of MDM were obtained by slowly evaporating the filtered solution at room temperature for 3–5 d and one crystal was identified by single crystal diffraction as containing 94% S -MDM and 6% R -MDM. Bulk quantities of MDM (94 S : 6 R ) were obtained by dissolving 100 mg of the commercial S -MDM in EtOH at room temperature, and removing the solvent quickly using a rotary evaporator (Büchi, Germany) under a vacuum achieved by a diaphragm pump (Vacuubrand, Germany), with the rotary flask rotating at a speed of 40 rpm while being immersed in a water bath at 50 °C. 24 The resulting white powdered product was isolated and allowed to dry in the fume hood overnight. Powder X-ray Diffraction (PXRD): The PXRD patterns were collected on a STOE STADI MP diffractometer with a Cu Kα radiation (1.540 Å) using a linear position-sensitive detector. The tube voltage and amperage were set at 40 kV and 40 mA respectively. Each sample was scanned between 3.5 and 45.5° 2θ with an increment of 0.05° at a rate of 2° min –1 . The samples were prepared as transmission foils and the data were viewed via STOE WinXPOW POWDAT software. 25 Differential Scanning Calorimetry (DSC): DSC was conducted on a TA Instruments Q1000. Samples (1–5 mg) were placed in nonhermetic aluminum pans and scanned in the range of 25 to 200 °C at a heating rate of 10 °C min –1 under a continuously purged dry nitrogen atmosphere (flow rate 80 mL min –1 ). The data were viewed and analyzed by TA Universal Analysis software. FT-IR Spectroscopy (IR): FT-IR spectra were recorded on a PerkinElmer UATR Two spectrophotometer using a diamond attenuated total reflectance accessory over a range of 400–4000 cm –1 . Four scans were taken at 4 cm –1 resolution for each sample, and the spectra were measured over the range of 400–4000 cm –1 . Single crystal X-ray diffraction (SCXRD): An optical microscope was used to choose a suitable crystal for diffraction. SCXRD data was performed using a Bruker APEX II DUO with monochromated Cu Kα radiation . The structure was solved and refined by the SHELX suite of programs in the Bruker APEX software. 26 , 27 All non-hydrogen atoms were refined by using anisotropic displacement parameters while hydrogen atoms were fixed in geometrically calculated positions using the riding model, with C–H = 0.93–0.98 Å, O–H = 0.82 Å and N–H = 0.86–0.89 Å, and Uiso (H) (in the range 1.2–1.5 times Ueq of the parent atom). For MDM (94 S : 6 R ), there is disorder in two of the four crystallographically independent MDM molecules due to the R -MDM impurity, which was modeled in two conformations in 88:12 ratio. For S -MDM- l -Pro and S -MDM- d -Pro cocrystals, there was disorder in the proline carbon that is beta to both the nitrogen and the carbon bonded to the carboxylic acid, which was modeled in two conformations in 50:50 and 85:15 ratios, respectively. PLATON was used for the analysis of potential hydrogen bonds and short ring interactions. 28 , 29 Mercury 2022.2.0 and DIAMOND 4.6 were used for viewing structures and creating diagrams. 30 Crystallographic parameters are listed in Table 1 . Hirshfeld surface analyses and two-dimensional (2D) fingerprint plots were carried out using the CrystalExplorer 21.5 program. 31 NMR spectra were recorded on either a Bruker Avance 300 MHz NMR spectrometer 1 H (300 MHz) or on a Bruker Avance 400 MHz NMR spectrometer 1 H (400 MHz) and 13 C (100.6 MHz). All spectra were recorded at room temperature (20 °C) in deuterated methanol ( d 4 -CD 3 OD), using tetramethylsilane (TMS) as an internal standard. Chemical shifts are reported in parts per million (ppm) relative to TMS, and coupling constants are expressed in Hertz (Hz). The enantiopurity of the commercial S- MDM from Sigma-Aldrich, synthesized S- MDM and the single crystal of MDM (94 S : 6 R ) were determined by chiral high-performance liquid chromatography (HPLC) analysis on a Lux Amylose-1 column, purchased from Phenomenex. The HPLC parameters employed included a mobile phase of hexane/isopropanol = 90:10, a flow rate of 1 mL min –1 , a temperature of 25 °C and a detection wavelength of 210 nm. HPLC analysis was performed on a Waters Arc with a Waters 2998 PDA Wavelength UV Detector. All solvents employed were of HPLC grade. Based on the molecular structures of mandelamide and both coformers, it was anticipated that the well-known amide–amide homosynthons and amide-acid heterosynthons would be observed in their crystal structures . A search of the CSD was undertaken to identify common supramolecular synthons for compounds containing a hydroxyl group in the α position to a primary or secondary amide functional group . The R 2 2 (8) homosynthon between two amides is commonly observed in 82 structures, 58 of which are single component crystals. Only one structure containing the amide-acid R 2 2 (8) heterosynthon has been reported (Refcode NUGFAX 33 ). Single crystals of racemic and enantiopure S -MDM were grown from THF and MeOH, respectively, and the structures determined as shown in Figure 4 and 5 , respectively. The ellipsoid plots are shown in Figure S14 . Hydrogen bonds and π–π interaction geometries are displayed in Tables S2 and S3 , separately. (±)-MDM crystallizes in the monoclinic P 2 1 / c space group with Z ′ = 1. As shown in Figure 4 a, two (±)-MDM molecules formed a R 2 2 (11) motif through the N–H···O and C–H···O hydrogen bonding. The hydrogen-bonded network is further extended by O–H···O hydrogen bonds between two (±)-MDM molecules. Along the b axis, an R 4 2 (8) motif is created among four (±)-MDM molecules via N–H···O hydrogen bonding . S -MDM crystallizes in the P2 1 2 1 2 1 space group with Z ′ = 1. As shown in Figure 5 , every two S -MDM molecules formed a R 2 2 (9) dimer between the hydroxyl group and the amide group in a tail-to-tail manner through the N–H···O hydrogen bonding. The 3D hydrogen-bonded network is further stabilized by O–H···O hydrogen bonds between two S -MDM molecules. Interestingly, during this study a third crystalline form of MDM was isolated from the solvent mixture of THF and toluene. Analysis of the SCXRD showed that this contained enantioenriched MDM (94 S : 6 R ) which results in a very different structure relative to either the enantiopure or racemic forms. The chiral HPLC results on another crystal from the same batch are consistent with the structural analysis. . As shown in Figures S21 and S22 , the crystal arrangement along the b axis in both the major and minor components of MDM (94 S : 6 R ) exhibits similarity to the crystal packing observed in (±)-MDM, rather than the expected resemblance to S -MDM, despite the fact that S -MDM constitutes 94% of MDM (94 S : 6 R ). The single crystals of S -MDM were obtained from the synthesized S -MDM which contains 100% of S -MDM, while the formation of the MDM (94 S : 6 R ) could be attributed to the commercial starting material being <100% S . According to the chiral HPLC analysis, the commercial S -MDM contained 96% S -MDM and 4% of R -MDM . PXRD analysis of the bulk material for (±)-MDM, S -MDM, and MDM (94 S : 6 R ) match the theoretical PXRD based on the single crystal analysis, Figure S11 . The formation of MDM (94 S : 6 R ) may be rationalized either on the basis of solvent effects, since it was observed by crystallization from a THF/toluene mixture, or fast crystallization using the rotatory evaporator, which is a method that can produce new crystalline forms. 24 To investigate whether MDM forms a solid solution, a 50:50 mixture of (±)-MDM and R -MDM was crystallized from methanol and analyzed by PXRD . The peak at approximately 2θ = 19–20° matches all forms of MDM. It has low intensity broadening at lower 2θ (18–19°), which is the region where a peak is only observed in MDM (94 S : 6 R ). The structural analysis results revealed that the expected R 2 2 (8) motif between two MDM molecules is not present in any of the crystal structures of MDM. Instead, motifs 1–4 are present in these three crystal structures. Motif 1 and 3 are not found in reported structures, while motif 2 was observed in four reported structures (Refcodes: VAFVIL, 34 DEZKUR, 35 NOLCOG, 36 YENDEC 37 ) based on the CSD search. In addition, motif 4, consisting of four MDM molecules in (±)-MDM and MDM (94 S : 6 R ), can also be found in two reported structures (Refcodes: DEZLEC 35 and YENDEC 37 ). The two main hydrogen-bonding functional groups in MDM are the amide and hydroxyl groups. As shown in Table S1 and Figure S3 , the characteristic IR bands of the N–H and O–H stretches in (±)-MDM and MDM (94 S : 6 R ) are both increased compared with those in S -MDM. In contrast, the stretching vibrations of C=O in these two solids display a decrease compared to S -MDM. As shown in Figure S8 , the melting point of (±)-MDM is 133–135 °C, which is in line with the reported data. 38 DSC analysis of the MDM (94 S : 6 R ) reveals its melting point is slight lower than that of S -MDM. In the book “Introduction to Stereochemistry”, Mislow examined the most common diastereomeric phase relationships that occur between two stereoisomers of similar substances. 39 One out of the four scenarios could explain the thermal behavior of MDM (94 S : 6 R ). In this case, introducing a small amount of impurity (i.e., R -MDM) can result in a decreased melting point compared to the pure component ( S -MDM). S -MDM- S -MDA and S -MDM- R -MDA cocrystals crystallized in the same space group (P 2 1 2 1 2 1 ) of the orthorhombic system and have similar unit cell parameters ( Table 1 ). Hydrogen bonds and π–π interaction geometries are displayed in Tables S5 and S6 , separately. S -MDM- S -MDA crystallizes with one S -MDM molecule and one S -MDA molecule in the asymmetric unit, Figure 7 a. These two molecules are connected via C11–H11···O23 and O23–H23···O3 discrete hydrogen bonds, forming a R 2 2 (8) motif. Two asymmetric units link through N1–H1A···O23 and C14–H14···O3 discrete hydrogen bonds, generating a four-molecule motif . In the other four-molecule motif , one S -MDM molecule and one S -MDA molecule interact through N1–H1B···O25 and C4–H4···O25 discrete hydrogen bonds, forming a similar four-molecule motif via O5–H5···O25 and O5–H5···O21 discrete hydrogen bonds. These two motifs are further assembled by an O25–H25···O5 hydrogen bond. The asymmetric unit of S -MDM- R -MDA contains one S -MDM molecule and one R -MDA molecule, which are connected via N1–H1A···O21 and O25–H25···O3 discrete hydrogen bonds, forming an R 2 2 (9) motif. Along the c axis, the asymmetric unit links two adjacent units to extend the 3D structure of the cocrystal through O–H···O hydrogen bonds (forming an R 1 2 (5) motif), and N–H···O hydrogen bond, respectively . Additional hydrogen bonding between S -MDM and R -MDA molecules is observed in a tail-to-tail manner along the b axis, where an R 1 2 (5) motif is created via O–H···O hydrogen bonds . The DSC data for the S -MDM- S -MDA and S -MDM- R -MDA cocrystals show single endothermic peaks at 85 and 81 °C, respectively, with the melting point of the cocrystals lying lower than those of the corresponding starting materials . As shown in Table S1 and Figures S4 and S5 , the −NH 2 , −OH, and C=O bands of S -MDM exhibit shifts in both cocrystals. All the observed differences indicated that those three moieties are involved in the formation of hydrogen bonds in the different cocrystals. As shown in Figure S12 the PXRD patterns for both S -MDM- S -MDA and S -MDM- R -MDA cocrystals match with the simulated patterns extracted from the SCXRD analysis, indicating these cocrystals can be reproduced in bulk quantities by the LAG method. The products were the same irrespective of the source of S -MDM (synthesized or commercial) used in the experiments. A stoichiometrically diverse diastereomeric cocrystal system between S -MDM and l / d -Pro was obtained. Hydrogen bonds and π–π interaction geometries are displayed in Tables S7 and S8 , respectively. S -MDM- l -Pro cocrystallized in the orthorhombic P 2 1 2 1 2 1 space group with one S -MDM and two l -Pro molecules in the asymmetric unit. As shown in Figure 9 a, S -MDM links l -Pro 1 through O5–H5···O27 hydrogen bond and connects l -Pro 2 via N–H···O and C–H···O hydrogen bonds, forming an R 2 2 (8) motif. R 1 2 (4), R 2 1 (5), and R 3 3 (8) motifs between l -Pro molecules interlink the chain , stabilizing the 3D hydrogen-bonded network of S-MDM- l -Pro cocrystal along the a axis . The S -MDM- d -Pro cocrystal crystallizes in the monoclinic space group P 2 1 and the asymmetric unit consists of two S -MDM molecules and two d -Pro molecules (Z′ = 2). As shown in Figure 10 , two S -MDM molecules and two d -Pro molecules can be regarded as the crystal packing building block, where R 4 4 (16) motif and R 4 3 (11) motif are created among four S -MDM molecules and two d -Pro molecules via N–H···O and O–H···O hydrogen bonds. An R 4 4 (13) motif between four d -Pro molecules is also observed in this building block through N–H···O hydrogen bonding. The 3D hydrogen-bonded network is extended by connecting different building blocks through O5–H5···O58 and C28–H28···O58 hydrogen bonds. Meanwhile, N–H···O hydrogen bonds between four S -MDA molecules also contribute to the stabilization of the crystal structure, forming two R 3 3 (11) motifs. Adifference of melting point between the S -MDM- l -Pro and S -MDM- d -Pro cocrystals can be observed from the DSC plots . The S -MDM- l -Pro cocrystal shows a single endothermic peak at 208 °C and the melting point of S -MDM- d -Pro cocrystal is 166 °C. Both of these are in between that of the individual components. The IR data show differences in the ν N–H , ν O–H , and ν C=O , indicating reconstruction of hydrogen bond networks in those solids and the formation of new crystalline solids . The experimental PXRD patterns of S-MDM- l -Pro and S-MDM- d -Pro cocrystals were found to compare well with the simulated PXRD patterns obtained from the SCXRD data . The different sources of S -MDM used in the cocrystallization experiments did not influence the products obtained. A 2014 CSD search of the existing enantiospecific and diastereomeric cocrystals demonstrated that among 44 multicomponent structures containing two optically active compounds, 38 (86%) systems behave enantiospecifically. 40 This reveals that even a small change in the structure of the cocrystallizing component, such as a change in absolute and/or relative stereochemistry, can lead to changes in secondary interactions and steric effects, ultimately changing the outcome of cocrystal formation. 40 − 44 Flood et al. explored the formation of enantiospecific and diastereomeric cocrystals by employing crystal structure prediction and molecular simulations, indicating that despite the similarity in the predicted hypothetical crystal structure and hydrogen-bonding geometries, variations in aromatic interactions and lattice energy were instrumental in favoring the formation of an enantiospecific cocrystal instead of a diastereomeric cocrystal pair. 45 Therefore, for the formation of a diastereomeric cocrystal pair, more changes in the hydrogen bonding network and molecular arrangement are required in order to reduce the influence of the secondary interactions and steric effects to the total cocrystal stabilization energy. 40 As mentioned earlier, diastereomeric cocrystals of S -MDM with S / R -MDA have similar crystallographic data, and the stoichiometric ratio between S -MDM and the coformers are the same. However, the hydrogen bonding between the two components in these cocrystals differ significantly. As shown in Figure 11 , binary level hydrogen-bonding motifs are present in S -MDM- S -MDA (motif 5) and S -MDM- R -MDA cocrystals (motif 6), respectively. For the S -MDM- S -MDA cocrystal, only the hydroxyl group from the carboxyl group of S -MDA, serving as both hydrogen-bonding donor and acceptor, is engaged in the hydrogen bond formation, while both the oxygen atom of the carbonyl group and a hydrogen atom (H11) from the benzene ring of S -MDM are involved in the hydrogen bond construction. In contrast, for the S -MDM- R -MDA cocrystal, hydrogen bonding occurs between the carbonyl oxygen atom and the hydroxyl group of R -MDA and the amide group of S -MDM. Motif 5 is not found in any structures through the CSD search, whereas motif 6 was presented in two reported (Refcodes: VASWOC 46 and ZZZRJG01 47 ). These orientationally restrictive interaction motifs determine the formation of diastereomeric cocrystal pairs between S -MDM and S / R -MDA. 48 Moreover, the different contacts in these two cocrystals can be visualized by their 2D fingerprint plots . Hydrogen bonding in the S -MDM- S -MDA cocrystal constitute a bigger proportion compared with those in S -MDM- R -MDA cocrystal, while in contrast, van der Waals interactions account for a larger percentage in the S -MDM- R -MDA cocrystal. These significant differences lead to the remarkable changes in the crystal packing for this diastereomeric pair. Compared to the S -MDM- S / R -MDA diastereomeric cocrystal pair, the differences between the S -MDM- l -Pro and S -MDM- d -Pro cocrystals are more significant. Apart from the dissimilar motifs (motif 7 from S -MDM- l -Pro, motifs 8 and 9 from S -MDM- d -Pro) resulting from different functional groups in two cocrystals and their distinct 2D fingerprint plots and corresponding contact contributions , the primary factor that overcame the obstacle of stabilization free energy for cocrystal formation is the varying stoichiometric ratios of S -MDM and l / d -Pro. This is similar to the recent report by Leyssens and co-workers for l -Pro with mandelic acid. 20 Given the different outcomes in terms of stoichiometry when using the diastereomeric pairs of S-MDM with either MDA or proline, a series of screening experiments were conducted with S-MDM and S / R -MDA and l / d -Pro in 1:1, 1:2, and 2:1 ratios. Based on the PXRD analysis, the product 1:1 ratio is of high purity without the existence of the diffraction peaks from either S -MDM or S / R -MDA. The PXRD pattern of the new phase of S-MDM with l -Pro in a 1:2 ratio was obtained, while for the 1:1 and 2:1 ratios, excess S-MDM was present as well as the 1:2 product. For the d -Pro system, new diffraction peaks of S-MDM- d -Pro were found using a 1:1 ratio. Excess S-MDM was detected when a 2:1 ratio was used and excess d -Pro found using a 1:2 ratio. These grinding experiment results are in line with the solution crystallization results. To demonstrate the potential of the MDM as a cocrystal system for chiral resolution, a series of slurry experiments involving (±)-MDM and l -Pro in molar ratios ranging from 1:1 to 1:5 were undertaken ( Table S10 ). The PXRD results revealed that at high proportions of l -Pro, particularly 1:4 and 1:5 ratios, the R -MDM: l -Pro (or S -MDM- d -Pro) cocrystals were not detected. Due to challenges in the determination of the enantiopurity of proline, the resolution experiment was undertaken using (±)-MDM and l -Pro as a proof of concept. Thus, a sample of (±)-MDM and l -Pro (in 1:3–1:5 molar ratios) was slurried in MeOH for 3 d. Separation of the solid from the liquid phase and analysis of each component revealed that the solid consisted predominantly of S -MDM- l -Pro by PXRD. Notably, the enantiopurity of S -MDM in the solid phase with 1:5 ratio is 96.1%ee, confirming the chiral resolution is possible through this cocrystal system . Further investigations are underway to explore the potential of MDM for chiral resolution through cocrystallization. In summary, the crystal structures of (±)-MDM, S -MDM and MDM (94 S : 6 R ) were identified and fully characterized in this work. Additionally, this study reports the synthesis and characterization of two novel diastereomeric cocrystal pairs of S -MDM with both enantiomers of mandelic acid ( S -MDM- S -MDA and S -MDM- R -MDA) and proline ( S -MDM- l -Pro and S -MDM- d -Pro). The S -MDM- S -MDA and S -MDM- R -MDA cocrystals have similar unit cell parameters and the same stoichiometric ratio (1:1), yet a significantly different hydrogen bonding between the two coformers plays a critical structure determining role. The formation of S -MDM- l -Pro and S -MDM- d -Pro diastereomeric cocrystals proceeds with different stoichiometries, similar to a recent report of proline with mandelic acid, 20 although the structure determining features are very different. The feasibility of utilizing MDM and l -Pro as a cocrystal system for chiral resolution was explored. This work revealed that S -MDM can be effectively resolved by cocrystallization with l -proline. | Review | biomedical | en | 0.999996 |
PMC11697380 | Importantly however, the area of specificities in cognitive processing in visually impaired individuals has been rather unexplored. Especially in the subgroup of blind learners it is likely that such specificities exist, due to known particularities in visual-spatial mental modeling and spatial navigation of this group . The resulting complexity of conveying abstract cognitive concepts to learners with visual impairments has been documented in other educational fields such as science and music . In this study, we focus on the field of programming education, and explore how blind and low vision learners approach the computational concept of abstraction. By observing these learners during programming assignments with the educational robots the Bee-bot and Blue-bot, we assess their approach to and experience of this concept through concrete behaviors. This will provide insight into how the process of abstraction emerges in this group of learners. Ultimately, these insights can contribute to understand specificities within their cognitive processing in the context of computational concepts, as well as to provide tailored educational support. A core computational process is abstraction, which involves viewing a situation at various levels of detail, and deciding what details we need and can ignore . It can be seen as a form of problem solving, as in the model by Perrenet where four layers of abstraction are described to understand how novice learners approach programming tasks. In the original model, these layers were identified in Computer Science education students’ thinking. The layers included the problem layer (the highest layer, where a verbal description of the problem is provided), the design layer (where a detailed depiction of the solution is provided without a reference to the specific programming language), the code layer (referring to the code in the specific programming language) and the execution layer (which involves running the code or referencing to the output, the lowest level). The model has been applied in the context of elementary school-level learners , where concrete observable behaviors of young learners while working with an educational robot have been operationalized for each layer . This enables identifying which layer a learner is engaged in during an assignment. Behaviors include tactile expressions (for instance, pointing towards the robot), verbal expressions (describing the route of the robot) and observing the environment, route, or robot carrying out the task. In addition to observing the behaviors within layers, the model can also be used to assess how learners switch between the layers through pattern analyses . Previous research with the educational robot revealed that young learners spend little time on the problem layer but do switch between layers in a matter that suggests debugging (switching back to the code layers after the execution) and redesigning (switching back to the design layer). This challenge in conveying abstract knowledge to learners with visual impairments can be grounded in specificities in visio-spatial mental modeling and spatial navigation in this group. This is a highly complex area. Generally, qualitative differences can exist for specifically blind compared to sighted individuals in how spatial information is encoded, as a result of the absence of visual experience and the quantitative advantage of vision over other senses . However, performance is influenced by several factors, including the onset of blindness, other experiences, taught or developed compensatory mechanisms as well as the specific spatial processing aspects involved or task used. Ultimately, visio-spatial mental images of blind individuals can in practice be functionally equivalent to those of sighted individuals, but differences on specific mental imagery tasks can also be identified. To further understand the implication of this complex picture within the context of young learners with visual impairments’ education, it is recommended to focus on distinct spatial contexts and representations within a particular discipline . Within programming education, the core concept of abstraction provides a suitable starting point. The approach of the layers of abstraction and the previously identified behaviors emphasize how more complex tasks and approaches entail less direct manipulation and consequently require more mental modeling. In order to explore this, we use the educational robots Bee-bot and Blue-bot, since these are widely applied in early programming education and have been proven to be at the concrete level relatively accessible for learners with visual impairments . Further, the two types of this bot provide different options for the dimension of control within a programming task, with the Bee-bot being directly programmed with buttons on the bot and the Blue-bot having the option to be programmed through an external device. Our research question is: which patterns and specific behaviors, approached from the layers of abstraction model, do children with visual impairments engage in when working on a programming task with the Bee-bot or Blue-bot? Specifically, we will assess how the children move through the abstraction layers during a task, and which behaviors they show within the different layers. In our understanding of the four layers, we follow previous work on this model . Consequently, the problem layer refers to the most abstract level where the problem is discussed, the design layer involves a depiction of the solution, the code layer involves being directly concerned with the code as applicable in the specific tool or language, and the execution is the least abstract layer where the code is ran or where there is preoccupation with the output. In our study, first, frequency of the different layers and switching between the layers can reveal whether learners with visual impairments engage in these higher levels of abstraction, that require mental representation of the problems they work on. Second, how exactly these learners engage in these layers, that is what type of concrete behaviors and practices, are being employed, can indicate what information is used and needed to build these mental representations. This also illuminates the extent to which the original model of the layers of abstraction, based on sighted learners , is applicable to other learners with certain specificities. Together these insights explore how the cognitive concept of abstraction in the context of programming is experienced by learners with visual impairments. Our primary interest lies in blind children, however given the low prevalence of this group as well as the unexplored nature of this topic in learners with visual impairments overall, we include in our study pairs of learners with visual impairments with each pair containing at least one blind child. The primary focus of this qualitative study was to explore visually impaired learners’ experience of the concept of abstraction in a programming assignment, in order to gain insight into the reality of this topic as experienced by our subjects. Fitting with such a design and focus, we intended to obtain trustworthiness of our study through establishing the four criteria of Guba: trust value, applicability, consistency, neutrality . In our collection of the data, we followed a tailored approach fitting the subjects’ specific setting and needs, providing space to find and express their experience. Further, we documented this approach, our sample, and the findings in detailed descriptions (see the relevant sections of the methods and the results sections). As such, we established truth value by staying close and true to the direct experience of subjects and documenting this experience in detail. Further, applicability refers to the extent to which (a type of) generalization is aimed for. In this study, this was limited to enabling transferability to similar participants and contexts by providing details on these participants and contexts. Third, consistency is established in the results section by working with a detailed coding scheme that allows for both pre-defined and newly observed behaviors, and in addition through providing full descriptive pictures for each pair of learners. Finally, neutrality is established again through staying close to our participants’ experience and documenting these experiences in detail. The educational floor-robot Bee-bot and the more advanced version Blue-bot were both used. These robots have the same basic look and functions, shaped as a bee with a clear front (distinguished by the protruding eyes and nose) and seven buttons on top which can be used to move the bot forward, backwards, turn right or left, pause, and run or erase the program. The type of functions are distinguished by different shapes and colors of the buttons. The bot makes sounds when it is being programmed (with different sounds for a step, for erase, and for run) and when it executes the program (making a sound for each step and a different sound at the end). Whereas the Bee-bot can only be programmed with the buttons on top, the Blue-bot has the option to be programmed externally using the accompanying materials (the tactile reader and tactile reader cards), or the Blue-bot app on a PC or tablet device (the latter was not used in the current study). The tactile reader is an external card holder, which connects to the Blue-bot through Bluetooth. A total number of nine cards, that hold the same five functions as the buttons on top of the robot, can be placed in this holder. Compared to programming the bot with the buttons on top, this external device makes it possible to lay out the program. The original cards indicate their function (step forward, pause, etc.) with a small picture. Since this is unsuited for blind learners, adapted tactile versions of these cards were used (previously created by one of the expertise centres and explored in some of the schools), which contained small tactile shapes attached to the cards below the original pictures. One consideration in the design of the tactile shapes was to find an appropriate alternative for the arrow shape, which had been proved in these previous explorations to be a difficult concept to convey to braille learners. Throughout this study, the tactile versions of the Blue-bot cards were used, as well as (in order to compare) the original versions. Finally, for the environment in which the bots were programmed (see Assignments below), either the wooden board that distinguishes different plates indicating the steps of the Blue-bot and that can be build into a maze was used or loose Kapla blocks to create the environment or a maze. Once the children had been divided up into pairs and matched to a tester, the tester explained the constructive interaction protocol (described in the next section), after which the video recording was started. Next, the tester checked whether the children had understood the explanation on the bot and if needed provided additional instructions. The assignment always started with the children programming a few steps in order to move the bot from one child to the other. The tester then choose an appropriate assignment to continue, out of several available worked-out assignments with different levels (including having the bot move from point A to point B within an open environment or within a maze, or having the bot perform a dance with a repeated pattern). The children were also allowed to (co-)design the environment and/or think of the end goal for the bot themselves. The children always worked with the bot in a structured manner towards a specific goal. This set-up was designed and carried out following the recommendation for an individually guided, tailored and flexible approach in research with children with impairments . In addition to the specific content of the assignment being adapted to the children’s level, tailored extra instruction was provided when required and the teacher was present to intervene for instance when a child got too distracted. Further, in order to gain insights into children’s experience while working with the bots, the think-aloud method of constructive interaction was used . Constructive interaction uses the set-up of a collaboration between children in order to create a natural situation for them to verbalize their experiences . We explicitly stimulated this by providing the children with an elaborate instruction on verbalizing your thoughts at the start, including a concrete example . The children were instructed to work together and try to verbalize what they were thinking of the material and what they were doing. The tester reminded them throughout the assignment, using neutral prompts (“don’t forget to think aloud”, “what are you doing now”). The sessions were all recorded individually on video. These recordings were processed by coding and transcribing verbal and non-verbal behavior, using a detailed pre-defined coding scheme in line with a theory driven thematic analysis approach . Continuing on establishing trustworthiness for our study as described in the research design section above, our data analyses were also aimed at capturing the experience of reality of our subjects. This was obtained first of all by staying close to this experience in coding our data, and second by taking into account the learners’ overall approach to the assignment and providing a full picture for each pair of participants on how they proceeded through the assignment. The coding scheme consequently included the primary focus of the study, distinguishing the four layers of abstraction, but also information on additional aspects of the learners’ experience. In addition, the overall impression of the sessions is included in the descriptions in the results as well. The scheme included 17 categories of behaviors referring to specific features of usability and accessibility (for instance-independent use, needed assistance, positive or negative experience) and the computational practices. The behaviors were based on previous insights on the use of programming materials with sighted and visually impaired learners . Concerning the coding of the computational practices, for each layer, plausible pre-defined behaviors were hypothesized based on previous work with sighted learners and fitting the Bee-bot and Blue-bot (pre-specified behaviors for each layer can be found in Tables 3–6 ). In addition, for each layer it was possible to indicate non-anticipated behaviors. This enabled the important intention of the research set-up to explore the current subjects’ behaviors in an open way. For all seven sessions included in this study, a coding scheme was completed, including verbatim transcriptions of verbal behaviors. Behavior occurring during one time or stimulus could be coded within multiple layers, for instance, when children were simultaneously discussing the end point and the steps towards it, which would be coded as pre-defined behavior in the problem as well as design layer. Further, multiple behaviors could also be coded within one layer, when for example children were following the output while discussing whether the outcome was anticipated, which would be coded as two pre-defined behaviors within the output layer. With the detailed and elaborate coding scheme, we aimed to establish consistency in our study, allowing for full descriptive pictures where observed behaviors are embedded in context and connected to the overall experience. Further processing was conducted by first taking an inventory of the frequency of and switching between the layers, by creating frequency tables and a graph for each session representing the switching per layer. We followed the previous paper by Faber et al. with this representation through graphs of young learners’ switching through the abstraction layers. Second, the behaviors within the layers were inventoried by creating frequency tables for the pre-specified behaviors within each layer and structuring the open answers for non-anticipated behaviors into patterns. Finally, information from other (not computational practice-related) categories was scanned to obtain an overall picture of the course of the assignment as well as any specificities for each session. Microsoft Excel was used for the coding scheme’s, further processing of the data and creation of the graphs was done in the Statistical Package for the Social Sciences (SPSS), version 27. Table 2. Occurrence of layers within and across sessions. Percentages are relative to specific session. 1 2 3 4 5 6 7 Total Problem Layer 24 19 17 5 8 4 5 82 (8.9%) (7.3%) (6.3%) (3.7%) (6.5%) (3.6%) (7.2%) (7.0%) Design Layer 83 106 54 35 21 26 18 343 (30.6%) (40.5%) (19.9%) (26.1%) (17.1%) (23.4%) (26.1%) (27.6%) Code Layer 112 104 73 61 35 18 28 431 (41.3%) (39.7%) (26.9%) 45.5%) (28.5%) (16.2%) (40.6%) (34.7%) Execution layer 52 33 127 33 59 63 18 385 (19/2%) (12.6%) (46.9%) (24.6%) (48.0%) (56.8%) (26.1%) (31.0%) Total 271 262 271 134 123 111 69 1241 Other 94 43 124 73 30 58 43 Table 3. Behaviors within problem layer. Behaviors Low vision Braille Total Anticipated Point to starting point 8 (28.6%) 11 (15.1%) 19 (18.8%) Point to end point 6 (21.4%) 13 (17.8%) 19 (18.8%) Discuss starting point 2 (7.1%) 8 (11.0%) 10 (9.9%) Discuss end point 9 (32.1%) 14 (19.2%) 23 (22.8%) Non anticipated 3 (10.7%) 27 (37.0%) 30 (29.7%) Total 28 73 101 Table 4. Behaviors within design layer. Behaviors Low vision Braille Total Anticipated Point to route 3 (2.1%) 19 (7.9%) 22 (5.7%) Following route 13 (8.9%) 22 (9.1%) 35 (9.0%) Describing route 89 (60.1%) 89 (36.8%) 178 (45.9%) Counting steps route 12 (8.2%) 7 (2.9%) 19 (4.9%) Non anticipated 29 (19.9%) 105 (43.4%) 134 (34.5%) Total 146 242 388 Table 5. Behaviors within code layer. Behaviors Low vision Braille Total Bee-bot Anticipated Make program 0 8 (2.9%) 8 (2.6%) Follow program 1 (3.2%) 15 (5.5%) 16 (5.2%) Press one button 22 (71.0%) 149 (54.2%) 171 (55.9%) Press multiple buttons 0 64 (23.3%) 64 (20.9%) Erase steps 7 (22.6%) 38 (13.8%) 45 (14.7%) Non anticipated 1 (3.2%) 1 (.4%) 2 (.7%) Total 31 275 306 Blue-bot Anticipated Make program 1 (.8%) 4 (3.0%) 5 (1.9%) Follow program 2 (1.6%) 4 (3.0%) 6 (2.3%) Take card 49 (39.8%) 51 (38.1%) 100 (38.9%) Put card in reader 44 (35.8%) 47 (35.1%) 91 (35.4%) Take card out reader 12 (9.8%) 8 (6.1%) 20 (7.8%) Change order cards 2 (1.6%) 2 (1.5%) 4 (1.6%) Non anticipated 13 (10.6%) 18 (13.4%) 31 (12.1%) Total 123 134 257 Table 6. Behaviors within output layer. Behaviors Low vision Braille Total Anticipated Execute program 14 (18.4%) 109 (31.2%) 123 (28.9%) Follow bot 45 (59.2%) 165 (47.3%) 210 (49.4%) Relate outcome 7 (9.2%) 40 (11.5%) 47 (11.1%) Predict outcome 6 (7.9%) 27 (7.7%) 33 (7.8%) Non anticipated 4 (5.3%) 8 (2.3%) 12 (2.8%) Total 76 349 425 The overview of the occurrence of the four layers ( Table 2 ) as well as the pattern analysis indicates how the children within the seven sessions move and switch through the layers of abstraction while they work on their assignments with the Bee-bot (Sessions 1, 2, 3) and Blue-bot (Sessions 4, 5,6,7). Most sessions lasted around 30 min, session 7 lasted only 15 min. Behavior unrelated to the layers is coded with 0 and displayed in the graphs and included in Table 2 as well. Overall, it can be seen that within all sessions all layers occur. The problem layer occurs least frequently (on average about 7% of the time), as the graphs show it differs per session at which point during the session this usually is. In sessions 1, 2, 3 and 5 the problem layer arises all through the session, whereas in sessions 4, 6, and 7 there are one or two occasions where this layer occurs. The design, code and execution layer usually each take in between 20% and 40% of the behaviors, with some exceptions (for instance the execution layer occurring less often in session 2, and the code layer occurring quite frequently in session 4). In some sessions, the design layer stands out by being less frequently engaged in compared to the code and execution layer (sessions 3 and 5 most clearly) while in session 2 the design layer occurs most often. “Other” behaviors are seen throughout all sessions in between processes related to abstraction. Consistently across sessions, these other behaviors mostly involve the children listening to instructions, generally discussing the material or their collaboration with it, or distractions and actions outside of the material and assignment. Figure 1. Session 1 pattern of layers. Line graph for session 1 describing the pattern of moving through the layers of abstraction with the Bee-bot. The X-as represents time during the assignment and the Y-as represents the four abstraction layers. After a start where the children spent some time at the code and execution layer, the graph shows the children most frequently switch between the design and code layer. Six dense stretches going back and forth between these layers can be seen. In between these stretches the children visit the execution layer, and mostly during the second half they also go to the problem layer. Figure 2. Session 2 pattern of layers. Line graph for session 2 describing the pattern of moving through the layers of abstraction with the Bee-bot. The X-as represents time during the assignment and the Y-as represents the 4 abstraction layers. The graph shows 6 stretches of switching back and forth between the design and code layer, spread through the assignment. At the start or during each of these stretches the problem and execution layer are also visited once or twice. Figure 3. Session 3 pattern of layers. Line graph for session 3 describing the pattern of moving through the layers of abstraction with the Bee-bot. The X-as represents time during the assignment and the Y-as represents the 4 abstraction layers. The graph shows a not very dense pattern of mostly switching between the code and execution, and less frequently, design layer. The problem layer is switched to on occasion all through the assignment. Looking in more detail at the individual graphs, complemented by the accompanying behavior and atmosphere during the different sessions, several observations can be made. A general trend is that sessions 1, 2 and 3 (the Bee-bot sessions) have a denser pattern compared to sessions 4, 5, 6, and 7 (the Blue-bot sessions). Further, especially in sessions 1 and 2 the dense pattern involved several stretches of quickly switching back and forth between coding and designing. Taking a closer look at these sessions, session 1 concerned two braille learners who often relied on their residual sight by bringing themselves very close to the material. The children preferred to work by themselves and the coding-designing stretches always involved one child programming step by step by pressing the button (code layer) and moving the bot through the environment along to plan the next step (designing). Session 2 consisted of one low vision and one braille learner. The latter did not have any residual sight and relied upon the audio function of the bot and tactile exploration both while programming and while following the bot go on his route, as well as upon quite some verbal and tactile assistance by the tester and the other child. The two boys worked enthusiastically and well together. Whereas Figure 2 shows similar coding-designing stretches as in session 1, in session 2 this always involved both children working together while dividing the tasks, with one child coding and the other child designing. In most of the stretches it was Child1 (low vision) who took on the design and Child2 who coded, only in the third stretch this was the other way around. As the graph indicates, this stretch, which takes places between 1300–1500 seconds within the assignment, is a bit slower paced compared to the other stretches. The tester intensely guided the braille child here in tactilely exploring the maze to think of the next step. In session 3 both children were braille learners (Child1 had some very limited residual sight) primarily using tactile and auditory access, receiving some support by the tester or each other for instance in confirming which button they were to press or in getting oriented. The graph in Figure 3 shows a calmer pattern, which includes the execution layer more frequently in between coding and designing. This reflects the children working on smaller sub-parts of the program which were tested in between. Generally, both boys worked together through the different layers, though Child2 was a bit more active. Figure 4. Session 4 pattern of layers. Line graph for session 4, describing the pattern of moving through the layers of abstraction with the Blue-bot. The X-as represents time during the assignment and the Y-as represents the 4 abstraction layers. The graphs shows a spacious not very dense pattern switching between code and execution, and code and design layer, moving to the problem layer twice at the end of the first half of the assignment. Figure 5. Session 5 pattern of layers. Line graph for session 5, describing the pattern of moving through the layers of abstraction with the Blue-bot. The X-as represents time during the assignment and the Y-as represents the 4 abstraction layers. The graph shows a spacious not very dense pattern switching between the 4 layers. During the second half the graph becomes somewhat more dense and there is a stretch going back and forth between the design and code layer, and a stretch going back and forth between the code and execution layer. Figure 6. Session 6 pattern of layers. Line graph for session 6, describing the pattern of moving through the layers of abstraction with the Blue-bot. The X-as represents time during the assignment and the Y-as represents the 4 abstraction layers. The graph shows a spacious not very dense pattern switching mostly between the code, execution, and (less often) design layer). During the second half of the assignment the children switch 3 times to the problem layer. The behaviors the learners displayed within the layers are categorized into anticipated (observed in or referred from previous research) and non-anticipated (first observed in our study). These anticipated and non-anticipated behaviors are indicated in Tables 3 to 6 , specified per session as well as by vision type (braille or low vision learner). First, Table 3 provides the behaviors occurring within the problem layer. All anticipated behaviors are engaged in by both low vision and braille learners, though no single behavior is consistently present across all sessions. Discussing the endpoint is overall most frequent within the behaviors occurring within this layer. Further, it can be noticed that relatively more non-anticipated behaviors are engaged in by the braille compared to the low vision learners, taking in almost 40% of all behaviors amongst the braille learners. The inventory of these non-anticipated behaviors showed that they most commonly involved an alternative way to be occupied with the start or ending. This included placing the Bee-bot at the start (multiple times by both children in session 1), discussing the start position/stance of the Bee-bot (once in session 3), coding the final step for the Blue-bot in advance (session 5, by a low vision child), or discussing different routes towards the end. Some additional non-anticipated behaviors that occurred were being generally occupied with the plan or environment, making a drawing of the environment (in session 1) or depicting the goal of the assignment in a physical way. The latter occurred in session 6, and involved Child 1 standing up and physically taking several turns to explain to the other child what kind of turn the bot took (“Look, look, I take a step, turn, step, step, turn, step, turn”). In this study, we answered the research question: which patterns and specific behaviors, approached from the layers of abstraction model, do children with visual impairments engage in when working on a programming task with the Bee-bot or Blue-bot? We assessed how nine children (six of whom were blind, three of whom had low vision) move through the four layers of abstraction during programming assignments. Furthermore, we specifically observed which concrete behaviors they employed within each layer. The four layers include the problem layer (the most abstract level where the problem is discussed), the design layer (where a solution is sought), the code layer (where there is direct involvement with the code) and the execution layer (the least abstract level where the code is ran or output is involved) . The exact behaviors low vision and blind learners engage in within the different layers can show how they concretely approach the process of abstraction with these bots. Our learners show a mix of anticipated actions, known from sighted learners , and spontaneous alternative actions. Furthermore, the extent to which alternative behaviors were used was clearly higher in the problem and design layer (compared to the code and output layer), where they were also more frequently employed by the blind learners (compared to the low vision learners). Taken together, in more abstract layers, more alternative approaches are taken in by blind learners. In interpreting this, it should be taken into account that this observation entails both that alternative actions are required, and that they are possible. The background of the model of the layers of abstraction stipulates that more abstract layers involve moving away from the concrete material, the specific programming language and environment, and move towards an approach to the problem in abstract terms . In the code and output layer, the blind learners engage regularly in anticipated behaviors, they are actively involved in the assignment by pressing the bot, using the cards and reader, and following the bot. Alternative access for them is inherent in these behaviors themselves: they can see, feel, or hear when they press the buttons as well as when they follow the moving bot. This indicates that at these levels it is mostly about the concrete accessibility of the bot and confirming again the fitting “hybrid” nature of the Bee-bot and Blue-bot. At the more abstract design and problem level however, it becomes more about how the materials facilitate abstract thinking, and the presence of alternative behaviors especially in the blind learners suggests exactly both the need for such behaviors and their possibility while working with the bots. A second approach towards abstract concepts shown by our visually impaired learners was physical enactment. This could be seen in the placement of a hand in the route to indicate the position of the bot (in the design layer) and in the interesting case of a blind student physically depicting a turn in order to understand what the type of turn made by the bot entails within the currently planned route (in the problem layer). For a sighted novice learner, it can already be difficult to grasp how the bot makes a “turn in place” without moving a step. Comprehending the size and type of the turn and placing it in the mental image of the route can be even more challenging for a blind learner. Acting out the spatial concept of a turn can be seen as an embodied cognition approach towards spatial thinking and abstract concepts . This approach, which entails that mental processes are mediated by body-based systems which include body shape, movement, and the interaction of the body with the environment, has been explored as an educational strategy within STEM fields and specifically within mathematics . In the latter field, it has also been explored how embodied cognition can especially help blind learners . Within the field of computer science education however this approach to facilitate the understanding of abstraction notions has only recently been proposed and has not yet been considered for our specific group of learners. Embodied cognition can take several forms, including hand gestures and acting out concepts or representations spatially which could be, as our case example suggests, highly beneficial for learners with visual impairments to conceptualize computing concepts by representing, interpreting, reasoning and communicating about these concepts . Finally, interestingly this connects to unplugged programming. Unplugged programming activities are often of a physical nature and are moreover known to be activating, engaging and particularly inclusive . Consequently, it can be all the more valuable to further consider how the physical nature of unplugged activities can not only be motivating but also facilitate the learning of abstract computing concepts especially for blind learners. The approach of this qualitative study was to capture our subjects’ experience, within this highly specific and under-explored area of abstract computational concepts in learners with visual impairments. Within our data collection, analysis, and documentation of results, we focused on staying close to the experience of the subjects, in a tailored and detailed manner . Consequently, our small and specific sample was fitting within this aim and approach, and generalization was not the primary intent. However, there are some limitations connected to our approach and set-up. First, we included solely learners with visual impairments, as opposed to sighted individuals as well. The direct comparison of the latter set-up would enable a fuller interpretation of young learners’ computational practices and underlying mental modeling and abstract thinking using the Bee-bot and Blue-bot in programming assignments. Given the complexities and diversity within the group of learners with visual impairments, a focused investigation into low vision and blind children can be seen as a suited first step. Relatedly, we do consider transferability of our findings to similar participants and contexts possible. The diversity of our sample should be carefully taken into account here, in terms of both vision and other specificities. Currently, our findings might be most directly applicable to special education settings, where this diversity is always present and adapted to. In other settings, the insights and ideas generated in this study can be further explored. Second, in the interpretation of our findings, the concrete accessibility of the set of materials of specifically the Blue-bot for blind and low vision learners should be taken into account. Although adapted tactile versions of the Blue-bots cards, designed for learners with visual impairments, were used in our study, these cards are still in a development phase. Consequently, the set of Blue-bot materials is currently not completely (validated) accessible. This could impact how well learners with visual impairments can engage in computational practices with the material, and it emphasises the need to continue to improve accessibility at the concrete level of materials. At the same time, however, how well individuals with visual impairments can work or learn with a not entirely accessible material is also in line with their daily context, where specifically in the case of computers and technology often it is necessary to “work around” accessibility issues . Third, in our data processing we relied upon one coder using a detailed coding scheme that was partially fixed and partially allowed for new observations within fixed categories. This approach fitted our qualitative study set-up, yet there is the risk that personal interpretation of the coder could have impacted the interpretation of the behaviors. Our findings show that learners with visual impairments, using the Bee-bot and Blue-bot, engage in a formal computational way of working within the process of abstraction, including iterative actions of redesigning and debugging. Further, they engage in these computational practices using a mix of behaviors known from sighted learners as well as, especially the blind learners in the more abstract layers, alternative behaviors. The content of the latter indicates the preference to be physically involved and keep track of the bot and the plan. Moreover, it suggests how embodied cognition in the form of physical enactment can be helpful to grasp an abstract concept and mental representation. Overall, the previous operationalization of the model of the layers of abstraction in sighted learners can be meaningfully applied to low vision and blind learners, when elaborated with specific tactile and physical behaviors. Furthermore, such behaviors can be further established, possibly as part of an embodied cognition approach within inclusive computer science education, in order to encourage teachers to support learners with visual impairments in their conceptualization of abstract notions and mental representations within programming education. | Study | biomedical | en | 0.999996 |
PMC11697393 | A total of 20 young, healthy participants aged between 19 and 29 years (M = 24.15 ± 3.08) took part in the study, including 11 female and 9 male participants. All participants were native German speaking, non-smokers, and reported not to suffer from any neurological or psychiatric conditions. They did not take any medications (except oral contraceptives), iron, or vitamin supplements. Individuals reporting difficulties to fall or stay asleep, nightmares, or other sleep disorders were also excluded. All participants reported to follow a regular sleep/wake schedule with >6 hours of sleep per night and no shift work, night duties, or long-distance flights with jet lag in the 6 weeks prior to the experiment. Only participants were included who assessed themselves as individuals “dreaming on occasion” and normally having at least “fairly good recall” of their dreams. The study followed a within-subject design, examining the correspondence of dream reports obtained in a single experimental night to each of three different task plans with different execution status (completed, uncompleted, and interrupted). The task plans were entitled “Setting the table,” “Tidying the desk,” and “Getting ready to leave.” The first two scripts were derived from another study , while the third one was newly developed for the purpose of this study. Each task plan was assigned to one of the three execution statuses. Participants learned the scripts for all task plans in the evening and performed them either afterward (for the completed and interrupted conditions) or in the next morning (uncompleted). The assignment of task plans to the execution status conditions was balanced across conditions (completed – C, interrupted – I, uncompleted – U) distributing as follows: “Setting the table” (C = 7; I = 7; U = 6), “Tidying the desk” (C = 6; I = 6; U = 8), “Getting ready to leave” (I = 7; C = 7; U = 6). Also the order of execution status conditions for the tasks to be performed in the evening was balanced across participants such that the “completed-interrupted” order occurred nine times and the “interrupted-completed” order occurred 11 times. During the nocturnal sleep period, dream reports were gathered during awakenings from either rapid eye movement (REM) sleep or NREM stage 2. The participants were informed that the experiment investigated the effect of sleep on memory for specific task plans and that awakenings for dream reporting would occur. The experimental procedure is illustrated in Figure 1A . All subjects reported to the laboratory at 8:00 pm. First, a questionnaire regarding participant data was completed to ensure all experimental inclusion criteria were met. Then, EEG electrodes were attached, and the participants completed the Stanford Sleepiness Scale (SSS) and a mood questionnaire, followed by the Regensburg Word Fluency Test (RWT) and a Vigilance Task (VT), both assessing executive cognitive functions. After the initial learning phase, an immediate recall test was performed where each task title was presented, and the participant was asked to recall (verbally) the five subtasks of the plan in the correct order. It was ensured that the participants memorized the exact wording and order of the subtasks. If they did not achieve 100% correct recall on all three task plans during two consecutive recall tests, additional learning trials were conducted. For the experimental sleep interval, lights were turned off at the participant’s habitual bedtime. After about 3 hours of sleep or latest at 2:00 am, the first awakening occurred. It was ensured by visual inspection of the ongoing polysomnographic recordings, that before an awakening the sleep stage was stable for at least 10 minutes. Awakenings were done up to six times per night, from NREM stage 2 and REM sleep to cover both NonREM and REM sleep-associated dreams [ 42–45 ]. Subsequent awakenings always were carried out after the participant had regained sleep for at least 30 minutes. For awakening, lights were turned on and the participant was addressed by their name and asked to sit up and put on a headset for voice recording. A standard set of questions followed: (1) Tell me everything that was going through your mind before you were woken up. (2) Can you remember any details? (3) And further? (4) Was it a dream or a thought? and (5) Was it pleasant, unpleasant, or neutral? Question 3 was repeated unless the participant explicitly reported not having any further memory. Lights were turned off for the participant to return to sleep once no further details came into their mind. The participant was awakened the next morning at about his/her usual wake-up time. The SSS, mood questionnaire, RWT, and VT were performed a second time about 30 minutes after awakening. Throughout the night, the EEG was recorded from six channels (F3, F4, C3, C4, P3, and P4) referenced to two electrodes attached to the mastoids (M1 and M2) using a BrainAmp DC amplifier (BrainProducts, Munich, Germany). The ground electrode was placed on the forehead. Impedances were always kept below 5 kOhm. Additionally, vertical and horizontal eye movements were measured (VEOG and HEOG) as well as an electromyogram (from two electrodes placed on the chin). Signals were band-pass filtered between 0.3–30 Hz (EEG and EOG signals) and 5–150 Hz (EMG signal), sampled at 500 Hz and stored for offline analyses. Visual scoring of 30-second polysomnographic records followed the criteria outlined by Rechtschaffen and Kales . For the rating-based approach, two colleagues, sleep experts with no special experience in the assessment of dreams, were asked to rate the dream reports according to a standardized scale derived from a previous report by Schredl . The first part of the scale required to rate the general extent of alignment between the reported dream and the specific task plans between 0 and 8 (indicating no vs. high correspondence). The second part of the scale aimed at a similarity rating based on the occurrence of certain core elements characterizing the task plans. For this, for each task plan, 11 core elements, i.e. objects, and activities that characterized the plan, were selected. The rating required to assign, for each of these core elements, a score between 0 and 2 with 0 indicating that the element did not occur in a dream report, 1 indicating that the element occurred metaphorically or indirectly, and 2 indicating that the element was directly named in the dream report, with the sum of the 11 scores defining the similarity between the dream report and the respective task plans. Raters were blinded as to the execution state conditions and as to whether a dream was reported after a NonREM or REM sleep awakening. For the second analysis , we used a large language model to objectively quantify the extent of dream incorporation. Dream incorporation was measured by the degree of semantic similarity between the task plans and the dream reports. Semantic representations of the task plans and dream reports were extracted using the transformer-based representational model Bidirectional Encoder Representations from Transformers (BERT) , a natural text embedding model capable of quantifying semantic textual similarity . Specifically, we used the German version (GermanBERT) which is pretrained on written German texts, i.e. German Wikipedia articles, German OpenLegalData, and German news articles. The parameters of the model were trained by (1) splitting the input texts, i.e. the dream reports and task plans, into tokens representing semantic units (words, subwords) of the input texts, (2) masking some tokens and feeding back the corrupted sentence (with masked tokens) as input into the model, and (3) asking the model to reconstruct the original tokens. Since BERT was pretrained on written language, the transcripts of the dream reports were additionally edited to remove filler words and to correct grammatical errors. Similarly, the task plans were prepared for these analyses by transforming the description of subtasks in bullet points into full sentences. Then, each dream report was summarized automatically using a BERT model fine-tuned for text summarization before giving it as input to the GermanBERT. The GermanBERT encoder embeddings (vectors that take into account single text units and respective semantic relationships to other units) were used as a representation of the dream. While these embeddings are influenced by sentence length and parts of speech, this information is not explicitly encoded in the embeddings. We then encoded each of the five subtasks in each of the three task plans into embeddings following the same procedure (without prior summarization). Cosine similarity between the embeddings was calculated resulting in a similarity score for each dream report with each action of the task plans, for which then the maximum similarity score for each task is chosen. Cosine similarity measures how closely aligned two vectors are, regardless of their magnitude, by calculating the cosine of the angle between them. In our study, we used cosine similarity to quantify the semantic relatedness between dream reports and task descriptions, with scores ranging from −1 (opposite meaning) to 1 (identical meaning). Because similarity cosine values for the dream reports fell into a more limited range at the higher end of the scale , we thresholded cosine values using only values greater or equal to 0.85 to focus the analysis on dreams with high similarity scores. We then compared the number of dream reports with the above criterion similarity scores for a given task plan, between the execution status conditions completed, interrupted, and uncompleted. Similarity scores for dream reports were generally analyzed using analyses of variance (ANOVA) including repeated measures factors representing the executive status conditions (completed, interrupted, and uncompleted) and the sleep stage (NREM stage 2, REM) prior to the obtained report, with subsequent post-hoc t -tests used to specify the significance of pairwise comparisons. Pearson’s correlation coefficients were used to assess interrater reliability in the analyses based on subjective ratings. Given that similarity scores for the dream reports with the above criterion similarity in the AI language model-based analyses were not uniformly distributed among execution status conditions and task plans, we focused on the statistical analysis of these scores on nonparametric testing using χ 2 tests. Prior to testing, the number of dream reports above the criterion in each execution status condition and for each task plan was additionally divided by the total number of dream reports obtained for each execution condition and task plan. The χ 2 test was also used to detect deviations from equal distributions of dream reports collected in different sleep stages, conditions, and tasks. The level of significance was set to p = .05 for all statistical tests, and p = .01, in case of directed one-tailed testing of hypotheses. A total of 117 awakenings were performed, equally distributed across REM sleep and NREM stage 2, i.e. 59 (50.4%) awakenings were performed in REM sleep and 58 (49.6%) awakenings in NREM stage 2 sleep ( p = .926, χ 2 test). A dream was recalled in 86 (73.5%) of the awakenings. Of these 86 dream reports, 51 (59.3%) were collected after REM sleep awakenings and 35 (40.7%) after NREM stage 2 sleep awakenings, resulting in a trend toward more dream reports collected after REM sleep than NREM stage 2 awakenings ( p = 0.084, χ 2 test). Since participants’ ability to recall dreams at each awakening differed, the number of dream reports was not evenly distributed among task plans and execution status conditions ( Table 1 ). SSS sleepiness scores averaged (mean ± standard deviation) 3.1 ± 0.85 in the evening before sleep, and 2.5 ± 0.76 in the next morning. Performance scores on the RWT averaged 16.4 ± 4.81, in the evening, and 16.6 ± 4.32 in the next morning. The two raters only moderately agreed on their ratings of the dream reports. Although significant, both correlations between their ratings of the general similarity between task plans and dream reports as well as correlations of their judgments based on the occurrence of core elements of the task plans (objects, activities) in the individual dreams were only of medium size (0.52 < r < 0.65, p < .01, Pearson’s correlation). ANOVA performed on ratings collapsed across both raters did not reveal any significant difference in similarity ratings between any of the execution status conditions or awakenings from REM or NREM stage 2 (all p > .67 for respective ANOVA factors). Values collapsed across general similarity ratings and core element-based ratings indicated for one of the rater's highest dream incorporation for the completed task plans (1.02 ± 0.20), medium for uncompleted plans (0.98 ± 0.20), and lowest for interrupted task plans (0.80 ± 0.17) whereas for the other rater, values were highest for the interrupted task plans (1.20 ± 0.22), medium for completed plans (0.78 ± 0.14) and lowest for the uncompleted tasks (0.70 ± 0.15). The overall insufficient agreement between our raters is consistent with a great body of findings in this field of dream content analysis and led us to switch to an AI-based approach. Here, we determined the semantic similarity of dream reports by applying a large language model (GermanBERT) to transcripts of the reports . Cosine similarity scores were calculated for the whole texts, indicating the similarity between a dream report and one of the three task plans. For all task plans, similarity scores ranged between 0.54 and 0.90, with maximum frequencies in the 0.84–0.90 range , with this distribution indicating that a minimum similarity (of around 0.54) to each of the three task plans is basically reached by any of the dream reports. The direct comparison between task plans, on the other side, indicated that the task plan “Setting the table” yielded distinctly higher similarity scores than the two other task plans ( F (1.040,88.43) = 116.9, p < .0001, for ANOVA main effect of task plan), with this effect being independent of the execution status condition the task plan was assigned to . This finding points to an a priori difference in our task materials, with a higher likelihood for the “Setting the table” plan to be similar to a dream report than for the other two plans. Given that the frequency distribution of similarity scores indicated that each dream report shows at least a minimum similarity to any of the three task plans (of around 0.54), we focused our analyses on only the dream reports exhibiting substantial similarity to one of the tasks, adopting a criterion similarity score of ≥0.85. Indeed, we assumed that higher similarity scores are associated with a higher probability that this similarity was related to specific features of one of the three task plans. Note, although the ≥0.85 criterion is arbitrary, virtually the same results were obtained with lower criteria, up to ≥0.75. Comparing each dream report with each of the three task plans, we revealed that out of all 86 dream reports, 29 reached a semantic similarity score ≥0.85 to the task plan assigned to the uncompleted execution status condition, 24 dream reports reached the ≥0.85 criterion for the interrupted tasks, and 20 for the completed task plans. Although descriptively this pattern concurred with our hypothesis of an increased incorporation into dream reports of contents from uncompleted and interrupted task plans in comparison with completed task plans, it did not reach significance (χ 2 (2,73) = 1.67, p = .4439, for the comparison between execution state conditions), which we hypothetically attributed to the fact that our task plans showed a priori differences in the likelihood of being highly similar to a dream report with the highest likelihood for the “Setting the table” task plan . Indeed, analyzing separately similarity scores for the different task plans, we found that significantly more dream reports were semantically similar to the “Desk tidying” and “Getting ready to leave” task plans when they were uncompleted (Desk tidying—24, and Getting ready to leave—46) or interrupted (Desk tidying—31, and Getting ready to leave—27) compared to being completed (Desk tidying—5, Getting ready to leave—25; Desk tidying: χ 2 (2,60) = 18.10, p < .001; and Getting ready to leave: χ 2 (2,98) = 8.23, p < .05; for the comparison across all three execution state conditions, see Figure 2C for pairwise comparisons). On the other hand, for the “Setting the table” task plan no such pattern was obtained (χ 2 (2,99) = 2.24, p > .30). We found no comparable differences between the execution status conditions in separate analyses of dream reports obtained after REM sleep awakenings (all p > .36) or after NREM stage 2 sleep awakenings (all p > .33, χ 2 test). Moreover, an exploratory control analysis of word counts for dream reports with the highest similarity to task plans revealed no significant differences between the execution status conditions ( p = .5052, F (2,28) = 0.7053). To further validate our large language model-based approach, in a second analysis, we made use of a forced choice method where dream reports were allocated to one of the three execution status conditions after a sentence-wise comparison of dream reports and task plans . This forced choice approach appeared to be also favorable against the backdrop that the similarity scores of an individual dream report for the three different task plans were generally rather close to each other, i.e. showed relatively low variability in comparisons with the high variability of similarity scores among the different dream reports (averaged across task plans; F (1,83) = 0.0014, p < .002, for a direct comparison between respective variances). This sentence-wise analysis revealed similarity scores ranging between 0.78 and 0.90 for each of the three task plans, with maximum frequencies around 0.86 . Similarity scores again significantly differed for the three task plans ( F (2, 249) = 8.7, p < .0005, for ANOVA main effect of task plan), with this effect being independent of the execution status condition the task plan was assigned to ( F (4, 249) = 0.64, p = .63, for ANOVA task plan × execution status interaction). When we assigned each dream report to the execution status condition with the highest similarity score for this dream report, we found that the lowest number of dream reports, i.e. 20 reports, were assigned to the completed condition, the number of assigned reports was intermediate (28 reports) for Uncompleted task plans, and highest for interrupted task plans (38 dream reports ( χ 2 (2,86) = 5.678, p < .01, one-tailed χ 2 test, for the comparison across all three conditions, Figure 3B and C ). In this study, we explored whether intentions for future actions influence dream content. Employing an AI-based large language model analysis, we show that task plans that have not been completed before sleep and, hence, remain active during sleep, influence the content of a dream to a greater extent than tasks that are completed before sleep. Specifically, tasks whose execution was interrupted before sleep or whose execution was anticipated for the morning after sleep produced dream reports of greater semantic similarity to these tasks than task plans that were completed before sleep. Whereas firm evidence has been accumulated that dreams incorporate past experiences , especially if they are emotional [ 54–56 ], our findings provide first-time experimental evidence that dreams also incorporate anticipated experiences, i.e. future plans. In psychological terms, our findings relate to the well-known Zeigarnik effect or the intention–superiority effect which describes the phenomenon that a planned action is better retained in memory or in a heightened state of activation as long as the plan is not executed [ 57–59 ]. It is assumed that a “tension” sometimes carrying also an emotional tone , drives ongoing processing of memory representations connected to the plan, as long as it is not executed. This tension and associated processing of plan-related representations is not necessarily conscious, and we here provide evidence that it extends into sleep biasing the content of dream reports. This conceptual view is in line with multiple studies showing that sleep promotes problem solving on tasks that remained unsolved before sleep, probably due to a subliminal ongoing processing of the problem . Indeed, the incorporation of experienced content into dreams has likewise been linked to an ongoing reprocessing of respective memory representations that not only supports the consolidation of respective memory but simultaneously, expresses itself in dream reports that are semantically biased toward the reprocessed memory contents . Studies of prospective memories for plans and intentions have indicated a greater benefit for such memories of uncompleted plans from slow wave sleep (SWS) than REM sleep . Hence, assuming a direct link between processes of memory consolidation and dreaming, one might expect that dream reports after awakening from SWS show a greater similarity to the uncompleted task plans than reports after REM sleep awakenings. The present data remain inconclusive in this regard for two reasons. First, rather than in SWS, we awakened the participants in NREM stage 2 sleep during which the reprocessing of the to-be-consolidated memory representations might be less intense than in SWS, though evidence for such difference is mixed [ 62–64 ]. Second, our analyses relied on a rather small number of dream reports. The data set, hence, did not provide sufficient statistical power for reliable analyses on subsets separating dream reports between awakenings from NREM stage 2 and REM sleep, considering the size of f = 0.48 (G*Power version 3.1.9.7) for the effect of the plan execution status on dream report similarity for our analysis across all (NREM stage 2 and REM sleep) dream reports. Our finding of an incorporation of uncompleted task plans into dream reports appears to be especially noteworthy in that it derives from an objective large language model-based machine-learning approach, i.e. an approach which, except for a most recent study , has so far not been used for dream content analyses. This large language model-based approach overcomes the weakness of traditional analyses of dream reports based on subjective ratings which are notoriously unreliable suffering from modest inter-rater agreements . Here, using two independent raters to classify dream reports according to their similarity to the different task plans, we also found only rather low interrater reliability and, consequently no distinct differences in similarity between dream reports and task plans. Conceivably, we could have strengthened inter-rater reliability by a more intense prior training of our raters on “sham reports” including the discussion of discrepant scores between raters . Perhaps, we could have also enhanced our ratings if—like in other studies —we had additionally asked our participants themselves to rate the similarity of their dream reports with the task plans. Exclusively relying on ratings by other persons, our analyses did not yield any conclusive results. Nevertheless, although objective, our large language-model-based machine-learning approach bears several limitations, altogether calling for further confirmation of our main findings. First of all, we applied the large language model-based approach post-hoc, and only after the subjective ratings turned out to be unreliable. Related to this, our task plans were not particularly tailored for a large language model-based analysis of their semantic similarities. Basically, the task plans turned out to be too similar to each other, resulting in a large overlap between the task plans with respect to their similarity to the dream reports. While we adopted our task plans from a foregoing study targeting the persistent activation of intentions in memory, task plans with greater semantic differences in the activities, objects, and contexts might have increased the differences in similarity between plans and dream reports. Interestingly, the “Setting the table” plan yielded significantly higher similarity scores than the two other task plans, suggesting that certain activities may be more prone to dream incorporation than others. Obviously, future studies adopting new task plans should rule out such a priori differences in task plans before experimental use. Such future studies should also overcome other limitations of our study arising, e.g. from a rather crude assessment of sleep lacking occipital recordings which may be particularly important for a precise sleep scoring assessment and therefore relevant in analyses of (visual) dreams. Our large language model-based approach, using GermanBERT as an embedding extractor, revealed significant differences in dream report similarity that confirmed our a priori hypotheses, supporting the validity of this approach. However, statistical significance per se does not necessarily imply that this approach is also the most valid and optimal. It is to emphasize, however, that we could in principle (internally) replicate our findings here, using two different approaches, i.e. a text-based and a sentence-based, strategy of detecting similarity between dream reports and plans, in combination with a statistical assessment of different target parameters (number of above-criterion similarity reports vs. forced choice allocation of the maximum similarity report). This mutual confirmation further corroborates the validity of our approach, although there may be other more optimal strategies for detecting semantic similarity. An example is BERTScore which is a language generation evaluation metric based on BERT contextual embeddings. In contrast to our approach, in which we compute cosine similarities between embeddings of task plans and dream reports, it computes the similarity on a token level, taking into account their context via contextual embeddings, i.e. a strategy potentially allowing for more fine-grained comparisons between dream reports and task plans. Generally, the use of large language models for analyzing dream content is in its beginnings but, eventually, may turn out a promising tool also for other topics of dream research such as the differentiation of reports of dreams versus more or less emotional wake experiences as well as the differentiation of dream reports among individuals, with potentially important therapeutical implications. Whatever the case, GermanBERT administered to the present data set revealed results confirming our a priori hypotheses. Nevertheless, our approach and findings require further confirmation and external validation, ideally through application to other similar data sets, although other validation strategies are conceivable. | Study | biomedical | en | 0.999998 |
PMC11697404 | Health-related cultural norms serve as reference points that influence how individuals assess their health. These norms, however, are shaped by personal expectations and narratives, which play a key role in how people experience, interpret, and maintain their health . For instance, some evidence reveals that culturally specific practices can influence ‘objective health status’ by limiting the spread of diseases in epidemics or offering protection against communicable and non-communicable health threads . Although health assessments are well known to correlate with objective measures of health status, to date, there is limited empirical evidence of how culturally persistent such health self-assessments are, which limits their cross-country comparability. An opportunity to study cultural influences is by examining samples of individuals who have migrated to countries from a specific sending country where we can measure self-assessed health too. This is the case because the effect of culture partially varies with some slow-moving features such as language and traditions . Migrant samples allow studying the effect of cultural persistence once we control for citizenship regulations, welfare institutions, or the duration of an individual's residence in a country. The intuition behind the methodology is that health assessment priors can be conceived as portable reference points of what is regarded as ‘good’ or ‘bad’ health. Hence, a measure of the cultural persistence of health assessments can be extracted by examining the systematic association between migrants’ health assessments and the health assessments of individuals from their home countries. This is possible in surveys that contain large samples of immigrants from multiple sending and host countries to mitigate potential selection biases. In this paper, we investigate the relationship between the health assessments of migrant individuals—whose parents or themselves were not born in the host country—and the average health assessments of their (or their parents') sending (or home) country. We draw upon seven waves of the European Social Survey (ESS) 2004 - 2016 containing self-reported health records from 30 different European member states. The ESS is unique in that it contains a consistent measure of self-assessed health and allows us to include several controls for important alternative explanations that could drive the association between migrants and their home countries' health assessments. Such controls can help identify some of the potential sources of migrant selection (for example, time in the host country or citizenship), as migrants may differ from population averages in key observable dimensions. We make several contributions to the literature. First, we advance the discussion on the cultural determinants of health assessments, an area that has been underexplored thus far. Our findings also extend beyond the health assessments of first-generation immigrants and measures of happiness . Specifically, we provide evidence that culture exerts a long-term influence by shaping the reference points individuals use when assessing their own health. If we were to compare two individuals in the same health state but who assess their health differently, then this difference could be interpreted as stemming from different cultural reference points in the assessment of their own health status. Second, previous research has used individuals' health in their country of origin as an instrument to exploit the exogenous variation in health assessments, allowing for the examination of its impact on labor market decisions , but it does not examine the cultural transmission mechanisms. This paper considers a number of potential threats, biases and potential genetic effects by adding objective measures of health. Finally, this research contributes to the so-called ‘epidemiological approach’ literature that compares immigrants' preferences to the average preferences of people in their countries of birth which has been used to explain the use of traditional medicines , and differences in savings . We study the cultural persistence of such assessments, how robust such persistence is to the inclusion of country of residence fixed effects and different subsamples. Next, we examine a number of mechanisms to understand different explanations for the cultural effect. Its worth mentionning that the paper most closely related to ours is Roudijk et al. , which explores how country-of-origin influences health and well-being assessments. However, this literature does not address the cultural persistence or the mechanisms that underpin such health assessments. Culture and health . Culture refers to a system of shared understandings and values that can influence the reference points individuals use in making health assessments . Such shared values can act as triggers (or barriers) for certain behaviours, such as seeking health care, or spending time in or near natural landscapes . It is conceivable that cultural reference points exert a direct impact on how people perceive health, for instance influencing how illness and pain are perceived. More specifically, health professionals in some European countries such as Belgium, Switzerland, and Germany, employ the term “Mediterranean syndrome” to refer to individuals who “are known for their tendency to present with diffuse complaints and exaggerate pain” . Nonetheless, the health advantage of migrants declines with time spent in the host country. For instance, the health of Latin American migrants to the United States appears to deteriorate as they stay in the country longer, indicating an unhealthy adaptation . However, other evidence suggests that the longer an immigrant stays in the country, the better their health . Indeed, although some evidence suggests that health benefits are lost in childhood , and many health conditions worsen across generations, exposure to a new environment can trigger the adoption of native behaviors . Yet, such healthy migrant advantage disappears in European countries which might be explained by the fact that migrants come from a larger set of sending countries compared to the United States, and there is a large variation in host cultures . Constant et al. did not find evidence of a healthy migrant effect in Europe. Hence, Europe is an ideal setting to study the cultural persistence of health assessments, given its large variation in cultures and lesser exposure to migrant selection. Our primary variable of interest is self-reported health, which is assessed subjectively on a five-point scale ranging from very good to very bad. The question posed is: “How is your health in general?” Respondents can choose from the following options: very good, good, fair, bad, or very bad (Table A1 in the Appendix for details). It is important to acknowledge that while self-reported health is the most used measure of health, it is not without its biases and can show inflated responses and significant cross-country variation . Given that health assessments are a proxy for latent health, cultural biases in self-assessments are a proxy for cultural effects on health. To analyze some of these effects, we carry out subsample analysis where the composition of the countries differs, alongside analysis of measures of health that are not directly self-reported. Our key explanatory variable refers to the average health assessments in the sending country, specifically distinguishing both the father and mother's country of birth for second generation migrants. Given that the correlation of health assessments can be explained by other potential pathways, we include several controls. Such controls capture individual-specific conditions that can independently influence the way health is individually assessed. Furthermore, given that health declines with age and exhibits gender and household-specific differences, we control for several socioeconomic and demographic characteristics (gender, age, and household size). Institutional explanations for an association in migrant's health assessments such as citizenship status are also considered. These are important measures, as in some countries migrant's citizenship is not automatic after birth. Our data also contains records on how long individuals have lived in the country of residence, and whether they belong to a minority ethnic group. Alongside educational attainment, we include main occupational activity and household net income quintile, which measure socio-economic determinants of health. The baseline specification includes wave controls. Based on the above considerations, we examine the association between migrants’ health assessments and that of their sending country using a reduced form estimate that draws on the following specification: (1) H i j t = ρ H ¯ j + φ X i t + μ t + ε i j t where H i j t is self-reported health of first (second) generation migrant i from the sending country j at time t, H ¯ j refers to the sending country health assessment for either first or second-generation migrants retrieved from the World Value Survey, X i t refers to individual-specific controls that could bias our estimates of cultural persistence, and μ t are fixed wave effects. Our coefficient of interest is ρ , measuring the association between the migrant's health assessment and the average health assessment in the sending country. ε i j indicates random parameter, which may include country-of-residence fixed effects. Country of origin fixed effects are not included in this literature as they absorb the entire effects of cultural norms and values influencing country health assessments. To account for the arbitrary correlation of error terms among individuals from the same country of origin, standard errors are clustered at the individual's country of origin. For robustness purposes, we estimate both linear probability models and ordered probit models. The results are presented in standardised coefficients to compare the mean between the first and second generations; marginal effects for nonlinear models are also included. We run several specifications in addition to our baseline models to investigate heterogeneous effects and address potential biases. We focus on specifications that distinguish between paternal and maternal lineage for second-generation migrants. This is important when second-generation migrants come from different countries, hence we can distinguish the influence of the maternal and paternal country of origin. We consider cohort differences, as early life health assessments may reflect differences in reference points for what constitutes "good health" when compared to other categories, whereas later life health assessments may reflect true differences in health status. Heterogeneous effects by gender and region are also analysed. Other estimates include regional and country of residence fixed effects to account for any unobserved time-invariant characteristics, lags in average health assessments of the home country as migrants might not observe contemporaneous values when making their judgements, and other measures of wellbeing. In addition, we define cohorts based on gender and year of birth and restrict our analysis to migrants from European countries who have similar rights in both host and sending country. Given that mobility restrictions within Europe are less stringent for European citizens, the analysis of this subsample of migrants allows examining potential sources of unobserved heterogeneity that could not be entirely controlled for with destination country fixed effects. Cultural Persistence: In Panel A of Table 1 , we report the regression estimates for first-generation migrants only. We examine estimates both without and with controls (columns 1–2 and 3–4, respectively), using inear and nonlinear models. Given that migrants’ behaviors might change with exposure to the host country, we then include citizenship status and time in the country since arrival (columns 5–6). More specifically, we specify five dummy variables: whether individuals have spent less than 1 year in the country of residence (reference), between 1 and 5 years, between 6 and 10 years, between 11 and 20 years, and more than 20 years. In all cases, the estimates suggest a large and significant coefficient of health assessments of the migrants’ home country consistent with the hypothesis of cultural persistence. As expected, the size of the cultural persistence coefficient declines with the inclusion of socio-economic and demographic controls. We find that spending up to ten years in the host country increases cultural attachment to the country of origin, and the coefficient is even larger when migrants have been in the country of residence for more than 20 years. Table 1 Cultural persistence of health status. Baseline models. Table 1 OLS Oprobit OLS Oprobit OLS Oprobit (1) (2) (3) (4) (5) (6) Panel A. First-generation migrants Self-assessed health at country of origin 0.880*** 0.974*** 0.600*** 0.774*** 0.584*** 0.757*** [0.248] [0.048] [0.169] [0.033] [0.165] [0.031] (0.150) (0.169) (0.060) (0.084) (0.058) (0.081) Citizen of country of residence 0.029 0.038 (0.025) (0.032) Time in country of residence Within last year (reference) 1 to 5 years 0.036 0.083 (0.052) (0.083) 6 to 10 years 0.092* 0.167* (0.055) (0.086) 11 to 20 years 0.153** 0.253*** (0.063) (0.096) More than 20 years 0.280*** 0.417*** (0.067) (0.102) Observations 24,880 24,880 24,880 24,880 24,457 24,457 R 2 / Pseudo R 2 0.06 0.02 0.29 0.12 0.29 0.12 Panel B. Second-generation migrants Self-assessed health at country of origin 0.758*** 0.892*** 0.573*** 0.766*** [0.225] [0.031] [0.170] [0.024] (0.083) (0.106) (0.055) (0.079) Observations 22,319 22,319 22,319 22,319 R 2 / Pseudo R 2 0.05 0.02 0.25 0.11 Wave fixed effects Yes Yes Yes Yes Yes Yes Controls No No Yes Yes Yes Yes Notes: The dependent variable is self-assessed health of first- and second-generation migrants who live in European countries (SAH=1 very good,…, SAH=5 very bad). Standardised coefficients (OLS models) and average marginal effects on the probability of the worst self-assessed health (Oprobit models) are in brackets. Standard errors (in parenthesis) are clustered at the country-of-origin level. Specifications with controls (columns 3–6) include gender, age, education, marital status, household size, religion, whether belongs to minority ethnic group, employment status, and household income (quantiles). * p < 0.1; ** p < 0.05; *** p < 0.01. Gender Effects . In Table 2 we report the results for both first- and second-generation migrants (Panel A and B, respectively), splitting the sample by gender. Consistently, we find significant and large coefficients that do not differ considerably by gender. A change in one standard deviation in the country-of-origin's self-assessed health increases migrants’ self-assessed health by nearly 0.60 scale units (16%) irrespective of gender (columns 1 and 2). Table A8 in the Appendix again distinguishes paternal and maternal lineage (panels A and B, respectively). The effect decreases to 0.50 scale units (15 % compared to the mean) on maternal lineage among men. However, among women, the effect is virtually the same for second-generation migrants of both maternal and paternal lineage. Table 2 Cultural persistence of health status. Heterogeneous effects by gender. Table 2 OLS Oprobit Female Male Female Male (1) (2) (3) (4) Panel A. First-generation migrants Self-assessed health at country of origin 0.608*** 0.578*** 0.774*** 0.763*** [0.172] [0.162] [0.037] [0.027] (0.066) (0.059) (0.092) (0.084) Wave fixed effects Yes Yes Yes Yes Controls Yes Yes Yes Yes R 2 / Pseudo R 2 0.30 0.27 0.12 0.11 Observations 13,822 11,058 13,822 11,058 Panel B. Second-generation migrants Self-assessed health at country of origin 0.587*** 0.556*** 0.773*** 0.760*** [0.174] [0.166] [0.025] [0.023] (0.064) (0.053) (0.095) (0.074) Wave fixed effects Yes Yes Yes Yes Controls Yes Yes Yes Yes R 2 / Pseudo R 2 0.26 0.23 0.11 0.10 Observations 12,062 10,257 12,062 10,257 Notes: The dependent variable is self-assessed health of first- and second-generation migrants who live in European countries (SAH=1 very good,…, SAH=5 very bad). Standardised coefficients (OLS models) and average marginal effects on the probability of the worst self-assessed health (Oprobit models) are in brackets. Standard errors (in parenthesis) are clustered at the country-of-origin level. Controls include gender, age, education, marital status, household size, religion, whether belongs to minority ethnic group, employment status, and household income (quantiles). * p < 0.1; ** p < 0.05; *** p < 0.01. Age and Geographical Effects . Next, we explore other specifications to try to disentangle whether our estimates could be partly attributed to genetic transmission rather than cultural transmission ( Table 3 ). Specifically, we split the sample by age group (panels A and B for first- and second-generation migrants, respectively) and region of Europe (panels C and D for first- and second-generation migrants, respectively). In the first case, we find statistically significant effects for all age groups that roughly correspond to age quartiles (35 years or less, 36 to 50 years, 51 to 65 years, 66 years, and more), although we find a very clear positive gradient. These results suggests that, even among younger age groups where individuals typically exhibit very good self-assessed health, we still find consistent evidence of cultural transmission, implying that that there are relevant differences in cultural reference points when making health self-assessments across individuals. Significant results, on the other hand, are found for five regions based on country of residence (North, South, Center, East, and West), though with significant variations. The coefficients for first-generation migrants, for example, are estimated to range from 0.169 in the South to 0.673 in the North. When we look at second-generation migrants, however, we find no evidence of cultural transmission in the Southern and Eastern countries. That is, cultural persistence is primarily driven by cultural persistence in Northern and Central European countries. 2 Table 3 Cultural persistence of health status. Heterogeneous effects by age group and regions of Europe. Table 3 (1) (2) (3) (4) (5) AGE GROUP 35 years or less 36–50 years 51–65 years 66+ years Panel A. First-generation migrants Self-assessed health at country of origin 0.239*** 0.613*** 0.707*** 0.927*** [0.081] [0.192] [0.217] [0.270] (0.051) (0.072) (0.060) (0.118) Wave fixed effects and controls Yes Yes Yes Yes R 2 0.04 0.11 0.19 0.20 Observations 6677 6965 5914 5324 Panel B. Second-generation migrants Self-assessed health at country of origin 0.444*** 0.544*** 0.649*** 0.710*** [0.156] [0.172] [0.196] [0.206] (0.060) (0.067) (0.078) (0.095) Wave fixed effects and controls Yes Yes Yes Yes R 2 0.09 0.18 0.18 0.14 Observations 7666 6136 5346 3171 Regions of Europe North South Center East West Panel C. First-generation migrants Self-assessed health at country of origin 0.673*** 0.169** 0.508*** 0.376*** 0.248*** [0.221] [0.045] [0.104] [0.107] [0.049] (0.101) (0.075) (0.091) (0.107) (0.086) Wave fixed effects and controls Yes Yes Yes Yes Yes R 2 0.35 0.17 0.22 0.37 0.16 Observations 8093 2187 6517 5174 2843 Panel D. Second-generation migrants Self-assessed health at country of origin 0.625*** −0.062 0.369*** 0.147 0.266** [0.253] [−0.012] [0.072] [0.043] [0.042] (0.081) (0.112) (0.084) (0.090) (0.108) Wave fixed effects and controls Yes Yes Yes Yes Yes R 2 0.24 0.30 0.24 0.38 0.15 Observations 6459 760 6342 5559 3156 Notes: The dependent variable is self-assessed health of first and second generation migrants who live in European countries (SAH=1 very good,…, SAH=5 very bad). OLS estimates, standardised coefficients are in brackets; standard errors (in parenthesis) are clustered at the country of origin level. Controls include gender, age, education, marital status, household size, religion, whether belongs to minority ethnic group, employment status, and household income (quantiles). * p < 0.1; ** p < 0.05; *** p < 0.01. Migrant Selection . To test for potential selection into migration, we limit our analysis to migrants from EU countries with comparable rights and institutional development in both their country of origin and destination. Table 4 differentiates between samples of individuals born in EU countries and those who reside in EU countries but might be born elsewhere. This enables us to determine whether the effects are driven by migration from some of the non-EU countries represented in our sample. Again, we find large and significant coefficients across all regressions. When we examine the effect among migrants born in the EU, we still find evidence of cultural persistence across all generations. Table 4 Cultural persistence of health status. Subsample of migrants within the European Union (EU). Table 4 EU residents (Parents) Born in the EU EU residents and (parents) born in the EU (1) (2) (3) Panel A. First generation migrants Self-assessed health at country of origin 0.593*** 0.515*** 0.316*** [0.173] [0.102] [0.066] (0.061) (0.076) (0.082) European regions fixed effects Yes Yes Yes Wave fixed effects Yes Yes Yes Controls Yes Yes Yes R 2 0.28 0.22 0.21 Observations 16,419 9693 6683 Panel B. Second generation migrants Self-assessed health at country of origin 0.575*** 0.425*** 0.401*** [0.180] [0.087] [0.084] (0.064) (0.086) (0.085) European regions fixed effects Yes Yes Yes Wave fixed effects Yes Yes Yes Controls Yes Yes Yes R 2 0.21 0.20 0.19 Observations 13,973 10,382 6791 Notes: The dependent variable is self-assessed health of first and second generation migrants who were born (or whose parents were born) and/or live in European Union countries (SAH=1 very good,…, SAH=5 very bad). OLS estimates, standardised coefficients are in brackets; standard errors (in parenthesis) are clustered at the country of origin level. Controls include age, education, marital status, household size, religion, whether belongs to minority ethnic group, employment status, and household income (quantiles). * p < 0.1; ** p < 0.05; *** p < 0.01. We also consider migration selection using a two-step procedure. First, we use a probit model to estimate the likelihood of migration (Table A10 in the Appendix); the estimated parameters are then used to calculate the inverse Mills ratio, which is then added to the estimates that consider cohorts to link individuals' self-reported information and that of the country of origin (Table A9 in the Appendix). Our estimates suggest that the difference in the coefficient of country-of-origin self-reported health after including the Mills ratio from the coefficient calculated before is not statistically significant (95 %CI: −0.087, 0.028 for first-generation estimates, and 95 %CI: −0.102, 0.076 for second-generation estimates). Other measures . We also estimate the baseline specification for life satisfaction rather than self-reported health ( Table 5 ). This provides additional evidence of the effect of other measures of self-assessed well-being. Table 5 suggests robust evidence of cultural transmission when such measures are employed . Table 5 Cultural persistence of life satisfaction. Table 5 First generation migrants Second generation migrants (1) (2) (3) (4) (5) Life satisfaction at country of origin 0.440*** 0.307*** 0.290*** 0.288** 0.281*** [0.173] [0.122] [0.115] [0.115] [0.112] (0.103) (0.064) (0.061) (0.118) (0.056) Citizen of country of residence −0.093** (0.040) Time in country of residence Within last year (reference) 1 to 5 years −0.033 (0.136) 6 to 10 years −0.166 (0.129) 11 to 20 years −0.266** (0.124) More than 20 years −0.298** (0.141) Wave fixed effects No Yes Yes No Yes Controls No Yes Yes No Yes R 2 0.03 0.11 0.12 0.01 0.13 Observations 27,431 24,829 24,410 24,295 22,239 Notes: The dependent variable is life satisfaction of first- and second-generation migrants who live in European countries. OLS estimates, standardised coefficients are in brackets; standard errors (in parenthesis) are clustered at the country of origin level. Specifications with controls (columns 2, 3, 5) include gender, age, education, marital status, household size, religion, whether belongs to minority ethnic group, employment status, and household income (quantiles). * p < 0.1; ** p < 0.05; *** p < 0.01. In addition, we employ height and weight information collected in the seventh round of the ESS to define body-mass index (BMI), a more objective health measure, to run the baseline models. Age-standardized BMI averages per country for males and females were drawn from the NCD Risk Factor Collaboration. Table A12 in the Appendix first shows the results for self-reported health for round 7 of the ESS. The standardized coefficients of the variable of interest in the specifications with controls (columns 2–3) are only slightly lower than for the full sample (0.14 instead of 0.17). The results for BMI are also significant (columns 4–6), but the standardised coefficients are only half those for self-reported health, suggesting that the positive association for the subjective health measure does reflect, at least in part, the cultural persistence in how health is assessed, rather than the underlying health status. This is important given that the evidence suggests that the correlation between BMI and self-reported health is negligible. Finally, we consider potential differences in social norms. Specifically, add as a control the opinion on the statement “ men should have more right to a job than women when jobs are scarce ”, with response options: agree, neither agree not disagree, disagree , since it may also bear cultural information that may affect the health report in the destination country. We chose this variable because it is one of the few measuring attitudes that is available in both the ESS and the WVS. The results (not shown but available on request) are practically identical to those reported in Table 1 , namely the (standardised) coefficients of the variable of interest in the models with controls remain significant and around 0.17. This paper studies the hypothesis of cultural persistence in health self-assessments in a large and heterogeneous sample of Europeans. We have documented evidence of an association between migrants’ health assessments and that of their home countries (or that of their parents), which we argue capture what can be regarded as evidence of ‘cultural persistence’ in health assessments. This has been a question traditionally ignored in the evaluation of health programs across countries. Specifically, we document a clear association between subjective health assessments of first and second-generation immigrants (residing in 30 different European host countries and over 90 sending countries) and that of their home country. Our findings suggest evidence that migrants' health assessments are associated with the average health status in their sending country, net of socio-demographic characteristics and other relevant controls. We report evidence that the correlation is stronger among older individuals and those residing in Northern Europe. We leverage on a large cross country variation which we beleive attenuates the likelihood of selection bias. We estimate that one standard deviation change in self-reported health in the sending country is associated with an increase in migrants' self-reported health of about 0.17 standard deviations. Our interpretation of the results is that cultural reference points matter in making health assessments and are persistent across generations. Other explanations include some potential negative assimilation when health behaviors and cultural beliefs of the host country are perceived as advantageous, or the presence of selection bias in return migration which we cannot examine in our data as we cannot identify returning migrants. Finally, estimates are limited by any potentially unaccounted selection and the presence of genetic and epigenetic effects, alongside common migration wave-specific effects. | Study | biomedical | en | 0.999997 |
PMC11697409 | Animal venoms harbor a complex blend of salts, amino acids, biogenic amines, neurotransmitters, peptides, and proteins, strategically targeting various receptors crucial for the survival of venomous creatures . These venoms, along with their toxins, exhibit diverse pharmacological properties, serving as valuable resources for investigating cellular and molecular functions. Certain venom components are pivotal in human ailment and have inspired the development of novel therapeutic interventions . Toxins derived from aquatic venomous organisms represent a valuable reservoir of natural compounds for both academic inquiry and practical applications. However, challenges persist in the acquisition and preservation of venom extracts, leading to the underutilization of aquatic animal venoms, especially those from fish species, as a largely untapped wellspring of novel medicines and pharmacological compounds . Peptides, for example, are molecules found in living organisms and play a crucial role in many biological processes [ , , , ]. Their widespread occurrence and functional versatility enhance their therapeutic promise [ , , ]. Peptides are increasingly dubbed the "Goldilocks" chemical modality, characterized by their intermediate size, which combines the advantageous features of small molecules and biologics. This includes high target specificity, minimal off-target effects, and distinctive pharmacokinetic profiles . Nevertheless, while a select few peptides have secured FDA approvals and others are in various stages of clinical trials , it's noteworthy that peptides have a longstanding legacy of contributing to human health spanning over a century. From insulin to vasopressin, and more recently, tirzepatide, peptides have played pivotal roles in healthcare . In the last years, sales of peptide drugs exceeded $70 billion, with 10 non-insulin peptide drugs among the top 200 best-selling drugs, representing a substantial portion of the pharmaceutical market . Peptides sourced from venom have been investigated for their potential in biotechnological applications . While the majority of these peptides stem from a restricted range of venomous terrestrial animal groups, bioactive compounds from fish venoms have also been successfully identified and studied . The therapeutic potetinal of venom/toxin-derived peptides is apparent, attributed to their heightened specificity, stability, and comprehensive evaluation of pharmacokinetic characteristics . These peptides also demonstrate therapeutic attributes, notably antimicrobial properties (AMPs) . The efficacy showcased by these substances is associated with their physicochemical characteristics, such as net charge, hydrophobicity, and solvent accessibility. These properties, in turn, govern their mechanisms of action, selectivity, and specificity towards their targets . AMPs are peptides that exert their main effects on membranes, primarily by disrupting the integrity of the plasma membrane of their cellular targets . The ability of AMPs to penetrate cells appears to bolster their antimicrobial effectiveness by engaging and disrupting intracellular components such as macromolecules and organelles . However, this cellular uptake and permeability of AMPs may be linked to varying degrees of cytotoxicity. Through the design and synthesis of AMPs, it is possible to create peptides with finely tuned membrane translocation and cellular uptake abilities, coupled with reduced or minimal adverse impacts on membrane stability and cellular health. This is demonstrated by examples like buforin II, derived from the stomach tissue of the Asian toad Bufo bufo garagrizans , and its derivatives , as well as other AMPs . While numerous venoms harbor AMPs, it's worth noting that other families of biologically active peptides can also be present, including cell-penetrating peptides (CPPs) . CPPs comprise short sequences typically consisting of a few amino acids up to <40 residues. These peptides possess physicochemical and biological characteristics enabling them to traverse cell lipid membranes and facilitate intracellular transportation of various molecular cargoes. This transport can occur in the form of covalent conjugates or noncovalent complexes . Remarkably, many of the structural and physicochemical traits found in AMPs are also present in CPPs . Moreover, both types of peptides predominantly act on the cell membrane, inducing pore formation through diverse mechanisms that ultimately result in cellular apoptosis. These attributes not only enable their application as independent treatments but also facilitate the investigation of synergistic effects with various approved medications. These peptides can serve as adjuvants for compounds targeting the intracellular milieu, aiding in their effective delivery to the active site and bolstering treatment efficacy by combating infection or disease through diverse mechanisms of action . Undoubtedly, the advancement of novel antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs) represents a highly promising frontier in biotechnology and therapeutic pharmacology, particularly amidst the rise of strains resistant to conventional antibiotics . Nevertheless, there are notable constraints concerning the commercialization of AMPs and CPPs. These include elevated production expenses and time-intensive procedures, especially in the context of recombinant techniques, limited efficacy in animal models, heightened vulnerability to protease degradation, and diminished activity in specific physiological environments . All these shortcomings can be addressed through the application of in silico methods to assist the design of AMPs and CPPs, termed in silico study . In silico studies represent a logical progression adjuvant to in vitro methods, whereby biological and physiological processes are simulated using computer models. This approach enables researchers to explore a virtually limitless array of parameters, providing more insights or predictions regarding potential outcomes . There is a growing body of literature documenting the utility of in silico studies in predicting, designing, and modifying AMPs and CPPs, underscoring their promise as valuable approaches . Recent progress in analytical methodologies, such as the integration of genomics, mass spectrometry, and proteomics, has greatly facilitated scientists' exploration of venom compositions . Coupled with contemporary high-throughput screening techniques for venom compounds, the ability to predict novel molecules encoded within toxins marks a significant advancement toward harnessing the complete therapeutic capacity of animal venoms. Leveraging high-performance technologies, it is now feasible to anticipate new molecules derived from toxins, thereby tapping into the therapeutic potential inherent in these molecules. Moreover, biologically active peptides sourced from venomous animals indigenous to South America demonstrate significant and varied activities, presenting promising prospects as clinical candidates . One such example is the TnP family of synthetic cyclic peptides discovered in the venom of Thalassophryne nattereri , a venomous fish inhabiting the northern and northeastern coastlines of Brazil [ , , ]. In a previous study, our group employed in silico techniques to design 57 peptides derived from the T. nattereri family of toxins, known as Natterins . Natterins have been identified as the primary agents responsible for the major toxic effects induced by T. nattereri venom, including local edema, and intense pain progressing to necrosis [ , , ]. The predicted peptides exhibit a molecular mass ranging from 965.08 Da to 2704.06 Da, a net charge spanning from -2 to +7, a hydrophobic moment (μH) varying from 0.044 to 0.627, and a hydrophobic ratio ranging from 9 to 50 %. These characteristics facilitate their interaction with the microorganism membrane through adoption of an α-helix formation, ultimately leading to membrane disruption. The present study explores the antimicrobial and antiviral properties of two selected peptides identified as promising. Upon analyzing the findings outlined in De Cena et al. , peptides NATT2_06 and NATT4_01 emerged as particularly noteworthy candidates with potential antimicrobial and antiviral attributes. Among the 57 peptides delineated in the investigation , these specific peptides showcased physicochemical characteristics deemed vital in antimicrobial and antiviral peptides as documented in existing literature. These characteristics include optimal membrane-binding potential, cellular localization both inside and outside the membrane, minimal toxicity and allergenicity, alongside ADMET parameters falling within the expected range for such molecules. Our study demonstrates that these peptides exhibited mild inhibitory effects on the growth of both Gram-positive and Gram-negative bacteria, as well as fungi, over a brief period. They demonstrated comparable inhibitory actions concerning viral replication both intra and extracellularly, without manifesting any toxic effects in vitro or in vivo . Lastly, stability and membrane interaction assessments were conducted to pave the way for these peptides to emerge as potential prototype compounds. The peptides were designed by using a template and physicochemical base method as described in Conceição et al., and de Cena et al., . The NATT peptides amino acid sequence was derived from Natterins toxins from Thalassophryne nattereri and was used as a template protein. The peptide physicochemical properties were calculated through various tools, including ProtParam ( http://web.expasy.org/protparam ) , PepCalc ( https://pepcalc.com/ ), Heliquest version 2 ( https://heliquest.ipmc.cnrs.fr/cgi-bin/ComputParams.py ) , and APD3 for complementary properties ( https://aps.unmc.edu/prediction/predict ). All mentioned software was used with its default parameter configuration. The synthesis of NATT peptides was performed manually on solid phase following a 9 fluorenylmethoxycarbonyl (Fmoc)/ tert-butyl ( t -Bu) protocol , in polypropylene syringes fitted with a polyethylene porous disk. Solvents and soluble reagents were removed in vacuum. Commercially available reagents were used throughout without purification. Fmoc-Rink-MBHA resin (0.71 mmol/g) was used as solid support since it provides C-terminal peptides amides. Fmoc group removal was achieved with piperidine-DMF (3:7, 2 + 10 min). Coupling of commercial Fmoc-amino acids (4 or 3 equiv) were performed using DIC (4 or 3 equiv) and Oxima (4 or 3 equiv) in DMF under stirring at room temperature for 4 or 8 h The completion of the reactions was monitored by the Kaiser test for amino acid bearing a primary amine and by Chloramil test for the proline residue bearing a secondary amine. For each coupling and deprotection step, the resin was washed with DMF (6 × 1 min) and CH 2 Cl 2 (3 × 1 min) and aired-dried. After coupling of ninth amino acid residue, NMP was used instead DMF. Peptide elongation was performed by repeated cycles of Fmoc removal, coupling and washings. Once the synthesis was completed, peptidyl resins were subjected to the N-terminal Fmoc removal. Then, the peptides were cleaved by treatment with TFA-H 2 O-TIS (95:2.5:2.5) for 2 h Following TFA evaporation and diethyl ether extraction, the crude peptides were purified by reverse-phase column chromatography, lyophilized, analyzed by HPLC, and characterized by high resolution mass spectrometry (HRMS) and proton nuclear magnetic resonance (1H-NMR) (Supplementary material). Peptide stability was evaluated under various conditions. To investigate the impact of temperature on peptide structure, samples were incubated at 37 °C and 60 °C for 24 h. A control sample was kept at -4 °C. To evaluate the distribution under acidic conditions, the peptides were dissolved in a 0.074 M HCl solution, resulting in a pH of 3. For evaluation under basic conditions, the peptides were dissolved in a 0.18 M NaOH solution, resulting in a pH of 11. A control sample was maintained at pH 7. Additionally, samples were exposed to trypsin solutions at a concentration of 20 µg/mL. All samples underwent analysis using High-Performance Liquid Chromatography (HPLC) coupled with Mass Spectrometry . The HPLC system comprised LC-10AD mobile phase pumps, an Ultrasphere C-18 column (5 µm; 4.6 × 250 mm), a UV-vis SPD-10AV detector set at a wavelength of 220 nm, and a mass spectrometer operating in electrospray ionization (ESI) mode with a quadrupole separator. Reference strains of Pseudomonas aeruginosa (ATCC 15,442), Staphylococcus aureus and the fungus Candida auris were evaluated in this study. All strains were stored in 20 % (v/v) glycerol at -80 °C. Before the assays, cells were seeded on Cetrimide agar for P. aeruginosa , agar Cled for S. aureus and Sabouraud dextrose agar for C. auris , and grown at 37 °C for 48 h The assessment of antimicrobial activity of the synthesized peptides was conducted following the analytical parameters outlined in the CLSI and NCCLS methods. Strains were cultured in appropriate media (Mueller Hinton Broth for bacteria and Brain Heart Infusion for fungi) for approximately 24 h at 37 °C. The inoculum was adjusted to a concentration of 10 3 cells/mL at different absorbances (630 nm for P. aeruginosa and S. aureus , and 530 nm for C. auris ). Different concentrations of peptides were tested in sterile 96-well plates, with each well containing 200 µL (10 µL of peptide and 190 µL of inoculum in culture medium). Plates were incubated at 37 °C in a bacteriological incubator. Absorbance readings were taken by spectrophotometry at the respective wavelengths using a microplate reader (Synergy H1, BIOTEK, USA) after 2, 4, 6, 12, and 24 h. Tetracycline at 1 mg/mL was used as a positive control for bacteria, and the respective inoculum of each microorganism was used as a negative control. After assessing biomass to determine the number of viable cells following the time-kill assay and biomass evaluation at different time points, this was subjected to Colony-Forming Unit (CFU) assessment. The CFU counting assays were performed following the method described in Herigstad . This method involved the removal of 20 µL aliquots from the samples in the wells, followed by serial dilution in 180 µL of 0.9 % saline solution and plating on agar plates, which were then incubated for 24 h. After this period, the number of colonies on the plates was counted, and the number of cells per mL (CFU/mL) in the original culture was calculated. Murine fibroblast cells (L929) viability was assessed in the presence of the peptides using the cytotoxicity assay . L929 cells were prepared in RPMI medium for the assay. After 24 h, 10 µL of each peptide concentration (3.125 µM, 6.25 µM, 12.5 µM, 25 µM, and 50 µM) were added to a 96-well plate with cells (190 µL) and incubated for 24 h at 37 °C and 5 % CO 2 . Next, 200 µL from each well was removed, and 100 µL of 3-[4,5-dimethylthiazol-2-yl]−2,5-diphenyltetrazolium bromide (MTT) 0.5 mg/mL was added to the wells. The plate was incubated for 3 h under the same conditions. Then, 100 µL from each well was removed, and 100 µL of DMSO (dimethyl sulfoxide) was added as a positive control, and PBS was added as a negative control. The multi-well plate was quantified by absorbance at 540 nm using a spectrophotometer (Synergy H1, BIOTEK, USA). To perform the hemolytic test, human RBC was obtained from a volunteer donor. After centrifugation at 1000 xg for 5 min, the RBC pellets were resuspended in 5 % (vol/vol) sterile saline at different concentrations of peptides NATT2_06 and NATT4_01, and subsequently incubated at 37 °C for 1 hour. The supernatants were transferred to a 96-well plate, and the absorbance at 570 nm (A570) was measured. 0.12 % DMSO and 1 % TritonX-100 were used as negative and positive control, respectively . The hemolysis rate was calculated as Eq 1: H e m o l y s i s % = A ( s a m p l e ) − A ( D M S O ) / A ( T r i t o n ) − A ( D M S O ) x 100 A low-passage stock of the Chikungunya virus was also employed for antiviral activity. First, the viral propagation and titration were performed in the adherent African green monkey kidney epithelial (Vero) cells . The virus was grown in Vero cultured in MEM medium (Gibco, Waltham, MA, USA) supplemented with 10 % fetal bovine serum (FBS, Gibco), 100 U/mL penicillin, and 100 μg/mL streptomycin (Gibco, Waltham, MA, USA)-M10) for 48 h Then, the supernatant was collected, harvested, and titrated as previously described . The titer obtained for CHIKV was 9.3 × 10 6 PFU/ml. The immortalized human hepatocytes derived from the hepatocarcinoma of a 57-year-old Japanese individual were cultured. The Huh-7 were grown in Minimum Essential Medium (Advanced MEM, Gibco®, USA), supplemented with 10 % (v/v) heat-inactivated fetal bovine serum (FBS) (Gibco®, USA), 100 U/mL penicillin, and 100 μg/mL streptomycin (PenStrep – Gibco®, USA), along with 2 mM L-glutamine (Gibco®, USA), and maintained in a humidified atmosphere with 5 % CO 2 at 37 °C. To analyze the antiviral potential of peptides, Huh-7 cells were seeded in 48-well flat-bottom plates (Sarstedt®, Germany) at a density of 6.5 × 10 4 cells/well in MEM supplemented with 10 % FBS, 100 U/mL penicillin, 100 μg/mL streptomycin, and 2 mM L-glutamine. Cells were incubated for 12 h at 37 °C to allow cell adhesion to the wells. After this period, two types of assays were conducted, considering the peptides' ability to cross the plasma membrane, as described in de Cena et al., and based on a previous study . The first assay, named "post-treatment" , involved removing the culture medium from the wells, washing with phosphate-buffered saline (PBS, pH 7.0), followed by CHIKV inoculation in MEM supplemented with 2 % FBS at a multiplicity of infection (MOI) of 1 for 2 h to allow homogeneous viral adsorption. The inoculum was then removed, and the peptides were added in triplicate at different concentrations , serially diluted at a 1:2 ratio in culture medium with the same supplementation mentioned. Cells were monitored for 12 h. The second assay, named "co-treatment" , involved removing the culture medium from the wells, washing with PBS, followed by simultaneous inoculation of CHIKV and peptides, diluted in the same manner in culture medium with the same supplementation, in triplicate. The same multiplicity of infection (MOI) of 1 for 2 h allows homogeneous viral adsorption. Cells were also monitored for 12 h. In all situations, positive controls were included with only CHIKV inoculation without peptide addition, and negative controls were included without CHIKV or peptide addition. At the end of each period, supernatants from each well were collected in a viral lysis buffer (AVL) and stored at -80 °C until viral RNA extraction and molecular characterization ( Fig. 1 (C)). The flowchart below outlines the step-by-step methodology applied in the two assays. All viral manipulation procedures were conducted in accordance with WHO and PAHO regulations in a suitable Biosafety Level 2 (BSL-2) laboratory, following the biosafety guidelines of ANVISA. Fig. 1 CFU/mL (Log10) of different peptides. (a) NATT2_06 against P. aeruginosa , (b) NATT4_01 against P. aeruginosa , (c) NATT2_06 against S. aureus , (d) NATT4_01 against S. aureus , (e) NATT2_06 against C. auris , (f) NATT4_01 against C. auris tested at 0, 2, 4, 6, 12 and 24 h. Statistical significance was calculated using two-way analysis of variance (ANOVA) in Prism version 8.0 (GraphPad, USA) and represented as ***** p < 0.0001 for all tested concentrations, **** p < 0.0001 for four tested concentrations, *** p < 0.0001 for three tested concentrations, and * p < 0.0001 for only one tested concentration. Fig. 1 After conducting the assays, each viral RNA in the samples' supernatant was extracted using the QIAamp® Viral RNA Mini Kit (QIAGEN®, Germany). In this process, each sample underwent the extraction and purification of viral genetic material (+ssRNA) using extraction columns, with resuspension in nuclease-free ultrapure water using materials and reagents provided by the manufacturer. For the molecular characterization of NATT peptide treatment, RNA samples extracted from the plaque challenge assays' supernatants and viral stock dilutions underwent quantitative reverse transcription PCR (RT-qPCR) assays. CHIKV Primers and specific probe were synthesized by Sigma Life Science®, with 5-carboxyfluorescein (5-FAM) as the fluorophore and Minor Groove Binder (MGB-NFQ) as the fluorescence quencher . For the RT-qPCR reaction, the AgPath-ID® One Step RT-qPCR kit (Applied Biosystems®, USA) was used for each extracted RNA in duplicate. The reverse transcription reaction was carried out at 45 °C for 10 min, followed by 40 amplification cycles at 95 °C for 15 s and 60 °C for 45 s on the PCR StepOne Plus® thermocycler (Applied Biosystems®, USA). Data analysis was performed using StepOne® software, version 2.3 (Applied Biosystems®, USA). C T (Cycle Threshold) values were established for each sample based on the threshold automatically set by the software. All detection and quantification of viral RNA was done by real-time PCR of each sample . All the results of cycle threshold (Ct) were compared to a standard curve, which was obtained by carrying out serial dilutions from the pure stock (PFU), as previously described . For this study, the methodologies described by Mylonakis et al. and Jorjão et al. , with some modifications were employed. G. mellonella larvae in their final larval stage were used for the experiment. Ten randomly selected G. mellonella larvae of similar weight and size (250 to 350 mg) were used per group in all assays. Syringes (Hamilton Inc., USA) used for injections were sterilized with peracetic acid (Henkel - Ecolab GmbH, Düsseldorf, Germany) according to the manufacturer's instructions prior to inoculation. Each larva was injected with 10 µL of each peptide (NATT2_06 and NATT4_01) at a concentration of 50 µM into the last left proleg. A control group was injected with PBS to assess overall viability. The number of deceased G. mellonella was recorded every 24 h after peptide injection, with monitoring continuing for 7 days across three independent experiments. Larvae were considered dead if they showed no movement upon touch. The experiment concluded either when all larvae in the experimental group had died or transitioned into the pupal form . Large unilamellar vesicles (LUVs) of POPC:POPG (1:1), POPC:POPG (2:1) and POPC:POPG: Chol (2:1:1) with 100 nm of diameter were used as membrane model systems. The lipids were solubilized in chloroform in a round-bottom flask, and the organic solvent was evaporated under a gentle nitrogen flow to form a thin lipidic film. This film was then placed under vacuum overnight. The lipidic film was rehydrated with 10 mM phosphate buffer, pH 7.4, followed by ten freeze/thaw cycles to produce a suspension of multilamellar vesicles. Large unilamellar vesicles (LUVs) were obtained by extruding the multilamellar vesicles through 100 nm pore-size polycarbonate filters . Circular dichroism spectra of 50 µM of the peptides in 10 mM HEPES buffer, 50 mM NaF, pH 7.4, in the absence or presence of large unilamellar vesicles (LUVs), were acquired at 25 °C in the 190–260 nm wavelength range using 0.1 cm quartz cells in a JASCO model J-815 spectropolarimeter (Tokyo, Japan). Each final spectrum corresponded to an average of five scans, which were subsequently corrected for buffer or LUV baseline. Zeta potential was measured to evaluate changes in the surface charge of the LUVs (POPC:POPG (1:1), POPC:POPG (2:1) and POPC:POPG: Chol (2:1:1)) in the presence of NATT2_06 and NATT4_01. Assays were performed in a Zetasizer Nano ZS (Malvern Instruments, Malvern, UK) equipped with a 633 nm HeNe laser and disposable ζ cells with gold electrodes. LUVs suspensions were fixed at 200 mM prepared in Mueller-Hinton broth (MHB) to a final concentration of 1 × 10 8 CFU/mL. Each peptide solution from 0 to 30 uM, was added to the LUVs solution. Peptide-treated bacterial suspensions were dispensed into ζ cells and allowed to equilibrate for 15 min at 25 °C. The suspensions were mixed with peptides for 30 min at 37 °C. Values of viscosity and refractive index were set at 0.8872 cP and 1.330, respectively. The electrophoretic mobility of each sample was calculated, and the ζ potential was measured using the Smoluchowski equation, as previously described . Various studies have evaluated the prediction of antimicrobial and cell-penetrating peptides [ , , , ]. Together, these studies contribute to the realm of peptide prediction and classification, offering promising applications in drug development and delivery. A prior study utilized in silico analysis to predict and characterize novel AMPs and CPPs derived from natterins of T. nattereri . Subsequently, the current work evaluated the activity of two selected peptides through in vitro and in vivo assays. The purify of chemically synthesized peptides was confirmed through High Performance Liquid Chromatography (HPLC) and High Resolution Mass Spectrometry (HRMS) . Peptides synthesized to a purity of over 99 %, appearing as white powder, were utilized for in vitro and in vivo activity assessment. Table 1 Table 1 Properties of the peptides. Table 1 Peptide SEquence MW (g/ mol) Charge (at pH = 7) Theoretical PI Boman index NATT2_06 TTLRPKLKSK 1171.45 +4 11.26 3.02 NATT4_01 LYVAKNKYGLGKL 1466.79 +3 9.83 0.08 Following exposure to various temperature and pH conditions, as well as treatment with Trypsin protease, peptides at a concentration of 100 μg/mL underwent analysis using high-performance liquid chromatography coupled with mass spectrometry . Upon subjecting the peptides to a thermal treatment of 60 °C for 24 h, no degradation peaks indicative of peptide breakdown was detected. Consequently, it can be inferred that the peptides remained stable at 37 °C, given their resilience to degradation at 60 °C. The [ M + H ] +1 values observed were 1170, 1197, and 1465, respectively, compared to the control at -4 °C, as shown in Figures S8 and S9. Based on the HPLC chromatograms and mass spectra analysis, all three tested peptides displayed resistance to temperatures up to 60 °C. Previous research has consistently demonstrated the robust stability of antimicrobial peptides (AMPs), even at extreme temperatures such as 121 °C. For instance, Baindara et al. noted that the antimicrobial activity of Penisin peptide remained intact when incubated at temperatures up to 100 °C for 30 min, albeit showing a notable decline at 121 °C. Similarly, peptides including HKPLP, Cap18, Cap11, Cap11–1-18m², Cecropin B, Cecropin P1, Melittin, Indolicidin, and Sub5 have exhibited thermal resilience, withstanding temperatures of up to 100 °C in various assays . Additionally, Georgalaki et al. reported the remarkable thermal stability of the food-grade antibiotic macedocin, which retained its activity even after short-term heating, long-term incubation for up to four weeks at 30 °C and autoclaving at 121 °C for 20 min . The peptides underwent exposure to diverse pH environments, spanning from 3, 7, to 11. Under pH fluctuations between 3 and 7, none of the three tested peptides exhibited notable structural changes, suggesting their stability at pH 3. However, at pH 11, all three peptides experienced structural degradation, evidenced by distinct mass peaks observed in Figure S9e, differing from those in Figure S10c and d, corresponding to pH 3 and 7, respectively. In mass spectrometry assays, an alkaline pH can lead to diverse outcomes. Specifically, in the analysis of proteins and peptides, an alkaline environment may induce unintended chemical modifications in certain samples, impacting ionization, fragmentation, and the detection of reference molecules . Additionally, the peptides underwent treatment with a trypsin solution at a concentration of 20 μg/mL. Among them, the peptide NATT2_06 displayed notable changes in mass, as evidenced by both HPLC and MS, as depicted in Figure S12. Regarding the assessment of the NATT4_01 peptide, there were no notable shifts observed in the chromatograms, as illustrated in Figure s13. Nonetheless, upon examination of the mass spectra, fragments of the peptide were discernible. This observation suggests potential cleavage occurring between the Lys (K) and Arg (R) residues within the peptide sequence (LYVAKNKYGLGKL). The overall stability in plasma of NATT2_06 and NATT4_01 can be estimated using the Cavaco et al. equation that relates half-life (t 1/2 ) in serum with sequence-related physicochemical properties . Worthy of note, t 1/2 of NATT2_06 is 9.3 and 90.2 min for NATT4_01, a 10-fold increase, which is in line with our finding . Prior research indicates that when AMPs such as Cap18, Cecropin P1, Cecropin B, Melittin, and Indolicidin are incubated with trypsin, their antimicrobial activity is entirely lost within just 30 s. In the case of the peptide Cap11, this loss of activity occurs after 15 min of incubation. Conversely, a brief exposure of up to 5 min to trypsin enhances the antimicrobial activity of peptides such as Cap11–1-18m² by a factor of 2 . Furthermore, investigations involving the peptide Penisin revealed no decline in its inhibitory activity following a 6-hour incubation with trypsin . The specificity of trypsin in cleaving peptide bonds stems from its active site, which comprises specific amino acid residues (Lys and Arg) that selectively interact with particular amino acid residues within the polypeptide chain. This specificity grants trypsin the capability to identify and cleave peptide bonds at precise locations within the protein molecule. It's noteworthy that the interplay between AMPs and trypsin can be intricate and variable, suggesting the necessity for further analyzes concerning the behavior of AMPs with other proteolytic enzymes. The antimicrobial efficacy of the tested peptides (NATT2_06 and NATT4_01) against microorganisms was assessed by determining the colony-forming units per milliliter (UFC/mL) using the micro drop technique for cell counting at time intervals of 2, 4, 6, 12, and 24 h. When evaluated against P. aeruginosa , peptides NATT2_06 displayed remarkably similar activities, exhibiting significant inhibition across all tested concentrations at 2, 4, 6, and 12 h. However, at 24-hours, only the 3.1 μM concentration ceased to exhibit statistically significant inhibition, as shown in Fig. 1 (A). The peptide NATT4_01 similarly demonstrates inhibition at all concentrations at 2, 4, and 6 h. At 12 h, it showed activity only at 50 μM, and at 24 h, no antimicrobial action was observed. A general analysis of the antimicrobial activity of the peptides reveals that they exhibit action between 2 and 12 h. The 6-hour incubation period shows the highest activity of the peptides and concentrations tested. Possibly, after this period, the peptide loses its efficacy, allowing the microorganisms to resume growth. Liu et al. successfully employed an integrated in silico-in vitro approach to discover bioactive peptides, marking a milestone in leveraging these methods to design molecules surpassing the potency of native peptides . Given the pronounced cationicity of antimicrobial peptides (AMPs), electrostatic interactions play a pivotal role in their binding to the negatively charged cell membrane . Computational analyzes have underscored that net charge and amphipathic characteristics stand out as the most statistically significant physicochemical attributes distinguishing anti-Gram-negative AMPs from others . Notably, the two peptides under evaluation exhibit a positive charge of +3 and +4, respectively NATT4_01 and NATT2_06 facilitating their interaction with the negatively charged bacterial membrane. Research indicates that high cationicity in synthetically designed AMPs correlates with heightened in vitro antibacterial efficacy and minimal cytotoxicity, up to a threshold of +8, beyond which there's an escalation in hemolytic activity . However, it's noteworthy that lower cationicity has also been associated with peptides demonstrating activity in vivo . While the interaction between peptides and membranes is indeed a crucial aspect of AMP function, with many AMPs acting directly on microorganisms' cell membranes through affinity for certain lipid components, thereby disrupting membrane integrity and creating pores or channels, it's essential not to solely attribute their mechanism of action to electrostatic attraction or hydrophobic interactions . This perspective is supported by extensive research, including studies involving the cell-penetrating peptide penetratin at 100 μM, also known as the protein transduction domain. Penetratin, a 16-residue cationic peptide (RQIKIWFQNRRMKWKK-NH 2 ), derived from the third helix of the Antennapedia protein homeodomain, has been patented as a carrier peptide (or cargo transporter) for drug delivery into cells . Notably, a single change of tryptophan 6 to phenylalanine in the AMP abolished its membrane transfer properties, indicating that lipid binding alone may not be sufficient for AMP activity . Similarly, a W2G mutation in cecropin, an AMP predominant in insect cell-free immunity, nearly eradicated antibacterial activity . There are numerous parallels between CPPs and AMPs . Both types demonstrate antimicrobial effects and possess the capability to transport cargo molecules into cells. For instance, the renowned peptide LL-37 can translocate into eukaryotic cells at concentrations lower than those required for bacterial lethality, when adjusted for equivalent concentrations of divalent cations . However, it's pertinent to highlight a key distinction: AMPs are perceived to possess the ability to traverse bacterial membranes autonomously, without necessitating a transport mechanism, whereas CPPs are primarily internalized via active endocytosis . This discrepancy might indicate a fundamental difference in how peptides gain entry into prokaryotic versus eukaryotic cells. Among the peptides under examination, only NATT4_01 is categorized as non-CPP. However, it's noteworthy that this characteristic didn't compromise its effectiveness against P. aeruginosa , as it still exhibited significant activity, albeit at a lower level, following statistical analyzes. Recently, peptides having the ability to traverse reversibly the blood-brain barrier (BBB), referred to as BBB peptide shuttles (BBBpS), were found to be a class of peptides distinct from CPP . BBBpS are thus membrane-active peptides related but distinct of CPP, as CPP are related but distinct from AMP. According to the quantitative methodology adopted by Cavaco et al. , both NATT2_06 and NATT4_01 have moderate to high propensity (Cavaco's score function, S = 0.7) to traverse the BBB. This result opens an avenue for both peptides to be antimicrobial and target organs protected by physiological barriers. This is particularly relevant for NATT4_01 given its long t 1/2 . When assessed against a strain of S. aureus , peptide NATT2_06 demonstrated significant inhibition for all tested concentrations and time intervals ranging from 2 to 12 h. On the other hand, peptide NATT4_01 exhibited significant antimicrobial activity at all tested concentrations starting from 4 h. The peptides demonstrate antimicrobial efficacy against S. aureus within a time frame spanning from 4 to 12 h. Beyond this period, inhibition notably declines and ceases to maintain statistical significance, suggesting that after 12 h, the efficacy of peptide NATT2_06 diminishes, allowing bacterial growth to resume. Furthermore, it's noteworthy that the observed action does not conform to a dose-response pattern, as inhibitory activity fluctuates among the tested concentrations, and the highest concentration does not consistently correspond to the most pronounced antimicrobial effect. S. aureus stands as a prime example of a Gram-positive bacterium that poses a significant global threat to human and animal health . Correspondingly, findings from the current study align with those of Zhang et al. , who reported inhibitory effects of porcine beta defensin 2 (pBD2) against S. aureus within a timeframe of 1 to 8 h, demonstrating up to 80 % microbial survival inhibition within 4 h at a concentration of 150 µg/mL . In comparison, the peptides examined in this study exhibited microbial survival inhibition rates ranging between 32 % and 48.8 % within the same period. Despite the peptides synthesized in this study displaying lower inhibition rates, it's noteworthy that the tested concentrations were lower, ranging from 3.16 µg/mL (NATT2_6 at 3.125 µM) to 73.3 µg/mL (NATT4_01 at 50 µM). Consequently, these findings indicate promising progress in the pursuit of potential antimicrobial agents. In the study of Mohamed et al. , antimicrobial assays against various clinical and drug-resistant strains of S. aureus were conducted using synthetic peptides RRIKA, RR, KAF, and FAK. They found that the RRIKA peptide exhibited antimicrobial activity at concentrations ranging from 2 to 4 μM, while RR showed activity ranging from 8 to 32 μM. Conversely, the KAF and FAK peptides showed no activity against all tested strains up to 64 μM . Our data reveals a significant observation: the highest concentration doesn't always result in the most favorable outcome for both AMPs and PPCs. In both studies, the concentration range showing substantial inhibitory activity was between 2 and 32 μM, despite higher concentrations being evaluated. In addition to being assessed against strains of both gram-positive and gram-negative bacteria, the peptides underwent testing against a strain of the fungus C. auris , recognized as the most pathogenic species within its genus . Upon evaluating cell viability results, as depicted in Fig. 1 e and f, it becomes evident that all peptides tested exhibit a range of inhibitory effects against C. auris between 6 and 12 h. Conversely, peptide NATT4_01 effectively inhibited the growth of C. auris at all tested concentrations at both 6 and 12 h. Fig. 2 (a) Cell viability of mouse embryonic fibroblasts L929 (MEFs) treated with peptides. (b) Hemolytic activity of NATT2_06 and NATT4_01 peptides in erythrocytes. The mean standard deviation of three independent experiments is presented. Fig. 2 AMPs known to target fungi can bind to chitin, disrupting the integrity of the fungal cell wall by increasing its permeability or forming pores . Both peptides tested in our study displayed significant inhibition at least at one of the five concentrations assessed against C. auris , indicating potential affinity for chitin similar to the 36-amino acid peptide described by Pushpanathan et al. . Many AMPs exhibit a broad spectrum of antifungal activity, proving effective against various fungal species, including drug-resistant pathogens like Candida albicans, Aspergillus fumigatus , and Cryptococcus neoformans . Candidiasis represents an opportunistic infection impacting immunosuppressed and hospitalized individuals, leading to global concerns. The escalating pharmacological resistance among Candida species and the emergence of multidrug-resistant C. auris pose significant public health challenges . AMPs have undergone extensive investigation to assess their efficacy against C. auris . Studies have shown that numerous AMPs exhibit noteworthy antifungal activity against C. auris in in vitro experiments. This activity encompasses the capability to impede biofilm formation and fungal growth, induce damage to the cell membrane, and facilitate fungal cell death . Among the peptides evaluated, it is evident that NATT4_01 displayed superior results in inhibiting the growth of C. auris , spanning concentrations from 3.125 to 50 μM (equivalent to 4.58 μg/mL to 73.3 μg/mL NATT4_01). Other peptides documented in literature have also exhibited inhibitory effects against C. auris , including histatin-5 at 7.5 μM . Histatin-5, the predominant 24-amino acid product resulting from histatin-3 cleavage, demonstrates the most potent antifungal activity among all histatins . Moreover, recent findings indicate that the peptide LL-37 is capable of inhibiting and eradicating C. auris at concentrations ranging from 25 to 200 µg/mL . Comparing these results with our peptides, it is evident that all fall within a similar concentration range, affirming our progress in the pursuit of effective antifungal agents. Despite numerous efforts to develop antimicrobial peptides (AMPs) as antibiotics, one obstacle hindering the progress of many synthetic AMPs is their unknown toxicological profile upon systemic administration . Recent studies have explored the toxicity of antimicrobial peptides across various cell types and organisms . The results of cytotoxicity testing against L929 cells revealed that the peptides exhibited viability exceeding 75 % across all tested concentrations (ranging from 3.125 to 50 μM), as illustrated in Fig. 2 a. This suggests that the peptides did not induce significant toxicity in these cells. Hoskin and Ramamoorthy investigated the toxicity of various antimicrobial peptides (AMPs) on both normal and cancer cells, underscoring the significance of determining the therapeutic index for these molecules. Moreover, the variability in the toxicity of AMPs has been documented, with certain peptides exhibiting selectivity for bacterial cells, while others may impact eukaryotic cells as well. Consequently, further research is imperative to elucidate the interplay between the structure, antimicrobial activity, and toxicity of AMPs across different cell types . In this study, the structure of the tested peptides, along with their antimicrobial activity and in vitro and in vivo toxicity, were assessed. However, it's crucial to acknowledge that the observed in vitro toxicity effects may not necessarily mirror in vivo toxicity or the specific actions on target cells. To access the hemolytic activity, the peptides NATT2_06 and NATT4_01 were incubated with the erythrocytes for 1 h at 37 °C. The highest percentage of observed hemolysis was 2 %, for NATT2_06 at 50 μM . For antimicrobial peptides to be viable for systemic applications, it's crucial for them to demonstrate low toxicity against erythrocytes . The absence of hemolytic activity in the tested peptides is advantageous, as many AMPs are restricted in their use due to their significant hemolytic properties . The outcomes of in vitro assays align with the findings proposed by De Cena et al. , which classified peptides NATT2_06 and NATT4_01 as unlikely to induce hemolysis based on in silico analyzes , further corroborating results obtained with L929 fibroblastic cells, indicating no adverse effects on cell growth. In their research, Ebbensgaard et al. emphasize the correlation between hydrophobicity and hemolytic activity, illustrating how the substitution of specific amino acid residues can augment peptide hemolytic activity when paired with particular amino acid residues crucial for antimicrobial efficacy (such as Leu, Ile, and Thr). Notably, both NATT2_06 and NATT4_01 peptides examined here contain Leu and Thr residues. The study indicates that merely reducing hydrophobicity or achieving a low hydrophobic moment value isn't adequate to annihilate a peptide's antimicrobial activity; instead, the amino acid composition holds significant importance . The reality is that numerous AMPs possess hemolytic properties and can disrupt mammalian cells. Balancing the minimization of cellular toxicity with the maximization of antimicrobial effectiveness poses a significant challenge in the clinical application development of AMPs. The potential for cytotoxicity is an important consideration when it comes to antimicrobial peptides. A common characteristic of positively charged AMPs is nonspecific toxicity. Most known antimicrobial peptides are cationic and cytotoxic . The peptides NATT2_06 and NATT4_01 underwent antiviral assays to validate the findings from the in silico studies outlined by de Cena et al. . In vitro assessments were conducted to evaluate the antiviral efficacy of each peptide in blocking Chikungunya virus (CHIKV) infection or progression. Two distinct assays were performed to gauge the antiviral activity at various stages of CHIKV replication. The outcome was determined based on the peptides' capacity to reduce plaque-forming units (PFU) in the supernatants of Huh-7 cell culture infected 12 h post-infection (h.p.i). The inhibitory effects of peptides NATT2_06 and NATT4_01 on CHIKV replication were dose-dependent, with peptide concentrations ranging from 1.5625 to 50 μM. However, minimal antiviral activity against CHIKV was noted in cells treated with either NATT2_06 or NATT4_01 . Fig. 3 The effect on viral load reduction in post-infection treatment with peptides (a) NATT2_06 and (b) NATT4_01 during the adsorption and replication stages of CHIKV in Huh-7 cell. The p-value results (* p < 0.05, ** p < 0.01, and *** p < 0.001) were calculated using Student's t-test with and without Welch's correction for samples with a normal distribution or the Mann-Whitney test (6.25 μM) for samples with a non-normal distribution, using the CHIKV group as a control sample. (c) Percentage of viral inhibition at different concentrations of the NATT2_06 peptide: 19.12 % (50 μM), 11.09 % (25 μM), 16.25 % (6.25 μM) and 16.63 % (3.125 μM). The green bars represent the levels of viral load inhibition in the presence of NATT2_06 in post-infection treatment of CHIKV. The green bars represent the levels of viral load inhibition in the presence of NATT2_06 post-infection CHIKV treatment. Inhibition was assessed in a dose-dependent manner. Inhibition was assessed in a dose-dependent manner. Fig. 3 Fig. 4 The effect on the reduction of viral load in co-treatment during CHIKV infection with the peptides (A) NATT 2_06 and (B) NATT4_01. The p-value results were calculated using the Student's t-test with and without Welch correction at all concentrations, using the CHIKV group as the control sample. (C) Percentage of viral inhibition under different concentrations of the peptide NATT4_01: 22,62 % (50 μM), 25,31 % (25 μM), 22,08 % (6,25 μM) e 22,44 % . The figure shows the results of three independent experiments. Fig. 4 For the first assay, the treatment with peptides commenced 2 h post-viral infection, following the adsorption and entry stage of the virus into the cell. The final viral load recovered by the cells after the 12-hour assay is illustrated in Fig. 3 a and b. Notably, the inhibitory effect on CHIKV replication was solely evident with the NATT2_06 peptide. At a concentration of 12.5 μM, NATT2_06 exhibited a reduction about ten times in the quantity of plaque-forming units (PFU) (4.11 ± 0.45 Log 10 (PFU/mL), < 0.0001), corresponding to a 21.41 % inhibition of viral load compared to the untreated control (5.23 ± 0.73 Log 10 (PFU/mL)) . The inhibitory effect of NATT2_06 treatment persisted up to a concentration of 3.125 μM (4.36 ± 0.65 Log 10 (PFU/mL)). Conversely, no significant reduction in viral load was observed with NATT4_01 treatment in this assay, indicating no inhibition of CHIKV replication . The studies outlined by de Cena et al. categorize the NATT4_01 peptide as "Non-CPP," unlike NATT2_06, which is classified as "CPP." These classifications offer insight into the assay results. If a peptide fails to penetrate the cell, it cannot interact with the internalized virus, thereby lacking inhibitory activity, as observed with NATT4_01. In a second assay, peptide treatment was administered concurrently with viral infection. The final viral load recovered by the Huh-7 cells after the 12-hour assay is illustrated in Fig. 4 a and b. Notably, antiviral activity against CHIKV was solely observed in the treatment with the NATT4_01 peptide. A reduction of about 10 times in the plaque-forming units (PFU) at a concentration of 12.5 μM (3.94 ± 0.18 Log 10 (PFU/mL), p < 0.0001) was evident, with a corresponding decrease in viral load of 29.26 % , compared to the untreated control (5.57 ± 0.69 Log 10 (PFU/mL)). This effect of NATT4_01 persisted up to the concentration of 3.125 μM (4.32 ± 0.74 Log 10 (PFU/mL)). In this assay, no significant reduction in viral load was observed with treatments using NATT2_06, indicating no activity of the peptides on CHIKV . Arthropod-borne viruses (arboviruses), such as the chikungunya virus (CHIKV), are the primary pathogens of interest for global public health [ , , ]. Therefore, there is a growing need to develop new drugs to treat these viral infections. In this context, AMPs obtained from animal venoms stand out as promising compounds for exhibiting strong antiviral activity against emerging arboviral pathogens . These peptides may have direct antiviral effects on viral particles or replication cycles or exert indirect antiviral effects by modulating the host's immune response . Numerous peptides are undergoing clinical trials as potential antimicrobial agents, owing to their promising antiviral activity against specific viral pathogens and distinctive mechanisms of action. Examples include Myrcludex B, Hepalatide (L47), Adaptavir, and Aviptadil . Additionally, T20© (enfuvirtide) is a peptide currently utilized in the combined therapy of HIV-1 infections, inhibiting the entry of HIV into human cells by preventing viral fusion with the cell membrane. Despite being the sole commercially available peptide for this purpose, T20© faces limitations such as a low genetic barrier to drug resistance and a short in vivo half-life [ , , , ]. Lima et al. investigated the efficacy of Latarcin 1 peptide against CHIKV at varying concentrations ranging from 0.5 to 50 μM, alongside assays involving the NATT peptides. As outlined by Rothan et al. , Latarcin 1 peptide exhibits multifaceted action throughout the viral replication cycle, including entry, assembly/release, fusion, and replication stages. Interestingly, Latarcin 1 demonstrated diminished inhibitory potential against CHIKV during the pre-treatment phase compared to simultaneous addition with the virus inoculum and post-infection treatment. This observation suggests that the peptide exhibits greater potential for inhibiting viral activity when administered alongside the virus or after viral infection, akin to the actions observed with NATT4_01 and NATT2_06 peptides, respectively. Hence, it is plausible that the tested peptides may exert their effects at distinct stages of viral replication. Antiviral peptides are characterized by their cationic nature and amphipathic properties, making them promising candidates for therapeutic applications . These attributes are particularly advantageous in combating enveloped arboviruses like flaviviruses and alphaviruses. The viral envelope originates from host cell membranes, comprising lipid rafts, sphingolipids, and cholesterol, thereby exhibiting an amphipathic nature and negative charge. Consequently, cationic peptides can electrostatically interact with this viral structure, leading to direct virucidal effects or interference with virus binding and fusion during the viral life cycle within host cells . Moreover, they have the capability to disrupt endoplasmic reticulum membranes, thus impeding exponential virus replication . The main advantages of peptides over small chemical compounds are specificity, tolerability, potency, rarer side effects (since the decomposition products are amino acids), and commercial scalability. Moreover, peptides have the potential to interact at the active site of large proteins where protein-protein interaction is essential. Identifying compounds has become much easier now with advances in structural and genomic technologies. However, short half-life, solubility, bioavailability, stability, and natural peptide delivery are the main challenges faced by these peptides . The in vivo toxicity of peptides (NATT2_06 and NATT4_01) was assessed using G. mellonella larvae . The highest concentration tested in all assays was 50 µM (equivalent to 58.6 µg/larvae for NATT2_06 and 73.3 µg/larvae for NATT4_01). No significant toxicity was observed in any of the peptide samples tested. Over the course of the 7-day experiment, only three deaths occurred in the group treated with peptide NATT2_06 (on days 5, 6, and 7), while no deaths were observed in the group treated with NATT4_01. Fig. 5 Toxicity of NATT2_06 and NATT4_01 in G. mellonella. Fig. 5 G. mellonella larvae have emerged as a valuable model for assessing both the in vivo toxicity and efficacy of antimicrobial agents . Notably, there exists a robust correlation between the toxicity of food preservatives observed in Galleria larvae and that in rats, underscoring the model's potential for evaluating in vivo toxicity of various compounds . Insects possess a highly sensitive immune response, and the introduction of foreign material, such as pathogens or pathogen-associated material, can trigger a potent antimicrobial immune response within the insect, rendering it resistant to subsequent infections—a phenomenon known as priming . Moreover, the simplicity and precision of inoculation and control procedures have established Galleria as the predominant model organism in larval studies [ , , ]. Circular dichroism (CD) spectroscopy indicated that the secondary structure of these peptides remained unaffected upon interaction with POPC:POPG LUVs. However, the incorporation of cholesterol into the LUVs (POPC:POPG:Chol) slightly altered only the secondary structure of NATT4_01 . The zeta potential, derived from the mobility of cells in an electric field under defined pH and salt conditions, offers insights into cell surface charge . Assessments of zeta potential using model membrane systems revealed variations across different peptides, indicating that NATT4_01 more efficiently achieves charge neutralization in both POPG:POPC (2:1) and POPC:POPG:Cholesterol (2:1:1) setups . Fig. 6 Zeta-potential for membrane model systems in the presence of (A) NATT4_01 and (B) NATT2_06. Bars represent the zeta-potential range. POPC:POPG (1:1) (orange squares); POPC:POPG (2:1) (pink triangles) and POPC:POPG:Chol (2:1:1) (blue circles). The lipid concentration was kept constant at 200 mM, while peptide concentration ranged from 0 to 30 μM. Fig. 6 Moreira Brito investigated the synthetic antimicrobial peptide LyeTx Ib Cys, derived from LyeTx I found in the venom of the spider Lycosa erythrognata . Their study revealed that when subjected to zeta potential tests on POPC:POPG LUV membranes, it elicited an increase in the membrane's surface charge, even at relatively low concentrations ranging from 20 μM to 40 μM . These concentrations mirror those used in assays with peptides NATT2_06 and NATT4_01, indicating that synthetic antimicrobial peptides indeed interact with membranes. Another investigation into zeta potential analysis in POPC:POPG LUVs, this time utilizing the lipopeptide polymyxin B, indicates that in the presence of the peptide, the zeta potential data display a trend towards less negative values. This outcome suggests that initial electrostatic interactions play a significant role in peptide binding . Despite interacting with LPS, there is not complete neutralization of the membrane, as seen in assays with peptides derived from natterins, hinting that polymyxin B may not fully access the negative charges of LPS aggregates, similar to the behavior expected from the peptides studied by our group. Moreover, Domingues and colleagues propose that most cationic peptides can prompt aggregation of negatively charged lipid vesicles at concentrations considered high. Their study also highlights that many hydrophobic peptides can interact with neutrally charged lipids and induce their aggregation, suggesting these properties hold promise in the design of new peptides with antibiotic activity . This study investigates the intricate dynamics of antimicrobial and cell-penetrating peptides (AMPs and CPPs) derived from Natterin toxin, exploring their stability, antimicrobial efficacy, cytotoxicity, and antiviral activity. The findings underscore the peptides' robust stability under varying temperatures and pH conditions, alongside notable resistance to proteolytic degradation. The antimicrobial assays reveal significant efficacy against P. aeruginosa, S. aureus and C. auris , with varying degrees of inhibition observed across different time intervals and concentrations. Moreover, the minimal cytotoxicity and hemolytic activity demonstrated by the peptides enhance their potential as viable therapeutic agents. The antiviral assays, although revealing limited efficacy against the Chikungunya virus, highlight distinct stages of viral replication where the peptides may exert their effects. Additionally, the in vivo toxicity assessment using G. mellonella larvae provides promising indications of the peptides' safety profiles. Finally, the zeta potential measurements offer insights into the peptides' interactions with model membranes, further elucidating their potential mechanisms of action. As the landscape of antimicrobial resistance continues to evolve, the continuous exploration and refinement of AMPs and CPPs are imperative. This study not only contributes valuable data to the existing body of knowledge but also guides the way for future research endeavors aimed at harnessing the full therapeutic potential of these peptides. The journey towards effective antimicrobial and antiviral agents is arduous, yet the insights gained from this research offer a light of hope in the field of drug development and delivery. | Review | biomedical | en | 0.999997 |
PMC11697424 | Recent years have seen great advances in the treatment of oncology patients. Still, toxicity related to radiotherapy and chemotherapy treatment, or the combination of both, remains high. Cancer patients who undergo this type of therapy often present with symptoms that severely impair their clinical, functional, and nutritional outcome. Specifically, radiotherapy to the pelvic region has been found to be a main cause of nutritional deterioration, mainly due to radicular enteritis, which causes diarrhea, mucositis, abdominal pain, and, to a lesser extent, constipation ( 1 ). Diarrhea related to cancer treatment (DRTO) is a side effect that causes deterioration of the patient’s nutritional status, treatment interruptions, frequent hospitalizations, and impairment of quality of life ( 2 , 3 ). The prevalence of DRTO can reach up to 74% of cancer patients, depending on radiation doses, cancer treatment, female sex, low BMI, advanced age, and having undergone abdominal surgery ( 3 ). It is essential to treat DRTO early and perform the most appropriate intervention to minimize its progression to more severe states that could condition the continuity of cancer treatment and its survival ( 4 ). In clinical practice, early and precise nutritional intervention can favor the control of diarrhea, cover nutritional needs, and promote good nutritional status ( 5 ). Cancer patients frequently present with a high risk of malnutrition per se due to the tumor itself, its location and extension, the oncologic treatment received (surgery, radiotherapy, chemotherapy), the toxicity related to it, the metabolic changes that develop, and their social environment ( 6 ). Previous studies have shown that malnutrition leads to a higher rate of hospital admissions, longer hospital stays, a lower quality of life, and higher mortality related to a decrease in the tolerance of oncologic treatments ( 7 ). Considering the negative effects of malnutrition in cancer patients, it is essential to detect it early and provide optimal nutritional support to minimize its progression. Given the high prevalence of DRTO and malnutrition in the cancer patient, it is striking that clinical practice guidelines focus their recommendations on the pharmacological treatment of diarrhea but do not specifically address the nutritional support needed by patients ( 3 , 8 – 10 ). The nutritional support plan will range from dietary advice (DA) to the use of commercial formulations, including oral nutritional supplements, enteral tube feeding, or even parenteral nutrition, depending on the severity and persistence of symptoms ( 11 ). Oral nutritional supplements may prove the most common and effective tool to treat both symptoms, as long as adequate adherence to treatment is achieved ( 12 ). A peptide diet (PD) may be a nutritional therapy option for patients with DRTO due to its ease of absorption, suppression of pro-inflammatory cytokine production, and maintenance of mucosal integrity ( 13 – 15 ). There are few published studies, however, on the efficacy of nutrition with intestinal peptides in patients with diarrhea associated specifically with colorectal cancer therapy, although there are studies with enteral supplementation with glutamine that show positive results in improving the severity and symptomatology of patients with radicular enteritis ( 16 ). The main studies on PD published to date have been conducted in cancer patients undergoing chemo-radiotherapy treatment but at the level of the oral mucosa, esophagus, stomach, or pancreas, showing heterogeneous results ( 17 – 24 ). For all these reasons, Sanz-Paris et al. ( 25 ) published an algorithm on the nutritional management of DRTO from an oligomeric formula. Based on this algorithm, these authors presented results on the clinical and nutritional efficacy of the implementation of this protocol in clinical practice, with very promising results ( 26 ). In 2023, Peña Vivas et al. ( 27 ) published a clinical study demonstrating that supplementation with PD reduces DRTO with respect to a polymeric diet, affecting the functional and nutritional improvement of the patient with rectal cancer in neoadyuvancy. The aim of the study was to evaluate the efficacy of nutritional supplementation with a glutamine-enriched peptide diet (PD) compared to exclusive dietary advice (DA) on gastrointestinal toxicity, interruption of radiotherapy treatment, and nutritional status in patients with rectal cancer undergoing neoadjuvant chemo-radiotherapy. Adult patients with a diagnosis of adenocarcinoma of the rectum (confirmed by biopsy) in treatment with neoadjuvant chemo-radiotherapy were recruited. Patients with severe renal, cardiac, respiratory, or hepatic disease, pregnant or lactating women, or patients with an allergy or intolerance to any of the ingredients of the formula under study were excluded. Intestinal toxicity: Using the Common Toxicity Criteria version 5.0 of the National Cancer Institute (CTCAE v5.0), the degree of gastrointestinal toxicity associated with cancer treatment was evaluated: nausea, vomiting, abdominal pain, intestinal mucositis, diarrhea, and constipation. In addition, the following were collected: total volume radiation dose (cc), minimum, average and maximum radiated bowel (percentage and Gy), and the volume of radiated bowel (V40 < 150cc) in short cycle and long cycle. Nutritional status: Anthropometric data were collected (weight, height, calculation of the percentage of weight lost, and calculation of the body mass index), body composition (percentage of fat mass and percentage of fat-free mass), analytical data (total protein, albumin, prealbumin, C-reactive protein, cholesterol, and triglycerides), and a diagnosis of malnutrition was made following the GLIM criteria. Following the ESPEN recommendations for cancer patients, all patients received dietary recommendations to increase energy and nutrient intake through regular dietary intake. Moreover, patients in the intervention group that received the peptide diet were instructed to take 1–2 containers of the nutritional supplement daily (according to their nutritional needs to be covered) from day 1 of radiotherapy until the time of surgery, continuously, for a total of 12 weeks. A statistical study was carried out using the SPSS 22.0 program (IBM). Quantitative variables were evaluated for normal distribution with the Kolmogorov–Smirnov test and expressed as mean and standard distribution. A comparison between quantitative variables was performed with Student’s t -test. Qualitative variables are expressed as absolute frequencies and percentages. For the comparison between variables, the chi-square test and the calculation of the relative risk (RR) with its 95% confidence interval were used. Subanalysis was performed by tumor stage, oncologic treatment (short or long), and diagnosis of malnutrition. A p -value of less than 0.05 was considered significant. Fifty-four patients diagnosed with rectal adenocarcinoma under neoadjuvant treatment were initially selected. Fifty-one patients were randomized uniformly to the peptide-diet group (25 subjects) or the dietary-counseling group (26 subjects). No enrolled patients were excluded, and all completed the intervention and follow-up period . Table 2 presents the demographic and clinical parameters, with no differences found between intervention groups. Globally, the 52.9% received chemotherapy treatment with capecitabine, 33.3% with FOLFOX (leucovorin calcium, fluorouracil, and oxaliplatin), 11.8% with XELOX (capecitabine and oxaliplatin), and 2.0% did not receive chemotherapy treatment, with no differences among the intervention groups. Regarding associated metabolic pathologies, 19.6% had diabetes mellitus, 39.2% had dyslipidemia, and 5.9% had heart disease, with no differences between intervention groups. There were no differences between groups in the prevalence of nausea, vomiting, and abdominal pain at the visits performed, but there were differences in the presence of intestinal mucositis and diarrhea at the final visit, with more in the group that received DA ( Table 3 ). When grouping the toxicity grades at ≥ 2, it was observed that toxicity related to the development of diarrhea was confirmed as more frequent in the DA group at the intermediate visit, with a RR of 0.218 (95% CI = 0.052–0.923) and at the final visit, with a RR of 0.103 . This situation was also confirmed in the development of mucositis at the final visit , with a RR of 0.405 (95% CI = 0.280–0.584). In the subanalysis performed by radiotherapy treatment (long or short), in both cases it was again confirmed that mucositis at the final visit was more prevalent in the DA group (long: 33.3 vs. 0%, p = 0.023; short 30.8 vs. 0%, p = 0.036). With respect to diarrhea, it was more frequent in the DA group at the final visit (long: 54.6 vs. 7.7%, p = 0.012; short: 38.5 vs. 8.3%, p = 0.047). In the sub-analysis performed by stage, in stage III mucositis at the final visit was more prevalent in the DA group (38.9 vs. 0%, p = 0.002), as was diarrhea (38.9 vs. 5%, p = 0.011). In the sub-analysis stratified by nutritional status, among patients with malnutrition, the prevalence of mucositis at the final visit was significantly higher in the DA group (30 vs. 0%, p = 0.024). Additionally, the incidence of diarrhea was greater in the DA group at both the intermediate visit (45.5 vs. 6.7%, p = 0.020) and the final visit (50 vs. 6.7%, p = 0.013). Among patients with adequate nutritional status, no significant differences were observed in the incidence of diarrhea. However, mucositis at the final visit remained more prevalent in the DA group (35.7 vs. 0%, p = 0.034). A lower rate of interruptions was observed in the group treated with PD (0%) than in the DA (11.5%), although it did not reach statistical significance ( p = 0.070). In the subanalysis by stage, stage III patients receiving DA were observed to have a higher frequency of interruptions of radiotherapy treatment (15.8 vs. 0%, p = 0.049). In the sub-analysis performed by malnutrition, patients with malnutrition who received DA were observed to have a higher frequency of interruptions in radiotherapy treatment (18.2 vs. 0%, p = 0.040). In both groups a deterioration of nutritional status was observed, especially in the DA group . Regarding anthropometric and body composition parameters, no differences were detected throughout the evolution ( Table 5 ). Regarding the analytical analysis, differences between groups were detected in the values of prealbumin in the final determination, but they were not consistent compared to the initial parameters ( Table 5 ). Of the total patients, 41 underwent surgery (20 in the DA group and 21 in the PD group). No differences were observed between groups in the surgical complications evaluated ( Table 6 ), nor in hospital stay [8.84 (10.64) days in the PD group vs. 8.60 (12.11) days in the DA group, p = 0.944]. Gastrointestinal toxicity, especially diarrhea and mucositis, are frequently present in patients with colorectal cancer. In our study, the comprehensive treatment of both clinical situations with a peptide enteral nutrition formula enriched with glutamine reduced the digestive toxicity associated with oncologic treatment much more than the usual clinical practice consisting of dietary advice. The PD diet achieved an improvement in DRTO with respect to the group that received DA exclusively. Specifically, stage III patients and patients with malnutrition presented a lower incidence of diarrhea when receiving PD compared to those who followed standard clinical practice with DA. Focusing exclusively on the peptide diet, the study by Sanz-Paris et al. ( 26 ) determined the number of stools and their consistency with the Bristol scale but did not measure the intestinal toxicity of diarrhea with the CTCAE 5.0 scale, making the results of our studies difficult to compare. The study by Peña Vivas et al. ( 27 ) did measure the presence of diarrhea with the CTCAE 5.0 scale but did not determine the degrees of toxicity, which were recorded in our study. In this case, at the final visit the prevalence of toxicity was 8% in PD and 45% in DA, values very similar to those detected by this group (5% in the PD group and 85% with a polymeric diet), also achieving in both cases a statistically significant reduction in RR in favor of the PD group ( 27 ) [RR of 0.103 (95% CI = 0.020–0.537) vs. RR of 0.059 (95% CI 0.015–0.229)]. In addition to an improvement in DRTO, an improvement in intestinal mucositis was observed in the group that received PD, an aspect of great clinical effect for the patient. In the literature reviewed, only in the study by Peña Vivas et al. ( 27 ) was this variable evaluated, and in both cases a decrease in RR in favor of PD was observed [RR of 0.405 (95% CI = 0.280–0.584) vs. RR of 0.202 (95% CI 0.102–0.399)]. Certain metabolic alterations and potential improvements may arise from the effects of the test diet (PD), likely attributed to specific bioactive components such as extra virgin olive oil (EVOO), glutamine, and the omega-3 fatty acids EPA and DHA. EVOO is rich in the phenolic compound oleocanthal, which exerts potent anti-inflammatory effects by inhibiting cyclooxygenase (COX) enzymes, specifically COX-1 and COX-2, key mediators in the biosynthesis of pro-inflammatory molecules. Attenuation of chronic inflammation, both at the intestinal and systemic levels, may significantly optimize metabolic function by reducing oxidative stress and downregulating the production of pro-inflammatory cytokines, such as IL-6 and TNF-α. This systemic anti-inflammatory effect may enhance nutrient utilization efficiency and facilitate the restoration of energy metabolism compromised by oncologic treatments ( 28 ). Glutamine, a conditionally essential amino acid, plays a pivotal role in the energy metabolism of enterocytes (intestinal epithelial cells). Under conditions of metabolic stress, such as those induced by cancer therapies, glutamine demand escalates due to its critical involvement in cellular repair and regenerative processes. Exogenous glutamine supplementation via the test diet may promote intestinal homeostasis by upregulating protein synthesis, reducing intestinal permeability, and preserving epithelial barrier integrity. These effects could enhance nutrient absorption and attenuate the protein catabolism linked to systemic inflammation and treatment-induced toxicity, thereby supporting improved nutritional status ( 29 ). EPA and DHA are implicated in mitigating metabolic dysfunctions triggered by cancer therapies and in enhancing patient immune function through modulation of inflammatory pathways and cell membrane fluidity ( 30 ). Finally, hydrolyzed proteins are characterized by an accelerated absorption profile within the gastrointestinal tract, leading to a more rapid increase in plasma amino acid levels. This faster digestion rate enables a quicker entry of amino acids into circulation, thereby augmenting the anabolic response in skeletal muscle. Additionally, hydrolyzed proteins reduce splanchnic amino acid extraction, thereby increasing peripheral availability to tissues such as muscle, and enhancing postprandial protein synthesis ( 31 ). One of the most noteworthy results of the study is the reduction in the number of interruptions of radiotherapy treatment in the group that received PD when the onco-logic stage was III and they were malnourished. This variable was not evaluated in any of the studies reviewed, so it could shed light on the clinical effect of specific nutritional treatment with peptide formulas in patients with rectal cancer and malnutrition. Regarding the effect on nutritional status as measured by GLIM criteria, both groups recovered during follow-up, although it was more effective in the PD group. In the case of anthropometric, body-composition, and analytical variables, there were no statistically significant differences between intervention groups. Other studies carried out with elemental and peptide diets ( 22 – 24 ) and peptide diets ( 26 , 27 ) also showed an improvement in nutritional status at the anthropometric and analytical levels, but with greater robustness. This situation could be justified by a major methodological difference, since our study did not solely include patients at risk of malnutrition, which could have influenced the results obtained in the improvement of nutritional status. No differences were detected in the frequency of surgical complications or hospital stay between groups. This situation could be explained by the fact that the peptide formula under study was not enriched by immunonutrients (arginine and nucleotides), nor did it contain the doses of omega-3 (EPA and DHA) that have been shown to be effective in the clinical improvement of the surgical patient (2–3 g/day) ( 6 ). As strengths of the study, it should be noted that this is the first study in which the efficacy of a peptide enteral nutrition formula was evaluated during interruptions of radiotherapy treatment. This shows that specific nutritional support with a peptide formula goes beyond the simple recovery of the oncologic patient’s nutritional status and also has an effect on their clinical improvement, reducing digestive symptoms that condition their overall evolution and tolerance of oncologic treatment. This may be due to, among other possible factors, the use of partially hydrolyzed protein, the fact that the fat intake is mainly from MCT, or the fact that glutamine, the main amino acid of the enterocyte, has been supplemented. In conclusion, the glutamine-enriched peptide diet had a protective effect on the development of gastrointestinal toxicity associated with antineoplastic treatment, specifically on the development of DRTO and intestinal mucositis, and reduced the interruptions of oncologic treatment in patients with colorectal cancer undergoing radiotherapy and chemotherapy. | Study | biomedical | en | 0.999996 |
PMC11697425 | Substance use is a major public health concern globally ( 1 ). Disability-adjusted life years (DALYs) are often used as a measure of impact of disease states or health behaviors on health-related quality of life. One DALY represents the loss of the equivalent of one year of full health. In 2016, 4.2% of DALYs globally were attributable to alcohol use, while 1.3% were attributable to other substance use ( 1 ). Annually, about 11.8 million deaths are linked to substance use ( 2 ), with alcohol alone causing three million deaths worldwide ( 3 , 4 ). In North America, in 2019, substance use disorders (SUDs) ranked 5 th for years lived with disability (YLDs) and 15 th for years of life lost (YLLs) ( 5 ). Among countries in South and North America, Canada ranks second in terms of DALYs ( 5 ). Furthermore, Canada experiences approximately 67,000 deaths each year as a result of substance use ( 6 ). Based on data from the 2012 Canadian Community Health Survey – Mental Health, about 6 million Canadians (21.6%) met the criteria for SUD in their lifetime ( 7 ). In Nova Scotia, the lifetime prevalence of SUD was 30.2%, the second highest in the Canadian provinces ( 8 ). SUD and mental illnesses often co-occur ( 9 ). Substance use can exacerbate symptoms of mental illnesses, while conversely, mental illnesses can drive individuals towards substance use as a form of coping or self-medication ( 10 ). A study conducted in Ontario showed that the prevalence of SUD varies from 17.1% among individuals with anxiety disorder to 34% among individuals with personality disorder ( 11 ). Moreover, polysubstance use is common among those with mental health disorders. For example, a study conducted in Nova Scotia showed that the prevalence of comorbid alcohol and cannabis use disorders among patients with a psychotic disorder was 50.0%, while the prevalences of alcohol use disorder alone and cannabis use disorder use alone were 12.5% and 20.8%, respectively ( 12 ). SUD among individuals with mental illnesses can lead to misdiagnosis, delayed intervention, relapse, poor prognosis, and poorer overall health ( 13 ). Thus, understanding the substance use profile among individuals with mental health needs is essential for tailoring effective interventions, addressing their specific needs, managing comorbidities, and improving treatment outcomes ( 14 ). Most of the current studies about substance use are predominantly centered on clients already engaged in mental health and addiction services or on the general population. Less attention has been paid to individuals in the early stages of help-seeking. To the best of our knowledge, no study has investigated substance use profiles among the ‘pre-clinical’ population of those seeking mental health and addiction (MHA) services but who have yet to see a clinician. Also, no study has examined substance use disparities based on gender, race, or ethnicity, and socio-economic status among this population. Understanding how gender, ethnicity, and income sources intersect to influence substance use patterns among individuals seeking MHA services is essential for designing prevention and treatment strategies tailored to an individual’s unique needs. Additionally, understanding the epidemiology of substance use and its intersectionality with socio-demographic features in this population has significant implications for planning MHA services. Furthermore, exploring substance use profiles among individuals with mental health needs is pivotal for developing early interventions, personalized treatment plans, and targeted resource allocation. Examining the various routes of substance administration among individuals seeking MHA services can provide insight into potential health risks, helping to inform harm reduction strategies. Therefore, the objectives of this study were to investigate, among MHA intake clients in Nova Scotia: 1) the prevalence of substance use by gender, ethnicity, and income source; 2) the routes of substance administration; and ( 3 ) factors associated with substance use. All clients aged 19-64 years who were assessed by MHA Intake between January 2020 and December 2021 were included. The MHA Central Intake was established in 2019 by the Department of Health and Wellness of Nova Scotia as the entry point of MHA specialty services in the province and is the primary single entry-point of MHA services in Nova Scotia for this age range (individuals 18 years and younger are service through the child and adolescent system, and those 65 years and older are referred directly to geriatric specialty services). Individuals with symptoms of mental health and/or addiction problems living in any region of Nova Scotia (Northern, Eastern, Western, and Central zones) can directly contact MHA central intake using a toll-free telephone number. This central intake screens individuals with mental health and/or addiction problems and promptly links them to the appropriate level of mental health and addiction care. The intake process involves a semi-structured interview with the client by a clinician (e.g., clinical therapist, social worker, or registered nurse) over the telephone or in person. The information gathered during the interview was recorded on the electronic Intake Assessment form, which, once finalized, becomes an integral part of the individual’s permanent health record ( 15 ). This study was a secondary data analysis using existing de-identified data. Given the large number of clients in the database and vast area where they lived, obtaining informed consent from each client was not feasible. This study was approved by the Research Ethics Board of the Nova Scotia Health Authority. Individuals at higher risk of suicide were referred to psychiatrist for further evaluation, while those at mild and moderate risk received support from health professionals and psychologists working in the MHA Central Intake. The current substance use screening by MHA Intake included current use of alcohol, cannabis, hallucinogens, inhalants, opioids, sedatives/hypnotics, stimulants, and tobacco. The frequency of using these substances was evaluated via a questionnaire and included options of 2-4 times a month, 2-3 times a week, four or more times a week, and daily. The method(s) of administering each substance used was also queried, including oral, intravenous, inhaling, intramuscular, subcutaneous, smoking, snorting, transdermal patch, and/or rectal administration routes. The frequency of substance use was recoded into three categories of use: occasional use (2-4 times a month), frequent use (2-3 times a week, and four or more times a week), and daily use. The following mental health problems were assessed and current/provisional diagnoses were made based on the client’s report: depression, anxiety disorder, bipolar disorder, attention-deficit/hyperactivity disorder, adjustment disorder, autism, eating disorder, neurocognitive disorder, obsessive-compulsive disorder, personality disorder, psychotic disorder, posttraumatic stress disorder, and substance use disorder. We aggregated the presence of current or past provisional diagnoses of mental health disorders into a single variable with three levels: no mental health disorder (coded as 0); provisional diagnosis of one current/past mental illness (coded as 1); or two or more provisional diagnoses of current/past mental illnesses (coded as 2). Clients were assessed to determine if they had experienced current/past psychosocial stressors in the following areas: childhood adversity, abuse or other trauma, economic/financial, educational/school, ethnic/cultural, spiritual/religious beliefs, family and/or significant relationship, social relationships, housing or legal issues, leisure/recreational, military, parent/guardian–child conflict, or physical health/disability, and how these stressors affected their functioning ( 15 ). In this analysis, we classified psychosocial stressors into three categories: the absence of any such stressors (coded as 0); the experience of one such stressor (coded as 1); or the experience of two or more psychosocial stressors (coded as 2). We first examined the frequency of each variable, its distribution, and rates of missing values. We selected 128 variables with missing values for imputation based on our objectives. Multiple Imputation by Chained Equations (MICE) was used to impute variables with missing values (missing at random). We opted for MICE as our method of choice because of its flexibility in generating multiple predictions for each missing value. This approach relies on the variable’s distribution, the observed values for a given participant, and the correlations observed in the dataset for other participants ( 16 , 17 ). In this study, the imputed variables with missing values ranged from 0.004% (for bromazepam [a sedative/hypnotic] route of administration) to 20.9% (impact of mood symptoms on functioning). Traditionally, a small number of imputations (five to ten) are commonly used ( 18 , 19 ). However, to achieve a better estimate of standard error, a higher number of imputations are recommended, which is at least equal to the average percentage rate of missing values, as a rule of thumb ( 18 , 19 ). Considering the average percentage rate of missing values in our study (i.e., 0.76%), we used five imputations with a maximum iteration of 20. The imputed datasets were used to complete variables with missing values and Rubin’s rules were used to pool estimates in our analysis ( 20 ). Descriptive statistics were used to report on socio-demographic characteristics of the sample and rates of use of each substance. To reduce the complexity of the analysis and increase the interpretability of the results, for objective one, the frequency of using substances such as alcohol, opioids, stimulants, cannabis, hallucinogens, sedatives/hypnotics, tobacco, and other substances (nitrous oxide, cough syrup, caffeine pills) was aggregated to yield one composite variable labelled “frequency of substance use.” To derive this composite frequency of substance use variable, we retained the highest frequency score from among the individual frequency of alcohol, opioid, amphetamine/methamphetamine, cocaine, cannabis, hallucinogens, sedatives, and/or other substance use items. For example, if the client’s responses were ‘not using’ for alcohol, ‘2-4 times a month’ for opioids, ‘2-3 times a week’ for cocaine and amphetamine/methamphetamine, and ‘daily’ for cannabis, their overall frequency of substance use was coded as ‘daily’. Then, the client’s overall frequency of substance use was re-coded into three categories: occasional use (2-4 times a month), frequent use (2-3 times a week and four or more times a week), or daily use. We then calculated the proportion of the sample who were using substances and the proportions using at each frequency category. We also computed these two substance indices as a function of gender, ethnicity/race, and income source. For objective two, we calculated the proportion each route of administration for users of each substance. Multinomial logistic regression was employed to investigate factors associated with occasional, frequent, and daily substance use compared to abstaining from substance use. First, we included demographic and socio-economic variables as predictors in the multinomial logistic regression model without introducing any interactions between variables. Then, two-way interactions between gender and other predictor variables were included in the multinomial logistic regression model, along with demographic and socio-economic variables, history of mental and physical illnesses, suicide risk, and psychosocial stressors. Pooled adjusted odds ratios and corresponding 95% confidence intervals were used to estimate the strength of association. The analysis was conducted utilizing R software (version 4.2.3). Among the participants, 36.1% used a substance daily, while 10.0% and 12.4% used it frequently and occasionally, respectively. A significantly higher prevalence of daily substance use was observed among men (44.7%, p < 0.001) than among women (29.3%), non-binary individuals (32.3%), and those who did not specify their gender (36.7%). Among homeless participants, 69.7% reported daily substance use, which was about two times higher than the prevalence observed among individuals living in private homes, apartments, or rented rooms (35.3%) (see Table 1 ). Multinomial logistic regression modelling revealed that men were more likely to engage in occasional (aOR =1.48, 95% CI: 1.24, 1.76), frequent (aOR =2.12, 95% CI: 1.77, 2.54), and daily substance use (aOR = 2.60, 95% CI: 2.27, 2.97) than women. Also, non-binary individuals or those not specifying their gender had higher odds of occasional (aOR = 1.19, 95% CI: 1.00, 1.41), frequent (aOR =1.23, 95% CI: 1.02, 1.48), and daily (aOR =1.39, 95% CI: 1.21, 1.58) substance use compared to women. In comparison to individuals residing in a private home, apartment, or rented home, individuals experiencing homelessness or residing in other living conditions had increased odds of daily substance use (aOR = 1.93, 95%CI = 1.57, 2.37). Non-White individuals, as compared to those of White ethnicity/race, had higher odds of daily substance use when their income source was from social assistance or disability (aOR = 2.82, 95% CI: 2.08, 3.82), or employment insurance or pension (aOR = 1.68, 95% CI: 1.16, 2.42). The presence of two or more mental illnesses currently or in the past was associated with increased odds of occasional, frequent, and daily substance use compared to no mental health conditions. In comparison to the absence of psychosocial stressors, experiencing two or more psychosocial stressors was associated with higher odds of engaging in occasional, frequent, and daily substance use (see Table 4 ). In this study, about one-third (36.1%) of our sample of individuals seeking mental health and addictions services reported daily substance use. More specifically, the observed prevalence of daily opioid (69.0%) and cannabis (60.1%) use in our study was higher than the prevalence of daily opioid (40%) and cannabis (36%) use reported in a study conducted in Vancouver ( 21 ). The observed differences may be due to variations in the study populations. Our study population consisted of individuals with mental illnesses and addiction, while the Vancouver study focused on individuals who use drugs and experienced chronic pain. Additionally, the Vancouver study had a smaller sample size (1,476 participants) compared to our study, which may contribute to the observed differences. In our study, a large proportion of daily amphetamine/methamphetamine (52.4%), sedative/hypnotics (50.8%), and cocaine (42.6%) use was reported. The high prevalence of daily opioids use among clients of MHA Intake may lead to opioid use disorder and exposes these individuals to overdose risk ( 22 ). The high prevalence of daily substance use in our study can be attributed to the unique nature of our study population: individuals in the early stage of seeking mental health and addiction treatment services. These individuals may use substances daily as a form of self-medication for symptoms of mental health problems ( 23 ). Additionally, the high prevalence of substance use, particularly daily substance use, observed in our study has important clinical implications since using substances can either exacerbate the existing mental health problems or lead to the development of new conditions (e.g., addiction, physical health problems) and drop out once they are engaged in services ( 24 ). Furthermore, the high prevalence of daily substance use in our study implies the importance of an integrated care model that addresses both mental health problems and substance use simultaneously, as well as targeted prevention and intervention strategies aimed at reducing substance use among vulnerable individuals. Also, this finding indicates the need for a broader and more nuanced approach to understanding how substance use interacts with mental health problems and psychiatric medications. The high prevalence of polysubstance use observed in our study (44.4%) has significant clinical implications. Polysubstance use not only exacerbates symptoms of mental health problems but can also interfere with the efficacy of psychiatric medications ( 25 , 26 ). Additionally, the concurrent use of various substances can mask underlying mental health problems and complicate their treatment ( 27 ). Moreover, polysubstance use can increase the risk of overdose, cognitive dysfunctions, and aggressiveness including violent criminal behavior ( 25 ). Using various substances, particularly when novel psychoactive substances are used for adulteration, can lead to in unpredictable health consequences and complicated treatment and harm reduction efforts ( 25 , 28 ). Our study found a significant variation in substance use across socio-demographic characteristics. In line with previous studies ( 29 , 30 ), we found a high prevalence of daily substance use among men (44.7%) compared to women (29.3%) and non-binary/gender non-specified individuals (36.7%). This gender difference can be at least partially attributed to sociocultural factors, including societal norms, expectations, and culturally-sanctioned gender roles ( 30 ). Though the prevalence of daily substance use among women was lower than among men, women are at higher risk of experiencing acute and long term consequences of substance use than men ( 30 ), making the relatively high rates of daily use seen among women in our sample (29.3%) of clinical concern. Among homeless individuals, about two-thirds (69.7%) were engaged in daily substance use. This could be due to the fact that substance use disorder can lead to job loss, disruption of social ties, and loss of housing, which results in homelessness ( 31 ). In Canada, for instance, about 25% of Canadians reported that substance use was responsible for their most recent housing loss ( 32 ). On the other hand, homelessness-related stress may also lead to substance use to cope ( 33 ). An individual’s socio-economic condition significantly influences their substance use and the development SUD. Poverty not only increases substance use but also exacerbates the risks associated with SUD ( 34 ). In line with studies conducted in the USA ( 35 – 37 ), we found a very high prevalence of daily substance use among individuals with income sources from social assistance or disability support (41.3%) and employment insurance or pension (41.1%). This could be due to individuals with economic problems resorting to substance use to cope with difficult life situations and stress related to financial hardships ( 38 ). Additionally, individuals with insecure sources of income may face challenges in accessing mental and addiction treatment services, and as a result, substances may be used as self-medication ( 36 ). We also found that the majority of non-White men with income sources from social assistance/disability (60.9%) and employment insurance/pension (56.4%) engaged in daily substance use. Since non-White races/ethnicities were disproportionately using substances, developing targeted interventions and promoting equitable access to treatment and support services are crucial. In this study, the presence of two or more mental health problems was associated with increased odds of daily substance use. This could be due to the fact that individuals with mental health problems may turn to substance use as a self-medication to temporarily alleviate symptoms of mental illnesses ( 39 ). Additionally, individuals with mental health problems may use substances to cope with stress, as a source of pleasure, and for socialization purposes ( 39 , 40 ). Conversely, in the longer term, substance use affects the brain’s neurobiology and leads to changes in mood, cognition, and behavior, which contribute to the development of mental illnesses or exacerbation of symptoms ( 39 ). We also found that having two or more psychosocial stressors was associated with all levels of substance use: occasional, frequent, and daily. This may stem from the tendency of individuals facing psychosocial stressors to utilize substances as a coping mechanism ( 41 ). Over time, these stressors can increase the risk of initiating substance use and developing addiction ( 42 ). This study is the first provincial-level analysis providing evidence regarding substance use disparities, considering the intersection of gender, ethnicity, and income sources among clients seeking MHA services. This type of study is instrumental in identifying and developing plans to address health equity concerns and instituting intervention strategies that consider the unique needs of various subgroups in society. Also, what makes our study the first in Canada is the unique nature of our study population: individuals seeking MHA services with symptoms of unconfirmed mental health problems and addiction. However, our study has also some limitations. We used a cross-sectional study design that cannot establish a temporal relationship, making it difficult to know if, for example, social disability and mental illnesses precede and/or follow substance use. Moreover, due to social desirability bias, clients may not disclose detailed information about illegal drug use or even deny using it. Additionally, this study may not generalizable to all individuals with mental health problems and addiction across Canada. Moreover, we did not gather data regarding tobacco and other substance use frequency. Additionally, although the prevalence of opioid use was high, we did not gather data regarding opioid overdose and related emergency department visit or hospitalization. Also, we did not use standard tools or DSM-5 criteria to assess mental health problems. The prevalence of daily substance use was high in our sample of individuals seeking mental health and addictions services and varied by participant socio-demographic characteristics of gender identity, ethnicity/race, and/or income source. The highest prevalence of daily substance use was observed among non-White men whose income source was from social assistance or disability support and employment insurance/pension, indicating that prevention and treatment approaches should address these individual and structural level factors contributing to daily substance use. Being homeless/other living conditions (Group home, transition house, jail, halfway house, hostel, and hotel), having two or more medical or mental illnesses (current or past), and experiencing two or more psychosocial stressors were associated with daily substance use; further studies are needed to understand the temporal relationship between these variables and daily substance use. | Review | biomedical | en | 0.999998 |
PMC11697427 | Non-alcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver disease and a significant cost to the global health system . It is anticipated that the prevalence of NAFLD will increase in tandem with the rise in disorders of glycolipid metabolism, as the progression of NAFLD is closely linked to obesity and insulin resistance. Nevertheless, not much is understood about the pathogenesis of NAFLD. Genetic susceptibility variation, environmental factors, insulin resistance, and alterations in the gut microbiome are believed to be involved in the complex interactions . The interaction between these factors results in the excessive accumulation of lipids in liver cells and changes in lipid metabolism, which ultimately contribute to the development of NAFLD. In addition, the microbiota is responsible for the regulation of the balance between pro-inflammatory and anti-inflammatory signals, which can result in inflammation and the development of non-alcoholic steatohepatitis (NASH). A progressive form of NAFLD, NASH has the potential to progress to cirrhosis and hepatocellular carcinoma (HCC) and is presently the most common reason for liver transplantation. While there has been consistent progress in the identification of therapeutic targets, pathogenesis, and epidemiology of the disease, the therapeutic area has experienced the most sluggish progress. There are currently no FDA-approved pharmaceuticals to treat this disease, and it is imperative that appropriate therapeutic targets be identified. Thus, it is imperative to gain a comprehensive understanding of the pathogenesis of NAFLD and the role of the microbiome in its occurrence and development. This knowledge may be beneficial for the diagnosis of the disease, the identification of new therapeutic targets, and the potential for the microbiome to be used as an early clinical warning system for NAFLD. Over the past decade, the gut microbiome has emerged as a significant regulator of substrate metabolism and energy homeostasis in the host. Abnormalities in the structure and, particularly, the function of the microbiota are anticipated to influence the metabolism of the brain, adipose tissue, muscle, and liver. There is a strong correlation between the development of intestinal host-microbial metabolic axes and metabolic diseases and microbial components or metabolites, including lipopolysaccharides, secondary bile acids, dimethylamine, and trimethylamine, as well as compounds produced by carbohydrate and protein fermentation . In recent years, there has been exploration of the potential mechanisms by which the intestinal microbiota regulates NAFLD. The transfer of harmful microbes and their derived metabolites to the liver through a disrupted intestinal barrier is one of the hypothesized mechanisms. This process results in an inflammatory response in the liver and the co-occurrence of steatosis with dietary factors or metabolite-induced interactions. The notion that gut bacteria affect liver homeostasis is derived from the near anatomical interaction between the gastrointestinal tract and the liver, which is frequently referred to as the “gut-liver axis.” The liver is the initial organ to drain the stomach through the portal vein, which is a critical component of the link between host-microbial interactions. Portal blood contains additional microorganisms that actively or passively migrate from the intestines to the bloodstream, in addition to nutrients. This results in the liver being one of the organs that is most susceptible to gut bacteria and bacteria-derived metabolites . Nevertheless, there are limited direct studies on the hepatic microflora, and the precise mechanism of action for the dysregulation of the hepatic microflora that contributes to the development of NAFLD remains unclear. This is expected to result in alterations in the pertinent terminal metabolites in the liver tissues of patients. At present, there is a lack of consensus regarding the specific microorganisms and metabolites present in patients with NAFLD. The identification of specific NAFLD metabolome phenotypes can assist in the development of additional diagnostic tools and therapeutic interventions. Our research directly investigates the microbes and their metabolites in the liver. The basic biological characteristics of microbial composition and metabolomics in the liver tissues of NAFLD patients may offer valuable insights into the disease mechanisms and physiological functions of the host. Consequently, the objective of this investigation was to examine the microbial composition and metabolite characteristics of liver tissue in two distinct coyotes: patients with NAFLD and normal controls. Additionally, the study sought to determine the impact on NAFLD through the regulatory influences between the two. We enrolled 13 patients (≥18 years) who were newly diagnosed with NAFLD at Yan’an Hospital in Kunming, Yunnan Province, from July 2020 to March 2024. A control group of 12 non-NAFLD subjects, matched by age, sex, and ethnicity, was concurrently recruited. All participants in the control group were asymptomatic volunteers with a standard diet and no recent or chronic illnesses. Liver biopsies were conducted on participants exhibiting abnormal pre-specified imaging criteria, and the biopsies were evaluated blindly, with outcomes determined by the consensus of two expert pathologists. The incidence of NAFLD was ascertained via biopsy. Table 1 presents the demographic information of the subjects enrolled in the study. All subjects granted informed consent to partake in the study. The Medical Ethics Committee of Yan’an Hospital Affiliated to Kunming Medical University approved this study. The FastDNA ® Spin Kit for Soil (MP Biomedicals, China) was employed to extract the genomic DNA of liver tissue samples. To assess the purity and integrity of the genomic DNA, it was extracted using 1% agarose gel electrophoresis. Nanodrop 2000 was employed to ascertain the purity and concentration of genomic DNA. The V3–V4 hypervariable region of the 16S r RNA gene was amplified by primers Primer F = Illumina adapter sequence1 + GTGCCAGCMGCCGCGGTAA and Primer R = Illumina adapter for each sample sequence2 + GGACTACHVGGGTWTCTAAT. The Illumina Miseqbenchtop sequencer (Illumina, United States) was employed to sequence the amplified libraries, which were constructed using purified PCR products. A double-terminal sequencing strategy of 2 × 250 bp was employed. In QIIME2 , the original sequencing data underwent quality filtering, noise reduction, splicing, and dechimerization. Feature tables and representative sequences were subsequently generated. Additional analysis was conducted in the DADA2 (v 1.6.0) pipeline , and amplicon sequence variants (ASVs) were acquired. The confidence threshold was 0.8, and the RDP classifier algorithm was used to compare the taxonomic attribution of ASV representative sequences with the Ribosomal Database Project (RDP) (version 11.5) database. Alpha diversity analysis was implemented to evaluate the diversity and richness of each group. Species, observed, and Chao1 The richness was analyzed using observed species and ACE, while the diversity was analyzed using Shanno, Simpson, InvSimpson, and Coverage. The vegan package from the R project (v2.5.6) was employed to execute the calculations, and the ggplot2 package (v3.3.0) was used to visualize the results. The difference in ASV composition between various samples is measured by beta diversity, which is assessed using principal component analysis (PCA) and principal coordinate analysis (PCoA). Both of these methods are appropriate for the supervised analysis of high-dimensional data. The vegan package (v2.5.6) employs the similarity analysis (ANOSIM) function to determine the importance of beta diversity. Metastats software was employed to compare the microbiota characteristics of healthy controls and NAFLD patients. The non-targeted metabolites in liver samples were identified using liquid chromatography-mass spectrometry (LC-MS). Weigh the correct amount of sample in a 2 mL centrifuge tube and add 1,000 μL tissue extract [75% (9:1 methanol: chloroform)]. The steel ball was filled with 25% H 2 O (stored at −20°C) and placed in a tissue grinder. It was ground at 50 Hz for 60 s, ultrasonic at room temperature for 30 min, placed on ice for 30 min, centrifuged, concentrated, and dried. Twenty microliters of each sample was combined into QC samples, while the remaining samples were examined using LCMS. The samples were separated using an ACQUITY UPLC ® HSS T3 1.8 μm (2.1 × 100 mm) column (Waters, Milford, MA, United States) at 40°C. Mass spectrometry was carried out at a flow rate of 0.30 mL/min. The mass spectrum data were collected using a Thermo quadrupole-electrostatic field orbital trap high resolution mass spectrometer (Thermo Fisher Scientific, United States) with an electrospray ion source (ESI) that may operate in either positive or negative ion mode. The original data is first converted to mzXML format using MSConvert in the ProteoWizard package . In addition, RXCMS was utilized for peak detection, filtering, and alignment. Metabolites are identified using exact mass numbers (<30 ppm) and MS/MS fragmentation patterns, then matched with HMDB, MassBank, LIPID MAPS, mzCloud, and KEGG. To detect differences between groups, we employed orthogonal projection to potential structure discriminant analysis (OPLS-DA) or the discriminant analysis model (PLS-DA). To reduce the risk of overfitting, the model parameters R 2 and Q 2 were calculated to determine the model’s interpretability and predictability. The OPLS-DA model calculates the variable importance in projection (VIP). The p -value was calculated using the paired t -test in one-dimensional statistical analysis, with p1 serving as the screening criterion for meaningful differential metabolites. Metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG). The route analysis was carried out on the MetaboAnalyst7 database. Quantitative data conforming to normal distribution were expressed as mean ± SD, and t -test was used for comparison between groups. Quantitative data of non-normal distribution were expressed as median (Q1, Q3), and Mann–Whitney U test was used for comparison between groups. p < 0.05 was considered to be statistically significant. All statistical analyses were performed using the SPSS Statistics 27 software package. p < 0.05 was considered significant difference. Mean age, fasting blood glucose, ALT, AST were not significantly different between the two groups. The number of NAFLD group with type 2 diabetes mellitus, BMI, total cholesterol level, degree of steatosis, lobular inflammation, fibrosis stage, NAFLD activity score (NAS) were significantly higher than the control group. The demographic information of the participants included in the study is shown in Table 1 . A total of 2,693 high-quality reads were obtained from 25 liver tissue specimen samples. Furthermore, a total of 823 ASVs were obtained. Of these ASVs, 143 were shared among the three groups, while 357 and 323 ASVs were specific to the control and NAFLD groups, respectively . We used the LEfSe approach to find the biometric traits that separate the two sample groups. The LEfSe results confirmed that the bacteria most likely to explain the difference between the two groups are the order_Enterobacterales (genus_ Escherichia - Shigella ), order_Mycobacteriales (family_Nocardiaceae, family_Nocardiaceae, genus_ Rhodococcus ), order_Pseudomonadales (family_Pseudomonadaceae, genus_ Pseudomonas ), and order_Flavobacteriales (family_Weeksellaceae, genus_ Chryseobacterium ). The NAFLD group was evidently devoid of the order_Xanthomonadales, order_Sphingomonadales (genus_ Sphingobium ), and order_Lysobacterales (genus_ Stenotrophomonas ) . Alpha diversity analysis was performed to determine the richness and diversity of species in each group. The primary metastats used in the Alpha diversity analysis were observed, Chao 1, ACE, Shannon, and Simpson. The results indicated that there was no significant difference in flora richness between the NAFLD group and the control group ( p > 0.05) . PLS-DA analysis showed that there was significant separation of liver microbial community structure between NAFLD and control groups, as shown in Figure 3B . ANOSIM based on UniFrac distances was calculated . At the order level, the control group had considerably larger ratios of Lactobacillales (16.34% vs. 7.66%), Xanthomonadales (3.70% vs. 1.25%), and Sphingomonadales (2.25% vs. 0.98%) than the NAFLD group. The NAFLD group had a larger number of Enterobacterales (12.10% vs. 3.42%), Corynebacteriales (8.04% vs. 6.13%), and Bacteroidales (2.07% vs. 0.79%) than the control group . The abundance values of Xanthomonadales and Sphingomonadales in the NAFLD group were considerably lower than those in the control group ( p = 0.004, p = 0.008), whereas the abundance values of Flavobacteriales in the NAFLD group were significantly greater than those in the control group ( p = 0.019) . Escherichia - Shigella (10.07% vs. 0.99%), Rhodococcus (6.76% vs. 3.53%), Enterococcus (8.89% vs. 1.03%), Helicobacter (2.72% vs. 0.07%), and Pseudomonas (15.83% vs. 10.05%) were significantly more prevalent in NAFLD than in the control group in terms of genus. Stenotrophomonas (3.22% vs. 1.24%), Lawsonella (1.06% vs. 0.21%), and Helicobacter (2.72% vs. 0.07%) were the most prevalent bacteria in the control group . The control group exhibited a substantially lower abundance of Rhodococcus , Escherichia - Shigella , and Sphingobacterium than the NAFLD group ( p = 0.007, p = 0.009). The abundance value of Pseudomonas ( p = 0.038) increased considerably, and it was classified as phulum_Pseudomonadota . Conversely, the abundance values of Stenotrophomonas , Sphingobium , and Lawsonella in the control group were significantly higher than those in the NAFLD group ( p = 0.014, p = 0.011, p = 0.031). The abundance of Turicella in the phylum actinomycetota and the family Corynebacteriaceae also increased significantly ( p = 0.017) . The OPLS-DA results demonstrated that the hepatic bacterial flora exhibited differences in metabolite profiles between the NAFLD group and the control group (R2Y = 0.458, Q2Y = 0.994). This suggests that the bacterial metabolites in the liver of NAFLD were transformed, and the metabolic level differences between the two groups could be clearly observed. Four hundred two annotable differential metabolites were screened from the NAFLD group and control group using VIP >1 and p < 0.05 as the first principal component of the OPLS-DA model. This included 78 up-regulated metabolites and 14 down-regulated metabolites . Five metabolites with significant differences in up-regulation and down-regulation between the NAFLD group and the control group were evaluated after further adjusting the screening conditions ( Table 2 ). The majority of them are carboxylic acids and their derivatives, steroids and steroid derivatives, azagands and complexes, fatty acyl groups, fatty acids and conjugates, linolenic acid metabolism, nucleotide metabolism, glycerophospholipids, and glutathione synthase. Significant differential metabolism is observed in the lipid metabolism of these pathways . The metabolic pathway enrichment results were derived from the KEGG pathway database. The findings indicated that the primary metabolite enrichment pathways were linoleic acid metabolism, ABC transporter, phagocytosis, necrotic apoptosis, and calcium signaling pathways. Linoleic acid metabolism was the most significant contributor to metabolic differences . The relationship between hepatic flora and metabolite groups was analyzed using Pearson correlation analysis. In order to investigate potential sources of metabolites in the liver, we conducted a generic-level analysis of the correlations between the liver flora and metabolites. It was determined that the bacteria Lawsonella , Stenotrophomonas , and Sphingobium , which are abundant in the liver of the control group, and the bacteria Rhodococcus , Chryseobacterium , and Escherichia - Shigella , which are significantly more abundant in the liver of NAFLD patients, have a strong correlation with differential metabolites, as illustrated in Figure 6 . Lawsonella was positively correlated with glutathione and benzaldehyde, and negatively correlated with carboxyspermidine, (2R) -2-hydroxy-3-(phosphonatooxy)propanoate. Pipecolic acid and myriocin were positively correlated with Stenotrophomonas . 13-oxoODE is negatively correlated with Sphingobium , while lithocholic acid glycine conjugate is positively correlated with Escherichia - Shigella and Sphingobacterium and negatively correlated with Sphingobium and Stenotrophomonas . Rhodococcus was positively correlated with dehydroepiandrosterone. Chryseobacterium and Lawsonella were positively and negatively correlated with carboxyspermidine. There is increasing interest in elucidating the microbiome’s role in the pathophysiology of MAFLD, with numerous gut bacterial communities identified in various studies as components of microbial patterns in NAFLD. Intestinal flora significantly influences health, and its imbalance is associated with the expedited advancement of NAFLD. Intestinal bacteria and their metabolites directly access the liver via the portal vein and indirectly influence the onset and progression of NAFLD, either directly or through signaling pathways mediated by their constituents . Our research directly examined the microbiota in human liver tissue, minimizing the confounding influence of intestinal microflora, with the diagnosis of NAFLD relying on liver imaging and biopsy. We concentrated on NAFLD and the control group, demonstrating that the bacterial DNA signature in the liver of NAFLD is greatly influenced by the host phenotype. The hepatic bacterial community composition in the two groups was equivalent at many levels; nevertheless, substantial variations were noted in abundance analysis, diversity measurement, and the predictive utility of bacterial DNA. Distinct metabolites in the samples from the two groups influenced the onset and progression of NAFLD, and we additionally identified the association between the varying bacterial communities and metabolites. Nonetheless, individuals with NAFLD (validated by liver needle biopsy) exhibited no significant differences in the Metastats analysis of α diversity in liver bacteria when compared to controls without NAFLD. In contrast, other researchers reported diminished bacterial diversity in NAFLD subjects, potentially attributable to the sample size in our study. In our study, the disparity between the two groups was substantial, although the variance within the two groups was minimal. Consequently, a notable disparity in the amount of hepatic flora existed between the two groups at varying levels. The prevalence of Enterobacteriales, Corynebacteriales, and Flavobacteriales in the NAFLD group, together with Escherichia - Shigella , Rhodococcus , and Chryseobacterium in the NAFLD group, was significant. The levels were markedly elevated compared to the control group, and were devoid of Lactobacillales, Xanthomonadales, Sphingomonadales, Stenotrophomonas , Lawsonella , and Sphingobium , which constituted a considerably smaller proportion than in the control group. This may facilitate the progression of the condition. Escherichia - Shigella has been shown to induce steatohepatitis and fibrosis in non-obese rats through the secretion of msRNA 23,487 . Escherichia - Shigella is linked to steatosis and necrotic inflammatory activity, whereas Shigella is related with fibrosis and necrotic inflammatory activity . Rhodococcus is intimately associated with the phenotype of NAFLD and can effectively differentiate between NAFLD patients and healthy non-NAFLD individuals . Chryseobacterium is a non-fermentative gram-negative bacterium. It is a conditional pathogen that remains non-infectious under typical conditions but may induce infection when the immune system is compromised. Flavobacteriaceae and Porphyromonadaceae have markedly proliferated in the intestines of animals subjected to a high-fat diet, although have not been documented in human intestines . Conversely, Stenotrophomonas rectified ecological imbalances in individuals with NAFLD, stabilized inflammatory cytokine expression and mucosal immune function, and mitigated NAFLD and its associated hazards . Lawsonella participates in the metabolic pathways of fatty acids, nucleotides, and carbohydrates. Bacteroidetes and bacilobacteria are believed to significantly contribute to intestinal homeostasis, comprising over 90% of the bacteria present in healthy human intestines , a finding corroborated by our research results. NAFLD patients have a deficiency in the protective effects of beneficial bacteria, which are essential for combating inflammation and stabilizing hepatic immunity, hence exposing the liver to bacteria that readily induce immune suppression and inflammatory responses. Analysis revealed that bacterial metabolites in the liver of both groups were highly enriched, with a notable difference between the two groups. N1-Acetylspermidine, an acetyl-derivative of polyamines, is up-regulated in the liver of NAFLD patients and may serve as a valuable biomarker linked to the course of nonalcoholic fatty liver disease . Irregular steroids and steroid derivatives dehydroepiandrosterone may influence the progression of NAFLD and engage in lipid metabolism, however the function of its signaling in the pathogenesis of NAFLD is not yet elucidated . 13-oxoODE is an oxidized lipid derivative of linoleic acid (LA) and correlates with the histological severity of NAFLD . GDP participates in the metabolism of fructose and mannose. Conversely, glutathione, a down-regulated metabolite, has the potential to ameliorate NAFLD . The metabolic route exhibiting the most significant variation is linoleic acid metabolism, a process of fatty acid synthesis and degradation, which regulates blood glucose levels and facilitates the oxidation of saturated fatty acids while diminishing the synthesis of cholesterol and triacylglycerol. To elucidate the pathogenicity of metabolites, it is essential to comprehend the bacterial origins of these metabolites and their interrelationships. To further investigate the bacterial origins of metabolites in the liver, we performed a comprehensive examination of both bacteria and metabolites within the liver. Lawsonella is classified within the phylum Actinomycetota, class Actinomycetes, order Mycobacteriales, and family Lawsonellaceae. Benzaldehyde, which has a positive correlation, inhibits fat formation in normal human liver cells and reduces the onset of NAFLD, potentially linked to the metabolic product aldehyde oxidase 2. Furthermore, glutathione is favorably correlated with the production of glutathione synthetase, which plays a role in glutathione metabolism, and the synthesis of glutathione mitigates NAFLD . Lawsonella exhibits an inverse correlation with the metabolite (2R)-2-hydroxy-3-(phosphonatooxy)propanoate ethyl propionate, which is found in all eukaryotes, ranging from yeast to humans. Ethyl propionate is connected with various known ailments, including autism, irritable bowel syndrome, ulcerative colitis, and non-alcoholic fatty liver disease. Furthermore, it has been linked to congenital metabolic problems, such as celiac disease. Ethyl propionate, a volatile organic molecule, has been recognized as a fecal biomarker for C. difficile infection . Stenotrophomonas is classified within the phylum Pseudomonadota, class Gammaproteobacteria, order Lysobacterales, and family Lysobacteraceae. Ecological disturbances in NAFLD patients can be ameliorated by stabilizing the expression of inflammatory cytokines and enhancing mucosal immune function . The former had a favorable correlation with pipecolic acid and myriocin, respectively. Metabolomic studies of serum and liver indicated that the former contained a non-coding amino acid. The study findings demonstrated that early consistent exercise may improve the anti-inflammatory immune response in middle-aged male mice via epigenetic modulation of immune metabolism. The hepatic production of pipecolic acid is pivotal , being intricately linked to fatty acid synthase and fatty acid desaturase, and constitutes a significant component of the lipid metabolism route. Insufficient pipecolic acid can result in fatty acid oxidation disorder, bile acid synthesis defect, and long-chain fatty acid transport deficiency, culminating in lipid metabolism problem. The latter suppressed ceramide and lipid buildup while enhancing fibrosis in liver tissue samples from rats subjected to a high-fat diet (HFD), and myriocin also markedly ameliorated liver inflammation and apoptosis in HFD rats . Sphingobium is classified under phylum Pseudomonadota, class Alphaproteobacteria, order Sphingomonadales, and family Sphingomonadaceae. The negatively correlated 13-oxoODE, an oxidized lipid derivative of linoleic acid, correlates with the histological severity of NAFLD and facilitates the evolution of NASH by elevating oxidized fatty acids . Rhodococcus is classified within the phylum Actinomycetota, class Actinomycetes, order Mycobacteriales, and family Nocardiaceae. Aberrant synthesis and metabolism of positively linked substances dehydroepiandrosterone and catecholamines may be linked to the onset of NAFLD . Levels of 16 hydroxydehydroepiandrosterone sulfate (16-OH-DHEA-S) elevated with the advancement of fibrosis . Chryseobacterium is classified under phylum Bacteroidota, class Flavobacteriia, order Flavobacteriales, and family Weeksellaceae. Carboxyspermidine, positively associated with Chryseobacterium , serves as a novel biomarker for NAFLD progression, with elevated levels correlating with the condition . 11-Dehydrocorticosterone, which is positively correlated with metabolic syndrome , exhibits a substantial association with NAFLD and a negative correlation with glutathione. The glycine conjugate of lithocholic acid is elevated in the intestines of patients with NAFLD , potentially linked to fatty acid oxidation dysfunction and positively connected with Escherichia - Shigella and Sphingobacterium . It had a negative correlation with Sphingobium and Stenotrophomonas . Phylum Bacteroidota, class Sphingobacteriia, order Sphingobacteriales, family Sphingobacteriaceae Escherichia - Shigella is classified within the phylum Pseudomonadota, class Gammaproteobacteria, order Enterobacterales, and family Enterobacteriaceae. This work used multi-omics to connect hepatic microbiota and metabolites. Correlation analysis indicated that the liver microbiota not only modulates inflammation and immunity but also regulates lipid synthesis, metabolism, and transport via associated metabolites, influences hepatic fat accumulation, and significantly impacts the enhancement or exacerbation of inflammation and fibrosis. This study highlights that metabolic disorders resulting from bacterial imbalance in the liver are significant contributors to the pathogenesis of NAFLD, and investigating the relationship between specific metabolites and bacterial flora may ultimately aid in regulating bacterial flora function in NAFLD treatment. This study has certain drawbacks. The limited sample size necessitates external validation via larger samples and multi-center experiments. However, confounding variables can be efficiently managed by enlisting healthy participants matched by age, gender, and ethnicity. This study was a case–control study. Although our data indicate a functional relationship among the bacteriome, metabolome, and illness, causality remains undetermined, and the mechanism behind this functional correlation requires additional investigation. In conclusion, examining the correlation between the human hepatic microbiota and NAFLD reveals distinct bacterial communities and metabolic traits, hence presenting new opportunities for researchers to investigate the possibly advantageous effects of specific nutrient supplementation. This study establishes an experimental foundation for developing prospective diagnostic and therapeutic targets in the future. | Review | biomedical | en | 0.999997 |
PMC11697428 | Advancements in diffusion magnetic resonance imaging (dMRI) have facilitated our understanding of the brain's intricate architecture and organization . By measuring the diffusion of water molecules within the brain tissue, dMRI provides valuable information to investigate the connectivity and assess the white matter (WM) microstructure of pathways in the brain. Voxels in the WM can contain different axonal fiber populations with complex configurations . Each one of these populations is called fixel , which denotes the discrete component of a fiber element . Fixels and their properties, like orientation and tissue metrics, are fully determined by the voxel in which they reside. Local modeling allows for estimating these fixel properties at each voxel of the dMRI data . Tractography can use these locally estimated fixel orientations to reconstructs the trajectories of the WM, which are often called streamlines . Additionally, tractometry has emerged as a useful method for quantitative analysis of the WM pathways. It encompasses the streamlines obtained with tractography at the macroscopic level with the metrics obtained from a local modeling method at the microscopic level. This combination enables the analysis of microstructural changes by extracting quantitative metrics along specific WM anatomical tracts. Tractometry insights could potentially serve as a valuable tool for investigating WM characterization and degeneration associated with neurological disorders, such as multiple sclerosis (MS) , Alzheimer's disease , and traumatic brain injury , among others. Diffusion Tensor Imaging (DTI) is a single fiber method traditionally used to estimate properties of the fixels, averaging the diffusion properties of all the fixels within a voxel. Thus, DTI results in a loss of important information of the fixels, especially when different fiber populations with different properties or lesions are present within the same voxel. This presents an important problem in estimation because WM tissue contains between 66% to 90% of voxels with crossing fibers . DTI Limitations motivated the development of more advanced acquisition and local modeling techniques. Multi-shell H igh A ngular R esolution D iffusion I maging (HARDI) was originally developed to provide anisotropy measures beyond DTI metrics that are more robust to crossing fibers and sensitive to WM alterations, making also tractography more robust . HARDI allowed to develop techniques that estimate multiple fixels within a voxel. Notable examples of these techniques are: Q - b all I maging (QBI) , the M ulti- T ensor M odel (MTM) , and C onstrained S pherical D econvolution (CSD) . In particular, MTM is a straightforward extension of DTI that represents each one of the fiber populations in the voxel by a different diffusion tensor. However, the estimation of MTM parameters is an ill-posed challenging problem, that requires very high SNR data and large computational resources, restricting it's routine clinical use. The dMRI signal arising from the WM is composed of several compartments. Thus, taking advantage of HARDI, several techniques were developed to decompose the dMRI data into contributions from various compartments. An example of these multi-compartment methods procedures is the model in Novikov et al. , which depicts the dMRI data as a combination of I ntra- C ellular (IC), E xtra- C ellular (EC) and ISO tropic (ISO) contributions. Other hybrid methods are based in the MTM like the F ree- W ater DTI (FW-DTI) , which fits for each voxel a bi-tensor model including an anisotropic tensor for tissue compartment and an isotropic tensor for a free water compartment. The DI stribution of A nisotropic M icr O -structural e N vironments with D iffusion-weighted imaging (DIAMOND) and the M ulti- R esolution D iscrete- S earch (MRDS) are more general MTM-based methods, which fits up to three restricted anisotropic tensors for the restricted and hindered diffusion compartments and one isotropic tensor for the free diffusion compartment. DTI metrics are the most widely used metrics for tractometry . Although DTI metrics have the potential to be biomarkers, they have inconsistent sensitivity to characterize the WM as they are easily biased. For example, the common F ractional A nisotropy (FA) metric is informative about changes in WM microstructure caused by pathology, but crossing fibers bias it. FA decreases in fiber crossing voxels because oblate tensors are obtained, which leads to an alteration in the resulting FA tract profile in the tractometry as shown in Figure 1 . These alterations can be confused with alterations derived from WM degeneration, which is also illustrated in Figure 1 , leading to erroneous or ambiguous interpretations. Moreover, in the presence of crossing fibers together with pathology, FA increases, which could seem counterintuitive. However, this could happen, when only one of the fiber populations in the crossing is affected by the pathology, then the resulting single-tensor may become sharper, see Figure 2 . Approaches that have studied other DTI metrics, like the radial diffusivity (RD) metric, have shown that RD is a promising biomarker for demyelination . However, they have reported that RD can be inconsistent, presenting challenges in its reliability and reproducibility and resulting in misleading results. Besides, co-existing inflammation, edema, and crossing fibers can significantly impact on the DTI metrics at the same time . Multi-fixel methods have further expanded the scope of tractometry, resulting in tract-specific analyses less impacted by crossing fibers. Remarkable examples are the Automated Fiber-tract Quantification , the Connectivity-based Fixel Enhancement , the Fixel-Based Analysis framework , the Tractometry_flow pipeline and, recently, the UNRAVEL framework . Other tractometry frameworks have combined DTI metrics with other metrics including fixel-based metrics like the A pparent F iber D ensity (AFD). For example, the framework called Profilometry performs a simultaneous analysis of DTI metrics and other metrics, resulting in tract profiles as parameterized curves in a multi-dimensional space. Nonetheless, the crossing fibers bias in DTI metrics still limits it. Besides, these types of multi-fixel methods face several challenges and limitations. As example, frameworks informed with CSD metrics such as AFD, while sensitive, do not have a straightforward biological interpretation; moreover, they could be biased as CSD employ a fixed response function across the entire WM . On the other hand, previous tractometry results using MTM fixel-based metrics are not free of limitations. For instance, they need more complex multi-shell dMRI acquisitions and are limited to a maximum of 2 fixels per voxel . This is insufficient in many brain regions, e.g. the centrum semiovale, where 3 fiber populations cross from the corticospinal tract, corpus callosum, and superior longitudinal tract intersect. Additionally, fixel-FA estimation has shown to be affected by high levels of noise and inconsistent through scan-rescan experiments as a consequence of MTM fitting being numerically unstable . MTM-based methods generally struggle to accurately estimate the required number of tensors per voxel ( N ). These methods tend to overestimate the value of N as a direct consequence that a single diffusion tensor does not properly represent the dMRI signal (even when a single fixel is present) for b-values higher than 1ms/μm 2 , needing more tensors to fit the per voxel signal . Between the MTM-based methods, MRDS offers a balanced trade-off in terms of model complexity and accuracy when using short-acquisition-time clinical multi-shell dMRI data . MRDS has proven to be a noise-robust and accurate multi-fixel method for estimating the directions of the fixels and their metrics. In addition, MRDS has been histologically validated in a rat model of unilateral retinal ischemia in which only one of the optic nerves was damaged. This nerve lesion was correctly detected by MRDS at the region where the optic nerves cross (optic chiasm) . Moreover, MRDS has shown to be capable of recognizing 3 fiber populations in regions-of-interest (ROI) like the centrum semioval when using clinical in vivo multi-shell dMRI data. A recent work has proposed to use the T rack O rientation D ensity I maging (TODI) as a useful spatial regularizer for a more accurate and robust estimation of N in MRDS. The T rack O rientation D istribution (TOD) image estimated with TODI presents an increased amount of spatial consistency compared with the fiber orientation distribution (FOD) image obtained with constrained spherical deconvolution (CSD) . In this paper, we propose a novel tractometry pipeline to address several current limitations of tractometry informed with multi-fixed methods. Our proposed pipeline combines multi-tensor fixel-based metrics estimated with MRDS and the Tractoflow and Tractometry_flow pipelines. The proposed pipeline provides fixel-based tensor metrics that are robust to crossing fibers and noise. Provided fixel-based metrics have the potential to be biomarkers for pathologies like demyelination and can be useful for the characterization and study of underlying WM anomalies in patients with pathologies such as MS. Most of the previous tractometry studies in pathology used DTI metrics, then, our multi-tensor pipeline results can be straightforwardly situated in their context and compared with them. Finally, the pipeline is tested on both synthetic phantom dMRI data and clinical dMRI in vivo data from a large healthy control and MS groups with a scan-rescan experiment, highlighting the robustness and potential of our approach when studying WM anomalies in patients with such neurological disorders. A synthetic phantom was generated based on the geometry of a previously published dMRI phantom , see Figure 3 . The size of the phantom is 50 × 50 × 50 voxels with an isotropic dimension of 1.0mm. Similar to Caruyer et al. , our synthetic phantom has 20 distinct bundles showing a complex fiber crossing configuration and volume contamination with C erebro S pinal F luid (CSF). Each bundle in the phantom exhibits unique diffusivities and axonal dispersion characteristics. The diffusivities of each bundle were tuned to mimic those found in healthy human brains . We have simulated a phantom dMRI signal for each individual bundle and the whole volume signal without noise and without dispersion. Then, DTI was fitted to each individual bundle signal as well as the whole dMRI signal, and the tensor metrics were extracted. This simulated dataset was employed as Gold Standard (GS) to compare results with the experiments on in-vivo dMRI data. A multi-compartment model also known as Standard Model (SM) , was adopted to simulate this phantom signal by including three types of microstructural environments: intracellular (IC), extracellular (EC), and isotropic (ISO). Each environment was simulated with a given volume fraction denoted by f ic , f ec and f iso , respectively. The IC space was modeled with cylinders of zero radius ( sticks ), the EC space with a cylindrically symmetric tensor ( zeppelin ), and finally, the ISO space was modeled as a free diffusion compartment ( ball ) . Three datasets were generated with known GS. The radial EC diffusivities were simulated based on Fieremans et al. . Thus, the EC space tortuosity D 0 / D ec ⊥ , which quantifies how the diffusion is affected by cellular and extracellular structures within tissue, was defined as the ratio of free diffusivity D 0 = 2 μ m 2 / ms over the EC diffusivity D ec ⊥ . Therefore, the intracellular volume fraction f ic was most sensitive to axonal loss. Besides, it was most sensitive to demyelination. The first dataset incorporated D ic ∥ and D ec ∥ diffusivities within a healthy range sampling a Gaussian distribution with a mean of 2μm 2 /ms and variance of 0.01μm 2 /ms, while D ec ⊥ = 0 . 48 μ m 2 / ms , f ic = 0.65 and f ec = 1− f ic . On the other hand, the second dataset simulated, in some bundles, conditions associated with demyelination on MS. Specifically, in regions with demyelination f ic = 0.55 and D ec ⊥ = 0 . 71 μ m 2 / ms , while in regions without damage, the values remained the same as in the first dataset. Finally, our third dataset simulated conditions related to axonal loss. For this case, f ic = 0.35 and D ec ⊥ = 0 . 59 μ m 2 / ms in regions with lesion and regions without lesion maintained the same control values as the first dataset. All datasets were generated with a high and realistic noise level ( SNR = 12). The isotropic diffusivity D iso and volume fraction f iso were fixed equal to 3μm 2 /ms and 0.05, respectively. Axonal dispersion was modeled with a Watson distribution . The κ value of each bundle used as the parameter for the Watson distribution was sampled from a Gaussian distribution with mean 20 and variance 0.01. Lastly, we used the same protocol of the in-vivo data described below. Two groups of participants were recruited from the University of Sherbrooke (UdS) and the Center Hospitalier Universitaire of Sherbrooke (CHUS) community. The first group was a healthy control (HC) group with 26 adults, and the second group has 22 relapsing-remitting MS patients. Both groups had a gender proportion of 75% women and 25% men. Diffusion MRI data was acquired using a clinical 3T MRI scanner (Ingenia, Philips Healthcare) using a 32-channel head coil. Each subject was scanned 5 times over 6 months and a 4-week interval (±1 week) with a total acquisition time of 20 minutes for each session. MRI acquisitions were obtained for each subject at roughly the same time daily to mitigate potential diurnal impacts, i.e. morning subjects underwent all sessions in the morning with a permissible 2-3-hour variation. Finally, 6 of the 26 healthy control subjects were discarded for several reasons, including problems during the scan or processing. Thus, the HC group employed for the experiments had 20 subjects. All MRI images were aligned respect to the anterior commissure-posterior commissure plane (AC-PC), which is an anatomical reference defined by two small bundles in the brain. One bundle located in the front part of the brain, and the other in the back. This ensured consistency in the orientation and position of the images when analyzing them across scans and subjects. In addition, 3 type of data were included: The preprocessing of the dMRI data was performed using the Tractoflow pipeline . This includes the brain and WM masks extraction, T1 registration and tractography. The dMRI data was denoised using the MP-PCA method. Brain deformation induced by magnetic field susceptibility artifacts was corrected . Motion artifacts corrections and slice-wise outlier detection were performed . Image intensities were normalized to reduce the bias by the magnetic field . The brain mask was obtained from the bet command from FSL . Specifically, Tractoflow performed an extraction on the b = 0 image. Then, the obtained mask was applied to the whole DWI to remove the skull and prepare the DWI for the T1 Registration. Tractoflow performed brain extraction after Eddy/Topup correction to have a distortion-free brain mask. Tractoflow processed the T1 image using eight different steps. First, Tractoflow preprocessed the T1 image including denoising, correcting and resampling steps for the T1 image. Then, the T1 image was registered on the b = 0 and FA images using the nonlinear SyN ANTs (antsRegistration) multivariate option, where the T1 image is set as moving image and the b0 and FA images are set as target images. After registration, Tractoflow extracted gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) partial volume masks using fast from FSL. These maps were used to compute the exclusion and inclusion maps for tractography , which are anatomical constraints for the tracking . The fiber tracking was also done using the Tractoflow pipeline. The seeding mask employed in the tractography was the obtained WM mask. The tractogram was generated employing the anatomically constrained particle filter tracking (PFT) algorithm . This algorithm utilized the FOD image obtained with the Multi-Shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD) , along with the inclusion map, exclusion map, and a seeding mask to guide the tractography process. The seeding mask employed in the tractography was the extracted WM mask. A fully detailed explanation of the whole Tractoflow pipeline can be found in Theaud et al. . Additionally, Tractoflow includes strategies to avoid premature track termination when tracking MS patients. The seeding mask in MS patients was filled using a lesion-corrected WM mask. During the tracking process, if a peak in the FOD image is coherent and well-defined, the tracking continues even if the voxel is inside a WM lesion, increasing anatomical accuracy and consistency in obtained tractograms for MS patients. This step can be omitted as the tractogram can be generated with any fiber tracking technique. The tractogram from fiber tracking was then processed with the TODI method to obtain a TOD image. Subsequently, the resulting TOD image was segmented to produce discrete fixels . Then, the fixel-based image was converted into a Nu mber of F iber O rientations (NuFO) scalar image, where the number of fixels was counted in each voxel. A threshold peak amplitude was utilized to prune the spurious peaks, such that any lobe for which the maximal peak amplitude was smaller than 0.1 was omitted. Finally, this NuFO image was used as input MOSEMAP in MRDS, which is better described in the next step (Section 2.3.4). The MTM represents the diffusion signal S i at each voxel as: where M is the number of unitary gradient orientations g i , N is the number of tensors, and α j is the fraction of the j -th diffusion tensor D j . Assuming axial symmetry, then D j is parameterized by the unitary principal diffusion direction (PDD) θ j , the axial ( λ j ∥ ) and radial ( λ j ⊥ ) diffusivities, such that: The bundle-specific parameters of the MTM were non-linearly estimated using the MRDS method for N = 1, N = 2, and N = 3, resulting in 3 multi-tensor fields (MTFs), see Figure 4 . More than three fixels can be estimated, albeit with increased computation time and reduced precision for the estimated parameters. Besides, N ≤ 3 has been reported to be a reasonable threshold . Initial diffusivities for the non-linear estimation of parameters in Equation 2 were obtained from DTI at brain WM voxels with a high probability of containing only one fiber. Given that high b-value diffusion signals are not fully represented with the diffusion tensor, causing an overestimation in N . Thus, the original statistical model selection in MRDS, which provides a model selection map (MOSEMAP) with the value of N that better describes the diffusion signal at each voxel, is replaced for the NuFO scalar map obtained with TODI in step 2.3.3. The TODI NuFO scalar map merges the 3 MTFs into a reevaluated and refined MTF with the spatially smoothed information provided by the tractogram , see Figure 4 . From this improved MTF, fixel-FA, fixel-MD, fixel-AD, and fixel-RD maps were generated. The fixel-FA map maintains the same spatial dimensions as the original DWI Each voxel may contain multiple tensors. Then, an extra layer was added to store the multiple fixel-FA values obtained at each voxel. The scalar fixel-FA value was obtained for every tensor within a voxel, computed as the standard FA . This resulted in a 4D-dimensional fixel-FA map. The computation of fixel-RD, fixel-AD and fixel-MD maps was analogous to the computation of the fixel-FA map. Similarly, a map storing the PPDs of the MTF was computed. These maps were used as input for the tractometry step. The tractogram was segmented into major bundles employing the RecoBundlesX pipeline, see Figure 4 . RecoBundlesX recognizes bundles by comparing the subject's tractogram with a template (or atlas) through a similarity metric based on their shapes. This algorithm is re-evaluated multiple times with parameter variations and label fusion because RecoBundlesX is a multi-atlas and multi-parameter approach. We used the atlas in Rheault , which is designed specifically to be used with RecoBundlesX, and it was built from delineation informed with anatomical priors . After RecoBundlesX identified a large number of WM bundles, tracks were visually inspected to ensure their quality. The Superior Longitudinal Fasciculus (SLF), Arcuate Fasciculus (AF), Pyramidal Tract (PYT), Inferior Longitudinal Fasciculus (ILF), Inferior Fronto-Occipital Fasciculus (IFOF), Middle Cerebellar Peduncle (MCP) and Cingulum (CG) bundles were selected to showcase the pipeline's capabilities. Selected bundles comprise a large set covering most of the brain, showing complex crossing fiber configurations, which is why they are frequently studied in the literature . In the experiments with MS patients, we have chosen the AF, ILF, IFOF and PYT bundles, which have clinical implications in the context of MS studies . The AF bundle connects the frontal and temporal lobes, crucial in speech communication. On the other hand, the ILF bundle connects the occipital and temporal lobes. Its functionality includes visual processing, tracking and recognition of objects and obstacles. Like AF and ILF, the IFOF bundle is involved in speech communication and visual processing tasks, transporting signals from the frontal to occipital and temporal lobes. The PYT connects the spinal cord with the cerebral cortex. It is essential in voluntary control movements. Therefore, when MS lesions appear in the AF, ILF, IFOF, and PYT bundles, several symptoms are experienced by MS patients. These symptoms include difficulties in speech and comprehension, visual deterioration, visual memory problems, attention issues, and affected motor coordination. The proposed pipeline employed the Tractometry_flow pipeline, which delivers metric maps along each individual input bundle. Then, each metric map was projected through every bundle to obtain a tract profile. We adapted the Tractometry_flow pipeline to support multi-tensor fixel-based metrics. The closest-fixel-only strategy was used to map the contribution of the multi-fixels estimated by MRDS to a given streamline. In Figure 5 , we show violin plots comparing single-tensor (blue) and multi-tensor (green) metrics. Horizontal lines refer to the GS (red) and the mean of each distribution. Single-tensor metrics exhibit several discrepancies with respect to the GS, most DTI distributions are bimodal, such that one of the peaks is close to the GS, while the other is underestimated for FA and AD, and overestimated for RD. For each bundle, we accounted for the proportion of voxels containing 1, 2, and 3 fiber populations using the NuFO map obtained with TODI, i.e., we accounted for the proportions of N . These proportions are at the top of Figure 5 . By inspecting percentages of N shown in Figure 5 , it is reasonable to assume that DTI bimodality is caused by crossing fiber biases. In Figure 5 , bundles with a high proportion of N = 2 and N = 3 have a more pronounced bimodality; this is particularly evident for the 13th bundle. In contrast, it can be seen in Figure 5 that the mean of the estimated fixel-FA and fixel-AD are similar to the GS value in all bundles. The relative error of fixel-FA and fixel-AD is around 10% as it is reported in Table 1 , reaching a relative error as low as 2.7% in some bundles where the average relative error is 5.6%. It is important to note that, for fixel-based metrics, the relative error of bundles with a high count of 2 and 3 crossing fibers is similar to the relative error in bundles exposing mostly single fiber composition. As an example, percentages shown in Figure 5 exhibit that bundles 5 and 10 mainly have no crossing fibers, while bundle 11 has, for the most part, crossing fibers. However, the relative error of fixel-FA for bundles 5, 10, and 11 in Table 1 are around 3%. Even for the bundle 13, which is one of the most challenging bundle as it has a high proportion of crossing fibers, the relative error remains at 7%. Values in Table 1 exhibit a higher relative error for fixel-RD compared with fixel-AD and fixel-FA, but still less relative error than RD in general. Additionally, bundle 2 shows abnormal relative errors compared to the other bundles. Looking at the obtained tractogram, the streamline count for bundle number 2 after segmentation is 104, which is insufficient to cover the whole bundle's volume, resulting in an increased relative error. Thus, results in bundle 2 should be interpreted with caution because of the low number of streamlines. Bundle 10 has almost 100% single fiber composition. It is the only bundle where the relative error of RD is less than the one reported in fixel-RD. This suggests that, in the absence of crossing fibers, DTI's RD may be more accurate than fixel-RD. Besides, fixel-RD violin plots in Figure 5 indicate that, in general, fixel-RD tends to underestimate the GS value, which is congruent with the relative errors reported in Table 1 . In the MTM fitting with MRDS the isotropic volume fraction is overestimated, see Appendix A . Since the synthetic data was generated using a multi-compartment model and MTM does not fully represent the signal for the b-values in our protocol, then the ISO compartment may be partially explaining the contribution of the EC compartment (see Appendix A for more details). Therefore, this underestimation of the fixel-RD metric might be related to the overestimation of the isotropic volume fraction. Similar to Figure 5 , in Figure 6 violin plots on the 13th bundle are reported for the 3 simulated scenarios detailed in Section 2.1: healthy control case, demyelination, and axonal loss. Additionally, tractometry results on the same bundle for the 3 different scenarios can be found in Figure 6B . In the healthy control scenario, the limitations of DTI in capturing the overall WM microstructure configuration are evident. Tract profiles informed with standard DTI metrics are biased by crossing fibers as FA, RD, and AD tract profiles have variations along the bundle, while the GS does not. In particular, FA tract profile decreases and RD tract profile increases when the value of N increases, see Figure 6B . In contrast, the robustness of the multi-tensor fixel-based metrics estimated with MRDS is evident as they provide tract profiles independent of the underlying fiber configuration, see Figure 6B . In the demyelination scenario, results with DTI metrics in Figure 6 showed limited sensitivity to changes in the WM microstructure. In the region with a lesion, tract profiles exhibit variations, but they do not correspond with the GS. Contrarily, results with multi-tensor fixel-based metrics show enhanced sensitivity, detecting reductions in FA and increase in RD associated with simulated demyelination while maintaining robustness to noise and crossing fibers, see Figure 6 . Like the demyelination scenario, DTI metrics exhibit limitations in detecting axonal loss, particularly in regions with crossing fibers. Despite the differences in the three simulated scenarios, results in Figure 6 show no substantial differences in DTI metrics. This makes it impossible to distinguish between different scenarios. Results with multi-tensor fixel-based metrics are less contaminated by fiber crossing artifacts, which allows to detect variations in the tract profiles related to lesions. Obtained tract profiles informed with fixel-based metrics underestimate the GS RD, which is expected and congruent with the results investigated in Figure 5 . Although results with multi-tensor fixel-based metrics overestimate the GS FA and underestimate the GS RD, they are accurate in shape and sensitive to small variations. For experiments on in-vivo data, we focus only on FA and RD metrics and their fixel-based counterparts, as MS research and literature report that FA and RD are potential biomarkers closely related to microstructure anomalies and demyelination . Figure 7 illustrates tract profiles for different major bundles in the left hemisphere of the healthy participants. According to Table 2 , tract profiles obtained with MRDS fixel-FA and fixel-RD metrics show an overall reduction in the correlation with the value of N compared to FA and RD metrics. Tract FA profile decreases in locations where N is high and vice versa. In contrast, tract fixel-FA profiles exhibit more robustness to crossing fibers. Additionally, tract profiles informed with fixel-based multi-tensor metrics show FA similar to the ones reported in healthy WM of human brain. Based on the literature, FA values in the healthy human brain WM generally range between 0.60 and 0.85, depending on the specific tract or region. For example, FA in the corpus callosum was reported to be between 0.72 and 0.78 , and between 0.73 and 0.76. FA in the internal capsule was reported to be between 0.70 and 0.80 , and around 0.75. Finally, FA in frontal WM was found to be between 0.60 and 0.70 , and between 0.60 and 0.65. Results on the healthy control group dataset follow the patterns observed in the experiments on the control synthetic dataset. Like Figure 5 , tract profiles obtained with DTI-based metrics consistently show lower FA and higher RD values compared to the fixel-based metrics across every bundle. Figure 7 shows average tract profiles computed from the HC cohort, which includes different subjects scanned in different time stamps. Tract profiles in Figure 7 show visually low variability overall. In Table C1 , the standard deviation (SD) is presented for tract profiles informed with both fixel-based and DTI metrics. The SDs are computed within-subject and between-subject for each bundle and each section of the bundle. The SD from tract fixel-FA profiles is generally higher than the tract FA profiles, though they remain comparable overall. Additionally, Table C2 presents the results of the ANOVA test conducted to compare the mean of 5 tract fixel-FA and fixel-RD profiles resulting from the five scans of sub-015 (one of the subjects exhibiting the highest variability), see Appendix C . The ANOVA test shows the F-statistic and p-value across the 20 locations (labels) of the selected bundles. The results revealed significant differences in the means for various bundles at specific labels. Notably, Label 2 exhibited a statistically significant effect in the PYT_L bundle with an F-statistic of 3.9618 (p = 3.84E-04) and in the ILF_L bundle . Similarly, Label 8 demonstrated significant findings in the SLF_L and MCP bundles. Additionally, Label 12 showed highly significant results in the AF_L and PYT_L bundles. Our pipeline was applied to the MS dataset for a set of relevant bundles in the context of MS studies: AF, ILF, IFOF, and PYT. Differences between MS patients and HC group-averaged tract profiles are studied in Figure 8 . In locations adjacent to lesions, fixel-FA tract profiles show lower values than the healthy control group. Moreover, on the ILF and IFOF bundles, fixel-FA values are beyond the second variance, which may indicate degradation of the WM integrity. Besides, fixel-RD tract profiles are consistently elevated compared to healthy controls in regions with lesions, suggesting widespread expected demyelination. The spatial extent of the lesions correlates with the extent of the changes in both metrics. Figure 9 displays FA and fixel-FA maps along the IFOF_L bundle in the patient 004. Both single- and multi-tensors are visualized in the region of the bundle. Each tensor is colored according to its FA value. Additionally, Figure 9 compares the tensor renders in two different ROIs within the bundle. The ROI outlined in blue has MS lesions while the orange outlined ROI is located at the normal-appearing white matter. Several crossing fibers are present in each ROI as the IFOF bundle crosses with other bundles, such as the PYT and ILF bundles. The FA map shows darker areas in both ROIs, corresponding with the shape, and decreased FA showed by the tensors. No significant differences in FA values are appreciated between the two ROIs. On the other side, the fixel-FA map is darker only in the ROI with the lesion. However, unlike the FA map, fixel-FA map shows higher values and fewer dark spots in the crossing fiber ROI. This indicates that fixel-FA is more robust to crossing fibers. In addition, multi-tensors show FA values within a healthy control range in the crossing fiber ROI, highlighting their potential to differentiate between crossing fibers and lesions. In this work, we address the crossing fiber bias from DTI metrics used in tractometry, by instead using multi-tensor fixel-based measures obtained from multi-shell HARDI acquisitions. Multiple b-vale diffusion-weighted data is mandatory for reliable parameter estimation in MTM-based methods such as MRDS . Our multi-shell acquisitions remain clinically feasible (~30 minutes). Previous works in literature have reported limitations when informing tractometry with multi-tensor fixel-based metrics. Multi-tensor fitting is computationally demanding, highly affected by noise, and requires extensive high-quality dMRI HARDI acquisitions, which are time-consuming and challenging to find in clinical settings . Because of this, previous tractometry methods informed with multi-tensor fixel-based metrics have been limited up to 2 fixels per voxels, resulting insufficient in many regions of the brain . MTM methods generally struggle to accurately determine the number of fixels at each voxel, which is especially challenging in regions with complex fiber configurations. Choosing MRDS as a framework to estimate the multi-tensor fixel-based measures and using TODI to inform MRDS's model selection with tractography regularization allowed us to address these limitations in the current state-of-the-art. MRDS accounts for the presence of up to 3 fixels within each voxel plus an isotropic compartment, allowing for more accurate characterization of the fixel-specific tract profiles and being robust to fiber-crossing. MRDS is relatively fast when estimating the diffusivities in the resampled WM at 1 mm isotropic resolution (~1 h of computing time per subject). Moreover, it has been shown to be accurate and robust to noise (SNR = 12) when using clinical-grade dMRI data and protocols. Finally, the new model selection applied in MRDS allows for improvement in the estimation of the required number of tensors per voxel, taking advantage of the spatial regularization provided by tractography. In the experiments with synthetic dMRI data, relative errors for fixel-based metrics indicate that multi-tensor fixel-based metrics estimated with MRDS are robust to crossing fibers and sensitive to WM anomalies . When comparing the tract profiles obtained with fixel-based multi-tensor metrics to traditional single-fixel tensor metrics, a difference in sensitivity was observed. Tract profiles informed with multi-tensor fixel-based metrics distinguish between crossing fibers and scenarios like axonal loss and demyelination by assessing the underlying fiber configuration and WM tissue metrics. We tested our proposed tractometry pipeline on several WM bundles of the in-vivo healthy control group: SLF, AF, CG, IFOF, PYT, ILF and MCP. We compared the obtained tract profiles informed with multi-tensor fixel-based metrics with tract profiles informed with single-tensor metrics along these bundles, focusing on their robustness to crossing fibers . The robustness of the multi-tensor fixel-based metrics to crossing fibers is evident across all examined bundles. Besides, our findings indicate that tractometry informed with multi-tensor fixel-based metrics is consistent, reliable, and not significantly affected by random noise or crossing fibers. As expected, single-tensor metrics exhibit a notable fluctuation when the estimated number of crossing fibers per voxel ( N ) along the bundle increases or decreases. This pattern suggests that single-tensor metrics are highly influenced by crossing fibers. According to the literature , tract profiles informed with multi-tensor fixel-based metrics exhibit FA values in a range that is considered normal for healthy WM. This alignment suggests that multi-tensor fixel-based metrics provide more accurate representation of the WM integrity. Contrary, singles tensor metrics fail to estimate FA values considered normal in the WM because they are biased by crossing fibers. In Section 3.2 we quantitatively and qualitatively explored the within-subject and between-subject variability of the tract profiles. The consistent low SDs values for the tract profiles indicate minimal variability within and between subjects. Despite the higher variability in multi-tensor fixel-based tract profiles, they remain within acceptable limits. This suggests that multi-tensor fixel-based informed tract profiles are more accurate, but less precise than DTI informed tract profiles. Moreover, we conducted an ANOVA test to evaluate the differences in mean fixel-FA and fixel-RD metrics across 20 locations of several bundles in sub-015. The overall rejection rates across the labels suggest a high level of consistency in the measurements, with an average rejection rate of 40%. However, our findings indicate that the tract profiles of certain bundles are significantly influenced by the anatomical location, revealing significant differences in the means of fixel-FA and fixel-RD across different regions of the brain. These results underscore the importance for careful interpretation of tract profiles as certain bundles, particularly in subjects with pronounced variability. While Rojas-Vite et al. provided a solid foundation for the application of fixel-based metrics provided by MRDS, further validation using animal models remains essential. Particularly, in the context of demyelination and tractometry. Understanding the intricate changes in the obtained fixel-based metrics associated with demyelination is crucial for accurately interpreting the alterations detected by our method. Future studies utilizing animal models have to be driven for a more comprehensive assessment of our approach's sensitivity to demyelination and its correlation with histological outcomes. We compared relapsing-remitting MS patients to a group of healthy subjects with similar age and brain configuration . The proposed pipeline shows to be sensitive to WM anomalies related to relapsing-remitting MS disease. The single MS patient tract profiles exhibit values that clearly deviate from the healthy control group. These deviations are potentially related to MS pathology as they occur around lesion location. A similar behavior is reproduced in the synthetic data simulating demyelination . Therefore, differences between group-averaged and individual MS patients' tract profiles in Figure 8 are assumed to be a consequence of the disease. In general, for all bundles, MS patients consistently show reduced fixel-FA and increased fixel-RD compared to healthy controls. In Section 3.3, we made a comparison between the tract profiles of the HC group and two MS patients (sub-004-ms and sub-022-ms) of the MS group. Although a group comparison (HC vs. MS) may be done, the inherent group-averaging may not be beneficial because of the variability of MS lesions among MS patients. MS lesions can appear in different regions along the brain, and the severity of these lesions varies between patients . Averaging these tract profiles across patients could lead to loss of critical information that is essential for understanding the individual differences within the MS group. Nonetheless, it is important, as a future work, to design an analysis for the entire MS cohort, which will provide a more comprehensive understanding of these dynamics. Moreover, we recognize the importance of developing a framework for explicit comparison of Wallerian degeneration, which would provide valuable insights to the MS research community. Finally, we acknowledge the need for a more comprehensive analysis comparing FA and RD values in the normal-appearing white matter of MS patients with those of healthy controls, which could further enhance our understanding of the integrity of WM in regions without visible lesions. In a previous study , tractometry with dMRI metrics was investigated in young adults with relapsing-remitting MS. They reported significant abnormalities in the WM microstructure in WM bundles similar to those we used. In particular, reduced FA and increased RD were observed, indicating demyelination, which aligns with our reported results. Additionally, specific changes in fiber density and complexity were noted, indicating axonal degeneration. In Chamberland et al. a study using tractometry was conducted on MS patients with optic neuritis. It was found a limited ability to differentiate between various types of lesions like demyelination and axon loss using dMRI metrics, which is consistent with our findings. In another example , tractometry informed with single tensor and other advanced fixel-based metrics was used to investigate the association between diffusion MRI-derived measures and neuropsychological symptoms of MS. They focused on WM fascicles that are associated with cognitive dysfunction in the presence of lesions. Our approach could offer several benefits to this kind of studies. For example, MTM metrics may replace standard DTI metrics in their analysis. The integration of these new metrics should be direct, as MTM metrics have the same biological and geometrical interpretation as DTI metrics without the crossing fiber bias. This could provide a more robust and accurate depiction of microstructural WM changes in MS patients. MTM metrics like fixel-RD would allow for a more precise and sensitive characterization of demyelination and other alterations, including axon loss. Robust multi-tensor metrics could improve the reliability of longitudinal studies by providing consistent and accurate measures over time. This would facilitate the monitoring of disease progression. By incorporating multi-tensor fixel-based tractometry analysis, researchers and clinicians may underscore the advantages of multi-tensor fixel-based metrics in improving the fidelity of studies. One of the main limitations in the current literature is that RD metric can be contaminated in regions with crossing fibers and lesions, leading to erroneous interpretations and conclusions, making RD unstable as a biomarker . This work addresses this limitation by offering a tractometry pipeline robust to crossing fibers, suggesting the fixel-RD metric as a more robust biomarker for demyelination. Our pipeline shows that multi-tensor fixel-based methods could be a robust alternative to DTI, in which familiar metrics such as FA or RD are now specific to a particular fixel or track, with similar biological/geometrical interpretation. This facilitates the contextualization of these MTM metrics regarding many studies utilizing DTI metrics. Besides, it is unnecessary to include other fixel-based metrics such as AFD, which has challenging biological interpretability. AFD reflects the density of axonal fibers within a voxel, but not necessarily their functional status or health . Thus, an increase or decrease in AFD does not directly translate to improved or deteriorated neurological function, requiring additional context . Moreover, pathological conditions like demyelination or axon loss can alter diffusion properties in ways that are not straightforward to disentangle, making it hard to pinpoint the exact biological cause of changes in AFD . In this work, we utilized a simulated phantom that incorporates different compartments to simulate WM microstructure to evaluate our proposed method. However, it is important to acknowledge the limitations of this phantom as it only serves as an approximation that does not capture the full complexities of the human WM. Membrane permeability and vascularization are examples of factors that were not considered in these simulations. Future work should focus on validating the proposed method using more realistic phantoms, such as the proposed by Callaghan et al. and Villarreal-Haro et al. . Our results showed a decrease in RD precision when a single fiber population is present. This is concordant with what has been reported in other multi-fiber methods . Including a free water tensor in MRDS enhances results accuracy and mitigates potential biases, particularly when analyzing patient data. Nonetheless, this inclusion decreases sensitivity in the estimated fixel-based diffusivities due to the increased complexity of fitting 4 tensors (3 anisotropic and 1 isotropic) instead of 3 with MRDS. Besides, for acquisition schemes including high b-values the estimation of N and the isotropic volume fraction is affected, see Appendix A . Hence, the isotropic compartment may partially explain the contribution of the extra-cellular part of the dMRI signal, resulting in a reduction of the RD as shown in results with synthetic data. In the future, we consider that it will be important to study in depth the impact of including a free water compartment in MRDS and their implications in other lesions like edema as it is still an open question. In our study, we demonstrated that the proposed method effectively detects variations in tract profiles associated with lesions, both in synthetic simulations and in-vivo data. This capability underscores the potential of our approach for identifying abnormalities in complex fiber crossing regions. However, it is important to note that while our method shows promise in detecting lesions, future work is necessary to further investigate its performance in accurately assessing the actual severity of detected lesions. Although the obtained results underscore the capabilities of the proposed pipeline to identify WM lesions while being robust to crossing fibers, it cannot discriminate between demyelination and axonal damage. This is congruent to previous studies, which found that RD is sensitive to several microstructural changes different from demyelination, such as axonal deterioration, edema, and inflammation . More advanced models like SM can distinguish between changes occasioned by axonal integrity and changes due to demyelination, but they still use one single tissue kernel per voxel, not per fixel. Similar to Dayan et al. , our robust multi-tensor fixel-based metrics can be combined with these advanced methods, leading to a more sophisticated pipeline with a different type of metrics. Additionally, the employment of M agnetization T ransfer I maging (MTI), which is sensitive to myelin content, could help to differentiate between demyelination and axonal injury. However, it is necessary to extend the developed phantom for simulating not only dMRI acquisitions but also MTI acquisitions in order to validate the results on in-vivo data. Another important aspect to consider is the high amount of false-positive streamlines in the tractogram and recognized bundles . While segmenting the tractogram and focusing the analysis on known tract bundles, false-positive streamlines can lead to inconsistencies in the tract profiles of the tractometry analysis, like overestimating the tract profiles from the estimated fixel-based metrics. Additionally, they can introduce more noise and variability into the analysis, hindering reproducibility. This can reduce the sensitivity of tractometry analysis to detect genuine alterations in WM between control subjects and patients, resulting in misinterpretations and erroneous conclusions. Fortunately, there are methods like COMMIT that assign weights to individual streamlines in the tractogram by solving a convex optimization problem. This enables the detection of false-positive streamlines, which can be removed by discarding streamlines with weight equal to 0. As future work, COMMIT can be integrated into the pipeline to obtain a pipeline more robust to false-positive streamlines. In conclusion, our work focuses on creating a robust tractometry framework informed by tractography-regularized multi-tensor fixel-based metrics. It demonstrates its capabilities to address the crossing fibers bias and lesions, increasing the sensibility in both simulated and real-world scenarios. This study makes several key contributions to the field of WM imaging analysis. First, developing a simulated phantom with challenging and customizable geometry, incorporating different WM scenarios by using the standard model (healthy tissue, demyelination, and axon loss). This phantom provides a controlled environment to systematically evaluate and compare different imaging techniques and models. This allows us to verify the accuracy and robustness of our proposed methods against various fiber configurations and pathologies. Second, our proposed pipeline informed with the multi-compartment framework MRDS–three anisotropic and one isotropic compartment–marks a substantial methodological advancement. This pipeline goes from raw data to tract profiles informed with track-specific tensor metrics. By combining tractography robust to lesions and accurate multi-tensor fixel-based metrics, our pipeline achieves more robust, precise, and sensitive representations of the WM microstructure, particularly in regions with complex crossing fiber configurations or lesions related to pathologies. This approach addresses limitations in the current state-of-the-art methods. Thirdly, we evaluated the proposed tractometry pipeline in a cohort of 20 healthy individuals. Our results demonstrate the superiority of MTM over DTI, highlighting MTM's enhanced ability to capture detailed microstructural information and resolve crossing fiber geometries. The increased sensitivity of MTM metrics provides more accurate assessments of white matter integrity. Finally, applying our tractometry pipeline to a cohort with relapsing-remitting MS further underscores the clinical relevance of our work. Our qualitative analysis demonstrates the sensitivity of the pipeline in detecting WM anomalies related to demyelination. This is particularly important in diseases like MS, where it is important to differentiate between crossing fibers and lesion contamination. Pipeline's capabilities to delineate these anomalies offer an improvement over those that only include DTI metrics for studying and monitoring MS and potentially other neurological conditions. | Review | biomedical | en | 0.999996 |
PMC11697429 | In this article, we report our experiences of participant selection in different traditional, modern, and community-oriented qualitative methodologies—auto/ethnography, narrative inquiry, participatory action research, ethnography, case study, grounded theory, and phenomenology, and some thinking points for consideration. Qualitative research is gaining popularity in social science and educational research for exploring human experiences and feelings. Humans aligned to the phenomenon are the main information and/or data sources. Aspers and Corte noted that this popularity should be “Seen in a historical light, what is today called qualitative, or sometimes ethnographic, interpretative research—or a number of other terms—has more or less always existed.” (p. 141). Denzin and Lincoln stated, “Qualitative research is a situated activity that locates the observer in the world. It consists of a set of interpretive, material practices that make the world visible. These practices transform the world. They turn the world into a series of representations, including field notes, interviews, conversations, photographs, recordings, and memos to the self.” (p. 3). Thus, qualitative research plays a crucial role in exploring complex phenomena and gaining in-depth insights into human experiences. Further, Pyo et al. remarked that “Qualitative research is conducted in the following order: (1) selection of a research topic and question, (2) selection of a theoretical framework and methods, (3) literature analysis, (4) selection of the research participants and data collection methods, (5) data analysis and description of findings, and (6) research validation.” (p. 12). However, there is always back and forth between different processes, making it iterative. Despite the popularity of qualitative research, novice researchers often struggle with the intricacies of participant selection for exploring human experiences aligned with their feelings, emotions, and perceptions. So, selecting the appropriate research participants is crucial to conducting any qualitative research and/or inquiry that directly influences the rigor—credibility, and richness of the data collection and/or generation. However, inappropriate choice of participants and data collection may lead to methodological flaws and compromised study outcomes. With advantages, disadvantages, and characteristics, the participant selection procedure in qualitative research is considered purposeful sampling with referred criteria in general and co-researchers in particular. These selection procedures are often based on problem, purpose, research question, and theoretical referents. In this article, we have explored participants' selection procedures, drawings from our experiences and understanding. We offer some thinking points for consideration in qualitative methods and the nuanced differences and uniqueness in each of the chosen methodologies by offering a practical guide for consideration for novice and/or veteran researchers regarding participant selection in seven qualitative methodologies—auto/ethnography, narrative inquiry, participatory action research, ethnography, case study, phenomenology, and grounded theory. Choosing appropriate participant selection procedures is essential to enhance the quality of qualitative studies. This paper serves as a comprehensive guide for novice and/or veteran researchers, offering a step-by-step approach to participant selection in the chosen qualitative research method, taking care of challenges, and offering practical solutions based on our studies. In this article, we report our experiences of participant selection in each methodological tradition, as the process is essential for ensuring quality in qualitative research findings and/or outcomes . Addressing the ethical considerations and practical tips for selecting participants, this article offers a participant selection procedure in seven qualitative methodologies (1) auto/ethnography, (2) narrative inquiry, (3) participatory action research, (4) ethnographic study, (5) case study, (6) grounded theory, and (7) phenomenology. As the authors have also embedded reflective experiences and some thinking points for consideration of our research journey in relation to participant selection, we have used the first-person pronoun “I” in subsequent sections. The case study can be explanatory, exploratory, or descriptive. So, the case study design selection depends on the study's overall purpose . An explanatory case study seeks to identify the causal factors that explain a particular case. The primary focus of such a study is to explain “why” and “how” certain conditions come into being and why certain consequences of events occur or do not occur . An exploratory case study explores the context of the phenomenon, and its primary purpose is to investigate or identify the new research question that can be used extensively in succeeding research studies. Likewise, the primary purpose of a descriptive case study is to describe a phenomenon in detail in its real-life situation in which it occurred . In terms of the number of cases, it can be single and multiple case study research in which the researcher tries to have a holistic understanding of a unique, extreme, or critical case in a single case study, whereas, in contrast, the researcher explores the similarities or differences in multiple case studies. Sampling in a case study involves selecting representatives from a larger population for an in-depth analysis of the issue to be studied . Qualitative case studies employ purposive sampling to illustrate the phenomenon of interest and present an in-depth understanding of the case of the study. In case study, like other qualitative research, participants are selected in terms of their relevance to the research topic or question(s) . Like other qualitative studies, even in case study research, researchers need to define participant selection criteria clearly and should have a specific purpose behind selecting the case(s), that offers valuable insights into the phenomenon under investigation. Similarly, considering the access and feasibility of data regarding availability, willingness to participate, the practicality of data collection methods, and continuity of the data collection process until it reaches the saturation point is also critical for determining samples. Moreover, the case study researchers select their cases gradually, not limiting the number of participants chosen until the data reaches saturation. Regarding this, Glesne and Peshkin suggest that if the stories are repeated among the participants and no new information is added to the research by any new participants, researchers need to stop selecting new participants. For the sound, undulated, and unbiased study of the phenomenon under study, a case study involves multiple sources of data collection, like participant/nonparticipant observation, in-depth interviews, audio/video recordings, field notes, focus group discussion (FGD), conversations in a natural setting, and study of documents (whether of books, archival manuscripts, signs, physical artifacts, and so on) . The concept of conducting an “unbiased” study is highly debated, even within qualitative research . In constructivist credo, Lincoln and Guba noted that researchers do not need to assert their impartiality but should instead embrace a dialogical approach with their participants or co-researchers. Gergen further emphasizes that achieving excellence in qualitative research is less about striving for objectivity and more about fostering strong relationships with research participants. The multiple sources of data collection are crucial in the case study, which does not seek to offer a more or less unbiased representation, but it can be used to enhance dialogically generated insights and increase the richness and quality of the findings, which is likely to be more convincing and accurate . At the same time, due to the bulk of data from multiple sources, sometimes there is the risk of the researchers being lost in the data. Romm expanded the discussion to provide more detailed accounts of what acting responsibly toward research participants means. Therefore, Baxter and Jack suggest proper organization and analysis of the data as each data source is one piece of the “puzzle,” each contributes to the researcher's holistic understanding of the phenomenon. Thus, the emphasis is not solely on the professional researcher's comprehension but on co-creating understanding and insight . As grounded theory aims to build a theory grounded in the data, participant selection is crucial. Grounded theory adopts theoretical sampling as an effective strategy as it is a dynamic, iterative process that is driven by emerging theory . Researchers continuously gather and analyze data using this sampling method by letting the developing theory determine where, when, and from whom further data should be collected . As a result, this method enables researchers to concentrate on their areas of interest, find theoretical gaps as they occur, and ensure that the final theory is thorough and solidly supported by the available data. A broad research question or area of interest is usually the starting point for theoretical sampling. The initial data collection process may involve document analysis, observations, or interviews, depending on the research context. Furthermore, the emerging theory guides the sampling decisions as they are gathered and analyzed. In this regard, the researcher looks for more subjects or data sources that can elaborate on that idea . Ensuring that the final theory is thorough and firmly based on the data enables the researcher to expand and improve the theory as the study goes on. Another element to consider during the grounded theory sampling process is reflexivity. In qualitative research, reflexivity is crucial, especially when using techniques like grounded theory, where the researcher's work is closely linked to gathering and analyzing data . By practicing reflexivity, researchers become conscious of their own prejudices and how they might affect the way they conduct their work, including how they choose to sample. Neill emphasized the importance of reflexivity to ensure that researchers' preconceptions do not influence the sampling process, keeping it aligned with the emerging theory's requirements. Charmaz argued that grounded theory can never be a completely objective representation of phenomena. Researchers should transparently disclose how their theories have been constructed or co-constructed . Mills et al. offer a comprehensive discussion of the nuances within grounded theory and constructivist approaches, including those explicitly promoted by Charmaz and other proponents. By practicing reflexivity, researchers can address potential biases, enhancing the quality and credibility of their data collection. This process strengthens the rigor of grounded theory and the research relationship. The process of theoretical sampling is a continuous cycle of data collection, analysis, and refinement rather than a linear one. To find trends, concepts, and categories, newly acquired data is instantly examined and contrasted with the database. A key component of grounded theory, constant comparison, ensures that the evidence supports the developing theory . The process of theoretical sampling persists until theoretical saturation is achieved, which transpires when supplementary data ceases to advance the theory . At this stage, the researcher can be sure that the theory appropriately explains the phenomena being studied and is well-supported by the data. Likewise, another important component of grounded theory is the consideration of ethical issues. The requirements of the developing theory dictate the sampling procedure. Because of this, researchers need to consider any potential ethical ramifications before making any sampling decisions. Researchers should take into account concerns about participant impact, informed consent, and confidentiality, according to Conlon et al. . By doing this, researchers can ensure that participants are treated fairly and respectfully during sampling. The rigor and credibility of the research are enhanced by important aspects of sampling in grounded theory, including reflexivity, sample size, diversity, and ethical considerations. Phenomenology is a unique qualitative form of inquiry into lived experiences of human existence, and it aims to understand those experiences from the participants' perspectives . This methodology is ingrained in early 20th-century European philosophy, which comprises the use of thick descriptions of close inquiry of lived experience to understand how meaning is created through personified insights and perceptions . Digging deep into participants' lived experiences that reflect their life's pains and gains is a challenging job for a researcher. For instance, “a study on the lived experiences of pregnant women with psychosocial support from primary care midwives will recruit pregnant women varying in age, parity and educational level in primary midwifery practices” . It is essential to appropriately select research phenomena and participants and formulate research questions for a phenomenological study to capture the essence of the participants' shared experience and construct meaning from their experiences. The term “sample within phenomenological methodology should not refer to an empirical sample as a subset of a population”, but to a wisely chosen group of human beings that share in-depth insights into the essence of the subject being studied aligned to transformative intents. Phenomenological researchers primarily employ purposive, snowball, and maximum variation strategies for selecting their research participants . These strategies help researchers delve deep into their participants' lived experiences about the phenomenon being studied. Purposive sampling is a key data collection strategy in phenomenological study as it enables researchers to select participants with a rich array of lived experiences of the phenomenon under study and willing to provide rich, thorough, and evocative data . The participants are selected based on their lived experiences, knowledge of the phenomenon being studied, and their verbal efficiency in describing their group or (sub)culture . The participants can provide rich descriptions of their experiences with the phenomenon, collaborating with the researcher to explore its essence and construct meaning. Maximum variation sampling is another significant strategy to gather intentionally heterogeneous data for phenomenological research . The researcher selects participants with a wide range of characteristics and experiences about the phenomenon being explored. The data collected from such a selection of participants can yield varied information from a wide range of perspectives and identify important common patterns . The participants share experiences of a phenomenon being explored in a phenomenological study . Phenomenological study focuses on gathering the depth and quality of the information primarily through interviews and observations rather than the number of participants. There are different opinions regarding the number of participants in qualitative research, including phenomenology. For example, Polkinghorne suggests interviewing 10 to 25 participants, and Moustakas recommends that a researcher should take between 5 and 25 participants. However, the gathering of data continues until saturation occurs or when the data no longer reveals new insights or themes from the participants . The researcher encourages and probes their participants to describe their experiences in detail during the unstructured interviews and semi-structured interviews and observes them in the context where the phenomenon being explored is experienced . A researcher can use an unstructured interview if they have a limited understanding of the topic and want to rely on their participants' information to lead the conversation and a semi-structured interview in order to obtain in-depth data from the participants . The data is expected to reach a point of saturation from the participants, ensuring that no new understandings or themes would emerge from further participants . Saturation occurs when the data no longer reveals new information or themes, and further interviews or data collection yields redundant information . The primary emphasis of a phenomenological study is on the richness and saturation of the information, so selecting appropriate participants is a critical methodological process. A researcher can obtain in-depth, diverse, and evocative insights into participants' lived experiences by employing purposive, snowball, or maximum variation sampling strategies, ensuring that the selection process benefits both researchers and participants. The number of participants is decided when the insights or themes start getting repeated, affirming the study encapsulates the essence of the phenomenon being explored. Hence, an appropriate selection of participants helps researchers construct an in-depth understanding of human experiences based on the viewpoints of those who have experienced them. The nuanced differences and unique aspects of participant selection across these methodologies (as shown in Table 2 ) highlight the importance of a thoughtful and deliberate approach. However, considering the problem, purpose, research question, and theoretical framework, researchers can ensure that their participant selection process aligns with the goals of their study and enhances the overall quality of their research without compromising their methodology. This article also emphasized the iterative nature of qualitative research design, such as auto/ethnography, where participant selection is not a one-time decision in qualitative research design but an ongoing process that may require adjustments as the study progresses as an emerging nature of the qualitative inquiry. Ethical considerations, our experiences, and some thinking points in this serve as valuable guidelines for researchers to navigate the complexities of participant selection. Further, by subscribing to participatory action research (PAR), the third author redefines the traditional sampling concept by emphasizing the research process's collaborative nature. In contrast to conventional qualitative research, where the researcher chooses the participants, PAR entails inviting people to join as co-researchers who share responsibility and dedication to the research objectives. This approach fosters a sense of ownership and active engagement among all participants, enhancing the relevance and impact of the research. The process of identifying co-researchers in PAR is complex and multifaceted. It requires understanding of the stakeholders, their shared problems or goals, and the broader vision they aim to achieve. This collaborative approach ensures that the research is grounded in the real-world experiences and aspirations of the community involved. Methods such as purposive sampling and respondent-driven sampling can be adapted to fit the unique needs of PAR, ensuring that the co-researchers are well-suited to contribute meaningfully to the research process. The participatory nature of PAR encourages a problem-solving mindset, where researchers and co-researchers work together to address issues and generate knowledge. And this idea of collaboration (and co-generation of meaningful insights) is not confined to PAR. It can enter much qualitative research . This collaborative effort often leads to richer, more nuanced data that might be captured through something other than traditional research methods. The inclusion of non-academic participants who bring diverse perspectives and experiences further enriches the research outcomes. PAR transforms the research process into a collective journey of inquiry and action, where the roles and responsibilities are shared, and the knowledge generated is co-created. This approach not only enhances the quality and relevance of the research but also empowers the participants, fostering a deeper connection to the research outcomes and their potential impact on the community. Researchers can create more inclusive, impactful, and ethically sound research practices by embracing the principles of PAR. Likewise, the fourth author added that ethnographic studies require a deep understanding of the socio-cultural context and careful consideration of participant selection. Unlike other qualitative methodologies, ethnography emphasizes “participant selection” oversampling to ensure that the chosen individuals reflect the research site's cultural dynamics and enable (critical) exploration of power relations. This approach respects the complexity of human societies and honors the cultural contexts of the participants. The process begins with selecting a research site based on specific criteria, which significantly influences participant selection. Flexibility is crucial, as the choice of site and participants can be iterative, adapting to emerging insights during the research process. Detailed consideration of socio-cultural, economic, and political dynamics is essential to delve deeply into the intricacies of the culture being studied. The number of participants in ethnographic studies typically ranges from six to ten, focusing on in-depth interviews and observations. This small size allows for rich qualitative data while ensuring comprehensive coverage of the research questions. Achieving theoretical saturation is key, ensuring that the data collected is sufficient to address the research objectives effectively. Ethnographers employ various participant selection techniques, with purposive sampling being the most common. This method identifies information-rich participants who can provide deep insights into the cultural norms and behaviors of the community. The goal is to represent diverse groups and select participants who can contribute meaningfully to the study. Providing detailed profiles of participants enhances the transparency of the research process. This can be done through summary tables or detailed bio-sketches, offering a clear rationale for the selection and demonstrating how the participants' characteristics align with the research goals. Ultimately, ethnographic research is about understanding and interpreting the lived experiences of individuals within their cultural contexts. By carefully selecting participants and considering the socio-cultural dynamics, researchers can produce rich, nuanced insights that contribute to a deeper understanding of human societies. Next, in qualitative case study research, the fifth author reported that qualitative case study offers a robust method for exploring complex phenomena within their real-life contexts. This approach is particularly valuable in social science and educational research, where understanding the intricacies of specific cases can provide deep insights into broader issues. Case studies can be explanatory, exploratory, or descriptive, each serving different research purposes. The selection of cases, whether single or multiple, is guided by the research questions and the need to understand the phenomenon in depth. Also, it is important to consider who is setting the research questions so that the research can benefit participants, especially those most marginalized in the social fabric (and, for that matter, the ecological fabric). Single case studies focus on unique or critical cases, while multiple case studies explore similarities and differences across several cases. Participant selection in case studies is a critical process that involves purposive sampling to ensure that the participants are relevant to the research questions. Defining clear selection criteria and considering factors such as availability, willingness to participate, and data collection feasibility are essential. The goal is to continue selecting participants until data saturation is achieved, ensuring additional participants add no new information. The richness of case study research lies in its use of multiple data sources, including observations, interviews, recordings, field notes, focus group discussions, and document analysis. This triangulation of data sources enhances the credibility and depth of the findings. However, researchers must be cautious of the potential for data overload and ensure proper organization and analysis to maintain a clear focus on the research objectives. Thus, qualitative case study research provides a comprehensive and nuanced understanding of the studied phenomena. By carefully selecting participants and employing multiple data collection methods, researchers can produce convincing and accurate findings, contributing valuable insights to the field. All the above, the sixth author added that grounded theory stands out as a powerful qualitative research methodology, particularly valued for its ability to generate theories directly from systematically gathered and analyzed data. This bottom-up approach provides researchers with authentic insights into social interactions, processes, and behaviors within their natural environments, making it especially effective for exploring complex and poorly understood social issues. Participant selection in grounded theory is a dynamic and iterative process known as theoretical sampling. This method allows the emerging theory to guide the selection of participants, ensuring that data collection is continuously refined and focused on filling theoretical gaps. This iterative process, combined with constant comparison, ensures that the developing theory is robust and well-supported by the data. Reflexivity is another crucial element in grounded theory, helping researchers remain aware of their biases and ensuring that these do not influence the sampling process. By practicing reflexivity, researchers can enhance the quality and credibility of their data collection, thereby strengthening the overall research process. Theoretical sampling continues until theoretical saturation is achieved, meaning no new data significantly advances the theory. This ensures that the final theory is comprehensive and accurately reflects the phenomena being studied. While a typical sample size for grounded theory research is around 25 interviews, it may extend to 30 to develop theoretical constructs thoroughly. Diversity in the sample is also essential, as it ensures that the emerging theory captures the complexity of the phenomena under study. Researchers must consider a range of experiences, backgrounds, and perspectives to develop a well-rounded theory. Ethical considerations are paramount in grounded theory research. Researchers must ensure that participants are treated with respect and fairness, addressing issues such as informed consent, confidentiality, and the potential impact on participants. Researchers must examine the likely consequences with participants and ways of setting research questions and proceeding with the research. By adhering to these ethical standards, researchers can enhance the rigor and credibility of their studies. Overall, grounded theory provides a flexible and rigorous framework for developing theories that are deeply rooted in empirical data. By carefully considering participant selection, reflexivity, sample diversity, and ethical issues, researchers can produce robust and meaningful theories that contribute significantly to our understanding of complex social phenomena. Finally, the seventh author adds that phenomenology offers a thoughtful approach to understanding the lived experiences of individuals, focusing on capturing the essence of these experiences from the participants' perspectives. Rooted in early 20th-century European philosophy, phenomenology employs thick descriptions and close inquiry to uncover how meaning is constructed through personal insights and perceptions. Selecting participants for phenomenological study is a critical process that goes beyond traditional sampling methods. Instead, it involves purposive, snowball, and maximum variation strategies to ensure that participants provide rich, detailed accounts of their experiences. Purposive sampling allows researchers to choose individuals who have deep insights into the phenomenon, while snowball sampling helps identify additional participants through recommendations from initial subjects. Maximum variation sampling ensures a diverse range of perspectives, enhancing the depth and breadth of the data collected. The number of participants in phenomenological research varies, with recommendations ranging from 5 to 25 participants. The key is to continue data collection until saturation is reached, meaning no new themes or insights emerge from additional data. This ensures that the study captures the full essence of the phenomenon being explored. Interviews, both unstructured and semi-structured, are primary data collection methods in phenomenology. These interviews allow participants to describe their experiences in detail, providing the rich, evocative data needed to understand the phenomenon thoroughly. Observations in the context where the phenomenon occur further enrich the data. The success of a phenomenological study hinges on the careful selection of participants and the depth of the data collected. By employing appropriate sampling strategies and focusing on the richness and saturation of the information, researchers can construct a comprehensive understanding of human experiences grounded in the authentic perspectives of those who have lived them. These days, many qualitative researchers point out that the construction should not lie in the hands of professional researchers but must be a co-construction with participants, with the intent that the research will benefit (marginalized) participants. This approach not only enhances the credibility and depth of the research but also provides valuable insights into the complexities of human existence. | Study | other | en | 0.999997 |
PMC11697431 | The term cognitive impairment/dementia alludes to a continuum, a progressive and changing syndrome, which leads to successive disabilities and the loss of personal autonomy (i.e., to dependence on third parties) ( 1 ). Due to its enormous biological, psychological, and social complexity (both in terms of the patient and their caregiver), we consider dementia to be one of the best examples of complex chronic psychogeriatric diseases, and it will therefore always be at the center of reflection and psychogeriatric intervention. Geriatrics and Old Age Psychiatry both support a holistic approach as the essential tool to deal with this complexity ( 2 , 3 ). In the health care field, the concept of need can be applied at both the collective and individual level ( 4 ). At population level, it serves as a tool to organize clinical management and design health policies. Likewise, the study of individual needs, once assessed and prioritized, allows for personalized interventions. The combination of decades of daily experience in the clinical approach to dementia and our research experience with CANE, an instrument used frequently to assess needs in psychogeriatrics, has led us to believe that needs assessment is an inextricable part of the comprehensive psychogeriatric assessment ( 5 , 6 ). We are convinced that a care model based on a sufficiently thorough and operationalized study of the needs of the subject and their caregivers, while not essential, will greatly facilitate the comprehensive approach, the operationalization of the biopsychosocial model (more often advocated than actually put into practice), and, ultimately, person-centered care ( 7 – 10 ). Models that assess the condition of older persons based on needs assessment take these aspects into account, identifying the need itself (understood as a deficit), the relevance or appropriateness of third-party assistance (whether from the family, the community or the health system) and how these needs vary over time. There are several theoretical models that integrate needs and their relationship with morbidity and disability, such as that of Miranda-Castillo et al ( 11 ), Shmidt et al ( 12 ) or the studies conducted by Kitwood ( 13 ). These models are not mutually exclusive; they share common points, while emphasizing different aspects. Thus, the Miranda-Castillo model relates the person’s needs to the clinical, social and caregiver spheres. Schmidt focuses on morbidity (in cognitive impairment) as the main element of loss of autonomy, adding the importance of knowing how the person copes with difficulties in maintaining their autonomy and what needs are most important to them. The latter idea is also emphasized in Kitwood’s publications, promoting person-centered interventions and, ultimately (along with previous models), highlighting the importance of meeting needs, relating them to autonomy, the state of well-being and quality of life. The aim of this study is to examine the relationship between needs and functional capacity/dependency in people with cognitive impairment/dementia, establishing the hypothesis that people with cognitive impairment will have a greater number of needs and a higher level of disability and dependency, and that the severity of cognitive impairment is in correlation with an increased number of needs (both met and unmet). This is a community-based, cross-sectional, descriptive epidemiological study of morbidity and other health-relevant conditions. It is based on a reanalysis of data from a community-based epidemiological study conducted in Santiago de Compostela, Spain, of people over 65 years of age ( 14 , 15 ). It complies with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) criteria ( 16 ). The main defining morbidity variables in the project were: the Spanish 30-item version of the Minimental State Examination (MMSE) ( 17 ), the 30-item version of Yesavage’s Geriatric Depression Scale (GDS-30) ( 18 ), a self-referenced questionnaire of chronic diseases common in older persons ( 19 ) and a brief ad hoc questionnaire of Likert scale type questions on health self-perception. The clinical diagnosis of dementia was made according to the International Classification of Diseases (ICD-10) ( 20 ). The MMSE score was used to classify the level of cognitive impairment. Other clinical variables included the frequency of the subjects’ visits to their primary care physician/specialists and whether they had been hospitalized recently. The Katz Index ( 21 ) was used to assess functional level , as well as the Barthel Index ( 22 ) (for the basic activities of daily living) and the Lawton & Brodie Scale ( 23 ) (for instrumental activities of daily living). The needs assessment was carried out using the Camberwell Assessment of Need for the Elderly (CANE) ( 24 , 25 ). This instrument analyzes 24 biopsychosocial needs of older persons and, if applicable, the caregiver’s overburden and need for information. This needs assessment is carried out from the triple perspective of the professional/researcher, of the caregiver (in the case of a dependent person) and of the subject themselves. For readers interested in gaining a deeper understanding of the instrument, we refer them to the two editions of the detailed manuals published on the subject ( 24 , 25 ). It is worth noting that this is a semi-structured interview, in which “older people are fully involved in the needs assessment process and there is a special section noting their own views and their satisfaction with the amount of help received” ( 24 ). The professional (in this case, the researcher) bases their assessment of needs for each CANE item on the answers given by both the older person and their caregiver, also incorporating any contextual information from other research instruments. This approach allows each CANE item to be classified into one of three statuses: “no need”, “met need” or “unmet need”. The quantitative analysis of the CANE for a specific subject provides the total number of needs (differentiating between “met needs” and “unmet needs”). When group data are reported, the mean values of “met needs”, “unmet needs” and “total number of needs” are usually analyzed. Tables 3 and 4 show the contingency tables for the MMSE variable (cognitive impairment). The Barthel Index, the Lawton and Brodie scale and the Katz Index ( Table 3 ), as well as the contrast of these variables, show a statistically significant relationship with these 3 scales ( Table 4 ). People with cognitive impairment have a greater level of dependency, perceived as a worsening of the ability to perform basic and instrumental activities of daily living. As indicated in the Material and Methods section, for the multivariate analysis a Generalized Linear Model of the negative binomial family with logarithm as the link function was performed to try to elucidate the important predictors in relation to the unmet needs response variable (count variable). The aim of the present study was to show the importance of studying needs as part of the comprehensive psychogeriatric assessment of persons with cognitive impairment/dementia and how increased needs, in particular unmet needs, are related to the level of cognitive impairment and dependency. The most frequent needs found in the study sample are related to physical health (85.5%), visual and auditory deficits (40.9%), distress and anxiety (37.1%) and the state of the home (36.5%), relating in general to the domains of self-care and the physical and psychological sphere. Variable results have been found in other studies, although most are related to the physical, psychological and environmental spheres (social variables). This is the case of the study conducted by Tiativiriyakul et al. ( 28 ), that of Hoogendijk E et al. ( 29 ) and that of Magalhaes Sousa R et al. ( 30 ). Regarding the most frequent unmet needs in people with cognitive impairment, the study found them to be memory, home care, financial management and physical health. In their research based on the Actifcare Cohort Study, Gonçalves-Pereira M et al ( 32 ) reported that unmet needs were mostly related to companionship, stress/anxiety and activities of daily living. These results were also found in the study by Mazurek et al ( 33 ), as well as in the study by Tobis S et al ( 34 ), Kerpershoek K et al ( 35 ) and Miranda-Castillo C et al. ( 11 , 36 ). The most frequent total needs (met and unmet) in our study of people with cognitive impairment were home care, personal care, financial management, nutrition, mobility, memory, anxiety/stress, activities of daily living, and continence. In the study conducted by Bohlken J et al ( 37 ), psychological disorders (related to mood, anxiety/stress), limitation in activities of daily living and memory disorders were highlighted. In their study, Tapia-Muñoz T et al. ( 38 ) described home care, nutrition and self-care as the most frequent needs. In the study carried out by Van der Ploeg ES et al ( 39 ), comparing the needs of people over the age of 65 with and without dementia living in a nursing home, it was found that the most frequent needs were those related to housing, financial management, continence, medication, memory, risk of (accidental) self-injury, companionship and activities of daily living (as in our study). This result was the same as that reported by Worden A et al ( 40 ) or the study by Hancock G et al ( 41 ). In the present study, one of the main results is the relationship between the diagnosis of cognitive impairment/dementia and needs, finding that they are related to an increase in the number of needs (both met and unmet) and an increase in dependency. Furthermore, the severity of the cognitive impairment has been shown to increase needs (both met and unmet). The main conclusion of our study is that the use of an instrument that allows the analysis of a large number of biopsychosocial needs, such as CANE, provides essential information for a comprehensive psychogeriatric assessment of the person with dementia and will facilitate the implementation of personalized care, as recommended in virtually all good clinical practice guidelines for psychogeriatric and, more specifically, for dementia care ( 2 , 3 , 7 – 10 ). Another potential weakness of the study is reliance on outdated data. The study is based on the reanalysis of data from an epidemiological study conducted two decades ago, which might affect the relevance of the findings in the current context. Without a doubt, the needs of the population change over time, as a consequence of advances in knowledge and in social protection systems (although they may also regress), and of other social and political changes, for example. But our study is not a descriptive study of the prevalence of the current needs of older persons in Galicia (although it was at the time). The aim of this study is to analyze the structure of the concept of needs, using data to demonstrate something that is conceptually reasonable to expect: that the study of needs is not opposed to or an alternative to clinical diagnoses or functional assessment, but rather it adds another layer of knowledge about the health status of the older population and helps professionals to make better clinical decisions and social interventions. Due to the fact that the study analyzes the relationship between different types of variables: diagnoses of mental and somatic disorders, functionality, and formally assessed needs, we consider that the age of the sample does not constitute an impediment. This could have been the case if this analysis had been performed on a very specific sample of patients (for example, health care services that no longer exist or whose functioning have been greatly modified over two decades). But we consider that this analysis is valid due to the fact that it is a community sample that is representative of the general population of Galicia. While many things have changed in the past two decades in this territory, we believe that the sociological and healthcare structure and social resources have not experienced radical changes. One of the strengths of the CANE, the instrument chosen to measure needs, is that it includes (in three separate columns) the perspective of the user and the caregiver, as well as the perspective of the professional, making it favorable over other instruments that measure needs. In other words, the caregivers of all the dependent older persons in our study provided insight into the needs of these individuals. Likewise, users always provided their own perspective of their needs, except in cases of dementia so advanced that it prevented dialogue with the patient (which marks the natural limitation of collecting the user’s perspective of their needs). In all cases, with the information provided by the subject and the caregiver (where applicable), the research team drew up the professional opinion on the needs of that older person (i.e., the epidemiological study has mimicked the use of CANE in routine clinical practice). It seems appropriate to point out that the field study that generated these data was one of the first in the history of CANE, if not the first, to be used in an epidemiological survey ( 14 ). As indicated in the brief description of the methods used to prepare the study sample, it includes not only subjects with all levels of severity of cognitive impairment and physical dependence, but also “healthy” subjects (regarding cognitive impairment, depression, Basic Activities of Daily Living and Instrumental Activities of Daily Living). This enables a holistic and detailed analysis of the relationship between needs and the successive stages of cognitive impairment. | Review | biomedical | en | 0.999997 |
PMC11697483 | Schwannomas are tumors originating from the Schwann cells of peripheral nerves. Approximately 25–45% of schwannomas occur in the head and neck region , followed by the limbs . Schwannoma in the pancreas is extremely rare. Pancreatic schwannomas are usually solid or cystic benign tumors, though some may have a tendency for malignant transformation , and their pathogenesis remains unclear. It primarily affects individuals between the ages of 20 and 50, with no gender preference. Most patients present with gastrointestinal symptoms, such as nausea, vomiting, and indigestion, although some cases are asymptomatic. Currently, the treatment for pancreatic schwannomas primarily involves surgical resection. Pancreaticoduodenectomy (PD) and distal pancreatectomy (DP) are the main surgical approaches reported in most cases, with only one report detailing a case where central pancreatectomy (CP) was performed . In this article, we present a report on a 44-year-old female patient with pancreatic schwannoma and diabetes who underwent CP, and conduct a review of the relevant literature. In 2021, a 44-year-old female presented to a local hospital with upper abdominal discomfort. Abdominal Computed Tomography (CT) revealed a pancreatic mass, and she was subsequently transferred to our hospital for further treatment . Physical examination showed a deep mass in the upper abdomen, approximately 7 cm × 7 cm in size, with a hard consistency and poor mobility. No other significant abnormalities were noted on the rest of the examination. The patient had a 2-year history of type 2 diabetes with poor medication control. Tumor markers, including CEA, CA19-9, and CA72-4, were within normal limits, but neuron-specific enolase (NSE) was elevated. Insulin: 36.57 mIU/L, C-peptide: 1.9 nmol/L, albumin: 37.6 g/L and LDH: 201 U/L. Abdominal CT revealed a 64 mm × 54 mm mass in the body of the pancreas, with clear borders and no enhancement on the slice, and mild dilation of the main pancreatic duct was observed, but no evidence of metastasis was found . Due to the patient’s financial constraints, she refused a magnetic resonance imaging (MRI) scan, which hindered the accuracy of our diagnosis. MRI, with its ability to assess tumor characteristics through various sequences such as T1 and T2, can more accurately display the tumor’s morphology and its relationship with surrounding tissues, which is extremely helpful for diagnosing solid tumors. Clinically, pancreatic cystic tumors are more common than pancreatic schwannomas, and the CT features of pancreatic solid pseudopapillary neoplasms (pSPN) can closely resemble those of pancreatic schwannomas. Based on the patient’s symptoms and laboratory results, our preliminary diagnosis was pSPN. The Royal Marsden Hospital score indicated a low-risk group, suggesting a relatively favorable prognosis . After obtaining informed consent from the patient and her family, our treatment team performed an exploratory laparotomy. During surgery, a mass approximately 8 cm × 7 cm × 4 cm was palpated in the body of the pancreas. Therefore, the patient underwent CP and Roux-en-Y pancreaticojejunostomy. The postoperative CT images of the patient are shown in Fig. 3 , the direction indicated by the arrows in pictures A and B shows the pancreatic remnant, while the arrows in pictures C and D indicate the location of Roux-en-Y pancreaticojejunostomy, with the pancreatic stent clearly visible at the central part of the pancreas. Frozen section analysis was performed on the mass that was completely resected. The frozen section labeled “pancreatic mass” revealed tumor cells that were polygonal or round in shape, uniform in morphology, and arranged in cords or blocks. Foam-like stromal cells were observed. We suspected that the mass was a pSPN, which needs to be differentiated from pancreatic neuroendocrine neoplasm. The paraffin section showed that the tumor cells had round or polygonal nuclei, with fine granular chromatin. Some nuclei contained visible nucleoli, and the cellular boundaries were not well defined. The cells were arranged in a palisading pattern in some areas, and the other were arranged in a rope-like or fascicular pattern, or in a pseudo-glandular or sheet-like arrangement . Immunohistochemical results : S100 (+), P53 (+), CK5/6 (-), CD56 (+), CD68 (+), Ki − 67 hot zone (< 5% +), NSE (+). The diagnosis was pancreatic schwannoma. Fig. 3 Postoperative CT images after Roux-en-Y Pancreaticojejunostomy. A : Arterial phase of contrast-enhanced CT; B : Venous phase of contrast-enhanced CT; C : Arterial phase of contrast-enhanced CT; D : Venous phase of contrast-enhanced CT; Note: The arrows in pictures A and B indicate the residual pancreatic head; The arrow in picture C indicates the location of the anastomosis of pancreaticojejunostomy; The arrow in picture D indicates the residual pancreatic tail Fig. 4 Pathological photograph. A : Resected pancreatic schwannoma; B : H&E ×100, a large amount of foamy histiocytes were deposited in the interstitium; C : H&E ×200, the epithelioid tumor cells were arranged in strips and sheets, and the stroma was collagenous; D : H&E ×200, lymphocyte aggregation at the edge of the tumor After surgery, the patient developed abdominal pain and fever. Amylase and lipase levels in the abdominal drain fluid were elevated, indicating a Grade B pancreatic fistula. The patient was treated symptomatically with fasting, nutritional support, antibiotics, and gastric lavage. After these treatments, her symptoms resolved, and the amylase levels in the drain fluid returned to normal. Preoperatively, her fasting venous blood glucose was approximately 8–15 mmol/L, controlled by oral medications, but with poor efficacy. Postoperatively, her fasting venous blood glucose fluctuated between 12 and 20 mmol/L with insulin therapy. After her feeding, subcutaneous insulin injections were used to maintain blood glucose levels below 11.1 mmol/L. About 40 days after surgery, her treatment was adjusted to oral hypoglycemic medications, and her venous blood glucose was stabilized at around 10 mmol/L. At a 32-month follow-up after discharge, no tumor recurrence was observed, and the patient’s blood glucose was controlled below 11.1mmol/L with only oral antidiabetic drugs. The patient fully understood the purpose of this case report and its contents, and she signed an informed consent form allowing the publication of her relevant medical information. Schwannomas are tumors originating from Schwann cells, which surround the myelinated nerve fibers. Schwannomas are generally benign, with approximately 10–15% undergoing malignant transformation . These tumors are most commonly found in the limbs, neck, mediastinum, retroperitoneum, and posterior nerve roots of the spinal cord . The majority of patients present initially with a painless mass, and other signs and symptoms vary depending on the tumor’s anatomical location . Zhang included 75 reported cases of pancreatic schwannomas, with abdominal pain being the most common symptom (44%), followed by asymptomatic patients (31%), and other symptoms include weight loss, mass, and jaundice . Pancreatic schwannomas are extremely rare , and their growth pattern is similar to that of schwannomas found in other parts of the body. However, pancreatic schwannomas typically present with nonspecific abdominal pain . The most common location for pancreatic schwannomas is the head of the pancreas, followed by the body, tail, and uncinate process . A literature search was conducted in September 2024. The MeSH term “pancreatic schwannoma” was used in searches on both PubMed and China National Knowledge Infrastructure (CNKI). The PubMed search for the past decade yielded 38 articles describing 41 detailed cases of pancreatic schwannoma in the English literature. The CNKI search for the past decade identified 4 articles describing 4 detailed cases of pancreatic schwannoma in the Chinese literature (Detailed documents are provided in the supplementary materials ). We analyzed and summarized the 45 cases of pancreatic schwannoma identified from the searches, with clinical and pathological data summarized in Table 1 . Table 1 Summary of clinicopathological data from all 45 cases of pancreatic schwannoma reported in the recent 10 years N (%) or Mean ± SD Age (year) ( n = 45) ≤ 30 4 30–60 22 ≥ 60 19 55.43 ± 14.839 Sex ( n = 45) Male 15 Female 30 Male: Female 1:2 Symptoms ( n = 41) Abdominal pain 20(48.78%) Abdominal bloating 2 (4.88%) Diarrhea 1(2.44%) Nausea/ Vomiting 3(7.32%) Indigestion 3(7.32%) Weight loss 4(9.76%) Jaundice 2(4.88%) No symptoms 17(41.46%) Tumor location ( n = 45) Head 19(42.22%) Head + body 6(13.33%) Body 11(24.44%) Body + Tail 1(2.22%) Tail 8(17.78%) Nature of tumor on imaging ( n = 44) Soild 28(63.64%) Cystic 9(20.45%) Soild + Cystic 7(15.91%) Preoperative diagnosis ( n = 36) Pancreatic Schwannoma 16(44.44%) Pancreatic cystadenoma 8(22.22%) Pancreatic solid pseudopapillary neoplasm 8(22.22%) Neuroendocrine neoplasm 1(2.78%) Acinic cell carcinoma 1(2.78%) Pancreatic cancer 2(5.56%) Accuracy 35.60% Surgical methods ( n = 40) Enucleation of tumor 10(25.00%) Pancreaticoduodenectomy 9(22.50%) Distal pancreatectomy 11(27.50%) Central pancreatectomy 2(5.00%) Conservative treatment 8(20.00%) Note: Because some patients come in with multiple symptoms, the percentage in the symptoms column will be greater than 100% Due to the lack of specific diagnostic methods, preoperative diagnosis of pancreatic schwannoma is challenging. In the absence of pathological results, imaging is often a key tool for preoperative diagnosis. On CT, pancreatic schwannomas typically present as well-defined, round or oval masses with clear borders, marked cystic degeneration, and punctate calcifications. CT contrast enhancement shows localized cystic changes within the tumor, with areas of low density and no enhancement . Malignant transformation of pancreatic schwannomas is characterized by rapid growth, infiltration of surrounding tissues, and the presence of irregularly shaped, solid, heterogeneous masses, with possible lymph node metastasis . Additionally, the tumor may show the formation of vascular thrombosis. On MRI, a well-defined pancreatic mass appears as heterogeneous high signal intensity on T2-weighted images, with distinct low signal intensity on T1-weighted images, and high signal intensity on diffusion-weighted imaging. The mass shows mild enhancement in the arterial phase, with further enhancement in the portal venous and delayed phases. These imaging features suggest a possible diagnosis of pancreatic schwannoma . The diagnosis of schwannoma requires differentiation from other pancreatic tumors, such as pancreatic cystic tumors, pancreatic neuroendocrine neoplasms, pancreatic solid pseudopapillary neoplasms (pSPN), and pancreatic cancer. Pancreatic cystic tumors primarily present as cystic lesions on imaging, characterized by fluid-filled dark areas, often with multilocular structures and minimal solid components, which differ significantly from pancreatic schwannomas. Pancreatic neuroendocrine neoplasms share both cystic and solid components, similar to schwannomas, but neuroendocrine neoplasms tend to exhibit a dense vascular pattern, leading to homogeneous enhancement on contrast-enhanced CT , which is not consistent with the imaging features of pancreatic schwannomas. pSPN are also mixed solid-cystic masses, making them difficult to distinguish from pancreatic schwannomas. Moreover, pSPN can also present as cystic masses or calcified cystic tumors . Although pancreatic schwannoma and pSPN have similar imaging findings, pSPN does not express NSE, whereas pancreatic schwannoma does. Therefore, these two diseases can be differentiated through a combination of imaging studies and laboratory examinations. Early pancreatic cancer can present as a solitary solid mass similar to pancreatic schwannoma. However, pancreatic cancer has distinct features, such as elevated CA-199 levels, significant enhancement on contrast-enhanced CT and clear signs of tissue invasion, which help differentiate it from pancreatic schwannomas. Compared to CT, PET/CT is more sensitive for the diagnosis of pancreatic cancer . Therefore, in our data, the misdiagnosis rate for pancreatic cancer is relatively low. Since the first case of endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) was performed in 1997 , EUS-FNA has been very helpful for the preoperative diagnosis of pancreatic schwannoma [ 20 – 24 ]. With the development of technology, the sensitivity of EUS-FNA for determining the nature of a tumor can exceed 90%, with a specificity of over 97% . This technique plays a crucial role in formulating precise treatment plans, not only optimizing medical decisions but also significantly improving treatment outcomes and prognosis for patients. Currently, the diagnosis of pancreatic schwannoma mainly relies on histopathology and immunohistochemical staining. Pancreatic schwannomas are uniform, yellow-brown nodules with clear boundaries and an intact capsule observed macroscopically [ 27 – 29 ]. Microscopically, they typically exhibit two types of tissue structures: Antoni A and Antoni B. The Antoni A area is characterized by a rich presence of spindle-shaped cells, usually arranged in a palisade pattern or forming Verocay bodies . Tumor cells in the Antoni A area have very few mitotic figures, typically less than 5 mitotic figures per 10 high-power fields . In contrast, the Antoni B area has fewer tumor cells, which are arranged in a sparse network-like structure. There is a large amount of fluid and mucinous matrix within and between cells, forming cystic structures, typically exhibiting degenerative changes such as myxoid changes, cyst formation, stromal hemorrhage, and calcification . On CT, Antoni A-type pancreatic schwannomas appear as low-density solid masses with an uneven enhancement pattern, occasionally with multiple septal enhancements. Antoni B-type pancreatic schwannomas tend to appear as homogeneous cystic or multiple masses . The more vascularized Antoni A areas typically show enhancement, while Antoni B areas show no enhancement . Almost all benign schwannomas contain abundant S100 (+) cells, while only about 50% of malignant schwannomas show S100 (-), suggesting that S100 can be used as an initial marker to differentiate between benign and malignant schwannomas [ 30 , 35 – 38 ]. NSE is a glycolytic enzyme isozyme primarily found in the cytoplasm of central and peripheral neurons, as well as neuroendocrine cells, and is an important marker for diagnosing various neuroendocrine neoplasm . Through literature review, we found that pancreatic tissue-derived tumors rarely express this enzyme . Therefore, the strong positive staining for S100 and NSE in this case provides solid evidence for the diagnosis of pancreatic schwannoma. Most schwannomas grow slowly, with an average growth rate of 1.2 mm per year . Small schwannomas can be monitored periodically . However, for symptomatic schwannomas, surgical treatment is necessary. Regarding surgical options for pancreatic schwannoma, in cases with a confirmed diagnosis, complete resection can achieve the therapeutic goal. However, if the preoperative diagnosis is unclear, the tumor should be completely resected during surgery, and frozen section pathology should be performed to determine the extent of resection. In a previous review of 65 cases of pancreatic schwannomas, Fukuhara et al. found that schwannomas most commonly occur in the head of the pancreas (40%), followed by the body (23.1%), tail (10.8%), and uncinate process (10.8%). The most common treatment approach is pancreaticoduodenectomy (34%), followed by distal pancreatectomy (25%) and enucleation (14%). The pancreas is a key organ responsible for secreting various hormones and digestive enzymes. Insulin and glucagon are secreted by the β-cells and α-cells of the pancreas, respectively, and play a central role in glucose metabolism . Pancreatic resection can be categorized into two main types: partial and total. Total pancreatic resection results in complete loss of both endocrine and exocrine functions of the pancreas, leading to difficulty in achieving glucose control . In contrast, partial pancreatic resection preserves both the endocrine and exocrine functions of the pancreas, making it easier to manage blood glucose levels compared to total pancreatic resection. Partial pancreatic resection can be further subdivided into pancreaticoduodenectomy (PD), distal pancreatectomy (DP), and central pancreatectomy (CP). After PD, about 50% of the pancreatic tissue remains, which leads to a reduction in the secretion of insulin and glucagon . For patients with preexisting diabetes, this operation may worsen their condition. Additionally, PD significantly alters the digestive system and reduces exocrine function , making it unacceptable for patients with non-malignant tumors who do not require radical surgery . After DP, approximately 30-40% of the pancreatic tissue remains . Compared to PD, DP has a relatively smaller impact on the structure of the digestive system. However, this operation inevitably involves the removal of a considerable amount of healthy pancreatic tissue, which can significantly affect the postoperative recovery of pancreatic function . In contrast, CP preserves more pancreatic tissue (and sometimes the spleen), which greatly facilitates the recovery of pancreatic function post-surgery. Studies have shown that the incidence of new-onset diabetes after CP is lower than after PD and DP , suggesting that CP has a lesser impact on pancreatic function and a better blood glucose control for diabetic patients. However, CP also has certain drawbacks. Due to the necessity of carefully managing both ends of the pancreatic remnant, CP requires longer operating times and is associated with a higher incidence of pancreatic fistula compared to PD and DP. A meta-analysis by Bi et al. comparing the advantages and disadvantages of DP and CP supports this conclusion. The surgical time in the DP group was significantly shorter than CP group, but intraoperative blood loss was higher in the DP group. Regarding postoperative complications, the incidence of pancreatic fistula in the CP group (36.9%) was significantly higher than DP group (20.2%). The incidence of severe postoperative complications (Clavien-Dindo grade III or higher) in the CP group (21.8%) was also higher than DP group (12.8%). However, the incidence of endocrine insufficiency after surgery in the CP group (6.7%) was much lower than DP group (20.6%), and the incidence of new-onset or worsened diabetes in the CP group was also lower than DP group . On the other hand, another article indicated no significant difference in the probability of pancreatic fistula between the CP and DP groups . This discrepancy may be attributed to the surgeon’s technical skills, suggesting that CP can minimize its drawbacks and effectively prevent postoperative metabolic disorders through precise technique and enhanced postoperative care, ultimately ensuring a higher quality of life for patients after surgery. Additionally, after comparing 34 patients in the CP group and 262 patients in the DP group, Chen YW et al. found that no new-onset or worsening diabetes occurred in the CP group, while 40 patients in the DP group developed endocrine insufficiency after surgery ( P < 0.05), and the incidence of exocrine insufficiency was significantly higher in the DP group . Some studies have pointed out that poor blood glucose control increases the risk of surgical site infections . Therefore, CP can preserve both endocrine and exocrine pancreatic functions postoperatively, reducing the incidence of new-onset or worsening diabetes , which offers long-term benefits for the patients. In this case, the patient’s diabetes remained stable after surgery, with oral medication treatment, demonstrating the therapeutic value of CP for patients with pancreatic schwannomas and diabetes. In conclusion, pancreatic schwannoma is a rare disease that presents unique challenges in both diagnosis and treatment. Due to the lack of specific clinical symptoms and typical imaging features, the preoperative misdiagnosis rate remains high, making it a significant challenge to improve diagnostic accuracy. However, once diagnosed, surgical treatment typically yields favorable outcomes and prognosis. In this case, we chose CP and achieved significant therapeutic success. Our treatment experience, combined with findings from previous literature, suggests that CP may be a more ideal surgical approach for patients with pancreatic schwannoma and diabetes. | Clinical case | biomedical | en | 0.999997 |
PMC11697502 | Arteriovenous fistulas (AVFs) of the filum terminale (FTAVFs) are rare vascular malformations that can present with symptoms ranging from low back pain (LBP) to severe radiculopathy . Overall, vascular malformations of the spine are relatively rare (3% of all spinal arteriovenous shunts), with lesions occurring caudal to the conus medullaris infrequently observed . FTAVFs are perimedullary arteriovenous malformations (AVMs) that are found on the surface of the pia mater and are without a capillary bed between arterial and venous systems . These lesions are classified as type IV arteriovenous malformations of the spinal cord and are subcategorized into type IVa, type IVb, and type IVc by Anson and Spetzler . Type IVa lesions are low-flow AVFs supplied by a single anterior spinal artery (ASA) branch. Type IVb lesions are intermediate-flow fistulas with multiple arterial feeders. Type IVc lesions are high-flow fistulas supplied by several ASA or posterior spinal artery branches . Over time, these fistulas contribute to the development of myelopathic or radicular symptoms, secondary to abnormal vascular flow and venous congestion, resulting in arterial insufficiency . Treatment for FTAVFs includes endovascular embolization or open microsurgical resection . The treatment choice is made for each patient individually, depending on vascular characteristics and institutional resources . Importantly, lesions that are not completely obliterated surgically or endovascularly are at high risk of recurring with worsening of symptoms. In this report, we present the case of a 64-year-old male who presented to the hospital with lower back pain and proximal bilateral lower extremity weakness. Additionally, we provide a current literature review of reported cases of FTAVFs. A 64-year-old male of African descent presented to the emergency room with lower back pain and bilateral lower extremity weakness of several months’ duration. His only neurological deficit was 4/5 strength in the bilateral lower extremities, most notably proximally in the hip flexors and extensors. An outpatient MRI of the thoracic spine demonstrated cord edema from T7-conus medullaris and multiple flow voids consistent with intradural vessels overlying the spinal cord, which progressed to T1-L2 cord edema on the preoperative MRI scan . A spinal digital subtraction angiogram (DSA) demonstrated a perimedullary arteriovenous fistula spanning the L2-5 vertebrae supplied by the ASA originating from the artery of Adamkiewicz . Angiographic embolization of the lesion under general anesthesia was offered and scheduled. Somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs) were monitored for the procedure. A 5-Fr Cobra tip femoral angiography sheath was introduced through the left femoral artery and advanced cranially through the descending aorta under fluoroscopy during the procedure. Contrast dye and overlay mapping were then utilized to identify the artery of Adamkiewicz, which originated at the level of the left L2 intervertebral foramen. Once the AVF was isolated on fluoroscopy, a preembolization trial with lidocaine and pentobarbital greatly diminished SSEPs in the lower extremities, with a similar loss of MEPs. Due to the loss of neuromonitoring signals, it was considered unsafe to proceed with the embolization, and open surgical treatment was planned. In situations where endovascular embolization results in loss of neuromonitoring, open approaches are preferred as occlusion of the feeding artery/arteries can be rapidly reversed by removing the temporary clip to avoid permanent detriment to the spinal cord, which may not be readily resolved during embolization procedures. Following team and patient discussions, microsurgical obliteration of the AVF through an open surgical approach was planned. Following the L2-4 laminectomy, the dura was longitudinally opened under microscopic visualization. After proper extradural hemostasis was achieved, the dura was opened longitudinally and tacked up to the laterally dissected paraspinal musculature. At this point, the cauda equina and filum terminale came into view. A prominent arterialized vein coursing alongside the filum was identified. Indocyanine green (ICG) video angiography confirmed arterialization of the vein at the lower end of L4 with contiguous vessels visualized going caudally and another traveling cephalad. A temporary clip was then applied just cephalad to the site of the AVF, and intraoperative angiography confirmed occlusion of the AVF. No signal change from baseline in neuromonitoring occurred. A permanent clip was then deployed cephalad to the first clip, followed by bipolar cauterization of the filum terminale between the two micro-vascular clips . The filum terminale was divided, and adequate closure was then achieved in a multilayer fashion. No surgical specimen was sent for pathologic diagnosis. The patient tolerated the procedure well and his lower extremity weakness was mildly improved compared to presurgical assessment. Postoperative spinal angiography displayed resolution of the FTAVF. Ten days following discharge, while in acute rehab, the patient experienced severe shortness of breath and was diagnosed with a saddle pulmonary embolism. Interventional thrombectomy was attempted and successful. A right lower extremity deep venous thrombosis (DVT) was identified with compression ultrasonography (US). Due to contraindications for antiplatelets and anticoagulants, an inferior vena cava (IVC) filter was placed. The patient was stabilized and discharged to subacute rehabilitation. Here, we present a case of a 64-year-old male patient presented with myelopathic symptoms of the lower extremities. The patient’s symptoms had quickly progressed from LBP to lower extremity pain and weakness, for which an MRI with and without contrast of the lumbar spine was appropriately performed, demonstrating spinal cord edema from T1-L2. Further investigation revealed a FTAVF at the level of L2-L5, originating from the artery of Adamkiewicz. Endovascular intervention was planned. However, following changes in neuromonitoring during the endovascular approach, the patient underwent successful open surgical intervention. Spinal AVMs are rare tortuous vascular lesions that often arise in pediatric populations . In 1987, Rosenblum et al. proposed a four-tier classification system for spinal AV shunts . In 1992, Anson and Spetzler further developed the system by adding subclassifications for type IV lesions (Table 1 ) . The lesion in the present case fits with a type IVa AV shunt . These lesions are low-flow, high-pressure systems that are often unstable and unpredictable. Due to the low flow in this system, ischemia can occur in the supplied tissue, a condition known as "Foix-Alajouanine syndrome" or "subacute necrotizing myelopathy." This involves progressive congestive ischemia of the spinal cord, which develops over months or years . Progressive myelopathy, radiculopathy, LBP, and bladder or bowel incontinence may also occur during the course of the disease. Due to the high pressure of this system, these lesions are vulnerable to rupture, resulting in hemorrhaging into the subarachnoid space. Rapid, excruciating back pain is often the first symptom, classically referred to as “Coup de poignard of Michon" . Efficient diagnosis and treatment are crucial to avoid catastrophic outcomes in these patients, which may involve permanent damage to the spinal cord and possibly death. In cases of FTAVFs, the main cause of neurological symptoms is unlikely to be due to direct ischemia or compression of the AVF on the FT or adjacent nerve roots. The cauda equina typically has adequate space to maneuver and the FT rarely carries any meaningful neurologic signals. The symptoms are thought to be primarily due to the venous congestion caused by the AVF, affecting the levels of cephalad to the fistula, and can cause myelopathic or radicular symptoms . On MRI, venous congestion is visualized in the form of spinal cord edema at the spinal levels, where congestion has impacted normal vascular dynamics . Importantly, this edema, and presumably the venous stasis, is typically improved or eliminated when FTAVFs are promptly treated . Many patients affected by FTAVFs also present with lumbar spinal stenosis, leading some to hypothesize that longstanding neural compression and inflammation can contribute to AVF formation . In the cases indexed in this literature review, 17 cases reported the presence of lumbar spinal stenosis (nine cases reported the absence of lumbar spinal stenosis and 32 cases failed to report the absence or presence of stenosis). The presence of concurrent lumbar stenosis has the potential to mask the true cause of symptoms, especially when symptoms are primarily radicular, causing a delay in diagnosis. Treatment for FTAVFs may include surgical, endovascular, or radiotherapeutic management. The surgical approach has previously been established as the modality of choice, with the first successful treatment in 1916 . This approach involves occlusion of the receiving vein of the shunt, with definitive interruption of other spinal draining veins. This is crucial for successful treatment, as occlusion of arterial feeders may result in re-establishment of the fistula via recruitment of new arterial feeders, which can lead to relapsing symptoms . Surgical management has been shown to be the most definitive treatment . However, endovascular treatment has recently seen a surge in popularity in treating spinal AVFs . Many institutions utilize endovascular techniques as first-line treatment as it is less invasive. While no difference has been seen when comparing complication rates between surgical and endovascular management for spinal AVFs, embolization is associated with a much higher failure rate, with patients often having to return for open surgery or repeat endovascular embolization . Finally, stereotactic radiosurgery has also been described in the literature as a means to treat dural AVFs . However, it has not been established as a mode of treatment for perimedullary AVFs, and with the availability of other effective treatment options, radiosurgery is currently not recommended as a management option in most AVF cases . We indexed and reviewed 24 articles with 59 cases in the literature that reported FTAVFs with either progressive myelopathy and/or radiculopathy. The identified feeding vessel(s) and subsequent draining vein(s), chosen treatment options, complications, and outcomes are shown in Table 2 . The patients' ages ranged from 3 to 84 years, with 38 males, nine females, and two unidentified. FTAVFs were more common in males, which is consistent with previously published literature . We compared the approach of treating AVFs (for which both endovascular and microsurgical approaches have been frequently utilized) by observing outcomes and intraoperative or postoperative complications. Both approaches offered positive outcomes, resulting in improvement, if not resolution of symptoms in a majority of cases. However, in previously reported cases of FTAVF, endovascular treatment was associated with more complications (46.7%), with failed embolization being the reported complication in all cases, requiring repeat embolization or subsequent microsurgical intervention. There were two cases of microsurgical complications in which patients experienced worsening urinary symptoms. Cases treated with microsurgery reported higher success rates with complete resolution being identified in 14 of the 59 cases. Compared to endovascular approaches which had no cases reporting complete symptom resolution. Finally, microsurgical management reported two cases where symptoms were unchanged, compared to one case that was approached endovascularly. This case demonstrates the importance of early identification and treatment of AVFs, as well as the importance of a multidisciplinary therapeutic approach. In this case, endovascular embolization was attempted; however, it was aborted due to loss of neuromonitoring signal, and open surgical management was scheduled. Successful treatment was achieved with microsurgery, with improvement immediately postoperatively. While endovascular management is often highly successful in treating FTAVFs, surgeons should be prepared for microsurgical treatment if embolization fails or is unsafe to proceed. | Clinical case | biomedical | en | 0.999998 |
PMC11697511 | Tradeoffs in life-history strategy are key features in animal evolution . These tradeoffs often involve differential investments in life-history traits such as growth rate ; reproductive maturation, timing, and fecundity ; or resistance to stress , predation , or disease . The fitness costs and benefits of these investments are often context-dependent and shifts in ecological or environmental conditions can favor some life-history strategies over others , sculpting trait evolution within animal lineages and reshaping ecological communities. Global climate change is shifting the patterns and prevalence of disease in many animal taxa, while increasing the virulence of some pathogens . Identifying evolutionary tradeoffs and resulting trait correlations associated with disease susceptibility can therefore help predict how species survival will shift with climate change. Although much research on evolutionary tradeoffs focuses on the traits of animals themselves, it is also well documented that the physiology , fitness and even behavior of many animals are influenced by their microbiomes. Animal microbiomes have been linked to multiple key life-history traits, including growth , development rate , fecundity , stress resistance , and disease susceptibility . It therefore seems likely that microbial symbiosis is an important aspect of animal life-history tradeoffs and may correlate with host traits over long periods of animal evolution. However, testing the potential relevance of microbial symbiosis for life-history strategy evolution over long time periods is challenging. The reef-building corals that have evolved over 425 million years represent a diverse group of animals, including an estimated >1600 species , with an extensive fossil record, and a well-known variety in both life-history strategy and microbial symbiosis [ 16 – 18 ]. As such, they present a valuable opportunity to explore connections between microbes and life history strategy. These animals also have special ecological and societal importance, as corals are foundational to reef ecosystems that support some of the most biodiverse assemblages on the planet and the livelihoods of many coastal communities . Yet the ancient diversity of coral reefs is currently threatened by global climate change, which is driving both dramatic mass bleaching events and increased prevalence and severity of disease outbreaks . Alongside research on how coral health is affected by both well-studied (e.g., Symbiodiniaceae [ 20 – 22 ]) and emerging (e.g., corallicolids, fungi ) microbial eukaryotes, extensive research has demonstrated that present-day communities of coral-associated bacteria and archaea (hereafter ‘coral microbiomes’) play a myriad of roles in host biology that could impact disease susceptibility. These include antimicrobial production , predation of pathogens , jamming of quorum-sensing systems , and passive competition for space and resources. Yet these microbiomes are also influenced by host traits , local environmental factors, and ecological context , including host disease susceptibility patterns within and among species . While this supports a connection between present-day coral life-history, microbiome structure and disease susceptibility, these data do not directly allow for statistical testing of evolutionary hypotheses about potential roles of microbial symbiosis in life history tradeoffs. Clarifying whether microbiome structure and coral life-history traits correlate over coral evolution globally will contextualize studies of extant coral symbiosis and disease at local or regional scales. Several lines of research have created a strong foundation on which such comprehensive comparative evolutionary analyses can be built. Coral disease patterns have been intensively researched, and an increasing number of datasets are now openly available . Well-curated global databases of coral physiological traits have been established and mapped to coral life-history strategies . Finally, several large cross-species studies of corals and their microbiomes have been launched. These advances provide an opportunity to compare host trait data and microbiome structure from across the coral tree of life. Here, we test whether microbiome structure correlates with two key aspects of coral life history strategy: disease susceptibility and growth rate. To address this question quantitatively, we first characterized the microbiome composition from visibly healthy samples of 40 coral genera using 16S rRNA gene amplicon sequencing results from the Global Coral Microbiome Project (Supplementary Data Table S1 a), and subsequently combined these data with coral growth rates from the Coral Trait Database , and genus-level long-term disease prevalence data from several tropical regions around the globe . These long-term disease datasets included the Florida Reef Resilience Project data (FRRP, https://frrp.org/ ) , Hawaiʻi Coral Disease Database (HICORDIS) , and new data covering eastern Australia (this study; Supplementary Data Table S1 b). With the resulting microbiome structure, coral growth rate, and disease data across a global distribution of coral genera (Supplementary Data Table S1 c), we compared these traits using methods that account for phylogenetic correlations using a time-calibrated multi-gene reference tree of corals . Fig. 1 Conceptual overview of data sources integrated for the project. ( A ) Map of sampling locations for coral microbiomes analyzed in the manuscript. Pie charts show the proportion of coral samples from families in the Complex clade (cool colors) and Robust clade (warm colors). Samples were collected from coral mucus, tissue, and endolithic skeleton (see Methods). ( B ) Schematic representation of data integration for the project. Coral microbiome data (as shown in A) were combined with long-term disease prevalence data from 3 projects (the Florida Reef Resilience Program (FFRP), the Hawaiʻi Coral Disease Database (HICORDIS), and data from Australia (this study)), as well as coral trait data from the Coral Trait Database, and a molecular phylogeny of corals (see Methods). To integrate data from these disparate sources, all annotations were pooled at the genus level. The end product was a trait table of microbiome, taxonomic, physiological, and disease data across diverse coral genera The microbiome of corals is often dominated by a few highly-abundant taxa that demonstrate species-specificity , though why these highly-abundant microbial taxa differ across coral diversity is unknown. To test this, we first identified a restricted set of dominant bacterial or archaeal taxa in visibly healthy corals retrieved from mucus, tissue, and skeleton samples of 40 coral genera. (‘Dominant taxa’ were defined as those that are most abundant on average within all samples from a given portion of coral anatomy in a given coral genus). Thirty-eight of the coral genera were dominated by the bacterial classes 𝛼 - or γ-proteobacteria, which are known to include common coral associates , with more detailed taxonomy revealing that the number of dominant bacterial and archaeal genera across compartments is also somewhat limited . For example, only 17 genera of bacteria or archaea accounted for the dominant microbial genus in the tissue microbiomes of all 40 coral genera (this number excludes 4 unclassified ‘genera’ that could not be classified to at least the order level). Mucus and skeleton showed similar trends, with only 16 and 25 dominant genera, plus 2 or 4 unclassified genera, respectively. Across coral-associated bacterial or archaeal genera, Pseudomonas was most commonly dominant in mucus (31.4% of coral genera), while Endozoicomonas was most commonly dominant in tissue (18%) and Candidatus Amoebophilus (13.5%) was most commonly dominant in skeleton microbiomes. However, whether differences in microbiome structure and dominant microbes across coral diversity influence differences in coral physiology is not yet well understood. Fig. 2 Dominant microbes in the coral microbiome. ( A ) Dominant bacterial or archaeal genera in coral mucus (cyan), tissue (orange), or skeleton (purple) microbiomes. Pie wedges represent the fraction of coral host genera in which the labeled bacterium is more abundant than all other bacterial or archaeal taxa. Cyan shades represent microbes dominant in mucus, oranges represent microbes dominant in tissue (but not mucus), purple shades represent microbes dominant in skeleton (but not mucus or tissue). Endozoicomonas , which is of special significance later in the paper, is highlighted in aqua. ( B ) Bar charts showing correlations between microbiome alpha and beta diversity metrics and disease, represented by the R 2 for PGLS correlations. Alpha diversity metrics include richness, evenness (Gini index), and dominance (Simpson’s index), and weighted UniFrac beta diversity metrics including the three principal component axes (PC1, PC2, PC3) that represent measures of community structure. Significant relationships ( p < 0.05, Supplementary Data Table S4 ) are marked by an asterisk (*). ( C ) Bubble plot showing correlations between dominant microbial taxa and coral disease prevalence. The size of each triangle represents the R 2 for PGLS correlations between disease susceptibility and microbial relative abundance for each listed taxon in either all samples (top row), mucus samples (cyan row), tissue samples (orange row), or skeleton samples (purple row). Colored points were significant ( p < 0.05, FDR q < 0.05) and hashed points were nominally significant ( p < 0.05, FDR q > 0.05; Supplementary Data Table S7 a). Points that were not significant or had too little data ( n < 5) for reliable testing are marked in white. Taxa whose relative abundance is significantly correlated with disease are marked in bold on the x-axis We visualized the evolution of coral disease susceptibility and multiple measures of microbiome diversity using ancestral state reconstruction , then tested whether microbial alpha or beta diversity correlated with disease susceptibility using phylogenetic generalized least squares (PGLS). We found no evidence for an effect of microbiome ecological richness or evenness (considered individually) on disease susceptibility (Supplementary Data Table S3 ), and limited evidence for an effect of microbiome composition on disease susceptibility (Supplementary Information; Supplementary Data Table S4 ). However, given that cross-species differences in a limited number of dominant microbes were very notable in the data, we hypothesized that corals with highly abundant bacterial taxa might display more disease vulnerability. To quantify this, ecological dominance among identified amplicon sequence variants (ASVs) was calculated using Simpson’s Index, which estimates the probability that two species drawn from a population belong to the same group, and thereby incorporates aspects of both richness and evenness simultaneously. We correlated Simpson’s Index against coral disease prevalence for either all coral samples, or those in mucus, tissue, or skeleton considered individually. In coral tissue, microbiome dominance significantly correlated with disease, explaining roughly 27% of overall variation in disease susceptibility across coral species . No other combination of alpha diversity measure and compartment correlated with disease after accounting for multiple comparisons . Thus, microbiome dominance as measured by Simpson’s Index was a far stronger predictor of coral disease susceptibility than 𝛼 -diversity measures that considered either richness or evenness individually. Regionally-specific analysis, which eliminates potential confounders due to the global nature of the comparison, recaptured this dominance-disease relationship (Supplementary Information; Supplementary Data Table S3 b). Further testing showed that corals dominated by γ-proteobacteria drove the dominance-disease trend, suggesting a specific microbial genus (rather than a general ecological feature) might be responsible for this striking correlation (Supplementary Information; Supplementary Table S3 c). Bacteria in the genus Endozoicomonas are among the most-studied γ-proteobacterial symbionts of corals. In several species Endozoicomonas forms prominent aggregates known as CAMAs (coral associated microbial aggregates) in coral tissue . In species where members of genus Endozoicomonas are common, decreases in relative abundance during coral bleaching or disease are frequently observed , suggesting a commensal or mutualistic rather than opportunistic relationship with host health, although evidence exists for the potential of Endozoicomonas to form relationships with corals along the entire spectrum of symbioses (i.e., beneficial, commensal, and/or antagonistic; see ). Further, it has previously been observed that the family Endozoicomonadaceae shows by far the strongest signal of cophylogeny with coral hosts among tested bacterial families in coral tissue . In the present dataset, Endozoicomonas was also the single genus that most typically dominated coral tissue microbiomes . We therefore tested whether the signal of microbiome dominance on disease susceptibility could be explained by the abundances of dominant taxa, and found that across all corals in our dataset (regardless of whether Endozoicomonas was present and/or dominant; n = 40 genera), Endozoicomonas relative abundance explained the majority of variation in ecological dominance among coral tissue microbiomes . Further, the relative abundance of Endozoicomonas in coral tissue alone explained 30% of variance in overall disease susceptibility , exceeding the signal from ecological dominance. Endozoicomonas remained significantly correlated with disease susceptibility after testing multiple linear models with depth, temperature, extent of turf algae contact, latitude and overall microbiome richness as confounders (Supplementary Data Table S5 b & c). Neither commonly opportunistic microbes in corals (Supplementary Data Table S6 ), nor other dominant microbes (Supplementary Data Table S7 ) showed similar patterns ( Supplementary Information ). Thus, our prior results linking ecological dominance and overall disease susceptibility appear to be largely explained by changes in Endozoicomonas relative abundance over coral evolution. Fig. 3 Endozoicomonas correlates with growth and disease. Phylogenetic independent contrast in Endozoicomonas relative abundance in coral tissue , correlated against ( A ) contrast in microbial dominance in coral tissue (assessed by Simpson’s Index), ( B ) constrast in coral disease susceptibility (estimated from integrated long-term coral disease prevalence data) and ( C ) coral growth rate (mm per year) from the Coral Traits Database. Dotted red lines in panels A-C indicate the null expectation that if traits are uncorrelated, change in the x-axis trait will not correlate with changes in the y-axis trait, with contrasts instead distributed equally above or below the dotted line. Statistics from phylogenetic generalized least squares (PGLS) regression for A-C are available in Supplemental Data Tables 5 and 9. ( D ) Modeled strength and direction of causality between Endozoicomonas relative abundance, disease susceptibility and growth rate during coral evolution using both Brownian Motion (blue) and Pagel’s Lambda (green, dotted) evolutionary models. The thickness of the lines represents the averaged standardized path coefficients of the top competing models based on CICc values (Supplementary Data Table S11 ) Endozoicomonas is often linked to metabolic benefits to the coral host (but see ), including a potential role in steroid processing . Experimental studies have shown that decreases in its relative abundance are typical with disease or other health stressors such as bleaching . This suggests that the striking correlation between Endozoicomonas and disease is not due to pathogenesis by Endozoicomonas . There are several possibilities for how a non-pathogen might nonetheless increase disease, including opportunity costs in host biology (e.g., in innate immunity, permissiveness to CAMA formation), tradeoffs in microbial symbiosis (e.g., dominance of Endozoicomonas vs. more diverse and potentially flexible microbiome associates with benefits for pathogen defense or resilience to environmental change), or tradeoffs driven by host physiological changes induced by Endozoicomonas (e.g., in steroid hormone processing). However, regardless of mechanism, if maintenance of high relative abundances of Endozoicomonas has fitness costs, they may be balanced by benefits to the host – at least under some conditions. If symbiosis with Endozoicomonas did play a causal role in coral life-history tradeoffs, we hypothesized that we would see a positive correlation between a beneficial coral trait and Endozoicomonas that counterbalances the correlation between Endozoicomonas and disease. Given that Endozoicomonas is thought to be a metabolic mutualist of corals, and it has recently been suggested to facilitate faster coral growth , growth rate seemed like a likely candidate for a potential benefit explaining the persistence of coral- Endozoicomonas associations. Depending on the mechanism of action, any such Endozoicomonas - growth correlations might depend merely on the presence of Endozoicomonas , or alternatively on its relative abundance. Using data from the Coral Trait Database (CTDB) we tested whether Endozoicomonas relative abundance was correlated with growth rate in corals where we detected Endozoicomonas (i.e., the effect of relative abundance alone) and in all corals (i.e., the combined effect of presence and relative abundance). In both cases, we limited this analysis to only corals with replicated growth rate data ( > = 5 replicates in the CTDB). While the relative abundance of Endozoicomonas was not correlated with growth rate across all coral genera (tissue PGLS: R 2 = 0.11, p = 0.17, FDR q = 0.37; Supplementary Data Table S8 a), across coral genera where Endozoicomonas was detected and replicated growth rate data were available ( n = 17 genera), its relative abundance in tissue was strongly correlated with growth rate (tissue PGLS: R 2 = 0.31, p = 0.024, FDR q = 0.024; Supplementary Data Table S8 b). Unlike for disease susceptibility, several additional microbes showed anatomically-specific correlations with the growth rate of their coral hosts, including strong positive correlations between growth and uncultured Rhodobacteria (Family: Terasakiellaceae) and negative correlations between growth rate and the archaeal genus Nitrosopumilis . However, Endozoicomonas appears unique in its association with both growth and disease. Overall, Endozoicomonas may in part explain, or at least correlate with, about a third of known growth rate differences between coral genera. Across the coral genera surveyed in our dataset, initial, low-level symbiosis with Endozoicomonas does not correlate with growth rate, but subsequent expansions of the relative abundance of Endozoicomonas within coral microbiomes co-occur with both higher average growth rates and greater disease susceptibility. Having seen that Endozoicomonas is correlated with both disease susceptibility and growth-rate in corals, we investigated if these correlations were stronger or weaker than any direct correlation between disease and growth rate in our dataset. Across genera with both growth rate and disease prevalence data, the correlation between growth and disease susceptibility had only a modest effect size and was not statistically significant. Thus, in this dataset Endozoicomonas showed stronger associations with both growth and disease than these factors showed with one another, regardless of whether the analysis was conducted across all coral genera (tissue PGLS: R 2 = 0.12, p = 0.17, FDR q = 0.17; Supplementary Data Table S10 a) or just those where Endozoicomonas was present (tissue PGLS: R 2 = 0.06, p = 0.37, FDR q = 0.37; Supplementary Data Table S10 b). This suggested that Endozoicomonas relative abundance might not merely mark tradeoffs between growth and disease but may play some causal role in one or both processes. The univariate correlations between Endozoicomonas , host disease susceptibility and growth rate raise the question of the direction of causality by which these factors have become non-randomly associated during coral evolution. Using phylogenetic path analysis (Methods), we compared 14 models of the relationship between Endozoicomonas relative abundance, disease susceptibility, and growth rate . As is common in this type of analysis, more than one model was consistent with the data. However, none of the top models using either Brownian Motion (Supplementary Table S11 b) or Pagel’s lambda (Supplementary Data Table S11 c) suggested that disease influenced growth rate or vice versa without the influence of Endozoicomonas , and all significant models include Endozoicomonas . Thus, while the precise feedback remains to be determined, causality analysis suggests that, in some capacity, Endozoicomonas likely mediates growth rate and disease. Our comparative results across coral genera suggest that the total relative abundance of microbes in genus Endozoicomonas is linked to shifts in host disease susceptibility and growth rate over coral evolution. However, Endozoicomonas is comprised of many strains that may differ in their interactions with coral hosts. For example, Endozoicomonas phylotypes in nearby corals may differ in genomic features like capacity for reactive oxygen species scavenging that could have implications for host-microbial symbiosis . Moreover, our cross-compartment analysis showed anatomically-specific differences in associations between Endozoicomonas and host traits: Endozoicomonas relative abundances were significantly associated with disease susceptibility and growth rate in tissue, but only disease susceptibility in mucus. In past literature and our results, Endozoicomonas are most abundant in tissue . Therefore, differences in associations between host traits and mucus- or tissue-associated Endozoicomonas may simply reflect somewhat less statistical power in mucus (where Endozoicomonas is less abundant) vs. tissue, and in our growth rate analysis ( n = 17 genera) vs. disease susceptibility analysis ( n = 40 genera). However, these results also raise the question of whether stable sub-populations of Endozoicomonas in mucus vs. tissue have distinct effects on host physiology. To test for any differences among mucus- vs. tissue-associated Endozoicomonas , we characterized the distribution of Endozoicomonas ASVs across coral compartments. Our dataset contained 123 Endozoicomonas ASVs. Of these, 23 abundant ASVs explained 95% of total Endozoicomonas reads, while the remainder were relatively rare. After removing ASVs with < 10 counts, we sorted the remaining Endozoicomonas ASVs according to the compartment in which they showed highest abundance. This yielded 15 ASVs that were most prevalent in mucus, 42 in tissue and 3 in skeleton. We then analyzed the relative abundance of these compartment-specific pools separately to see which, if any, would recapture associations between genus Endozoicomonas and host disease susceptibility. In this more nuanced analysis, the pool of Endozoicomonas ASVs associated with tissue showed a strong relationship with disease susceptibility , while ASV pools associated with both mucus and skeleton showed no association with disease (PGLS mucus R 2 = 0.02, p = 0.37, FDR q = 0.56; skeleton R 2 = 0.008, p = 0.57, FDR q = 0.57) (Supplementary Data Table S12 a). Thus, associations between Endozoicomonas relative abundance in mucus and coral disease susceptibility appear to derive from ASVs that have highest relative abundance in bulk tissue samples, but appear in mucus at lower relative abundance – consistent with evidence from fluorescence imaging showing Endozoicomonas can aggregate within multiple coral tissues, including tentacles, mesenteries, and calicodermis . In contrast to the strong association between total Endozoicomonas relative abundance in coral tissues and host growth rate, the association between tissue-enriched Endozoicomonas ASVs and growth rate was not significant in the top model (PGLS FDR q > 0.05; Supplementary Data Table S12 b). This may indicate that ASVs excluded in this analysis are important to the Endozoicomonas – growth rate association, perhaps due to contributions from ASVs common in multiple compartments or the summed influence of multiple rare ASVs. Experimental tests on diverse Endozoicomonas strains will be important to track the dynamics of Endozoicomonas across coral anatomy, and delineate any direct, strain-specific effects on disease susceptibility or growth rate. We found positive correlations between the total relative abundance of Endozoicomonas in coral tissue and the host traits of growth rate and disease susceptibility. This finding complements and contextualizes ongoing work on the mechanisms underlying the coral- Endozoicomonas symbiosis and the potential role of Endozoicomonas as a metabolic mutualist . It also echoes findings of correlations between life-history strategy and microbiome structure in other important marine invertebrates, such as that between predator defense and microbial abundance in marine sponges . The mechanism by which corals with high proportions of Endozoicomonas become more vulnerable to disease are not yet known, but may shed light on their role in coral symbiosis . Because these results rely on relative abundance, it is not yet clear whether differences in absolute abundances of Endozoicomonas also vary. Importantly, anatomically-specific variation in true abundances may complicate relative abundance in bulk tissue – for example, if coral taxa vary greatly in absolute microbial abundances outside of CAMAs (similar to low vs. high microbial abundance sponges ), those differences could alter apparent Endozoicomonas relative abundance. If the pattern of relative abundance reported here corresponds to absolute Endozoicomonas abundances, potential explanations fall into three main categories: ecological, structural, or immunological. Many coral microbes (but not Endozoicomonas ) are thought to protect against pathogenic disease by mechanisms such as antibiotic secretion , direct predation , jamming of quorum signaling , and through physically occupying space close to host tissues that may restrict binding sites for opportunists and pathogens. In theory, it is possible that high dominance of Endozoicomonas may impact the overall diversity or richness of the coral microbiome, effectively restricting the diversity of potential microbial defenses that may benefit the health of the coral. Similarly, Endozoicomonas may interact directly or indirectly with other microbiome members in a way that reduces microbially-derived host defenses. However, that Endozoicomonas are frequently observed in discrete CAMAs complicates this possibility, as any effects on microbes outside the local area of these CAMAs would have to rely on indirect consequences of Endozoicomonas -coral interactions or secreted factors. Nevertheless, if this hypothesis were correct, the reductions in the abundance or relative abundance of Endozoicomonas that are often reported in diseased coral phenotypes (e.g., ) would then be adaptive on the part of the host, by allowing proportionally greater growth of other, more protective microbes. This hypothesis could be tested by microbial inoculation experiments that increase Endozoicomonas abundances prior to or concurrent with disease exposure, with the prediction that this would increase disease severity (although care must be taken to exclude nutritional benefits from corals directly eating the Endozoicomonas confounding the results). More systematic studies of whether high abundances of Endozoicomonas are exclusively found in visible CAMAs could also speak to the plausibility of this ecological hypothesis, by clarifying the likely routes for interaction between Endozoicomonas and other coral-associated microbes. In addition to ecological interactions, the Endozoicomonas - disease susceptibility correlation may also arise as a result of host traits that are permissive for the formation of microbial aggregates. As the cellular processes involved in establishing mutualism, commensalism and pathogenesis often overlap, the same host-microbe interactions that allow Endozoicomonas and some other microbes like Simkania to aggregate within coral tissues may also be more permissive towards invasion by pathogens. So far known coral pathogens have not been reported to be present within CAMAs. However, other structural mechanisms are possible. For example, the density, morphology, or diversity of septate junctions — which form epithelial barriers similar to tight junctions in chordates — might, in theory, influence the ability of both Endozoicomonas and pathogenic microbes to enter coral tissues. This idea could be tested by examining cellular morphology, sequence similarity, and/or gene expression of septate junctions and their constituent components in coral species in which CAMAs did or did not form. Finally, it is possible that coral immunological strategies that permit symbiosis with high abundances of Endozoicomonas also tend to make corals more vulnerable to pathogens. Coral species vary in immune investment (as measured by immune parameters like melanin abundance, phenoloxidase activity, etc.), and low immune investment has been observed to correlate with disease susceptibility . Some theory predicts that the evolution of more permissive immunological strategies is favored by symbionts that provide metabolic benefits to the host . In corals specifically, immune repertoires in key gene families such as TIR-domain containing genes vary greatly between species, which has been hypothesized to influence microbiome structure . Indeed, in sequenced coral genomes the copy number of some of these, such as IL-1R receptors, appear to correlate with several features of coral microbiomes, including Endozoicomonas abundance . Thus, symbiosis with Endozoicomonas may promote lower immune investment in corals, which in turn increases disease susceptibility. This hypothesis could be tested by comparing the length of coral- Endozoicomonas associations, to see whether longer histories of association lead to low immune investment, or by examining selection on innate immune genes in low vs. high Endozoicomonas coral lineages (e.g., by dN/dS ratios). A related immunological explanation would occur if Endozoicomonas itself achieves high relative abundances by suppressing aspects of host immunity. Genomic studies of host-associated Endozoicomonas identified variation in the proportion of eukaryote-derived genes and domains as a key feature of strain variation, including some domains thought to suppress immunity-induced apoptosis . Endozoicomonas has also recently been suggested to play a role in coral hormone homeostasis , which could have multiple physiological effects on coral tissues (even those not in direct contact with CAMAs), including potentially influencing both growth rate and immunity. If representatives of diverse strains could be cultured, experiments adding exogenous Endozoicomonas might clarify whether Endozoicomonas strains have any direct effects on coral immunity, and if so whether they differ from strain to strain. Animals evolved in a microbial world. The resulting interactions between animal hosts and their associated microbes influence organismal fitness, and the history of these interactions across generations may influence eco-evolutionary patterns. Using evolutionary analyses of coral microbiomes, we provide evidence that symbiosis with Endozoicomonas may mediate growth vs. disease resistance tradeoffs. While further manipulative studies are necessary to confirm this finding and determine the directionality of the relationship, evidence for this trend across the coral tree of life is compelling. Our comparative approach suggests that Endozoicomonas -dominated lineages of corals may grow more quickly under ideal conditions but are more likely to succumb to coral disease. Because much other work has shown that coral disease is exacerbated by global and local stressors such as climate-change driven heat waves or local pollution events , this may make Endozoicomonas- dominated coral especially vulnerable to environmental change . It has even been suggested that high dominance of one microbial taxon in the coral microbiome may have a stabilizing effect on the rest of the community , thereby limiting the flexibility of the microbiome to functionally adapt through restructuring when exposed to environmental stressors . Fig. 4 Endozoicomonas dominance facilitates life history tradeoffs. Conceptual hypothesis on the role Endozoicomonas dominance in coral microbiomes (teal icons, top row) plays in the tradeoff between growth and defense under varying environmental conditions. Endozoicomonas -dominated microbiomes may ( A ) provide a metabolic advantage for growth under normal environmental conditions (top left), but ( B ) lack the ecological, structural or immunological defenses against pathogen invasion, and therefore become susceptible to disease under stressful environmental conditions (top right). In contrast, microbiomes not dominated by Endozoicomonas (bottom left) grow slower, but may have lower disease susceptibility in stressful environmental conditions (bottom right) If microbial symbiosis does play a causal role in coral life history tradeoffs in the present day, then identifying microbes underlying those tradeoffs may benefit microbiome manipulation for targeted coral conservation and restoration strategies. For example, microbial screening (e.g., ) could help identify Endozoicomonas -dominated coral species or populations that may be more susceptible to disease and drive the conservation and protection of these individuals or their habitats. Identification of these target corals is perhaps most relevant for coral restoration initiatives that include breeding, nursery propagation and out-planting, where coral health is monitored closely and predicting disease susceptibility can inform decision-making. Depending on the mechanism underlying the Endozoicomonas- disease susceptibility correlations reported here, Endozoicomonas -dominated corals may further represent strong candidates for microbiome engineering (e.g., human-assisted manipulation of host-associated microbes or the application of probiotics ) to enhance host resilience in anticipation of stress events by decreasing microbiome dominance. That said, we emphasize that microbiome manipulation and other restoration initiatives are not replacements for efforts to decarbonize global economies to limit greenhouse gas emissions. The results presented here provide the first evidence of a likely microbe-mediated life-history tradeoff in Scleractinian corals. Further exploration of this and other such potential tradeoffs may shed light on the evolutionary interplay between microbes and the physiology and ecology of their animal hosts. 16S rRNA sequence data were obtained from visibly healthy coral DNA extractions collected and processed for the Global Coral Microbiome Project (GCMP). This included coral samples taken from Eastern and Western Australia that were used in a previous study by Pollock and co-authors in addition to coral samples taken from the Red Sea, Indian Ocean, Coral Triangle, Caribbean, and Eastern Pacific. All samples compared in this study were collected, processed, and sequenced using consistent protocols as outlined below. In total, 1,440 coral, outgroup, and environmental samples were collected. Of these GCMP samples, the 1,283 scleractinian coral and outgroup samples were used in the present study (Supplementary Data Table S1 a). These comprise 132 species and 64 genera of corals originating from 42 reefs spanning the Pacific, Indian, and Atlantic oceans. Excluding outgroups, these data included an average of 22.3 ± 3.3 samples per genus, with a minimum of n of 2 in the genus Lithophyllon (Supplementary Data Table S1 a, d). The collection and processing of these coral samples followed the methods outlined in Pollock et al. and are compatible with samples processed for the Earth Microbiome Project . Briefly, three coral compartments were targeted for each sample: tissue, mucus, and skeleton. Mucus was released through agitation of coral surface using a blunt 10mL syringe for approximately 30 s and collected via suction into a cryogenic vial. Small coral fragments were collected by hammer and chisel or bone shears for both tissue and skeleton samples into sterile WhirlPaks (Nasco Sampling, Madison, WI). All samples were frozen in liquid nitrogen on immediate return to the surface prior to processing. In the laboratory, snap frozen coral fragments were washed with sterile seawater and the tissue was separated from skeleton using sterilized pressurized air at between 800 and 2000 PSI. Tissue and skeleton samples were then preserved in PowerSoil DNA Isolation kit (MoBio Laboratories, Carlsbad, CA; now Qiagen, Venlo, Netherlands) bead tubes, which contain a guanidinium preservative, and stored at -80℃ to await further processing. Outgroup non-scleractinian Anthozoans were also opportunistically collected and stored similarly, including healthy samples of the genera Millepora (hydrozoan fire coral), Palythoa (zoanthid), Heliopora (blue coral), Tubipora (organ pipe coral), and Xenia and Lobophytum (soft corals). Bacterial and archaeal DNA were extracted using the PowerSoil DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA; now Qiagen, Venlo, Netherlands). To select for the 16S rRNA V4 gene region, polymerase chain reaction (PCR) was performed using the following primers with Illumina adapter sequences (underlined) at the 5’ ends: 515 F 5′− TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG GTG YCA GCM GCC GCG GTA A − 3′ and 806R 5’− GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA GGG ACT ACN VGG GTW TCT AAT − 3′). PCR, library preparation, and sequencing on an Illumina HiSeq (2 × 125 bp) was performed by the EMP . All raw sequencing data and associated metadata for the samples used in this study are available on Qiita (qiita.ucsd.edu) under project ID 10895, prep ID 3439. 16S rRNA sequencing data were processed in Qiita using the standard EMP workflow. Briefly, sequences were demultiplexed based on 12 bp Golay barcodes using “split_libraries” with default parameters in QIIME1.9.1 and trimmed to 100 bp to remove low quality base pairs. Quality control (e.g., denoising, de-replication and chimera filtering) and identification of amplicon sequence variants (ASVs) were performed on forward reads using deblur 1.1.0 with default parameters. The resulting biom and taxonomy tables were obtained from Qiita and processed using a customized QIIME2 v. 2020.8.0 pipeline in python (github.com/zaneveld/GCMP_global_disease). Taxonomic assignment of ASVs was performed using vsearch with SILVA v. 138 (see below). Coral mitochondrial reads obtained from metaxa2 were added to the SILVA repository to better identify host mitochondrial reads that may be present in the sequencing data . We refer to this expanded taxonomy as “silva_metaxa2” in code. After taxonomic assignment, all mitochondrial and chloroplast reads were removed. The bacterial phylogenetic tree was built using the SATé-enabled phylogenetic placement (SEPP) insertion technique with the q2-fragment-insertion plugin to account for the short-read sequencing data, again using the SILVA v. 138 database as reference taxonomy. The final output from this pipeline consisted of a taxonomy table, ASV feature table and phylogenetic tree that were used for downstream analyses. Potential contaminants from extraction and sequence blanks ( n = 103 negative controls) were identified and removed using the decontam package in R v. 4.0.2 with a conservative threshold value of 0.5 to ensure all ASVs that were more prevalent in negative controls than samples were removed ( n = 662 potential contaminants). The final feature table consisted of a total of 1,383 samples, 195,684 ASVs, and 37,469,008 reads. Disease data were gathered from long-term multi-species surveys in the Florida Keys (the Florida Reef Resilience Program (FRRP), https://frrp.org/ ), Hawaiʻi (HICORDIS ), and Australia (this study). Disease counts for Australian corals were collected over a period of 5 years across 109 reef sites and 65 coral genera (Supplementary Data Table S1 b). At each of the 109 reefs, we surveyed coral health using 3 replicate belt transects laid along reef contours at 3–4 m depth and approximately 20 m apart using globally standardized protocols . Depending on the reef location, belt transects were either 10, 15, or 20 m in length by 2m width making the area surveyed at each reef between 60 and 120m 2 . Within each belt transect, we identified each coral colony over 5 cm in diameter to genus and classified it as either healthy (no observable disease lesions) or affected by one or more of six common Indo-Pacific coral diseases (according to Lamb and co-authors ). Together with the FRRP and HICORDIS data, the combined disease dataset contained 582,342 coral observations across 99 coral genera (Supplementary Data Table S1 c). Because many of these disease observations identified corals only to genus, disease prevalence data were summarized at the genus level. All three resources represent coral surveys over time, ranging from 5 to 16 years. We chose such long-term datasets in an attempt to minimize the potential effects of specific events (e.g., bleaching in a single summer) and instead to capture more general trends in disease susceptibility across species, if such trends were present. Summarizing these data at the genus level was thus part of a comparative strategy, enabling us to extract overall trends and average out local circumstances, so that we could find holobiont features that control disease resistance that may protect some corals but not others. When summarizing at the genus level, individual counts of healthy corals or corals with specific diseases were summed within coral genera across these datasets. To ensure sufficient replication, we excluded coral genera with fewer than 100 observed individuals. This minimal count was selected because it is the lowest frequency at which diseases with a reasonably high frequency (e.g., 5%) can be reliably detected. (With 100 counts, there is a > 95% chance of detecting at least one count of any disease present with > = 5% prevalence; cumulative binomial, 100 trials, success chance = 0.05). Because only very rarely observed taxa were removed, this filtering preserved 99.8% of total observations. Ultimately, our genus-level summary produced a table with 581,311 observations across 60 coral genera (Supplementary Data Table S1 d). For a breakdown of disease susceptibility by coral host genus, see Supplementary Fig. S5 A. Statistical summaries of microbiome community composition were calculated for each sample in QIIME2 , and then summarized within anatomical compartments and coral genera. These summaries of coral microbiome alpha diversity were richness , evenness (the Gini Index), and Simpson’s Index, which combines both richness and evenness. Thus, each combination of coral genus and anatomical compartment — such as Acropora mucus — was assigned an average α-diversity value. Simpson’s Index, which is of particular importance in these results, is at its highest when a single taxon is the only one present in microbiome, and at its lowest when there are both a large number of taxa, and all taxa have equal abundance (or relative abundance). Thus, this measure is reduced both by community richness and community evenness (Simpson’s Index is closely related to Simpson’s Diversity, which is calculated as 1 - Simpson’s Index, such that more rich or even communities produce higher values). The summarized, genus-level disease susceptibility data compiled from all disease projects, and the summarized genus-level microbiome diversity data (see above) were combined to form a trait table that was used in subsequent evolutionary modeling. Additionally, the relative abundance of ‘dominant’ microbes analyzed in this study was averaged within genera and added to this genus-level trait table. Starting with a published multigene time-calibrated phylogeny of corals that we had previously used to demonstrate phylosymbiosis in corals , we randomly selected one representative species per genus to produce a genus level tree. This approach was preferred over several alternatives — such as trimming the tree back to the last common ancestor of each genus and reconstructing trait values — because it required fewer assumptions about the process of trait evolution. As microbiome data were not available for all genera on the coral tree (e.g., temperate deep-sea corals), the tree was further pruned (preserving branch lengths) to include only the subset of branches that matched those with microbiome data. To examine the influence of microbiome structure on coral traits, we pulled growth data from the Coral Trait Database from all coral genera that matched those with both microbiome and disease data, and were collected using consistent metrics (mm/yr). This resulted in growth rate data from 18 coral genera that were subsequently combined with our genus-level trait table . Shared evolutionary history induces correlations in traits between species that violate the requirement of standard statistical tests that observations must be independent and uncorrelated. Thus, special care must be taken to account for phylogeny in comparative analysis. We first applied Felsenstein’s phylogenetic independent contrasts (PIC) to visualize our cross-genus trait correlations using the phytools R package . This method removes the effect of any shared evolutionary histories by calculating differences in trait values (contrasts) between sister taxa. We next examined the relationships between traits using information-theoretic model selection (that is, comparison of AICc scores) to identify phylogenetic generalized least squares (PGLS) models of evolution that best explained the observed distribution of microbiome α- or β-diversity and disease susceptibility (as continuous evolutionary characters) in extant species. We tested 4 evolutionary models in the caper R package . In the first model, we used PGLS with no branch length transformation (i.e. holding λ, 𝜹, κ = 1). Thus, this first model is equivalent to PIC. In the next 3 models, we transformed branch lengths on the tree by allowing the model to fit either λ, 𝜹, or κ (see below) using maximum likelihood estimation, while fixing the other 2 parameters at 1. We refer to these 4 models as PGLS, PGLS + λ, PGLS + 𝜹, and PGLS + κ. For detailed explanations of each parameter, please refer to Supplementary Data Table S13 . Typically, these models estimated very low λ (~ 0), indicating little or low phylogenetic inertia. Multiple comparisons were accounted for by calculating q values for false discovery rate (FDR) control. Significant relationships between the two traits suggests that they are evolutionarily correlated. All statistics reported represent the best PGLS model results. Additionally, ancestral state reconstructions of key traits were visualized using the contmap function in the phytools R package , which in turn estimates internal states using fast maximum-likelihood (ML) ancestral state reconstruct as implemented in the fastAnc phytools function. Observing that A and B are correlated famously does not guarantee that A causes B. However, non-random correlation between A and B does imply some causal association - though there are many possibilities (A causes B, B causes A, a positive feedback loop exists between A & B, some external factor C causes both A and B, etc.). Path analysis represents hypotheses of causality using directed acyclic graphs, then tests the different strengths of association predicted under different hypotheses of causation to test which are consistent with data. The cross-species nature of these data further necessitated use of phylogenetic path analysis, which also accounts for expected trait correlations among related genera. Hypotheses of the direction of causality between microbiome (specifically Endozoicomonas ), disease, and growth rate were tested using a phylogenetic causality analysis performed in the R package phylopath . This analysis tests the ability of different models to explain correlations in trait data. For example, does selection for a high growth rate in turn drive selection for increased Endozoicomonas relative abundance, which then increases disease susceptibility, or does symbiosis with Endozoicomonas itself separately increase disease and growth? Fourteen potential causality models were tested to incorporate all biologically plausible pathways between Endozoicomonas relative abundance, disease susceptibility, and growth rate . The top performing causality models according to CICc values (using both Pagel’s λ and Brownian Motion models of evolution) were averaged for interpretation and visualization. ASVs annotated as Endozoicomonas at the genus level were extracted from the rarefied QIIME2 coral microbiome feature table. Differences in ASV diversity within the Endozoicomonas genus was assessed by PERMANOVA of Weighted UniFrac or Aitchison beta-diversity distance matrices. For analysis of Endozoicomonas ASVs by compartment, Endozoicomonas ASVs were pooled according to whether they had greatest relative abundance in mucus, tissue or skeleton. The relative abundance of these compartment-specific pools was then regressed against host traits using PGLS, as outlined above. | Study | biomedical | en | 0.999997 |
PMC11697514 | Central pancreatectomy has emerged as an effective therapeutic alternative for the management of benign and low-grade pancreatic tumors, especially in cases where the preservation of pancreatic function is crucial, and minimizing morbidity associated with more radical resections, such as distal pancreatectomy or pancreatoduodenectomy, is desired . Unlike these traditional techniques, central pancreatectomy allows for the resection of localized neoplasms without significantly compromising the surrounding pancreatic tissue, resulting in a reduced risk of postoperative pancreatic insufficiency . Recent studies support central pancreatectomy as a valid therapeutic option in selected clinical contexts, where multidisciplinary preoperative evaluation plays a fundamental role in case selection. This article presents a clinical case of central pancreatectomy to contribute to the understanding of its role as a therapeutic option in the treatment of low-grade pancreatic tumors and its impact on pancreatic tissue preservation. Pancreatic fine-needle aspiration (FNA) was performed (Table 1 ), with citrine-colored fluid aspirated and carcinoembryonic antigen (CEA) < 1.8 ng/mL. The biochemical analysis of the fluid showed amylase of 144 U/L; glucose of 102 mg/dL; immunohistochemistry, chromogranin A diffusely positive in neoplastic cells; synaptophysin, diffusely positive in neoplastic cells; and Ki-67, proliferation index estimated at <1%, consistent with well-differentiated grade 1 neuroendocrine tumor (G1 NET). The surgery was performed through a midline laparotomy, with the opening and section of the gastrocolic ligament, providing access to the lesser sac and full exposure of the pancreas. Macroscopically, a soft pancreas with a well-defined, partially exophytic cystic lesion in the neck, approximately 20 mm in its largest diameter, was identified, involving almost the entire thickness of the pancreatic parenchyma . The lymph node dissection of group 8 was performed, along with the dissection of the common hepatic artery and splenic artery. The dissection of the pancreatic groove to the left of the mesenteric vessels allowed the creation of a retro-pancreatic tunnel without complications. To the left, the splenic vein was identified in its usual position. The tunnel was completed using blunt dissection and an esophageal retractor to encircle the pancreatic body, achieving wide proximal and distal margins . The transection of the pancreatic neck was performed using an Endo GIA (Covidien, Dublin, Ireland) 60 mm purple cartridge, and distal pancreas resection was completed with monopolar energy . The Wirsung duct was identified with a diameter of approximately 2-3 mm . The jejunum was transected 20 cm distal to the previous entero-entero anastomosis of the gastric bypass. A transmesocolic loop was brought up, and a Blumgart pancreatojejunostomy was created with 10 separate duct-to-mucosa Prolene 5-0 stitches . A side-to-side mechanical entero-entero anastomosis was performed. Two Blake drains were placed in the pancreatic bed, with distal ends adjacent to the pancreatic stump and exteriorized through the right flank. On postoperative day 4, both drains had minimal output (drain I, 10 cc; drain II, 59 cc) with amylase levels of 5878 and 59 mg/dL, respectively (Table 3 ). Traditional approaches, such as distal pancreatectomy and pancreatoduodenectomy, involve more extensive resections, which may result in a higher risk of postoperative complications, including diabetes and malabsorption . Authors such as Iacono et al. support the idea that central pancreatectomy may be superior to distal pancreatectomy in certain contexts, particularly for patients with benign or low-grade tumors. Central pancreatectomy offers greater pancreatic tissue preservation and a lower rate of severe complications compared to distal pancreatectomy, making it the preferred surgical option in selected cases . The choice of surgical technique should consider not only the type of tumor but also the patient's clinical characteristics and the surgical context. Multidisciplinary clinical evaluation is essential for decision-making, where surgical planning plays a fundamental role. In terms of pancreatic function preservation, a recent study by Lee et al. directly compared central pancreatectomy, distal pancreatectomy, and duodenopancreatectomy. Central pancreatectomy demonstrated significant advantages in terms of pancreatic functional preservation, with favorable long-term outcomes not only for pancreatic function but also in preserving pancreatic mass, which ultimately translates into a lower incidence of postoperative diabetes and an improved quality of life for patients . Regarding the effectiveness and safety of central pancreatectomy, a recent systematic review and meta-analysis established its feasibility for both open and minimally invasive techniques. Although minimally invasive techniques offer additional benefits in terms of reducing surgical trauma, recovery time, and hospital stay, central pancreatectomy remains effective in open surgery . The adoption of minimally invasive techniques developed in recent years represents a current challenge, with laparoscopic approaches being a safe technique and an important advancement in surgical practice . Despite the numerous advantages of central pancreatectomy, the technique requires a high level of skill and experience from the surgeon, which may limit its application in centers with less experience in pancreatic surgery. In our case, the surgical team had extensive experience in pancreatic surgery, enabling central pancreatectomy to be considered as a therapeutic option, with thorough preoperative evaluation and planning. We believe that continuous training in minimally invasive techniques and the establishment of standardized protocols play a fundamental role in improving surgical outcomes in these cases. The reviewed studies support central pancreatectomy as a valid therapeutic option and, in certain cases, a preferred choice, particularly with the current advancements in minimally invasive techniques. | Clinical case | clinical | en | 0.999996 |
PMC11697539 | Dupilumab is a fully human monoclonal antibody against the interleukin (IL)-4 receptor alpha subunit (IL-4Rα). Binding of the monoclonal antibody to the IL-4Rα inhibits the signaling of IL-4 and IL-13, the 2 major cytokines secreted by CD4 + T-helper 2 (Th2) cells. 1 Dupilumab has been approved for the treatment of moderate-to-severe atopic dermatitis (AD) not adequately controlled by topical therapies and has become the first monoclonal antibody for the treatment of AD. 1 Cutaneous T-cell lymphomas (CTCLs) are characterized by mature CD4 + T-helper cells that are remarkably Th2-biased with strong inhibition of Th1 responses. 2 , 3 Blocking IL-4/IL-13 signaling pathways by anti-IL-4 neutralizing antibody reduces the proliferation of mycosis fungoides (MF) cells. 2 IL-4 and IL-13 are the major cytokines transforming the tumor-associated macrophages (TAMs) to M2 macrophages that promote cancer progression and treatment resistance, and dupilumab reduces the pro-tumor phenotype of M2 macrophages. 4 However, previous studies have shown that IgG4 is highly expressed in various types of tumor tissues, such as pancreatic cancer, 5 gastric cancer, 6 and others. IgG4 reduces the expression of CD206, CD163, and CD14 on the surface of M2 macrophages, increases the production of CCL-1, IL-10, and IL-6, induces the M2b-like macrophage phenotype, which impairs the tumor cell phagocytosis function and the function of anti-cancer effector cells. 7 Therefore, dupilumab, a fully human IgG4 monoclonal antibody, may induce macrophage polarization to M2b, mediating tumor tolerance and ultimately leading to cancer progression. Previous studies have reported that dupilumab may cause the worsening of existing tumors prior to the antibody therapy or may drive the appearance of typical tumors, in AD or refractory pruritus patients during or after dupilumab treatment. These tumors include CTCLs, other skin tumors, hematologic tumors, and solid tumors. For example, dupilumab treatment unmasked the atypical lymphoid infiltrates or MF in patients with refractory presumed AD and pruritus. 8 In this article, we collected and analyzed the relevant cases reported in the literature to explore the safety of dupilumab treatment for AD or refractory pruritus and the possible mechanisms of dupilumab on tumors. A systematic search in the PubMed database was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . We used the keywords “dupilumab AND cancer” and “dupilumab AND tumor” without limitation of article type to search the English publications in PubMed from 2017, the beginning of dupilumab for clinical use, until August 2024. A total of 148 full-text and eligible articles were retrieved, of which 43 articles were analyzed in this study after excluding the eligible articles without reported case(s) (n = 105). Fig. 1 PRISMA flow diagram of systematic review Fig. 1 A total of 90 patients, including AD, presumed AD, and other diseases with refractory pruritus, were reported in the 43 articles. Their demographics, preliminary diagnosis and treatment, dupilumab treatment method, the tumor diagnosed before or after dupilumab treatment, tumor type, TNM (tumor, node, and manifestation) classification and stage, changes of tumor and skin lesions after dupilumab treatment, and outcome of the patient were extracted from the 43 articles. A 600 mg dupilumab loading dose followed by 300 mg dupilumab every 2 weeks was used in most cases. The therapeutic effect was evaluated mainly according to the changes in skin rash, pruritus score, and quality of life index. We paid special attention to the occurrence of tumors and changes in tumor progression to evaluate the effect of dupilumab treatment on tumors. The summary statistics of 90 patients are shown in Table 1 , and the specific clinical characteristics are shown in Table S1 . They were aged 22–82 years old. Except for 7 patients whose gender was not specified, 50 patients were males and 33 were females, with a male-to-female ratio of 1.52:1. The course of dupilumab treatment ranged from 1 injection to several years. It is important to note that all patients had concomitant tumors or newly developed tumors after the use of dupilumab. Table 1 Demographic characteristics and changes of disease in patients with concomitant or newly emerging tumors treated with dupilumab. Table 1 Demographic characteristic Patients, No. (%) Male sex 50 (55.6) Female sex 33 (36.7) Unknown sex 7 (7.8) Age 22–82 years old Dupilumab treatment duration Once to 30 months Tumor characteristic Pre-exist before dupilumab treatment 62 With primary tumors 57 (63.3) CTCL misdiagnosed as AD 3 (3.3) With primary solid tumors, new CTCLs 2 (2.2) Present after dupilumab treatment 30 With new tumors 28 (31.1) With primary solid tumors, new CTCLs 2 (2.2) Tumor type CTCL 34 (37.8) Other skin tumors 9 (10) Hematological 24 (26.7) Solid tumors 26 (28.9) Tumor changes Stable 14 (15.6) Progression 10 (11.1) Response 13 (14.4) Not available 53 (58.9) Primary dermatological changes No response 24 (26.7) Response 40 (44.4) Not available 26 (28.9) AD, atopic dermatitis; CTCL, Cutaneous T-cell lymphoma. A total of 62 patients had the tumors before dupilumab treatment. 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 Most patients showed improvement in AD rash and pruritus, and only 8 patients showed aggravation of rash and pruritus. 8 , 13 , 37 The changes in tumors after dupilumab treatment are listed in Table 2 . It seems that dupilumab has no significant negative effects on these tumors, except that more CTCL patients under dupilumab treatment are required to be observed to draw a conclusion. Table 2 Patients with pre-existing tumors and changes in the tumor after dupilumab treatment. Table 2 Tumor No. Stable Death or progression Partial response Remission or very good response Changes not available Skin (CTCL: MF, SS, CTCL-NOS) 10 5 (case 1,5,6,7,25) 3 (case 19,21,26) 2 (case 33,34) Skin (melanoma, squamous cell carcinoma, angiosarcoma) 9 3 (case 39,40,41) 2 (case 38,42) 2 (case 35,36) 2 (case 33,37) Hematological (multiple myeloma, lymphoma) 21 1 (case 53) 3 (case 54,59,61) 2 (case 38,62) 2 (case 52,60) 13 (case 43,44,45,46,47,48,49,50,51,55,56,57,58) Solid tumors 25 10 (case 78,79,82,83,84,85,86,87,88,90) [16,17,27.28,34] 1 (case 89) 2 (case 42,76) 2 (case 77,80) 10 (case 15,31,66,67,68,69,70,71,72,75) P.s.: case 15 and case 31 were originally diagnosed with solid tumors and presented CTCL after dupilumab treatment. Case 33,38 and 42 had overlapping multiple tumor types, so they were counted twice. A total of 30 patients presented tumors after dupilumab treatment (1 patient subjected to only 1 injection of dupilumab is excluded from the analysis). 8 , 10 , 14 , 21 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 Interestingly, 23 of the 29 patients developed CTCLs. Most authors believed that the pre-existing malignant T-cell clone may overgrow in a changed immune microenvironment. For example, some patients were first diagnosed with presumed AD (cases 1, 2, 3, 4, 16) and actually were the presentations of CTCL at an early stage; they developed typical CTCL symptoms after dupilumab treatment. 8 , 49 Therefore, careful examination of the refractory patients with atypical AD lesions to identify the possibility of concomitant tumors, especially CTCLs, before dupilumab treatment is recommended. On the other hand, in some patients with long-standing AD, eczematous or psoriasiform lesions confirmed by multiple biopsies, CTCLs occurred following dupilumab treatment (cases 24,27,28,29,30,63,64). 36 , 41 , 42 , 46 , 48 Type 2 immunity, illustrated by T helper 2 lymphocytes (Th2) and downstream cytokines (IL-4, IL-13, IL-31) as well as group 2 innate lymphoid cells (ILC2), is important in host defense and wound healing. 50 In CTCL, the expression of STAT4, an important transcription factor of Th1 lymphocyte subsets, is upregulated in the early stage. However, with the development of the disease, the expression of signal transducer and activator of transcription 4 (STAT4) is usually lost, leading to the switch from Th1 to Th2, causing cancer progression and immunosuppression, which is associated with worse clinical prognosis. 51 The 2 most common CTCLs, advanced MF and SS, are often associated with eosinophilia and high IgE levels. 52 IL-4 and IL-13 are the main cytokines that drive the Th2 response and inhibit Th1/Th17 differentiation. 53 , 54 They are also important growth factors in primary cutaneous lymphoma, where IL-13 acts on tumor lymphocytes in an autocrine manner. 2 Both are involved in stimulating B-cell differentiation, IgE production, eosinophilic growth, and aggregation 53 , 54 , 55 and have been confirmed as irritants of chronic pruritus. 53 , 54 Therefore, inhibition of the IL-4/13 pathway can theoretically improve the clinical symptoms of CTCL. It has been reported that IL-13 is an autocrine factor in CTCL. CTCL cells produce IL13 and express IL13 receptors, which can induce the growth of CTCL cells. Moreover, pSTAT6 was highly expressed in CTCL lesions, implying the activation of the IL4/IL13 pathway. It was confirmed that blocking IL-4 and IL-13 had a synergistic effect on inhibiting the growth of CTCL cells. Interestingly, blocking IL-13Rα2 revealed an even stronger inhibition of tumor growth, considering that IL-13 binds to IL13Rα2 more strongly than IL13Rα1. 2 , 56 Therefore, blocking the heterodimer formed by IL-4Rα and IL-13Rα1 may increase the binding of IL-13 to the IL-13Rα2 site. Effectively increasing the IL-13 shunt in the tumorigenic pathway may achieve a tumor promotion effect. TAMs are abundant tumor-associated macrophages in the tumor microenvironment (TME). 4 Macrophages can account for more than 50% of solid tumors and play an important role in cancer progression. 57 , 58 The high permeability of TAMs is associated with poor prognosis. 59 , 60 , 61 , 62 , 63 They are usually classified as either an antitumor phenotype (M1-like) or a tumor-friendly phenotype (M2-like). Most TAMs exhibit an M2 phenotype that supports tumor growth, immune escape, and metastasis 4 and promotes therapeutic resistance through various mechanisms. 57 , 60 , 64 , 65 , 66 , 67 M2 TAMs can also counteract the effect of cytotoxic agents on cancer cells through the secretion of survival signals and cathepsins. 64 , 65 IL-4 and IL-13 are the main cytokines that polarize macrophages into the M2 subpopulation. 4 Therefore, blocking the IL-4/IL-13 pathway may have anticancer effects. However, the tumor microenvironment is complex and dynamic and cannot be fully simulated by in vitro models. In an animal model of prostate cancer, drug inhibition of IL4Rα did not affect tumor growth. 4 Therefore, further in vitro and in vivo tests are needed to evaluate the effect of targeting the IL4/IL13 pathway in different tumors. Dupilumab is a fully human monoclonal antibody of the immunoglobulin G4 (IgG4) subclass. IgG4 antibody have a unique affinity profile for Fc gamma receptors (FcγRs) and support phenotypical macrophage changes towards an M2b-like state. 68 Macrophages express FcγRIIa which is involved in antibody-dependent cellular phagocytosis (ADCP). 69 Since IgG4 has a low affinity for FcγRIIa and a higher affinity for inhibitory FcγRIIb than for other IgG subclasses and only acts as an inhibitory effect when other FcγRs are co-involved, 70 it is possible that IgG4 may dampen FcγR immune activation by co-engaging FcγRIIb together with the engagement of any other FcγRs by antigen-specific IgG1. Furthermore, since IgG4 is not able to trigger complement-dependent cytotoxicity (CDC), 71 any tumor specific IgG4 antibody competing with tumor specific IgG1 antibody indirectly reduces IgG1-mediated CDC. Therefore, IgG4 is a key to immune tolerance in cancer. Previous study has found that IgG4 inhibited IFNγ signaling via FcγRI, and favoring an M2b-like phenotype, 72 which plays a role in the formation of CTCL by secreting various chemokines, 52 such as CCL-1, IL-10, and IL-6. 68 CCL1 secretion is critical to maintain the M2b phenotype in mice and humans, 73 while IL-10 has been found to impair the differentiation of infiltrated monocytes into mature dendritic cells (DCs), thereby compromising the competent host anti-tumor immune response. 74 Through the analysis of CTCL patients and animal experiments, it has been proved that M2-like phenotype macrophages play an important role in the tumorigenesis of CTCL, and the depletion of macrophages inhibits tumor growth in a mouse model. 75 In conclusion, as an IgG4 monoclonal antibody, dupilumab may promote tumor immune escape by affecting macrophage polarization and cytokine secretion. In our study, most AD patients with tumors showed improvement in AD symptoms, tumor stabilization, or regression after treatment with dupilumab. Only a few patients showed tumor progression, which was mainly MM and CTCL. Combined with the above analysis, we suggest that in most cases, dupilumab has no effect on tumor progression or even prevents tumor progression by blocking the IL-4/IL-13 pathway and/or inhibiting the transformation of TAMs to the M2 phenotype. However, dupilumab may promote tumor progression by blocking IL-13Rα1 and then increasing IL-13 binding to IL13Rα2, thus promoting tumor progression in some tumors, especially CTCLs. The effect of dupilumab on tumors may be determined by whether IL4/IL13 signaling plays a dominant role in tumors. Different tumors have distinctive signaling pathways with pro-tumor and tumor-suppressive roles. Furthermore, advanced CTCLs are aggressive. It is unclear whether dupilumab treatment worsens the disease or whether it is the natural course of the disease's progression. Due to the limited sample size, more observations and studies are needed to determine the effect of dupilumab on primary tumors. Some patients with dupilumab treatment developed new tumors, including CTCL in the majority, as well as other skin tumors, hematological tumors, and solid tumors such as bladder cancer. For individual patients with new solid tumors such as bladder cancer, the authors noted that there was no significant correlation between the occurrence of tumors and the use of dupilumab. 39 However, we should pay special attention to CTCL. A group of patients with an initial diagnosis of AD confirmed pathologically atypical lymphocyte infiltration following dupilumab treatment. 44 Over an average of 9.8 months after dupilumab treatment, the density, distribution pattern, and composition of lymphatic infiltrates gradually changed from reactive to neoplastic patterns. 44 Previous data have supported the progressive development of CTCL in the context of chronic inflammatory processes such as AD driven by exogenous and endogenous factors. 76 Therefore, dupilumab may be a potential trigger for the initial progression of benign lymphocyte tissue infiltration, leading to clonal expansion of T lymphocytes and subsequent malignant transformation. Although this study was limited by its retrospective design and sample size, it reminds us that careful clinical, histopathological, and immunohistochemical evaluation should be performed before and during the treatment of refractory AD and that continuous skin biopsy is necessary. As described above, individual patients were misdiagnosed with AD and were given dupilumab treatment. After the treatment, the clinical symptoms worsened, and the diagnosis of CTCL was confirmed by multiple biopsies and other relevant examinations. Because CTCL can simulate multiple clinical manifestations of inflammatory skin diseases, especially in the early stages, repeated biopsies are required to assist in a definitive diagnosis. The prognosis and treatment regimens of AD and CTCL are largely different. Therefore, it is necessary to perform multiple or repeated pathological biopsies on refractory AD patients or AD patients with atypical skin lesions to determine whether there is a diagnosis of CTCL. We summarized the literature on tumors in the setting of dupilumab use and came up with the following conclusions: dupilumab is theoretically safe in patients with concomitant tumors, but a small number of patients, especially those with CTCLs, developed progression of the primary tumor after treatment. Although a clear correlation between dupilumab therapy and tumor progression cannot be demonstrated, dupilumab does not have a definite effect on preventing tumor progression. In clinical studies of dupilumab for other diseases, such as asthma, chronic rhinosinusitis with nasal polyps, and eosinophilic oesophagitis, a small number of patients had serious adverse events related to neoplasms, which was ultimately determined without significant relationship to dupilumab treatment. 77 , 78 , 79 , 80 Due to the small sample size of relevant studies and the characteristics of the advanced malignant behavior of CTCL itself, it is still uncertain whether dupilumab causes tumor progression, and it is necessary to pay close attention to tumor changes during treatment. In addition, early CTCL is easily misdiagnosed as AD, which has a distinctive prognosis and treatment, so it is necessary to make a clear and definitive diagnosis. In a cross-sectional study of dupilumab-associated MF, the more advanced disease stage at the time of MF diagnosis during dupilumab use, the shorter the treatment duration to MF onset. In addition, older age and male sex seem to have a higher risk of advanced MF. 81 Therefore, close monitoring of elderly men and late-stage MF patients with serial biopsies and close observation of clinical changes may be warranted. If patients show refractory or atypical lesions of AD, the possibility of CTCL should be considered. We suggest that biopsy criteria should be lowered before the application of biologics, and close follow-up should be conducted during treatment to evaluate the presence of CTCL based on clinical manifestations, pathological biopsies, TCR rearrangement, and immunological tests. Once a diagnosis of CTCL is established, caution is recommended to discontinue dupilumab and aggressively pursue lymphoma-related therapy. Thus, a multidisciplinary committee with oncologists is recommended to jointly assess the patient's condition and guide more precise treatment. In conclusion, dupilumab, as the first monoclonal antibody approved for the treatment of moderate to severe AD, makes a significant contribution to improving the quality of life of patients with AD and AD-like symptoms. However, its safety and efficacy in the context of cancer remain unclear. In this study, we conclude that tumors are not an absolute contraindication for dupilumab, but careful evaluation before and during treatment is warranted. Limitations of this review include small sample size, incomplete clinical data of patients, short mean follow-up time, and inability to know the long-term prognosis of patients. More clinical reports and mechanistic studies are needed to clarify the safety and efficacy of dupilumab in the tumor setting. Here, we summarize the use of dupilumab in the tumor setting and cases of new tumors after dupilumab treatment in the literature and describe the demographics, clinical characteristics, therapeutic responses, and clinical outcomes of these patients. Based on these findings, we conclude that dupilumab is not an absolute contraindication for tumor use, but also does not have a definite tumor therapeutic effect. We recommend early and repeated testing in refractory AD patients with atypical lesions to identify the possibility of concomitant tumors, especially CTCLs. We suggest that the criteria for biopsy may be lowered appropriately and note that negative or ambiguous results do not preclude a diagnosis of CTCL. Dupilumab can be discontinued out of caution when the tumor is detected and active tumor-related therapy is initiated. It is necessary to follow up closely before and during treatment and to monitor the occurrence and development of tumors. | Other | biomedical | en | 0.999996 |
PMC11697550 | The incidence of hepatocellular carcinoma (HCC), the predominant form of primary liver cancer, is increasing more rapidly than that of any other cancer in the United States. 1 HCC is frequently diagnosed at advanced stages, making surgical resection or liver transplantation for curative purposes challenging. 2 , 3 Recent advancements in first-line treatments for advanced HCC involve combination therapies that include immune checkpoint blockade (ICB), specifically anti-programmed cell death protein/ligand 1 (PD-1/PD-L1) antibodies, combined with anti-angiogenics. 4 Despite these advances, the response rates to these therapies hover around 15%–20%, with median survival for patients with advanced unresectable HCC ranging from 1 to 2 years. 5 For patients who respond, tumor-infiltrating lymphocytes (TILs) correlate strongly with better outcomes. Patients with high TILs in their tumors typically experience better responses to the combination of ICB and anti-angiogenic therapy, leading to increased overall survival rates. 6 , 7 , 8 , 9 However, HCC tumors often lack tumor-associated antigens that are strongly and consistently expressed and capable of triggering anti-tumor immune responses or being targeted through adoptive immunotherapy or vaccination. In addition, the presence of cells that suppress T cell responses in the tumor microenvironment (TME) further limit immune response against tumors. 6 In this context, developing strategies to increase immune responses within HCC tumors may open new opportunities for maximizing therapeutic potential for patients with advanced HCC. Oncolytic viruses (OVs) have emerged as a promising strategy to enhance the immunogenicity of tumors by directly lysing cancer cells and inducing an immune response. 10 , 11 OVs promote tumor cell death and increase the recruitment and activation of TILs within the TME. This dual action can potentiate the effects of standard therapies, such as ICB and anti-angiogenic agents, by modifying the immune landscape of the tumor and making it more receptive to treatment. 10 , 11 Among the attractive class of OVs, the Rhabdoviridae family, specifically the vesiculovirus genus, has garnered significant interest due to their inherent advantages over other viral vectors, including board tropism, fast replication in cancer cells, cytoplasmic replication, genetic manipulability, and low human seroprevalence. 11 Here, we show that Jurona virus (JURV), 12 a member of the vesiculovirus genus, effectively induces cytolytic activity in HCC cells in vitro and delays tumor progression in vivo . Moreover, we demonstrate that JURV is safe and elicits systemic anti-tumor immunity, inhibiting growth in both virus injected and distal tumors in a syngeneic HCC model. Furthermore, administration of JURV remodeled the TME by enhancing the activation of tumor-specific cytotoxic T cells and, when combined with ICB, improved survival in an aggressive orthotopic murine model of HCC. These results lay a foundational basis for further exploration of JURV and the combination of JURV with ICB as a novel therapeutic approach in HCC treatment. We obtained JURV from the University of Texas Medical Branch World Reference Center for Emerging Viruses and Arboviruses (Galveston, TX). It has been isolated from Haemagogus sp. and a human in northern Brazil. 12 A laboratory-adapted viral clone of JURV was generated using sequential plaque purifications in Vero cells . RNA sequencing was applied to confirm the full-length JURV genome (10,993 bp) as described previously. 13 Analysis of the genome of JURV showed an identical genome organization as that observed in Vesicular stomatitis virus (VSV) and Morreton virus (MORV) , two other members of the Rhabdoviridae family. 5 Infectious JURV was recovered from a full-length cDNA clone (GenScript) comprising genes encoding for the nucleoprotein (JURV-N), phosphoprotein (JURV-P), matrix protein (JURV-M), glycoprotein (JURV-G), and RNA-directed RNA polymerase L protein (JURV-L), as described in the materials and methods . We assessed the in vitro cytotoxicity of JURV in various human and murine HCC lines, including HEP3B, PLC, HuH7, HEPA 1–6, and RILWT. These cell lines were infected with JURV at multiplicities of infection (MOIs) of 0.1, 1, and 10 . With an MTS cell viability assay at 72 h post-infection, we observed a reduction in cell viability across all cell lines, with differences in response to each cell type. HEP3B and PLC cells showed a ∼30% reduction in cell viability irrespective of the MOI , while the other cell lines showed MOI-dependent cell cytotoxic effects, mostly reaching ∼30% at high MOI. Crystal violet staining was performed 3 days post-infection with an MOI of 0.1. It showed that JURV infection resulted in the substantial loss of adherent cells in most cell lines, except HuH7 , indicating that the MTS assay might have underestimated JURV’s oncolytic impact. In addition, the viral kinetic analysis revealed that JURV amplification reached around 10 6 plaque-forming units (PFU)/mL viral titers in HCC cell supernatants as early as 10 h post-infection , indicating JURV’s high infectivity and fast replication capability in these HCC cells. Figure 1 Oncolytic JURV is effective at inducing oncolysis in HCC cell lines (A) Monolayers of human HCC (HEP3B, PLC, HuH7), murine HCC (HEPA 1–6 and RILWT) were seeded at a density of 1.5 × 10 4 /well in 96-well plates and infected with JURV at an MOIs of 10, 1, or 0.1, respectively. The percentage of cell viability was determined 72 h post-infection using a colorimetric assay (MTS, Promega) and calculated as percent of noninfected control cells. The discontinued lines on the graphs indicate the cutoff percentage for resistance (>50% cell viability above the line) and sensitivity (<50% of cell viability, below the line). Data were collected from multiple replicates over three independent experiments. Bars indicate mean ± SEM. (B) Crystal violet staining. Cancer cells were plated at 5.0 × 10 5 /well in a 6-well plate and rested overnight. The following day they were infected with JURV at an MOI of 0.1. Cells were fixed and stained with crystal violet 72 h post-infection, and images were captured at 10× magnification on an Olympus IX83 Inverted Microscope System. (C) HCC cells were plated in 6-well plates at 2.0 × 10 5 /well and infected with JURV at an MOI of 0.1. Supernatants from infected cells were collected at different time points, and viral titer was determined using a TCID 50 (50% tissue culture infective dose) or PFU method on Vero cells (1.5 × 10 4 ). Data are plotted from two independent assessments of TCID 50 for each point with mean ± SEM. We assessed the safety profile of JURV using two doses (1.0 × 10 7 or 1.0 × 10 8 TCID 50 [50% tissue culture infective dose]) of JURV that are around 5- to 50-fold higher than the toxic threshold for VSV (1 × 10 6 PFU). 14 Doses were administrated to non-tumor-bearing healthy mice either intranasally (i.n.) or intravenously (i.v.). Our analyses, including post-infection body weight monitoring and histological examination of key organs (brain, liver, or spleen), revealed a mild weight loss (10%–15%) in the initial 3 days but no significant histopathological changes in the brain, liver, or spleen . Importantly, there were no marked differences in clinical signs such as paralysis, death, fur condition, or serum markers of drug-induced toxicity between the JURV-treated and control groups, indicating that high-dose JURV administration is not associated with severe adverse effects in this model. Figure 2 Effects of low and high doses of oncolytic JURV on body weight and hemogram in mice Non-tumor-bearing female C57BL6/J of age 6–8 weeks were administered single doses of PBS, 1 × 10 7 TCID 50 of JURV, or 1 × 10 8 TCID 50 of JURV (A) intranasally (i.n.) or (B) intravenously (i.v.). Body weight was recorded twice a week in both the i.n. and i.v. cohorts to assess drug-related toxicity. Three mice per group in each cohort (i.n. or i.v.) were sacrificed 3 days post-infection, and blood, brain, and liver were harvested to assess the short-term toxicity. Hematoxylin and eosin (H&E) staining (brain, spleen, and liver) are shown for i.n. and i.v. administration (C), where black arrows indicate that samples were within normal limits. Green arrows indicate necrosis, single cell, macrophage, sporadic. Yellow triangles indicate pigmentation increased in macrophages, red pulp, and white pulp. Next, we evaluated whether the observed in vitro cell killing capacity of JURV is associated with its capacity to induce an oncolysis-dependent tumor cell killing in vivo . We injected intratumorally (i.t.) three doses of JURV into human HEP3B xenografts. We used luciferase-tagged HEP3B cells to monitor tumor growth during the first 3 weeks of treatment. Bioluminescence imaging revealed significant tumor inhibition in JURV-treated mice compared with phosphate-buffered saline (PBS) controls, evident from the first week post-injection . We observed a significant reduction in tumor growth (>90%) in the JURV-treated group . However, while the HEP3B xenograft mice exhibited tumor reduction, we also noted some weight loss , which could be due to tumor volume reduction. In addition, NOD scid mice, being severely immunocompromised, are susceptible to viral infections, which likely contributed to this effect as well. In contrast, in immunocompetent HCC models, JURV-treated mice maintained stable body weight, further supporting the safety and tolerability of JURV in hosts with intact immune systems as described elsewhere in this manuscript. Figure 3 Assessment of JURV-mediated oncolysis in Hep3B xenografts Female NOD.Cg-Prkdcscid/J mice ( n = 6/group) were inoculated subcutaneously with HEP3B cells tagged with a luciferase reporter protein. When the average tumor volume reached 80–120 mm 3 , mice were divided into two groups and received i.t. injections with either PBS or JURV at a dose of 1.0 × 10 7 TCID 50 (days 0, 7, and 14). (A) Tumor volume was recorded twice weekly until the humane endpoint, or end of the study (day 21). HEP3B tumors treated with PBS or JURV were harvested and analyzed for changes in protein expression. (B) Volcano plot of protein expression differences in HEP3B tumors treated with PBS vs. 1 × 10 7 TCID 50 of JURV. (C) 3D pie slices of the numbers of differentially expressed proteins (DEPs) in HEP3B tumors injected with PBS vs. 1 × 10 7 TCID 50 of JURV. (D) Heatmap of the top 20 DEPs upregulated or downregulated in HEP3B tumors injected with PBS vs. 1.0 × 10 7 TCID 50 of JURV. DEPs were determined using the limma-voom method as described in material and methods section. A fold-change |logFC| ≥ 1 and a false discovery rate (FDR) of 0.05 were used as a cutoff. The logFC was computed using the difference between the mean of log2(JURV) and the mean of log2(PBS), that is, mean of log2(JURV) – mean of log2(PBS). (E) Graph showing top-scoring canonical pathways significantly enriched by treatment with 1.0 × 10 7 TCID 50 of JURV in the HEP3B tumors. We have previously demonstrated that the responsiveness to type I IFN production or viral kinetics in vitro by infected cancer cell lines does not always correlate with the in vivo efficacy of OVs. 13 Consequently, we conducted a proteomics analysis of tumor tissues to identify changes, specifically focusing on proteins involved in the anti-viral pathway, following intratumoral delivery of JURV in HEP3B tumors. The analysis of 2,088 proteins showed that a storm of 160 differentially expressed proteins (DEPs) were upregulated, and 170 DEPs were downregulated in the JURV-treated vs. control group tumors . Key upregulated proteins, including VIM, 15 LCP1, 16 COL6A3, 17 HSPG2, 18 NAMPT, 18 and STAT1, 19 are associated with the activation of the mTORC2/AKT pathway, whose inhibition reduces the expression of type I IFN genes (IFN-α/β) during TLR triggering . A subcutaneous syngeneic HEPA 1–6 HCC model was used to evaluate the anti-tumor efficacy of JURV. The treatment regimen included three i.t. doses of JURV within 3 weeks. A significant delay in tumor growth was observed in mice treated with JURV compared with PBS-injected control , with no adverse effects . In addition, to investigate further the potential abscopal effect and the broader systemic immune response triggered by JURV, we implanted bilateral Hepa 1–6 tumors subcutaneously on both flanks of the mice. JURV was administered i.t. exclusively to the right flank tumors. Interestingly, this treatment led to tumor regression on both the treated and untreated sides, indicating a potential systemic anti-tumor response . However, we recognize the complexity of accurately evaluating the abscopal effect. Further studies are required to thoroughly assess JURV’s ability to induce local and systemic immune responses capable of eradicating distant tumors. Figure 4 Evaluation of the anti-tumor efficacy of oncolytic JURV in an immuno-competent murine HCC model HEPA 1–6 cells were implanted into the right flanks of female C57BL6/J ( n = 7/group; Jackson Laboratory). (A) When the average tumor volume reached 80–120 mm 3 , mice were administered 50 μL i.t. injections containing PBS (vehicle) or 1 × 10 7 TCID 50 units of JURV were injected (inj.) into tumor-bearing mice at days 0, 7, and 14. Tumor volume was recorded twice weekly. Tumors were harvested at the end of the study for downstream analysis. (B) In the abscopal model (dual flanks), HEPA 1–6 cells (1 × 10 6 cells/mouse) were first subcutaneously grafted into the right flanks and were categorized as “primary” tumors. Simultaneously, we performed distant HEPA 1–6 tumor grafts (1 × 10 6 cells/mouse) into the left flanks of these mice. Mice in the dual-flank group received 50 μL i.t. injections of 1 × 10 7 TCID 50 units of JURV only on their right flanks once a week for 3 weeks. Data plotted as mean ± SD; ∗∗ p < 0.001, ∗∗∗ p < 0.0001. Area under the curve for tumor growth was compared by one-way ANOVA with Holm-Sidak correction for type I error. The first day of JURV or PBS injection was defined as day 0. t-SNE (t-distributed stochastic neighbor embedding) plot showing variable composition of tumor-infiltrating lymphocytes in JURV-treated tumors. Viable CD45 (12,500 events per tumor) were clustered by t-SNE. (C) Global cell density by t-SNE for each tumor treatment group. (D) Heatmap level of expression of each cellular marker across all groups. (E and F) Analysis of tumor-infiltrating immune cells following i.t. injection of oncolytic JURV in murine HCC tumors. The parent gate used is the live CD45+CD3+ population. We analyzed the changes in the immune landscape in murine HCC treated with JURV by flow cytometry. With t-SNE analysis , we observed that JURV treatment-induced tumor growth delay was associated with a significantly altered TME to favor a more robust immune response. This effect was evidenced by increased markers of activated and proliferating T cells (CD44, Ki67), cytotoxic markers (CD8, GzmB), and IFN-γ production , with PD-1 expression suggesting a potentially active immune response. Our results indicate that JURV effectively recruits cytotoxic T lymphocytes and modulates immunosuppression, a key feature of durable immunotherapy responses. Hepa 1–6 HCC tumors were subjected to transcriptional profiling to discern the gene and pathway alterations occurring following treatment with JURV, compared with controls treated with PBS. Differentially expressed genes (DEGs) were analyzed using the limma-voom method. 20 Our data showed that, among the 22,786 genes, 203 DEGs were upregulated and 464 DEGs were downregulated (2-fold change >2, p < 0.055). Several of the top 10 upregulated DEGs, Myo3a, 21 Cd209c, 22 Trim67, 23 St8sia2, 24 and Wnt5b 25 are associated with immune response pathways . Many of the enriched cellular signaling pathways, such as the B cell receptor signaling, IL-15 signaling, and phagosome formation, identified by IPA analysis are related to the activation of the host’s innate and adaptive immune responses . Furthermore, to better understand the mechanism of JURV-induced anti-tumor activity, we analyzed the DEPs and DEGs from the transcriptomic and proteomic data . In the associated DEGs/DEPs, we identified the top 30 enriched features that are significantly upregulated or downregulated in the JURV group compared with the PBS-treated control group. Among the upregulated features, S1pr3, 26 Tnpo1, 27 Psmb1, 28 Ddt, 29 Ncor2, 30 and Slc04c1 31 have been identified in inflammation, host immune response against microorganisms (virus, bacteria), and tumorigenesis. These studies reveal potential molecular mechanisms involved in the JURV-induced anti-tumor activities. Figure 5 Proteogenomic changes in murine HCC injected with oncolytic JURV (A) Volcano plot of murine HCC tumor mRNA expression differences for PBS vs. JURV (1.0 × 10 7 TCID 50 ). (B) 3D pie slices of the numbers of differentially expressed genes (DEGs) between PBS vs. JURV. (C) Heatmap of the top 20 DEGs upregulated or downregulated in PBS vs. JURV. DEGs were determined using the limma-voom. (D) Graph showing top-scoring canonical pathways significantly enriched by treatment with PBS vs. JURV. A MixOmics supervised analysis was carried out between DEPs and DEGs based on Log2 fold change values. Log2 fold change of DEG × Log2 fold change of DEP > 0 with a p value of DEG and DEP < 0.05 were considered associated DEGs/DEPs. (E) DEG/DEP expression heatmap of the 30 most upregulated and downregulated features DEG/DEP in PBS vs. JURV. To comprehensively evaluate the therapeutic efficacy of oncolytic JURV across diverse TMEs, we employed distinct experimental approaches tailored to each model: i.t. injections for the non-metastatic Hepa 1–6 model and intraperitoneal (i.p.) injections for the metastatic RILWT model. Building on the observed effects of JURV in delaying tumor growth, modulating the TME, and activating immune effectors critical for anti-tumor immunity, we further investigated the synergistic potential of combining i.p. administration of JURV with anti-PD-1 therapy in an orthotopic RILWT mouse model. Employing immunocompetent C57BL6/J mice with RILWT HCC cells implanted orthotopically, we administered i.p. injections of JURV (1.0 × 10 7 TCID 50 ) weekly for 3 weeks from day 7 post-tumor implantation, either alone or combined with anti-PD-1 antibody (5 mg/kg given twice weekly for 3 weeks). Kaplan-Meier survival analysis showed significant improvements in survival for mice treated with anti-PD-1 antibodies , JURV , and notably the combination of JURV and anti-PD-1 antibodies , compared with PBS-treated controls . The combination therapy notably outperformed both anti-PD-L1 antibody alone and JURV alone without inducing adverse clinical events . Furthermore, RILWT-cured and treatment-naive mice were rechallenged subcutaneously with 5.0 × 10 5 RILWT cells to evaluate long-term immunity. Interestingly, all mice previously treated with JURV, anti-PD-1, or their combination, successfully rejected the implanted RILWT cells, contrasting with the tumor development in treatment-naive mice . These findings suggest the induction of a robust tumor-specific immune response by the treatments highlighting the potential of JURV in combination with anti-PD-1 therapy as a potent strategy for HCC treatment. Figure 6 JURV synergizes with checkpoint inhibitors to significantly control tumor growth and prolong survival compared with single treatments in the metastatic HCC orthotopic mouse model (A) Kaplan-Meier survival curves illustrate the probability of survival over time for RILWT tumor-bearing mice ( n = 10/group) treated with PBS (vehicle), JURV alone, anti-PD-1 antibodies alone, and the combination of JURV and anti-PD-1. Median survival times are indicated for each treatment group, with the combination therapy showing significantly extended survival compared with all other groups . (B) Body weight changes of the mice are plotted over time post-treatment, serving as an indirect measure of general health and treatment tolerability. Data points represent mean body weights with error bars indicating standard deviation. (C) Tumor growth post-rechallenge demonstrates individual tumor progression for each treatment cohort. JURV, αPD-1, and their combination notably inhibit tumor growth, which correlates with enhanced survival rates and suggests induction of tumor-specific immune responses. Statistical significance for survival rates was calculated using log rank (Mantel-Cox) tests, with the following notations: ns, not significant; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001. Tumor volume and body weight data were analyzed using repeated measures ANOVA with post hoc tests appropriate for multiple comparisons. In this study, we have demonstrated the oncolytic efficacy of JURV in targeting murine and human HCC cell lines in vitro , as well as its capability to delay tumor growth and prolong survival in murine cancer models of HCC. Our data show that JURV modulated the TME by enhancing the infiltration of cytotoxic T cells and recruiting diverse immune effectors. When used in combination with anti-PD-1 antibodies, JURV greatly enhances tumor regression and improves survival rates in orthotopic HCC models. This survival benefit remained effective when surviving mice were rechallenged with subsequent tumor implantations, strongly indicating a tumor-specific immune response. This work further investigated the safety profile of JURV, underscoring its lack of neurotoxic and hepatotoxic effects, thus making it a promising candidate for oncolytic viral therapy. This safety, combined with its effectiveness, was demonstrated in HEP3B xenografts, where JURV’s anti-tumor activity led to the complete eradication of human HCC in tumor-bearing mice. This outcome correlated with our in vitro cytotoxicity assays and the activation of IFN-associated proteins, as described in our earlier publication. 13 , 32 Moreover, our data reveal the protective effect of exogenous type I IFN, which reduces JURV-induced cell killing in a dose-dependent manner. This suggests that normal tissues with intact IFN responses are likely protected from viral infection, further supporting JURV’s tumor specificity and safety. Our comprehensive analysis of proteomic and transcriptomic data uncovered various molecular pathway alterations and changes in gene expression in HEPA 1–6 tumors following treatment with i.t. injections of JURV. Transcriptional profiling identified several DEGs associated with immune response pathways, such as Myo3a, Cd209c, Trim67, St8sia2, and Wnt5b. Enrichment analysis highlighted major immune-related signaling pathways, including B cell receptor and IL-15 signaling. By integrating transcriptomic and proteomic data, we observed the upregulation of proteins such as S1pr3, Tnpo1, and Psmb10, which are involved in inflammation, immune response, and tumorigenesis. However, we acknowledge several limitations in the models used. The subcutaneous Hepa 1–6 tumor model, while providing important insights into localized tumor-immune interactions, does not fully replicate the complex TME or metastatic behavior typical of HCC. In addition, the use of human cell lines in xenograft models presents challenges due to species-specific immune system differences, which may affect the translational relevance of our findings. To address these limitations, future studies employing orthotopic or patient-derived models are necessary to validate our observations and refine therapeutic strategies. Our results also align with the concept of locoregional oncolytic virotherapy as reported in other therapies. 33 It shows that JURV-mediated oncolysis effectively induces tumor growth delays in both primary and distant tumors, demonstrating its ability to trigger an abscopal effect that is less commonly observed in other therapies for HCC. 34 In summary, we demonstrated that JURV effectively induces cancer cell death and stimulates anti-tumor immunity in HCC. Moreover, we showed that the combination of JURV with anti-PD-1 antibodies provides additional survival benefits in preclinical HCC models. This study not only highlights the potential of JURV as a potent therapeutic option for HCC treatment but also introduces an innovative strategy with the potential to overcome challenges such as low immunogenicity and immunosuppression safely and potently. The addition of JURV in the field of oncolytic viral therapy promises to broaden the clinical application of OVs in cancer treatment, providing new avenues for therapy optimization. The procedure used for JURV recovery was as in Lawson et al. 35 In short, 6-well plates were used to plate BHK cells at a density of 5 × 10 5 cells/well. At an MOI of 10, the cells were infected with a vaccinia virus that encodes T7 polymerase. Following 1 h incubation, excess vaccinia was removed and cells were transfected with 2 μg pJURV, 1 μg pN, 0.8 μg pP, and 0.4 μg pL (the N, P, and L plasmids were constructed in the pCI vector) using 12.5 μL of Lipofectamine LTX transfection reagent (Life Technologies, Grand Island, NY) following the manufacturer’s instructions. The cells were incubated in Opti-MEM Reduced-Serum Medium (Gibco) at 37°C for 48 h. The cells were cultured in Opti-MEM Reduced-Serum Medium (Gibco) for 48 h at 37°C. The culture medium was taken out after 48 h, twice filtered through a 0.2-μm filter, and then placed on top of fresh BHK cells in a 6-well plate. After 48 h, the culture medium was taken out, centrifuged at a low speed, filtered through a 0.2-μm filter, titrated on new Vero cells, and kept in storage at −80°C. This study used a panel of three human HCC cell lines: HEP3B , PLC, HuH7 , and two murine HCC cell lines: HEPA 1–6 and R1LWT (RRID: CVCL_B7TK). We also used several murine solid tumor cells, including colon carcinoma cells , skin melanoma cells , and prostate cancer cells . All cell lines were cultured at 37°C with 5% CO 2 in medium supplemented with antibiotic agents (100 μg/mL penicillin and 100 μg/mL streptomycin). HEP3B, PLC, and HuH7 were maintained in Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS). We maintained HEPA 1–6, RILWT, BHK-21 , and Vero cells in DMEM with 10% FBS. BHK-21, Vero, HEP3B, PLC, HuH7, HEPA 1–6, CT26, and BF16-F10 cells were obtained from the American Type Culture Collection (Manassas, VA). The RILWT cell line derived from RIL-175 cells was from Dan G. Duda, PhD, Massachusetts General Hospital, Boston, MA. Viral amplification was done by infecting confluent (∼80%) Vero cells in T-175 flasks of JURV at an MOI of 0.001. At 48 h post-infection or when cytopathic effects were observable, supernatants of virus-infected cells were collected from the flasks. The viral stocks were purified using 10%–40% sucrose-density gradient ultracentrifugation followed by dialysis. The titer (TCID 50 ) of the rescued virus was determined by the Spearman-Kärber algorithm using serial viral dilutions in BHK-21 cells. BHK-21 and Vero cells were obtained from the American Type Culture Collection. For all cytotoxicity assays (96-well format), 1.5 × 10 4 HEP3B, PLC, HuH7, HEPA 1–6, or RILWT cells were infected with JURV at the indicated MOIs of 10, 1, or 0.1 in serum-free Gibco Minimum Essential Medium (Opti-MEM). Cell viability was determined using a Cell Titer 96 AQueous One Solution Cell Proliferation Assay (Promega, Madison, WI). Data were generated from six replicates from two independent experiments ± SEM. Five hundred thousand HEP3B, PLC, HuH7, HEPA 1–6, or RILWT cells were infected with oncolytic JURV in 6-well plates at an MOI of 0.1 for 1 h. Supernatants of virus-infected cells were removed, and cells were washed with PBS and incubated at 37°C until analysis. At 72 h after infection, cells were fixed with 5% glutaraldehyde and stained with 0.1% crystal violet to visualize the cellular morphology and remaining adherence indicative of cell viability. Pictures of representative areas were taken. Two hundred thousand HCC cells were plated in each well of a 6-well plate in 2 mL of complete DMEM. After allowing cells to rest overnight, we infected them with JURV at an MOI of 0.1 for 1 h. Supernatants of virus-infected cells were removed, cells were washed with PBS, and fresh medium was added. At 10, 24, 48, and 72 h, the supernatant was collected and stored at −80°C. Viral titers (PFU/mL) were determined with serial dilutions of the supernatant on Vero cells. Data were generated as means of two independent experiments ± SEM. The following antibodies were used for flow cytometry analysis: CD45-FITC , CD3-BUV395 , CD4-BUV737 , CD8-Percp-Cy5.5 , CD44-BV711 , CD335-PE/Dazzle594 , PD-1-PE , Ki67∗-BV605 , Granzyme B∗-APC , IFN-γ∗-BV421 , CD11b-PE-Cy7 , F4/80-BV51.00 , CD206-AF700 , I-A/I-E-BV786 , and L/D-efluor780 . Female C57BL/6J mice ( n = 6 mice/group) were administered PBS, a moderately high viral dose (1.0 × 10 7 TCID 50 ), or a high viral dose (1.0 × 10 8 TCID 50 ) i.n. (25 μL in each nostril) or i.v. (50 μL/mouse). Body weight, temperature, behavior, and clinical signs were monitored by a board-certified veterinarian at least three times a week to detect any signs of toxicity. At 3 days post-infection, three mice per group were sacrificed, and blood and animal tissues (brain, liver, and spleen) were collected and subjected to hematoxylin and eosin staining to assess short-term toxicity and viral biodistribution. The remaining mice were monitored for 30 days. Female NOD.Cg- Prkdc scid /J mice were subcutaneously inoculated with HEP3B cells expressing a firefly luciferase reporter gene on the right flanks ( n = 6–7/group). When the average tumor volume reached 80–120 mm 3 , mice were administered 50 μL i.t. injections of JURV (1.0 × 10 7 TCID 50 ) or 50 μL of PBS (controls) once weekly for 3 weeks. Tumor volume was measured twice weekly until the end of the study (day 21), or the humane endpoint as described above. We also recorded mouse body weight and clinical observations twice per week. Tumor-bearing (HEP3B) mice were anesthetized with isoflurane and imaged once a week (days 0, 7, and 14) with an IVIS Xenogen imaging system to assess virus-induced changes in tumor growth. Anesthesia was induced in an induction chamber (2%–5% isoflurane), after which the mice were placed in the imaging instrument and fitted with a nose cone connected to a vaporizer to maintain the isoflurane concentration (0.5%–2%) during the procedure. This range of concentrations produces a level of anesthesia that prevents animal movement during scanning. If the respiratory rate accelerates or slows, the isoflurane concentration is increased or decreased. We used a heated animal bed, heating pads, and, if necessary, a heating lamp to ensure that body temperature was maintained both before imaging and during the procedure. Each mouse received an i.p. injection of D-luciferin . Anesthetized mice were placed into the IVIS Xenogen imaging system on their stomachs. Imaging of each group of mice took less than 10 min. This was a non-invasive imaging procedure, and no restraints were needed. To evaluate the in vivo therapeutic efficacy of oncolytic JURV in a syngeneic mouse HCC model, we injected 1 × 10 6 HEPA 1–6 cells in 100 μL of cold RPMI into the right flanks of immunocompetent female C57BL6/J mice ( n = 7–8/group; Jackson Laboratory) using 1 mL syringes. Mice were monitored weekly for palpable tumors or any changes in appearance or behavior. When average tumors reached a treatable size (80–120 mm 3 ), mice were randomized into the respective study groups—PBS (controls) and JURV. Dosing began within 24 h of randomization. Depending on the treatment regimen, mice were administered 50 μL i.t. injections of either PBS or JURV (1 × 10 7 TCID 50 units) on days 0, 7, and 14. To establish syngeneic bilateral HCC tumors (dual flanks), in additional groups of mice, HEPA 1–6 cells (1 × 10 6 cells/mouse) were first subcutaneously grafted into the right flanks (resulting in tumors at ∼14 days) and categorized as “primary” tumors. Simultaneously, we performed distant HEPA 1–6 tumor graft injections (1 × 10 6 cells/mouse) into the left flanks of these mice. Mice in the dual-flank groups only received 50 μL i.t. injections of 1 × 10 7 TCID 50 units of JURV on their right flanks once a week for 3 weeks. Tumor volume and body weight were measured twice weekly using a digital caliper and balance following randomization and initiation of treatment. Tumor volume was calculated as (longest diameter × shortest diameter 2 )/2. During the first week of treatment and after each injection, mice were monitored daily for signs of recovery for up to 72 h. Mice were euthanized when body weight loss exceeded 20%, when tumor size was larger than 2,000 m³, or for adverse effects of treatment. Mice were sacrificed 28 days following the first JURV dose administration, at which time tumors and blood were collected for downstream analysis. To evaluate the in vivo therapeutic efficacy of oncolytic JURV in a syngeneic mouse orthotopic HCC model, 1.0 × 10 6 luciferase-expressing RILWT cells were surgically implanted into one of the lobes of the liver of a syngeneic orthotopic HCC mouse model. Following 14 days after tumor implantation, mice were randomized ( n = 10/group) and grouped. To determine the safety and efficacy of the JURV (1.0 × 10 7 TCID 50 ) and/or anti-mPD-1 were administered i.p. Tumor size was measured by bioluminescent imaging 14 days after tumor implantation for animal randomization and once weekly for 60–90 days. Body weight was measured twice weekly. During the first week of treatment and after each injection, mice were monitored daily for signs of recovery for up to 72 h. Mice were euthanized when body weight loss exceeded 20% or for adverse effects of treatment. Mortality during the survival study was assessed using the log rank test to compare the differences in Kaplan-Meier survival curves. RILWT-cured or treatment-naive C57BL/6J mice were rechallenged by subcutaneously inoculating 5.0 × 10 5 RILWT cells. Tumor growth was monitored for 30 days post-implantation. Hepa 1–6 tumors ( n = 3 samples/group) were excised and dissociated on day 18, 3 days after the last JURV injection, using a mouse tumor dissociation kit (Miltenyi, cat. no. 130-096-730) with a gentleMACS Octo Dissociator (Miltenyi) according to the manufacturer’s protocol. CD45 + cells were isolated with mouse CD45 (TIL) microbeads (Miltenyi). Cells were incubated with Fixable Viability Stain 510 for 15 min at 4°C, followed by anti-Fc blocking reagent for 10 min before surface staining. Cells were stained, followed by data acquisition with a BD LSRFortessa X-20 flow cytometer. All antibodies ( Table S1 ) were used following the manufacturer’s recommendation. Fluorescence Minus One control was used for each independent experiment to establish gating. For intracellular staining of granzyme B, cells were stained using an intracellular staining kit (Miltenyi), and analysis was performed using FlowJo (TreeStar). Forward scatter and side scatter cytometry were used to exclude cell debris and doublets. Hepa 1–6 ( n = 3 samples/group) FFPE scrolls were processed for DNA and RNA extraction using a Quick-DNA/RNA FFPE Miniprep Kit with on-column DNase digestion for the RNA preps . RNA was assessed for mass concentration using the Qubit RNA Broad Range Assay Kit with a Qubit 4 fluorometer . RNA quality was assessed with a Standard Sensitivity RNA Analysis Kit on a Fragment Analyzer System . Sequencing libraries were prepared using TruSeq Stranded Total RNA Library Prep Gold . RNA DV200 scores were used to determine fragmentation times. Libraries were assessed for mass concentration using a Qubit 1X dsDNA HS Assay Kit with a Qubit 4 fluorometer . Library fragment size was assessed with a High Sensitivity NGS Fragment Analysis Kit on a Fragment Analyzer System . Libraries were functionally validated with a KAPA Universal Library Quantification Kit . Sequencing was performed to generate paired-end reads (2 × 100 bp) with a 200-cycle S1 flow cell on a NovaSeq 6000 sequencing system (Illumina). We examined the mRNA and protein expression profiles of Hepa 1–6 tumors treated with PBS, JURV, anti-PD-1, or JURV + anti-PD-1. Three replicates were used to analyze each of the untreated (PBS) and treated groups. The tumor samples were sequenced on an NGS platform. The files containing the sequencing reads (FASTQ) were then tested for quality control using MultiQC. 36 The Cutadapt tool trims the Illumina adapter and low-quality bases at the end. After the quality control, the reads were aligned to a mouse reference genome (mm10/GRCm38) with the HISAT2 aligner, 37 followed by counting reads mapped to RefSeq genes with feature counts. We generated the count matrix from the sequence reads using HTSeq-count. 38 Genes with low counts across the samples affect the false discovery rate, thus reducing the power to detect DEGs; thus, before identifying DEGs, we filtered out genes with low expression utilizing a module in the limma-voom tool. 39 Then, we normalized the counts by using TMM normalization, 40 a weighted trimmed mean of the log expression proportions used to scale the counts of the samples. Finally, we fitted a linear model in limma to determine DEGs and expressed data as mean ± standard error of the mean. All p values were corrected for multiple comparisons using Benjamini-Hochberg FDR adjustment. After identifying DEGs, enriched pathways were performed using the Ingenuity Pathway Analyses (IPA) tool to gain biological insights. The statistical difference between groups was assessed using the nonparametric Mann-Whitney U test R module. The limma-normalized transcript expression levels and the normalized protein intensities were integrated using two independent methods. Firstly, the mixOmics package (Omics Data Integration Project R package, version 6.1.1) was implemented to generate heatmaps of the associated DEPs/DEGs as described previously. 41 Secondly, the MOGSA package was used to generate heatmaps of the top 30 upregulated or downregulated DEPs/DEGs between the various groups. 42 All numerical variables were summarized using mean ± standard error. A one-way ANOVA model assessed the association of the numerical variable to an experiment factor. Post hoc means were compared between experiment groups after adjusting for multiple comparisons using Turkey’s method. Sequencing data were analyzed after controlling for false discovery rate using a Benjamini-Hochberg method. Time-to-event data were analyzed using Kaplan-Meier curves and compared between groups using a log rank test. Paired comparisons were conducted using paired t tests and/or Wilcoxon signed rank tests. Statistical analyses were performed using GraphPad Prism . p values <0.05 were considered statistically significant. | Other | biomedical | en | 0.999999 |
PMC11697552 | Corrosion is the process by which materials deteriorate because of reactions with their environment. It is often seen as the degradation or irreversible destruction of the surface of metal due to chemical reactions involved in the translation of pure metal to a more chemically unchanging form (such as hydroxides, oxides, sulphides) in a corrosion-prone environment. Corrosion-prone setting may be of solid, liquid or gas form. Corrosion of metals is quite a complex and a worldwide phenomenon [ , , , ]. The settings are called electrolytes, while transference of ions (anions and cations) forms two reactions. In a situation of two different metals in an electrolyte, the less noble metals perform as anode and become corroded while the more noble metal perform as cathode and become protected. In a conducting solution, zinc tends to corrode while copper is more likely to remain protected. This is due to the electrochemical series; zinc has a lower electrode potential, making it more susceptible to oxidation and corrosion, while copper has a higher electrode potential and is less prone to corrosion . The high tendency of aluminium and its alloys to resist corrosion is due to the development of a compact, adherent inert oxide film, which is amphiprotic and dissolves to a great extent on exposure of the metal to alkaline or acids solutions. The deterioration of aluminium and its compounds in aqueous solutions comes with substantial cost implication. It is therefore essential to introduce inhibitors to shield the metal from corrosion. Many organic compounds are deployed as corrosion inhibitors for aluminium and its compounds in alkaline and acidic media. The inhibitive performance of these compounds hinge on the chemical composition of the inhibitor, the metal's surface charge, and the type of contact between the molecules of the inhibitor and the surface of the metal. Often, inhibitors perform by sticking to the surface of the metal and creating a coating that shields it. Typically, inhibitors are dispersed from a solution; a portion are part of the preparation of protective coatings [ , , , , ]. Majority of the organic materials deployed as inhibitors are very costly and naturally toxic. Hence, there is need to find non-toxic, eco-friendly, natural and low-cost inhibitors for shielding of alloys and metals from corrosion in aqueous solutions. A viable substitute to these organic compounds has been found to be expired drugs, since they possess the above desirable properties, and have been found to adsorb on metallic surfaces. They create layers and precipitates on the surface of the metal, leading to the obstruction of anodic and cathodic sites. Some of the drugs that have been successfully applied include atenolol drug , cimetidine , antithyroid drugs , among others. The current study intends to advance the usage of drugs as corrosion inhibitor, by using danacid for such purpose. Danacid tablet is a compound of magnesium trisilicate which is used for the management of hyperacidity, heartburn, dyspepsia, peptic ulcer disease and reflux esophagitis. Due to its adsorptive and eco-friendly properties, expired danacid has been found as a good candidate for corrosion inhibition. In our previous publication, danacid was applied as a corrosion inhibitor of aluminium in sulphuric acid medium . In the present study, it is deployed as a corrosion inhibitor of aluminium in HCl media with the aid of electrochemical impedance spectrometry (EIS), potentiodynamic polarization (PDP), quantum chemical computations, as well as modeling and optimization using artificial neural network (ANN) response surface methodology (RSM). In this work, varied concentrations of the inhibitor were prepared. Ten grams of the expired drug was mixed with 1 L of HCl solution. The solutions of the inhibitor were set at concentrations of 0.1–0.9 g/L from the stock solution [ , , ]. Chemical analysis of the expired danacid was done with a GC-MS as well as FTIR spectroscopy (Cary 630 model from Agilent Technologies), as previously reported . The thermometry of the process had been reported . The reaction number (RN) and the inhibitor efficiency (IE) were respectively obtained with Equations (1) , (2) ) . (1) R N = T m − T i t (2) I E ( % ) = ( 1 − R N i n h R N u n i h ) ∗ 100 The gravimetric method had previously been reported. The corrosion rate (CR), weight loss (Δw), surface coverage, and IE, were respectively computed through the application of Equations (3) , (4) , (5) , (6) as previously reported [ , , , , , , , , ]. (3) C R = w i − w f A t (4) Δ w = w i − w f (5) θ = ω 0 − ω 1 ω 0 (6) I E % = ω 0 − ω 1 ω 0 ∗ 100 The linearized form of Arrhenius model was deployed to estimate the activation energy of the inhibition process as shown by Equation (7) . (7) Ln ( CR ) = Ln A − ( E a R ) 1 T where CR, E a , A, T and R respectively denote the corrosion rate, activation energy, frequency factor, temperature and gas constant. By denoting the metal's corrosion rates at T 2 and T 1 as CR 2 and CR 1 , Equation (8) is obtained . (8) Ln ( C R 2 C R 1 ) = Ln A − ( E a 2.303 R ) ( 1 T 1 − 1 T 2 ) As previously reported , Q ads (kJmol −1 ) was calculated with Equation (9) . (9) Q a d s = 2.303 R [ log θ 2 1 − θ 2 − log θ 1 1 − θ 1 ] ∗ T 2 . T 1 T 2 − T 1 where R denote the gas constant, θ 2 and θ 1 and correspondingly designate the degree of surface coverage at T 2 and T 1 . The θ data was deployed to evaluate the usability of different isotherm models, such as the Langmuir, Flory-Huggins, Frumkin, and Temkin model as respectively depicted by Equations (10) , (11) , (12) , (13) , as previously reported . (10) C θ = 1 K a d s + C (11) log [ ( θ C ) ] = log K a d s + x log ( 1 − θ ) (12) log [ ( C ) × ( θ 1 − θ ) ] = 2.303 log K a d s + 2 α θ (13) θ = − 2.303 log K a d s 2 a − 2.303 log C 2 a where C, θ, K a d s , x, α respectively denote the concentration of the inhibitor, degree of surface coverage, adsorption equilibrium constant, size parameter and the lateral interaction term. The free energy of adsorption ( Δ G a b s ) was estimated with Equation (14) . (14) Δ G a b s = − 2.303 RT log ( 55.5 K ) Molecular modeling and quantum chemical techniques were used to determine the molecular composition and adhesive properties of the inhibitor (danacid) as previously reported [ 19 , , , ]. Comparison of ANN and RSM was done to evaluate their analytical and valuation capabilities with statistical tools namely, root mean square error (RMSE), standard error of prediction (SEP), mean absolute error (MAE), as shown in Equations (15) , (16) , (17) ) . (15) R M S E = ( 1 n ∑ i − 1 n ( Y p r e d . , i − Y exp . , i ) 2 ) 1 / 2 (16) S E P = R M S E Y exp . a v e ∗ 100 (17) MAE = 1 n ∑ i = 1 n | ( Y exp . , i − Y p r e d . , i ) | Table 1 depicts the effect of the concentration of the expired danacid on the RN and IE. The RN was determined by the fraction of change in temperature to the maximum time attained. The expired danacid's concentration range stretched from 0.1 to 0.9 g/L. The RN decreased with increase in inhibitor concentration (IC). The IE was evaluated as a function of reaction number (in the presence and absence of the inhibitors). The IE increased with increase in IC and reduction in RN . Table 1 Influence of danacid concentration on the IE and RN. Table 1 IC (g/L) RN ( o C/min) IE (%) 0.0 0.3409 0.1 0.1052 69.14 0.3 0.0703 79.38 0.5 0.0414 87.86 0.7 0.0156 95.41 0.9 0.0231 93.22 The loss in weight of a metal sample in its area multiplied by the duration the experimental work was carried out defines the rate of metal dissolution. The main merit of this method is that it is convenient and simple to determine corrosion conditions and little inhibitor dosage is needed for additional experiments. The disparities of protection efficiency and dissolution rate in the protected and unprotected media are presented in Table 2 . Results displayed in Table 2 shows that danacid is a possible candidate for aluminium protection in acidic environments indicating a slowdown in reaction rate in the inhibited solution in comparison to the uninhibited solution. Close inspection of Table 2 indicates that dissolution rates increased as the temperature was made to rise with the highest values obtained at 323 K in all the systems studied. The corrosion IE rises by increasing the danacids's concentration and is further evident as a result of the large part of active constituents of the inhibitor on the corroding surface of the metal. Conversely, protection efficiency reduced to a great extent as the temperature was increased. This is due to the fact that rise in temperature scatters the extract molecules from the aluminium surface (breaks the heterocyclic bonds found in the danacid, hence, decreasing the surface coverage) . Table 2 Results of weight loss of Al in HCl. Table 2 Time (h) Temperature (K) Inhibitor conc. (g/L) Weight loss (g) CR (g/cm 2 h) IE (%) SC (θ) 5 303 0.0 0.097 0.0022 0.3 0.050 0.0011 48.45 0.4845 0.7 0.036 0.0008 62.89 0.6289 0.9 0.021 0.0005 78.35 0.7835 313 0.0 0.109 0.0024 0.3 0.059 0.0013 45.87 0.4587 0.7 0.042 0.0009 61.47 0.6147 0.9 0.035 0.0008 67.89 0.6789 323 0.0 0.129 0.0029 0.3 0.069 0.0015 46.51 0.4651 0.7 0.051 0.0011 60.47 0.6047 0.9 0.045 0.0010 65.12 0.6512 4 303 0.0 0.091 0.0025 0.3 0.049 0.0014 46.15 0.4615 0.7 0.036 0.0010 60.44 0.6044 0.9 0.028 0.0008 69.23 0.6923 313 0.0 0.100 0.0028 0.3 0.056 0.0016 44.00 0.4400 0.7 0.041 0.0011 59.00 0.5900 0.9 0.035 0.0010 65.00 0.6500 323 0.0 0.114 0.0032 0.3 0.065 0.0018 42.98 0.4298 0.7 0.054 0.0015 52.63 0.5263 0.9 0.046 0.0013 59.65 0.5965 3 303 0.0 0.069 0.0026 0.3 0.041 0.0015 40.58 0.4058 0.7 0.034 0.0013 50.72 0.5072 0.9 0.023 0.0009 66.67 0.6667 313 0.0 0.080 0.0030 0.3 0.049 0.0018 38.75 0.3875 0.7 0.039 0.0014 51.25 0.5125 0.9 0.033 0.0012 58.75 0.5875 323 0.0 0.085 0.0031 0.3 0.053 0.0020 37.65 0.3765 0.7 0.044 0.0016 48.24 0.4824 0.9 0.036 0.0013 57.65 0.5765 The Q ads and E a for the corrosion control of Al in HCl solution with danacid are shown in Table 3 , Table 4 . The E a was computed using the Arrhenius model. The E a attained in this study is > 80 kJ/mol, which indicates that the inhibitor molecules’ adsorption on the surface of the metal conforms to the physical mechanism of adsorption . Heat of adsorption is an important thermodynamic property since it shows the straight connection with the degree of surface coverage. Negative values were recorded for the Q ads in this work as shown in Table 4 . This shows that the adsorption of the inhibitor on the surface of the metal is exothermic. Table 3 E a for the corrosion control process. Table 3 Temperature (K) CR (mg/cm 2 h) E a (kJ/mol) 303 0.889 32.81 313 0.444 323 1.306 333 2.083 343 2.806 Table 4 Q ads for the corrosion control process. Table 4 IC (g/L) Q ads (kJ/mol) 0.1 −85.822 0.3 −102.246 0.5 −105.096 0.7 −98.062 0.9 −108.677 Langmuir, Frumkin, Temkin, and Flory-Huggins models were used to examine the experimental results for controlling aluminium corrosion in HCl media with expired danacid as inhibitor as presented in Table 5 . The Langmuir, Temkin, Frumkin, and Flory-Huggins plots are correspondingly depicted in Fig. 1 , Fig. 2 , Fig. 3 , and Fig. 4 (a, b). Table 5 Adsorption parameters. Table 5 Adsorption Isotherm Tempe-rature (K) R 2 K ads ΔG ads (kJ/mol) Isotherm property Langmuir Isotherm 313 0.999 0.9649 −10.360 323 0.9807 0.360 −8.043 Temkin Isotherm 313 0.9572 9606008.00 −52.300 a −8.3929 323 0.8513 9921.7702 −35.504 −5.6034 Frumkin Isotherm 313 0.9939 0.0043 3.729 α 3.4717 323 0.9691 0.0613 −3.288 2.0616 Flory-Huggins Isotherm 313 0.8308 14.6690 −17.443 x 0.9252 323 0.6115 4.6946 −14.941 0.9097 Fig. 1 Langmuir model graph. Fig. 1 Fig. 2 Temkin model graph. Fig. 2 Fig. 3 Frumkin model graphs: (a) 313K, (b) 323K. Fig. 3 Fig. 4 Flory-Huggins model graphs: (a) 313K, (b) 323K. Fig. 4 Correlation coefficient values of 0.999 and 0.999 respectively recorded at 313 K and 323 K show that Langmuir model term gave the finest fit to the results of the experiment. By comparison of equations (10) , (11) , (12) , (13) ) with the isotherm plots, the adsorption parameters K, a, x, and α were evaluated . The isotherms and their corresponding parameter values are displayed in Table 5 . PDP measurements were carried out in uninhibited and inhibited acid media containing different concentrations of expired danacid to gain further insight about the behaviour of Al in 1 M HCl. From Fig. 5 , it is clear that the presence of danacid suppressed the anodic and cathodic reactions. The Tafel polarization factors recorded from the PDP experiments such as corrosion current density (i corr ), corrosion potential (E corr ), cathodic (β c ) and anodic (β a ) Tafel slopes are all presented in Table 6 . No certain trend is seen in the corrosion potential shifts in danacid's presence; hence, the inhibitor can be regarded as mixed-type inhibitor. Fig. 5 PDP curves of Al in 1 M HCl in the uninhibited and inhibited solution. Fig. 5 Table 6 Polarization parameters. Table 6 System E corr (mV) I corr (μA/cm 2 ) b a (mVdec −1 ) b c (mVdec −1 ) sc (θ) IE (%) 1 M HCl −478.4 208.7 88.6 46.8 1 M HCl+ 0.5 g/L DNC −477.6 27.4 93.6 54.7 0.8687 86.87 1 M HCl + 0.7 g/L DNC −472.4 12.8 87.6 42.5 0.9387 93.87 As depicted in Table 6 , there are no considerable changes in the β c and β a values in the presence of the inhibitor, hence, cathodic and anodic reactions are not affected and the danacid's inhibition action is majorly due to the geometric obstructive effect implying the decrease of the reaction area on the aluminium surface by obstructing the active reaction sites, which does not affect the corrosion reaction mechanism during the inhibition process. Table 6 reveals that the addition of danacid decreased both cathodic and anodic currents and did not reveal any considerable shift in E corr , which also prove that expired danacid is a mixed-type inhibitor . The inhibition efficiency (η) of expired danacid was computed with Equation (18) . (18) η = [ 1 − i c o r r i c o r r 0 ] × 100 where i c o r r 0 and i corr respectively denote the corrosion current densities in the absence and presence of danacid. Measurements of EIS were implemented in 1 M HCl and with different danacid concentrations to give insight into the corrosion behaviour and the adsorption mechanisms. Additionally, EIS was undertaken as rapid and precise technique to evaluate corrosion rates at the aluminium/1 M HCl boundary in the presence and absence of inhibitors. Fig. 6 a shows the Nyquist plot while Fig. 6 b and c respectively shows the Bode phase angle and Bode modulus plots without and with two concentrations of the inhibitor. Table 7 gives the values of the EIS parameters calculated by fitting the EIS spectra along with the inhibition efficiency (IE, %) values computed with Equation (19) . (19) I E ( % ) = R c t i − R c t 0 R c t i × 100 where R c t i and R c t 0 are charge transfer resistances in presence and absence of inhibitor, respectively. Fig. 6 EIS spectra of Al in 1 M HCl in the uninhibited and inhibited media: (a) Nyquist (b) Bode phase angle and (c) Bode modulus plots, respectively. Fig. 6 Table 7 Impedance parameters of Al in HCl. Table 7 System R s (Ωcm 2 ) R ct (Ωcm 2 ) n C dl (Fcm 2 ) IE (%) 1 M HCl 1.723 39.7 0.88 7.124E-5 1 M HCl + 0.5 g/L DNC 1.686 532.4 0.88 7.221E-5 92.54 1 M HCl + 0.7 g/L DNC 1.716 802.7 0.89 7.172E-5 95.05 As presented in Fig. 6 , the Nyquist plots demonstrate similarity in behaviour with and without the inhibitor signifying that their existence in 1 M HCl did not change the mechanism of the process. It is worthy of note that the Nyquist semicircles diameter in the inhibited media rise gradually, this became more noticeable upon adding cumulative amounts of danacid to the 1M HCl as demonstrated by a substantial rise in charge transfer resistance values, with associated reductions in C dl values as shown in Table 7 . The decrease in the value of C dl in danacid's presence is due to the reduction in local dielectric constant and a rise in the electrical double layer's thickness, owing to the shift taking place between inhibitor and water molecules throughout the adsorption process . From the results displayed in Table 7 , the IE increased from 92.54 to 95.05 % as the concentration of danacid was increased from 0.5 to 0.7 g/L. GC-MS results of danacid had previously been reported. The peaks show numerous heterocyclic compounds found in danacid. The components found include 1-methyl-4-(1-methyl ethyl)-Cyclohexanol, dl-Menthol, trans-13-Octadecenoic acid, Dotriacontane, 9,12-Octadecadienoic acid, 1-chloro- Hexadecane, n-Hexadecanoic acid, 1-chloro-Octadecane, eicosyl vinyl ester, buty l,2-methylpropyl ester, Carbonic acid, tetradecyl ester, cis-Vaccenic acid, 9-Octadecenoic acid, among others . The IE of expired danacid is credited to its adsorption on aluminium surface. In order to show the relationship between the inhibiting property and quantum chemical parameters of expired danacid, DFT computations were made with DFT electronic structure programme DMol 3 executed in Materials Studio Software. The HOMO and LUMO obtained from the optimized molecular structure are depicted in Fig. 7 . It has been well established that the reactivity of an inhibitor can be characterized in terms of its HOMO and LUMO. The HOMO depicts the electron donation while LUMO depicts the electron acceptance capability of the inhibitor molecules. From the frontier orbital theory's postulation, E HOMO represents a species' aptitude to donate electrons, signifying that species with higher value of E HOMO are more likely to achieve the best inhibition efficiency. On the other hand, E LUMO depicts a species' ability to accept electrons, hence, an effective inhibitor usually has low values of E LUMO . A molecule's energy gap (ΔE) is represented by the difference between E HOMO and E LUMO of the molecule. Low values of ΔE signifies that a molecule is likely to give high inhibition efficiency. The electron density, optimized structure, LUMO, HOMO, side view, top view, and front view of danacid (Mg 2 O 8 Si 3 ) molecule on the Al surface are correspondingly shown in Figures (7(a, b, c, d, e, f, g)) . The DFT parameters are depicted in Table 8 . Fig. 7 Danacid (magnesium trisilicate (Mg 2 O 8 Si 3 ) model: (a) Electron density, (b) Optimized structure, (c) LUMO, (d) HOMO, (e) Side view, (f) Top view, (g) Front view. Fig. 7 Table 8 DFT parameters. Table 8 Inhibitor (molecule) E HOMO E LUMO Energy gap (eV) Molecular mass (gM −1 ) Adsorption Energy (eV) Danacid −6.026 −3.703 2.323 260.857 −137 To assess the collaboration between the inhibitor molecules and the Al surface, the adsorption energy (E ads ) of each scheme was computed with Equation (20) . (20) E Interact = E total – ( E DNC + E Al ) From Table 9 , the maximum inhibitor efficiency for the corrosion protection of Al in HCl was documented as 94.65 %, at the IC of 0.7 g/L, temperature of 313 K, and time of 4 h. The high IE value signifies that the inhibitor is fitting for checkmating corrosion of aluminium in HCl media. There is also an observed rise in the concentration of the inhibitor with rise in IE. It may be related to the nature and effect of molecular construction on their inhibition properties. Table 9 RSM result for corrosion protection of Al in HCl with danacid. Table 9 Std Run Factor 1 A: IC g/L Factor 2 B: Temperature K Factor 3 C: Time h Response IE % 2 1 0.9 303 3 81.03 1 2 0.5 303 3 68.11 7 3 0.5 323 5 66.14 9 4 0.5 313 4 87.63 12 5 0.7 323 4 84.64 14 6 0.7 313 5 94.23 5 7 0.5 303 5 79.55 10 8 0.9 313 4 92.98 20 9 0.7 313 4 94.65 3 10 0.5 323 3 56.97 11 11 0.7 303 4 88.77 19 12 0.7 313 4 94.65 18 13 0.7 313 4 94.65 17 14 0.7 313 4 94.65 15 15 0.7 313 4 94.65 16 16 0.7 313 4 94.65 13 17 0.7 313 3 91.67 4 18 0.9 323 3 77.01 6 19 0.9 303 5 83.14 8 20 0.9 323 5 79.95 Table 10 displays the ANOVA model of the inhibitor efficiency of Al in HCl. The F-value of 47.59 indicates the model is vital since there is 1 out of 100 probabilities that an F-value of up to 47.59 could ensue owing to noise. P-values <0.0500 specify model components are vital. In this study C 2 , B 2 , A 2 , AC, AB, A, B, C are vital model terms. The predicted R 2 of 0.8447 is in decent vicinity with the Adjusted R 2 of 0.9567; i.e. the variance is < 0.2. Adequate Precision ratio of 22.767 designates a satisfactory signal. The model obtained can be deployed to explore the design space [ , , , , , , , , ]. Table 10 ANOVA of Quadratic model. Table 10 Source Sum of Squares df Mean Square F-value p-value Model 2284.92 9 253.88 47.59 <0.0001 significant A-Inhibitor concentration 310.36 1 310.36 58.18 <0.0001 B-Temperature 128.81 1 128.81 24.15 0.0006 C-Time 79.64 1 79.64 14.93 0.0031 AB 37.58 1 37.58 7.05 0.0241 AC 30.26 1 30.26 5.67 0.0385 BC 0.2592 1 0.2592 0.0486 0.8300 A 2 126.09 1 126.09 23.64 0.0007 B 2 295.80 1 295.80 55.45 <0.0001 C 2 46.82 1 46.82 8.78 0.0142 Residual 53.35 10 5.33 Lack of Fit 53.35 5 10.67 Pure Error 0.0000 5 0.0000 Cor Total 2338.27 19 Std. Dev. 2.31 R 2 0.9772 Mean 84.99 Adjusted R 2 0.9567 C.V. % 2.72 Predicted R 2 0.8447 Adeq Precision 22.7673 The coded mathematical model for this study is given as Equation (21) . It is valuable for classifying the comparative impact of the factors by comparison of the factor coefficients. The model in terms of coded factors could be deployed to make estimates about the response for specified levels of each factor . (21) IE = + 95.62 + 5.57 A − 3.59 B + 2.82 C + 2.17 AB − 1.95 AC − 6.77 A 2 − 10.37 B 2 − 4.13 C 2 The equation in terms of actual factors is presented as Equation (22) (22) IE = − 9944.83889 − 35.46102 ∗ Inhibitor concentration + 63.87921 ∗ Temperature + 48.27441 ∗ Time + 1.08375 ∗ Inhibitor concentration ∗ Temperature − 9.72500 ∗ Inhibitor concentration ∗ Time − 169.28409 ∗ Inhibitor concentration 2 − 0.103714 ∗ Temperature 2 − 4.12636 ∗ Time 2 Fig. 8 (a–d) show the graphical results of IEs of danacid for controlling aluminium corrosion in HCl media. Feasibility of the inhibitor was defined with predicted against actual IE and 3-D graphs. Fig. 8 a shows the plot of predicted versus actual IE. The points aligned on the best fitted line, signifying that the model attained successfully defined the experimental IE of danacid . Fig. 8 (a) Experimental against predicted values (b) IE against IC and temperature (c) IE against IC and time (d) IE against temperature and time. Fig. 8 The 3-D graphs reveal the combined effect of temperature, IC and time on the IE of danacid. At the different optimum values of the parameters, the IE of danacid for the corrosion protection of aluminium in HCl medium was recorded as 93.57 %. The high IE value recorded confirms the appropriateness of the inhibitor. The evaluation of ANN and RSM results is displayed in Table 11 . A comparative analysis was implemented to establish the predictive abilities of ANN and RSM using some statistical models as presented in Table 12 . From the statistical investigation performed, ANN gave improved estimate compared to RSM, as authenticated by the lesser values of error parameters such as MAE, RMSE, and SEP . Table 11 ANN and RSM predicted results for corrosion protection of Al in HCl. Table 11 Std Run Factor 1 A: IC g/L Factor 2 B: Temperature K Factor 3 C: Time h Response 1 IE % Experimental RSM values ANN values 2 1 0.9 303 3 81.03 80.29 80.8682 1 2 0.5 303 3 68.11 69.59 68.7234 7 3 0.5 323 5 66.14 67.61 66.8716 9 4 0.5 313 4 87.63 83.28 87.0722 12 5 0.7 323 4 84.64 81.66 84.2616 14 6 0.7 313 5 94.23 94.32 93.2762 5 7 0.5 303 5 79.55 79.48 79.477 10 8 0.9 313 4 92.98 94.42 92.1012 20 9 0.7 313 4 94.65 95.62 93.671 3 10 0.5 323 3 56.97 58.44 58.2518 11 11 0.7 303 4 88.77 88.84 88.1438 19 12 0.7 313 4 94.65 95.62 93.671 18 13 0.7 313 4 94.65 95.62 93.671 17 14 0.7 313 4 94.65 95.62 93.671 15 15 0.7 313 4 94.65 95.62 93.671 16 16 0.7 313 4 94.65 95.62 93.671 13 17 0.7 313 3 91.67 88.67 90.8698 4 18 0.9 323 3 77.01 77.80 77.0894 6 19 0.9 303 5 83.14 82.40 82.8516 8 20 0.9 323 5 79.95 79.20 79.853 Table 12 Comparison of ANN and RSM models. Table 12 Parameters RSM ANN RMSE 1.6330 0.7617 SEP 1.9215 0.8963 MAE 1.2630 0.6698 The results depicted in Table 13 shows the optimum concentration of the inhibitor, temperature, time and IE of danacid. Value of optimum IE was obtained as 93.57 %, which indicates that the inhibitor is appropriate for controlling aluminium corrosion in HCl media. The result obtained here was validated with a percentage deviation of 0.68 %. Table 13 Optimum values. Table 13 Media Optimum IC (g/L). Optimum temperature (K) Optimum time (h) Optimum IE (%) Al in HCl with danacid 0.67 313.36 3.80 93.57 From the results obtained in this work, expired danacid revealed excellent IE for aluminium in 1 M HCl medium. The IE of danacid increased with increase in its concentration and got to 78.35 % at 0.9 g/L, from the gravimetric study. The IE was however found to reduce with increase in temperature. Polarization results show that the expired danacid acts as a mixed-type inhibitor. The adsorption study carried out demonstrate that the inhibition process followed Langmuir isotherm. PDP measurements indicate rise in charge transfer resistance and IE with rise in IC, reaching an IE of 93.87 % at 0.7 g/L. The value of activation energy recorded in this work indicates that the inhibitor molecule's adsorption on the aluminium surface conforms to the mechanism of physical adsorption. Results of computational calculations using density functional theory show good reactivity of expired danacid on the aluminium surface and correlates with the results obtained by electrochemical measurements. Optimization of process parameters established the optimum points for the inhibition process while ANN modeling improved the inhibition efficiency values predicted by RSM. Hence, danacid proved to be a viable inhibitor for aluminium corrosion. | Study | biomedical | en | 0.999997 |
PMC11697557 | Recently, novel materials called high entropy alloys (HEAs) were developed by Yeh et al. [ , , , , ]. Two definitions were provided for HEAs. The basis of the first definition is the chemical composition and the basis of the second one is their configurational entropy. In the first definition, HEAs must contain at least five elements, where each of their concentrations varies between 5 and 35 %. In the second definition, the configurational entropies of HEAs must exceed 1.5R (R is gas constant), regardless of whether they are single-phase or multiphase at room temperature. High hardness, good wear properties and excellent corrosion resistance are observed properties of high entropy materials [ , , , , ]. Casting , powder metallurgy , cladding , spraying , mechanical alloying and welding procedures are the current methods used to produce the HEA systems that have been routinely considered in different studies. Among these, AlNiCoCrFe coatings produced via the tungsten inert gas (TIG) process represent a promising avenue for enhancing the performance of materials in corrosive environments. The TIG process, known for its ability to produce high quality welds and coatings, allows for precise control over the microstructural characteristics of the deposited alloy . However, there is abundant literature that notes that HEAs contain multiple elements as a bulk material. Furthermore, the dimensions and form of bulk ingots made using pointed methods were limited. Hence, some scientists have been trying to probe the two-dimensional production of a layer of high entropy alloy on cheap metallic substrates [ , , ]. One of the current methods is laser processing [ , , ] and another current and low-cost methods of surface hardening and alloying is the use of tungsten inert gas (TIG), which can easily provide the strong and sound bond between layer and substrate . The layer preparation of multi element alloys has seldom been considered. In situ synthesized high-entropy alloy coatings have been focused with the production approach of the multicomponent system with the TIG procedure . For instance, Jie-Hao Chen et al. have fabricated a HEA layer on steel substrate using the tungsten inert gas process in which they used Ni, Co, Cr, Mo and Al. In another experiment, Y.C. Lin had successfully blended two powder mixtures of NiCrAlCoW and NiCrAlCoSi as an initial material. The coating was produced by TIG on plain carbon steel substrate to produce an in situ synthesized layer. Qiu et al. investigated the electrochemical properties of Al2CrFeCoxCuNiTi HEAs coatings prepared on Q235 steel by laser cladding. The results showed that the coating with x = 2 showed better corrosion resistance than 304 stainless steel in the H 2 SO 4 solution. The effect of Al on corrosion properties of AlxFeCoNiCuCr HEAs coatings prepared on AISI 1045 steel was reported by Ye et al. . The results indicated that AlxFeCoNiCrTi coatings prepared by laser cladding are better than 314L stainless steel in corrosion resistance, in which Al1.8FeCoNiCrTi coatings serve the best. This study aims to investigate the possibility of the production of coatings containing high entropy alloys on plain carbon steel substrate using the tungsten inert gas process under controlled conditions to achieve a suitable coating and improve surface properties and increase the corrosion resistance of the substrate. In this way, the same equimolar powder mixtures were used on AISI 1050 medium carbon steel. The effect of electrical current on the microstructure, phases and properties of the coatings during the TIG process has not yet been investigated. The substrate samples were AISI 1050 carbon steel with thickness of 20 mm, length of 400 mm and width of 20 mm. the samples were cut from a slab. The elements Aluminium, Cobalt, Chromium and Nickel were used as the principal elements in the powdered form. The powders were with purity higher than 99.5 % and particle size of approximate 60 to 120 μm. These elements were blended to prepare a base material for the layer. After blending for 8 h, 5 wt% polyvinyl alcohol was added to the mixtures as a binder. The ball milling machine was used to blend and mix the powders with 250 r/min speed. Then, a uniformed mixture was applied as a pre-layer on the surface of 20 × 40 mm 2 of AISI 1050 carbon steel. The thickness of the pre-layer was 1 mm. In the next step, samples were heated to 70 °C for 4 h to dry, so that the moisture would evaporate. Finally, surface alloying was carried out by using TIG. As a side note, it is important that the powder mixture does not contain Iron. The steel substrate provided the Iron element during surface melting. An inverter TIG welding machine (Pars-Digital PSQ 250 AC/DC) was used with a constant voltage of 220 V an electrical current of 90, 110 and 130 A to heat and melt the pre-layer and substrate. The substrate was moved by a CNC table at a speed of 4.3 mm s−1. At the same time, Argon gas was used to protect the arc. The torch moved several times on all the points of part of the surface with an overlap of 50 %. The specimens after being cut, mounted and polished were characterized by optical microscopy (OM), a scanning electron microscope , an energy dispersive X-ray spectroscope (EDS, Oxford Inca, Oxford Instruments) and X-ray diffraction (XRD, X'pert) to determine the present phases in the layer. Also, the hardness was measured by a hardness tester (Hysitron Inc., TriboScope® nanomechanical test instrument) in a Vickers unit with a load of 0.5 kg. All electrochemical measurements were performed using the ivium vertex potentiostat in 1M HCl solution at ambient temperature after 30 min immersion. A conventional three-electrode cell was used with the bare and coated samples as the working electrodes, the platinum electrode as the counter electrode and the saturated calomel electrode as the reference electrode. It should be noted that all the reported potential in this text refers to SCE. The EIS measurements were carried out by impressing a 10 mV amplitude of ac signal and a wide frequency spectrum of 100 kHz–0.01 Hz on the OCP. Polarisation measurements were performed at a scan rate of 1 mV/s. At 90 A , the points showed about 9 at% Al, 10 at% Cr, 10 at% Co, 11 at% Ni and 60 at% Fe. At 110 A , the concentration of Fe was decreased, and the concentration of other elements was increased. The points displayed about 17 at% Al, 18 at% Cr, 16 at% Co, 17 at% Ni and 32 at% Fe. Unlike 110 A, at 130 A , with the increase of the electrical current, the concentration of Fe increased while the concentration of other elements decreased. The points displayed about 6 at% Al, 6 at% Cr, 7 at% Co, 7 at% Ni and 74 at% Fe. Finally, with regard to high entropy alloy definition, the results showed that high entropy alloy coating was produced at the electrical current of 110 A. SEM image of the coatings obtained at 110 A determined the stable phases in the coating. Their chemical compositions (characterised by EDS) are shown in Fig. 5 . A phase (shown by letter A) in a matrix with another phase (shown by letter B) was observed at 110 A. The average concentration of Fe at points A and B was 32 and 27 at%, respectively. Fig. 6 represents the XRD results that were obtained from the layer formed using an electrical current of 110 A. The results illustrated that at the current of 110 A, the surface layer included BCC and FCC phase structures. One of the main problems in this kind of coating is the possibility of the formation of intermetallic compounds. But a significant presence of intermetallic compounds was not observed in samples. The optical microscope (OM) images of the layer at different electrical currents are displayed in Fig. 7 a-c, which indicate a dendritic structure throughout the entire coating under different conditions. Also, no voids or cracks were observed in the entire coating. Fig. 5 BSE micrographs obtained from the cross-section of the coatings produced at a TIG current of 110 A. Fig. 5 Fig. 6 XRD spectra obtained from the coating that was produced using a TIG current of 110 A. Fig. 6 Fig. 7 OM microstructure of the cross-section of the coating/substrate at TIG currents of (a) 90, (b) 110 and (c) 130 A. Fig. 7 In order to investigate the corrosion behaviour of the samples, an electrochemical impedance test was performed on the samples. Electrochemical impedance is regarded as one of the practice tests to evaluate the behaviour of the interface between coatings and solution. The Nyquist plots for the corrosion of bare and coated samples in 1M HCl at ambient temperature are depicted in Fig. 9 . As can be seen, in the HCl solution, all electrochemical Nyquist curves had the same shape, such that the compact semicircle could be clearly seen. Fig. 9 also displays that the diameters of the electrochemical Nyquist curves were visibly different from each other. The analysis of these curves was performed based on the equivalent circuit, as shown in Fig. 10 . In this circuit, R-Q is attributed to the charge transfer reaction; it has been replaced by the constant phase element due to the non-ideal dual-layer capacitor. R L is the resistance of the induction process and L represents pseudo-inductance. The results of this analysis are presented in Table 1 . Fig. 9 EIS Nyquist curves recorded on the bare and coated samples in 1M HCl solution at ambient temperature. Fig. 9 Fig. 10 Equivalent circuit model for impedance data fitting. Fig. 10 Table 1 The result of Nyquist plots for the corrosion of bare and coated samples in 1M HCl. Table 1 TIG Current Rs Rp CPE n R L L (Hcm2) (Ωcm 2 ) (Ωcm 2 ) (μSs n cm -2 ) (Ωcm 2 ) substrate 10.5 62.5 545.3 0.84 4.35 9.77 90 20.5 271.1 68.2 0.85 44.16 1.95 110 16.3 834.2 23.7 0.77 419.6 13.56 130 22.9 566.2 45.7 0.82 290.7 8.17 In order to more accurately investigate the electrochemical properties of the coatings on the substrate, an electrochemical polarisation test was performed on the samples. Fig. 11 shows the results of this test in 1M HCl solution. As can be seen, by applying the coating to the steel specimen, the polarisation curves were shifted to the left and the lower current densities. On the other hand, by increasing the current in the TIG process from 90 to 110 A, the polarisation curve first moved to the left; with a further increase in current, it moved slightly to the right. As shown in Fig. 11 , in the HCl solution, the passive layer was not formed even on the 110 A sample. Table 2 shows the results of the analysis of electrochemical polarisation curves. Fig. 11 Potentiodynamic polarisation curves of bare and coated samples in 1M HCl at ambient temperature. Fig. 11 Table 2 The results of the analysis of electrochemical polarisation curves in HCl solution. Table 2 sample i corr (μA/cm 2 ) E corr vs. SCE (mV) substrate 572.2 −489.5 90 127.2 −407 110 39.7 −450 130 63.6 −429 The relationship between the electric current and the depth of the melted layer is significant because it determines the chemical composition of the coating, especially the part of the coat composition provided by the substrate iron which melts along with the pre-coat. Also, the increase in the depth of the molten pool results in a longer solidification time, which allows time for the distribution of the alloy elements. Therefore, the forces applied to the molten weld pool in the TIG welding process, such as Buoyant forces, the Lorentz force, and Shear stress are increased, and there is sufficient time to apply them. As a result, the liquid mixing will be improved reasonably in the molten weld pool to provide the necessary conditions for creating a uniform layer. The volume of the molten weld pool plays a critical role in creating a desirable layer, especially for high entropy coatings. This is because if the molten pool is shallow the amount of iron entering the pool will not be sufficient, and the iron required to create the high entropy alloy, which should be at least 5 at%, will not be available. On the other hand, if the electric current (and consequently the depth) of the molten pool is very high, the amount of iron entering the molten pool exceeds the maximum permissible value of 35 at% and does not meet the requirement for a high entropy alloy. Therefore, it is essential to achieve optimum electric current. Contrary to the previous supposition that formation of multiple intermetallic compounds, as suggested by statistical thermodynamics and also the Boltzmann equation, indicates that increasing the number of elements in an alloy reduces the possibility of solid solution formation , it is stated that as the number of elements increases, the entropy of the system also increases. This reduction in Gibbs free energy (ΔG) variation enhances the possibility of solid solution formation. Accordingly, another requirement for the formation of high entropy alloys is the presence of at least five elements in the compound. A comparison of these conditions with the actual governing conditions of the coatings shows that the layer created under the 90A current cannot be a high entropy alloy because its iron content is greater than 60 at% . However, contrary to the expectation that at 110 A with increased electric current and depth of the molten weld pool the iron content would increase, instead the iron content decreased to 32 at% and other elements were in the range of 16–17 at% . At 90 A, the force applied by the electric arc on the pre-coat was not probably sufficient and the powders compacted on the substrate moved forward, so that a small portion of the initial layer melted and the majority of the molten pool was formed from the iron of substrate. However, at 110 A the electric arc provided the force required to prevent the motion of the pre-shield, and in addition to the substrate, a majority of the pre-coat also melted and formed the compounds of the molten weld pool. By increasing the current to 130A and increasing the depth of the molten weld pool, the iron content increased to 73 at% and exceeded the threshold determined for the high entropy alloys. The results of the XRD on the coat created by the 110A current showed that the coat consists of two-phase structures of FCC and BCC solid solutions, BCC being the matrix and FCC the second phase structure. The XRD analysis and calculation of Bragg's law reveal the presence of a BCC phase with a lattice constant of a = 0.286 nm and an FCC phase with a lattice constant of a = 0.357 nm. Cai et al.’s results confirm obtained results because they calculated lattice constant for FCC and BCC 0.366 and 0.287, respectively. The SEM images in Fig. 5 are also in full agreement with the XRD results, as two distinct phases (one shown by A and the other by B) can be distinguished. As shown in Fig. 5 , phase A has approximately 32 at% of iron and Phase B has approximately 27 at% of iron. The phase richer in iron has a BCC phase structure while the phase with a low amount of iron has an FCC phase structure. The atomic percentage of iron in two mentioned phases is similar, which is why the color contrast between the two phases is not significantly different . However, according to the evidence obtained and the findings of Cai et al., the FCC phase is stable for iron amounts below 30 atomic percent while the BCC phase is stable for higher amounts of iron. Both of them have five major elements, and all the elements have concentrations between 5 and 35 at%, and thus are high-entropy alloys. On the other hand, previous studies show that the AlCrCoNiFe compound is prone to the formation of a solid solution. Also, in the calculations based on Eqs. (2) , (3) , (4) ) and the atomic percentages obtained from the shielding elements , the value of Ω the parameter introduced by Yang and Zhang was 1.7, and the formation of the solid solution could be predicted from the diagrams presented by Yang et al. . (2) ΔS mix = RΣX i LnX i (3) Ω=(T m .ΔS mix ) / |ΔH mix | (4) T m = ΣX i (T m ) i The main reason for the lack of intermetallic compounds is rapid solidification. Because different atomic radii in the HEA composition leads to the increase of the solid-liquid interface energy and the difficulty of the long-range diffusion of atoms in the crystal lattice, thus favouring the nucleation of solid solution and decreasing the growth rate of intermetallic compounds. Zhang et al. delicately studied the influences of laser rapid solidification on the microstructure and phase structure in the HEA coatings. They calculated the nucleation incubation time for various competing phases and indicated that the growth of intermetallic compounds will be hampered if the solidification rate is sufficiently high. As shown in Fig. 7 , the microstructure of the shield was completely dendritic. Similar microstructures have been observed in various studies . The major determinants in the formation of microstructures by solidification are the temperature gradient ratio and growth rate . In the molten pool resulting from the TIG process, with the development of solidification, the ratio of the gradient to the rate of formation is reduced and the cells are formed on the surface rather than deeper. The direction of cell growth is toward the substrate, which is influenced by the transfer of heat into the molten weld pool. Compact semicircles in electrochemical Nyquist curves indicate that the electrochemical behaviour of the samples is controlled by the charge transfer process . In addition to the compact semicircle at high frequencies, there is a small loop at the low frequencies. This semicircle, which is below the y-axis, can be the result of the release of the adsorbed ions on the surface and the separation of the corrosion products from the surface . The diameter of Nyquist curves has a direct relation to corrosion resistance; so, the higher diameter of the curves indicates higher corrosion resistance . According to Fig. 9 , in the HCl solution, the diameter of the semicircle of the substrate was smaller than that of the coated samples, thus indicating improvement in corrosion resistance by creating a high entropy coating on the surface. The corrosion resistance of the samples can be predicted as follows: Substrate< 90A < 130A < 110A The polarisation resistance can be used to evaluate the corrosion resistance of the samples. The higher the value of Rct, the higher the density of protective coating, which means the better the effect of isolating corrosive medium. It is clear from Table 1 optimum coating (formed via 110 A) has a higher Rp value of 834 cm 2 compared to the other HEA coating. The lack of a passive layer in electrochemical polarisation curves can be explained through the reactions occurring on the surface. In fact, after placing the sample with the coating in the solution, an oxide film consisting of iron oxides, nickel oxide, aluminium oxide, cobalt oxide and chromium oxide was formed on the surface. However, in the acidic solution at 25 °C, the ΔG 0 value related to the reaction of these oxides with hydrochloric acid was negative (except aluminium). For example, the reaction for nickel oxide is as follows: NiO + 2HCl → NiCl 2 + H 2 O, ΔG 0 = −90 kJ/mol Following this reaction, the oxide form of oxygen is replaced by a chlorine ion, leading to the formation of a metal chloride, which is a water-soluble compound . According to the results of the polarisation test, by creating a coating with a current of 90 A, the corrosion current density of the steel substrate in the HCl solution was decreased from 572 to 127 μA/cm 2 ; by increasing the current up to 110 A, the corrosion current density in the HCl solution was decreased to reach its minimum. Indicating that this coating provides the best corrosion protection in comparison to the other coatings and suggest a much slower dissolution of the 110A sample coating in 1M HCl solution than other samples. According to Table 2 , by creating the coating, the corrosion potential was shifted to the positive values; from a thermodynamic point of view, this indicated a lower corrosion tendency for the substrate [ , , , , , ]. The comparative analysis indicates that the TIG process for AlNiCoCrFe coating on plain carbon steel offers favorable outcomes in terms of coating thickness and bond strength, making it a viable option for applications requiring robust surface protection. The TIG process resulted in a thicker coating about 700–800 μm compared to PVD and plasma spraying about 5–10 and 100–150 μm, respectively, which can enhance durability but may also affect thermal performance. About hardness, While PVD exhibits superior hardness about 1200–1300 HV , the hardness of the TIG-coated samples about 600–700 HV is still adequate for many applications, providing a good balance between toughness and wear resistance and it is similar to plasma spraying and laser cladding methods. The bond strength achieved with the TIG method is competitive about 30–40 MPa, ensuring good adhesion to the substrate, which is crucial for performance in demanding environments. While, other current methods such as plasma spraying , PVD and laser cladding [ , , ] have an average bonding strength about 25, 15 and 35 MPa respectively. (1) The TIG welding method was utilised successfully to form high entropy alloy coating of AlNiCoCrFe with high density and hardness on a steel substrate. (2) The electrical current and depth of the melted layer produced during the surface treatment had a significant effect on the formation of a high entropy alloy. (3) In this study, the optimum electrical current was 110 A, at which a high entropy alloy with BCC and FCC phase structures was produced. Lower currents could not escape the powders from the steel substrate surface. Higher currents also formed a layer with more depth, which resulted in an increase in the Fe concentration above the critical depth needed to produce a high entropy alloy. (4) The layer coated had a microhardness of about 518–658 HV at the surface. There was a significant difference between the coating and substrate. (5) Electrochemical results showed that coating prepared by 110A electrical current exhibited the optimum corrosion resistance in 1M HCl solution, which had higher corrosion potential, lower corrosion current density and higher charge-transfer resistance than the other coatings. | Study | biomedical | en | 0.999997 |
PMC11697579 | Infectious diseases remain a leading cause of morbidity and mortality worldwide, estimated to cause more than 10% of deaths and 28% of disability‑adjusted life‑years (DALYs) attributed to all causes in 2019, with human immunodeficiency virus (HIV), tuberculosis and malaria being the key contributors . Outbreaks of Ebola and coronavirus disease 2019 (COVID‑19) in recent years have led to unprecedented numbers of deaths and cases. New pathogens continue to emerge in animal and human populations, as demonstrated by the emergence of severe acute respiratory syndrome (SARS) in 2003, highly pathogenic avian influenza in poultry and humans in 2004/2005, swine flu in 2009, Middle East respiratory syndrome coronavirus (MERS‑CoV) in 2013, Zika in 2016, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) in 2019 and recently, the monkeypox virus in 2022 and 2024 . Mathematical models are being increasingly used to understand the transmission of infections and to evaluate the potential impact of control measures or interventions in reducing morbidity and mortality. Mathematical modelling underpinned most of the critical decisions made by the UK government during the COVID‑19 pandemic, including the decision to implement a nationwide lockdown in March 2020, lay down a road map for release from lockdown in February 2021 and implementation of public health interventions in December 2021 during the omicron wave . In the USA, modelling projections for different COVID‑19 scenarios by the Institute for Health Metrics and Evaluation COVID‑19 Forecasting Team also informed crucial policy decisions . The use of mathematical disease models in public health policy is well adapted to the decision‑making process for epidemic and endemic diseases in high‑income countries . Even organisations such as the World Health Organization (WHO) and the Joint United Nations Programme on HIV/AIDS (UNAIDS) have relied on findings from mathematical modelling studies to make crucial choices around selection of the intervention and vaccination strategies for diseases such as influenza, Ebola, HIV and COVID‑19 . It is encouraging to see African countries demonstrating global leadership in infectious disease mathematical modelling through successful north–south collaborations. Organisations such as South Africa’s Modelling and Simulation Hub Africa (MASHA), the South African Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA) and the Centre for Infectious Disease and Epidemiology Research (CIDER) have played a key role in increasing outputs related to disease modelling studies by African researchers. They have also supported national governments in making model‑informed evidence‑based policies . Training and mentorship have been identified as key approaches to strengthening mathematical modelling capacity in Africa and elsewhere . A deliverable‑driven mentor‑led learning‑by‑doing model of capacity building for policy‑makers and public health professionals in Africa was an effective training model for building the capacity in mathematical modelling of diseases . In India, these disease models are not fully integrated into the policy‑making process, primarily due to limited capacity in building mathematical models, lack of trust in the findings given the many assumptions and data limitations and the reluctance of policy‑makers to apply the model findings to formulate policies . Much of this lack of trust or reluctance in adopting the model findings stems from the lack of knowledge about how these models are conceived, constructed and calibrated. During the COVID‑19 pandemic, although several mathematical models were proposed to understand the evolving disease in India, they did not feed into the process of decision‑making . The possible reasons could be: (i) large variations in the model predictions and assumptions, breeding mistrust in the model results; (ii) criticisms around use of simple mathematical models to describe complex processes; (iii) use of models to describe real‑life epidemic scenarios being relatively new; and (iv) lack of knowledge about what goes on in the building of such models. Previous training models in India were short‑term courses predominantly based on didactic teaching ranging from 2 to 5 days and covering only the basics of infectious disease modelling without being deliverable‑driven and devoid of long‑term mentorship. Following the COVID‑19 pandemic, several disease modelling experts have come together to form groups such as the National Disease Modelling Consortium and the Indian Scientists’ Response to CoViD‑19 (ISRC) to develop India‑specific disease models to aid national policy‑makers make informed decisions and improve disease control and elimination efforts . However, training and mentorship have never been at the forefront of their agenda. Recognising this gap in training, the Department of Health Research (DHR), which is the Government of India body for research in India, released a call for applications under the Human Resource Development Scheme to design long‑term capacity‑building programs in key priority areas, including infectious disease modelling. In response to the call, we proposed a 3‑month post‑graduate (PG) certificate course in infectious disease modelling in hybrid mode to the DHR in order to build a team of infectious disease modellers who can prove to be a great asset in tackling future pandemics and emerging threats. Following sanction by the DHR, we developed the course structure and curriculum and delivered the first cycle of the course during July to September 2024, producing the first cohort of 20 infectious disease modeilers in the country. The course curriculum was guided by Kolb’s experiential learning theory, which is an andragogical approach to learning focussing on real‑world experiences and practical applications . This was the first such course on infectious disease modelling in India. The structure, content and key components of the first course, along with the strengths, challenges and way forward from the participants’ and facilitators’ perspective, are discussed in this paper below. The details of the course structure and the delivery of the course are described in Table 1 and Figure 1 . Supplementary appendix 1 shows the details of the curriculum including the week‑wise course content and the teaching learning methods. The course was delivered via a six‑step process: 8‑week online course: This was delivered through live online video lectures, online software demonstrations and exercises, once weekly live discussion forums, bi‑weekly assignments and reading materials. The total duration of teaching was around 45 hours per week. Bi‑weekly assignments: After the completion of every 2 weeks, assignments were given. All four assignments had to be submitted within the specified deadlines. (Milestones 1‑4). Project work: A project‑based assignment was given wherein they will be practically applying the principles learnt. The final project report had to be submitted before the completion of the 10th week of the course. The format in which the project report is to be submitted is given in Supplementary appendix 2 (Milestone 5). Online revision and discussion classes in Week 11. 3‑day contact programme: A 3‑day contact programme was held in the 12th week of the course to discuss and revise the key concepts, clarify doubts and give them a mentored hands‑on practice on the key exercises. Exit examination: The contact programme was followed by an exit examination the very next day. Table 1 Details about the course structure and delivery. DOMAIN COURSE DETAILS Approach Deliverable‑driven hands‑on approach to training with intensive mentorship during practicum and in‑person sessions Mode of delivery Hybrid mode (online video lectures, discussions and demonstrations, offline contact programme and exit examination) Target trainees Public health professionals, medical college faculty, biostatisticians, microbiologists and scientists working in the domain of infectious diseases Duration of the course 12 weeks (including 8 weeks of online training, followed by assignments and project submission, face‑to‑face contact session and examination) Deliverables Four assignments and project work Course advertisement Advertised within the priority organisations, professional networks and on social media such as LinkedIn, Facebook and Twitter Trainee selection and number A total of 24 participants were selected on the basis of their previous clinical, programmatic or research experience in the domain of infectious diseases out of 224 applications received. Course format Live online video lectures, online hands‑on practical exercises and software demonstrations, live discussion forums and Q&A, case studies, journal clubs, bi‑weekly assignments and project submission, 3‑day contact session followed by final exit examination Course fee No course fee was charged to the participants. However, the participants had to bear their cost of travel, accommodation and other expenses during the face‑to‑face contact session and the exit examination Assessment Formative assessment: Four assignments (25 marks each) Summative assessment (100 marks): End‑of‑course exit examination (75 marks) Theory (50 marks) Practical (25 marks) End‑of‑course project submission (25 marks) Course feedback and evaluation Participants evaluated the structure and content of the training at the end of each week of training through formal and informal feedback mechanisms to inform subsequent sessions. In addition, trainees provided overall evaluation of the course at the end of the training, including training logistics. Figure 1 Details about the course structure, modes of delivery, milestones, deliverables and assessment methods. Course structure, teaching/learning methods, milestones, deliverables, and assessment methods Self‑administered, semi‑structured questionnaires were emailed to the course participants ( n = 20) via Google form after completion of the face‑to‑face offline sessions. Anonymous feedback was collected to get appropriate responses without any desirability bias. Identifying information and email IDs were not collected. The questionnaire included closed‑ended quantitative and open‑ended qualitative variables. The quantitative variables included feedback on the overall course content, learning objectives, balance between theory and hands‑on, delivery of the course, contribution of the course towards learning and skill and responsiveness of the facilitators. A five‑point Likert scale was used to record the responses. The qualitative variables included open‑ended questions assessing strengths (what worked well?), weaknesses (what did not work well?) and suggestions to improve the delivery of the course in subsequent cycles. Facilitators’ feedback regarding the strengths and weaknesses of the course and suggestions for improvement in the subsequent cycles was also taken in a meeting of the facilitators after the course. Manual descriptive content analysis of the textual responses to the open‑ended questions was carried out by two authors (J.P.T. and P.D.), who are experienced in qualitative research. Themes were generated in consensus using standard procedures by a deductive approach . Any disagreement between the two authors was resolved by mutual discussion. The participants were contacted again by email or telephone in case any clarification was required. Statements in italics represent direct quotes from the participants. Out of 224 applicants, a total of 24 participants were selected for the first cohort. The mean age of the participants was 37.7 years (standard deviation 4.9), ranging from 29 to 48 years. About 42% ( n = 10) were female. Most of them belonged to a public health background ( n = 16, 66.6%), followed by biostatisticians ( n = 4, 16.7%) and microbiologists ( n = 4, 16.7%). Those from a public health background came from diverse domains including medical college faculty, scientists from ICMR institutes, junior and senior residents, national consultants working with the World Health Organization, state‑level public health administrators, etc. Only one of them (4.2%) had attended a course on mathematical modelling of infectious disease before. Of the 24 selected participants, 20 (83.3%) successfully completed the course; 1 dropped out of the course very early in the first month and the remaining 3 could not attend the contact workshop due to other competing personal or professional commitments, and thus were ineligible for the final exit examination. This is the first study using a mixed methods approach to evaluate learner’s perceptions of an innovative 3‑month hybrid training program in infectious disease modelling targeting mid‑career professionals in India. This paper describes the structure, curriculum and delivery of the course and also highlights the strengths and challenges in training the first cohort of disease modellers along with recommendations for the subsequent cohort. | Review | biomedical | en | 0.999995 |
PMC11697582 | Building upon the studies of Aron & Aron , it is proposed that the ability to detect and respond to internal and external stimuli, including interactions with others, may be particularly pronounced in individuals with Sensory Processing Sensitivity (SPS). This temperament trait is characterized by 1) deep cognitive processing of stimuli, 2) heightened emotional reactivity, 3) vulnerability to sensory overstimulation, and 4) heightened awareness of subtleties in the environment, including people’s emotional states . In detail, fMRI studies on individuals with SPS demonstrate that this trait is associated with deep processing and heightened responsiveness to emotional signals from others. These studies highlight increased activation in brain regions associated with reward processing, memory, emotion, empathy, and awareness. For this reason, the role of the parenting environment in the development of emotional regulation strategies in individuals with SPS has also been studied. Specifically, it is reported that adverse environments, poor parenting, or lack of social support have a worse impact on children with SPS , often resulting in depression and anxiety problems in adulthood . Conversely, environments with low levels of stress and adequate emotional support promote greater creativity , improved social skills , and more effective emotional regulation strategies . Recently, the Sensory Processing Sensitivity Questionnaire (SPSQ) was developed to incorporate the positive aspects of the SPS that the HSPS failed to include, as noted by some studies . To do this, items from both the HSPS and the Adult Temperament Questionnaire (ATQ) were taken . The social and affective dimensions included a social-affective sensitivity and an understood associative sensitivity. This dimension is made up of seven items. Through statements such as: Sometimes I notice sad eyes hidden by a smile ; I’m usually surprised when a person’s tone of voice doesn’t match their words , or; When people are uncomfortable, I know how to calm them down , it seems focused primarily on recognition of emotional states and heightened awareness of subtleties. These characteristics of the SPS are fundamental to understanding the effect of interactions with other people. Still, it is necessary to consider different elements of interest that have not been sufficiently explored in this sense, such as overstimulation and intense emotional experience. It should be noted that since cutoff scores for the scale used to assess SPS presence have not been defined, low, medium, and high levels were determined using total scores obtained from the HSPS (Highly Sensitive Person Scale) (minimum, maximum, first and third quartiles, mean, and median). The defined levels were low, 22 to 62 points; medium, 63 to 88 points; and high, 89 to 114. Once a version of 25 items (five dimensions with five items each) was finalized, it underwent content validity assessment by five expert judges. They reviewed each statement’s wording and syntax to ensure each item corresponded accurately to its designated dimension or characteristic. The 25 proposed items were retained because they achieved Aiken’s V and Kendall’s W scores above 0.80, indicating high agreement regarding relevance and clarity. Highly Sensitive Person Scale (HSPS) . Translated and adapted for the Mexican population . It is a self-report scale designed to measure the degree of sensitivity in adults. It consists of 17 items with Likert-type responses ranging from 1 (Not at all) to 7 (Extremely), which are answered based on the person’s feelings. All items are scored in the same direction, so higher scores indicate higher sensitivity. Principal component analysis suggested a two-factor solution that explained 30% of the variance: 1) processed sensitivity (PS) with 13 items, and 2) low sensory threshold (LST) with 4 items. Reliability analysis reported an α coefficient of 0.89. Interpersonal Reactivity Index (IRI) .Translated and adapted for the Mexican population . It is a self-report scale with 28 items designed to assess empathy in a multidimensional manner. It comprises a cognitive component with two dimensions, perspective taking and fantasy , and an affective component with two dimensions: empathic concern and personal distress . These two components are distributed across four subscales with 7 items each. Each dimension has 7 items, and responses are given on a Likert scale with five response options, where higher scores indicate a higher presence of the measured dimension. The reported reliability for the total scale was α = 0.81. Before conducting the Exploratory Factor Analysis (EFA), Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity were performed as measures of the adequacy of the data for factor analysis. A KMO value above 0.5 and a significance level below 0.05 for Bartlett’s test were set as criteria. The EFA was conducted using the principal axis factoring method with varimax rotation. Criteria for retaining items included factor loading greater than 0.45 on a single factor, factor loadings not exceeding 0.30 on other factors, and item content congruence with the factor. CFA was conducted using the maximum likelihood method. Model fit was evaluated using various indices. Absolute fit indices included χ 2 and the standardized root mean square residual (SRMR). Comparative fit indices included the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Akaike Information Criterion (AIC). The Root Mean Square Error of Approximation (RMSEA) was also used to assess model parsimony . For the χ 2 test, a non-significant result indicates a good fit. CFI and TLI values should exceed 0.90 (higher values indicate better fit). SRMR values below .08 are considered acceptable, with lower values indicating better fit. RMSEA values should be below .08 for acceptable fit or close to .05 for good fit . AIC, an unbounded selection criterion, compares models fitted to the same data; smaller values indicate a better fit. Reliability was estimated using Cronbach’s alpha and McDonald’s Omega coefficients, with a confidence level of 95%. Values above 0.7 were considered acceptable . Convergent validity was assessed through correlation analysis, evaluating the relationship between the HISS’s total scores and its factors and the HSPS’s and IRI’s total scores and factors. The KMO index (0.85) and Bartlett’s test (p < 0.001) indicated appropriate values for conducting exploratory factor analysis (EFA). The principal axis factoring method with varimax rotation was used for the 25 items initially included in the HISS. Firstly, five dimensions were obtained based on criteria of eigenvalues greater than 1. However, considering the factor loadings of each item, their theoretical coherence, and their grouping within factors, 11 items describing dimensions of emotional reactivity and empathetic response were discarded. With the remaining 14 items, EFA revealed a three-factor solution using the same extraction method and rotation. These items exhibited loadings above 0.45 in their respective factors, collectively explaining 47% of the total variance (see Table 1 ). The three derived dimensions from the analysis were: factor 1, awareness of subtleties , explaining 19% of the variance with 5 items; factor 2, overstimulation , explaining 15% of the variance with 5 items; and factor 3, persistent effect , explaining 13% of the variance with 4 items. Regarding the χ 2 test, it is important to note that none of the models showed a satisfactory result, as this test should be non-significant to confirm a good fit. However, Littlewood and Bernal point out that χ 2 is prone to error when applied to large samples, i.e., more than 200 observations. Therefore, other indices are recommended for model confirmation. Models 1, 3, and 4 exhibit a χ 2 /df value <5, suggesting an acceptable fit . Internal consistency for the total HISS score and each factor was assessed using Cronbach’s alpha and McDonald’s omega coefficients (see Table 3 ). The total HISS score yielded α = 0.803 and ω = 0.804. Factor 1, awareness of subtleties , showed the highest indices, similar to those obtained for the total score. The other factors ( overstimulation and persistent effect ) demonstrated values above 0.6 for Cronbach’s alpha and close to 0.7 for McDonald’s omega, which are considered acceptable (see Table 3 ). As evidence of convergent validity, a correlation analysis was conducted between the final version of HISS and its factors with HSPS and IRI scores and their respective dimensions. As observed in Table 4 , only the correlations between the overstimulation dimension (HISS) and empathic concern (IRI) were not statistically significant. The results of correlations between the total scores of the scales show moderate correlations, with the highest correlation observed between HISS and HSPS, followed by the correlation between HSPS and IRI (see Table 4 ). The results of the exploratory factor analysis suggest that the items designed to capture dimensions of emotional reactivity and empathic response are grouped into a single factor. This may indicate a need for more specificity in item wording and potential shared content between these two dimensions. Given that these aspects of SPS have primarily been studied using methods such as fMRI, it is crucial to explore them with more precise and differentiated assessments, considering they refer to heterogeneous constructs. In particular, the moderate correlation between the dimension of overstimulation in the HISS and the HSPS with its factors aligns with expectations based on findings reported by Montoya-Pérez et al. . Their study revealed a moderate correlation between the total score of the HSPS and the DESR-E, an instrument designed to assess difficulties in emotional regulation. Consequently, they suggested a potential bias in the HSPS structure towards problems associated with SPS. The results of the current study appear to confirm this hypothesis, especially considering that the dimension of personal distress in the IRI also moderately correlated with the HSPS and its factors, similar to the overstimulation dimension in the HISS. Similarly, these moderate correlations between the HSPS and the HISS support the notion that high interpersonal sensitivity can be considered an integral aspect of the SPS trait. The current research findings suggest that a comprehensive assessment of SPS should consider sensitivity to physical stimuli and sensitivity in interpersonal relationships. Furthermore, the positive relationship between interpersonal sensitivity and empathy, as measured by the IRI, supports the idea that the SPS trait could confer an evolutionary advantage under favorable environmental conditions (such as parenting). | Study | biomedical | en | 0.999997 |
PMC11697585 | Migraine is a primary headache disorder typically characterized by recurrent attacks of disabling headache and associated with relevant personal and societal burden ( 1 ). The detailed pathophysiological mechanisms causing migraine are still elusive, however, there is evidence that central iron metabolization might play a role ( 2 , 3 ). Evidence suggests that iron imbalance, particularly iron deficiency or overload, may be associated with migraines through mechanisms involving oxidative stress, neurotransmitter regulation, and vascular health ( 4 , 5 ). Iron is a metabolically very active component. Some of the reasons for high iron levels in brainstem structures include overproduction of transferrin, increased iron uptake reflecting increased activity, and sequestered iron following cell damage regardless of the mechanism, abnormally high or low iron affects homeostasis and is a marker of altered function ( 5–7 ). Various magnetic resonance imaging (MRI) methods enable measurement of iron in vivo in the human brain ( 8 , 9 ). R 2 (=1/ T 2 ) or R 2 ∗ (=1/ T 2 ∗ ) relaxometry are one of the most commonly used MRI based iron mapping techniques, as there is a strong linear relationship between R 2 and R 2 ∗ values with the underlying iron content in brain structures ( 10 ). Several MRI studies observed altered iron sensitive quantitative MRI measures in various brain structures of patients with migraine, indicating an iron accumulation compared to healthy controls ( 6 , 7 , 11–14 ). This increase in iron content was associated with pain processing and the frequency of migraine attacks ( 7 , 11 , 14 ). Although, there is a strong correlation between R 2 ∗ and iron content in gray matter, several confounding factors, such as variations in tissue microstructure, myelin content and water content, exist which counteract the effect of iron on R 2 ∗ in white matter ( 15 , 16 ). In white matter, the high amount of myelin leads to a strong confounding effect on R 2 ∗ , as both, an increase in iron and an increase in myelin, leads to higher R 2 ∗ values and vice versa. Furthermore, R 2 ∗ in white matter is sensitive to the orientation of anisotropic tissue structures with respect to the B 0 field of the MRI system ( 17 , 18 ). There two main sources of R 2 ∗ anisotropy in the brain are (I) myelinated nerve fibers ( 17 , 18 ) and (II) the anisotropic component of the vasculature (larger vessels tend to run in parallel with nerve fiber tracts) ( 19 ). However, the fiber orientation dependency of R 2 ∗ can be utilized to separate the effect of iron and anisotropic structures on R 2 ∗ in white matter. Therefore, R 2 ∗ is combined with the fiber angle, estimated using diffusion tensor imaging (DTI), within each white matter voxel to compute the fiber orientation independent (isotropic) and fiber orientation dependent (anisotropic) R 2 ∗ components ( 20 , 21 ). Although, there are many studies on structural MRI in patients with migraine ( 22 , 23 ) there is limited data on MRI measurements during an acute migraine attack. Therefore, the aim of this study was to investigate if there are dynamic fluctuations in isotropic and anisotropic R 2 ∗ , which can be related to tissue components, such as iron, in the brain during a migraine attack. To achieve this, we acquired, quantitative MRI, including R 2 ∗ relaxometry and DTI of a patient with migraine on 21 consecutive days, comparing migraine-free days and 2 days with an acute migraine attack. A 26-year-old male patient diagnosed with episodic migraine with aura according to ICHD-3 ( 1 ) criteria since the age of 15 years participated in this study. He reported aura symptoms as mainly flashing lights lasting 15–20 min in the left visual field, mostly before the onset of the migraine headache. The participant had a history of 4 to 5 migraine attacks per month before entering the study. During the study the participant experienced two migraine attacks (on day 12 and day 16), which fulfilled the criteria for a migraine attack as defined by the ICHD-3 ( 1 ). The attacks lasted up to 24 h and were always localised on the left frontal side. Pain maxima ranged from 70–80 on a visual analogue pain scale of 0–100, with 100 representing maximum pain. Individual migraine attacks were always preceded by a prodrome, characterised mainly by tiredness and yawning. After the migraine attacks had subsided, a postdrome phase was clinically observed, characterised by fatigue and impaired concentration. During the 21 consecutive scanning days, the participant voluntarily decided not to take any preventive or acute medication and refrained from any other medication. The participant was a non-smoker, did not drink alcohol during the study period and maintained a constant daily routine prior to each measurement. The subject’s informed consent was obtained in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Medical University of Innsbruck. Written informed consent was obtained from the subject for the publication of any potentially identifiable images or data included in this article. MRI was performed at the same time on each day on a 3 T MR system (MAGNETOM Skyra, Siemens Healthineers, Erlangen, Germany) using a 64-channel head coil. The following sequences were acquired in this study: For structural overview and tissue segmentation, a 3D T 1 weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence with echo time (TE) = 2.1 ms, repetition time (TR) = 1,690 ms, inversion time (TI) = 900 ms, flip angle = 8° and a 0.8 × 0.8 × 0.8 mm 3 isotropic resolution. For the estimation of the white matter fiber angle θ , a diffusion weighted single-shot echo-planar imaging DTI sequence with TE = 92 ms, TR = 9,600 ms, flip angle = 90°, 30 isotropically distributed diffusion directions, b -value = 1,000 s/mm 2 , three images with b -value = 0 s/mm 2 and a 2 × 2 × 2 mm 3 isotropic resolution. For quantification of R 2 ∗ relaxation, a multi-echo gradient echo (GRE) sequence with TE = 4.92, 9.84, 14.7, 19.6, 24.6 and 29.51 ms, TR = 35 ms, flip angle = 15°, and 0.9 × 0.9 × 0.9 mm 3 isotropic resolution. R 2 ∗ maps were computed voxel by voxel assuming a mono-exponential relaxation using MATLAB 2019a (The MathWorks Inc., Natick, Massachusetts, United States). DTI data was analyzed with the FMRIB Software Library (FSL version 6.0.5.1) using FSL DTIFIT ( 24 , 25 ) to calculate the diffusion tensor model and estimate the eigenvalues and eigenvectors. To correct for distortions induced by eddy currents and head motion FSL’s eddy_correct was used. The fiber angle θ was calculated as the angle between the first eigenvector and the direction of the main magnetic field B 0 for each voxel, where θ = 0° represents fibers parallel to B 0 and θ = 90° represents fibers perpendicular to B 0 . For plotting fiber orientation dependent R 2 ∗ , the fiber angle θ was divided into 18 intervals of 5° and voxels from the entire white matter were pooled. R 2 ∗ anisotropy ( Equation 1 ) ( 20 ) was calculated based on orientation dependent R 2 ∗ according to T 1 weighted images were used for automated segmentation of white matter, deep gray matter and cortical brain structures using FreeSurfer software (version 7.3.2). Automated tissue segmentation was performed independently on each day. A list of all segmented brain regions can be found in Table 1 . Statistical analysis was performed using R (version 4.0.3, The R Foundation for Statistical Computing, Vienna, Austria). A Shapiro–Wilk test was used to test for normal distribution of the data. Depending on the data distribution, a t -test or Mann Whitney U test was used to assess differences of R 2 ∗ in various brain regions of the left and right hemisphere. To test if R 2 ∗ was different on days with migraine, an analysis of variance (ANOVA) or Kruskal–Wallis test was used depending on the distribution of the data. As post-hoc tests, pairwise t -test or Mann Whitney U test were applied. For p -value adjustment Bonferroni correction was used. We repeated the entire analysis, excluding days right after the migraine attack from the migraine-free days, to investigate if there are potential changes on these days. Study personnel was fully blinded regarding migraine status (migraine-free vs. migraine) until completed data acquisition and computation of the quantitative MRI parameter maps. Prominent focal veins were visualized on the left hemisphere, and asymmetry in the appearance of the cortical vessels was more prominent on the left side in the susceptibility weighted imaging (SWI). Enlarged and clustered perivascular spaces (PVS) are visualized in 3D T 1 weighted images in the deep grey matter bilaterally, mildly accentuated on the left side. Accentuated PVS were also present in the supratentorial white matter, specifically in the centrum semiovale, with no clear side difference. PVS in this patient were considered radiological within normal ranges. No diffusion restrictions suggesting ischemic processes-were present and the cerebral fluid system was normal. Overall, structural MRI was without findings and considered radiological within normal range. Figure 1 shows representative T 1 weighted images, average R 2 ∗ maps of all migraine-free days, average R 2 ∗ maps of all migraine days and the R 2 ∗ difference (Δ R 2 ∗ map) between migraine-free and migraine condition of a axial (top row) and coronal (bottom row) slice. The Δ R 2 ∗ map highlight areas which are altered during a migraine attack, indicating an increase in R 2 ∗ in red and a decrease in R 2 ∗ in blue. During migraine attacks, R 2 ∗ was found to be significantly altered in various brain regions. Overall, an increase in R 2 ∗ is predominantly observed in brain regions of the left hemisphere, whereas a decrease of R 2 ∗ is predominantly observed in brain regions of the right hemisphere. In the caudate, R 2 ∗ increased by 4.9% from 20.6 1/s to 21.6 1/s ( p = 0.021) in the left hemisphere and decreased by −5.3% from 20.8 1/s to 19.7 1/s ( p = 0.114) in the right hemisphere, from migraine-free days to during migraine, respectively . These alterations in R 2 ∗ are also evident in the Δ R 2 ∗ map shown in Figure 1 . R 2 ∗ increased in the left ventral diencephalon by 5.7% from 23.0 1/s to 24.3 1/s ( p = 0.011) and left cerebral white matter by 1.9% from 21.0 1/s to 21.4 1/s ( p = 0.021) on days with migraine. During a migraine attack, R 2 ∗ decreased in the right superior frontal cortex by −1.9% from 15.6 1/s to 15.3 1/s ( p = 0.026), in the right caudal middle frontal cortex by −3.0% from 16.6 1/s to 16.1 1/s ( p = 0.021) and in the right pericalcarine cortex by −4.6% from 19.7 1/s to 18.8 1/s ( p = 0.046). R 2 ∗ orientation dependency was assessed to separate isotropic and anisotropic R 2 ∗ contributions in cerebral white matter at each day. On average, R 2 ∗ increased with increasing fiber angle from 19.6 ± 0.3 Hz at 0° to 22.1 ± 0.3 Hz at 90° (12.6%, p < 0.001) in the left cerebral white matter and from 19.8 ± 0.3 Hz at 0° to 21.9 ± 0.4 Hz at 90° (10.7%, p < 0.001) in the right cerebral white mater. Grouping by condition, revealed alterations in isotropic and anisotropic R 2 ∗ during a migraine attack compared to migraine-free days as shown in Figure 3 . In the left cerebral white matter R 2 ∗ increased by 1.8% ( p = 0.021) and R 2 ∗ anisotropy decreased by −1.9% ( p = 0.853), where as in the right cerebral white matter R 2 ∗ decreased by −1.0% ( p = 0.286) and R 2 ∗ anisotropy decreased by −16.6% ( p = 0.011). R 2 ∗ anisotropy differs between left and right cerebral white matter by −9.2% ( p = 0.009) on migraine-free days and by −23.6% ( p < 0.001) on days with migraine. In contrast to the literature, where quantitative MRI in patients with migraine is mainly acquired in cross-sectional studies, we aimed to identify potential short-term changes in quantitative MRI to study tissue composition during the migraine cycle, and in particular during an acute migraine attack. To the best of our knowledge, this is the first study in which quantitative MRI was acquired on multiple consecutive days in a patient with migraine, including days with imaging during an acute migraine attack. The results of this study suggest that R 2 ∗ relaxometry is suitable for detecting short-term changes in brain tissue composition that are indicative of central iron involvement during an acute migraine attack. We propose that the observed changes in R 2 ∗ in deep grey matter and cortical brain regions are related to changes in iron content, whereas in white matter an increase in iron content is accompanied by microstructural changes related to anisotropic tissue components, such as vascular structures. Fluctuations in iron content and anisotropic tissue components during a migraine attack are fully reversible within the time period observed. Several studies observed higher R 2 , R 2 ∗ or magnetic susceptibility values in patients with migraine compared to healthy controls, indicating an increased iron accumulation in various regions of the brain ( 22 ). An increased iron content was mainly observed in deep gray matter and cortical gray matter ( 6 , 7 , 11–14 , 26 ). Higher iron content in migraine patients were correlated with disease duration and the frequency of migraine attacks ( 6 , 7 , 12 , 26 ). Furthermore, it was shown that iron content in the basal ganglia differs between patients with chronic migraine compared to patients with episodic migraine ( 11 , 14 , 26 ). Studies conducted by Dominguez et al. ( 11 ), and Chen et al. ( 26 ) showed that patients with chronic migraine have an increased accumulation of iron in areas involved in the nociceptive network such as the red nucleus and periaqueductal gray (PAG). The role of the PAG as a contributory generator in migraine attacks warrants further investigation since several studies, such as the one conducted by Welch et al. ( 27 ), point to its importance in the development of migraine attacks. Welch et al. ( 27 ) demonstrated that iron homeostasis in the PAG may be affected by recurrent migraine attacks by observing a significant increase in mean R 2 ′ (= R 2 ∗ − R 2 ) and R 2 ∗ in patients with both episodic migraine and chronic daily headache, although there was no significant difference between the episodic migraine and chronic daily headache groups. In a recent study, investigated changes in iron deposition after treatment with erenumab showed lower R 2 ∗ values in the PAG and anterior cingulate cortex (ACC), indicating less iron deposition, in responders compared to non-responders after 8 weeks of treatment ( 28 ). Overall, the majority of studies investigating iron content in migraine indicate a general increased iron accumulation and differences in iron content between migraine types ( 22 ). In contrast to literature, our study allowed to investigate short-term dynamic alterations in iron content during the migraine cycle. By daily measuring R 2 ∗ we could observe both, an increase and a decrease in R 2 ∗ , in various regions of the brain being dependent on migraine attack status. In deep gray matter structures and in the cortex, these R 2 ∗ alterations are highly likely to be driven by iron changes. These dynamic alterations in R 2 ∗ differed not only between regions but also between hemispheres, indicating a shift in regional iron content. An increase in R 2 ∗ is predominantly observed in the left hemisphere, which was also the hemisphere where the pain was located. This could indicate that in these brain regions a higher demand of energy metabolism, and thus a higher need of iron, is present during a migraine attack ( 29 ). The accompanied decrease of R 2 ∗ in contralateral brain regions could indicate a shift of iron between the hemispheres. However, after the migraine attack, iron content in deep gray matter and white matter, reached the same level as on migraine-free days. Our results indicating a short-term alteration in brain iron levels during a migraine attack do not contradict an overall abnormal long-term iron accumulation in patients with migraine. In white matter the effect of iron on R 2 ∗ is overshadowed by the effect of diamagnetic myelin ( 15 , 16 ), and orientation effects of anisotropic tissue components. In white matter, there are two main sources of orientation dependency of R 2 ∗ : (I) the orientation of myelinated white matter fibers with respect to B 0 ( 17 , 18 ) and (II) the anisotropic part of the vasculature ( 30 ). Blood vessels have an isotropic component (capillary bed) and an anisotropic component (larger vessels). There is evidence that these larger blood vessels in white matter converge in parallel to main white matter fiber tracts and therefore contribute to the orientation dependent MR signal ( 31–34 ). Therefore, we acquired orientation dependent R 2 ∗ to differentiate between isotropic effects of iron and anisotropy effects of myelin and vascular components contributing to R 2 ∗ in cerebral white matter. By separating isotropic and anisotropic contributions to R 2 ∗ , it was possible to identify an increase in white mater iron content on days with migraine. To the best of our knowledge, this study is the first of its kind, which reported alterations in white matter iron content in patients with migraine. Our results could potentially indicate a shift in iron content from deep gray matter structures to white mater during an acute migraine attack. Beside changes in iron content, migraine is also associated with changes in vascular structures, including veins and perivascular spaces (PVS) ( 22 ). Breiding et al. ( 35 ) observed a higher total cerebral vein volume in patients with migraine compared to healthy controls. Furthermore, in patients with unilateral migraine, the veins were more prominent in one hemisphere ( 35 ). This is in line with our observation of more prominent venous structures in the left hemisphere of our patient. We observed that R 2 ∗ anisotropy is approximately 10% higher in the left hemisphere compared to the right hemisphere on migraine-free days, indicating higher venous and or PVS volume. On days with migraine, the difference in R 2 ∗ anisotropy between left and right hemisphere increased up to approximately 30%. This indicates an involvement of vascular mechanism’s during a migraine attack in addition to a slight increase in iron content in cerebral white matter. The decrease in R 2 ∗ anisotropy during migraine could be explained by a lower venous volume, or altered perfusion which is commonly observed in migraine ( 36 , 37 ) or by a reduction of the PVS volume. A closure of the PVS causing an impaired glymphatic flow during migraine was observed in a previous study ( 38 ). Studies investigating PVS in patients with migraine showed inconclusive results where both increased and decreased, PVS volume was observed compared to healthy controls ( 38–41 ). Overall, the majority of the studies indicated an increase in PVS volume. This would be inline with the observation of enlarged PVS in our patient. However, an overall higher PVS volume in patients with migraine does not contradict a decrease in PVS volume during an acute migraine attack. Altered mean arterial blood pressure can induce cerebral blood volume shifts, detectable through quantitative susceptibility mapping (QSM) ( 42 ). Changes in blood volume or deoxygenated hemoglobin concentration during acute migraines may decrease R 2 ∗ anisotropy, recovering post-attack. Besides vascular factors, R 2 ∗ anisotropy reflects axonal and myelin-related alterations. Granziera et al. ( 13 ) noted thalamic changes in migraine patients, including myelin and cellularity differences, along with iron content changes. Palm-Meinders et al. ( 6 ) observed elevated R 2 values in migraine patients initially, which were decreasing after 9 years. Thus, migraine-related iron changes might be obscured by age or disease-related tissue changes. Numerous studies used DTI to assess white matter alterations, showing heterogeneous results in migraine ( 43 ). Alterations of tissue microstructure in patients with migraine will clearly affect R 2 ∗ anisotropy in general. However, we observed that after migraine attacks, the altered R 2 ∗ anisotropy values reach the same level as on migraine-free days. These short-term alterations in R 2 ∗ anisotropy are more likely to be explained by vascular effects, rather than microstructural tissue changes linked to myelin, as dominant source of R 2 ∗ anisotropy. Although our results are solely based on a single patient, this study is the first-of-its-kind acquiring MRI on multiple consecutive days, comprising migraine-free days and days with acute migraine attacks. Acquiring MRI of multiple patients with migraine on 21 or more days, including days with a migraine attack, would be very challenging. It is worth to note that is extremely difficult to recruit patients which are willing to undergo an MRI examination during an acute migraine attack, especially if a refrain from taking any preventive of acute medication is required for study purpose. Furthermore, results of multiple subject could not directly be averaged, as multiple factors, such as unilateral or bilateral migraine location of the pain, duration and frequency of the migraine attacks, age and sex will influence the observed R 2 ∗ alterations. However, further multi-parametric MRI studies with a larger number of patients, ideally with image acquisition during migraine attacks, will be needed to further identify the cellular mechanism contributing to isotropic and anisotropic R 2 ∗ changes in migraine and to obtain reproducible results. Furthermore, future studies should also consider the severity of the disease, e.g., attack frequency and duration. Moreover, the exact onset and the end of the migraine attack cannot always be assessed with high accuracy due to clinical considerations such as sleep terminating the migraine attack. In our study, we observed a relative short time period of 21 days in comparison to years of disease duration, thus no conclusions about long-term alterations in iron content or vascular structures can be made from our results. Although, our results indicate that changes in iron content related to migraine attacks are reversible, a long-term alteration in iron content can still accompany short-term dynamic fluctuations in iron content. It is worth to note, that plotting of R 2 ∗ on each day in combination with the boxplots is important, as every day was assigned to only one condition (e.g., migraine-free or migraine), yet, as shown in Figure 2A , altered R 2 ∗ values can sometimes be observed already on days prior or after the migraine attack. This can lead to a bias in the boxplots and statistical analysis. In addition, potential outliers, as shown in Figure 2A can be identified by combining day plots with boxplots. While R 2 ∗ is highly sensitive to iron content, it can not be directly converted to an absolute iron concentration. Although, studies have shown a strong correlation between R 2 ∗ values and iron concentration in brain tissue, particularly in deep gray matter structures ( 44 ), the exact relationship is not perfectly linear and can vary across brain regions. An approximation of the altered iron content can be made based on a post-mortem study, where R 2 ∗ was validated as reliable measure for iron content using mass spectrometry ( 10 ). Langkammer et al. ( 10 ) reported a slope of 0.27 1/s per mg/kg iron for gray matter. Based on this study, the R 2 ∗ decrease of around 5% in the caudate would correspond to a change in iron content of approximately 3.7 mg/kg wet tissue. We conclude that migraine attacks lead to short-term changes in R 2 ∗ in specific brain regions, which further differ between the left and right hemispheres. Our study identified both specific brain regions with increased and brain regions with decreased iron content during a migraine attack. This suggests that different metabolic processes have an increased need for iron, which could potentially be resolved by shifting iron between brain structures. Furthermore, by separating isotropic and anisotropic R 2 ∗ components, we were able to distinguish between iron and non-iron related tissue changes in the cerebral white matter. Our observed decrease in R 2 ∗ anisotropy during a migraine attack suggests the involvement of vascular components, such as a decrease in PVS volume, a change in venous volume, or a blood pressure-induced shift in magnetic susceptibility during an acute migraine attack. However, the observed R 2 ∗ changes fully return to baseline after the migraine attack has resolved. This supports the involvement of vascular structures rather than changes in axonal fibre architecture and myelin content as the dominant source of R 2 ∗ anisotropy. In conclusion, the time-dependent mapping of R 2 ∗ during a migraine cycle opens new possibilities to study short-term changes in the brain during a migraine attack, which appear to be partially different from long-term tissue changes in migraineurs. Taken together, our results indicate dynamic alterations in iron metabolism and vascular processes during an acute migraine attack. | Study | biomedical | en | 0.999999 |
PMC11697586 | Diabetes mellitus is a metabolic disease characterized by hyperglycaemia that affects a large population, is highly dangerous and is difficult to treat ( 1 ). Peripheral neuropathy (DPN) is one of the most common complications in people with diabetes. It is characterized by numbness, pain, burning or other abnormal sensations in the limbs. The WHO predicts that by 2030, there will be approximately 360 million diabetic patients worldwide, and more than 50% of them may have DPN symptoms, and most diabetic amputations or disabilities are caused by DPN ( 2 , 3 ), and the quality of life of diabetic patients with DPN will be seriously reduced once it occurs, and even lead to the death of the patients. Studies have shown that the relative likelihood of death within 5 years of lower limb amputation due to diabetic foot ulcers is greater than for diseases such as prostate and breast cancer ( 4 ). In addition, amputation imposes a significant financial burden on both the healthcare system and society. In the United States, the total annual cost of care for symptomatic DPN (pain) and its complications (foot ulcers and lower limb amputations) is estimated to be between 460 million and 1,370 million US dollars. As much as 27% of the direct medical costs associated with diabetes are attributed to DPN ( 5 ). Diabetic peripheral neuropathy is usually irreversible. The medical community does not have a consistent and effective treatment plan to manage the disease. Treatment options at this stage are generally used to prevent disease transmission and complications. Most treatment options tend to use symptomatic treatment such as nerve nutrition and improvement of neural microcirculation ( 6 ). Commonly used drugs include alpha-lipoic acid, which is an antioxidant stress, neurotrophins such as vitamin B1, B12, gangliosides, nerve growth factor, etc., and drugs to improve neural microcirculation include prostaglandin E1, scopolamine and hexacosanolone coccolithophore, etc. ( 7 ). Drugs are mainly used to relieve neuropathic pain and sensory abnormalities, but they cannot solve the problem of decreased nerve function. There is still a lack of specific therapeutic measures, and there are no effective treatments and medications, and most of the medications have certain side effects, and patients must be able to tolerate the side effects of drug therapy ( 8 , 9 ). In addition, there are individual differences between patients, and western medical treatment has more adverse effects and poor long-term results ( 10 ). Acupuncture is a characteristic external treatment method of traditional Chinese medicine, which mainly plays a therapeutic role through various physical stimulation effects on acupoints. Nowadays, this therapy has been widely used in the treatment of diabetic complications, including diabetic foot, diabetic bladder, diabetic peripheral neuropathy and so on. From the clinical study report, acupuncture can significantly reduce the symptoms of numbness, pain and superficial sensory impairment of the extremities in patients with DPN, with certain efficacy and fewer side effects ( 11 ). In addition, acupuncture has the advantages of multi-targeting and bidirectional regulation of the mode of action ( 12 ). It is currently believed that the pathological mechanism of DPN is closely related to inflammation, oxidative stress, endoplasmic reticulum stress, microvascular lesions, neurotrophic disorders and immune dysfunction ( 13 ), and its pathological changes are peripheral nerve demyelination or axonal degeneration ( 14 ), or both. Acupuncture has the ability to modulate inflammatory reaction, oxidative stress, ER stress, increase peripheral nerve blood flow, ameliorate microangiopathy, increase neurotrophic factor content, ameliorate peripheral nerve electrophysiological function, promote axonal and myelin repair, and so on. The key to the therapeutic effects of acupuncture in DPN may be found in the above mechanisms ( 15 ). A recent systematic review of acupuncture for the treatment of diabetic peripheral neuropathy concluded that acupuncture can effectively improve the neurological and clinical symptoms of diabetic peripheral neuropathy, but further work is needed to develop a uniform standard for the treatment of diabetic peripheral neuropathy with acupuncture ( 16 ). Although we also use acupuncture for the treatment of diabetic peripheral neuropathy in the clinic, there is a lack of systematic data studies on the efficacy of acupuncture for the treatment of diabetic peripheral neuropathy. Therefore, a meta-analysis was performed in this paper to summarize the randomized controlled trials of acupuncture for diabetic peripheral neuropathy published by previous investigators. The search will include major Chinese and English databases such as PubMed, Web of Science, Cochrane Library, AMED, CINAHL, China Knowledge Network (CNKI), Wanfang Database and Wipro Database (VIP), supplemented by references to included trials, clinical trial or research registry platforms, expert consultation and gray literature. All publications in Chinese and English from the time of the library’s inception to 30 December 2023 will be searched, regardless of country or article type. The key search terms were composed of the following group terms: “acupuncture,” “acupuncture,” “electropuncture,” “fire needle,” “plum blossom needle,” “acupoint,” “auricular acupuncture” and “peripheral nervous system diseases,” “diabetic peripheral neuropathy,” “DPN” and “randomized controlled trial,” “RCT,” “random,” “blind,” “control.” Treatment efficacy; sensory nerve conduction velocity (SNCV) of the median, common peroneal and tibial nerves; motor nerve conduction velocity (MNCV) of the median, common peroneal and tibial nerves; and visual pain scale (VAS); Symptom scores. The following were excluded: duplicate publications; unavailability of original literature; literature with incomplete or questionable data; non-RCT literature such as case studies, case series, qualitative studies and uncontrolled trials; comorbidities with other causes of peripheral neuropathy; and patients in the study group who had received other TCM drugs or therapies during the course of their disease. The literature was screened independently by two researchers in accordance with the inclusion and exclusion criteria and the literature search strategy, and the retrieved literature was imported into Endnote20 software for duplicate checking and removal. The title and abstract were read first to exclude literature that clearly did not meet the inclusion criteria. The remaining literature was read in full and screened again to identify literature that met the inclusion criteria. In the case of conflicting opinions, the third researcher was asked to participate in the discussion for assessment. The extraction of data was conducted independently by two investigators, encompassing fundamental details pertaining to the study, such as title, author, publication date, and journal, alongside essential study characteristics, including mean age, gender, sample size, subgroups, measures, treatment duration, follow-up duration, and outcome measures. The quality of the included trials was assessed by the researchers using the risk of bias assessment tool recommended in the Cochrane Handbook for Systematic Evaluators, and the results of the assessment included the following six items: whether the random allocation method was appropriate, whether allocation concealment was correctly applied, whether blinding was correctly applied, whether there was no selective reporting of results, whether outcome data were complete, whether there was any other risk of bias. Meta-analysis was performed with the use of RevMan 5.3 software. For dichotomous variables (e.g., clinical effectiveness), the relative risk (RR) was used as the effect size, and for continuous variables (e.g., sensory nerve conduction velocity SNCV), the mean difference (MD) was used as the effect size, and the 2 effect sizes were expressed by 95% CI. The degree of heterogeneity was determined by the I2 and p values; when the heterogeneity between studies was small (I2 ≤ 50%, p > 0.05), a fixed-effects model was chosen; when heterogeneity between studies was present (I2 ≥ 50%, p < 0.05), a random-effects model was chosen. A p value less than 0.05 signifies a statistically significant difference. Sensitivity analysis was performed to verify the robustness of the results of the heterogeneity tests by excluding studies case-by-case. In addition, the Baujat plot was used to further characterize the contribution of each study to overall heterogeneity and identify high heterogeneity studies. If more than 10 studies were available, publication bias was assessed using funnel plots. In addition, Egger test or Peters test was used to further formally test for potential publication bias. A total of 1,423 studies were identified in eight databases. A total of 446 articles were removed due to duplication. A total of 884 studies were screened by reading titles and abstracts, leaving 93 articles. After reading the full text of 93 articles, 73 articles were excluded for the reasons described in Figure 1 . Finally, 20 studies ( 17–36 ) met the inclusion criteria and were meta-analyzed . A total of 20 papers were included in this review, all of which were published, including 6 in English ( 17–20 , 26 , 36 ) and 14 in Chinese ( 21–25 , 27–35 ), involving a total of 1,455 patients. All studies reported comparable baseline data between groups. All trials included adults, and the mean age of participants ranged from 45 to 81 years, with all being middle-aged to older adults. Eleven trials compared AT with medication ( 19 , 21 , 23–27 , 31–34 ), and four trials used AT + medication as an intervention and medication alone as a control ( 22 , 26 , 30 , 35 ), with one trial having two intervention groups: AT and AT plus medication ( 26 ). Three trials compared AT with sham AT ( 18 , 20 , 36 ). Three trials compared AT + conventional treatment with conventional treatment ( 17 , 28 , 29 ). The results of the risk of bias assessment are displayed in Figure 2 . According to the Cochrane Risk of Bias Assessment Tool, 20 studies mentioned randomization and all of the literature used the random grouping method, of which 11 articles used the random number table method ( 20 , 22 , 23 , 25 , 26 , 28 , 30 , 32 , 34–36 ), three used the method of using computer random grouping ( 17 , 18 , 29 ), and the rest of the articles did not mention the specific random grouping method; one study ( 18 ) mentioned allocation concealment by using sealed opaque envelopes, one study ( 17 ) had randomization performed by a single research nurse and notified the study doctors and patients of the allocation results by telephone, and the rest did not describe the method of allocation concealment; 2 studies ( 18 , 20 ) blinded patients using a sham-needle technique, the remaining studies referred to in-process blinding during the trial, and blinding of patients or clinicians was considered to be at high risk due to the variable differences between the intervention and control groups; 1 study ( 17 ) referred to blinding of data analysts, the remaining studies did not; 2 studies ( 17 , 18 ) reported off-case results, the remaining studies did not report off-case results, with good data completeness; no selectivity was reported in all studies, with a low risk of other biases. Eleven RCTs were included ( 19 , 21 , 23–27 , 31–34 ), and the heterogeneity test showed that the heterogeneity between studies was small (I 2 = 7%, p = 0.37), and a fixed-effects model was adopted. The results of the meta-analysis showed that the overall efficacy rate of patients treated with acupuncture was significantly better than that of patients treated with drugs, and the difference was statistically significant . Four RCTs were included ( 22 , 26 , 30 , 35 ). Heterogeneity analysis revealed substantial heterogeneity among the studies (I 2 = 77%, p = 0.004), necessitating the application of a random-effects model. The meta-analysis demonstrated that the combined use of acupuncture and drug therapy yielded a higher overall efficacy rate compared to drug therapy alone, with a statistically significant difference . Two RCTs were included ( 28 , 29 ), with a high level of heterogeneity observed (I 2 = 88%, p = 0.003), prompting the adoption of a random-effects model. The meta-analysis revealed no significant difference in the overall efficacy rate between patients treated with AT plus usual care and those receiving usual care alone . Four studies ( 21 , 23 , 26 , 34 ) were analyzed, demonstrating low heterogeneity (I 2 = 40%, p = 0.17), thus supporting the use of a fixed-effects model. The meta-analysis indicated that patients in the acupuncture group experienced a statistically significant enhancement in median sensory nerve conduction velocity, compared to the drug group, with a mean difference of 2.61 . Two studies were included ( 26 , 35 ), and the heterogeneity analysis indicated minimal inter-study variability (I 2 = 49%, p = 0.16), leading to the adoption of a fixed-effects model. The analysis demonstrated that the protocol combining acupuncture and drug was superior to drug alone in enhancing median nerve sensory nerve conduction velocity in patients with diabetic peripheral neuropathy, with the difference being statistically significant . In a study ( 20 ), the protocol for the acupuncture treatment group was found to outperform the sham needle group in improving sensory nerve conduction velocity in the median nerve of patients with diabetic peripheral neuropathy (DPN). This superiority was accompanied by a statistically significant increase in median nerve conduction velocity, indicating a notable difference in the treatment outcomes . Four studies ( 19 , 23 , 26 , 34 ) were included, showing no significant heterogeneity (I 2 = 68%, p = 0.03), thus justifying the use of a fixed-effects model. The comparison revealed that the acupuncture treatment group outperformed the drug treatment group in improving motor nerve conduction velocity of the median nerve in patients with diabetic peripheral neuropathy, with the observed difference being statistically significant . Three studies ( 22 , 26 , 35 ) were analyzed, revealing negligible between-study heterogeneity (I 2 = 0%, p = 0.53), which supported the utilization of a fixed-effects model. The meta-analysis outcomes demonstrated that the acupuncture plus medication group exhibited superiority over the medication group in improving motor nerve conduction velocity of the median nerve in diabetic peripheral neuropathy patients, with a statistically significant discrepancy noted . One study was included ( 20 ), and the meta-analysis outcomes indicate that the acupuncture group protocol does not exhibit a significant difference compared to the sham needle group in enhancing the motor nerve conduction velocity of the median nerve in patients suffering from DPN . Four studies ( 23 , 26 , 31 , 34 ) were incorporated, and the heterogeneity analysis revealed minimal between-study variability (I 2 = 73%, p = 0.01), thus justifying the use of a fixed-effects model. The meta-analysis outcomes indicated that the acupuncture treatment group outperformed the drug treatment group in enhancing the sensory nerve conduction velocity of the common peroneal nerve in diabetic peripheral neuropathy (DPN) patients, with a statistically significant difference observed . Six studies ( 19 , 21 , 23 , 24 , 26 , 34 ) were included, and a significant level of inter-study heterogeneity was detected (I 2 = 0%, p = 0.60), prompting the adoption of a random-effects model. The comparison indicated that the acupuncture treatment group outperformed the drug treatment group in enhancing the motor nerve conduction velocity of the common peroneal nerve in diabetic peripheral neuropathy (DPN) patients, with a statistically significant difference . Two studies ( 22 , 26 ) were included, with a substantial heterogeneity found between them , prompting the use of a random effects model. The meta-analysis did not identify a statistically significant difference between the protocols of the acupuncture plus medication group and the medication group in terms of improvement in motor nerve conduction velocity of the common peroneal nerve in patients with diabetic peripheral neuropathy (DPN) . Three studies ( 24 , 32 , 33 ) were examined, and the heterogeneity test indicated a negligible degree of between-study heterogeneity (I 2 = 0%, p = 0.69), prompting the utilization of a fixed-effects model. The meta-analysis findings revealed that the acupuncture treatment group exhibited superiority over the drug treatment group in enhancing the sensory nerve conduction velocity of the common peroneal nerve in DPN patients, with a statistically significant difference detected . Three studies were included ( 21 , 32 , 33 ), and the heterogeneity test showed that there was a large heterogeneity between studies (I 2 = 52%, p = 0.15), and a random-effects model was adopted. All three studies showed that, compared with medication, the acupuncture group had a statistically significant effect on the improvement of motor nerve conduction velocity of the tibial nerve in DPN patients . Two studies were included ( 30 , 35 ), and the heterogeneity test showed a large heterogeneity between studies (I 2 = 84%, p = 0. 01), and a random-effects model was adopted. Meta-analysis results showed that the acupuncture plus drug group protocol was superior to the drug group in improving VAS in DPN patients, and the difference was statistically significant . Only one study ( 17 ) met the inclusion criteria, and it was noted that the acupuncture plus conventional group protocol exhibited a statistically significant improvement in VAS for DPN patients compared to the conventional group . Four studies ( 23 , 27 , 31 , 34 ) were selected, showing significant heterogeneity , which led to the use of a random-effects model. The Meta-analysis results showed that the Meta-analysis results showed that the acupuncture group was better than the drug group in improving the symptom score of DPN patients . Only one study ( 28 ) was deemed eligible for inclusion, and the comparative analysis outcomes revealed that the acupuncture plus conventional group protocol was more effective than the conventional group in enhancing VAS in DPN patients, with a statistically significant difference observed . A total of 47 adverse reactions were reported in the four included studies ( 20 , 30 , 33 , 36 ), including 2 cases of needle sickness, 18 cases of small hematomas, 1 case of localized swelling, 6 cases of pain, 1 case of itching, 7 cases of transient paresthesia, 1 case of cramps, 4 cases of transitory intensifying of DPN-related symptoms, 2 case of Mild dizziness, 1 case of chest pain, and 6 cases of tiredness. The total clinical effective rate is an important indicator of clinical efficacy. Therefore, a sensitivity analysis was conducted on the effective rate results for the acupuncture and drug groups that included the largest amount of data. By excluding studies individually, there was no significant change in the pooled effect size of the effective rate. From the results of Baujat chart, we found that three studies ( 21 , 27 , 31 ) led to the heterogeneity of Baujat chart results . As regards the high heterogeneity found in the comparison of acupuncture with drug vs. drug on effective rate (I 2 = 77%) and acupuncture vs. drug on MNCV in tibial nerve (I 2 = 93%), we performed the sensitivity analysis. By excluding studies individually, there was no significant change in the pooled effect size of the effective rate, but an apparently weak decrease in heterogeneity was observed when one study was excluded ( 22 ). Moreover, from the results of the Baujat plot, we found that two studies ( 22 , 30 ) unduly influenced heterogeneity as well as the pooled effect of the MNCV in tibial nerve . In the sensitivity analysis of nerve tibialis, the results showed that an apparently weak decrease in heterogeneity was observed when one study was excluded ( 32 ), and there were two studies ( 32 , 33 ) that contributed overly to the heterogeneity from the results of the Baujat plot . We drew the funnel plot and used Peters’ test to calculate the outcome of the total effective rate, which indicated no publication bias. However, publication bias in the outcome of effective rate may exist due to the asymmetrical funnel distribution and Egger’s test . The trim-and-fill method showed that it was necessary to fill four potential unpublished studies in the funnel plot . A meta-analysis was re-performed for all the studies, results show a heterogeneity test is low , using fixed effect model, The combined results of the effect indicators did not change significantly, indicating that the results were still statistically significant, with no reversal, so the combined results were robust . Our review aims to evaluate and refine the evidence from recent randomized controlled trials on acupuncture for the treatment of diabetic peripheral neuropathy. When comparing acupuncture with medication, conventional therapy, and sham acupuncture, our findings suggest that acupuncture is more effective in treating DPN and in improving nerve conduction velocity. Additionally, the combination of acupuncture and medication demonstrates a more significant improvement in nerve conduction velocity compared to medication alone. The combination of acupuncture and usual care improves DPN symptoms more effectively than usual care. In this meta-analysis, all included trials used acupuncture as a treatment option, and the clinical use of acupuncture for the treatment of DPN is varied, such as acupuncture, electroacupuncture, acupoint injection, and warm acupuncture. It can be seen that physical stimulation of acupoints is a safe and effective treatment for the potential treatment of diabetic peripheral neuropathy, and acupoints are an important basis for the efficacy of treatment, such as BL18, BL20, BL23, BL25, BL60, GB30, GB34, SP6 and ST36 are the most commonly used acupoints in the literature included in this study. Acupressure is an external Chinese medicine treatment, and both acupressure and acupuncture use physical stimulation of acupoints to achieve therapeutic effects. Therefore, these two therapies have similar mechanisms of action and clinical efficacy. According to a previous review published by Fu et al. ( 37 ), the combination of herbal foot bath and acupressure therapy effectively improved sensory nerve conduction velocity (SNCV), motor nerve conduction velocity (MNCV), overall efficacy rate and neuropathy syndrome score compared with various types of control groups, such as Western medicine, oral Chinese medicine, other Western symptomatic treatments and blank control, and there were no case reports of adverse effects. The results of this trial are consistent with the findings of this study, which suggest that herbal footbath combined with acupressure may be safer and more effective in the treatment of DPN. In contrast to previous systematic reviews, we identified 20 new randomized controlled trials ( 17–36 ) and successfully assessed the treatment evidence. Acupuncture, as a special therapy of Chinese medicine, has a wide range of indications, significant efficacy, safety and no side effects, and this review will identify the advantages and possibilities of using acupuncture in the treatment of DPN. In the early stages of DPN, the main manifestation is abnormal sensation in the limbs, which is distributed like a sock or glove, accompanied by numbness, pins and needles, burning, ants and ants, coldness, or as if stepping on a cotton pillow ( 38 ). This is followed by pain in the limbs, which is vague, tingling or burning, and is worse at night and during the cold period. In advanced stages, clinical manifestations of motor nerve disorders occur, such as hypotonia, muscle weakness to myasthenia and paralysis. Therefore, screening scales such as electromyography (EMG) for sensory nerve conduction velocity (SCV) and motor nerve conduction velocity (MCV) ( 39 ), Michigan Diabetic Neuropathy Score (MDNS) ( 39 ) and Toronto Clinical Scoring System (TCSS) ( 40 ) are important indicators of the patient’s condition. In a comparison of acupuncture with pharmacological therapies, the evidence showed that acupuncture had a significant effect on increasing treatment efficiency and was effective in improving sensory and motor nerve conduction velocities in the tibial and median nerves. Acupuncture was more effective than oral medication in relieving pain symptoms and improving quality of life in patients with painful diabetic peripheral neuropathy. Acupuncture showed better results than neurotrophic agents in improving circulation and significantly reducing clinical symptoms in patients with diabetic peripheral neuropathy of the lower extremities. The combination of acupuncture and medication was more beneficial than medication alone in improving nerve conduction velocity. The pathogenesis of DPN is highly complex. Current research suggests that it is primarily caused by metabolic disorders resulting from hyperglycemia, dyslipidemia, and insulin resistance. These disorders include abnormal glycolytic pathways ( 41 ), increased advanced glycation end products (AGEs) ( 42 ), and alterations in protein kinase C signaling pathways. These metabolic disturbances further enhance oxidative stress ( 43 ) and inflammatory responses ( 44 ), leading to endoplasmic reticulum stress, mitochondrial dysfunction, DNA damage, and inflammation, collectively contributing to the onset of DPN. There is a certain connection between the pathogenesis of DPN and the therapeutic mechanism of acupuncture. Acupuncture may exert its therapeutic effects by regulating the pathophysiological processes of DPN. Studies have shown that the mechanisms by which acupuncture treats DPN may include the regulation of neurotrophic factor expression, such as nerve growth factor (NGF) and calcitonin gene-related peptide ( 45 ). Additionally, acupuncture improves glycolipid metabolism, such as reducing the accumulation of advanced glycation end products (AGEs) ( 46 ), and inhibiting the secretion of inflammatory mediators, such as interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor- α (TNF-α) ( 47 ). However, the analyses of acupuncture for DPN did not show a significant combined effect, partly due to the limited number of trials included. Therefore, the role of acupuncture in the treatment of DPN needs to be further investigated future. We also observed that more acupuncture sessions may introduce some heterogeneity, possibly due to increased reporting bias and poor compliance with long-term protocols. Screening scales such as TCSS and MDNS scores were significantly lower after acupuncture treatment than before treatment, and compared with drug therapy treatment before and after treatment changes plus significant, Chinese medicine symptom scores and symptom scores were significantly lower goodness to increase the effectiveness of treatment, acupuncture in combination with drugs and conventional therapies is more favorable than drugs alone. Furthermore, the incidence of adverse effects was significantly lower with acupuncture compared to drug therapy. Based on our assessment, there is a high risk of bias in most of the included studies, which could lead to false positives, especially for subjects and staff blinded to an assessment. Blinding is difficult due to the specific nature of acupuncture therapy, where needles need to penetrate the skin and take time to remain there, whereas medications are taken orally as prescribed, where patients can easily distinguish whether they are receiving needles or medications, and where practitioners need to physically manipulate the patient’s skin to know the location and depth of the needles when performing acupuncture. Currently, the placebo needle methods commonly used in the design of acupuncture clinical trials include: acupoint/non-acupoint dermal surface needling, non-acupoint deep puncture needling, non-acupoint shallow puncture needling, specific acupoint dermal surface set of overlapping blunt-tipped needles, simulated dermal surface electrical stimulation, laser needling, and so on, but they all have certain limitations ( 48 ), and there is no accepted more mature method of placebo needling that can simulate the sensation of needle puncture without producing a therapeutic effect, which results in The experimental design of clinical trials of acupuncture is difficult to achieve strict blinded control. In addition, the clinical trials of acupuncture that have been conducted to date have not been able to blind the practitioners ( 49 ). Of the randomized controlled trials included in this review, only three trials ( 18 , 20 , 36 ) compared acupuncture with sham acupuncture. Two trials found that acupuncture was more effective than sham acupuncture in improving nerve conduction velocity. Another trial found that acupuncture was well tolerated and had no significant side effects. However, because of the differences in the outcomes between the two trials, they could not be analyzed together. So the placebo effect of acupuncture needs to be investigated further. There are also some limitations to this review. Firstly, many of the included trials had an unclear risk of bias, and most of the literature did not describe the specific method of random allocation and whether or not allocation was concealed. Second, despite the large number of randomized controlled trials, the outcome measures were very heterogeneous, which prevented large-scale meta-analyses. As a result, the level of evidence was mostly low or very low. Finally, most of the literature does not mention follow-up, and long-term effectiveness is more difficult to assess. In the future, studies of acupuncture for diabetic peripheral neuropathy should be more rigorously designed and focusing on randomized methods, allocation concealment, blinding, selection of objective and comprehensive outcome indicators, and long-term follow-up to provide high-level clinical evidence. Clinical trials with a large sample size should also be carried out to clearly demonstrate the benefits of acupuncture and to provide robust evidence for clinical decision making. In addition, further studies should be conducted to provide guidelines for the clinical use of acupuncture for diabetic peripheral neuropathy in terms of acupuncture points, duration, frequency and treatment cycles. In conclusion, acupuncture has the potential to be used as a routine treatment for diabetic peripheral neuropathy. Acupuncture has been shown to have better outcomes and fewer adverse effects than conventional Western medicine. The combination of acupuncture and pharmacological therapy is superior to pharmacological therapy alone for diabetic peripheral neuropathy. However, the level of evidence is low due to a high risk of bias and small sample sizes. To obtain high-quality and comprehensive evidence, future data from more rigorous clinical trials are needed. In addition, acupuncture has demonstrated better clinical outcomes for other complications of diabetes, such as diabetic nephropathy, diabetic foot, diabetic bladder and diabetic retinopathy, and has played an integrative role in improving the subsequent quality of life of diabetic patients. | Review | biomedical | en | 0.999995 |
PMC11697590 | Infectious diseases pose a long-term threat to human health and disrupt the normal social order. Infectious diseases are continuous and fast-spreading diseases that can be transmitted by an infected person to more than one person, exponentially increasing the total infected population. Common infectious diseases include swine influenza, avian influenza in birds, severe acute respiratory syndrome (SARS), coronavirus disease (COVID-19), dengue fever, and malaria. People are at risk of exposure to viruses and diseases that can affect their normal lives during daily commuting or by participating in social activities. The spread of infectious diseases has accelerated because of population growth and improved transportation systems. COVID-19 aroused extensive worldwide attention on infectious diseases during the related global pandemic. COVID-19 was rapidly dispersed internationally because of its wide distribution and difficulties with protection. Research into the impact of transportation on the spread of epidemics had increased during the SARS period ( 1 ). Some scholars observed that the contact rate was a key parameter in the study of the evolution of diseases ( 2 ). After the outbreak of COVID-19 in Wuhan, many scholars paid close attention to the pandemic ( 3 ) and conducted research using mathematical models ( 4 , 5 ). To predict the trend of a pandemic slowdown, there are articles which studied the outbreak of COVID-19 in Greece using a time series model, probability distribution, and a susceptible–infected–recovered (SIR) model ( 6 ). Some researchers noted that the COVID-19 pandemic could spread in family settings ( 7 ). Among the first scholars to study the spread of COVID-19 on buses, Edwards et al. ( 8 ) confirmed the effectiveness of surgical masks and the use of air conditioning systems to suppress the spread of the virus. Moghadas et al. ( 9 ) believed that the vast majority of COVID-19 incidences were related to a silent transmission caused by a combination of pre-symptomatic diagnosed patients and asymptomatic infected patients. The susceptible–exposed–infected–recovered (SEIR) model is suitable for the study of transmission trends because susceptible individuals do not always develop symptoms immediately after infection. Tang et al. ( 10 ) used the SEIR model to calculate and analyze data during the outbreak of the pandemic in Wuhan and explored the implementation effects of various intervention measures. Xue et al. ( 11 ) observed that the Omicron variant of COVID-19 was more infectious than the Delta variant and seasonal influenza; however, its mortality rate was lower. The high infectivity of the Omicron variant has ensured the continuation of COVID-19 infections, increasing the risk of infection among people. Prem et al. ( 12 ) used an age-structure-based SEIR model and observed that measures used to maintain a physical distance had different implementation effects in different age groups. Maintaining a certain social distance effectively reduced the incidence rate of infections in school-age children and the older adult. Transportation as a requisite for daily commuting should not only provide travel but also prevent the large-scale spread of epidemics ( 13 , 14 ). It is imperative to adopt effective epidemic prevention measures in transportation. There are researchers who have found epidemic prevention measures such as city closures and travel restrictions on domestic airlines were effective ( 15 ), based on passenger volume data from Japan's public transportation network. Anderson et al. ( 5 , 16 ) affirmed the contribution of vaccine developments, patient isolation, and self-protection to suppress the spread of epidemics. Some researchers posited that the channels connecting an epidemic area to other areas should be controlled during the early stages of an epidemic ( 17 ). When an epidemic spreads, relevant departments can effectively prevent the further spread of infections such as COVID-19 through the control of transportation hubs. Liu et al. ( 18 ) observed that the spread of an epidemic could effectively be prevented by implementing traceability measures to promptly isolate infected individuals and their close contacts. Rail networks are an important aspect of an urban public transportation system; they are also a critical component in epidemic prevention measures ( 19 ). Research into the transmission of asymptomatic infections in urban rail networks has received little attention in consideration of epidemic prevention measures. In this study, we included asymptomatic infected patients and explored the spread of infectious diseases and related factors within the subway system according to subway passenger flow. Beginning in 1927, the SIR model ( 20 ) marked the inception of mathematical modeling within the field of epidemiology. Since that time, a variety of mathematical models built upon this foundational framework have been continuously developed and extensively discussed. During the SARS epidemic, many researchers used the SEIR model developed by the SRI model as the basis to further explore and change the mathematical model to deal with the problems at that time ( 21 , 22 ). In the COVID-19 era, the SEIR model is still a common tool for many scholars to find ways to prevent the epidemic in the face of a more complex environment. Several studies have used SEIR models to talk about the changing patterns of the global pandemic ( 23 , 24 ) or to predict the effectiveness of government anti-epidemic policies ( 25 , 26 ). In this study, taking the subway in Z city as an example, the SEIR model was further innovated, and a model that can be used to simulate the spread of COVID-19 in the subway was proposed. We constructed an improved SEIR model called the SEIA (susceptible–exposed–infected–asymptomatic infected). The SEIA model considered asymptomatic infected patients to be virus spreaders. We studied the transmission mechanism of infectious diseases in subway systems with highly concentrated populations, based on the impact of changes in passenger flow and infection rates on the spread of infectious diseases. The key elements influencing the spread of infectious diseases in the subway were analyzed in the model calculations on the basis of the scale of exposed people. This enabled us to understand the spread of infectious diseases in a subway and further analyze and predict trends in the spread of diseases. Our research extends and complements prior theoretical research on the spread of infectious diseases in urban rail systems. The mathematical models of infectious diseases define the categories of different populations based on their different states and then divide them into a square or rectangular warehouse. Figure 1 depicts a warehouse model in which susceptible (S) is classified as a susceptible warehouse, infected (I) is classified as an infected warehouse, etc. The state of different types of people changes in a warehouse model, so researchers can reassign people to corresponding warehouses according to their new transformed state. If a susceptible (S) individual is infected, that individual is transferred to the infected warehouse. After treatment and recovery, infected (I) individuals enter the recovered warehouse. This model is usually represented by differential equations that can be used to predict the number of infected individuals, the scale of infections, and the duration of the epidemic. Common warehouse models include SI, SIR, and SEIR. The SIR warehouse model ( 20 ) is a relatively basic infectious disease model that is suitable for the study of diseases such as smallpox and parotitis, which occur quickly but produce antibodies after recovery to ensure no immediate re-infection. The initial total population is assumed in the model without considering the migration status of the population and increases or decreases in birth and death rates. The number of infected (I) individuals increases by β × S N × I within a certain period of time, and the total number of people is N = S + I . Recovered (R) comprises people who contain antibodies in their body after rehabilitation and who will not immediately be re-infected, so this number is not included in the total number of people. Susceptible (S) individuals are transformed into infected (I) individuals with a probability of β after contact with infected (I) individuals in the SIR model. Assuming that the recovery rate of infected (I) individuals from the state of illness to the state of recovery is γ, then infected (I) individuals are cured with a probability of γ after a period of treatment to become a recovered (R) individual. The specific formula for the SIR model is as follows: During the transmission process, infected (I) individuals have the ability to transmit disease after being infected and can spread to R 0 individuals on average during the period of disease ( R 0 = β / γ in the absence of any intervention measures). When R 0 > 1, the number of infected (I) individuals monotonically increases toward the highest value; when R 0 < 1, the number of infected (I) individuals monotonically decreases, leading to the final elimination of the disease. Susceptible (S) individuals are transformed into exposed (E) individuals with a probability of β after contact with infected (I) individuals in the SEIR model. There is an infection rate of σ in a population of exposed (E) individuals that causes exposed (E) individuals to be infected with the disease. Thus, infected exposed (E) individuals move from the exposed warehouse to the infected warehouse. Patients in the infected warehouse are cured based on a probability of γ after treatment and become recovered (R) individuals. Consequently, they move from the infected warehouse and enter the recovered warehouse. We primarily considered infectious diseases with latent periods that result in the creation of antibodies within a short period of recovery such as H1N1 and COVID-19. The SEIR model provides a greater alignment with research requirements than infectious disease models such as SI and SIR because of the addition of the exposed (E) and recovered (R) categories of population segmentation. The basic assumptions of classical infectious disease models do not consider factors such as population migration and natural death, so they are suitable only for the study of the short-term process of virus transmission in subway carriages. We chose the SEIR model as the basic model to study the transmission mechanism of infectious diseases in subways. The traditional SEIR model involves four types of people: susceptible (S), exposed (E), infected (I), and recovered (R). Infected (I) individuals may not experience a secondary transmission in a single ride because passengers may have a limited time traveling on a subway and there is a certain incubation period for exposed (E) individuals to transform into infected (I) individuals. The transmission model considers only the process of susceptible (S) individuals becoming exposed (E) individuals after they encounter infected (I) individuals. Susceptible (S) individuals may not actually be infected after coming into contact with infected (I) individuals. In this study, this latent population was categorized as exposed (E). The asymptomatic infected (A) parameter was added to the model to construct the improved SEIR model because asymptomatic infected (A) individuals also have the ability to spread infections. Our model included the following four population types: susceptible (S), exposed (E), infected (I), and asymptomatic infected (A), abbreviated as SEIA. According to the particularities of the subway environment, u t was introduced as the number of people boarding at time t (a certain stop) and g t was used to depict the number of people alighting at time t (a certain stop). We studied the spread of infectious diseases in subways using these two parameters to simulate the increase or decrease in the number of people in the subway when a train stops. In the formula, S t is the number of susceptible (S) individuals in the subway at time t . E t is the number of exposed (E) individuals in the subway at time t . I t is the number of infected (I) individuals in the subway at time t . A t is the number of asymptomatic infected (A) individuals in the subway at time t . r is the effective number of infected (I) and asymptomatic infected (A) individuals who encounter susceptible (S) individuals (the average number of carriers). β 1 is the probability of susceptible (S) individuals being infected after contact with infected (I) individuals. β 2 is the probability of susceptible (S) individuals being infected after contact with asymptomatic infected (A) individuals. ∑ g t is the total number of people alighting at all stops. The improved SEIR model added asymptomatic infected (A) individuals to the traditional model as the source of infection. This ensured its suitability for infectious diseases with a silent transmission such as the influenza of a virus and COVID-19. We assumed that the initial total population was N without considering the migration status of the population and increases or decreases in births and deaths. The formula for the increase in the number of exposed (E) individuals over a period of time is as follows: Not all susceptible (S) individuals are directly exposed to infection within the contact range of infected (I) or asymptomatic infected (A) individuals. This may expose certain susceptible (S) individuals to the range of the virus transmission. It is not guaranteed that susceptible (S) individuals exposed within the range of virus transmission will contract the virus; rather, they have an infection probability of β. Susceptible (S) individuals who encounter infected (I) individuals may be infected with a virus at an infection rate of r × β 1 . Susceptible (S) individuals who encounter asymptomatic infected (A) individuals may be infected with the virus at an infection rate of r × β 2 and can transform into exposed (E) individuals. A certain period of incubation is required before determining whether individuals have been infected with a disease and for symptoms to appear. The exposed (E) category only indicates the population that may be infected; it is not equivalent to those infected during a single ride or encounter with a subway. Certain susceptible (S) individuals transform into exposed (E) individuals after contact with infected (I) or asymptomatic infected (A) individuals with the probability of r × β 1 or r × β 2 . With each subway or stop, u t is added to the number of susceptible (S) individuals and g t is deducted from the number of susceptible (S) individuals. Infected (I) and asymptomatic infected (A) individuals decrease in proportion to g t ∑ g t with each stop. The process of virus transmission begins from the time infected (I) and asymptomatic infected (A) individuals enter the subway and ends when there are no infected (I) individuals in the subway. We used MATLAB to randomly iterate and generate the average passenger flow data of boarding and alighting. A scenario analysis can be employed to study the spread of infectious diseases by assigning different values for the effective contact number r . The analysis can compare differences in the number of exposed (E) individuals with the use of protective measures inside the subway or not and can judge the effectiveness of the prevention of infection. The degree of transmission of different infection groups can be studied according to the different values of the infection rate of infected (I) and asymptomatic infected (A) individuals. Subsequently, the degree of influence of the two groups of infected (I) and asymptomatic infected (A) individuals on exposed (E) individuals can be ascertained. This enables the identification of key factors in the spread of infectious diseases in subways, the simulation of trends in the spread of infectious diseases, and the exploration of disease transmission patterns. As a key parameter in the COVID-19 infection model, the infection rate β affected the number of exposed individuals. We analyzed the number of changes of infected and asymptomatic infected individuals under different infection rates by adjusting the transmission rate of symptomatic and asymptomatic infections β 1 and β 2 . We determined the extents to which the two types of infected people had an influence on the trends in the spread of infectious diseases. In Scenario 1, the transmission rate of infected individuals was β 1 = 0.2 and the β 2 infection rates of asymptomatic infected individuals were 0.2 and 0.5, respectively. The simulation results of different infection rates during the peak period are depicted in Figure 7 . The simulation results illustrated in Figure 7 revealed that the number of susceptible individuals during the peak period gradually decreased with an increase in the β 2 values during the peak period. The number of susceptible individuals increased when the trains continued to stop at stations and new passengers entered the subway. The change in the number of the susceptible individuals was not significantly different, indicating that the results of different values in the scenario had little effect on susceptible individuals. The curve of exposed individuals revealed a steady upward trend that decreased after the seventh station; nonetheless, it continued to grow. The number of exposed individuals increased from the first station of 2.58 to 16.45 at the tenth station when the β 1 value is 0.2. However, the number of exposed individuals increased from the first station of 5.36 to 33.13 at the tenth station when the β 1 value is 0.5. According to the different values of β 1 , the number of exposed individuals has a difference of 16.68. The number of susceptible individuals decreased with an increase in the β 2 values in Scenario 2. The β 2 values for asymptomatic infected individuals were the highest because of the β 1 transmission rate of infected individuals. Thus, the corresponding curve of the number of exposed individuals was also the steepest in the two scenarios. The number of exposed individuals reached 22.69 at the tenth station of the subway when the β 2 value is 0.2. Similarly, the number of exposed individuals reached 39.09 when the β 2 value is 0.2. According to the different values of β 2 , the number of exposed individuals has a difference of 16.4. We compared the different values of the β 1 infection rate of infected individuals in the two scenarios. When the β 2 infection rate of infected individuals was 0.2, the numbers of exposed individuals were 16.45 and 22.69, respectively, according to the different values of β 1 . When the β 2 infection rate of infected individuals was 0.5, the numbers of exposed individuals were 33.13 and 39.09, respectively, according to the different values of β 1 . From a horizontal comparison of the β 2 values in Scenarios 1 and 2, the number of infected individuals was >16. When comparing the β 1 infection rate of infected individuals with the β 2 infection rate of asymptomatic infected individuals, we observed that the β 2 values had a greater impact on exposed individuals. Infected individuals may consciously avoid travel and self-test their health at home when they experience symptoms such as fevers and coughs. Infected individuals often choose self-driving, walking, or using well-ventilated public transportation when there is an essential requirement to travel. Certain passengers who use the subway may also consciously reduce their contact with other passengers during the subway ride. It is difficult for asymptomatic infected individuals to ascertain whether they are infected as they do not present clinical symptoms. Asymptomatic infected individuals may maintain normal social activities and may not consciously maintain a social distance or reduce activities in crowded places. The transmission caused by asymptomatic infected individuals in subways is more covert, causing difficulties for subways and leading to an accelerated spread of infectious diseases. The patterns of disease transmission must be studied to ascertain the transmission process of infectious diseases in subways. In this study, we first determined the number of effective contacts, the infection rate of infected individuals. We also added asymptomatic infected individuals to the SEIR model together with infected individuals as the source of infection in the transmission process of infectious diseases in subways. | Other | biomedical | en | 0.999996 |
PMC11697591 | A significant number of prevalent human diseases are linked to climate fluctuations, and warming trends in recent decades have led to increased morbidity and mortality from diseases such as CVDs in many parts of the world ( 1–5 ). A meta-analysis of studies has demonstrated that CVD-related mortality increases with increasing ambient temperature, and that the risk of death from stroke increases by 3.8 per cent and the risk of death from CAD by 2.8 per cent for every 1°C rise in ambient temperature, and has demonstrated that the risk of CVD varies geographically and is affected by a number of underlying climatic conditions ( 6 ). To date, a large amount of literature has confirmed a strong correlation between high temperatures and increased mortality from CVD ( 7–10 ). However, most of these studies only focused on the impact of temperature as an influencing factor on the human body, while humidity was controlled as a confounding factor ( 11 ). With global warming, the earth’s climate is becoming warmer and wetter, and focusing only on temperature or humidity can no longer better quantify the impact of climate change on CVD. The complexity of the impact of climate on disease makes it challenging to study the relationship of a single factor in isolation, and because there is often a joint effect between the factors, a single-factor analysis cannot accurately reflect the real climate situation. Physiologically, it has been confirmed that high humidity at high temperatures can prevent the cooling effect of the cooling system. In a hot and humid environment, high humidity reduces the body’s own cooling ability ( 12–15 ), which can lead to an increase in the body’s core temperature and in turn put a strain on the cardiovascular system ( 16 ). On this basis, some scholars have begun to suggest that there may be a combined effect between temperature and humidity, and that this effect may exacerbate the damage to the cardiovascular system caused by high temperatures, leading to an increased risk of death ( 17 , 18 ). Of course, some scholars believe that high humidity in hot conditions may be a protective factor ( 19 , 20 ). To date, these conclusions are inconsistent, highlighting the need for a systematic investigation of the joint effects of humidity and temperature on CVD mortality under sweltering conditions. The effects of damp heat in various regions are likely to vary due to weather conditions, air pollution, socioeconomic status and demographic characteristics. Our study aims to use a dataset from Huizhou City, Guangdong Province, to investigate the combined effects of relative humidity and high temperature on CVD mortality, and to generalize the results to subtropical monsoon humid climate zones, especially those that experience hot and humid weather year-round. This will enable us to protect people at risk of cardiovascular disease and reduce their exposure to risk in advance of hot and humid weather, so as to prevent CVD mortality. The mortality data of permanent residents in Huizhou from 2015 to 2021 were retrieved from the death information registration and management department of Huizhou City. The mortality data encompassed fundamental individual information, such as gender, age, time of death, and cause of death. In accordance with the International Classification of Diseases, 10th revision (ICD-10, coding: I00-I99), the mortality data of CVDs were extracted, and on this basis, CAD and stroke were further screened out. All the above data is divided and analyzed in units of days. The general algebraic modeling system (GAMs) is suitable for analyzing complex nonlinear relationships between dependent variables and several explanatory variables. It is widely used in epidemiology and environmental health. The explanatory variables can be fitted using various smoothing functions to represent the degree of influence of each explanatory variable on the dependent variable. Since the effect of changes in THI on the risk of CVDs mortality is not limited to the observed time period and may also have a certain lag, the distributed hysteresis nonlinear model (DLNM) proposed by Gasparrini is introduced to model the relationship between exposure events and a series of future outcomes ( 22 ). Therefore, this study used a Poisson distribution GAM combined with DLNM to assess the association between THI and the risk of CVDs mortality in residents. Before establishing the model association, in order to avoid the collinearity of the factors in the model, the Separman correlation coefficient between the meteorological factors was tested ( 23 ). If the correlation between the two factors is strong (| r | > 0.8) ( 24 ), it indicates that the two variables are highly collinear and should not be included in the same model. In equation 2 : Y-the number of CVDs deaths on day t; E(Y t )-the expected value of the number of CVDs deaths on day t; lag–lag time; s-cubic spline function; df-degree of freedom parameter; α - intercept; TI t-lag -THI lagged by t days; W S t -mean wind speed on day t; time–time variable, with 7*year selected as the degree of freedom to control for long-term temporal trends; DOW-day of the week; Holiday–holiday as a confounding factor, added as a dummy variable. The regression coefficientβand standard deviation SE were estimated in accordance with equation 2 , and the relative risk (RR) and its 95% confidence interval (95% CI) were calculated ( 25 ). Please refer to equations 3, 4 for further details: Based on the above effects, the DLNM was constructed to predict the RR of CVDs deaths in residents under different THI values. First, a cross-base matrix was generated for the primary research factor THI, and the additional lag time dimension of the exposure-response relationship, that is, the combination of the two functions of prediction and lag effect, was combined into a two-dimensional matrix. The lag dimension was set to 7 days ( 26 , 27 ), and the model framework was as follows: To test the sensitivity of the model and the effect of THI in this study, the following Sensitivity analyses were performed to demonstrate the robustness of our model formulation: 1. Changing the time degrees of freedom 7*7; 2. Including CVD, stroke and CAD death data into the model for testing separately. The results calculated under different degrees of freedom were subjected to a significance t-test with α = 0.05 against the data from the main model. p < 0.05 indicates a statistical difference. All statistical analyses in this study were performed using R4.4.1 software, and the mgcv., dlnm, and ggplot2 packages were used to assess the impact of sweltering on the number of deaths from CVD and the two core disease types in different genders, as well as the cumulative lag effect and data visualization. Statistical tests were two-sided probability tests, with a test standard of α = 0.05. All results are expressed as RR and 95% CI, and a p value of <0.05 was considered statistically significant. Second, in a separate analysis of the data on deaths from coronary heart disease ( Tables 3 , 4 ), the risk of death from coronary heart disease increased with the cumulative sweltering effect for both men and women. The effect of perceived sweltering on coronary heart disease in men peaked at lag 1, with a 2.8% increase in mortality (RR, 1.028; 95% CI [1.009–1.048]), while in women the peak was reached on the cumulative lag day 2, with an increase in mortality of 3.5% (RR, 1.035; 95% CI [1.015–1.054]). The effect of perceived sweltering on the risk of death from stroke also had a cumulative effect. The effect of perceived sweltering on stroke in the total population peaked on day 2 of the lag, with an increase in mortality rate of 4.6%. Among men, the effect of perceived sweltering on stroke peaked on day 3 of the lag, with an increase in mortality rate of 5.4% (RR, 1.054; 95% CI [1.029–1.079]); the effect of feeling sweltering on stroke in women peaked on the second day of the lag, with an increase in mortality of 4.6% (RR, 1.046; 95% CI [1.023–1.069]). This may indicate that the effect of sweltering on stroke in men may be more serious and long-lasting. At the same time, by comparing Tables 3 , 4 , it can be found that the lagged effect of stroke mortality is longer and more severe than that of coronary heart disease mortality in the general population. There existed an evident nonlinear connection between THI and RR . When THI equaled 74.2 (corresponding to the minimum number of deaths), the average RR was the lowest, and subsequently, RR rose along with THI. Once THI exceeded a specific range, RR increased rapidly as THI increased. Furthermore, the impact of sweltering (THI > 75) on the human body possesses a cumulative lag effect. With the accumulation of the sweltering effect, the risk of CVDs death also accumulates, and the death risk reaches the peak on the 2nd lag day. As shown in Supplementary Table S2 , after adjusting for the degree of freedom of the long-term time trend, the impact of THI on CVD and mortality rates of the two core diseases did not change significantly, and the result of the t-test was p > 0.05, indicating that there was no significant difference between the changes in the data and that the model used in this study was reliable. This study first used the GAM to analyze the impact and lag effect of sweltering on CVD and mortality rates of two core diseases in different gender groups. It was found that the risk of CVD mortality in the total population increased by 3.0% under sweltering; and the RR showed a trend of first increasing and then decreasing with the increase in the number of lag days. In terms of cardiovascular disease, women showed more sensitivity; in terms of cerebrovascular disease, men showed more sensitivity. Then, DLNM was further used to predict the impact of different sweltering indices on the population of CVD mortality and their lag effects. These findings highlight the need to strengthen the prevention and treatment of cardiovascular and cerebrovascular diseases when sweltering weather occurs. At present, many literatures have confirmed various mechanisms to explain the increase in body temperature caused by the imbalance of the body caused by hot weather, which in turn leads to an increased risk of death from cardiovascular and cerebrovascular diseases ( 6 ). In a hot and humid environment, the body’s heat dissipation is limited ( 11–13 ), which is more likely to lead to an increase in body temperature after the body temperature is out of balance. The increase in body temperature after the body temperature is out of balance will ultimately lead to vascular damage and trigger the coagulation/fibrinolysis pathway ( 28 , 29 ). These physiological changes may lead to microvascular thrombosis or excessive bleeding, resulting in an increased risk of ischemic stroke and heart disease. In addition, high temperatures can lead to the destabilization of blood vessel plaques ( 30 ) and accelerate the progression of atherosclerosis, increasing the risk of acute coronary syndrome ( 31–34 ). Previous studies have also observed a strong correlation between high temperatures and mortality from CVDs. A meta-analysis of 266 studies showed that for every 1°C above the reference temperature, the risk of mortality from CVDs increased by 2.1%, and the risk of CAD increased by 2.8% ( 19 ), a retrospective study by Luo Q found that high temperatures increased CVD mortality by 3% ( 10 ). Increased body temperature increases the risk of cardiovascular dysfunction ( 35 ); Reduces coronary blood flow ( 36 ), High body temperature can also cause heart muscle damage shortly after exposure to heat ( 37 ). However, for women, the risk of death from coronary heart disease increases significantly with the cumulative sweltering effect. The research results of Zhao et al. also show that in extreme heat, coronary heart disease is more sensitive in women than in men ( 38 ). In addition, this study also independently proves the strong correlation between sweltering and stroke and CAD mortality. Similar results have also been observed in recent studies. In the study by Luo et al., every increase of 1°C above the reference temperature increased the cerebrovascular mortality rate by 2% ( 10 ), in this meta-analysis, it was observed that for every 1°C increase in temperature above the reference temperature, the risk of stroke increased by 3.8% ( 19 ), high temperatures have been shown to be a risk factor for ischaemic stroke (IS) ( 39 ), the higher the temperature of the brain, the greater the extent of the cerebral infarction ( 40 ). A retrospective article on animal models of high-temperature-induced cerebral ischaemia explains the specific molecular mechanisms of high-temperature-induced cerebral ischaemia: for example (1) more extensive disruption of the blood–brain barrier ( 23 , 24 ); (2) The number of potentially damaging ischaemic depolarizations in the ischaemic penumbra increases ( 41–43 ). Our results also show that the impact of sweltering on stroke is more significant than CAD, which is consistent with the research results of Liu et al. ( 19 ). However, there are differences with the research results of Luo et al. ( 10 , 44 ). The blood–brain barrier is very sensitive to temperature changes in the event of cerebral ischaemia, and high temperatures can lead to widespread damage to the blood–brain barrier ( 45 , 46 ), and after the blood–brain barrier is damaged, the accumulation of water in the brain and changes in ion homeostasis can aggravate heat injury ( 47 ). In addition, the loss of the blood–brain barrier leads to an imbalance in the immune system of the central nervous system, and the associated inflammatory response can further aggravate the deterioration of the stroke ( 48 ). There is a cumulative lag effect on the mortality rate of CVDs due to sweltering conditions. The effect of sweltering conditions on CVDs in women is greater than that in men. Mesdaghinia et al. showed that the short-term effect of heat exposure on the risk of CVDs in men was 1.1% (RR, 1.011; 95% CI [1.009–1.013]), and 1.4% (RR, 1.014; 95% CI [1.011–1.017]) for women 38. The cumulative lag effect of sweltering is more pronounced in women than in men for CVDs. Finally, the images in this study are different from the exposure-response curve images in the literature related to the relationship between high temperatures alone and CVDs ( 49 ). The images in this study are divided into two sections. In the first half of the images, as the THI increases, the risk of CVDs death increases slowly. Considering that when the ambient temperature is not too high, the body can still dissipate heat through heat radiation, even if the high humidity environment hinders the discharge of sweat, the body is not prone to body temperature imbalance. In the second half of the graph, as the THI increases, the body finds it difficult to cool down through heat radiation in a high-temperature environment. The body’s temperature is mostly cooled down by sweating, but in a high-humidity environment, the excretion of sweat is significantly hindered, which greatly reduces the body’s cooling efficiency in the same high-humidity environment, increasing the risk of elevated body temperature and, in turn, increasing the risk of CVDs ( 50 ), This result is consistent with the effect of wet bulb temperature on mortality. As the wet bulb temperature increases, the risk of human death also increases. When the wet bulb temperature reaches 35°C, the human cooling mechanism fails ( 11–13 ). Sweltering conditions can increase the risk of death from CVD, and the greater the THI, the more pronounced the increase in mortality, and beyond a certain range, the mortality rate increases significantly. There was also a gender difference in this effect, with the effect being more significant in women than in men. In addition, there is a cumulative lag effect of sweltering on CVD mortality, which generally peaks after 1–3 days. In addition, the lag effect is longer and deeper for stroke deaths than for CAD deaths. Studying the effects of sweltering on CVDs has important public health and clinical implications for the prevention of CVD deaths. | Study | biomedical | en | 0.999998 |
PMC11697592 | Stiff person spectrum disorders (SPSDs) are a rare group of neuroimmunological disorders characterized by progressive rigidity and triggered painful spasms of the limb muscles. Despite the first description by Moersch and Woltman in 1956 of the formerly coined “stiff man syndrome” ( 1 ) or as a gender-neutral term of “stiff person syndrome (SPS),” ( 2 ) this condition has a clinical spectrum that includes not only classical SPS but also other SPS variants, such as progressive encephalomyelitis with rigidity and myoclonus (PERM) ( 3 ). Classical SPS is the predominant clinical form and presents as an insidious onset with rigidity and stiffness of the trunk muscles, which advance to joint deformities, impaired posturing, and abnormal gait ( 1 , 3 ). Patients may also develop painful generalized muscle spasms triggered by unexpected stimuli and may be associated with other autoimmune disorders ( 3 , 4 ). The clinical features of SPS variants include focal or segmental SPS (“stiff limb syndrome”), jerky SPS, SPS with epilepsy, SPS with dystonia, cerebellar, and paraneoplastic variants ( 3 – 5 ). In addition to axial and limb muscle stiffness and diffuse myoclonus, patients with PERM (“SPS-plus syndrome”) exhibit relapsing–remitting brain stem symptoms, breathing issues, and prominent autonomic dysfunction ( 6 ). Despite significant advances in the treatment of SPSDs, the prognosis remains unpredictable, with an inadequate response in many patients, leading to severe disability and sudden death ( 5 , 7 ). Moreover, most patients receiving standard-of-care medications may require progressively higher doses, leading to intolerable adverse events ( 5 ), among other limitations of pharmacological interventions discussed later. Therefore, there is a need to identify innovative therapies in which we describe the potential use of extracorporeal photopheresis (ECP) as a rational approach for patients with SPSDs, specifically classical SPS. Of note, there are no case reports, patient cohorts, or clinical trials have been reported on the use of ECP in SPS yet. Accordingly, this study aims to propose ECP as a potential treatment for SPS by analyzing the current evidence supporting its clinical application. SPSDs are associated with high titers of autoantibodies to different antigens of inhibitory synapses, generating low level of synthesis and release of γ-aminobutyric acid (GABA) on presynaptic or postsynaptic neuronal junctions within the central nervous system (CNS), resulting in impaired functioning ( 3 , 8 ). Glutamic acid decarboxylase (GAD), a cytoplasmic enzyme with two isoforms (GAD67 and GAD65) that transforms glutamate into GABA, has been widely recognized as a primary target identified in classical SPS, predominately anti-GAD65 antibodies ( 3 , 8 ). However, other autoantibodies have also been reported, and various correlations with SPSD variants have been established, including antibodies against GABA receptor-associated protein and dipeptidyl-peptidase-like protein-6 (DPPX) in classical SPS, amphiphysin and gephyrin in paraneoplastic variants, and glycine receptor associated with PERM ( 3 , 9 ). The classical SPS etiopathophysiology has been explained by the B cell-mediated inhibition of GABAergic neurons and their synapses, whereas GAD65-specific T cells accumulated in the CNS could drive the intrathecal GAD65 IgG production ( 3 , 10 ). T cell-mediated cytotoxicity has also been reported in SPS, as GAD65-specific T cells can initiate cytotoxic immune responses ( 11 ). Despite evidence suggesting that GAD65-specific T cells are likely to be scarce and mainly confined to the naïve repertoire in blood ( 10 ), there is a systemic and oligoclonal immune response mediated by stable B cell clones ( 12 ) leading to serum titers that are 50-fold higher than cerebrospinal fluid (CSF) titers ( 4 ). Interestingly, the serum and CSF anti-GAD antibodies first reported by Solimena et al. in a patient with SPS, diabetes mellitus, and epilepsy ( 13 ) were not consistently correlated with the clinical fluctuations of the disease ( 4 , 11 ). These autoantibodies are directed to GAD65 intracellular antigens and have been postulated to interact with peptide fragments during GABA exocytosis on neuronal surfaces, exerting a change in the synaptic transmission by blocking either GAD function or synthesis ( 14 ). GAD65-specific memory T cells could enter the CNS and mount effector responses against GAD65-expressing neurons, including infiltrating CD8 + T cells ( 11 ) detected in the spinal cord of deceased patients with SPS, along with neuronal loss and axonal swelling ( 15 ). SPS immunotherapies are usually the first-line treatment and include corticosteroids, therapeutic plasma exchange, high-dose intravenous immunoglobulins (IVIg), and subcutaneous immunoglobulins (SCIg) ( 11 ). Anti-B cell therapies have recently been proposed as a rational approach in second-line therapies, along with mycophenolate mofetil, azathioprine, or a combination of therapies ( 4 , 5 , 11 ). Treatment with autologous anti-CD19 chimeric antigen receptor (CAR) T cells has also been successfully reported in a patient with refractory SPS ( 16 ). Third-line therapies include cyclophosphamide or a combination of therapies (e.g., IVIg and rituximab or mycophenolate mofetil) ( 11 ). Autologous non-myeloablative hematopoietic stem cell transplantation (HSCT) in disabled patients with SPS has also been reported, despite its variable beneficial effects (fourth-line therapies) ( 11 , 17 ). Commonly, SPS pharmacological treatment is combined with nonpharmacological interventions (e.g., selective physical therapy, deep tissue massage techniques, heat therapy, osteopathic and chiropractic manipulation, and acupuncture) in a multifaceted approach ( 11 ). Nevertheless, current pharmacological interventions lead to heterogeneous clinical responses and pose various limitations ( Table 1 ), which support exploring further strategies, such as ECP, that might be added to the SPS therapeutic armamentarium. ECP is a leukapheresis-based immunotherapy in which autologous leukocytes are exposed to a photosensitizing agent and ultraviolet-A (UVA) irradiation before being reinfused. The photosensitizing agent 8-methoxypsoralen (8-MOP) conjugates with the DNA of leukocytes upon UVA photoactivation, resulting in the inhibition of DNA synthesis and cell division and the induction of apoptosis, generating a cascade of events ( 18 ). During a regular ECP procedure, nearly 5%–10% of the total blood-circulating mononuclear cells are drawn and exposed to 8-MOP and UVA, and the susceptibility to ECP-induced apoptosis varies from cell to cell ( 18 , 19 ). For instance, B and T cells are highly susceptible to 8-MOP/UVA exposure, whereas monocytes and regulatory T cells (Tregs) are more resistant to ECP ( 18 ). ECP exerts “direct effects,” including apoptosis of treated leukocytes, followed by phagocytosis, which trigger cascades of downstream “indirect effects.” ( 20 ) Many cell interactions initiate a cascade of immunological changes, differentiation of monocytes into dendritic cells (DCs), and successive presentation of antigens ( 18 ). ECP-treated cells also recruit other modulators, such as phagocytes, via soluble and membrane-bound “find me” signals ( 21 ). The “indirect effects” of ECP include the eradication of (pathogenic) clonal cells, a shift in antigen-presenting cell (APC) populations, changes in cytokine secretion, and modulation of Tregs and regulatory B cells (Bregs) ( 20 , 22 ). Although the CNS has been considered an immunoprivileged site, current evidence shows the effective recruitment of immune cells across the blood–brain barrier (BBB) into perivascular and parenchymal spaces ( 23 ). T cell responses targeting CNS antigens are initiated in secondary lymphoid organs, and not in the CNS ( 10 ). In fact, activated T cells may penetrate the BBB, regardless of their specificity, and intrathecally are retained those T cells which encounter their cognate antigen ( 24 ). In this regard, Skorstad et al. indicated that GAD65-specific T cells may first be activated in the periphery and later accumulate in the CNS, including proliferation and promotion of B cell differentiation into GAD65 IgG-producing plasma cells within the intrathecal compartment of patients with SPS ( 10 ). Compared with serum anti-GAD65 antibodies, the CSF antibodies of patients with SPS exhibit a 10-fold higher binding avidity, indicating intrathecal synthesis by clonally restricted GAD65-specific B cells driven by local antigens within the confines of the BBB ( 4 , 10 ). Additionally, DCs involved in both primary and secondary immune responses can migrate not only into the perivascular space under degeneration and neuroinflammation ( 23 ), but also into the CSF-drained spaces of the CNS, even in the absence of neuroinflammation ( 25 , 26 ). Furthermore, DCs can traffic to peripheral lymphoid organs (e.g., cervical lymph nodes) and present CNS antigens to T cells in the periphery ( 26 ). Therefore, although the BBB may diminish the effects of ECP, the periphery–CNS trafficking of immune cells and anti-GAD65 antibody production can justify its investigational use in preclinical models and, eventually, in clinical trials. Unlike standard immunosuppressive therapies, ECP does not cause general immunosuppression; instead, it appears to exert complex specific effects ( 27 ) across different immune pathways ( 22 ). Analyzing the various immune specificities in the variations of the clinical phenotypes of SPSDs, we herein describe some potential mechanisms and caveats of ECP to be considered in the context of classical SPS. Previous clinical experience with ECP has been documented in other immune-mediated CNS disorders, such as MS, in which a few case reports and small clinical trials verified the safety of ECP, but the results were inconclusive in terms of efficacy ( 39 , 40 ). For instance, Besnier et al. reported that ECP transiently modified the course of severe secondary chronic progressive MS with a rebound after treatment discontinuation ( 41 ), and Cavaletti et al. reported evidence of adequate efficacy in a subgroup of patients with MS not responsive to or ineligible for standard immunomodulating treatments ( 42 ). Regarding the use of photopheresis in patients with classical SPS, our group has proposed to execute the termed OPTION study, a pilot open-label trial using ECP as an add-on investigational intervention comprised of one ECP cycle (two consecutive days) every other week for three months, followed by one ECP cycle every month for additional three months. This trial will evaluate safety outcomes as the primary endpoints, but the efficacy will be preliminarily assessed through changes in the Distribution of Stiffness Index (DSI) and Heightened Sensitivity Score (HSS) ( 43 ). Figures 1A, B summarize the main etiopathophysiological CNS events and postulated mechanisms of ECP in SPS, respectively. With the aforementioned pieces of evidence, being a well-tolerated and safe procedure with long-term effects in approved indications, ECP might overcome various gaps faced with current SPS treatments, which commonly provide a shorter duration of clinical improvement or variable beneficial effects ( 5 , 7 , 16 , 17 ). For instance, instead of the therapeutic approach of controlling disease symptoms (e.g., benzodiazepines and muscle relaxants), targeting some of the critical cells involved in the etiopathophysiology (e.g., anti-B cell therapies) or even “rebooting” the immune system (autologous HSCT), ECP possesses established immunologic effects that, in combination with those treatments, may gradually modulate the dysregulated immune response observed in SPS. Although the exact mechanism of action of ECP remains unclear and requires further studies in SPS, its wide-ranging immunomodulatory effects may be beneficial in this disabling disorder. By exploring the effect of ECP in preclinical models and formal clinical trials, this approach may also foster its use in SPS and potentially in other neuroimmunological diseases. | Review | biomedical | en | 0.999995 |
PMC11697594 | Organic fertilizer contains a large amount of organic material derived from organic waste, such as animal and plant remain after composting. Compared with inorganic fertilizer, organic fertilizer contains more trace elements and has the ability to regulate the soil structure and improve soil water conservation, fertility, and permeability , thereby promoting enzyme and microbial activity in soil. Moreover, the long-term application of organic fertilizer causes less damage to the environment compared with that of inorganic fertilizer . However, the application of organic fertilizers alone leads to a series of problems, such as slower fertilizer release, and in certain regions, higher costs compared to inorganic fertilizer . It also affects the normal growth and nutrient accumulation of tobacco plants. Therefore, the use of organic-inorganic fertilizer has shown a significant development trend . Studies have shown that organic-inorganic fertilizer can combine the advantages of organic and inorganic fertilizers thereby improving the yield and quality of tobacco . The correlation between yield increase and quality improvement in tobacco has not been fully established, which may be influenced by numerous factors, including the fertilizer type, chemical composition, and tobacco variety. Gaining a deeper understanding of the relationship between yield increase and quality improvement under the application of organic-inorganic fertilizer is conducive to the continuous and stable increase in the yield, quality, and economic benefits of tobacco crops. The organic-nitrogen ratio in organic-inorganic fertilizer significantly impacts the fertilization effects on tobacco. A nutrient release rate corresponding to a 25% organic-nitrogen ratio in the fertilizer aligns with the tobacco plant’s growth and development requirements. This alignment is beneficial for enhancing the agronomic indicators, yield, and quality of tobacco, as well as for coordinating the chemical composition within tobacco leaves. However, if the organic nitrogen ratio is too high, the organic-inorganic fertilizer may negatively affect the yield and quality of tobacco. The variety of tobacco is also an important factor that affects the yield of tobacco, and some varieties may show higher yield potential in specific environments because of their genetic characteristics . Additionally, the variety also affects the chemical composition of tobacco, such as nicotine content, total nitrogen content, reducing sugar, K content, among others , which directly determine the quality and taste of tobacco . China has a vast territory, and different planting areas plant different varieties according to their climate and environmental conditions . Therefore, establishing the quantitative relationship between varieties and changes in tobacco yield and chemical quality after the application of organic-inorganic fertilizer will help tobacco farmers choose the correct organic-inorganic fertilizer to suit their needs. Several basic field experiments have been carried out in different tobacco planting areas in China to study the different effects of organic-inorganic fertilizer on tobacco. The experiment results of Li et al. reported that the use of organic-inorganic fertilizer increased the yield (11.4%), output value (18.3%), and high-grade tobacco rate (11%) of the Y97 variety compared with the application of inorganic fertilizer alone, and the authors suggested that the application of organic-inorganic fertilizer with organic-nitrogen ratio of 25-50% was more conducive to the growth and development of tobacco and improved the yield and quality. Ma et al. found that the application of organic-inorganic fertilizer significantly increased the total sugar content (27.86%), the reducing sugar content (23.00%), sugar-to-nicotine ratio (72.60%), nitrogen-nicotine ratio (22.66%), and K content (6.21%) in tobacco leaves but reduced the total nitrogen content (5.29%) and nicotine (-27.21%), thus leading to a more balanced chemical composition. However, owing to the great differences in climatic conditions, soil physicochemical properties and field management measures in different regions, the experimental results obtained from different studies are inconsistent. To achieve production goals, a suitable organic-inorganic fertilizer ratio scheme should be formulated according to the chemical composition requirements, tobacco varieties, and other factors before planting and fertilization. Therefore, our study performed a meta-analysis of 169 peer-reviewed studies to (1) identify the specific effects of organic-inorganic fertilizer on tobacco yield and chemical components in tobacco leaves; (2) determine how different tobacco varieties and fertilizer components alter the effects of organic-inorganic fertilizer on tobacco; and (3) reveal the impact of applying organic-inorganic fertilizer on the balance between tobacco yield and quality. This study provides a scientific theoretical reference for improving the fertilization regime of tobacco. We searched relevant articles published between 1990-2023 from the China National Knowledge Infrastructure and Web of Science. The search keyword included “flue-cured tobacco” or “tobacco” and “organic fertilizer” or “inorganic fertilizer” and “yield” or “quality” or “chemical composition”. According to the data requirements of the meta-analysis and the purpose of this study, articles were screened using the following criteria: (1) tests in the article should include the application of inorganic fertilizer alone and organic-inorganic fertilizer for comparison; (2) the test materials and environmental background of the test sites should be described (the test sites are located in China); (3) the test results should include the mean and standard deviation of indicators, as these parameters are essential for meta-analysis. (4) the fertilizer treatment section should include the organic-nitrogen ratio; and (5) only one article from the same study can be selected. After screening and evaluation, 169 articles were finally obtained for follow-up analysis. For each study, we extracted the mean, standard deviation and sample size of tobacco yield, high-grade tobacco rate, output value, total nitrogen content, nicotine content, reducing sugar content, K content and Cl content (the chemical compositions come from the middle leaves of tobacco). These tobacco indicators are the primary subjects of study in Chinese research, reflecting the key aspects of tobacco yield and quality. Extract the mean and standard deviation directly from the article’s tables; use Origin 2023 to extract them from figures; and if only the mean is provided, calculate the standard deviation based on other parameters reported. The following relevant information was collected for analysis: climate conditions (planting site,average annual precipitation, average annual temperature, and average annual sunshine), soil conditions (pH, organic matter content, available nitrogen content, available phosphorus content, and available potassium content), field management measures (planting density, type of organic fertilizer, and organic-nitrogen ratio in mixed fertilizer), and tobacco varieties (K326, Y85, Y87, Y97, and others). A total of 632 sets of observations were selected from drawings and graphs in the 169 articles . Rosenthal’s fail-safe number was calculated to test publication bias in the studies, if its coefficient >5n + 10 (n is the sample size), then the variable had no publication bias ( Supplementary Table S1 ). The location distribution of each experiment in the meta-analysis is shown in Figure 1 . Meta-analysis is a quantitative analysis method that summarizes the results of several relatively independent similar studies and draws conclusions . To better study the effects of organic-inorganic fertilizer on the yield and chemical composition of tobacco and determine the different influences of other factors on the fertilizer’s effects, we performed a meta-analysis of the data in the database and used the log response ratio (lnRR) as the statistical effect value indicator . Individual lnRRs for each observation were calculated using Equation 1 : Owing to the large spatiotemporal span of the data in this study and the great differences in planting methods, climate conditions, soil physical-chemical properties in different regions, random effect model (REM) was selected for calculation. The meta-analysis weighted the log response ratio of each observation to obtain the variance (V), weighted factor (Wi), weighted log response ratio (lnRR++), and standard deviation of the weighted log response ratio (SD). They can be calculated using Equations 2 – 5 : Data processing and statistical analysis for the meta-analysis were performed using R version 4.3.1 by package “metafor” . Random forest analysis was carried out using the “rfPermute” package in R software, and all images were drawn using the “ggplot2” package in R software. Correlation analysis was performed to examine the pairwise relationships between the lnRR ++ of the indicators. Optimal model regression analysis was performed to explain the influence of fertilizer composition on the effect of organic-inorganic fertilizer and the relationship between yield and quality of tobacco. The omnibus test (Qm-test) was used to compare the response of indicators to application of organic-inorganic fertilizer among different subgroups. If the p-value of Qm< 0.05, it suggested a significant effect of this factor on the overall effect ( Supplementary Table S2 ). After performing an overall analysis of the 632 sets of data from all 169 studies, we found that the application of organic-inorganic fertilizer significantly increased the yield of tobacco leaves (3.4%), output value (10.1%), high-grade tobacco rate (10.3%), K content (3.76%), and reducing sugar content (5.5%) and significantly decreased the nicotine content (-5.6%) compared with inorganic fertilizer alone . However, significant changes were not observed in the Cl or total nitrogen content in tobacco leaves. From the network correlation analysis of indicator lnRR ++ , the output value (R=0.796, p<0.01), high-grade tobacco rate (R=0.234, p<0.01), total nitrogen content (R=0.177, p<0.01) and K content (R=0.168, p<0.01) in tobacco leaves was strongly positively correlated with yield. Notably, total nitrogen content and reducing sugar content had significant negative correlations (R=-0.214, p<0.01). We collected the organic-nitrogen ratio and the amount of total nitrogen in fertilizers used in different experiments and performed regression analysis of the optimal model with indicators lnRR. As shown in Supplementary Figure S1 within the range of total nitrogen collected in the study (0-116 kg/hm 2 ), the high-grade tobacco rate (p=0.013) and reducing sugar content (p=0.030) in tobacco leaves increased regardless of the total nitrogen, whereas the nicotine content (p=0.087) decreased. The tobacco yield (p<0.001) and output value (p<0.001) only when the amount of total nitrogen exceeded 30 kg/hm 2 . In particular, when the amount of total nitrogen was 50-60 kg/hm 2 , the application of organic-inorganic fertilizer effectively improved the yield, output value and high-grade tobacco rate of tobacco, reduced the nicotine content and increased the content of some chemical components. For the yield (p<0.001) and output value (p<0.001) of tobacco, within the range of organic-nitrogen ratio collected in the study (7-100%), their lnRR decreased as the organic-nitrogen ratio increased compared with inorganic nitrogen application alone, and the change of Cl content (p=0.003) was similar to yield . Notably, the tobacco yield decreased when the organic-nitrogen ratio exceeded 50%. Regarding the chemical indicators in tobacco leaves, we found that nicotine content (p=0.043) and total nitrogen content (p=0.023) decreased as the organic-nitrogen ratio increased. The reducing sugar (p=0.015) and K content (p=0.089) increased regardless of the organic-nitrogen ratio. When the organic-nitrogen ratio in fertilizer was in the range of 50-60%, the reducing sugar and K content showed the greatest increase, and the nicotine content also decreased significantly after fertilization. We analyzed the influence significance of various factors on the application of organic-inorganic fertilizer, including climatic conditions (planting site, annual average precipitation, annual average temperature, and annual average sunshine), soil conditions (pH, organic matter content, available nitrogen content, available phosphorus content, and available potassium content), planting density and tobacco varieties . Contrary to our initial expectations, climatic factors had a relatively low impact on the effectiveness of organic-inorganic fertilizer; they were not the primary determinants. In contrast, soil factors showed a more pronounced influence on the application of organic-inorganic fertilizer, with significant differences observed in tobacco yield, high-grade tobacco rate, nicotine content, and total nitrogen content under varying soil conditions. Notably, among the various factors assessed, planting density and tobacco varieties exerted the most significant influence on the application effects of organic-inorganic fertilizer. Four main tobacco varieties (K326, Y85, Y87 and Y97) that have been frequently studied and cultivated in China were selected and analyzed in this study. The yield and quality of K326 tobacco showed a weak in response to organic-inorganic substances, only K content was significantly increased (6.67%, p<0.001). Y85 and Y87 were closely related, their yield (5.59%, p<0.001; 5.82%, p<0.001), high-grade tobacco rate (14.92%, p<0.001; 11.04%, p<0.001), output value (11.94%, p<0.001; 10.78%, p<0.001) and reducing sugar content (4.25%, p<0.05; 5.82%, p<0.001) were significantly increased after applying organic-inorganic fertilizer. For Y97 tobacco, yield (4.03%, p<0.05), high-grade tobacco rate (5.94%, p<0.001), output value (5.71%, p<0.01), K content (5.46%, p<0.05) and total nitrogen content (6.06%, p<0.01) were all significantly increased after applying organic-inorganic fertilizer . Regression curves were constructed for the organic nitrogen ratio and indicators lnRR for the four main varieties. For K326, the results showed that organic-inorganic fertilizer with an organic nitrogen ratio below 50% slightly increased the yield (p<0.001), value (p<0.001), and the rate of high-grade tobacco leaves (p=0.013) of K326 tobacco. When the organic nitrogen was about 50%, the K content significantly increased . For Y85, yield (p<0.001) and output value (p<0.001) were increased when the organic-nitrogen ratio was less than 80%. Moreover, nicotine content (p<0.001) and total nitrogen content (p<0.001) in Y85 tobacco were significantly affected by the organic-nitrogen ratio. The higher the organic-nitrogen ratio, the greater the reduction in nicotine and total nitrogen . As shown in Figure 6 , among the four varieties, K326 and Y87 showed strong correlations with each indicator while Y85 showed the weakest correlations. The output value of all varieties was positively correlated with the yield (p<0.01), high-grade tobacco rate (p<0.01), and the total nitrogen content was positively correlated with the nicotine content (p<0.01). Reducing sugar content was positively correlated with yield (R=0.166, p<0.05), output value (R=0.095, p<0.05), and high-grade tobacco rate (R=0.071, p<0.05) in K326 tobacco. The yield of Y85 was positively correlated with the K content (R=0.319, p<0.05), and the K content was positively correlated with the reducing sugar content (R=0.333, p<0.05). In Y87, increase in the K content significantly increased the high-grade tobacco rate (R=0.309, p<0.01). Figure 7 ; Supplementary Figure S3 show the regression relationship between yield and other indicators of four varieties after applying organic-inorganic fertilizer. With an increase of yield, the high-grade tobacco rate (p<0.001, p=0.038, p=0.005, p=0.047) and output value (p<0.001, p<0.001, p<0.001, p<0.001) of the four varieties increased. Organic-inorganic fertilizer application can simultaneously improve yield and quality. However, except for K326, the curve between the high-grade tobacco rate and yield of the other three varieties is similar to a parabola, which means that the increase in the high-grade tobacco rate become declined when the yield increased to a certain extent and even had a negative effect. In Y85, K content (p=0.010) and reducing sugar content (p=0.045) increased while the total nitrogen (p=0.045) and nicotine (p<0.001) content significantly decreased as the yield increased . We found that the yield, output value and high-grade tobacco rate of tobacco were significantly increased after applying organic-inorganic fertilizer, and the increase in yield also affected the increase in output value and high-grade tobacco rate . Tobacco has strict nitrogen requirements at different growth stages . In the early growth stage, sufficient nitrogen is required to maintain the full growth of tobacco until flowering, and a high nitrogen supply is not subsequently required. The application of inorganic fertilizer alone provided nutrients required by tobacco in the early stage. Compared with inorganic fertilizer, the release of nutrients in organic fertilizer is slower . Therefore, the application of organic-inorganic fertilizer can provide tobacco with an appropriate nutrient supply during the entire growth and development period, thereby ensuring the nutrient absorption and growth of tobacco. Moreover, organic fertilizer can improve the plant root environment, enrich the types of soil microorganisms, enhance the activity of soil extracellular enzymes , and improve the content of nutrient element in the soil, which contributes to an increase in the tobacco yield and high-grade tobacco rate. However, compared with the increase of output value and high-grade tobacco rate, the increase of yield was small, indicating that the main effect of the application of organic-inorganic fertilizer on tobacco was not to increase yield but to improve the quality of tobacco . Superior quality tobacco exhibits a balance and coordination between carbohydrates and nitrogen compounds . Compared with inorganic fertilizer alone, the application of organic-inorganic fertilizer significantly increased the reducing sugar content in tobacco . Organic -inorganic fertilizer can improve soil invertase activity , and the invertase in the rhizosphere soil of tobacco can split disaccharides, increase the available carbon content in the soil, and promote the tobacco to absorb and use carbon to synthesize carbohydrates. Meanwhile, the application of organic fertilizer brought a large amount of humic acid to the soil . Humic acid can affect the physiological metabolism of tobacco by promoting root growth and nutrient absorption, thereby increasing the accumulation of reducing sugar content . Organic-inorganic fertilizer also reduced nicotine content in tobacco , and increased the sugar-to-nicotine ratio, which shifted the sugar-to-nicotine ratio to the high-quality range. The application of organic-inorganic fertilizer improved the situation of excessive N supply from inorganic fertilizer and slow N supply from organic fertilizer, making the N supply from fertilizer more stable and lasting. Nicotine and total nitrogen were positively correlated in the tobacco plants , thereby, adjusting the nitrogen was also conducive to the decrease of nicotine content . In general, organic-inorganic fertilizer has been found to harmonize the carbon-nitrogen relationship in tobacco and enhance tobacco quality. As mentioned above, the absorption of organic and inorganic fertilizers by tobacco is related to different growth periods. Therefore, if the organic-nitrogen ratio in organic-inorganic fertilizer is unbalanced, the content of available nitrogen in the soil during the early stages will be lower and the yield will be reduced . we found that organic-inorganic fertilizer with low organic-nitrogen ratio could increase tobacco yield, and when the organic-nitrogen ratio exceeded 50%, organic-inorganic fertilizer would reduce tobacco yield . The organic-nitrogen ratio can affect tobacco yield by controlling the synthesis and degradation of chlorophyll in leaves. Organic-inorganic fertilizer with 15%-30% organic nitrogen increases the chlorophyll content of tobacco leaves in the early growth stage, ensuring the normal degradation of chlorophyll in the later stage, and show normal yellowing maturation, which enhances the photosynthetic rate and promote the accumulation of tobacco dry matter, thereby helping to increase the yield of tobacco leaves. Whereas when organic nitrogen is more than 45%, the chlorophyll cannot be degraded normally at maturity, thus causing late ripening of tobacco and reducing the yield . The yield and output value required the amount of total nitrogen to exceed 30 kg/hm 2 to increase in Supplementary Figure S1 , which also confirms our thesis. When the ratio of organic nitrogen was within the range of 50-60% and the amount of total nitrogen was controlled within 50-60 kg/hm 2 , organic-inorganic fertilizer had the best effect on coordinating the chemical composition . At this ratio, organic-inorganic fertilizer can enhance the C metabolism in the sugar accumulation period and effectively regulate the N metabolism of tobacco , which is conducive to the coordinated development of carbon and nitrogen and the improvement of quality of tobacco. Liu et al. also found that when the organic nitrogen substitution ratio was 50%, the abundance of beneficial bacteria in soil could be maximized. These beneficial bacteria not only participate in the decomposition of soil organic matter and also accelerate the soil nitrogen cycle , but also directly carry out biological nitrogen fixation, carbon fixation, oxygen increase and perform other physiological activities to improve soil fertility . Beneficial bacteria help tobacco roots to better absorb and utilize nutrients and coordinate the chemical composition of tobacco leaves. In summary, we believe that the ratio of organic nitrogen applicable to increasing yield and coordinating chemical composition of tobacco is different. Only a small ratio of organic nitrogen can achieve a good yield-increasing effect, whereas a higher ratio of organic nitrogen is required to improve the chemical composition. This finding may play a guiding role in the scientific fertilization of tobacco and improving its economic benefits. Different tobacco varieties responded differently to the application of organic-inorganic fertilizer. The effect was the best in Y85 and Y87 varieties, followed by Y97, and was least effective in K326 . This variation is mainly attributed to differences in growth habits, genetic characteristics, root distribution, and nutrient requirements. K326 is widely planted in various regions of China . Observations of the microstructure of the tobacco leaf tissues and stomata showed that K326 had a balanced leaf thickness, tissue, and stomatal structure and good ecological adaptability, making it suitable for planting in most environments. Our study suggests that K326 can absorb sufficient nutrients for normal growth and development even with less effective fertilizers because of its good adaptation and resilience . Therefore, the yield and most chemical components of this variety did not change significantly after the application of organic-inorganic fertilizer, while only the K content in tobacco leaves increased significantly. Organic-inorganic fertilizer can promote the synthesis of more K in tobacco. The K content of K326 was approximately 2%, higher than that of the other varieties. More nutrients related to K synthesis need to be absorbed during growth and development . Consequently, the K content of K326 increased significantly after the application of organic-inorganic fertilizer, with an organic-nitrogen ratio of approximately 50% being particularly beneficial for enhancing K content in K326 . The yield and output value of Y85 and Y87 responded positively to organic-inorganic fertilizer . Chen et al., 2024 found that the leaf growth and dry matter accumulation rate of Y87 and Y85 were slower than those of K326 and other varieties. Thus, the application of organic-inorganic fertilizer is particularly beneficial for accelerating yield formation in these varieties. Compared with Y87, total nitrogen content and nicotine content in Y85 were more affected by organic-inorganic fertilizer . Total nitrogen and nicotine contents decreased significantly and continued to decline with an increasing proportion of organic nitrogen. Nicotine content in roasted tobacco leaves can neutralize the acidic substances produced by the burning of carbohydrates, which is conducive to the formation of a good taste , but when its content exceeds a certain range, it will have a certain negative impact on the taste . The nicotine content in Y85 tobacco leaves was higher compared to other varieties . The application of organic-inorganic fertilizer can reduce the nicotine content to a certain extent, which is helpful to improve the sensory quality of this variety. In conclusion, the application of organic-inorganic fertilizer significantly increased the K content of K326, improved the yield and quality of Y87 and Y85, effectively reduced the total nitrogen content and nicotine content of Y85 tobacco leaves, and coordinated the chemical composition of Y85 better. Due to the limited nutrients within crop, increasing crop yield and improving crop quality are two aspects that are closely related but contradictory. The high-grade tobacco rate is the ratio of tobacco leaves rated as superior relative to the total number of tobacco leaves. This parameter comprehensively considers the agronomic indicators, chemical quality, and sensory quality of tobacco, and its level directly reflects the quality of the tobacco. We found that after the application of organic-inorganic fertilizer, the yield and high-grade tobacco rate of the four tobacco varieties increased to different degrees . However, the high-grade tobacco rate does not continue to increase with an increase in yield, and when the yield increase reaches a certain value, the increase in high-grade tobacco rate begins to decline. When the yield is too high, plants use more energy and nutrients for the growth of stems and leaves, thus ignoring the formation of tobacco quality substances . Wang et al. also found that the relationship between tobacco yield and quality was similar to a parabola. When the yield ranged from 2040 kg/hm 2 to 2 775 kg/hm 2 , the quality of tobacco increased; however, after the yield exceeded 2775 kg/hm 2 , the quality of tobacco leaves began to deteriorate. The K and reducing sugar content in Y85 were positively correlated with the yield , and they both increased with an increase in yield . This is consistent with our expectations, because the essence of the increase in tobacco yield is that the leaves become larger and thicker, and the leaf area increases, which is more conducive to the photosynthesis. Photosynthesis is the main method of accumulating carbohydrates in plants, and an increase in photosynthesis increase the reducing sugar content . K participates in the movement of leaves and stomata and is an important cation that promotes the synthesis and transportation of photosynthetic and assimilation products. K deficiency will enhance stomatal and mesophyll resistance, and reduce the absorption of CO 2 at the leaf surface . Therefore, an increase in yield must be accompanied by an increase in photosynthesis, K content and reducing sugar content in Y85 tobacco leaves. Bilalis et al. found that plants need more nutrients to be transported to the leaves when the tobacco yield increases, which leads to the redistribution of some nutrients and the removal of some alkaloid substances from the roots. Total nitrogen and nicotine in the tobacco leaves may be reduced or transferred to other parts of the tobacco plant. This is consistent with the results of the present study, in where we found that an increase in yield led to a significant decrease in total nitrogen in Y85 tobacco leaves and a decrease in nicotine content . In conclusion, organic-inorganic fertilizer has the potential to coordinate the distribution of nutrients within tobacco plants and simultaneously improve yield and quality. However, further research is needed to achieve this goal. Our meta-analysis results showed that although the application of organic-inorganic fertilizer improved the yield of tobacco, the main effect was to improve the balance of the chemical composition and improve the quality of tobacco. Second, by analyzing the effects of organic-inorganic fertilizer components on the application effect, we concluded that organic-inorganic fertilizer with a low ratio of organic nitrogen (15–30%) was more beneficial for increasing tobacco yield while fertilizer with a medium and high ratio of organic nitrogen (50–60%) had a better effect on improving tobacco chemical quality. Application of organic-inorganic fertilizer had the best effect on Y85 and Y87 and improved the yield and quality, and it also effectively reduced the total nitrogen and nicotine content of Y85 tobacco leaves. It had the worst effect on K326, which only showed an increase in the K content. This study also concluded that organic-inorganic fertilizer simultaneously increased the yield and high-grade tobacco rate of the four main varieties under certain conditions. Moreover, organic-inorganic fertilizer also increased the reducing sugar and K content, reduced the nicotine content in Y85 while increasing the yield. | Study | other | en | 0.999997 |
PMC11697595 | Cerebral apoplexy, or stroke, is the third leading cause of disability in adults and the second leading cause of deaths globally ( 1 , 2 ). Post-stroke spasticity (PSS) is a form of increased muscle tone where pathological changes in the upper motor neurons lead to impaired sensory and motor controls. It is a motor disorder characterized by a velocity-dependent increase in tonic stretch reflexes with tendon hyperreflexia resulting in abnormal postures and movement patterns in stroke patients. It is a major contributing factor to high post-stroke disability rates ( 3–5 ). Studies have found the treatment cost to be higher in stroke patients with PSS than those without PSS ( 6 ). The pathogenesis of PSS is complex and various researchers have proposed different ideas and definitions ( 7–9 ). At present, modern medicine has made great progress in the treatment of PSS, including botulinum toxin injections, intrathecal baclofen pumps, etc., and “early detection, early treatment” has become a general consensus for the treatment of PSS in the clinic ( 10 , 11 ). This study was analyzed from the perspective of prevention. Through the investigation and study of the relevant samples, the study aims to understand the incidence of spasticity after stroke, screen the relevant risk factors of spasticity and construct a risk prediction model, which will further provide a reliable theoretical basis for exploring the early rehabilitation therapies, reducing the incidence of spasticity and slowing down the degree of spasticity. Therefore, this study investigated and studied the relevant samples to understand the incidence of PSS, and screened the relevant risk factors of spasticity to provide additional reliable theoretical basis for the effective prevention of PSS in clinical practice. This is a retrospective study. A total of 436 stroke patients who visited the Neurology Department of the Third Affiliated Clinical Hospital of Changchun University of Chinese Medicine from June 2020 to November 2020 were selected as study subjects, and finally 257 patients were included in the final analysis, and divided into 101 cases with spasticity and 156 cases without spasticity, depending on whether the individual patient experienced spasticity in the 6 months after the stroke (Any muscle considered to be in spasticity if a value of 1 or more in any muscle in the Modified Ashworth Scale) . From the electronic database of medical records, the investigators recorded information such as the age and gender of study subjects, their medical history in terms of smoking, drinking, hypertension, diabetes, and hyperlipidemia, their dominant hands (left or right hand), as well as observed and recorded the cerebral hemorrhage or infarction by side (left or right), the site of cerebral hemorrhage or infarction (frontal lobe, parietal lobe, temporal lobe, occipital lobe, thalamus, hippocampus, basal ganglia, cerebellum, midbrain, etc.). The volume of the cerebral hemorrhage or infarction was derived from MRI examinations and FLAIR images were used to measure the size of the focal area. Post-processing software was applied on the disclosed relevant sequence to outline the contours of the cerebral hemorrhage or infarction at each layer of the cerebral hemorrhage or infarction in order to automatically calculate the area, these areas were then added layer by layer, and finally multiplied by the layer thickness and inter-layer spacing to obtain the cerebral hemorrhage or infarction volume. Neurological deficit scores (NIHSS scores) and modified Ashworth scores assessed on admission in patients’ e-cases were collected. The data were summarized in a database of observation tables using Excel, then validated and checked for errors before statistical analyses were performed. Based on the diagnostic criteria for cerebral infarction and cerebral hemorrhage in the Chinese Guidelines for Diagnosis and Treatment of Acute Ischemic Stroke 2018 ( 12 ) and the Chinese Guidelines for Diagnosis and Treatment of Subarachnoid Hemorrhage 2019 ( 13 ). All cases were confirmed by cranial CT or MRI. The modified Ashworth method was used to evaluate the severity of limb spasticity. A final diagnosis was then carried out to confirm if there was limb spasticity. The sample size for this trial was estimated by calculating the required sample size based on logistic regression analysis in Medical Statistics, Second Edition ( 14 ): requires a minimum sample size of more than 10 times the number of independent variables, so as to reflect more realistically the relationship between the independent variable and the dependent variable. Statistical analyses of the data were performed using SPSS 27.0. Measurement data with a normal distribution was presented as _x ± s while data with a non-normal distribution was presented as M (IQR), and the Mann–Whitney U test was used to compare between the groups. Count data was represented by n(%), and the chi-square test or Fisher’s exact test was used for comparisons between the groups. Logistic regression was used to identify the factors that affect PSS. The difference is considered statistically significant if p < 0.05. As seen in Table 1 , comparisons between the groups showed that the differences in involvement of basal ganglia, cerebral hemorrhage or infarction volume, and NIHSS scores to be statistically significant ( p < 0.05), an indication that these may be factors that affect spasticity. However, the impact of confounding factors for spasticity was not accounted for. Therefore, a multivariate regression analysis that took into account the effects of confounding factors was carried out to identify independent factors that affect spasticity. As seen in Table 2 , results from the multivariate regression analysis showed that basal ganglia as the cerebral hemorrhage or infarction site, cerebral hemorrhage or infarction volume and NIHSS scores are independent influencing factors and independent risk factors for spasticity ( p < 0.05) . Specifically, spasticity is more likely to occur when the cerebral hemorrhage or infarction site is the basal ganglia, the larger the area of cerebral hemorrhage or infarction the more likely it is to lead to spasticity, while a higher NIHSS scores indicates a higher probability of spasticity. All other indicators are not independent influencing factors for spasticity. A risk prediction model for spasticity in stroke patients is derived with the multivariate logistic regression analysis: Logit (P) = 1.595 * Basal ganglia +0.084 * infarct volume + 0.208 * NIHSS scores – 2.092. The Hosmer-Lemeshow test results in Table 3 are X 2 = 13.828, and p = 0.086, which means that there is no significant difference between the predicted value and the actual value in the Hosmer-Lemeshow test. An evaluation of the goodness of fit using the ROC curve showed AUC (95% CI) = 0.786 (0.730–0.843), an indication of a high degree of model fit . Spasticity is a common post-stroke complication, and approximately one-third of stroke patients will experience spasticity within 3 months of onset of stroke ( 15–17 ). Spasticity is harmful to stroke patients, requiring them to undergo long-term rehabilitation and causing a series of physical and psychological problems that seriously affect their motor function and daily living activities ( 18 , 19 ). Studies have shown that patients with PSS often suffer from psychological problems such as depression and anxiety, cognitive impairment such as memory loss, and poor concentration ( 20–24 ). Post-spasm pain can also lead to sleep disorders. All these seriously affect the patient’s quality of life. In recent years, as modern medical science and technology develops, more clinical treatments for PSS have emerged, such as oral antispasmodics, botulinum toxin injections, physiotherapy, antispastic positioning, as well as acupuncture, moxibustion and traditional Chinese medicine ( 25–31 ). Currently, the most rapid and effective western medical treatments for spasticity are oral anti-spasmodic drugs and local injection of botulinum toxin ( 32 , 33 ). The early stage of PSS has also achieved better clinical outcomes through antispastic positioning ( 25 ). Acupuncture and moxibustion, massage (tuina) and traditional Chinese medicine have the advantages of simplicity and speed, and have achieved remarkable efficacy in the clinical treatment of PSS ( 34 ). If the point of treatment is too late, resulting in abnormal movement patterns and postures that have developed, the only way to treat it is through surgery, which is effective but has the disadvantages of a high coefficient of difficulty and high treatment costs ( 35 , 36 ). Therefore, early detection of spasticity and carrying out effective and rapid treatment are currently the focus of clinical treatment of PSS, which not only reduces related complications, but also shortens the treatment cycle and reduces the burden on the patient’s family. Clarifying the risk factors of PSS can help to detect and treat the functional disorders caused by PSS at an earlier stage, improve the rehabilitation efficacy of the patients, and enhance their ability to return to their families and society. There are many risk factors for PSS ( 37–39 ). NIHSS scores is an important indicator for assessing post-stroke neurological damage. A higher NIHSS scores means a more severe decline in the patient’s neurological functions ( 40 , 41 ). Studies have shown that PSS patients have relatively higher NIHSS scores ( 15 ). This study found the NIHSS scores to be significantly higher in the group with PSS when compared to the group with no PSS. There is a significant correlation between the incidence of PSS and NIHSS scores ( p < 0.05). In the multivariate analysis, NIHSS scores is an independent risk factor for PSS (OR = 1.515), and consistent with the findings of Ryu et al. ( 42 ). Relevant studies have also found ( 42 ) NIHSS scores to be a significant predictor of the occurrence of PSS. Basal ganglia refers to a group of nerve nuclei located deep in the brain, and comprises of the striatum, caudate nucleus, and globus pallidus. These sub-components play key roles in motor, emotional, cognitive, and focus. A damaged basal ganglia can lead to symptoms like muscle tone disorders, and spasticity ( 43 , 44 ). Studies have demonstrated a close relationship between the site of brain injury and the occurrence of spasticity ( 45 ). This study found the proportion of basal ganglia injury to be higher in patients with PSS than those without PSS. There was a significant correlation between the incidence of PSS and basal ganglia injury ( p < 0.05). Based on the relevant multivariate analysis, basal ganglia injury is an independent influencing factor for the incidence of PSS (OR = 6.693). Studies worldwide have also confirmed ( 46 , 47 ) that patients with basal ganglia injury have the highest risk of PSS. The size of cerebral hemorrhage or infarction also has a correlation with the occurrence of PSS, and this study showed that there was a significant difference in the comparison of the size of cerebral hemorrhage or infarction between patients with spasticity after stroke and those without spasticity ( p < 0.05), suggesting that large cerebral hemorrhage or infarction may be one of the factors influencing the development of limb spasticity after stroke. Related studies have shown that patients with less spasticity after stroke have smaller areas of cerebral hemorrhage or infarction, while the opposite is true for patients with severe spasticity ( 47 , 48 ). Some studies have found that the incidence of spasticity is higher in hemorrhagic strokes than in ischemic strokes, which may be related to the fact that hemorrhagic strokes have a higher degree of disability ( 49 ). Hemorrhagic stroke and ischemic stroke have very different pathological mechanisms. In addition to early local cerebral hemorrhage, hemorrhagic stroke is accompanied by a variety of pathological changes in the brain tissue in the hemorrhage area, such as ischemia, hypoxia, inflammatory response, neuronal degeneration, necrosis and apoptosis ( 50 ). Hemorrhagic stroke and ischemic stroke have different degrees and extent of damage to the central nervous system, and the onset of spasticity in different stroke types was not further analyzed in this study, the effects of cerebral hemorrhage and cerebral ischemia on spasticity will be further specifically analyzed in future studies. Other researchers have found that stroke patients with a history of previous stroke, that is, patients who are not the first stroke, have a higher proportion of spasticity, and the reason is related to the aggravation of brain tissue damage after a second stroke, and further neurological function damage leads to an increased likelihood of spasticity ( 51 ). It is now generally accepted that the incidence of spasticity is relatively low in the acute phase of stroke, and the incidence will gradually increase as the stroke course progresses and lengthens ( 52 ). Therefore, in this study, patients in the recovery period after stroke were selected as the research subjects to further clarify the importance of prevention of spasticity after stroke and early rehabilitation intervention on the recovery of neurological function after stroke. This study retrospectively analyzed data on the patient’s conditions and used multivariate logistic regression to identify factors that may influence spasticity, including the involvement of basal ganglia, cerebral hemorrhage or infarction volume, and NIHSS scores. Independent influencing factors for spasticity ( p < 0.05) include basal ganglia as the cerebral hemorrhage or infarction site, cerebral hemorrhage or infarction volume, and NIHSS scores. Due to the limited time and funding, this study has shortcomings in areas like study design and methods, and the results cannot comprehensively encompass all risk factors for PSS. Furthermore, this study adopts a retrospective approach, and is not able to dynamically track and observe the development of spasticity. In selecting the sample, only patients from a single center were selected for the study, so the generalization of the results to patients in other geographical regions should be approached with caution. In future research, a multicenter study with a larger sample size and longer follow-up duration will be conducted for a more comprehensive and in-depth investigation on the risk factors of PSS. A more scientific study plan will be adopted to provide a better scientific basis with regards to the prevention of PSS. Independent risk factors for Post-stroke spasticity include basal ganglia as the cerebral hemorrhage or infarction site, cerebral hemorrhage or infarction volume and NIHSS scores. | Study | biomedical | en | 0.999998 |
PMC11697598 | Among severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants, delta (B.1.617.2) and omicron (B.1.1.529) variant viruses caused a worldwide pandemic due to increased transmissibility compared to that of the Wuhan virus ( 1 ). It is urgent to elucidate the molecular mechanisms governing the onset and progression of these variants to develop effective strategies aimed at reducing recurrence rates and improving therapeutic potency. Metabolomics has the potential to enhance our understanding of host−pathogen interactions in infectious diseases. In particular, metabolomics has been widely applied for biomarker discovery and to investigate the immunometabolic response of individuals infected with various viruses, including more recently SARS-CoV-2 ( 2 – 7 ). Notably, targeting specific metabolic pathways that are crucial for viral replication can potentially disrupt virus growth and reduce infection severity ( 8 ). Depletion of GSH due to viral infection leads to disruption of the redox balance in the lungs and results in tissue damage ( 9 ). High kynurenine/tryptophan ratios were observed in the plasma of patients with moderate and severe COVID-19 ( 10 ). L-arginine is metabolized to L-ornithine in the urea cycle by arginase and serves as a substrate for the production of nitric oxide (NO) by nitric oxide synthase (NOS), a signaling molecule involved in inflammatory responses ( 11 ). Interestingly, arginine administration and modulation of nitric oxide (NO) production have emerged as promising therapeutic strategies with high potency in the management of patients with severe coronavirus disease 2019 (COVID-19) ( 12 , 13 ), but molecular mechanistic studies regarding arginine metabolism are still limited. Therefore, deeper metabolic pathway studies based on metabolomics are crucial to fully understand the arginine-NO metabolic pathway and its implications for therapeutic interventions in patients with severe COVID-19. The golden Syrian hamster model is valuable for studying pulmonary pathology during COVID-19 due to the high genetic similarity of these hamsters to humans ( 14 , 15 ). Recent years have seen continuous efforts to explore the pathophysiology of SARS-CoV-2 infection using various animal models such as hamsters, minks, and ferrets infected with the Wuhan virus, shedding light on changes including TCA cycle, purine metabolism, pentose phosphate pathway, kynurenine pathway and triacylglycerol accumulation ( 16 – 18 ). Moreover, multi-omics studies have elucidated the underlying mechanism involved in SARS-CoV-2 pathophysiology such as a shift toward enhanced glycolysis ( 19 ) and significant phospholipid metabolic alterations ( 20 ). However, metabolomic studies focusing on pulmonary pathophysiology in preclinical models of SARS-CoV-2 infection are still lacking. Current research on delta and omicron variant infections focuses on transcriptional changes in inflammatory mediators and specific genes but lacks a comprehensive view of systemic metabolic alterations in host-pathogen interactions ( 21 ). Here, we performed molecular profiling through metabolomic and transcriptomic analysis to acquire a comprehensive understanding of the systemic effects and metabolic alterations induced by SARS-CoV-2 variants, including delta and omicron. Overall, our study provided insights into how delta and omicron viruses manipulate host’s lung metabolism. We performed metabolomic profiling and integrated transcriptomic analysis, offering valuable insights into potential therapeutic targets for the treatment of SARS-CoV-2 delta and omicron variant infections in hamsters. Golden Syrian hamsters (6 weeks old, male) were purchased from Central Laboratory Animal Inc. (Seoul, South Korea). Our study examined male hamsters because male animals exhibited less variability in phenotype. The animals were maintained under a 12 h light and dark cycle and fed a standard diet and water ad libitum. The hamsters were divided into three groups (n=5/group): the negative control, delta variant and omicron variant groups. The hamsters were anesthetized, and thereafter, the infection was established by intranasally administration of 20 μL (10 5.0 TCID 50 /ml) of SARS-CoV-2 delta variant (B.1.617.2) or SARS-CoV-2 omicron variant (B.1.1.529). The body weights of all infected hamsters were monitored daily until sacrifice. Five hamsters from each group were sacrificed at 0, 4, and 7 days post-infection (dpi), and the lungs were collected to assess the metabolic changes following viral infection . Lung samples were divided for metabolic profiling, transcriptomic analysis, and H&E staining and were stored at -80°C until use. This study adhered to the guidelines of Jeonbuk National University and was approved by the Institutional Animal Care and Use Committee , and the experimental protocols requiring biosafety were approved by the Institutional Biosafety Committee of Jeonbuk National University . All animal experiments were carried out at the Animal Use Biosafety Level-3 (ABL-3) facility at the Korea Zoonosis Research Institute, which is certified by the Korea Disease Control and Prevention Agency of the Ministry of Health and Welfare (certification number: KCDC-16-3-06). To measure the viral loads of SARS-CoV-2 in lung tissue samples, quantitative real-time PCR was performed to detect the N gene of SARS-CoV-2 using TaqMan Fast Virus 1-Step Master Mix (Thermo Fisher Scientific, MA, USA) as previously described ( 22 , 23 ). One gram of tissue samples from all hamsters were placed into soft tissue homogenizing CK14 tubes (Precellys, Betin Technologies) prefilled with ceramic beads and DMEM and then homogenized using a Bead blaster 24 (Benchmark Scientific, NJ, USA). Viral RNA was extracted from the homogenized tissues using a QIAamp viral RNA Mini Kit (Qiagen) according to the manufacturer’s protocol. Real-time PCR was conducted using a CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). All animals were euthanized using an intraperitoneal injection of xylazine and succinyl choline at the end of the experiment. At necropsy, gross lesions in the lung were examined, and then the lung tissues were collected and fixed in 4% neutral-buffered formalin for 1 week. Tissues embedded in paraffin blocks were sectioned at a thickness of 4 μm and then mounted onto glass slides. The slides were deparaffinized in xylene, rehydrated through a series of graded 100% ethanol to distilled water and then stained with hematoxylin and eosin. All tissue samples were assessed by a blinded veterinary anatomic pathologist. To extract metabolites from lung tissue, 100 mg of lung sample was weighed and mixed with 600 μL of methanol/water (1:1, v/v) in a 1.5 mL Eppendorf (EP) tube containing zirconium oxide beads. The mixed sample was homogenized at 5,000 rpm twice using a Precellys 24 tissue grinder (Bertin Technologies, France) and centrifuged after homogenization. After adding 600 μL of chloroform, the sample was vortexed for 1 min and incubated at 4°C for 10 min. The mixture was centrifuged at 12,700 rpm for 20 min at 4°C. For the extraction of serum metabolites, 50 μL of each serum sample were mixed with 550 μL of chloroform/methanol mixture (2:1, v/v) and vortexed for 1 min. Next, 100 μL of water was mixed with the lung and serum samples, respectively and incubated at 4°C for 10 min. The mixture centrifuged at 12,700 rpm for 20 min at 4°C. Then, 150 μL of the upper aqueous supernatant from lung tissue and 50 μL supernatant from the serum were transferred into a new 1.5 mL tube and dried using a speed vac evaporator. The dried lung and serum extracts were redissolved in 200 μL of an acetonitrile/water mixture (75:25, v/v) containing internal standards (0.1 μg/ml betaine-D 11 , 10 μg/ml glutamate- 13 C 5 , 5 μg/ml leucine- 13 C 6 , 2 μg/ml phenylalanine- 13 C 6 , 10 μg/ml succinate- 13 C 4 , 10 μg/ml taurine- 13 C 2 , and 10 μg/ml uridine- 13 C 9 , 15 N 2 ). Liquid chromatography (LC)-electrospray ionization (ESI)-mass spectrometry (MS) analyses for metabolomics of lung tissue extracts were performed on a triple TOF™ 5600 MS/MS system (AB Sciex, Canada) combined with a UPLC system (Waters, USA). LC separations were carried out on a ZIC-HILIC column (2.1 mm × 100 mm, 3.5 μm; SeQuant, Germany). The column temperature and flow rate were set to 35°C and 0.4 mL/min, respectively. The mobile phases used were 10 mM ammonium acetate and 0.1% formic acid in water/acetonitrile (10:90, v/v) (A) and water/acetonitrile (50:50, v/v) (B). The linear gradient program was as follows: 1% B from 0 to 2 min, 1–55% B from 2 to 8 min, 55–99% B from 8 to 9 min, 99% B from 9 to 11 min, 99–1% B from 11–11.1 min, and 1% B from 11.1 to 15 min. The injection volume of the sample was 2 µL for both positive and negative ionization polarity modes. Quality control (QC) samples, which were pooled identical aliquots of the samples, were analyzed regularly throughout the run to ensure data reproducibility. The spectral data were analyzed by MarkerView™ (AB Sciex, Canada), which was used to find peaks, perform peak alignment, and generate peak tables of m/z and retention times (min). The data were normalized using the total area of the spectra. To identify reliable peaks and remove instrumental bias, peaks with coefficients of variation below 20 in QC samples were selected. Metabolites were identified by comparing the experimental data against an in-house library and the online database MS-DIAL. Total RNA from lung tissues was isolated and prepared using the TRIzol cell RNA extraction protocol. The libraries were prepared for 151 bp paired-end sequencing using a TruSeq Stranded mRNA Sample Preparation Kit (Illumina, CA, USA). Namely, mRNA molecules were purified and fragmented from 1 μg of total RNA using oligo (dT) magnetic beads. The fragmented mRNAs were synthesized as single-stranded cDNAs through random hexamer priming. By applying this single-stranded cDNA as a template for second strand synthesis, double-stranded cDNA was prepared. After the sequential processes of end repair, A-tailing and adapter ligation, cDNA libraries were amplified with polymerase chain reaction (PCR). The quality of these cDNA libraries was evaluated with an Agilent 2100 Bioanalyzer (Agilent, CA, USA). The libraries were quantified with a KAPA library quantification kit (Kapa Biosystems, MA, USA) according to the manufacturer’s library quantification protocol. Following cluster amplification of denatured templates, paired-end sequencing (2×151 bp) was performed using an Illumina NovaSeq 6000 (Illumina, CA, USA). The adapter sequences and the ends of the reads with a Phred quality score less than 20 were trimmed, and simultaneously, the reads shorter than 50 bp were removed by using cutadapt v.2.8 ( 24 ). Filtered reads were mapped to the reference genome related to the species using the aligner STAR v.2.7.1a ( 25 ) following ENCODE standard options (refer to “Alignment” of the “Help” section in the html report) with the “-quantMode TranscriptomeSAM” option for estimation of transcriptome expression level. Gene expression estimation was performed by RSEM v.1.3.1 ( 26 ) considering the direction of the reads that correspond to the library protocol using the option –strandedness. To improve the accuracy of the measurement, the “–estimate-rspd” option was applied. All other options were set to default values. To normalize the sequencing depth among samples, FPKM and TPM values were calculated. Based on the estimated read counts in the previous step, differentially expressed genes (DEGs) were identified using the R package TCC v.1.26.0 ( 27 ). The TCC package applies robust normalization strategies to compare tag count data. Normalization factors were calculated using the iterative DESeq2 ( 28 ) method. The Q-value was calculated based on the p value using the p.adjust function of the R package with default parameter settings. The DEGs were identified based on the q-value threshold less than 0.05 for correcting errors caused by multiple testing ( 29 ). We constructed a network based on correlation coefficients among the metabolites, transcriptome, and cytokines using Cytoscape v.3.10.1 ( https://cytoscape.org ). In the network graph, the metabolites and transcripts within the three selected metabolic pathways and significantly altered cytokines within the SARS-CoV-2 variant group are represented as nodes. The thickness of the lines connecting each node was determined by the Pearson’s correlation coefficient values. SIMCA-P+ v.16.0 (Umetrics, Sweden) was used to conduct multivariate analysis. All metabolite levels were scaled to unit variance prior to principal component analysis (PCA). PCA was applied to provide an overview of metabolomic data. All the results were analyzed using the Statistical Package for Social Sciences software, v.28.0 (SPSS Inc., USA) and plotted using GraphPad Prism, v.8 (GraphPad Software, Inc., USA). Statistical significance was assessed using one-way ANOVA with Tukey’s multiple comparisons post hoc test. After performing robust scaling on the metabolomics and transcriptomics data using Google Colab ( colab.research.google.com ), Pearson’s correlation analysis was conducted on the scaled data. Pathway analysis was performed in the MetaboAnalyst computational platform ( www.metaboanalyst.ca ) ( 30 ). To elucidate the immune response and pathogenic molecular mechanisms of SARS-CoV-2 variants, we used the hamster model for delta and omicron variant infection . After intranasal infection with the variants, the body weight of the hamsters was measured daily. In comparison to the non-infected control group, the groups infected with the delta and omicron variants showed significant weight loss, indicating a successful viral infection in the hamster model according to clinical signs . Specifically, the delta variant group demonstrated a more pronounced reduction in body weight than the omicron variant group, indicating a heightened severity of viral infection within the delta group. In addition, SARS-CoV-2 viral RNA copy numbers from lung tissue in both the delta and omicron variants showed significant increases at 4 and 7 dpi compared to those of the control group . However, no statistically significant difference was observed in the viral load between the two variants. Next, histological analysis of lung tissue was performed to evaluate pulmonary lesions . Histopathological changes such as perivascular inflammatory cell infiltration, pneumocyte hyperplasia, alveolar hemorrhages, and septal thickening were observed in the hamsters challenged with the delta or omicron variant at 4 dpi and 7 dpi. These findings indicate that SARS-CoV-2 variant viruses infect hamster lung tissues, with delta variant causing more significant inflammatory pathology compared to omicron variant. To investigate host-pathogen interactions and changes in host’s metabolism infected by delta and omicron variants, LC/MS-based metabolic profiling was conducted on lung tissues, a key target organ in SARS-CoV-2 pathology. A total of 5,427 and 3,110 peak features were detected in positive and negative ion modes, respectively. Tightly scattered quality control (QC) samples in principal component analysis (PCA) score plots indicated good analytical reproducibility during the LC/MS experiment . Regarding metabolic pattern recognition after infection with the delta variant, PCA score plots showed distinct separation between pre- and post-infection in both positive and negative ion modes , while lung tissue samples derived from hamsters infected with omicron were slightly separated between pre- and post-infection in PCA score plots. On the other hand, no significant differences were observed PCA score plots between pre- and post-infection in control group . These results suggest that SARS-CoV-2 variants can modulate lung metabolism, with the delta variant exhibiting a greater impact on lung metabolism reprogramming than the omicron variant. A heat map was generated to visualize the changes in the levels of 88 identified metabolite in the lung tissues of hamsters infected with the delta and omicron variants of SARS-CoV-2 . In both the delta and omicron groups, we observed significant elevation of the levels of several amino acids, including arginine, phenylalanine, asparagine, histidine, tryptophan, cystine, lysine, ornithine, serine, threonine and S-adenosyl-L-methionine (SAM), after variant infection. On the other hand, there were lower levels of taurine, allantoin and 1-methyladenosine after delta and omicron infection. Interestingly, the levels of S-adenosyl-L-homocysteine (SAH), cholic acid, glycochenodeoxycholic acid, malate, 3-hydroxy-3-methylglutaric acid, and kynurenine and the ratio of kynurenine to tryptophan were markedly increased only after delta infection. Next, to identify key metabolic pathways affected by SARS-CoV-2 variant infection at each distinct symptomatic phase (e.g., 7 dpi for delta and 4 dpi for omicron) ( 31 ), metabolic pathway analysis was performed based on differentially regulated metabolites specific to those time points . The results of metabolic pathway analysis revealed distinct changes specific to each variant group. Arginine biosynthesis and taurine and hypotaurine metabolism were important metabolic pathways for both the delta and omicron variants. In the delta variant group, tryptophan metabolism and glutathione (GSH) metabolism were identified as key metabolic pathways. Conversely, the omicron variant group showed arginine and proline metabolism, as well as histidine metabolism, played significant roles following infection. These results demonstrated distinct metabolic changes occurring in the lung tissue of hamsters as a direct consequence of infection with the SARS-CoV-2 variants. Based on the comprehensive examination of a heat map and pathway analysis, notable metabolic alterations were observed in three pathways: arginine biosynthesis, GSH metabolism, and tryptophan metabolism. The levels of most metabolites involved in arginine biosynthesis showed an increasing trend in both the delta and omicron groups compared to those pre-infection. In particular, significant accumulation of arginine and ornithine was observed after delta and omicron infection. In GSH metabolism, a remarkable increase in cystine and a decrease in GSH levels were observed in the delta variants at 7 dpi compared to those at 0 dpi. The levels of taurine were lower after delta and omicron infection than before infection. Within tryptophan metabolism, a significant increase in kynurenine levels was observed at 4 and 7 dpi, while tryptophan levels showed a decrease specifically in the delta group compared to those at baseline, indicating that tryptophan was being converted to kynurenine. To investigate systemic metabolic changes in response to coronavirus variants infections, we also examined alterations in those three pathways in the serum . Increased levels of citrulline and ornithine were observed in the serum, mirroring the trends identified in lung tissue for both the delta and omicron groups . Arginine levels showed an increasing trend in the serum of delta group, while a contrasting decrease was noted in omicron group. Additionally, a reduction in aspartate was observed in the serum. In the context of glutathione metabolism, a significantly reduction in cystine levels was observed in the serum, in contrast to the lung tissue. Additionally, there was an increase in both glutamine and GSSG levels in two variant groups. In tryptophan metabolism, we observed increase of kynurenine levels in both variant groups, mirroring the findings in lung tissue. The delta group exhibited a reduction in tryptophan whereas the omicron group exhibited an increase in tryptophan. Furthermore, a decline in kynurenic acid was also observed in the serum. Supplementary Figure S2 visually represents the individual trends of metabolite levels in three specific metabolic pathways between pre- and post-infection in lung tissue and serum in both the delta and omicron groups. We observed the correlation between the metabolic profiles of lung tissue and serum in the delta group at 7dpi and the omicron group at 4dpi , which exhibited distinct metabolic changes after infection. In the delta group, predominantly positive correlations were observed among various metabolites . Notably, arginine in lung tissue were positively correlated with arginine, glutamine and kynurenine in serum. Cystine in lung tissue were positively correlated with arginine, citrulline, proline, cystine and SAM in serum. Lung kynurenine also showed positive correlation with serum citrulline, proline and cystine. Conversely, in the omicron group, predominantly negative correlations were observed among various metabolites . Particularly, proline in lung tissue showed a significant negative correlation with ornithine, GSSG and SAM in serum. These findings underscore that the delta and omicron variants induce different metabolic alterations in the host’s lung tissue and serum following infection, and imply that a coronavirus infection impacts not only the pulmonary tissue but also has systemic effects throughout the body. Next, an RNA-Seq analysis was conducted to investigate the transcriptional alterations in genes linked to each of the three identified metabolic pathways, as outlined in the Kyoto Encyclopedia of Genes and Genomes (KEGG). The genes associated with the three metabolic pathways showed mostly similar trends of changes in transcription for both the delta and omicron variants, especially the magnitude of the significant change, which was much larger in the delta group than in the omicron group . In arginine biosynthesis, the levels of Ass1 were significantly increased at 7 dpi compared to those at 0 dpi in the delta group but not in the omicron group. The transcription level of most genes involved in GSH metabolism, including Gpx1, Ggt1, Gsr, Pgd, Anpep and Lap3, was significantly higher post-infection than pre-infection in the delta group but not in the omicron group. In tryptophan metabolism, increased patterns of transcription of genes related to kynurenine synthesis, including Tdo2 and Ido1, were observed, while the levels of Cyp1a1 were significantly lower at 7 dpi than at 0 dpi in the delta group. Based on metabolomic and transcriptomic analyses, we were able to identify altered metabolic pathways in response to the SARS-CoV-2 variants. By combining the two sets of analyses, the modified metabolic pathways could be depicted in a single figure . Upregulation of arginine biosynthesis and the urea cycle was observed with both the delta and omicron variants . An examination of the integrated metabolic pathway for GSH metabolism revealed distinct alterations in the context of the delta variant, wherein the synthesis of GSH was found to be suppressed, concomitant with an augmented production of cystine . In the context of the metabolic pathway related to tryptophan metabolism, enhancement of the synthesis of kynurenine was observed with only the delta variant . These findings demonstrate that SARS-CoV-2 variants induce alterations in the metabolic pathways of hamster lung tissue. Specifically, it was shown that the delta variant of the virus had a stronger impact on the lung metabolism of hamsters upon infection than the omicron variant. The levels of cytokines, including IL-6, IL-1β, IL-10, IFN-γ, tumor necrosis factor-α (TNF-α), and colony-stimulating factor (CSF), gradually increase with the severity of COVID-19 and play a crucial role in the immune response to SARS-CoV-2 infection ( 10 , 32 ). Thus, the mRNA levels of cytokines were examined to gain insights into their role in the immune response to SARS-CoV-2 delta and omicron variants . Most cytokine levels within the lung tissue were elevated after infection with the delta and the omicron variants, consistent with previous studies. In particular, we observed increase in the levels of cytokines known to contribute to cytokine storms as IL-1β, IL-6, IL-12A, IL-12B, IFN-γ, and TNF-α as well as various chemokines and CSFs upon infection with the COVID-19 variants . Moreover, these alterations were more notable in the delta group than in the omicron group. Next, a correlation analysis was conducted to explore the association between metabolites and genes involved in infection-induced altered metabolic pathways and all cytokines changed after infection . To visualize and interpret the modulation of metabolic pathways in relation to metabolic and transcriptional changes in the immune response, we created integrated metabolic network diagrams based on the correlation analysis of cytokines, metabolites, and genes for both the delta and omicron variants . For the delta variant, the network showed predominantly strong positive correlations among cytokines, metabolites, and transcripts . In particular, arginine exhibited positive correlations with major proinflammatory cytokines, such as CCL4 and CCL5. And proline and GSH were positively correlated with IL-12B . Additionally, oxidized glutathione (GSSG) and SAM were positively correlated with CCL8, while taurine exhibited a negative correlation with CXCL17 . Regarding transcriptome profiles, strong positive correlations were observed between genes and cytokines, mirroring the correlations between metabolites and cytokines . Genes related to arginine biosynthesis, such as Got1l1, Otc, and Asl, exhibited positive correlations with most cytokines. Notably, Asl showed a significant positive correlation with cytokines from the TNF and transforming growth factor-beta (TGF-β) families, while Arg1 and Otc showed negative and positive correlations with IL-12B, respectively. In GSH metabolism, Gpx1 and Pgd exhibited a negative correlation with IL-1β, while Anpep showed positive correlations with TNFAIP8L2, TNFSF12, TNFSF13b, and TGF-β1. Additionally, Lap3 was positively correlated with CCL12 and IL-18bp, and Gstt3 exhibited a positive correlation with CCL5. In tryptophan metabolism, Ido1 and Kynu showed positive correlations with CXCL10, TNFSF12, and TGF-β1, while Tdo2, Inmt, and Aldh7a1 exhibited positive correlations with IL-12B. For the omicron variant, the network primarily showed negative correlations . Specifically, all metabolites exhibited negative correlations with CCL5, CCL8, TNFAIP8L2, and CSF1, but showed positive correlations with XCL1 . No significant correlations were observed between genes related to arginine biosynthesis and cytokines for the omicron variant . In glutathione metabolism, Ggt1 and Pgd were negatively correlated with TNFSF10, while Gpx1 exhibited a positive correlation with TNFAIP8L2. Additionally, Anpep showed negative correlations with CCL5, CCL8, and CSF1. In tryptophan metabolism, Ido1 and Kynu showed negative and positive correlations with TNFSF10, respectively, while Cyp1a1 exhibited negative correlations with CCL5 and CSF1.These results indicate that SARS-CoV-2 variant infection triggers an inflammatory response associated with arginine biosynthesis, glutathione metabolism and tryptophan metabolism in the lungs of hamsters by modulating metabolite and transcript levels, and the delta and omicron variant viruses exert distinct inflammatory responses on hamster lung tissue, as evidenced by different correlations with cytokines. In this study, we investigated a comprehensive molecular mechanism in hamster lung tissue infected with delta and omicron SARS-CoV-2 variants by integrating metabolomics and transcriptomics. Following viral infection, arginine biosynthesis, GSH metabolism, and tryptophan metabolism were concurrently regulated at both the metabolic and genetic levels in lung tissue. Importantly, these metabolic pathways were notably associated with the production of inflammatory cytokines. Interestingly, the delta variant induced a stronger impact on lung metabolism and inflammatory responses compared to the omicron variant, according to metabolic profile patterns , levels of metabolites and genes , and changes in cytokine levels ( Supplementary Table S3 ). Additionally, these metabolic alterations were reflected in the serum, emphasizing the systemic impact of the virus on various metabolic processes. Viruses can influence host metabolic processes and induce physiological dysfunction ( 33 ). Understanding the pathophysiology of SASR-CoV-2 through the elucidation of molecular mechanisms via metabolomics and transcriptomics, as well as exploring metabolic interventions as novel therapeutic strategies, may contribute to the prevention and treatment of COVID-19. Hence, this study can provide potential molecular targets for therapeutic exploration in the quest for new drugs targeting the host pulmonary immune response following infection with delta and the omicron variants. In this study, an increase in arginine synthesis was observed in both the delta and omicron variant viruses. Arginine serves as a substrate for the generation of nitric oxide (NO), which is a signaling molecule in inflammatory responses. Previous studies reported decreased levels of arginine and a dysregulated urea cycle in plasma from patients with severe COVID-19 ( 34 , 35 ). Within the urea cycle, arginine is converted to ornithine and then recycled back to arginine via the enzymes Otc, Ass1, and Asl ( 36 ). Therefore, the increase in arginine levels can be derived from ornithine, as indicated by the upregulation of enzymes such as Arg1, Ass1, Otc, and Asl within the urea cycle. The alterations in arginine biosynthesis could be attributed to the actions of the urea cycle toward enhancing the reduction of elevated NO levels induced by the inflammation triggered by infection. Interestingly, some studies have suggested that arginine supplementation therapy in COVID-19 patients could improve immune function and reduce inflammation ( 34 , 37 – 39 ). Additionally, targeting arginine depletion by regulating arginine biosynthesis enzymes, aiming to inhibit viral replication may present a potential therapeutic strategy for the treatment of COVID-19 patients ( 36 ). Previously, a decrease in the levels of GSH along with an increase in the levels of GSSG was observed after coronavirus infection ( 40 ) indicating enhanced intracellular free radical generation and increased oxidative stress. Lung tissue functions as a reservoir for cellular thiols, primarily in the form of GSH. Viral infections deplete GSH and disrupt the redox balance in lung tissue, inducing cellular stress with lung damage ( 9 ). In patients experiencing hypoxemia due to SARS-CoV-2 infection, a reduction in serum cysteine has been reported, consistent with our research findings ( 41 ). Furthermore, we observed a significant increase in the levels of cystine, Ggt1 and Lap3. When GSH levels within the lung tissue are maintained, cystine from outside the cells enters and undergoes reduction to cysteine inside the cells ( 41 , 42 ). The decrease in GSH levels due to viral infection is anticipated to result from a reduction in serum cysteine levels required for GSH synthesis and the inhibition of the conversion of cystine to cysteine in lung tissue, leading to the accumulation of cystine. Thus, this study suggested that the alteration in GSH metabolism during SARS-CoV-2 variant infection can serve as an indicator of how the coronavirus affects oxidative stress and contributes to lung damage. In tryptophan metabolism, kynurenine, primarily known as an inflammatory marker, was significant enriched, along with a notable decrease in tryptophan levels after delta variant infection. Additionally, increased expression of genes such as tryptophan 2,3-dioxygenase 2 (Tdo2) and indoleamine 2,3-dioxygenase 1 (Ido1) was observed, indicating the enhancement of kynurenine synthesis after delta infection. Previous studies reported that kynurenine and tryptophan are associated with COVID-19 severity ( 35 , 43 , 44 ). Furthermore, Kynu and Ido1, which are involved in tryptophan metabolism, are upregulated during coronavirus infection. In particular, the reduction in tryptophan levels due to the action of Ido1 has long-term immunosuppressive effects ( 45 ). Consequently, our findings suggest that the enhancement of kynurenine synthesis represents a distinct inflammatory response in the lung tissue following infection with the delta variant. Interestingly, our findings are consistent with those of a previous human study. Li et al. found significant up-regulation in arginine metabolism and the urea cycle as well as tryptophan metabolism in plasma samples obtained from omicron patients compared to healthy controls ( 46 ). Notably, disruption of the urea cycle was observed, with a significant increase in ornithine cycle-related metabolites such as N2-acetyl-L-ornithine and asparagine, which were associated with cytokine storm. Additionally, these findings suggested that homoarginine and ornithine play a role in liver detoxification ( 35 ). Therefore, we suggested the potential for clinical application of SARS-CoV-2 research using the hamster model. The networks of cytokines and metabolic pathways suggested the presence of an inflammatory response and immune activation due to delta and omicron infection. Numerous studies have reported increased levels of inflammatory cytokines in COVID-19 patients, which supports our findings ( 32 ). Coronaviruses infect the respiratory tract and trigger a cytokine storm characterized by the production of inflammatory cytokines such as IL-1, IL-6, IL-8, IL-12, TNF-α, and other chemokines. This excessive release of inflammatory cytokines causes a rapid increase in cytokine levels in the bloodstream, leading to systemic inflammation. As a result, it can cause not only lung damage but also multiorgan failure, which is closely related to the severity of the disease. In patients with severe COVID-19, a high correlation was observed between circulating inflammatory cytokines, such as IL-6, CXCL10 (IP-10), and CSF1 (M-CSF), and arginine metabolism as well as tryptophan metabolism ( 43 ). Arginine is closely associated with inflammatory responses due to its essential role in T-cell activation, regulating both innate and adaptive immunity ( 47 ). These results reveal a strong correlation between TNF family cytokines and transcripts related to GSH metabolism, suggesting a potential link between the release of inflammatory cytokines and oxidative stress. The release of these inflammatory cytokines can potentially induce damage to lung tissue ( 48 ). Tryptophan metabolism is known to have the strongest correlation with IL-6 ( 43 , 49 ). Additionally, TNF-α, IL-6, and IL-1β induce elevated Ido1 expression in the context of immunosuppression in lung cancer progression ( 50 ). In conclusion, this study can be considered a notable advance as it included a comprehensive approach involving metabolic and transcriptomic profiling in animal models, which is relatively unexplored in the context of SARS-CoV-2 and its variants. We suggest that arginine biosynthesis, GSH metabolism and tryptophan metabolism are key metabolic pathways, shedding light on their relationship with the pulmonary immune response to both the delta and omicron infections. Furthermore, these pathways could be potential targets for therapeutic interventions aimed at mitigating the impact of these two SARS-CoV-2 variants. Overall, this study demonstrates that metabolic profiling with transcriptomic profiling is a valuable tool for exploring the immunometabolic responses associated with infectious diseases. | Study | biomedical | en | 0.999996 |
PMC11697606 | During the last decade, Bayesian geostatistical models have increasingly been used to determine spatio-temporal patterns of malaria risk, capture the effects of control interventions, and identify environmental and socioeconomic factors that are related to changes in the distribution of malaria risk . In most low- and middle-income countries, the data used to fit geostatistical models are mainly collected by national households surveys such as the Demographic and Health Surveys (DHS) and the Malaria Indicators Survey (MIS) . A two-stage sampling design was used to select survey clusters and households within clusters. The clusters included typically around 25 households per cluster and were geo-referenced according to their centroid. However, to ensure confidentiality of the health status of the enrolled individuals, the longitude and latitude of the cluster centroids were randomly jittered (displaced) from their original positions within a radius of 0 to 10 km according to the type of location (rural / urban) . Some studies have either assessed or mitigated the influence of imprecise geographical locations on model fit . In particular, studies on jittering DHS data have investigated the impact of spatial displacement on the estimates of the effects of distance-based covariates such as proximity to health services or areal covariates such as poverty measures defined in areas around a cluster location. These studies have been conducted in the field of HIV infection and using simulated and real data to assess the potential effects of location shift on model parameter estimates. However, within the Bayesian geostatistical modelling framework, studies assessing the effects of the cluster displacement on the pixel-level predictions of disease risk such as malaria and on the estimates of the covariates, for example climatic factors or control intervention effects are rather lacking. Our study assessed the influence of jittering of cluster locations on geostatistical model-based malaria risk estimates at high spatial resolution and on the estimates of the control interventions effects. A large simulation study using the jittered locations was carried out based on the MIS cluster locations and the random displacement procedure of DHS. Bayesian geostatistical models were applied on the simulated data and the results were compared with the non-jittered data. The Cameroon Malaria Indicator Survey (MIS) of 2011 was nationally representative and funded by the Global fund to fight AIDS, Tuberculosis and Malaria with the aim to collect malaria indicators additional to those in DHS and to compare the overall malaria parasite prevalence obtained by the MIS and DHS data . The MIS was conducted in 257 clusters randomly selected out of the 580 clusters of the Cameroon DHS 2011 and involved 6040 households and 4939 children aged between 6 and 59 months . Rapid Diagnostic Tests (First Malaria Response Antigen) were used for malaria screening of children with the approval of adults in charge . Apart from the malaria parasite data, the survey collected information on malaria interventions and socio-economic status proxies. Fig. 1 : Observed malaria parasite risk in children under 5 years at 257 MIS locations. Fig. 1 Data on malaria interventions was processed to create the following intervention coverage indicators as proposed by the Global Malaria Action Plan and Roll Back Malaria monitoring and evaluation group: (a) proportion of children in the households who slept under an insecticide treated-net (ITN) the night before the survey, (b) proportion of households in the cluster with at least one ITN, (c) proportion of households in the cluster with one ITN per two persons, (d) proportion of population with access to an ITN in their household. Adherence to the health system was calculated by the proportion of children with fever who sought treatment at hospital, tested and treated with the recommended anti-malaria drugs (Artemisinin-based combination therapy) during the last two weeks . One hundred datasets were generated from the original MIS data, each with randomly jittered cluster locations from the MIS coordinates according to the jittering algorithm used by the DHS program. In particular, clusters in urban areas were randomly displaced within a radius of 2 km; whilst 99 % of those in rural areas were shifted within a radius of 5 km from their original locations. The remaining 1 % of rural clusters were displaced up to a radius of 10 km, as these clusters remained sparsely populated . The simulated data differed from the MIS data in the cluster coordinates. The prevalence, intervention and socio-economic information were maintained the same as at the original locations. Bayesian geostatistical binomial regression models were fitted on the malaria parasite data (MIS and simulated ones) aggregated at cluster locations (See Additional file 1). The models incorporated geostatistical variable selection to identify the most important climatic and environmental covariates including their functional forms (i.e. continuous or categorical). The categorical covariates were derived by analyzing the relationship between malaria cases and continuous climatic predictors. The cut-off points were validated using linear regressions. In particular, a categorical indicator was created from each climatic predictor, taking the values 0, 1 and 2 which corresponds to the exclusion of the predictor from the model or its inclusion in continuous or categorical form, respectively (See Additional file 2). It was assumed that the indicator arose from a multinomial distribution with probabilities defining the variable-specific exclusion/inclusion probabilities (in continuous/categorical forms) in the model. A threshold of 50 % was considered for the probability of inclusion (i.e. posterior inclusion probability) into the predictive geostatistical model . The predictive performance of the models obtained from each simulated dataset was evaluated using the log predictive score comparing model-based predictions at the MIS locations with the observed MIS survey data . Bayesian kriging as described in the additional file 1 was separately applied on the observed data as well as on the simulated data with the best and least predictive performance (i.e. maximum and minimum log predictive score, respectively) namely Model 1a (MIS data), Model 1b (simulated data with the best predictive performance) and Model 1c (simulated data with the worst predictive performance). For each of the above models, a gridded surface of malaria parasite risk was estimated over 117,192 cells/pixels of 2 × 2 km 2 spatial resolution covering the country. To assess the effect of jittering on individual level covariates such as the ITN coverage indicators, geostatistical Bernoulli models were fitted on individual level data obtained from the observed MIS and the simulated data. As described above, three models were fitted i.e. Model 2a (applied to MIS data), Model 2b and 2c (applied to simulated data) with the best and worse predictive ability, respectively. We implemented geostatistical variable selection to identify the most important ITN indicators . The individual level models were adjusted for the confounding effects of the climatic predictors selected by the corresponding cluster-level model and the socio-economic proxies. Due to high correlation among the ITN indicators, only one indicator was allowed into the model. Covariates were statistically important when the corresponding Bayesian Credible Interval (BCI) did not include the one in the odds ratio scale, so the covariates were statistically significant if they did not include 0 in their BCI. Computation was performed on a dual processor workstation (2 × 2.6 Ghz, 128GB RAM). OpenBUGS version 3.2.3 (Imperial College and Medical Research Council, London, UK) was used for Bayesian model fit and prediction . Data management and analysis were carried out in R statistical software . Convergence was assessed by the Geweke statistic, visual inspection of the traceplots and achieved in less than 200,000 iterations . Maps were drawn in ArcGIS version 10.2.1 ( http://www.esri.com/ ) . The overall malaria prevalence estimated by the MIS data was 33 %. In the rural areas, 43 % of children were tested positive, meanwhile this proportion was 19 % in urban areas. The most affected areas were located in the North, East and South regions of Cameroon with a malaria risk of 57.2 %, 56.5 % and 50.9 %, respectively. The proportion of mothers that had attended university was 6.7 % and those without any education were 23.3 %. The proportion of households with at least one ITN was 46 %. Only 9 % of the population had access to an ITN in their household and 12 % of children with fever who sought treatment at hospital received a recommended Artemisinin-based combination therapy (ACT) during the last two weeks. Sixty-eight percent of households were most poor or poor. The geostatistical variable selection performed at the cluster level model (Model 1a) fitted on the original MIS data identified NDVI and altitude (in continuous form), EVI and DWB (in categorical form) and the presence of forest (binary) as the most important predictors of parasitaemia risk ( Table 1 ). Estimates of the final geostatistical model ( Table 2 ) indicated that the malaria parasite risk was positively associated with NDVI, EVI, and presence of forest, and negatively associated with altitude. The individual level model (Model 2a) fitted to the original MIS data selected the proportion of households with 1 ITN per 2 persons as the most important predictor ( Table 1 ). The association of this predictor with the parasitaemia risk was negative as shown in the final geostatistical model ( Table 2 ). Table 1 Posterior inclusion probabilities (%) of the climatic predictors and intervention coverage indicators based on the geostatistical variable selection applied to the three datasets i) observed MIS (cluster-level Model 1a, individual-level Model 2a) ii) simulated data with the best predictive ability (Model 1b, Model 2b) and iii) simulated data with worst predictive ability (Model 1c, Model 2c). Inclusion probabilities of the selected predictors are in bold. Table 1 Model Predictor MIS data Simulated data with best predictive ability Simulated data with worst predictive ability Excluded Continuous Categorical Excluded Continuous Categorical Excluded Continuous Categorical Model 1a, 1b, 1c: Cluster level RFE 36 18 46 56 18 26 26 10 64 NDVI ⁎ 12 83 5 2 98 0 20 58 22 LSTD 55 23 22 59 19 22 60 17 23 EVI ⁎ 16 17 67 0 0 100 12 42 46 DWB ⁎ 23 25 52 33 26 41 10 14 76 Altitude ⁎ 1 96 3 3 79 18 2 98 0 Forest ⁎ 34 – 66 41 0 59 36 0 64 Savannah 69 – 31 68 0 32 74 0 26 Cropland 72 – 28 81 0 19 75 0 25 LSTN 54 29 17 46 54 0 24 10 66 Model 2a,2b,2c: Individual level % of population access to an ITN in their household 84 16 – 87 13 – 82 18 – % of households with at least one ITN 100 0 – 84 16 – 98 2 – % of households with one ITN per two persons ⁎ 47 53 – 42 58 – 77 23 – % of children slept under ITN previous night ⁎ 69 31 – 87 13 – 43 57 – % of children with fever who received recommended anti-malaria drugs (ACT) 73 27 – 76 24 – 70 30 – ⁎ : the climatic or intervention indicator is selected. Table 2 Estimates (posterior median and 95 % BCI) of the geostatistical model parameters based on the cluster level (Models 1a, 1b, 1c) and the individual level models (Models 2a, 2b, 2c). Table 2 Factor MIS data Simulated data with the best predictive ability Simulated data with the worst predictive ability Model 1a Model 2a Model 1b Model 2b Model 1c Model 2c OR (95 % BCI) OR (95 % BCI) OR (95 % BCI) OR (95 % BCI) OR (95 % BCI) OR (95 % BCI) 0–30 mm 1 1 RFE 30–60 mm 0.83(0.24; 2.49) 2.54(0.86; 7.97) >60 mm 0.36(0.08; 1.65) 1.07(0.30; 3.98) NDVI 1.55 (1.12; 2.12) 1.33(0.97; 1.82) 1.63(1.18; 2.29) 1.31(0.96; 1.82) 1.6 (1.23; 2.09) 1.32(1.03; 1.70) EVI <0.21 1 1 1 1 0.21–0.38 1.90 (1.03; 3.51) 1.38(0.82; 2.33) 1.95(1.03; 3.67) 1.33(0.79; 2.22) >0.38 1.25 (0.51; 3.02) 0.92(0.41; 2.1) 1.24(0.49; 3.08) 0.9(0.40; 1.98) DWB <70 m 1 1 1 1 ≥ 70 m 1.82 (1.005; 3.45) 1.60(0.90; 2.86) 1.98(1.09; 3.95) 1.74(0.98; 3.17) Altitude 0.39 (0.26; 0.57) 0.37(0.25; 0.53) 0.53(0.3; 0.91) 0.42(0.25; 0.72) 0.38(0.24; 0.6) 0.35(0.23; 0.54) Forest No 1 1 1 1 1 1 Yes 1.55 (1.002; 2.39) 1.17(0.77; 1.79) 1.49(0.95; 2.34) 1.18(0.77; 1.81) 1.46(0.93; 2.28) 1.06(0.68; 1.62) LSTN_continuous 1.37(0.83; 2.29) 1.19(0.76; 1.89) 0–14 1 1 LSTN_categrical 14–18 1.82(0.29; 18.01) 2.06(0.63; 14.04) >18 1.45(0.21; 16.22) 1.87(0.53; 13.42) Gender Female 1 1 1 Male 0.99(0.86; 1.15) 0.99(0.86; 1.15) 1(0.87; 1.15) Area type Rural 1 1 1 Urban 0.55(0.38; 0.80) 0.54(0.38; 0.78) 0.56(0.39; 0.81) Wealth Index Most poor 1 1 1 Very poor 0.60(0.46; 0.76) 0.6(0.47; 0.76) 0.6(0.47; 0.76) Poor 0.66(0.49; 0.89) 0.66(0.48; 0.88) 0.65(0.48; 0.87) Less poor 0.46(0.32; 0.66) 0.45(0.31; 0.65) 0.46(0.32; 0.66) Least poor 0.39(0.25; 0.61) 0.39(0.25; 0.61) 0.38(0.25; 0.60 Education level of mothers No education 1 1 1 Primary 1.15(0.92; 1.43) 1.14(0.91; 1.42) 1.15(0.92; 1.44) Secondary 0.92(0.70; 1.22) 0.92(0.7; 1.21) 0.93(0.7; 1.22) University 1.03(0.57; 1.84) 1.03(0.56; 1.83) 0.98(0.53; 1.73) Age 0–1+ 1 1 1 1–2 1.31(0.96; 1.77) 1.32(0.97; 1.79) 1.34(0.99; 1.81) 2–3 2.29(1.70; 3.10) 2.30(1.71; 3.10) 2.32(1.73; 3.12) 3–4 2.57(1.90; 3.48) 2.59(1.92; 3.49) 2.60(1.93; 3.51) >4 3.49(2.62; 4.65) 3.38(2.51; 4.54) 3.40(2.54; 4.57) % households with 1 ITN per 2 persons 0.16(0.05; 0.47) 0.14(0.05; 0.44) % of children with fever in the last two weeks who received ACT 0.35(0.18; 0.66) Spatial parameters Posterior median Posterior median Posterior median Posterior median Posterior median Posterior median (95 % BCI) (95 % BCI) (95 % BCI) (95 % BCI) (95 % BCI) (95 % BCI) Spatial variance 1.81 (1.24; 2.92) 1.62(1.10; 2.76) 1.88(1.22; 3.6) 1.64(1.17; 2.5) 1.87(1.29; 3.13) 1.64(1.1; 2.8) Range (km) 1 154.8 (89.50; 292.96) 188.09(100.35; 353.63) 111.43(62.26; 217.13) 214.98(120.37; 487) 143.29(75.72; 301.36) 158.87(94.18; 321.1) 1: Smallest distance that spatial correlation is <5 %. Geostatistical variable selection applied to each of the simulated data identified 26 sets of climatic and environmental predictors that were included in the selected model ( Table A.2 in Appendix). Two simulated models among one hundred had the highest posterior inclusion probabilities equal to 19 % and 18 %. Both models included NDVI and altitude (continuous), EVI (categorical) and forest presence. Furthermore, the DWB was included in the second most frequent model (with inclusion probability of 18 %). The estimates of the effects of climatic predictors based on the selected models were overlapping between the simulated datasets . Altitude (in continuous form) was always statistically important and negatively associated with the parasitaemia risk. NDVI was positively associated and statistically important for the malaria parasite risk in most simulated data. Malaria parasite risk had a positive and most often statistically important relationship with the presence of forest, EVI and DWB. On the other hand, the importance of RFE, LSTN or LSTD on parasitaemia risk varied with the data. Fig. 3 Effects (posterior median, 95 % BCI) of the categorical covariates estimated by the selected geostatistical model for each simulated data (1−100) ordered according to the logarithmic predictive score values and of the data 0 corresponding to the observed data. Fig. 3 Fig. 4 Effects (posterior median, 95 % BCI) of continuous covariates estimated by the selected geostatistical model for each simulated data ordered according to the logarithm predictive score values of the models. Fig. 4 Table 1 presents posterior inclusion probabilities of the selected models based on the simulated data with the best (Model 1b) and worst predictive performance (Model 1c) and on the observed MIS data (Model 1a). The difference between Model 1b and Model 1a was that the former included LSTN and excluded DWB. Model 1c included RFE, LSTN which were not in Model 1a and excluded EVI. Regarding the selection of intervention indicators from the individual-level model, the Model 2b with the best predictive performance among the simulated data gave similar results to the true model (Model 2a). The model with the worse performance among simulated data (Model 2c) was not able to capture the statistically important effect of the malaria intervention indicator (i.e. proportion of households with 1 ITN per 2 persons and the proportion of children who slept under an ITN the previous night). The direction of the effects was estimated by the Bayesian geostatistical models ( Table 2 ). The global spatial patterns of disease risk in the East, North and Coastal parts of the country were well captured by the three cluster-level models. Maps drawn on the same scale clearly indicated similar geographic patterns predicted by the three models (Model 1a, Model 1b, and Model 1c), therefore the models with best and worst predictive performance were able to capture the disease risk distribution of the MIS dataset. However, the prediction uncertainties of Model 1b and Model 1c over the gridded surface were greater than the ones obtained from Model 1a . Fig. 5 : Malaria parasite risk estimates (median of predictive posterior distribution) among children less than 5 years, obtained from i) Model 1a (left), ii) Model 1b (center) and iii) Model 1c (right). Fig. 5 Fig. 6 : Predictive uncertainty (standard deviation of predictive posterior distribution) of estimated parasite risk among children less than 5 years, obtained from i) Model 1a (left), ii) Model 1b (center) and iii) Model 1c (right). Fig. 6 The spatial variance estimates and uncertainty obtained from the simulated data with the worst and best predictive abilities were close to the ones produced by the observed MIS data. The residual spatial correlation estimated by the different clusters and individual models remained high, indicating the presence of unmeasured spatially structured factors related to the geographic distribution of the parasitaemia risk. This study is the first to assess the effects of jittering of DHS/MIS cluster locations on the estimates of the geographical distribution of malaria risk and of the intervention effects obtained by Bayesian geostatistical modelling . A large number of jittered datasets were simulated from real data and geostatistical variable selection was applied to determine the impact of jittering on the model formulation. Different subsets of climatic factors in the simulated data were identified as important predictors of malaria risk. However, in 18 % of the datasets, the models included the same predictors with the fitted model obtained by the observed MIS data, while the model with the highest posterior inclusion probability (19 %) could not capture the statistically importance of the DWB predictor. NDVI and altitude were selected in more than 95 % of the simulated data and DWB was identified in 64 %. Furthermore, the jittering of cluster locations had an influence on the selected functional form of the climate predictors (continuous/categorical). These results showed that spatial displacement can influence the risk factor analysis and the estimation of the effects of malaria interventions on the disease risk. Similar findings have been reported for distance-based covariates in the study presented by Warren et al. . The direction of the relation between parasite risk, NDVI and altitude remained the same in all simulated data. In particular, the continuous form of NDVI was statistically important in most of simulations and as expected, the altitude was always negatively related and statistically important to the malaria parasite risk. Those associations were confirmed with the estimates obtained from the true dataset and findings from others studies . The jittering did not affect the direction of the relationship between malaria risk, NDVI and Altitude. However, the jittering had an effect on the uncertainty estimates of the covariate effects and therefore on their statistical importance. Warren et al. have also concluded that displacement of clusters led to an increase in the estimated uncertainty of the regression coefficient . In addition, Cressie et al. proved that in the presence of spatial location error, the prediction estimates and regression coefficients were influenced . According to the simulation, the proportion of households with 1 ITN per 2 persons or the proportion of children who slept under an ITN the previous night before the survey were identified as important predictors of the individual-level malaria risk model. Variable selection applied on the intervention coverage indicators revealed that jittering influenced their posterior inclusion probabilities into the model and therefore the inference about the effects of malaria interventions. This result could be due to the confounding effects of climatic predictors. All the wealth index categories were statistically important and negatively associated to the malaria parasite risk in the true model. The three individual level models showed that, posterior parameter estimates of socioeconomic factors were relatively stable, irrespective of the model. The socioeconomic factors were related to the individual risk rather than the malaria prevalence at the location level, and thus estimates of the socio-economic effects were not much influenced by the displacement of the clusters. Similarly, demographic factors also related to the individual were not affected by the jittering. The gender was not statistically important and a gradient of risk was noted in the age groups as already supported by other studies . The effect of the selected intervention indicator was statistically important and negatively associated to the parasitaemia risk regardless of the simulated dataset. Similar to the socioeconomic status and demographic factors, intervention effects were more likely to be higher at the individual and household than the community; therefore the changes of cluster locations did not influence the direction of the relationship between ITN coverage indicators and malaria parasite risk after adjusting for socioeconomic factors. The BCIs of the spatial correlation parameters of the true, best and worst models were overlapping and their spatial variances were not dramatically changed. Spatial range parameters depend on the cluster locations and were sensitive to the distance between locations. The individual level models overestimated spatial correlation especially for the model having the worst predictive ability. The change of cluster locations could lead to a misspecification of the spatial dependence structure of the disease risk . The geographical patterns obtained from the simulated data with the highest and lowest predictive performance were similar to the ones obtained from the true data. The relationship between malaria risk and the climatic factors was rather stable within the same ecological zone and therefore was not strongly influenced by the jittering of the locations. This result was expected since several studies showed a local interdependency between climatic factors within small buffer zones. . Moderate spatial modifications in the geographical positions of the clusters surveyed might have little influence on the estimation of the spatial patterns of malaria risk in Cameroon, especially when the climatic and environmental conditions are similar within the radius of the random displacement of locations. Nevertheless, the jittering of cluster locations has an impact on the selection of climatic predictors used to estimate the disease risk at high geographical resolution and could affect the interpretation of the relationship between malaria parasite infection with environmental and climatic factors that support the disease transmission. | Study | biomedical | en | 0.999999 |
PMC11697610 | Esophageal cancer is the eighth most common cancer worldwide and the sixth leading cause of cancer-related deaths . Statistics show that the 5-year survival rate is only 15–20 %, leading to over 500,000 deaths annually . By 2040, the global incidence of esophageal cancer is expected to reach 987,723 new cases, with 914,304 deaths . Current treatment modalities include surgical resection, radiation therapy, chemotherapy, and palliative care . Surgical resection of esophageal tumor tissues usually necessitates removal of the larynx, impairing vocal function and potentially leading to postoperative complications . Both radiation and chemotherapy have the widespread use, but lack selectivity for tumor cells and can result in severe side effects such as radiation pneumonitis, pleural effusion, and pericardial effusion [ , , ]. Consequently, there is an urgent need for novel therapeutic approaches aimed at improving survival rates and quality of life for esophageal cancer patients. In this context, photodynamic therapy (PDT) has emerged as a promising innovative treatment due to its higher selectivity and fewer systemic adverse effects [ , , , , , , , , , , ]. However, the conventional use of implanted optical fibers for PDT is plagued by complex equipment, cumbersome procedures, and increased patient discomfort, highlighting the need for more portable and effective light delivery systems for the esophageal cancer patients [ , , ]. Recently, some research groups have explored small devices for PDT, embedding light-emitting components within the body and powering them through wireless method to achieve effective internal illumination of living tissues [ , , , , , , , , ]. For esophageal cancer patients, tumor progression often leads to esophageal stenosis, causing eating difficulties [ , , ]. Esophageal stent placement is a widely used minimally invasive intervention that rapidly relieves dysphagia and obstruction symptoms, improving nutritional status and being widely employed in clinical treatments [ , , , ]. Therefore, the integration of small PDT unit and commonly used esophageal stricture-relieving stent may be a suitable system for the novel internal esophageal tumor treatment . Furthermore, it is also needed to dynamically adjust the position of the PDT unit based on the tumor's status in the esophageal to achieve efficient and precise treatment. The tumors have proliferative and metastatic properties, and during treatment, tumor progression can enlarge the lesion area or cause metastasis [ , , , , , , , ], weakening the therapeutic effect on regions distant from the light source. Soft robotics research has shown significant potential in the medical field, with flexible structures and excellent deformability suitable for operations in unknown and unstructured environments [ , , , ]. Developing soft actuators is a core task in soft robotics research, primarily responsible for driving or controlling the systems . Among different driving mechanisms, pneumatic soft actuator is simple in structure, cost-effective, highly efficient, quick in response, and environmentally friendly . Previously, our team used electrochemical pneumatic soft actuator to perform in vivo surgeries, e.g. inducing eye shape changes for the treatment of high myopia . Consequently, the pneumatic soft actuator for moving the PDT unit is suitable to integrate into the system. Here, we propose a wireless, battery-free, multifunctional therapeutic system that integrates a PDT module and an electrochemical pneumatic soft actuator into an esophageal stricture-relieving stent. This system not only alleviates esophageal stenosis symptoms and rapidly improves swallowing difficulties but also achieves precise and targeted treatment of tumor cells. The system comprises an esophageal stent, two piezoelectric transducers, an electrochemical pneumatic soft actuator, a micro light-emitting diode (μ-LED), flexible circuits, and biocompatible packaging. The μ-LED serves as the treatment module, providing a light source for PDT to activate photosensitizers that produce cytotoxic reactive oxygen species (ROS). The electrochemical pneumatic soft actuator, consisting of an electrolysis chamber and a long soft silicone tube track, houses the treatment module inside the track, allowing unidirectional movement along the track, with the entire actuator spirally wound inside the esophageal stent. When the tumor grows or metastasizes, the actuator can move the μ-LED to the new tumor site. The piezoelectric transducers convert external ultrasound waves into electrical energy, powering the therapeutic and actuation processes. These two processes are independently controlled by two piezoelectric transducers, each selectively responding to different external ultrasound frequencies, allowing independent and non-interfering operation. This innovative therapeutic approach holds promise for providing more effective and personalized treatment options for esophageal cancer patients, offering new avenues and methods for clinical intervention. Fig. 1 illustrates the structure and operation of the wireless, battery-free, and movable PDT system designed for esophageal tumor treatment. The system is installed at the site of esophageal stenosis caused by tumor growth, as shown in Fig. 1 A. Fig. 1 B depicts the overall structure of the system, which integrates a commercial nitinol esophageal stent as the framework, a therapy module that provides the PDT light source, and an electrochemical pneumatic soft actuator that supplies moving force and guidance for the therapy module. The wireless unit comprises two high-sensitivity lead zirconate titanate (PZT) piezoelectric transducers: PZT 1 and PZT 2. These transducers harvest energy from ultrasound waves and convert it into electrical power for the respective modules. The therapy module, consisting of PZT 2 and a μ-LED, utilizes the energy collected by PZT 2 to illuminate the μ-LED, providing the light source for PDT. The electrochemical pneumatic soft actuator comprises PZT 1, a control circuit, interdigitated electrodes, an electrolysis chamber, ionic solution, a track tube, and a piston. Using the energy harvested by PZT 1, the sophisticated configuration of the interdigitated electrodes and ionic solution induces electrolysis within the sealed chamber, generating bubbles that cause piston displacement, thereby controllably adjusting the position of the therapy module along the actuator track. The actuator is helically wound and adhered to the inside of the esophageal stent using a biocompatible flexible polymer (polydimethylsiloxane, PDMS). The helical structure ensures maximum coverage of the track within the stent, maximizing the area reachable by the therapy module. The entire system weighs 4.8 g and the integrated stent measures 7.8 cm in length. This compact and lightweight design facilitates minimally invasive implantation. Fig. 1 Overview of the esophageal stent for tumor treatment. (A) The stent installed in the affected area. (B) The structure of the stent. (C) Photograph showing various configurations of the actuator. (D) The sizes of PZT 1 and the therapy module, smaller than a grain. The width and gap of the interdigitated electrodes are 80 μm, finer than a syringe needle. (E) The actuator can be twisted and bent, demonstrating its capability to integrate into the esophageal stent. (F) The process of implanting the stent into the cancerous area. (G) The treatment process of the esophageal cancer using the movable PDT stent system. Fig. 1 Fig. 1 C shows the integrated design of the electrochemical actuator, which includes the control circuit, energy module, and interdigitated electrodes on a flexible circuit board. Fig. 1 D demonstrates that the volume of PZT 1 is smaller than a grain of rice, effectively minimizing the patient's sensation of a foreign object. The treatment module's compact size ensures frictionless movement within the actuator track. The interdigitated electrodes, fabricated using microfabrication techniques, have a width and gap of 80 μm, finer than a syringe needle. Fig. 1 E highlights the flexibility of the actuator's solution storage chamber, circuit, and track, which can be twisted many times without performance degradation, ensuring the maintenance of the helical structure within the stent. Fig. 1 F illustrates the implantation process of the system into the esophagus of an esophageal cancer patient. The system is folded and compressed into a sufficiently thin catheter, which is then entirely implanted into the cancerous region of the esophagus. Upon removal of the catheter, the stent rapidly expands and props the esophagus. Fig. 1 G outlines the operating procedure of the system. Ultrasound at 1 MHz is directed at PZT 2, activating it and lightning the μ-LED. The 660 nm red light irradiates the tumor area, inducing the production of ROS in the tumor cells, leading to tumor cell apoptosis. After treating this area, 680 kHz ultrasound is directed at PZT 1 to activate the actuator, moving and positioning the therapy module at the next tumor site. The process is repeated, treating each tumor site sequentially until all affected areas are cleared, after which the entire system is removed. Fig. 2 shows the characterization of the electrochemical pneumatic soft actuator. Fig. 2 A illustrates the exploded view and assembly process of the actuator. Given the excellent biocompatibility and flexibility of silicone , tubes of various diameters and lengths were selected to construct the actuator's transparent solution reservoir and control channels. Essential electronic components, including PZT 1, were soldered onto a custom-designed flexible circuit board, which was then encapsulated with PDMS to ensure biocompatibility for in vivo applications [ , , ]. PZT 1, a cylindrical component with a diameter of 3 mm and a height of 1 mm, operates at a resonance frequency of 680 kHz. Fig. 2 Structure and performance of the electrochemical actuator. (A) Exploded view and fabrication process of the actuator. (B) Workflow of the actuator in treating esophageal cancer. (C) Schematic of the actuator's energy harvesting circuit. (D) Simulation of 680 kHz ultrasound propagation. (E) Short-circuit current and open-circuit voltage peaks of PZT 1 at distances of 1 – 30 mm from the ultrasound source. (F) Current-voltage characteristics of the electrochemical actuator within the voltage range of 0 – 3 V. (G) Output power of the electrochemical actuator in different media after receiving ultrasound. (H) Volume of gas produced by the solution electrolysis. (I) Displacement of the therapy module caused by the actuator's operation. (J) Optical image of the therapy module displacement within 100 s. Fig. 2 To enhance electrochemical performance, we opted for interdigitated electrodes with smaller gaps and larger opposing surface areas. This design provides a stronger electric field for the electrolysis reaction: 2H₂O (liquid) → O₂ (gas) + 2H₂ (gas), thereby accelerating gas generation. Simultaneously, the gold electrodes, which exhibit stable chemical properties, ensure stable conditions for the electrolysis process. These interdigitated electrodes are sealed within a solution reservoir, printed on both sides of the circuit, and extend throughout the solution storage channel to guarantee complete immersion in the ionic solution. Following assembly, the ionic solution is injected into the storage tank, and a Vaseline piston is placed within the guide. To maintain sufficient conductivity, a 50 mmol/L NaOH solution was employed as the electrolyte . Sodium hydroxide (NaOH), a strong base, increases the concentration of OH⁻ ions, providing a higher availability of reactive ions at the electrodes. This facilitates current flow through the electrolyte, thus enhancing the actuator's electrochemical activity. Fig. 2 B shows the actuator's operation. A 680 kHz ultrasound source with a 70 % duty cycle activates PZT 1, which converts the ultrasound waves into electrical energy through the piezoelectric effect. The electrical signal is rectified and regulated before being transmitted to the interdigitated electrodes, inducing electrolysis in the sealed electrolysis chamber's ionic solution. This generates bubbles that push the piston in the track, positioning the therapy module near the tumor for precise treatment. If the tumor grows or metastasizes, reducing treatment effectiveness in distant areas, the actuator can reposition the therapy module to the new target location for efficient treatment. Fig. 2 C illustrates the circuit structure, where the rectifier bridge and capacitor provide a stable DC signal for the electrolysis of the ionic solution within the actuator. One Schottky diode in the rectifier bridge is replaced with a μ-LED, which serves as an indicator while maintaining rectification functionality. The dynamic process is shown in Movie S1. Fig. 2 D simulates the sound pressure propagation of 680 kHz ultrasound in water, which has acoustic properties similar to human tissue (Z_water = 1.48 MRayl, Z_tissue = 1.63 MRayl, average for human tissue). The simulation results indicate that ultrasound can easily transmit 5 cm through tissue with minimal attenuation, and the system can be remotely driven and collect stable ultrasound energy during operation. Fig. 2 E demonstrates the stable output of PZT 1 at different depths in various media, with the open-circuit voltage peak remaining around 10 V and the short-circuit current peak stabilizing around 15 mA, ensuring the device's reliable performance. The output of PZT 1 with a 70 % ultrasound duty cycle is shown in Fig. S1 (Supporting Information), and the current output with a 100 % duty cycle is shown in Fig. S2 (Supporting Information). Fig. 2 F shows the current-voltage characteristics of the actuator within a voltage range of 0–3 V. The device starts operating at 1.0 V, with the interdigitated electrodes in the actuator promoting the electrolysis reaction. To ensure adequate conductivity, a 50 mmol/L NaOH solution is used as the electrolyte. Fig. 2 G presents the actuator's power output at a depth of 10 mm in different media, with a power output of 14.9 mW in gel, 14.2 mW in tissue, and 12.7 mW in water. Fig. 2 H shows the volume-time relationship of gas production at room temperature, indicating that the device can stably provide a gas source during operation. This dynamic gas generation process is further illustrated in Supplementary Movie S2. Optical images also show significant bubble generation in the solution reservoir after 100 s. Fig. 2 I illustrates the displacement-time curve of the therapy module driven by the actuator's continuous operation. The actuator can consistently provide power, enabling the therapy module to move forward uniformly by 20 mm within 800 s. Fig. 2 J demonstrates the actuator's propulsion capability, moving the therapy module forward by one turn (45 mm) within 100 s. The dynamic process is shown in Supplementary Movie S3. All the materials used in the electrochemical pneumatic soft actuator are biocompatible, and the stable performance of the interdigitated electrodes, along with the actuator's excellent actuation capability, underscores its feasibility for in vivo operation, making future clinical translation possible. Fig. 3 characterizes the structure and performance of the treatment module. Fig. 3 A shows the structure and assembly process of the treatment module. A flexible circuit board is folded and soldered to the electrodes on both sides of PZT 2, with the other side connected to a μ-LED. The device is placed in the track of the actuator, resting solely on the outer end of the piston. The μ-LED (0.6 × 0.35 × 0.20 mm) and PZT 2 (1 × 1 × 0.6 mm, resonant frequency fc = 1 MHz) are small in size, ensuring the assembled module is compact and lightweight, allowing for easy movement within the actuator's track. Fig. 3 B illustrates the working process of the treatment module. The μ-LED is pushed by the actuator near the tumor. PZT 2 receives 1 MHz ultrasound and converts the acoustic energy into electrical energy, lighting up the μ-LED. The 660 nm red light illuminates the tumor injected with photosensitizer, performing PDT on the tumor. Fig. 3 C shows the circuit structure of the treatment module, where PZT 2 collects 1 MHz ultrasound, converts it into electrical energy, and transmits it to the μ-LED. Fig. 3 Structure and performance of the treatment module. (A) Exploded view of the treatment module. (B) Workflow of the treatment module in esophageal cancer therapy. (C) Energy harvesting circuit diagram of the treatment module. (D) Simulation of 1 MHz ultrasound propagation. (E) Open-circuit voltage output of PZT 2 at a 60 % duty cycle and 3 mm distance from the ultrasound source. (F) Short-circuit current and open-circuit voltage peak values of PZT 2 at distances of 1–30 mm from the ultrasound source. (G) Light power output of the μ-LED in the treatment module after receiving ultrasound in different media. (H) (I) Output performance of PZT 1 and PZT 2 at 680 kHz and 1 MHz, respectively, demonstrating that the actuator and treatment module operate without mutual interference. Fig. 3 Fig. 3 D presents a simulation of 1 MHz ultrasound propagation in water, indicating that ultrasound at this frequency can transmit with minimal attenuation through tissues, providing stable ultrasonic energy to the device. Fig. 3 E shows the real-time open-circuit voltage output of PZT 2 at a distance of 7 mm from the ultrasound source, with a peak value reaching 15 V, sufficient to light up the μ-LED. Fig. 3 F demonstrates that PZT 2 maintains stable output at various medium depths, with an open-circuit voltage peak around 15 V and a short-circuit current peak around 2.5 mA, ensuring stable operation at different depths. Fig. 3 G evaluates the light power emitted by the device in different media: 0.65 mW in gel, 0.58 mW in tissue, and 0.55 mW in water, providing a stable light source for PDT. The dynamic process of ultrasound activating the treatment module to emit light is shown in Supplementary Movie S4. Fig. S3 (Supporting Information) shows the transmittance of different wavelengths of light through the silicone track of the actuator. The transmittance of 660 nm light is 86 %, indicating that most of the light produced by the therapeutic module can be used for PDT. Additionally, Fig. S4 (Supporting Information) describes the penetration capability of 660 nm red light. As the thickness of the pork tissue increases (1, 2, 3, and 4 mm), the transmittance of red light decreases (4.8 %, 2.38 %, 1.3 %, and 0.8 %, respectively). When the thickness exceeds 5 mm, the transmittance drops to below 0.05 %. These results highlight the limited penetration ability of light through tissue, making it challenging for traditional external illumination to penetrate the tissue effectively. However, our wireless system overcomes this limitation, effectively delivering the PDT light dose to the target area. In terms of biological safety, we conducted further tests on the impact of the therapeutic module on local tissue . After operating continuously for 1 h, the muscle tissue containing the module showed a minimal increase of less than 2 °C (25.2 °C–27 °C), demonstrating that the system does not cause any thermal damage to the local tissue . Fig. 4 illustrates the efficacy of PDT treatment module in eradicating esophageal cancer cells, as well as the impact of light source distance on PDT effectiveness in vitro. Fig. 4 A illustrates the process of PDT conducted through the treatment module. The base layer comprises an ultrasound source with the treatment module positioned on ultrasound gel. When activated, the ultrasound triggers the μLED to illuminate, facilitating PDT. At the topmost layer, a cell plate containing the human esophageal squamous cell carcinoma (ESCC) cell line KYSE-150 (K150), pre-treated with the photosensitizer chlorin e6 (Ce6), is positioned for PDT. To assess whether the photosensitizer Ce6 exerts any effects on cells proliferation, we cultured the K150 with varying concentrations of Ce6 for 24 h. Our CCK-8 assay results confirmed that Ce6, even at a concentration of 32 μM, does not inhibit the viability of K150. . However, when Ce6 was present in conjunction with the μLED, PDT was effectively achieved. The efficacy of PDT was positively correlated with both the duration of illumination and the concentration of the photosensitizer . Specifically, at a Ce6 concentration of 16 μM and an illumination duration of 30 min, the cell viability of K150 was reduced to 23.66 %. Fig. 4 Significant cytotoxic impact of PDT on ESCC in vitro. (A) Schematic diagram of the PDT. (B) Cell viability of K150 under different concentrations of Ce6, n = 3. (C) The viability of K150 cells was assessed using the cell counting kit-8 (CCK-8) assay across various of Ce6 concentrations and exposure durations, n = 3. (D) The effect of PDT on the viability of K150 cells at varying treatment distances, n = 3. (E) WSI and microscopy imaging of Calcein-AM/PI staining in different treatment groups. Scar bars, 1000 μm for WSI and 50 μm for microscopy imaging. (F) Statistical analysis of dead cells proportion in different treatment groups, n = 3. Data are presented as mean ± SEM. Statistical analysis was conducted using ordinary one-way ANOVA with multiple comparisons, not significant (ns), P ≥ 0.05; ∗∗∗∗ P ≤ 0.0001. Fig. 4 To evaluate the impact of light source distance (LSD) on PDT efficacy, we further measured cell viability following treatment at varying distances. Results indicated a marked decrease in viability (to 9.63 %) when cells were positioned close to the light source. In contrast, when the LSD was increasing to 8 cm, the viability of K150 cells remained largely unaffected . Additionally, the Calcein-AM/PI staining method provided further insight into PDT-induced cytotoxicity in the K150 cell line. Whole slide imaging (WSI) of cell plates revealed a concentrated red fluorescence signal at the center of the light source, indicative of a higher cell death rate in this region. As the distance from the center increased, the red fluorescence signal diminished, while green fluorescence, marking viable cells, became more prominent. Quantitative analysis of fluorescence images confirmed that PDT induced cell death in over 88.94 % of cells, underscoring the effectiveness of PDT at close proximity to the light source. . During PDT, the cytotoxic agent singlet oxygen, produced via a type II photochemical reaction, is considered the primary mediator of PDT's biological effects [ , , ]. To detect singlet oxygen generation, we employed 1,3-diphenylisobenzofuran (DPBF) as a specific probe. A decrease in DPBF's relative absorbance indicates an increased rate of photodegradation, reflecting elevated ROS production. We selected porphyrin as a standard photosensitizer and compared it with Ce6 . Under identical PDT conditions, DPBF absorbance decreased significantly with Ce6, while the absorbance change was negligible with porphyrin, indicating that Ce6 continuously generates singlet oxygen over prolonged irradiation, whereas porphyrin produces only trace amounts. These results demonstrate that Ce6 has a higher singlet oxygen generation efficiency compared to porphyrin. Furthermore, UV–visible absorption spectra confirmed that Ce6 exhibits stronger absorption in the red-light spectrum than porphyrin, making it more suitable for red-light-activated PDT. To optimize light penetration depth, we selected a wavelength of 660 nm, as red light penetrates biological tissues more effectively than other wavelengths, such as ultraviolet and visible light. This allows it to reach several millimeters or deeper within tissue, enhancing the treatment effect. Furthermore, our implantable device effectively addresses the limitation of light penetration depth; by directly implanting the light source near the tumor, red light can reach areas inaccessible to external sources, thereby enhancing ROS generation. The efficiency of singlet oxygen generation is a critical factor in determining the effectiveness of PDT. To investigate the PDT yield, we employed the DPBF probe, and Fig. S11 reveals a notable reduction in the absorption intensity of DPBF's UV absorption spectrum after just 10 min of light exposure. This decrease provides strong evidence that our device generates a substantial yield of singlet oxygen during the PDT process. Furthermore, we utilized the DCFH-DA probe to quantitatively assess the ROS levels following PDT. The data indicated a marked increase in the mean fluorescence intensity (MFI) of ROS in the PDT group compared to the control group, highlighting the significant production of reactive oxygen species as a result of the treatment . To investigate the subcellular structures primarily responsible for ROS generation, we performed the MitoSOX staining to assess the expression levels of superoxide in the mitochondria. The experimental results showed that PDT group exhibited a significant increase in red fluorescence intensity, reflecting an elevated production of mitochondrial superoxide . As shown in Fig. 5 D, the substantially higher green-to-red fluorescence ratio in the PDT group quantitatively supports this trend, indicating a significant increase in mitochondrial oxidative stress following PDT treatment. Further, we employed JC-1 fluorescence to detect mitochondrial membrane potential, shown in Fig. 5 E and F. The green/red fluorescence ratio in the PDT group was 2.28, whereas other groups did not exceed 0.5 underscoring that PDT significantly promotes cancer cell apoptosis. Fig. 5 PDT significantly enhances the ROS generation and apoptosis in ESCC cells. (A) Representative images of ROS levels detected by DCFH-DA probe. Scar bars, 50 μm. (B) Statistical analysis of ROS in different treatment groups, n = 3. (C) Detection of mitochondrial superoxide expression by MitoSox red staining (red fluorescence indicates mitochondrial superoxide, green fluorescence marks the mitochondrion). Scar bars, 50 μm. (D) Quantitative analysis of mitochondrial superoxide, n = 3. (E) Fluorescence images of JC-1 staining (red fluorescence indicates JC-1 aggregates, green fluorescence indicates JC-1 monomers). Scar bars, 50 μm. (F) The corresponding statistical analysis of JC-1, n = 3. (G) Apoptosis detection by flow cytometry using the Annexin V-FITC/PI kit in different groups. (H) The corresponding statistical analysis of apoptosis, n = 3. Data are presented as mean ± SEM. Statistical analysis was conducted using ordinary one-way ANOVA with multiple comparisons, not significant (ns), P ≥ 0.05; ∗∗∗∗P ≤ 0.0001. Fig. 5 Additionally, flow cytometric analysis using an Annexin V/PI staining kit revealed that the PDT group exhibited the highest proportion of cells in both early and late stages of apoptosis . These results suggest that PDT induces significant oxidative stress and promotes apoptosis, effectively leading to the death of tumor cells. Fig. 6 shows the in-vivo efficacy of the PDT treatment module. To establish a breast cancer a mouse model, 100,000 4T1 cells were subcutaneously implanted into Balb/c mouse. When the tumor volume reached about 50 mm³, the treatment module was implanted deep into the tumor tissue. Following intra-tumoral injection of 0.5 mg/kg Ce6 for 5 h, a wireless ultrasound probe was used to drive the μ-LED emission and generate photodynamic effects for 30 min per day over a period of 8 days. During this process, there was no significant difference in the body weight changes between the control or experimental groups , indicating the biocompatibility and safety of the treatment. Compared to the Ctrl group, the PDT group exhibited a significant trend of tumor suppression, demonstrating a strong inhibitory effect on tumor growth, while the tumors volume in the Ce6 and LED groups continued to grow rapidly . Fig. 6 In vivo anti-tumor effect of the PDT treatment module. (A) Body weight of mice receiving various treatments, n = 5. (B) Average tumor growth curves for all groups, n = 5. (C) Tumor images collected from each group. (D) Average tumor mass collected from each group, n = 5. (E) Histological analysis of tumor tissue sections via H&E staining and Ki-67 immunohistochemical staining. Scale bar, 100 μm. (F) Quantitative analysis of Ki-67 immunohistochemical index, n = 5. All data are presented as mean ± SD, ∗ P < 0.05, ∗∗∗ P < 0.001, ∗∗∗∗ P < 0.0001. Statistical analysis was conducted using ordinary one-way ANOVA with multiple comparisons. Fig. 6 After the 4T1 cells were implanted for 18 days, the subcutaneous tumors were excised to evaluate the efficacy of the treatment . The average tumor mass in the PDT group similarly decreased by 79.1 %, demonstrating the effectiveness of the therapy . In addition, we performed hematoxylin and eosin (H&E) staining and Ki67 immunohistochemical staining to assess the histological changes in the tumors . The H&E staining showed a significant reduction in tumor cell density and damage of the tumor stromal structure in the PDT group, while the PDT group showed the lowest positive area rate of Ki67 . These findings collectively suggest that the PDT achieved through the treatment module significantly suppresses tumor growth, underscoring its potential as a viable therapeutic strategy for cancer treatment. The morphology of major organs indicates that our implantable treatment module has low side effects and high biocompatibility in vivo. Fig. 7 illustrates the biological safety of the integrated esophageal stent system. We co-cultured the device with mouse embryonic fibroblasts (NIH3T3) for 24, 48, and 72 h. Fluorescence microscopy imaging of Calcein-AM/PI staining showed that the stent could coexist with the cells for an extended period with minimal toxicity, as the proportion of cell apoptosis did not exceed 5 % . Fig. 7 Evaluation of the biosafety of the integrated esophageal stent system. (A) Fluorescence microscopy images of Calcein-AM/PI staining after co-culturing the integrated stent system with NIH3T3 cells for 24, 48, and 72 h. (B) Statistical analysis of the proportion of cell death in different time groups, n = 3. Data are presented as mean ± SD. Unpaired t -test, not significant (ns), P ≥ 0.05. Fig. 7 In the experiments, the ultrasound intensity used was 407.42 mW/cm 2 , which is below the FDA-approved threshold (peak intensity of 720 mW/cm 2 ) to prevent damage to esophageal tissue from high-intensity ultrasound. Furthermore, histological analysis was conducted on the surrounding skin tissue and esophageal region to assess the safety of ultrasound exposure. The results showed that, compared to the control group, there were no significant morphological changes in the skin tissue and corresponding esophageal region following ultrasound irradiation , indicating that our ultrasound parameters are biologically safe. In this study, we propose a novel esophageal stent integrated with a wireless, battery-free, and movable PDT unit, designed for flexible, precise, and real-time treatment of esophageal tumors. This system enables ultrasound-based wireless control of the PDT light source position, providing a novel approach for treating esophageal cancer patients. However, several challenges remain in translating this technology into clinical practice. The primary obstacle is the issue of light penetration and diffusion in heterogeneous tumor environments. One of the main challenges in applying PDT in clinical settings is the limited tissue penetration of light, particularly in deeper tumor tissues. Furthermore, in heterogeneous tumor environments, due to varying tissue densities and compositions, light diffusion may be uneven, potentially leading to suboptimal activation of photosensitizers and incomplete tumor treatment. To mitigate the limitations of light penetration, we utilized longer-wavelength red light, which is more tissue-penetrative and coincides with the absorption peak of Ce6. Additionally, we employed an implantable device to position the light source directly around the tumor. To ensure consistent treatment, we are considering the integration of photodetectors and fluorescence-based ROS sensors to monitor light distribution and ROS production. Concerns about potential off-target ROS effects in healthy tissues are valid. To address this, we leverage the selective accumulation of Ce6 in tumor tissues. Upon systemic or local administration, Ce6 preferentially concentrates in cancer cells due to metabolic changes and increased blood flow in the tumor microenvironment . Additionally, our system maximizes light delivery by positioning the μ-LED light source directly adjacent to the tumor, ensuring precise targeting of the treatment area. Furthermore, we are exploring the use of nanocarriers for the targeted delivery of photosensitizers, enabling controlled release and minimizing unintended damage to surrounding healthy tissues. Regarding regulatory approval and safety, the clinical application of this novel therapeutic system will require extensive regulatory approval processes, including demonstrating the biocompatibility and safety of all system components. The μ-LEDs, soft actuators, piezoelectric transducers, and all electronic and structural components must undergo rigorous biocompatibility testing to ensure they do not induce inflammation, toxicity, or immune responses in vivo. Furthermore, the long-term stability and potential degradation of biocompatible packaging must be evaluated to prevent any adverse effects such as material wear or breakdown. Finally, to enable widespread clinical adoption, the scalability of manufacturing this system must be addressed. The production of such a complex integrated system may face challenges related to cost, consistency, and reliability at large scales. It is crucial to establish standardized protocols for the assembly of the stent, μ-LEDs, actuators, and electronic components in a cost-effective and reproducible manner. Additionally, the integration of this system into existing clinical workflows must be considered to ensure ease of deployment and control. In conclusion, this system offers a novel strategy for esophageal cancer treatment. Overcoming the identified challenges and further optimizing its components will be essential for enabling its clinical translation, potentially providing a new, targeted therapeutic option for managing complex tumor environments. We have designed a wireless, battery-free, movable PDT unit for esophageal stents, enabling flexible and precise treatment of esophageal cancer. This system introduces a novel strategy utilizing an electrochemical pneumatic soft actuator to move PDT light sources in real-time to the vicinity of tumors, achieving precise and efficient treatment of esophageal tumors. The treatment module, characterized by its small size and wireless, battery-free operation, provides the light source for PDT therapy and moves frictionlessly within the track of the actuator. The flexible actuator adapts to various deformations, allowing it to spiral and cover a large area inside the esophageal stent, thereby extending the reach of PDT light sources. The actuator and treatment module exhibit different frequency selectivity to external ultrasound, enabling independent and interference-free operation. In vitro cell experiments have demonstrated the effective killing of tumor cells by PDT, with better efficacy observed at closer distances to the tumor, thereby establishing the practicality of this scaffold system. This innovative treatment approach holds promise for providing more effective and personalized treatment options for esophageal cancer patients, offering new ideas and methods for clinical intervention. A flexible copper-clad PI sheet (Cu/PI/Cu, 12/12.5/12 μm) served as the substrate. Patterned transmission and finger electrodes were achieved on the substrate through exposure, development, and etching steps. Holes (300 μm in diameter) were drilled on the substrate, with inner walls copper-plated to ensure electrical connection between top and bottom electrodes. A 75 nm gold layer was chemically deposited on the finger electrodes to prevent oxidation in the presence of NaOH solution. Key electronic components and power supply units, including capacitors, μ-LEDs, diodes, and PZT 1, were assembled on the substrate through soldering. A 6 cm length, 2.5 cm inner diameter, and 3.5 mm outer diameter silicone tube served as the actuator's solution reservoir. A 22 cm length, 1.5 cm inner diameter, and 2.5 mm outer diameter silicone tube served as the delivery track. Both tubes were compactly connected using PDMS: curing agent (SYLGARD 184, Dow Corning, USA) mixed at a 10:1 mass ratio, applied to one end of the track, and connected to the solution reservoir, cured at 70 °C for 3 h. The electrodes were placed into the solution reservoir, and the other end was sealed with PDMS, simultaneously encapsulating the external circuit, cured at 70 °C for 3 h. A 50 mmol L −1 NaOH solution (Shanghai, Macklin Biochemical Co., Ltd., Shanghai, China) was injected into the solution reservoir from the end of the track using a 30 cm long syringe, followed by injecting vaseline at the bottom of the track to act as a piston to prevent leakage. The treatment module was inserted into the track and placed near the vaseline piston. Optical image of PZT 1 is shown in Fig. S6 (Supporting Information), SEM images in Fig. S7 (Supporting Information), and the circuit board layout in Fig. S9 (Supporting Information). The structure and morphology of materials were studied using a SEM (GeminiSEM 300, Germany). The piezoelectric output of the wireless power unit was measured using an oscilloscope and KEITHLEY . Finite element simulations of ultrasound were conducted using COMSOL. Measure the voltage-current curves using an electrochemical workstation (CHI600E). The volume of gas produced was determined by the height of the liquid column. Human esophageal squamous cell carcinoma KYSE-150 cells were cultured in 1640 medium supplemented with 10 % fetal bovine serum and 1 % Penicillin/Streptomycin. Cells were then incubated at 37 °C in a humidified atmosphere containing 5 % CO 2 . Using a 48-well plate, 10,000 cells per well were seeded and treated with 300 μl gradients of Ce6 (1, 2, 4, 6, 8, 16, and 32 μM) in culture medium. After 24 h of incubation, cell viability was assessed using the CCK-8 assay (ABclonal Technology, China). In a 48-well plate, 10,000 cells per well were seeded. After incubating for 5 h in serum-free medium with a fixed Ce6 concentration of (0, 1, 2, 4, 6, 8, 16, and 32 μM) under 5 % CO 2 at 37 °C, the medium was replaced with serum-containing complete medium. Cells treated with Ce6 were then exposed to μ-LED (660 nm, 25 mW) for varying durations (0, 5, 10, 20, 30, and 40 min), and the different distances form light source to the bottom of the cell plates (0, 1, 3, 5, and 8 cm). Cell viability was measured using the CCK-8 assay after 24 h of incubation. K150 cells (100,000 per well) were seeded in 3.5 cm dishes and subjected to control, LED, Ce6, and PDT. After 24 h, the cells were stained with 500 μL of Calcein-AM/PI working solution (Beyotime Biotechnology, China) and incubated at 37 °C in the dark for 30 min. Following staining, WSI was performed using a cell imaging multi-mode microplate reader (Agilent BioTek Cytation 5, America). Additionally, K150 cells were also seeded in 48-well cell culture plates, treated and stained under the same conditions, and then imaged using a confocal fluorescence microscope (Nikon, Japan). Cells (100,000 per well) were seeded in 3.5 cm dishes. Cells from control, LED, Ce6, and PDT groups were incubated with DCFH-DA staining solution (Beyotime Biotechnology, China) at 37 °C for 20 min. ROS levels were measured using a confocal microscope (Nikon, Japan) with excitation and emission wavelengths set at 488 and 525 nm, respectively. Cells (100,000 per well) were seeded in a 6-well plate and subjected to PDT for 1 h. The mitochondria were stained with 5 μM MitoSOX Red (Beyotime, China) at 37 °C for 20 min to label mitochondrial superoxide. Following staining, the cells were washed twice with PBS. Subsequently, the mitochondria were further labeled with 0.1 μM Mito-Tracker Green (Beyotime, China) at 37 °C for 20 min. After another round of washing, the samples were observed using a laser scanning confocal microscope. Cells (150,000 per well) were seeded in 6-well plates and cultured for 48 h in control, LED, Ce6, and PDT conditions. Apoptosis levels were measured using the Annexin V-FITC/PI Apoptosis Kit (4abio tech, China). Cells (100,000 per well) were seeded in 3.5 cm dishes. Divide the cells into four groups: control, LED, Ce6, and PDT. After 24 h of cultivation, the mitochondrial membrane potential was measured by the JC-1staining kit (Beyotime Biotechnology, China). In this experiment, the 4T1 mouse breast cancer cell line was used for in vivo tumor-bearing treatment. First, 100,000 4T1 cells were subcutaneously injected into mice to establish the tumor model. Treatment was initiated when the tumor volume reached approximately 50 mm³. During the treatment period, the body weight and tumor volume of the mice were measured and recorded every two days. The treatment was administered daily for 8 consecutive days. After treatment, the mice were monitored, and tumor volume changes were recorded for an additional 10 days to complete the observation period. Biocompatibility Evaluation of Treatment Formulations NIH3T3 cells were cultured in DMEM supplemented with 10 % FBS and 1 % P/S for 24 h. Using a 96-well plate, 100,000 cells per well were seeded. Integrated scaffold slices were separately co-cultured with normal NIH3T3 cells for 24, 48, and 72 h, followed by Calcein-AM/PI staining to assess cell viability and death. Eight-week-old female SD rats underwent neck hair removal, followed by anesthesia. Gel was applied to the corresponding sites of the cervical and thoracic esophagus, and ultrasound treatment (with specified power) was administered daily for 20 min per session over a period of seven days. Afterward, the esophagus and corresponding skin were harvested for histological analysis using H&E staining to evaluate structural changes. The ordinary one-way ANOVA with multiple comparisons or unpaired t -test was used to perform statistical analysis using GraphPad Prism 9 (GraphPad Software, Inc., California, USA). Statistically significant was concluded at ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001, ∗∗∗∗ P < 0.0001. Data are presented as mean ± SD or mean ± SEM. | Review | biomedical | en | 0.999997 |
PMC11697617 | Loneliness has been traditionally understood as a subjective state that contrasts with the condition of physical isolation, implying an imbalance in the desired and achieved level of socio‑affective interaction . In an aging population, the loss of a spouse and diminishing social networks due to death of friends or reduced community integration are common triggers of loneliness . However, it is also critical to consider other factors that play a role in the subjective experience. Research from a Latin American study highlights urbanization of rural areas, retirement from careers, declining birth rates, departure of sons and daughters from the home, and the pervasive influence of technology that potentially isolates older adults in their homes as factors that can precipitate a state of loneliness. The study goes on to say that, while these may be associated with independence in some cultures, in a Latin American environment these may be considered risk factors that potentially alienate older members from a community . Frameworks from prior literature such as Disengagement Theory, which posits that adults intrinsically and responsively reduce the amount of social interactions they have as they age, help to give relevance to behavioral patterns that are seen with old age. The withdrawal from societal roles and interactions has widespread effects on levels of social connectivity that lead to exacerbated feelings of isolation . Personality traits such as poor social skills, shyness, and introversion can further predispose individuals to social isolation, complicating efforts to maintain robust social networks crucial for well‑being in later life. Conversely, higher levels of education and economic resources often serve as protective factors against loneliness, affording individuals greater opportunities for social engagement and support . The distinction between social isolation, defined as minimal contact with others, and loneliness, characterized by the subjective experience of lacking a meaningful social network, is crucial . Loneliness, whether emotional (lack of a close attachment) or social (absence of a broader social network), is increasingly recognized for its association with adverse health outcomes, including high risks of chronic diseases, depression, and decreased quality of life among older adults in Latin America . These implications underscore the urgent need for comprehensive strategies that not only connect older adults to community resources but also address their holistic well‑being through nutrition, physical activity, and spiritual support . The principles of loneliness from prior literature, outlined above, were used in the present study that sought to understand social and emotional loneliness (SEL) in San Vito de Coto Brus, Costa Rica. Trends in life expectancy for Costa Rica exceed other upper‑middle‑income countries (UMICs), including those in Latin America . A study that contrasted health risk factors between Costa Rica and the United States, a country with a life expectancy of 77 years , found lower rates of smoking, obesity, hypertension, and single‑person households in Costa Rica could contribute to this characteristic despite having a much lower GDP per capita compared with the United States . Yet, even with trends of higher life expectancy, it is important to recognize that healthy aging is multifactorial, and understanding how loneliness is implicated in this process is critical, as the burden of elderly populations in countries such as Costa Rica continues to increase. The prevalence of care dependency, or the degree of difficulty that people have performing regular activities of daily living, is about 10% for individuals over 65 years in Costa Rica, with the level of dependency only increasing with age. And with about 12% of the population being over the age of 65 years in Costa Rica , this only perpetuates issues such as familial abandonment and involuntary placement in long‑term care facilities . The primary instruments used for the present study were a content‑validated version of the 11‑item De Jong Gierveld Loneliness Scale , and sociodemographic questions that include age, sex assigned at birth, address, civil status, and level of education. The De Jong Gierveld Loneliness Scale was used for this study because the investigators were interested in differentiating feelings of missing an intimate relationship (emotional loneliness) from the feelings of missing a wider social network (social loneliness). Prior literature has demonstrated that the primary instrument has been validated in two different studies: one that looked at the reliability and validity of the 6‑item abbreviated instrument in France, Germany, the Netherlands, Russia, Bulgaria, Georgia, and Japan , and another that looked at the reliability and validity of the 11‑item instrument in Peru and other Spanish‑speaking countries . It was critical to find evidence of the instrument employed in a variety of populations—especially among Latin American populations—to provide evidence of reliability and validity in populations with different languages, cultures, and values. Information of all study participants was collected anonymously, and each participant was given a de‑identification code to ensure data remained anonymous. The standard scoring system available for the De‑Jong‑Gierveld Loneliness Scale was used to compute scores for participants in the study. For items 2, 3, 5, 6, 9, and 10, responses of “More or less” or “Yes’’ would receive a score of 1 for that item. Items 1, 4, 7, 8, and 11 needed to be reverse‑scored, so a response of “No” would receive a goal of 1 for that item. After summing the total from the collection of 11 items for each participant, overall social‑emotional loneliness (SEL) was categorized into not lonely (0‑2), moderately lonely (3‑8), severely lonely (9‑10), or extremely lonely (11) . A total of 63 individuals aged 65 years or older were sampled in the canton of Coto Brus, Costa Rica. In addition to the De Jong Gierveld scale for loneliness, general demographic characteristics were collected for the sample ( Table 1 ). In an analysis of the breakdown of the averaged composite score of total loneliness (TL) (3.37), emotional loneliness (EL) comprised a much larger proportion (2.69; 79.82%) compared with social loneliness (SL; 0.68; 20.18%), indicating that the loss of one close attachment during the aging process can have a profound effect on the individual evaluation of loneliness ( Table 2 ). Most of the study participants ( n = 35) classified themselves as moderately lonely, with a score on the De Jong Gierveld Scale falling between 3 and 8. Among the remaining participants in the study, most of them scored in the category of not lonely ( n = 25), whereas there were a few participants who scored in the category of severely lonely ( n = 3). There were no participants who scored in the category of extremely lonely (score = 11 on the De Jong Gierveld Scale). As a part of the preliminary analysis, the relationships between TL, EL, and SL were explored with each individual demographic factor. Given the age distribution of participants in the study, two distinct age categories (65–74 years and 75 years and above) were used for analysis as opposed to regression. While there appears to be a slight positive association of increasing age with the loneliness score on the De Jong Gierveld survey , especially among TL and EL, a more consistent distribution of individuals over the age of 65 years would be needed to draw any statistical conclusions. When sex assigned at birth was compared with loneliness, there also appears to be a positive association between male sex and loneliness score . While this association is supported in the literature, researchers ran into the issue of only sampling 21 males compared with 42 females. Once again, a more equal distribution of males to females in the sample would provide more information from which to draw any statistical conclusions. The next demographic factor, marital status, was divided into two categories: married and not married. The ‘not married’ category was composed of single ( soltero ), divorced ( divorciado ), widowed ( viudo ), separated ( separado ), and free union ( unión libre ) individuals. Participants who were married in the sample were only found to have slightly lower loneliness scores for TL, SL, and EL . And lastly, level of education provided the strongest association with lower loneliness levels. Prior literature has substantiated education as a protective factor in the development of loneliness, and the data in this preliminary analysis support this claim with evidence that level of education has stronger associations with lower loneliness levels compared with age, sex assigned at birth, and marital status . One of the notable strengths of the study lies in the use of a validated instrument with demonstrated reliability and validity across multiple countries [ 3 , 18 ‑ 20 ]. The study’s methodology benefits from the simplicity and clarity of the survey instrument, making it easy to administer and score. This approach enhances the feasibility of conducting similar studies in other communities or expanding the sample size to draw more robust statistical conclusions. The reliance on convenience sampling poses significant limitations. By sampling from community groups and nursing homes, the study may inadvertently exclude elderly individuals who experience severe loneliness and therefore do not actively participate in community activities. This could skew results toward a population with stronger social support networks, potentially underestimating the prevalence and impact of loneliness among the broader elderly population in Coto Brus. Additionally, the community‑centric nature of Coto Brus, where as in many other rural regions in Costa Rica, family plays a pivotal role , raises questions about whether the survey adequately captures the nuances between support from family versus friends. This distinction could influence how loneliness is perceived and experienced by individuals in this cultural context, suggesting a need for tailored survey questions that reflect these dynamics more accurately. This study points toward several avenues for future research. Firstly, expanding the sample size beyond convenience sampling methods would enhance the generalizability of findings. This would allow for more robust statistical analyses, exploring potential associations between loneliness and demographic factors such as age, sex assigned at birth, and marital status. Furthermore, future studies should delve into the relationship between loneliness and its potential impact on comorbid mental and physical health conditions among the elderly population in Coto Brus. Understanding these interrelationships could inform comprehensive healthcare strategies that address both the emotional and physical well‑being of older adults. One way to analyze the relationship of loneliness with comorbid mental and physical health conditions associated with older age would be to use the current EBAIS model of healthcare in Costa Rica. The establishment of the EBAIS model on a national scale in 1995 helped to revolutionize the delivery of healthcare throughout the country. The tenets of the EBAIS model, which include primary care, accountability, monitoring, and community involvement, have revolutionized what healthcare access can look like. Monitoring through the EBAIS model has also yielded an increase in the burden of noncommunicable diseases since 1990 as the proportion of individuals above 65 years old continues to increase . Given the association of loneliness in older adults with a variety of noncommunicable diseases, assessment of loneliness in rural regions of the country through local EBAIS clinics could be critical as we continue to build our understanding of the relationship between loneliness and aging in Costa Rica. | Study | biomedical | en | 0.999997 |
PMC11697618 | Harmful behaviors encompass a range of maladaptive behaviors that can be categorized as either self-oriented (e.g., non-suicidal self-injury or NSSI) or other-oriented (e.g., physical and verbal aggressive behaviors). Different harmful behaviors are often studied independently, even though research suggests that they frequently co-occur and share common underlying mechanisms . When both forms of harmful behaviors coincide, it is referred to as dual-harm . Temperament, defined as individual differences in emotional reactivity and regulation , may influence engagement in harmful behaviors . The present study focuses on the most visible or noticeable types of self- and other-oriented harmful behaviors, being non-suicidal self-injury (NSSI) and aggressive behavior, respectively, or a combination, termed dual-harm, and seeks to identify how temperamental traits differentiate harmful behaviors groups, i.e., no-harm, NSSI-only, aggression-only, and dual-harm (NSSI and aggressive behavior), from each other in a sample of emerging adults. Pairwise comparison of these four groups will provide insight into the unique and shared mechanisms underlying different types of harmful behaviors in emerging adults, which creates opportunities to improve models of psychopathology and inform the development of transdiagnostic strategies to reduce harmful behaviors rather than studying each type of behavior in isolation. NSSI refers to any socially or culturally unacceptable behavior inflicting direct and deliberate damage to an individual’s body tissue without suicidal intent , such as self-cutting and self-burning. Lifetime NSSI is estimated to occur in 18% of adolescents, 13% of emerging adults, and 5% of adults . The age of onset of NSSI is most often situated in mid-adolescence (i.e., 14–16 years old), with a second peak during emerging adulthood . Many individuals who start engaging in NSSI during adolescence also continue to self-injure during emerging adulthood . Across studies, women report slightly higher rates of NSSI than men, but the difference is usually smaller in community samples compared to clinical samples . Finally, dual-harm refers to the co-occurrence of self- and other-oriented harmful behaviors. A systematic review study by Shafti et al. provided evidence that dual-harm may not be a distinct clinical entity. Instead, it may emerge from the interaction of intrapersonal and interpersonal risk factors that can also be linked to both self- and other-oriented harmful behaviors. As such, individuals engaging in either self- or other-oriented harmful behaviors may be considered at risk for exhibiting the other . It is difficult to draw clear conclusions on the nature and extent of dual-harmful behaviors, as studies vary in their operationalization of dual-harm. There is a considerable body of research defining dual-harm as the co-occurrence of homicide and suicide , but other harmful behaviors can also be the focus of dual-harm studies. Spaan et al. , for example, measured aggressive behaviors and violent behaviors as other-oriented harm, and NSSI and suicidality as self-oriented harm. Harford and colleagues measured other-oriented and self-oriented harm with five items and four items (e.g., “felt like wanting to die”), respectively. O’Donnell and colleagues found that the majority of the studies on combined self-oriented and other-oriented harmful behaviors report a prevalence rate of dual-harm equaling or exceeding 20%. Dual-harm seems more frequent in men than in women, but these findings should be interpreted with caution as only a handful of studies has looked at sex differences in dual-harm . Importantly, individuals reporting dual-harm have more severe psychopathology and are more likely to die before the age of 35 than individuals who engage in either self- or other-oriented harmful behavior . While prior work has focused on intrapersonal and interpersonal risk factors of self-, other-oriented, and dual-harmful behaviors , still little is known about the role of reactive and regulative temperamental traits in relation to the co-occurrence of harmful behaviors. Previous studies have mainly investigated the influence of temperamental dimensions on other- and self-oriented harmful behaviors separately, without taking dual-harm into account. Temperamental dimensions have been identified as psychological risk factors underlying both types of behaviors . Therefore, we aim to investigate how reactive and regulative temperament dimensions differentiate the full spectrum of harmful behaviors, including individuals not engaging in harmful behavior (i.e., the no-harm group), only engaging in self-oriented harmful behavior (i.e., the NSSI-only group), only engaging in other-oriented harmful behavior (i.e., the aggression-only group), and reporting both types of behaviors (i.e., the dual-harm group). Temperament refers to individual differences in (1) emotional reactivity and (2) self-regulation, which are relatively stable across situations and over time . Reactive temperament has been described in the revised-Reinforcement Sensitivity Theory (r-RST) of Gray and McNaughton . The r-RST comprises three systems: a Behavioral activation system (BAS), a fight-flight-freeze system (FFFS), and a Behavioral inhibition system (BIS). BAS reflects a general approach tendency connected to reward sensitivity, positive affect, and extraversion . BAS consists of four empirically validated dimensions: BAS-Reward interest, BAS-Goal-drive persistence, BAS-Reward reactivity, and BAS-Impulsivity. BAS-Reward interest and BAS-Goal-drive persistence both reflect reward desire and are associated with respectively exploration and drive, whereas BAS-Reward reactivity and BAS-Impulsivity are activated in reaction to rewarding stimuli and are associated with respectively responsiveness and non-planning. In addition, FFFS and BIS reflect a general avoidance tendency connected to punishment sensitivity, negative affect, and neuroticism . In this study, FFFS refers to the flight-freeze system, excluding the fight-component . The flight-freeze system is conceptualized as an avoidance system promoting fleeing or freezing behavior in reaction to a stimulus, depending on the perceived danger of the stimulus. The flight-freeze system is associated with fear and panic . BIS functions as a conflict detector and regulator of BAS reactivity and flight-freeze reactivity: A conflict within or between them is followed by an increase in arousal, motivating individuals to resolve the detected conflict. For example, when a person encounters a situation where fleeing may coincide with a desire to stay (flight-freeze system activation vs BAS), BIS detects this conflict and increases arousal to motivate resolution, such as evaluating the level of threat and opting for the safer option. BIS is linked to increased anxiety . In terms of sex differences, women tend to report more BAS-Goal-drive persistence, BAS-Reward reactivity, flight-freeze reactivity, and BIS compared to men, and men tend to report more BAS-Reward interest and BAS-Impulsivity compared to women . Regulative temperament, described in terms of Effortful control (EC), is defined as the top-down capacity to moderate the reactivity of BAS, the flight-freeze system, and BIS to elicit adaptive behavioral responses . EC generally consists of three components: (1) attentional control is the ability to voluntarily focus or shift attention when needed, (2) activation control involves the ability to act even when lacking motivation, and (3) inhibitory control is the ability to voluntarily inhibit behavior . EC is positively associated with conscientiousness . Studies report no significant sex differences in EC in adults . We hypothesize that the likelihood of engaging in NSSI-only, compared to no-harm or aggression-only, will be positively associated with higher BIS . Additionally, we expect that individuals who engage in aggression-only will report lower BIS and higher BAS-Impulsivity than the group with no harmful behaviors or the individuals reporting NSSI-only . The hypothesis regarding the flight-freeze system is exploratory in nature. Considering dual-harm, the analyses are exploratory, but based on the strong link between dual-harm and borderline personality disorder , we assume that the odds of engaging in both NSSI and aggressive behavior, compared to no-harm, NSSI-only, or aggression-only, are positively associated with BIS and BAS and negatively with EC . Reactive temperament (BAS, flight-freeze system, and BIS) was assessed by means of the Brief-Reinforcement Sensitivity Theory of Personality Questionnaire . The B-RST-PQ consists of 37 items which are rated on a 4-point Likert scale ranging from 1 (Not at all accurate) to 4 (Highly accurate). BAS is split in four BAS-subscales: BAS-Reward interest (5 items; e.g., “I regularly try new activities just to see if I enjoy them”; α = .79), BAS-Goal-drive persistence (5 items; e.g., “I am very persistent in achieving my goals”; α = .85), BAS-Reward reactivity (4 items; e.g., “I find myself reacting strongly to pleasurable things in life”; α = 68) and BAS-Impulsivity (5 items; e.g., “I find myself doing things on the spur of the moment”; α = .73). The flight-freeze system consists of 6 items (e.g., “Looking down from a great height makes me freeze.”; α = .67 in the present study). Finally, BIS consists of 12 items (e.g., “I am often preoccupied with unpleasant thoughts.”; α = .91 in the present study). SPSS version 28 was used to analyze the data. A series of logistic regression models with two-sided significance tests were performed with the pairwise group comparisons as dependent variables, temperamental traits as independent variables, and age/sex as control variables. Odds ratios, 95% confidence intervals and Nagelkerke R 2 are reported. Odds ratios provide insight into the magnitude and direction of associations between predictor variables and the compared groups. An odds ratio higher than 1 indicates higher odds of belonging to the group under investigation compared to the reference group, whereas an odds ratio less than 1 suggests lower odds of belonging to the group under investigation compared to the reference group. Nagelkerke’s R 2 provides a measure of the model’s overall fit. Given the number of estimated logistic regression models, we conducted a Bonferroni correction to identify strong associations within each model, by dividing the significance level by the number of estimated models ( n = 6), resulting in a significance level of p < .008. The interactions between reactive temperament (BAS, flight-freeze system, and BIS) and regulative temperament (EC) were entered as a second block in each logistic regression model. As they were all found to be statistically non-significant, these were not presented in the manuscript. However, they are added in a supplementary table to the manuscript (Supplementary Materials 1). To further explore the role of significant predictors identified in the results, we will conduct additional ANOVA analyses for each independent variables that emerges as a significant predictor of subgroup membership. Detailed results of these analyses are provided in the supplementary materials (Supplementary Materials 2). Lifetime NSSI was estimated at 32.88% ( n = 220) and aggressive behavior was estimated at 46.34% ( n = 310). Of all participants, 38.86% ( n = 260) reported no harm, 14.80% ( n = 99) reported NSSI-only, 28.25% ( n = 189) aggression-only, and 18.09% ( n = 121) reported dual-harm . Table 1 displays the results of the six logistic regression analyses. The pairwise comparisons are structured as a continuum, ranging from no-harm to single-harm (either self-oriented or other-oriented harm) and dual-harm. This continuum allows us to capture a more nuanced understanding of self-oriented, other-oriented, and dual-harmful behaviors in relation to temperamental traits. Nagelkerke’s R 2 indicates that the sociodemographic and temperament variables in the model comparisons explain between 16.2% and 37.4% of the variance in group membership. After Bonferroni correction, the odds of belonging to the NSSI-only group over the no-harm group (comparison 1 of Table 1 ) are positively related to BIS. Individuals who only engage in NSSI show higher levels of BIS reactivity (anxiety) compared to individuals who do not engage in either NSSI nor aggressive behaviors (see also Supplementary Materials 2, for an overview of the mean scores of BIS across subgroups). The odds of belonging to the dual-harm group over the no-harm group (comparison 3 of Table 1 ) are positively associated with BIS reactivity and BAS-Impulsivity. These results indicate that individuals who engage in both NSSI and aggressive behaviors have a tendency to report higher BIS (anxiety) and BAS-Impulsivity (approach reward without planning) compared to those who engage in neither harmful behavior. The odds of belonging to the aggression-only group over the NSSI-only group (comparison 4 of Table 1 ) are positively associated with BAS-Impulsivity and negatively with BIS reactivity. This means that individuals who only engage in aggressive behaviors are more likely to exhibit higher levels of BAS-Impulsivity (approach reward without planning) and lower levels of BIS (anxiety) compared to individuals who only engage in NSSI. First, BIS, i.e., high anxiety, has a positive impact on the odds of belonging to the NSSI-only group versus the no-harm group. This finding is in accordance with previous studies that support higher BIS reactivity among individuals engaging in NSSI compared to those without NSSI . These individuals high in BIS often exhibit higher levels of affect-related dysregulation, as found in depression , which may drive individuals to the use of NSSI as an affect-regulatory strategy to alleviate intense negative emotions . Finally, high BIS and high BAS-Impulsivity increase the odds of engaging in both NSSI and aggressive behaviors over engaging in neither harmful behavior. The profile of high BIS and high BAS-Impulsivity is typically seen in individuals with borderline personality disorder who also report intense emotionality and high disinhibition and impulsivity . The above findings support interventions targeting emotional regulation and impulse control, as found in empirically supported treatments such as dialectical behavior therapy , to reduce dual-harmful behaviors . Multiple studies have demonstrated the efficacy of DBT in individuals engaging in NSSI and aggressive behaviors . Future research should seek to investigate the effectiveness of these interventions to those engaging in dual-harm. As far as the authors know, no studies compared temperamental reactivity between an NSSI-only group and a group with aggression-only or dual-harm. The results show that BIS (anxiety) has a negative impact on the odds of aggression-only as opposed NSSI-only, whereas BAS-impulsivity (impulsive action without thinking about the consequences of one’s behavior) positively impacts the odds of engaging in aggression-only above NSSI. These findings support earlier research that elevated BAS and reduced BIS are related to aggressive behaviors . These findings also align with the profile of psychopathy . Individuals with a weak BIS (primary psychopathy) do not experience sufficient anxiety to inhibit antisocial behaviors, whereas a strong BAS, especially BAS-Impulsivity (secondary psychopathy), drives aggressive behaviors due to the tendency to act without thinking . The inverse findings hold for NSSI-only group, compared to the aggression-only group. The NSSI-only group is characterized by high BIS and low BAS-Impulsivity reactivity compared to the aggression-only group. Several prior studies have supported a negative relationship between BAS-Impulsivity and NSSI , but the results so far were inconclusive and based on the original RST instead of r-RST. Elevated BIS and reduced BAS fits the temperamental profile of depression , which can explain the link between NSSI and depressive symptomatology . Presumably, individuals may resort to NSSI engagement to evoke positive feelings (missing due to low BAS) and/or to reduce their negative affect . Studies have not yet examined differences in temperamental reactivity between individuals who engaged exclusively in aggressive behaviors and those who engage in both aggressive behaviors and NSSI (dual-harm). The results indicate that high BIS, i.e., anxiety, increases the likelihood of engaging in dual-harm as opposed to only engaging in aggressive behaviors. These findings highlight the role of BIS in dual-harm, where elevated anxiety may contribute to a repetitive cycle of harmful actions. In contrast, the aggression-only group tends to report lower sensitivity to anxiety, suggesting the behavior is more impulsive rather than driven by internalized distress. These findings support the cognitive-emotional model of dual-harm which suggests that individuals prone to emotional instability, interpersonal difficulties, and maladaptive coping – factors all positively correlated with BIS – may be particularly susceptible to dual-harm. Clinically, the findings in this study support the need for tailored interventions for individuals engaging in different harmful behaviors. Evidence-based treatments of NSSI and aggression, such as Dialectical Behavioral Therapy or Cognitive-Behavioral Therapy , often include training strategies to replace harmful behaviors with more adaptive behavioral strategies. Based on the findings in this manuscript which show that BIS, BAS-Impulsivity, and EC differentiate between no-harm, NSSI-only, aggression-only, and dual-harm, we need to encourage individuals who engage in NSSI to develop emotion-regulating behaviors that are not harmful , whereas for individuals who engage in aggression, we recommend focusing on impulse-regulation skills . In the case of dual-harm, both emotion and impulse regulation skills are needed, which is the case in programs such as DBT. Although the present study offers valuable insights as one of the few studies currently available that considers temperamental dimensions underlying both self-oriented and other-oriented forms of harmful behavior, several limitations warrant consideration. The study used cross-sectional data from a community sample collected through snowball sampling. As we collected only limited sociodemographic information (age, sex, and nationality), our ability to assess the relevance of these findings across different demographic groups is restricted. Although this recruitment method is practical for reaching individuals who engage in NSSI and/or aggressive behaviors, it may limit the generalizability of the findings. Future research could address this limitation by employing randomized sampling methods. Replicating the study in clinical populations would also provide a more comprehensive understanding of these behaviors and allow for exploration of the potential benefits of therapeutic interventions on self-oriented, other-oriented, or dual-harm. Additionally, longitudinal research is needed to examine developmental trajectories and ascertain whether engaging in NSSI, aggressive behavior and dual-harm are more than merely associated with temperamental traits. Future studies should explore whether reactive and regulative temperament differentially predict NSSI, aggression, and dual-harm. Longitudinal research can also contribute to our understanding of engagement in NSSI and aggressive behavior as possible risks for more adverse outcomes over time. Moreover, in the present study, we included a combination of NSSI and aggressive behavior to operationalize dual-harmful behaviors. However, there is no consensus on which types of harmful behaviors should be included to constitute dual-harm, or on whether there should be a cutoff to establish recency or severity of the behaviors included. Future studies should examine a broader range of harmful behaviors in relation to each other, also considering their diverse characteristics, such as behavioral expressions, persistence, and thoughts related to the harmful behaviors. In that perspective, the work of Bresin offers a meaningful overview of diverse types of harmful behaviors (e.g., aggression, NSSI, as well as substance use, binge eating and gambling). In summary, the findings of this study reveal that reactive and regulative temperament are important transdiagnostic factors underlying engagement in self-oriented, other-oriented, and dual-harmful behaviors. Specifically, an elevated BIS and a decreased BAS-Impulsivity are linked to a greater likelihood of engaging in NSSI, as opposed to reporting no harmful behaviors or only aggressive behaviors. Conversely, a decreased BIS and an elevated BAS-Impulsivity are linked with a propensity for engaging in aggressive behaviors, compared to reporting no harmful behaviors or only NSSI. Individuals exhibiting dual-harmful behaviors demonstrate a deficit in EC, indicating lower levels of self-regulation, compared to individuals engaging only NSSI, and high BIS and BAS-Impulsivity compared to those engaging only aggressive behaviors or in neither NSSI nor aggressive behaviors. These differential associations highlight the nuanced interplay between temperamental traits and specific manifestations of harmful behaviors among emerging adults, which should be considered in future research and clinical practice. | Study | biomedical | en | 0.999997 |
PMC11697619 | In 2015, the Lancet Commission on Global Surgery (LCoGS) proposed six core surgical indicators to monitor access to safe and affordable surgical and anesthesia care . The indicators were developed to define, assess, and inform the surgical system on preparedness, service delivery, and cost‑efficiency. The first indicator measures the proportion of a country’s population living within 2 h of a bellwether‑capable facility, which provides cesarean section, laparotomy, and management of open fractures . This metric serves as a proxy for timely access to essential surgery, with an 80% 2‑h access (2HA) rate considered adequate . In this study, we conducted a geospatial analysis of access to emergency obstetric services within a 2‑h drive or 30‑min walk in Indonesia, focusing on the country’s substantial geographical barriers. There are two main objectives in this study: first, to determine the proportion of the reproductive‑age population in Indonesia that can reach a hospital with emergency obstetric services within a specified timeframe and, second, to identify areas lacking adequate access and suggest potential sites for infrastructure improvements. The purpose is to provide a comprehensive estimate of access based on population distribution, hospital locations, and road networks, which can assist the Indonesian government and other stakeholders in making informed national decisions. In this study, we adopted an observational cross‑sectional design to evaluate geospatial access to emergency obstetric surgery services in Indonesia. Secondary data sources included the hospital location from the Ministry of Health (MoH), obstetric gynecologist (OBGYN) practice location from the electronic management office (EMOP) of the Indonesian Society of Obstetrics and Gynecology (ISOG/POGI), and population estimates from the Facebook high‑resolution settlement layer (HRSL). We focused on women of reproductive age (15–49 years) and evaluated their access to hospitals with obstetric services within a 2‑h drive or 30‑min walk. The analysis identified underserved areas by mapping population density and hospital distribution and aimed to inform surgical workforce planning and infrastructure development in Indonesia. The latest maternal mortality ratio (MMR) data available for 2020 were obtained from www.bps.go.id . The LCoGS outlined timely access to surgical care as 2‑h access to a hospital providing surgical care. In addition, 30‑min access was measured to evaluate access to essential obstetric care, as recommended by the American College of Obstetricians and Gynecologists (ACOG). This suggests a 30‑minute benchmark for access to emergency cesarean sections (CS). We utilized the Network Analyst tool from ArcGIS Pro with a road network database sourced from the Indonesian Geospatial Information Agency (Badan Informasi Geospatial Indonesia), scaled at 1:250,000, to estimate walking and driving time. The walking speed was estimated to be 5 km/h; in contrast, the vehicle driving speed was set to 50 km/h. Service area maps were generated around each hospital with available OBGYNs, delineating areas reachable within 30 min of walking and 2 h of driving with a vehicle. Speed limits were embedded within the road network dataset, with the assumption that all patients always adhered to these speed limits. There are 2,855 hospitals across Indonesia with an available obstetric gynecologist (OBGYN) providing emergency obstetric surgical services . In Indonesia, 89.2% of 3,202 hospitals have an obstetrician‑gynecologist who can provide emergency obstetric surgical services. Overall, 94.5% of the population lives within 2 h of a hospital that provides emergency obstetric surgery, which is notably above the LCoGS target rate of 80% for every country by 2030. Among the seven island groups, five met the LCoGS indicator, ensuring that at least 80% of the population reached a hospital capable of performing emergency CS within 2 h of travel time. The total WRA population was highest in the Java Island group, with 99.2% having 2‑h access to emergency obstetric surgical care (EOSC). In contrast, the Maluku Islands and Papua had the lowest WRA populations and the lowest 2‑h access coverage at 69.2% and 60.7%, respectively . This is a novel study on the location and coverage of obstetric‑gynecological services in Indonesia nationally and in the provinces. In addition, it is the first study to utilize geospatial analysis in calculating reproductive‑age population estimates with access to emergency obstetric services in these provinces within a set time. This aligns with and adds to findings from similar studies in Southeast Asia, specifically in Malaysia and the Philippines, where the national 2HA target set by the LCoGS was achieved . However, regional disparities are evident in remote and underserved areas. These studies highlight that significant gaps in access persist locally, particularly in regions with challenging geography and lower population densities, despite averages meeting global standards. Geographical access to SAO care must be clearly defined, as it can facilitate the expansion of surgical care and strategy development to enhance geographic access for the population. SAO care includes a wide array of procedures, with obstetric care forming a crucial foundation that significantly affects WRA. Numerous studies have evaluated geographical surgical access using GIS software in various countries [ 5 , 6 , 11 , 17 – 23 ]. Any effort to scale up surgical care access requires a thorough evaluation of the distribution of facilities providing such care. The data gathered in this study should be integrated into Indonesia’s National Surgical, Obstetric, Anesthesia, and Nursing Plan (NSOANP), representing an initial critical step toward enhancing the surgical system and improving surgical care . The geospatial analysis results can inform national surgical planning and policy development, aiming to improve access to safe, affordable, and timely surgical care. The significant negative correlation between the maternal mortality ratio (MMR) and the number of active practicing obstetricians and gynecologists (APOs), as well as the OBGYN‑to‑10,000‑women‑of‑reproductive‑age (WRA) ratio, underscores the critical role of skilled obstetric care availability in reducing maternal deaths. This relationship suggests that provinces with a higher density of practicing obstetricians experience lower maternal mortality rates, likely due to improved access to timely and competent care during pregnancy and childbirth. This finding aligns with previous research indicating that the availability of human resources, such as obstetricians and midwives, is correlated with reduced insufficient referrals and better maternal outcomes . Conversely, the absence of a significant correlation between MMR and the number of hospitals with an available OBGYN (HAO) suggests that merely increasing the number of such facilities does not necessarily lead to improved maternal health outcomes. This discrepancy may be attributed to factors such as the uneven distribution of obstetricians across hospitals, service quality, and readiness variations, as well as barriers to accessing these facilities, including geographic distance and socioeconomic constraints. Studies have highlighted that, despite the presence of healthcare facilities, disparities in access and quality of care persist, contributing to high maternal mortality rates . Therefore, addressing maternal mortality requires a comprehensive approach that ensures equitable distribution of qualified healthcare providers and addresses barriers to accessing high‑quality maternal care. Indonesia has achieved a national CS rate of 17.6%, using the 2018 Indonesian Demographic Health Survey . This surpasses the World Health Organization’s recommended threshold of 10–15% for deliveries to reduce MMR levels ; however, the country still reports high MMR levels . This discrepancy underscores the significant gap in maternal health outcomes. While factors, including proximity to health facilities and urban residency, contribute to higher CS rates , they do not fully address the complexities of maternal mortality. This indicates potential gaps in the healthcare system, such as variations in the quality of care, rural access problems, and underlying health conditions in pregnant women, which are not resolved by higher CS rates alone. A deeper evaluation of these factors is essential to understand the persistently high MMR despite meeting the CS targets, suggesting the need for more comprehensive maternal healthcare strategies. The LCoGS identified three factors causing delays in patient care: (1) lack of knowledge about health systems, poor health‑seeking behavior, or distrust in the health system and cultural beliefs; (2) poor accessibility to healthcare facilities due to costs or infrastructure; and (3) insufficient capacity of health services to provide necessary care upon arrival . The 2‑h bellwether access in our geospatial analysis study only addresses the travel aspect of the second delay and does not account for travel costs, including ambulance services, which are not free in Indonesia and often face logistical challenges along with long response times . These travel costs and personal family needs can contribute to the first and second delays . In addition, a late referral system can worsen delays, leading to maternal mortality as evidenced by previous study. Therefore, further studies should consider these other “delays” to improve access to SAO care . Our study had some methodological limitations. First, the Indonesian archipelago, with over 17,000 islands and a total area of approximately 5.1 million km², is predominantly water‑logged (approximately 70%). Similar to a study conducted in the Philippines , we excluded boats and air transportation from our ArcGIS analysis. The variability in air and sea travel time influenced by the weather conditions and transportation type (from ferries to canoes), including challenging terrain in mountainous regions, such as Papua, where access may depend solely on helicopters or non‑commercial flights with infrequent schedules, made us standardize our measurements to land transportation times. Consequently, residents of remote islands requiring air or sea travel to reach healthcare facilities were categorized as outside the 2HA zone for hospitals with available OBGYNs. In this study, we considered other influential travel factors, such as economic, social, or cultural aspects. In this study, we assumed that hospitals can provide emergency obstetric surgical procedures 24/7. However, some facilities may not continue to operate, and the availability of OBGYNs and functional operating theaters is limited in certain regions. The surgical safety checklist for cesarean sections developed by the Society for Maternal‑Fetal Medicine (SMFM) may not be followed by all hospitals in Indonesia . Furthermore, the shortage of anesthesiologists in some areas may result in surgeries being performed without adequate anesthesia, which is suboptimal and can lead to undesirable outcomes . | Other | biomedical | en | 0.999998 |
PMC11697623 | In recent decades there has been increasing interest in the impact of research. Late phase clinical trials and systematic reviews of trials may find results that have the potential to improve health outcomes for people. However, there are often delays in the results influencing clinical practice. Previous research has found that it can take almost two decades, on average, for research results to go from discovery to practical application . These delays in implementing evidence-based approaches have serious implications for patients and the health care system. The most obvious effect is that, due to this delay, many patients and service users miss out on the benefits of evidence-based care [ 1 – 3 ]. These delays are not inevitable; for example, during the COVID-19 pandemic guidelines incorporating the latest evidence from trials and meta-analyses were developed at pace, and practice changed rapidly in response to emerging evidence . Against this backdrop, the concept of knowledge transfer and exchange has developed, which seeks to encourage the movement of research knowledge into action . Originally developed by the Canadian Institute of Health Research, many research funders now encourage grant applicants to think about how their research will be translated into action from this early stage of the development of ideas. This is of particular interest to public and charitable research funders, who want to be able to demonstrate to tax payers and donors that their investment in research has resulted in changes in policy and practice. Having a knowledge transfer and exchange strategy is a requirement of the Medical Research Council for University Units it funds, which includes our department. Part of the vision of our department is delivering a swifter and more effective translation of scientific research into patient benefits. Many models and frameworks to understand the knowledge to practice process exist [ 6 – 17 ], but these may be hard for busy clinical trialists to translate into practical actions. We therefore sought to develop a knowledge transfer and exchange strategy for our clinical trials unit, to support research teams to think through the actions they can take at different stages of their research to maximise and accelerate the impact of that research on policy and practice. This letter describes the strategy we developed, and how it was developed. The first step in developing the strategy occurred at a senior staff away day, where attendees were asked to list the activities they did as part of their studies to encourage knowledge transfer and exchange. These activities were grouped into 5 ‘strands’, described in Table 1 . Partnerships Communication Maximising the scientific value of our studies Strengthening capacity Learning and sharing Table 1 Description of the strands of our knowledge transfer and exchange strategy Strand Description Partnerships with external stakeholders Including collaborators involved in implementing our research; patient and public involvement, and stakeholder engagement activities Communication Activities to communicate about our research to various audiences, throughout the study process Maximising the scientific value of studies Actions to ensure our studies generate the range of evidence needed by stakeholders (such as including multi-disciplinary sub-studies) and that evidence is accessible to stakeholders (such as through open access publications and data sharing) Strengthening capacity Including efforts to build the capacity of our staff and partners around knowledge transfer and exchange, and to build the capacity of stakeholders to understand and apply the results of our studies Learning and sharing Evaluating the impact of our studies and knowledge transfer and exchange work to inform future studies; sharing our learning internally and externally, and seconding people to and from other organisations, so we can learn and share our knowledge with them Through discussion, the working group developed separate tables showing the activities happening at unit (Table 2 ) and study level , organised by strand as identified earlier in the process. The Working Group then developed checklists for studies at different stages (planning (from initial idea through to opening of the study), conduct (from opening to closing of the study), results (from analysis of results to publication), and translation of results (activities that take place after publication)). The checklists contain links to relevant guidance, to help teams think through what they should be doing to encourage knowledge transfer and exchange. Examples of the different activities being applied in different studies were compiled. Table 2 Unit-level knowledge transfer and exchange activities Strand Activities Partnerships with external stakeholders Patient and Public Involvement (PPI) Group PPI input to Quality Management Advisory Group PPI on Protocol Review Committee Engaging with other external stakeholders (long-term relationships lasting over generations of trials, and new partnerships developed to respond to current challenges and opportunities), including NGOs, professional bodies, guideline developers, healthcare commissioners, ethics committees, regulators and industry partners Communication Development and implementation of Unit Communications Strategy Maintaining communications channels including Vimeo, Soundcloud, MRCCTU website, LinkedIn, YouTube and Twitter Maximising the scientific value of studies Unit infrastructure supporting open access publication Unit infrastructure supporting data sharing SSG review to look for opportunities to embed methodology studies, and other ways to maximise the scientific value of our studies Identifying IP issues that need to be considered for a study Strengthening capacity Building internal capacity to develop and implement research impact strategies Building internal capacity to involve patients and the public in research and communication of results Building internal capacity to communicate research clearly Building external capacity to do high-quality research and apply methods developed at the unit Building external capacity to use/understand research Learning and sharing Seconding people into the unit with very specific skill sets to bring to the CTU, and those seeking to gain skills and experience to further their own careers within partner organisations Seconding unit staff to partner organisations Evaluating the impact of our research, and sharing case studies internally and externally Collect examples of impact of our research annually Monitoring our unit communication channels Sharing good practice and lessons learnt Fig. 1 Clinical study-level knowledge transfer and exchange activities There are numerous models and frameworks for knowledge transfer in the published literature [ 6 , 8 – 17 ]. Ward et al. found 28 different models in their 2009 review , from which they identified five common components of the knowledge transfer process, which overlap with the four research stages of our strategy (they go further than our research strategy, to research utilisation, which is beyond the scope of our strategy, as that is carried out by health care practitioners rather than researchers). Their problem identification and communication component links to some of our activities in the ‘planning stage’, particularly patient and public involvement to inform the research question; engaging with external stakeholders to inform research question and design; and building in multidisciplinary aspects needed to influence policy and practice. Their analysis of the context component is demonstrated in our activities of mapping key stakeholders to identify which organisations we should be engaging with; development of research impact strategies and capturing current guidelines/practice. Their knowledge transfer activities or interventions component could include many of the activities under the communication (‘distribution’) and partnership (‘linkage’) strands of our strategy, primarily at the results and translation of results stages. Where our strategy differs from many of the existing knowledge transfer models is its direct application to clinical trial, observational studies and meta-analysis research, explicitly focusing on the practical actions study teams and clinical trials units can undertake throughout the research lifecourse to enable impact. Many of the existing models and frameworks focus instead on the perspective of the (potential) information user, when seeking to apply evidence in practice , or identify factors for researchers to consider , or focus more narrowly on one strand of activities from our strategy . Our strategy considers not just the clinical implementation of study results, but also impact on science through data and sample sharing and methodological developments generated from the research. Another difference from most existing frameworks is our strategy identifies patient and public involvement as an essential part of knowledge transfer and exchange (within the partnership strand of activities), from identifying research questions through to advocating for the translation of results. As such, we hope our strategy will be of use to other researchers thinking about what they can do to maximise and speed the impact of their research. | Review | biomedical | en | 0.999997 |
PMC11697641 | Shoulder symptoms are a prevalent source of musculoskeletal pain and disability, affecting approximately one-quarter of the population . Shoulder imaging is frequently used to complement clinical examination and may detect abnormalities such as degenerative and traumatic rotator cuff injuries, labral and biceps pathology, glenohumeral and acromioclavicular joint arthritis (AC OA), subacromial bursal enlargement or inflammation, and fractures, most commonly fractures of the humeral head or clavicle . While it seems logical to associate these structural abnormalities with symptoms and to consider surgical correction if symptoms persist, many of these abnormalities are also commonly observed in asymptomatic individuals, particularly in the aging population [ 6 – 8 ]. Imaging modalities such as X-ray, ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI) have distinct strengths and limitations in identifying structural abnormalities. X-rays are cost-effective for bony structures but lack soft tissue assessment, while US provides dynamic imaging of soft tissues, depending on operator skill. CT excels in detailed bony anatomy visualization but has higher radiation exposure and limited soft tissue utility. MRI, the gold standard, effectively evaluates both soft tissue and bone but is costly and less accessible. The overall aim of the The SystematiC Review of shoUlder imaging abnormaliTies IN asYmptomatic adults (SCRUTINY) study was to summarize the prevalence of shoulder imaging abnormalities in asymptomatic adults. The primary objective of this paper was to assess the prevalence of abnormalities of the acromioclavicular (AC) joint and subacromial (SA) space from (a) population-based studies, and (b) other study populations, such as volunteers, healthcare-populations, and athletes. Our secondary objective was to compare the prevalence of imaging abnormalities in adults with and without symptoms from the same or comparable study populations. The SCRUTINY systematic review adheres to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) 2020 Statement and is registered with PROSPERO . This paper presents findings related to abnormalities of the AC joint and SA space. Part I of the SCRUTINY study details the findings concerning the glenohumeral joint, while Part III focuses on rotator cuff abnormalities. Observational population-based studies with asymptomatic adult participants (18 years and older) reporting on the prevalence of (i) AC OA, (ii) SA bursal abnormalities, (iii) SA space abnormalities, and (iv) SA calcification, as detected by X-ray, ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI), were included. Given the limited number of population-based studies—those conducted in general populations rather than recruiting from specific groups like athletes or individuals with particular characteristics—we also included research involving other groups, such as community volunteers, healthcare populations and athletes. Studies that reported on both asymptomatic and symptomatic shoulders, whether from the same individuals or different individuals within the same study population were also included. Detailed eligibility criteria are provided in Supplementary Table 1 . We conducted a comprehensive search of Ovid MEDLINE, Embase, CINAHL, and Web of Science from their inception up to June 12, 2023, without imposing language restrictions. The search strings used for each database are detailed in Supplementary Table 2 . Additionally, on June 16, 2023, we performed a backward and forward citation analysis of the included studies using Scopus. The titles and abstracts of identified studies were independently screened by five authors (SLS, RH, RJ, TI, and LR). Full-text papers of potentially eligible studies were then retrieved and thoroughly reviewed to determine their eligibility. Disagreements were resolved by a third author (RB or TI) in cases where consensus could not be achieved. Reasons for the exclusion of ineligible studies were documented. Pairs of reviewers (SLS & RH or TI & LR) independently evaluated each study using a modified version of the risk of bias assessment tool originally developed by Hoy et al. . This adapted version comprised seven items targeting essential domains for assessing the risk of bias in prevalence studies, mainly regarding selection bias and measurement bias. An overall judgment of the risk of bias was assigned as high, moderate, or low. Detailed information regarding the adaptations and guidance for conducting the risk of bias assessment can be found in Supplementary Table 3 . Using a pre-tested data extraction template, we extracted study details, participant demographics (population-based, athletes, or miscellaneous populations including community volunteers and healthcare populations), imaging modalities (X-ray, US, CT, or MRI), and prevalence findings (AC OA, SA bursa, SA space, SA calcification). In instances where studies conducted shoulder imaging but did not provide prevalence data categorized by shoulder symptom status, we contacted the first and last study authors via email to request this information. Given that most of the included studies presented prevalence data of AC joint and SA space abnormalities per shoulder and not per individual, we chose to analyze the data based on the number of shoulders rather than number of participants. Prevalence estimates and their corresponding 95% confidence intervals were calculated using the Freeman-Tukey double arcsine transformation and exact confidence intervals, with each calculation based on one shoulder per individual. Initially, our primary analysis was aimed at the general population. Due to clinical heterogeneity of the included studies, it was inappropriate to perform meta-analyses. We therefore conducted a narrative synthesis of the studies reporting the prevalence of imaging abnormalities in asymptomatic shoulders. We also performed a narrative synthesis of studies reporting the prevalence of structural abnormalities in both asymptomatic and symptomatic shoulders from the same individuals or study populations. However, studies comparing the prevalence of imaging abnormalities in asymptomatic individuals with a different group of participants experiencing symptoms (for example, comparing symptomatic athletes with asymptomatic non-athletes) were excluded from this analysis. Currently there is no specific Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework tailored for prevalence studies. Consequently, we adapted the GRADE approach for prognostic studies, as described by Iorio et al. (Supplementary Table 4 ). We evaluated the certainty of evidence independently for each outcome and study population. A total of 2457 records were identified through database searches, with an additional 1156 records obtained via other methods . Following a full-text review of 186 papers, 93 studies were excluded. The reasons for exclusion are detailed in Supplementary Table 6 . Studies that met the eligibility criteria but did not provide usable prevalence data are listed in Supplementary Table 7 . Fig. 1 PRISMA flow diagram showing search and screening results. Abbreviations: US, ultrasound; MRI, magnetic resonance imaging; SCRUTINY, the SystematiC Review of shoUlder imaging abnormaliTies IN asYmptomatic adults Overall, the SCRUTINY review included 90 studies reported in 93 publications. In this paper, 31 studies with usable data were included, comprising 4 X-ray [ 12 – 15 ], 11 US [ 6 , 16 – 25 ], 15 MRI [ 5 , 7 , 8 , 26 – 37 ], and 1 including both X-ray and MRI . No CT studies were found. There was one population-based study ( n = 20 shoulders) , 16 studies with miscellaneous populations (including volunteers , healthcare populations , a mixed population of volunteers and athletes , or a combination of volunteers and healthcare populations ) , and 14 studies reporting on athletes ( n = 708 shoulders) [ 13 , 15 , 19 , 23 , 25 , 28 , 31 – 37 , 39 ]. Among the athlete studies, four also included volunteers , with data presented separately for each group. Table 1 summarizes the characteristics of 31 studies reporting shoulder prevalence data across population-based, miscellaneous, and athletic groups. The single population-based study involved a longitudinal cohort of 4056 adults, with imaging findings from 30 participants aged 65 years on average, predominantly female (60%) . Table 1 Prevalence of acromioclavicular (AC) joint osteoarthritis (OA), subacromial (SA) bursa abnormality, SA space abnormality, and SA calcification in asymptomatic shoulders of population-based, miscellaneous, and athlete populations according to imaging modality (X-ray, ultrasound, and magnetic resonance imaging [MRI]) Study Study population Study location Mean age, years (range) Women, % No of participants (No of shoulders) AC-joint OA*, % (n/N) SA bursa abnormality†, % (n/N) SA space abnormality‡, % (n/N) SA calcification, % (n/N) X-ray and MRI studies Gill et al. 2014 Population-based X-ray MRI Australia 64.8 (56–74) 60 20 (20) 95 (19/20) 85 (17/20) 90 (18/20) 20 (4/20) 5 (1/20) X-ray studies Worland et al. 2003 Miscellaneous (volunteers) USA 60.2 45 (40–49) 54 (50–59) 65 (60–69) 77 (70–) 50.8 59 (118) 15 (30) 15 (30) 14 (28) 15 (30) NR NR 42.4 (50/118) 20 (6/30) 56.7 (17/30) 25 (7/28) 66.7 (20/30) NR Maquirriain et al. 2006 Miscellaneous (volunteers) Argentina 59.8 (51–76) 6 18 (36) 19.4 (7/36) NR NR 0 (0/36) Khoschnau et al. 2020 Miscellaneous (healthcare population and volunteers) Sweden 66 (50–75) 51 106 (129) 31 (40/129) NR NR NR Maquirriain et al. 2006 Athletes (former elite tennis players) Argentina 57.2 (51–75) 6 18 (36) 41.7 (15/36) NR NR 2.8 (1/36) Wright et al. 2007 Athletes (overhead, baseball pitchers) USA 29 (19–43) 0 57 (57) 47.4 (27/57) NR NR NR Ultrasound studies Wang et al. 2005 Miscellaneous (volunteers and athletes) Taiwan 21 NR 28 (56) 12.5 (7/56) NR NR NR Oschman et al. 2007 Miscellaneous (healthcare population, contralateral rotator cuff tear) South Africa 64 (40–83) 36 50 (50) NR 78 (39/50) 86 (43/50) NR Abate et al. 2010 Miscellaneous (healthcare population, with and without diabetes) Italy 71 (65–84) 38 80 (160) NR 18.8 (30/160) NR NR Ocguder et al. 2010 Miscellaneous (asymptomatic volunteers) Turkey 25 (18–33) 30 43 (86) NR 0 (0/86) NR 0 (0/86) Girish et al. 2011 Miscellaneous (healthcare population, males with knee problems) USA 56 (40–70) 0 51 (51) 64.7 (33/51) 78.4 (40/51) 5.9 (3/51) 3.9 (2/51) Iagnocco et al. 2013 Miscellaneous (healthy volunteers from four rheumatologic units) Italy 44.2 (20–85) 26 (20–29) 34 (30–39) 44 (40–49) 55 (50–59) 66 (60–) 54 97 (194) 21 (42) 19 (38) 20 (40) 19 (38) 18 (36) 25.7 (50/194) 7.1 (3/42) 7.9 (3/38) 17.5 (7/40) 47.4 (18/38) 52.8 (19/36) 11.3 (22/194) 0 (0/42) 7.9 (3/38) 15 (6/40) 15.8 (6/38) 19.4 (7/36) 2.6 (5/194) 0 (0/42) 0 (0/38) 0 (0/40) 10.5 (4/38) 2.7 (1/36) 18 (35/194) 2.4 (1/42) 10.5 (4/38) 25 (10/40) 5.3 (2/38) 50 (18/36) Sansone et al. 2016 Miscellaneous (healthcare population, females referred to routine gynecological screening) Italy 38.5 (18–60) 100 (509) NR NR NR 13.6 (69/509) Meroni et al. 2017 Miscellaneous (volunteers, working aged women) Italy 36.7 (19–56) 100 228 (456) 0.9 (4/456) 0.2 (1/456) 0 (0/456) 5.7 (26/456) Suzuki et al. 2021 Miscellaneous (volunteers) Japan 51.2 (33–65) 60 20 (40) NR 2.5 (1/40) NR 7.5 (3/40) Eliason et al. 2022 Miscellaneous (healthcare population, primary healthcare patients with unilateral shoulder pain) Sweden 45.0 (20–59) (20–29) (30–39) (40–49) (50–59) 53 115 (115) 14 (14) 19 (19) 35 (35) 47 (47) 13 (15/115) 0 (0/14) 21.1 (4/19) 17.1 (6/35) 10.6 (5/47) 73 (84/115) 35.7 (5/14) 89.5 (17/19) 71.4 (25/35) 78.7 (37/47) NR 17.4 (20/115) 0 (0/14) 10.5 (2/19) 20 (7/35) 23.4 (11/47) Brasseur et al. 2004 Athletes (veteran tennis players) France 55 (37–77) 43 119 (119) NR 22.7 (27/119) NR 25.2 (30/119) Ocguder et al. 2010 Athletes (overhead sports) Turkey 22 (17–40) 18 45 (90) NR 20 (18/90) NR 2.2 (2/90) Suzuki et al. 2021 Athletes (masters level swimmers) Japan 51.8 (33–65) 60 40 (60) NR 11.7 (7/60) NR 16.7 (10/60) Study Study population Study location Mean age, years (range) Women, % No of participants (No of shoulders) AC-joint OA*, % (n/N) SA bursa abnormality†, % (n/N) SA space abnormality‡, % (n/N) SA calcification, % (n/N) MRI studies Chandnani et al. 1992 Miscellaneous (volunteers) USA (25–55) NR 20 (20) 35 (7/20) 0 (0/20) NR NR Neumann et al. 1992 Miscellaneous (volunteers) USA 26 (22–45) 28 55 (32) 43.6 (24/55) 20 (11/55) 3.6 (2/55) NR Needell et al. 1996 Miscellaneous (volunteers without shoulder pain participating in a sports medicine study) USA 54 (19–88) 29 (19–39) 50 (40–60) 75 (61–88) 51 100 (100) 26 (26) 26 (26) 48 (48) 76 (76/100) 38.5 (10/26) 88.5 (23/26) 89.6 (43/48) 33 (33/100) 19.2 (5/26) 19.2 (5/26) 47.9 (23/48) 39 (39/100) 15.4 (4/26) 26.9 (7/26) 58.3 (28/48) NR Stein et al. 2001 Miscellaneous (healthcare population, other musculoskeletal complaint) USA 35 (19–72) 25 (19–30) 42 (31–72) 57 42 (50) (19)(31) 82 (41/50) 68.4 (13/19)90.3 (28/31) NR NR NR Barreto et al. 2019 Miscellaneous (volunteers with unilateral shoulder pain from the community) Brazil 39.4 (18–77) 46 123 (123) 73.2 (90/123) 52.8 (65/123) 13 (16/123) NR Su et al. 2020 Miscellaneous (male volunteers from the study institution) Taiwan 25.3 (22–29) 0 30 (30) NR 13.3 (4/30) NR NR Liu et al. 2021 Miscellaneous (volunteers, healthy non-athletic young adults) USA 24 (20–29) 66 29 (58) 0 (0/58) 0 (0/58) 1.7 (1/58) NR Miniaci et al. 2002 Athletes (professional male baseball pitchers) Canada 20.1 (18–22) 0 14 (28) 35.7 (10/28) 78.6 (22/28) 46.4 (13/28) NR Connor et al. 2003 Athletes (elite overhead athletes) USA 26.4 (18–38) NR 20 (40) NR 47.5 (19/40) NR NR Reuter et al. 2008 Athletes (Ironman participants) USA 35 (29–62) 29 7 (7) 71.4 (5/7) NR NR NR Del Grande et al. 2016 Athletes (male overhead athletes) USA 19.9 (17–22) 0 19 (19) 21.1 (4/19) 63.2 (12/19) NR NR Celliers et al. 2017 Athletes (elite swimmers) South Africa 18.9 (16–25) 45 20 (29) 34.5 (10/29) 34.5 (10/29) NR NR Hacken et al. 2019 Athletes (college and professional male ice hockey players) USA 22.1 (18–28) 0 25 (49) 8.2 (4/49) NR NR NR Lee et al. 2020 Athletes (elite volleyball players) USA 25.5 (21–30) 46 26 (26) 69.2 (18/26) NR NR NR Su et al. 2020 Athletes (male baseball players) Taiwan 25.6 (18–35) 0 68 (68) NR 55.9 (38/68) NR NR Cooper et al. 2022 Athletes (elite rock climbers) USA 34.1 (20–60) 42 50 (100) 28 (28/100) 79 (79/100) NR NR * = Osteophytes, joint effusion, bone oedema, bony ridging, elevated bone marrow signal, joint narrowing, joint degeneration, joint hypertrophy, articular surface irregularity, articular cartilage thinning, fissuring or degeneration, cortical irregularities, margin irregularity, bone sclerosis, erosions, osteoarthritis, synovial scarring, cystic change † = Bursal effusion, bursal thickening, bursal hypertrophy ‡ = SA space narrowing, SA spurs, SA enthesophytes, acromion osteophytes, acromiohumeral distance (abnormal/narrow), Type III acromion (hooked), SA impingement, AC joint osteophytes impinging the supraspinatus tendon All included studies were deemed to have a high overall risk of bias, primarily due to concerns about the representativeness of the target population limiting the generalizability of the findings. This was because the populations studied were not closely representative of the national population, lacked sample frame representativeness, or did not utilize random selection or consecutive series for sample selection . Additionally, there was variation in the outcome definitions across the studies (Supplementary Table 8 ). Fig. 2 Risk of bias summary: review authors judgments about each risk of bias item for each study providing prevalence data per shoulder. All 31 studies included in the review were judged to have a high risk of bias overall Twenty-two studies reported the prevalence of AC OA per shoulder. This included one population-based study that included both X-ray and MRI , 12 studies within the miscellaneous group (comprising 1 X-ray study with a mixed population of volunteers and healthcare patients ; 5 US studies involving healthy volunteers , a mixed group of volunteers and athletes , and healthcare populations ; 6 MRI studies with healthy volunteers , volunteers with unilateral shoulder pain , or other musculoskeletal conditions ), as well as one X-ray and seven MRI [ 32 – 37 , 39 ] studies of athletes. Additionally, one X-ray study reported on both athletes and a matched cohort of volunteers . Data categorized by age-group were available in four studies within the miscellaneous group [ 6 – 8 , 24 ] . Fig. 3 Studies reporting the prevalence of acromioclavicular osteoarthritis (AC OA) ( A ), subacromial (SA) bursa abnormalities ( B ), SA space abnormalties ( C ), and SA calcification per shoulder ( D ). Studies are arranged according to mean or midpoint age within each study population Fig. 4 Data stratified by age group were available in four studies reporting the prevalence of acromioclavicular osteoarthritis (AC OA) ( A ), three studies on subacromial (SA) bursa abnormalities ( B ), three studies on SA space abnormalities ( C ), and two studies on SA calcification ( D ). Overall, the data suggest a trend of increasing prevalence with age In the population-based study (20 shoulders, mean participant age 65 years) findings consistent with AC OA were observed in 19 shoulders (95%) on X-ray and 17 shoulders (85%) on MRI . Among the 21 studies with non-population-based samples , 483 shoulders (27%) had findings indicative of AC OA. The sample sizes varied from 57 to 129 shoulders in the three X-ray studies, 51 to 456 shoulders in the five US studies, and 7 to 123 shoulders in the 13 MRI studies. The prevalence of AC OA findings within the individual studies ranged from 6 to 47% for X-ray, 1 to 65% for US, and 0 to 82% for MRI. Twenty-one studies reported the prevalence of SA bursa abnormalities per shoulder. This included one population-based study that used MRI , 11 studies within the miscellaneous group (comprising 6 US studies with healthy volunteers and healthcare populations ; 5 MRI studies with healthy volunteers and volunteers with unilateral shoulder pain ), as well as one US and five MRI studies of athletes. Additionally, two US and one MRI study reported on both athletes and a matched cohort of volunteers . Data categorized by age-group were available in three studies within the miscellaneous group . In the population-based study, there were MRI abnormalities in the SA bursa in 18 shoulders (90%) . Among the 20 studies with non-population-based samples , 562 (27%) shoulders had SA bursa abnormalities. Sample sizes varied from 50 to 456 in nine US studies, and 20 to 123 in 11 MRI studies and the prevalence of SA bursa abnormalities ranged from 0 to 78% for US, and 0 to 79 for MRI. Eleven studies reported the prevalence of SA space abnormalities per shoulder. This included one population-based study that used X-ray , nine studies within the miscellaneous group (1 X-ray study with healthy volunteers ; 4 US studies in either healthy volunteers or healthcare populations ; 4 MRI studies with either healthy volunteers or volunteers with unilateral shoulder pain ), and one MRI study of athletes . Data categorized by age-group were available in three studies within the miscellaneous group . There were X-ray SA space abnormalities in four shoulders (20%) in the population-based study . Among the 10 studies with non-population-based samples , 172 shoulders (14%) shoulders showed SA space abnormalities. The sample size for the X-ray study was 118, while it ranged from 50 to 456 across four US studies and from 28 to 123 across five MRI studies. The prevalence of SA space abnormalities was 42% in the X-ray study, and ranged from 0 to 86% for US, and 2 to 46% in for MRI. Ten studies reported the prevalence of SA calcification per shoulder. This included one population-based study that used X-ray , five studies (all US) within the miscellaneous group comprising healthy volunteers and healthcare populations , and one US study of athletes . Additionally, one X-ray and two US studies reported on both athletes and a matched cohort of volunteers . Data categorized by age-group were available in two studies within the miscellaneous group . There was SA calcification in one shoulder (5%) in the population-based study . Among the nine studies with non-population-based samples , 198 shoulders (11%) showed SA calcifications. The sample size for the X-ray study was 72, while it ranged from 51 to 509 across eight US studies. The prevalence of SA calcifications was 1% in the X-ray study and ranged from 1 to 25% in the US studies. Ten studies examined the prevalence of imaging findings in both asymptomatic and symptomatic shoulders, as detailed in Supplementary Table 10 . Two studies included findings from both shoulders in participants with unilateral shoulder pain and four studies reported on asymptomatic and symptomatic shoulders from different individuals within the same study population . The remaining studies did not clearly specify whether they reported findings within the same individuals, separate individuals, or a mix of both . Seven studies investigated the prevalence of AC OA, including one X-ray study , one US study , four MRI studies , and one study that used both X-ray and MRI . These studies collectively examined 443 asymptomatic shoulders (ranging from 7 to 129 per study) and 378 symptomatic shoulders (ranging from 10 to 123 per study). In one population-based study, the prevalence of AC OA in asymptomatic shoulders was 85% on MRI and 95% on X-ray, while in symptomatic shoulders, it was 100% on both X-ray and MRI . Across all studies, the prevalence of AC OA varied from 13 to 95% in asymptomatic shoulders and from 20 to 100% in symptomatic shoulders . Fig. 5 Studies reporting the prevalence of both asymptomatic and symptomatic shoulders for acromioclavicular osteoarthritis (AC OA) ( A ), subacromial (SA) bursa abnormalities ( B ), SA space abnormalities ( C ), and SA calcification per shoulder ( D ). Numbers under the authors express the total amount of shoulders (asymptomatic/symptomatic) in the study Seven studies, consisting of 3 US studies [ 23 – 25 ] and 4 MRI studies , investigated the prevalence of SA bursa abnormalities. These studies collectively examined 506 asymptomatic shoulders (sample sizes ranging from 20 to 123 per study) and 350 symptomatic shoulders (sample sizes ranging from 10 to 123 per study). In the single population-based study, the prevalence of SA bursa abnormalities was 90% in asymptomatic shoulders and 100% in symptomatic shoulders . Across all studies, the prevalence of SA bursa abnormalities varied from 0 to 90% in asymptomatic shoulders and from 10 to 100% in symptomatic shoulders . Two studies, one that used X-ray and one that used MRI , investigated the prevalence of SA space abnormalities. These studies collectively examined 143 asymptomatic shoulders (ranging from 20 to 123 per study) and 133 symptomatic shoulders (ranging from 10 to 123 per study). In the single population-based study, the prevalence of SA space abnormalities was 20% in asymptomatic shoulders and 30% in symptomatic shoulders . Across all studies, the prevalence of SA space abnormalities varied from 13 to 20% in asymptomatic shoulders and from 15 to 30% in symptomatic shoulders . Five studies, consisting of one X-ray and four US [ 21 , 23 – 25 ] studies, investigated the prevalence of SA calcifications. These studies collectively examined 823 asymptomatic shoulders (ranging from 20 to 509 per study) and 271 symptomatic shoulders (ranging from 10 to 115 per study). In the single population-based study, the prevalence of SA calcification was 5% in asymptomatic shoulders and 20% in symptomatic shoulders . Across all studies, the prevalence of subacromial calcifications varied from 5 to 25% in asymptomatic shoulders and from 20 to 39% in symptomatic shoulders . This systematic review is the first to summarize the prevalence of AC joint and SA space abnormalities in asymptomatic shoulders. We identified one population-based study and 30 additional studies with various study populations. There was considerable variation in prevalence, age groups, genders, and outcome definitions across these studies, but structural changes were frequently observed in asymptomatic shoulders in both population-based and other study populations. Overall, all studies were assessed as having a high risk of bias and their prevalence estimates were judged to be of very low certainty. The prevalence of AC joint and SA space abnormalities was nearly as high in asymptomatic shoulders as in symptomatic shoulders except for subacromial calcification, which was more prevalent in symptomatic shoulders. Since imaging abnormalities are frequently observed in both asymptomatic and symptomatic shoulders, clinicians should exercise caution when linking these findings directly to a patient’s symptoms. Similar observations have been made regarding imaging findings of the glenohumeral joint , and in reviews of other painful musculoskeletal conditions [ 41 – 48 ]. Our review underscores the lack of reliable prevalence estimates for common shoulder imaging abnormalities. Our findings should therefore be interpreted with caution due to the high risk of bias of the included studies and the consequent very low certainty evidence. To establish the true age-specific prevalence of shoulder imaging abnormalities in the general population, further studies with large, representative samples are necessary. There is also a need to establish international consensus on clinically relevant outcome definitions which would facilitate better assessment of comparability across studies, and allow pooling of data across studies which would improve the precision of the prevalence estimates. To our knowledge, this is the first systematic review to synthesize the prevalence of imaging abnormalities in the AC joint and SA space. Previous reviews have reported on the prevalence of abnormalities of the rotator cuff and the glenohumeral joint , and one review has explored the link between imaging abnormalities and symptoms . We conducted a comprehensive literature search covering all commonly used imaging modalities. To improve comparability, we restricted our analysis to studies comparing symptomatic and asymptomatic shoulders within the same populations. We meticulously evaluated the risk of bias for each included study using a modified version of an established risk of bias assessment tool for prevalence studies , and we graded the certainty of evidence for each outcome using GRADE . Our review’s findings are limited by the quality of the available studies. The considerable variability in prevalence estimates across studies may be partly explained by their heterogeneity. Contributing factors include differences in study populations, potential selection bias even within the same population groups, and considerable variations in outcome definitions. Unlike findings related to the glenohumeral joint , age did not appear to have as large an impact on prevalence. Participants recruited from healthcare settings had a range of health conditions, such as contralateral shoulder pain , confirmed contralateral rotator cuff tears , and other healthcare issues , and the extent of upper extremity workload in athletes, may have also affected prevalence estimates. Differences in defining symptom status may also contribute to the wide range of prevalence estimates. Some studies relied solely on symptom questionnaires or interviews, while others also included clinical examinations. Some studies included participants with prior episodes of shoulder pain while others only enrolled individuals who had never experienced shoulder symptoms. The timeframe for defining asymptomatic shoulders also varied widely; definitions ranged from “no symptoms at recruitment” to specific durations such as one week, one month, one year, or longer. Additionally, some studies did not provide a clear explanation of symptom status or timeframe. There were also differences in how abnormalities were defined and assessed across studies. For example, AC OA definitions varied widely. Only two out of 14 MRI studies used the established Stein classification , while all included X-ray and ultrasound studies applied their own criteria which could include diverse findings such as osteophytes, joint effusion, bone oedema, joint narrowing, degeneration, hypertrophy, articular surface irregularity, sclerosis and cystic changes. Similarly, the assessment of SA bursa abnormalities differed. Criteria included bursal effusion, thickening and hypertrophy. Some studies considered size over 1 mm as abnormal , while others considered over 2 mm as abnormal . Although we applied a method to consistently count and report abnormalities, our approach was conservative, potentially leading to underestimation of the true prevalence. Additionally, we chose to report abnormalities per shoulder rather than per person. Some studies included both shoulders from the same individual, which could have biased the prevalence estimates if they assumed that if one shoulder was structurally normal then the other would be as well. However, most studies reported prevalence per shoulder or included findings for only one shoulder per person. Therefore, we deemed it inappropriate to report prevalence per person in this review. In future studies, we recommend assessing and reporting prevalence of symptoms and imaging abnormalities on both a per shoulder and per person basis. The true prevalence of AC joint and SA space imaging abnormalities in asymptomatic individuals remains uncertain, with estimates suggesting rates as high as 90 to 95%. Except for SA calcifications, which appear more common in symptomatic shoulders, these abnormalities occur almost as frequently in asymptomatic individuals as in those with symptoms. This highlights the importance of exercising caution when attributing causation of shoulder symptoms to imaging findings. Effective management of shoulder pain requires a comprehensive assessment of the patient’s medical history and a targeted physical examination. Imaging should be employed judiciously as a supplemental tool, primarily to confirm specific clinical suspicions or to exclude serious conditions such as tumors or infections. Finally, obtaining more accurate prevalence data is critical to guide evidence-based diagnostic and treatment strategies, ensuring appropriate interventions and minimizing unnecessary procedures. | Review | biomedical | en | 0.999997 |
PMC11697674 | Mucopolysaccharidosis type I (MPS I) is a rare autosomal recessive lysosomal storage disease (LSD) linked to pathogenic variants in IDUA gene. IDUA codes for the α-L-iduronidase enzyme and its deficit leads to lysosomal storage of glycosaminoglycans dermatan sulfate and heparan sulfate. Clinical features are variable, ranging from a severe form with onset before 1 year, to milder forms with later onset: Hurler-Scheie and Scheie types . The incidence of this pathology is estimated at 1 in 100,000 live births for Hurler type to 1 in 800,000 for Scheie type . In the majority of cases of Hurler syndrome, clinical signs appear after birth, and neonatal signs are rare. These clinical signs include musculoskeletal abnormalities (short stature, multiple dysostosis, thoraco-lumbar kyphosis), progressive thickening of facial features (protruding frontal bones, low nasal root with broad tip and anteverted nostrils, round cheeks, thickened lips), cardiomyopathy and valvular anomalies, sensorineural deafness, enlarged tonsils and adenoids. Developmental delay, particularly in speech, typically arises between 12 and 24 months, accompanied by progressive cognitive and sensory decline. Other manifestations include organomegaly, hernia, hirsutism, hydrocephalus, diffuse corneal . The first specific clinical signs only appear after a few months of life, linked to progressive lysosomal overload. MPS I with prenatal visceral presentation is particularly rare. While the combination of hepatosplenomegaly and coarse facial features is highly suggestive of a lysosomal disease in children, these signs have never been reported prenatally in MPS I according to our literature search. Prenatal diagnosis is performed mainly on family history, and a few cases of hydrops have been described, although this is much less frequent than in other lysosomal pathologies . We present what is, to our knowledge, the first case of prenatal MPS I diagnosed based on the presence of antenatal signs of overload, including hepatosplenomegaly and coarse facial features, as early as the second trimester of pregnancy. This diagnosis was confirmed through biochemical and genetic testing. A pregnant woman was referred by a partner center at 26.5 gestational weeks (GW) to the prenatal diagnostic center of Rennes (France). This was her second pregnancy following a previous delivery of a healthy infant. The couple was not consanguineous, their phenotype was normal, and they had no significant personal or family histories. Morphologic ultrasound examination conducted during the first trimester revealed a normal nuchal translucency of 2 mm (1.06 Multiple of Median (MoM), Crown-rump length: 77.6 mm) and a single umbilical artery. Additionally, vaginal bleeding related to a placental hematoma was observed. Ultrasound examination at 24.0 GW revealed hepatosplenomegaly and dysmorphic features, including a long and broad philtrum , as well as a few echogenic spots in the liver, spleen, peritoneum, and thymus . The cytomegalovirus (CMV) profile indicated long-standing immunity. Amniocentesis was performed at 26.7 GW for a chromosomal microarray analysis (CMA) and trio whole-exome sequencing (WES) examination. CMA was normal, but two likely pathogenic variants (class 4 according to ACMG classification) were identified by WES on IDUA gene: NM_000203.5:c.[590G > A]; [1139dup]; NP_000194.2:p.[(Gly197Asp)]; [(Leu381Alafs*18)] (Table 1 ). The presence of these two variants in compound heterozygous state raised suspicion of MPS I. No other variants of interest were identified. MPS I was next confirmed by enzymatic analysis in cultured amniocytes, with evidence of a deficiency in α-L-iduronidase activity (Table 1 ). Table 1 Biology results: genetic analysis results; α-L-iduronidase enzyme activity in cultured amniocytes Whole exome and targeted sequencing (fetal DNA) Allele 1: NM_000203.5(IDUA): c.[494-57G > A;590 G > A], inherited from the mother Allele 2: NM_000203.5(IDUA): c.1139dup, inherited from the father α-L-iduronidase enzyme activity in cultured amniocytes. Measured value Laboratory control α-L-iduronidase activity 0.3 µkat/kg 33.8 µkat/kg Hexosaminidase activity (control enzyme) 827 µkat/kg 1687 µkat/kg The couple elected for a medical termination of pregnancy, which was carried out at 35 GW. In France, pregnancy terminations for medical reasons are permitted until its term when a disease of particular severity is diagnosed in the fetus and is incurable at the time of diagnosis, as is the case for severe MPS I. At the parents’ request, only an external examination was performed. The infant’s birth biometrics were as follows: weight, 3140 g (94th percentile); length, 48 cm (80th percentile); occipitofrontal circumference 34 cm (83rd percentile). External examination confirmed hepatomegaly, with hepatic overhang of 4 cm and dysmorphic features, including coarse facial features, bulging or forward-projecting philtrum, broad nasal tip, micrognathia, thin upper lip vermilion, hypertelorism, plagiocephaly, microretrognatism, full and drooping cheeks, large, badly hemmed ears with bulky lobes, bulging eyes and marked suborbital folds . Placenta analysis showed single umbilical artery and micro vacuolized appearance of Hofbauer cells, compatible with lysosomal overload . Fig. 2 Post-termination studies. (A) External examination post-medical abortion at 35 GW; coarse facial features with broad philtrum, broad nasal tip, micrognathia, thin upper lip vermilion, hypertelorism, plagiocephaly, microretrognatism, full and drooping cheeks, bulging or forward-projecting philtrum, large, badly hemmed ears with bulky lobes, bulging eyes, marked suborbital folds. (B) Optical microscopic image showing vacuolization of Hofbauer cells (H&E stain; ×100) Given that the substitution variant (c.590G > A) is located at the canonical acceptor site of exon 6, we investigated the possible splicing impact. This was achieved through the use of a Minigene assay (as detailed in Gaildrat et al. ). In this construct, the c.590G > A variant is responsible for the appearance of a major transcript with complete retention of intron 5, as well as a few alternative transcripts with retention of the last 22, 25 and 28 nucleotides of intron 5. Complete retention of intron 5 leads to a premature stop codon, p.(Phe198Valfs*127). A second construction, using a longer sequence, revealed the complementary role of a 2nd rare variant (c.494-57G > A), in cis of the c.590G > A variant, also altering splicing. This variant creates an additional cryptic splicing site, resulting in the retention of the final 55 nucleotides of intron 4. This, in turn, leads to the formation of a premature stop codon (p.(Arg166Valfs*18)).)). These functional studies (enzyme activity and transcript studies) allowed us to reclassify these variants as pathogenic (class 5 according to ACMG classification). The etiology of fetal hepatosplenomegaly is multifactorial. It is crucial to determine the underlying cause, as some diagnoses are amenable to treatment or may have subsequent gestational implications (e.g., neonatal hemochromatosis). Major contributors include fetal infections, summarized by the acronym TORCH (Toxoplasmosis, Other infections (Parvovirus, Syphilis, Zika, Chickenpox, HIV), Rubella, Cytomegalovirus, Herpes Simplex). Hepatomegaly may also result from fetal anemia or hepatic tumor, such as hepatoblastoma, hemangiomas, mesenchymal hamartomas… . Among constitutional genetic causes, trisomy 21 is responsible in 5–10% of cases for transient abnormal myelopoiesis , a pre-leukemic syndrome which is responsible for hepatomegaly in fetuses and newborns . Wiedemann-Beckwith syndrome combines macroglossia, omphalocele, polyhydramnios, macrosomia and visceromegaly with hepatosplenomegaly . Lysosomal storage diseases (LSD) are a classic yet rare cause of hepatosplenomegaly, with few cases arising during the prenatal period and often associated with others signs like hydrops fetalis and/or fetal ascitis. Indeed, hydrops fetalis is the most frequent presentation indicator of lysosomal pathology, while associated antenatal hepatomegaly is seldom documented. In a context of nonimmune hydrops fetalis, the estimated prevalence of LSD is between 1.3 and 8% [ 10 – 12 ] with the most frequently diagnosed conditions (> 70% of cases) being mucopolysaccharidosis type VII , galactosialidosis and sialidosis , infantile free sialic acid storage disease , Gaucher disease , and GM1 gangliosidosis . In addition, a significant number of other lysosomal pathologies have been identified at least once as a cause of hydrops fetalis , including, but rarely, a few cases of MPS I. Another way LSD may manifest during the prenatal period is chondrodysplasia punctata, as observed in mucolipidosis type II and GM1 gangliosidosis , or multiple dysostosis, as in mucolipidosis type II . In MPS I, the earliest signs typically manifest after birth, and are often present from the first month of life but are not necessarily specific: breathing difficulties, otitis media, hearing loss, hernias, hypotonia, feeding difficulties . Consequently, the diagnosis is often made later, except in countries where newborn screening has been introduced . In the MPS I registry study of 115 individuals with Hurler form with no family history, the median age at diagnosis was 0.8 years . The most specific signs are kyphosis, corneal opacity, characteristic coarse facial features and hepatomegaly. However, hepatomegaly is classically one of the later signs, present in 61.4% of patients and detected after a median of 9.8 months in this study . In prenatal care, only a few isolated cases of MPS I with hydrops have been published , with most prenatal diagnoses being made because of family history. In France, pregnancy monitoring includes 3 systematic ultrasounds (first 9–11 WG, second 20–22 WG and third trimester 30–32 WG). It is challenging to diagnose lysosomal pathology prior to the second trimester ultrasound, given that the initial ultrasound signs were documented in the literature at this gestational age. Here, the fetus exhibited indications of visceral overload from the prenatal period. This severe expression of the pathology is consistent with genetic studies that identified two variants resulting in premature stop codons, and therefore probably no mRNA, targeted by non-mediated decay. To date, over 300 pathogenic variants have been described and reported in the IDUA gene . These include some over-represented variants (e.g., p.Trp402Ter, p.Gln70Ter, p.Pro533Arg) as well as more complex and difficult to interpret pseudodeficient alleles (e.g., p.His82Gln, p.Ala300Thr) . In severe forms, > 79% of genotypes include at least one nonsense/splice/frameshift variant; however, in many cases (i.e., > 20%), the combination of variants is unique to a single patient . The enzymatic studies corroborate this finding, with a marked decrease in α-L-iduronidase activity. Given the grave ramifications of this disease and the risk of recurrence (25%) for future pregnancies, a prenatal or pre-implantation diagnosis can be offered to the couple. From a genetic standpoint, it is noteworthy that only the c.590G > A variant of the c.[494-57G > A;590 G > A] complex allele was identified during the WES. The c.494-57G > A variant is located more than 50 bp from the intron-exon junction and was therefore not covered by WES (the presence of this second variant was confirmed by targeted sequencing in the fetus (Table 1 )). However, as it is upstream of the c.590 G > A variant, the c.494-57G > A has probably the greatest biological impact, although another substitution (c.589G > A p.(Gly197Ser)) on the same codon as the first variant has already been reported as pathogenic . Without being associated with the c.590 G > A variant, the c.494-57G > A variant alone might not have been detected, and the diagnosis of MPS I might therefore have been delayed or not made at all. In the absence of any previous description of signs of prenatal visceral overload in MPS I, as reported in this case, it is unlikely that the pathology would have been sought by targeted enzymatic techniques, as has been done historically, and as was done here following the genetic suspicion. The advent of prenatal genomics will provide better coverage of these intronic variants and therefore improve diagnostic results . This also underlines the importance of histological analysis of the placenta (and the fetus when feasible) in instances of suspected lysosomal disease, as this can assist in the diagnostic process when a definitive diagnosis has not been reached during the prenatal period. Microscopically, macrophage overload is a constant feature in lysosomal storage disorder, with macrophages being particularly rich in lysosomes. Lysosomal overloading is identified by the presence of vacuoles in affected cells, for example in the placenta, and particularly in Hofbauer cells . Vacuoles may be present as early as the first trimester, though they may only be visible under electron microscopy. In some cases, the location and composition of the vacuoles can assist in formulating a diagnosis . The chorionic villi from the placenta of fetuses with MPS1 displayed a remarkable degree of vacuolation of stromal cells , with vacuoles being relatively scarce within the cytotrophoblast and occurred with greater regularity in fibroblasts and endothelial cells . This case illustrates the growing interest in prenatal studies at the exome or genome level for the diagnosis of rare genetic diseases, making it possible to broaden the clinical spectrum of these diseases, and to make informed decisions for the current pregnancy, in particular when ultrasound signs are not specific, and carrying out a prenatal diagnosis for subsequent pregnancies. | Clinical case | biomedical | en | 0.999996 |
PMC11697680 | COVID-19 has had a severe impact on global public health systems and remains one of the most severe epidemics now over the world. As of January 2023, WHO reported about 750 million confirmed cases of COVID-19 globally, covering about 6.8 million deaths . The virus is constantly mutating, and the number of infected people continues to grow rapidly. According to statistics from outpatient and emergency clinics in various hospitals, about 40–50% of the patients are prone to serious illnesses and thus require hospital admission. Of these, 10–20% of patients are prone to critical illnesses and need to be admitted to intensive care units (ICUs) for supportive treatment. Consequently, the number of COVID-19 patients admitted to ICUs has markedly increased, and in some places, temporary ICUs have even been built in large numbers. Previous studies have shown that bloodstream infection (BSI) may occur in approximately 7% of COVID-19 inpatients . The incidence of BSI in COVID-19 patients in ICUs is 10–50%, which is a significant increase and a new record and is four times higher than that in non-COVID-19 patients . BSI can lead to bacterial or viral infections in various organs. In poorly treated or severe cases, it may lead to sepsis, resulting in fever and generalized pain, which gravely impacts the health. In severe cases, it can lead to the risk of death. ICU-BSI increases the risk of 30-day mortality by 40% . In addition, resuscitation therapy such as mechanical ventilation and endotracheal intubation in ICU increases the risk of BSI and poses a great challenge to anti-infective treatment [ 5 – 7 ]. COVID-19 infection may become a small-scale recurrent epidemic pattern in the future. Thereby, it is necessary to stratify patients at risk of BSI and take timely measures to reduce the occurrence of BSI. Recent studies found that gender, SAPS II score, underlying medical complications [e.g., diabetes mellitus (DM), hypertension], treatment-related factors (e.g., mechanical ventilation, intubation, ECMO), and drug-related factors (e.g., Tocilizumab, Methylprednisolone) might be associated with an increased risk of BSI in critically ill patients with COVID-19 in ICUs. However, the conclusions of various studies about the risk factors for BSI are inconsistent. Regarding gender, most studies found that men had a higher risk of developing BSI, which reached 60–70%, and men accounted for most COVID-19 admissions to ICUs [ 8 – 13 ]. Some studies concluded that gender was not associated with the risk of infection [ 14 – 18 ]. Interestingly, 2 studies found the risk of BSI was equal for males and females . DM, as the most common underlying disease in human beings, is statistically associated with a higher risk of BSI in most studies [ 8 , 10 , 13 , 15 – 18 , 20 , 21 ]. However, other studies concluded that DM did not lead to a higher risk of BSI . Additionally, the conclusions regarding hypertension were not clear and consistent. In most studies, statistical analyses suggested that hypertension caused a higher risk of BSI [ 10 – 13 , 16 , 18 , 19 ]; whereas some studies indicated no direct correlation between hypertension and the risk of BSI . Therefore, in this study, we aimed to determine the risk factors for BSI in COVID-19 patients in ICUs through a systematic review and meta-analysis. PubMed, Embase, Web of Science, and Cochrane Library were comprehensively searched for English papers up to July 2024, while references in related literature were manually checked. The following keywords were utilized to screen published clinical information on risk factors for BSI in COVID-19 patients in ICUs: “intensive care unit”, “covid-19”, “sepsis”, and “bloodstream infection”. All literature retrieved was imported into EndnoteX9 for paper screening. The search process was undertaken by two reviewers and any disagreements were addressed through discussion. (Table S1) Inclusion criteria covered: (1) study type: observational study; (2) patients: with COVID-19 in ICUs; (3) study content: risk factors for BSI in COVID-19 patients in ICUs. Exclusion criteria encompassed: (1) letters, reviews, conference proceedings, commentaries, and papers with unavailable full text and unsuitable types of publication; (2) with no available primary data; (3) published not in English. Based on the eligibility criteria, articles imported into EndnoteX9 were initially screened by reviewing the titles and abstracts. Ineligible articles were excluded, and the remaining articles were read through the full text to screen the eligible ones for meta-analysis. Relevant research data were extracted. All procedures were performed by two researchers (Ting Jiang and Jun Wang). The following data were extracted: (1) general information: authors, publication date, study area, study design, and period; (2) study characteristics: sample size, mean age, and gender distribution; (3) risk factors: SAPS II, hypertension, DM, chronic pulmonary disease, liver disease, immunosuppression, chronic kidney disease, heart disease, and tumors; and (4) treatment records: medication administration, treatment modalities, and duration of treatment. In studies where some information was lacking, we attempted to contact the authors by phone or email. In case of disagreement in literature screening and data extraction, a third researcher (Wei Wang) was consulted. Two researchers independently assessed the study quality based on the Newcastle-Ottawa Scale (NOS) for cohort studies and case-control studies. The NOS scale covered three dimensions and eight items, with a maximum score of 9 points. A score < 4 was defined as low quality, 4–6 as moderate quality, and ≥ 7 as high quality. The higher score implied a lower risk of bias. The quality of cross-sectional studies was evaluated using a scale recommended by the Agency for Healthcare Research and Quality. The scale consisted of 11 items. Answers included yes, no, and unclear. For the answer of “yes”, the item was assigned a score of 1. The higher the score, the higher the quality: low quality = 0–3; medium quality = 4–7; high quality = 8–11. If two researchers disagreed with quality assessment, a third researcher arbitrated. Statistical analyses were implemented using Stata 15.0 software. Categorical variables were analyzed using odds ratio (OR) while continuous variables using weighted mean difference (WMD). Data from the original studies were transformed before meta-analysis if they were not described as mean and standard deviation. All effect sizes were represented as 95% confidence intervals (CI). Heterogeneity was analyzed using I 2 . If there was no significant statistical heterogeneity between outcomes ( P ≥ 0.1, I 2 ≤ 50%), a meta-analysis was performed using a fixed-effects model, otherwise, using a random-effects model. For highly heterogeneous results, sensitivity analyses were performed on the results to validate the stability and reliability of the results. Sensitivity analyses were conducted on all outcomes. By eliminating the articles one by one, the stability of the remaining results was observed. For risk factors that included ≥ 10 articles, the Egger test was adopted to determine whether there was publication bias. The database searches retrieved 9099 relevant English articles. 6914 articles were obtained after duplicates were removed. Subsequently, the titles and abstracts were read to exclude the ineligible studies, leaving 301 studies. Finally, after reading the full text, 55 studies were enrolled in the meta-analysis [ 3 , 5 – 58 ]. The screening process is represented in a PRISMA flowchart. . The 55 included studies comprised 48 cross-sectional studies [ 5 – 9 , 11 , 12 , 14 , 15 , 17 , 18 , 20 – 23 , 26 – 58 ] (Table 1 ), 6 cohort studies , and 1 case-control study (Table 2 ), including 25,939 patients ranging in age from 18 to 94 years across Italy, CHN, UK, US, Spain, France, Greece, Austria, Singapore, India, Germany, Turkey, Switzerland, Sweden, and Portugal. The meta-analysis results of the research indicators are shown in Table 3 . Among them, 48 cross-sectional studies had average AHRQ scale scores > 7, and 6 cohort studies and 1 case-control study had average NOS scores > 7, implying that these articles were all of high quality. Table 1 Studies characteristics and quality. (cross-sectional study) Study Published time Country Study design Patients Age Gender(male) AHRQ grade Amit, M. et al. 2020 Israel Cross-sectional study 156 72(60–82) 108/48 9 Bonazzetti, C et al. 2020 Italy Cross-sectional study 89 61.5(53.1–68.7) 69/20 8 Fu, Guoping et al. 2020 CHN Cross-sectional study 51 60.94 ± 14.87(25–87) 27/14 8 Giacobbe, D. R et al. 2020 Italy Cross-sectional study 78 66 IQR 57–70 60/18 9 Anwar, Asad et al. 2021 UK Cross-sectional study 44 17–77 M59.5(IQR 50.5–64.5)/21–80 M59(IQR 49–67.5) 34/10 8 Bardi, Tommaso et al. 2021 Spain Cross-sectional study 140 61(57–67) 108/32 8 d’Humières, C et al. 2021 FRANCE Cross-sectional study 197 59(50–68) 148/49 8 Dupuis, Claire et al. 2021 FRANCE Cross-sectional study 303 61(53–70) 239/64 9 Grasselli, G. et al. 2021 Italy Cross-sectional study 774 62 (54–68) 597/177 9 Karruli, A. et al. 2021 Italy Cross-sectional study 32 68 [55.25–75] 23/9 9 Kokkoris, S. et al. 2021 Greece Cross-sectional study 50 Median age 64 36/14 8 Llitjos, Jean-Francois et al. 2021 FRANCE Cross-sectional study 176 63 (55–73) 134/42 8 Ong, C. C. H. et al. 2021 Singapore Cross-sectional study 71 52(39–66) 59/12 9 Ramos, Rafael et al. 2021 Spain Cross-sectional study 213 61(52–71) 110/103 8 Roedl, Kevin et al. 2021 Germany Cross-sectional study 223 69 (58–77.5) 163/60 8 Rollas, Kazim et al. 2021 Turkey Cross-sectional study 38 NR NR 9 Søgaard, K. K et al. 2021 Switzerland Cross-sectional study 41 64.8(54.7–72.1) 31/10 9 Suarez-de-la-Rica, A. et al. 2021 Spain Cross-sectional study 107 62.2 ± 10.6 76/31 8 Yakar, Mehmet Nuri et al. 2021 Turkey Cross-sectional study 249 71(61–80) 172/77 9 Yao, Ren-qi et al. 2021 CHN Cross-sectional study 35 64(59–67) 25/10 8 Zamora-Cintas, M. I. et al. 2021 Spain Cross-sectional study 54 NR NR 8 Zhang, J. et al. 2021 CHN Cross-sectional study 32 63.34 ± 12.48 20/12 9 Ahlstrom, Bjorn et al. 2022 Swedish Cross-sectional study 7382 63 (53–72) 5191/2191 9 Brücker, W. et al. 2022 Germany Cross-sectional study 61 66.4 ± 13.3 34/27 9 Caiazzo, L.et al. 2022 Italy Cross-sectional study 89 68.1 ± 9.3 66/23 8 Cidade, Jose Pedro et al. 2022 Portugal Cross-sectional study 118 63.3 ± 13.1 87/31 8 Ćurčić, M. et al. 2022 Croatia Cross-sectional study 692 NR NR 8 da Costa, R. L. et al. 2022 Brazil Cross-sectional study 191 69.66 ± 16.13 116/75 8 De Bruyn, A. et al. 2022 Belgium. Cross-sectional study 94 69.65 ± 11.29 55/39 9 DeVoe, C. et al. 2022 US Cross-sectional study 126 58.1 ± 17.9 85/41 8 Erbay, Kubra et al. 2022 Turkey Cross-sectional study 85 67.23 ± 13.05 54/31 8 Kozlowski, Bartosz et al. 2022 Poland Cross-sectional study 172 67.76 ± 11.16 112/60 8 Kurt, Ahmet Furkan et al. 2022 Turkey Cross-sectional study 470 66 ± 14.87 301/169 9 Lepape, Alain et al. 2022 FRANCE Cross-sectional study 4465 63.30 ± 11.68 3132/1333 8 Mantzarlis, K. et al. 2022 Greece Cross-sectional study 84 68.85 ± 12.17 56/28 9 Mustafa, Z. U. et al. 2022 Pakistan Cross-sectional study 636 NR 398/238 7 Pandey, M. et al. 2022 UK Cross-sectional study 299 NR 101/198 9 Roda, Silvia et al. 2022 Italy Cross-sectional study 22 61.36 ± 10.30 20/2 8 Routsi, C. et al. 2022 Greece Cross-sectional study 600 NR NR 8 Russo, A. et al. 2022 Italy Cross-sectional study 32 62.50 ± 10.99 21/11 8 Seitz, T. et al. 2022 Austria Cross-sectional study 117 57.2 ± 11.9 72/45 8 Torrecillas, Miriam et al. 2022 Spain Cross-sectional study 220 63.65 ± 12.69 169/51 8 Alenazi, T. A. et al. 2023 Saudi Arabia Cross-sectional study 118 60.97 ± 16.32 74/43 8 Alessandri, F. et al. 2023 Italy Cross-sectional study 138 62.20 ± 15.36 97/41 9 Bedenić, B. et al. 2023 Croatia Cross-sectional study 118 71 years (range 25–94) 78/40 8 Bonazzetti, C. t al. 2023 Italy Cross-sectional study 537 64.65 ± 11.15 402/135 9 Guanche Garcell, H. et al. 2023 Cuban Cross-sectional study 130 NR NR 8 Taysi, M. R. et al. 2023 Turkey Cross-sectional study 205 68.4 ± 13.1 119/86 8 AHRQ Agency for Healthcare Research and Quality, NR Not reported Table 2 Studies characteristics and quality. (Cohort study and case-control study) Study Published time Country Study design Patients Age Gender (male) NOS grade Cataldo, M. A et al. 2020 Italy Cohort study 57 62 ± 13 41/16 7 Garcia, Pedro David Wendel et al. 2020 European Cohort study 639 63 (53–71) 480/159 8 Zhang, H. et al. 2020 CHN Cohort study 38 64.76 ± 13.76 32/6 7 Massart, N. et al. 2021 France, Switzerland, Belgium Cohort study 4010 NR NR 8 Palanisamy, N. et al. 2021 India Cohort study 750 60 ± 17.71 562/188 8 Bartoszewicz, M. et al. 2023 Poland Cohort study 201 66.1 ± 12.1 114/87 8 Dupper, A. C. et al. 2022 US Case-control study 96 64.91 ± 9.51 57/39 8 NOS Newcastle-Ottawa Scale, NR Not reported Table 3 Outcomes of meta-analysis Risk factors No. of studies Heterogeneity Analysis Statistical model statistical method Effect estimate P I² P (95%CI) Hypertension 10 70.4% 0.000 Random-effects OR 1.30(0.92,1.83) 0.131 Chronic pulmonary disease 11 23.4% 0.221 Fixed-effects OR 1.07(0.90,1.29) 0.443 Diabetes 12 50.2% 0.024 Random-effects OR 1.34(1.04,1.73) 0.022* Gender 14 0.0% 0.059 Fixed-effects OR 1.28(1.10,1.50) 0.006* Liver disease 6 2.3% 0.402 Fixed-effects OR 0.86(0.47,1.58) 0.635 Immunosuppressive diseases 5 29.9% 0.222 Fixed-effects OR 1.11(0.88,1.40) 0.375 Chronic kidney disease 6 0.0% 0.751 Fixed-effects OR 1.20(0.78,1.84) 0.411 Heart disease 10 0.0% 0.550 Fixed-effects OR 1.00(0.85,1.17) 0.957 Tocilizumab 9 34.3% 0.144 Fixed-effects OR 1.04(0.74,1.46) 0.815 Tumors 9 10.2% 0.350 Fixed-effects OR 1.04(0.78,1.37) 0.807 ECMO 4 74.1% 0.009 Random-effects OR 2.70(1.17,6.26) 0.020* Tracheal intubation 4 67.8% 0.025 Random-effects OR 8.68(4.68,16.08) < 0.001* Mechanical ventilation 2 0.0% 0.385 Fixed-effects OR 22.00(3.77,128.328) 0.001* Methylprednisolone 2 13.5% 0.282 Fixed-effects OR 2.24(1.24,4.04) 0.008* Methylprednisolone + Tocilizumab 2 71.0% 0.063 Random-effects OR 4.54(1.09,18.88) 0.037* Steroids 3 87.6% 0.000 Random-effects OR 1.17(0.15,9.23) 0.882 Remdesivir 2 54.4% 0.139 Random-effects OR 0.80(0.14,4.41) 0.794 Dexamethasone 2 10.2% 0.291 Fixed-effects OR 1.64(0.85,3.15) 0.139 Renal replacement therapy 2 97.9% 0.000 Random-effects OR 0.86(0.11,6.57) 0.882 Central venous catheterization 2 0.0% 0.559 Fixed-effects OR 9.33(3.06,28.43) < 0.001* Length of stay in ICUs 8 0.0% 0.712 Fixed-effects WMD 10.37(9.29,11.44) < 0.001* SAPS II score 2 58.3% 0.122 Random-effects WMD 6.43(0.23,12.63) 0.042* WMD Weight mean difference, OR Odds ratio, CI Confidence interval * P < 0.05 Fourteen studies [ 8 – 21 ] explored the correlation between gender and BSI in COVID-19 patients in ICUs. A pooled analysis showed that male COVID-19 patients in ICUs were 28% more likely to develop BSI (OR = 1.28, 95% CI: 1.10–1.50, P = 0.006, I 2 = 0.0%). Fig. 2 Forest plot of univariate data associating BSI risk with ( A ) gender; ( B ) SAPS II score; ( C ) diabetes; ( D ) hypertension and ( E ) chronic pulmonary disease for patients with COVID-19 in ICU Two studies analyzed the correlation between SAPS II scores and ICU-BSI in COVID-19 patients. Meta-analysis showed that higher SAPS II scores were positively correlated with an increased incidence of BSI in COVID-19 patients in ICUs (WMD = 6.43, 95% CI: 0.23–12.63, P = 0.042, I 2 = 58.3%). Twelve studies [ 8 – 13 , 15 – 18 , 20 , 21 ] investigated the correlation between DM and BSI in COVID-19 patients in ICUs. Most of these articles did not indicate whether DM was a risk factor for BSI. Our pooled analysis unraveled that DM increased the occurrence of BSI in COVID-19 patients in ICUs by 34% (OR = 1.34, 95% CI: 1.04–1.73, P = 0.022, I 2 = 50.2%). There were conflicting results about the association between hypertension and BSI in COVID-19 patients in ICUs. Ten studies [ 8 , 10 – 13 , 16 – 20 ] were involved with mixed results. Meta-analysis demonstrated no correlation between hypertension and BSI in COVID-19 patients in ICUs (OR = 1.30, 95% CI:0.92–1.83, P = 0.131, I 2 = 70.4%). Because COVID-19 mainly attacked the respiratory system, we extensively investigated the correlation between chronic pulmonary disease and BSI in COVID-19 patients in ICUs through 11 studies [ 8 – 12 , 15 – 17 , 19 – 21 ]. Meta-analysis showed no correlation between chronic pulmonary disease and BSI in COVID-19 patients in ICUs (OR = 1.07, 95% CI: 0.90–1.29, P = 0.443, I 2 = 23.4%). Six studies investigated the correlation between liver disease and ICU-BSI in COVID-19 patients. Meta-analysis showed no correlation between liver disease and BSI in COVID-19 patients in ICUs (OR = 0.86, 95% CI: 0.47–1.58, P = 0.635, I 2 = 2.25%). Fig. 3 Forest plot of univariate data associating BSI risk with ( A ) liver disease; ( B ) chronic kidney disease; ( C ) heart disease; ( D ) immunosuppressive disease and ( E ) tumors for patients with COVID-19 in ICU Seven studies [ 9 – 11 , 15 , 19 – 21 ] investigating the association between chronic kidney disease and BSI in COVID-19 patients in ICUs were included. One article was excluded by sensitivity analysis and therefore six articles were included in the meta-analysis. It showed no correlation between chronic kidney disease and BSI in COVID-19 patients in ICUs (OR = 1.20, 95% CI: 0.78–1.84, P = 0.411, I 2 = 0.0%). Ten studies [ 9 – 11 , 13 , 15 , 16 , 19 – 21 ] investigating the correlation between heart disease and BSI among COVID-19 patients in ICUs were included. Meta-analysis showed no correlation between heart disease and BSI in COVID-19 patients in ICUs (OR = 1.00, 95% CI: 0.85–1.17P = 0.957, I 2 = 0.0%). All five studies showed no correlation between immunosuppression and ICU-BSI in COVID-19 patients. Meta-analysis also showed no correlation between immunosuppression and BSI in COVID-19 patients in ICUs (OR = 1.11, 95% CI: 0.88–1.40, P = 0.375, I 2 = 29.9%). Nine studies [ 8 – 11 , 17 , 19 – 21 ] investigating the correlation between tumors and BSI in COVID-19 patients in ICUs were included. Meta-analysis showed no correlation between tumors and BSI in COVID-19 patients in ICUs (OR = 1.04, 95% CI: 0.78–1.37, P = 0.807, I 2 = 10.2%). Four studies were included to investigate the association between tracheal intubation and ICU-BSI in COVID-19 patients. Meta-analysis revealed that tracheal intubation increased the risk of BSI in COVID-19 patients in ICUs by nearly 9-fold (OR = 8.68, 95% CI: 4.68–16.08, P < 0.001, I 2 = 67.8%). Fig. 4 Forest plot of univariate data associating BSI risk with ( A ) tracheal intubation; ( B ) mechanical ventilation; ( C ) ECMO; ( D ) CVC; ( E ) RRT and ( F ) Length of stay in ICU for patients with COVID-19 in ICU The correlation between mechanical ventilation and BSI in COVID-19 patients in ICUs was investigated by three studies . Since no heterogeneity was found ( P = 0.147, I 2 = 47.9%), a fixed-effects model was adopted and unraveled marked differences (OR = 4.98, 95% CI: 2.73–9.08, P < 0.001). After sensitivity analysis, the heterogeneity was greatly reduced ( P = 0.385, I 2 = 0.0%) when the study of Palanisamy, N et al. was excluded. The main source of heterogeneity might be the large sample size of their study, which tended to lead to unstable results compared to other studies with small sample sizes. Thus, this study was excluded because it led to a significant bias. The pooled analysis after exclusion using a fixed-effect model (OR = 22.00, 95% CI: 3.77–128.328, p < 0.001) showed statistically significant differences. The meta-analysis showcased that mechanical ventilation increased the risk of BSI by 22 times in COVID-19 patients in ICUs. The excluded study by Palanisamy, N et al. also showed that mechanical ventilation could increase the risk of BSI by 4-fold, in agreement with our results. Many critically ill patients have used ECMO for supportive care. Including four studies , we explored the correlation between ECMO and BSI among COVID-19 patients in ICUs. Meta-analysis manifested that ECMO increased the risk of BSI in COVID-19 patients in ICUs by nearly three times (OR = 2.70, 95% CI: 1.17–6.26, P = 0.020, I 2 = 74.1%). Two studies investigated the correlation between CVC and ICU-BSI in COVID-19 patients. Meta-analysis showed that CVC increased the Catheter-related BSI (OR = 9.33, 95% CI: 3.06–28.43, P < 0.001, I 2 = 0.0%). Two studies investigating the correlation between RRT and BSI in COVID-19 patients in the ICU were included. Meta-analysis showed no correlation between BSI and RRT in COVID-19 patients in ICUs (OR = 0.86, 95% CI: 0.11–6.57, P = 0.882, I 2 = 97.9%). Eight studies were included [ 12 – 14 , 17 , 18 , 20 , 21 , 53 ], all of which showed a strong correlation between the length of stay in ICUs and the occurrence of BSI in COVID-19 patients in ICUs. A meta-analysis showed that the longer the ICU stay, the higher the risk of BSI in COVID-19 patients in the ICU (WMD = 10.37, 95% CI:9.29–11.44, P < 0.001, I 2 = 0.0%). The correlation between Tocilizumab and BSI in COVID-19 patients was investigated in 10 studies [ 8 – 12 , 16 , 17 , 20 , 21 , 55 ]. One article was excluded by sensitivity analysis and therefore nine articles were enrolled in the meta-analysis. There was no correlation between Tocilizumab and BSI in COVID-19 patients in ICUs (OR = 1.04, 95% CI: 0.74–1.46, P = 0.815, I 2 = 34.3%). However, this may explain why Tocilizumab is widely used for severe and critically ill COVID-19 patients in ICUs under the guidance of guidelines. Fig. 5 Forest plot of univariate data associating BSI risk with ( A ) Tocilizumab; ( B ) Methylprednisolone; ( C ) Methylprednisolone and Tocilizumab combination; ( D ) Steroids; ( E ) Remdesivir and ( F ) Dexamethasone for patients with COVID-19 in ICU Two studies investigated the association between Methylprednisolone and ICU-BSI in COVID-19 patients. Meta-analysis signified that Methylprednisolone was linked with BSI in COVID-19 patients in ICUs (OR = 2.24, 95% CI: 1.24–4.04, P = 0.008, I 2 = 13.5%). Meanwhile, we found that the combination of Methylprednisolone and Tocilizumab significantly increased the risk for BSI in COVID-19 patients in ICUs (OR = 4.54, 95% CI: 1.09–18.88, P = 0.037, I 2 = 71%). All the studies on the risk of steroid use on ICU-BSI in COVID-19 patients were included, and the results were found only in 3 studies . Meta-analysis showed no correlation between steroid use and BSI in COVID-19 patients in ICUs (OR = 1.17, 95% CI: 0.15–9.23, P = 0.882, I 2 = 87.6%). This may be related to the fact that steroids are widely used as they are believed to improve the recovery of patients. Two studies investigated the association between Dexamethasone and ICU-BSI in COVID-19 patients. Meta-analysis showed no correlation between Dexamethasone use and BSI in COVID-19 patients in ICUs (OR = 1.64, 95% CI: 0.85–3.15, P = 0.139, I 2 = 10.2%) . The correlation between Remdesivir and ICU-BSI in COVID-19 patients was investigated in 2 studies . Meta-analysis showed no correlation between Remdesivir and BSI in COVID-19 patients in ICUs (OR = 0.80, 95% CI: 0.14–4.41, P = 0.794, I 2 = 54.4%). This may be related to the fact that Remdesivir is considered a potent drug for the treatment of COVID-19, with significant efficacy, and therefore is more widely used for severe and critically ill patients in ICUs. The stability of the results of the remaining articles was estimated by excluding each article in turn. Sensitivity analyses for gender, SAPS II score, DM, hypertension, chronic pulmonary disease, liver disease, heart disease, immunosuppressive disease, tumor, tracheal intubation, ECMO, CVC, RRT, length of stay in ICUs, and the use of Methylprednisolone, Steroids, and Remdesivir revealed that the results were relatively stable. In the sensitivity analysis of mechanical ventilation, the study by Palanisamy, N et al. greatly impacted the results, so the results were pooled after the exclusion of that article, and the results were more stable. Similarly, in the sensitivity study of chronic kidney disease, it was found that Massart, N et al. greatly influenced the results. After excluding the article and re-combining the results, the results were more stable. In the sensitivity study on the use of Tocilizumab, the study by Bonazzetti, C et al. greatly influenced the results. The results were more stable when the article was excluded, and the results were re-combined. The sensitivity analyses of other factors implied stable and insignificant changes, so these studies were retained. Publication bias was examined using the Egger test for the risk factor containing ≥ 10 articles. There was no publication bias for men ( P = 0.187), DM ( P = 0.142), hypertension ( P = 0.396), heart disease ( P = 0.592), and chronic pulmonary disease ( P = 0.671). In this meta-analysis, we aimed to identify risk factors for BSI in COVID-19 patients in ICUs. Among the studies with available data, 55 published English studies [ 3 , 5 – 58 ] investigating risk factors associated with BSI in COVID-19 patients in ICUs were included. Our findings showed that male, DM, tracheal intubation, mechanical ventilation, CVC, ECMO, Methylprednisolone use, higher SAPS II score, and longer ICU stay were risk factors for ICU-BSI in COVID-19 patients. In addition, hypertension, chronic pulmonary disease, liver disease, chronic kidney disease, heart disease, immunosuppression, tumor, RRT, and the use of Tocilizumab, Steroids, and Remdesivir neither increased nor decreased the risk of ICU-BSI in COVID-19 patients. Our analyses showed that male COVID-19 patients were at a higher risk of BSI in ICUs and that males also made up most of the ICU admission population. A previous study by Zamora-Cintas, M. et al. highlighted that male patients had a higher risk of BSI, in line with our results. COVID-19 virus infection mainly affects pulmonary function, resulting in more patients with pulmonary dysfunction being admitted to the hospital, and more severe patients need to be admitted to ICUs, which makes them more susceptible to BSI. However, we did not study whether smoking was a risk factor for BSI in COVID-19 patients in ICUs and the proportion of men who smoke., which might require more data support. Our findings also showed that the SAPS II score directly reflected the risk of BSI in COVID-19 patients in ICUs, in agreement with the findings of Massart, N et al. . SAPS II score, as an important evaluation component in ICUs, to some extent, also reflects infection indicators, which is directly related to our study. In short, a higher SAPS II score indicates a more severe condition and a worse prognosis . DM is a common chronic underlying disease in clinical practice. Our results showed that DM was also an associated risk factor for ICU-BSI in COVID-19 patients. This may be related to diverse complications associated with DM and the poor resistance of diabetic patients, which makes them susceptible to a variety of related infections and directly increases the risk of BSI . Our findings suggested that tracheal intubation substantially increased the risk of ICU-BSI in COVID-19 patients, in support of the findings of Bonazzetti, C et al. and Rollas, Kazim et al. . Tracheal intubation is a common resuscitation technique in ICUs and is essential to save patients in respiratory distress. It ensures that the patient receives an adequate supply of oxygen and provides mechanical ventilation support to maintain normal respiratory function . This makes mechanical ventilation support also a possible risk factor for BSI in COVID-19 patients in ICUs. Invasive treatment is highly likely to cause airway damage, and tracheal intubation may introduce bacteria or other pathogens, increasing the risk of infection in patients. It is also easy for micro-aspiration to occur after tracheal intubation, leading to lung infections . All these directly increase the risk of BSI. COVID-19 has become a specific infection that involves the pathophysiology of the lungs, including endothelial and epithelial changes, pulmonary embolism, and microvascular thrombosis. In addition, secondary infectious injury can cause acute lung injury and prolong mechanical ventilation . Zhang, J et al. showed that multiple invasive treatments were important risk factors for BSI. Early extubation and regular assessment of infection should be done, therefore early anti-infective therapy is important . CVC is widely used in the resuscitation of severe and critically ill patients, which is conducive to the measurement of central venous pressure, long-term medication, and large and rapid rehydration, thus preventing venous damage and repeated puncture. However, a common complication of CVC is deep vein thrombosis, which also leads to the invasion of external bacteria and infection. Patients present with persistent low-grade fever, and the simultaneous presence of bacteria and thrombus can exacerbate the infection . Our study revealed that prolonged CVC substantially increased the risk of ICU-BSI in COVID-19 patients. Therefore, there is a need for timely monitoring of the situation and increased measures for infection control and nursing care for CVC. ECMO serves as an important therapeutic tool to provide continuous extracorporeal respiratory and circulatory function for critically ill patients presenting with cardiopulmonary failure. Some studies showed that infections were highly susceptible to occurring after the use of ECMO, which was related to the fact that patients with low immunity were susceptible to systemic hematogenous infections, thus dramatically increasing the risk of fungal infections . This was in general agreement with the results of our study. Meanwhile, ECMO may result in renal failure in about 50% of patients, which may require RRT . However, our study found that RRT was not a risk factor for ICU-BSI in COVID-19 patients, which may need to be supported by more data. In particular, intuitive data in our study pointed out that the longer the treatment duration in ICUs, the higher the risk of BSI in COVID-19 patients. The possible reasons are as follows: first, the treatment time reflects the severity of the patient’s condition, and a longer ICU stay may mean that the patient’s condition is more critical; second, the longer the treatment time, the higher the chance of nosocomial infections , which is closely related to the prolonged use of antibiotics and ward management; third, severe and critically ill patients in ICUs have low resistance and need to be left with various passages during treatment, and most COVID-19 patients have coughing symptoms, which is prone to aerosol dissemination and transmission of infectious disease between the patients, thus greatly increasing the risk of BSI . In our study, the use of Tocilizumab, Remdesivir, Steroids, and Dexamethasone did not correlate with the risk of BSI in COVID-19 patients in ICUs, but the use of Methylprednisolone and the combination of Methylprednisolone and Tocilizumab directly increased the risk of ICU-BSI in COVID-19 patients. This is associated with the fact that patients receiving glucocorticoid therapy are more likely to require ventilatory support, vasopressors, and RRT . Glucocorticoids are widely used and effective drugs during hospitalization, especially in ICU, due to their anti-inflammatory and immunosuppressive effects. Glucocorticoids inhibit the inflammatory chemotaxis of cells and the rate of phagocytosis to reach inflammation sites. In addition, glucocorticoids increase the stability of cells so that cell membranes are less likely to rupture, and cells are less likely to release lysosomal enzymes to phagocytose bacteria to destroy inflammatory foci, thus decreasing the body’s immunity and making it susceptible to viral or bacterial infections . Glucocorticoids will also directly inhibit the body’s immune function, thus inhibiting the body’s fever, so that the fever symptoms are not obvious, which in turn masks the severity of the disease and delays the diagnosis and treatment, leading to further deterioration of the condition . Moreover, glucocorticoids also inhibit mucosal exudation and inflammatory exudation. For patients with respiratory tract infections, glucocorticoids inhibit the exudation of inflammatory secretions, so that patients reduce coughing, which is not conducive to discharging bacterial sputum out of the body through coughing. Additionally, it delays the detection and treatment, thus aggravating the infection and increasing the risk of BSI greatly . Therefore, it is crucial to monitor the use of glucocorticoids rationally according to the condition . Meta-analysis unraveled that the incidence of BSI in COVID-19 patients in ICUs was 19.9%, similar to the currently reported 10–50% incidence rate. Compared with previous studies, in which 7% of COVID-19 hospitalized patients may experience BSI , the incidence of BSI in COVID-19 patients in ICUs increased nearly threefold. The incidence of ICU-BSI in COVID-19 patients varied in different studies, mainly because the occurrence of BSI lies in the detection of blood cultures. Also, it is somewhat difficult to exclude sampling contamination and detection contamination, and most patients in ICUs receive various types of medications, which may affect the detection of BSI . This is the first systematic review analyzing risk factors for ICU-BSI in COVID-19 patients, and the data were reviewed by two investigators to ensure accuracy. By incorporating an extensive array of papers with high quality, our findings provide an accurate and reliable framework for promptly identifying the risk of BSI occurrence in COVID-19 patients in ICUs. In addition, our findings provide more comprehensive references of risk factors for ICU-BSI in COVID-19 patients for clinical treatment, which is a guide for early prevention of BSI. However, some limitations need discussion. First, the studies covered diverse ethnicities, populations, methods, and periods of investigation, which is reflected in heterogeneity. However, this may be due to differences in study design rather than actual differences in outcome measures. Therefore, we used sensitivity analyses and random-effects models to verify the result stability in the presence of high heterogeneity. Second, the diagnosis of BSI in COVID-19 patients treated with Tocilizumab may be difficult because patients often do not have fever and have low serum levels of typical inflammatory markers, requiring further study. Third, Data on diseases such as diabetes and oncology did not have specific types of data, so specific rich data are needed to study their association with BSI. Fourth, few of the included studies analyzed the impact of post-invasive treatment care measures on the occurrence of BSI in COVID-19 patients in ICUs. Therefore, in the future, more assessments of the impact of treatment details on ICU-BSI in COVID-19 patients and randomized controlled trials are needed to enhance the reliability. Our findings showed that males, higher SAPS II scores, DM, tracheal intubation, mechanical ventilation, ECMO, CVC, longer ICU stays, and Methylprednisolone use increased the risk of ICU-BSI in COVID-19 patients. It is imperative for future research to integrate these factors into a comprehensive predictive assessment framework to identify and intervene promptly in COVID-19 patients at high risk for ICU-BSI to improve treatment outcomes and promote patient health recovery. | Study | biomedical | en | 0.999998 |
PMC11697695 | Self-contained electrochemical nanotechnologies empowering direct single-cell studies have long been pursued to deepen our knowledge of cellular heterogeneity and behaviors as well as to promote precise pathological diagnosis and therapeutic strategies [ 1 – 3 ]. Accessing the native cytosol by a transmembrane nanoelectrode is the prerequisite for subsequent electrochemical decipher of the hidden biomolecular information and intracellular dynamics . After penetration, application of potential-resolved electrochemistry at the conducting nanotips could trigger redox reactions that correlate with specific endogenous species, which is at the heart of current single-cell electroanalysis [ 6 – 8 ]. Nevertheless, to yield electrical signals, occurrence of Faradic reactions at specific potentials and wired circuits are the necessity, which severely restricts this methodology to redox-involved events. To take it a step further, scientists have endeavored to exploit new nanoelectrochemistry and mechanism [ 9 – 12 ]. For example, using a single nanopipette, confined electrochemiluminescence has recently been exploited for wireless intracellular analysis . Photoelectrochemistry of engineered organic molecules and semiconductor hybrid has been used for studying intracellular oxidative stress . Utilization of the ionic current rectification could even realize the detection of intracellular H 2 O 2 and microRNA . Cell interior functions as a viscoelastic material, and cellular viscosity is crucial for the ubiquitous diffusion-dependent biological reactions and cascades . While changes in cellular viscosity has been correlated with many pathologies and malfunctions , the homeostatic nature of cellular viscosity and the intrinsic viscosity regulation mechanism as well as its potential biochemical/biophysical implications are still not well understood. Caragine et al. recently reported observation of the movement and fusion of cellular components for investigation of the viscous properties of the cell . Persson et al. then revealed that the cell tunes cytosolic viscosity to counter alternation of temperature and energy . To perform these, studies demand the ability to measure viscosity directly in a living cell, which could provide a benchmark for monitoring the viscosity and identifying the impacts of specific diseases on the cellular material properties. Despite their respective advantages, existing methods, e.g., fluorescent and rotational magnetic ones , are in concerns of low spatiotemporal resolution, biocompatibility, or bioorthogonality issues, such as possible cytotoxicity or destructiveness of the probe, disturbance of the intrinsic cellular chemistry, and their photobleaching or degradation inside cells. To date, scientists still lack an ideal single-cell viscometer that is highly simple, durable, and spatiotemporal-resolved. Originally found in nature, iontronics has rapidly evolved as an advanced technology based on sophisticated control of ions as signal carriers, underpinning the operating rationale of aqueous circuits made of rationally designed nanostructures and thereon the controllable ionic transport [ 21 – 23 ]. Working in aqueous environments, iontronic devices have shown to be promising for sensing, logic circuiting, and brain-machine interfacing. Herein, using the θ -type nanopores , we report an accessible iontronic single-cell viscometer with a high spatiotemporal resolution (see Supporting Information for experimental details). The nanotool was operated upon the potential-directed reversible ionic motion within the nanoscale cytosolic fluid bridging two adjacent nanopores, which would create recordable ionic currents of nanoampere level. Using a theory that describes how viscosity affects the current, the altered cellular viscosity and differentiable signals could be linked, and the accurate viscosity at specific time and position could also be inferred. Glucose deprivation and heat shock experiments further revealed the practicability of this nanotool of high spatiotemporal resolution. This work achieves an accessible single-cell viscometer and sheds light on futuristic study of single-cell material property and assessment of associated chemotherapeutic effects. As illustrated in Figure 1 (a), the nanotool was used to position at the specific intracellular site of a single living cell, and the local viscosity was transduced by the directional ionic motion driven by a step pulse voltage (SPV) from a patch-clamp system. Alternative application of the SPV (−1.0 V/+1.0 V) could induce reversible ionic motion passing through the two adjacent apertures, while the very short application time of ~400 ms was set to minimize the influence upon both the intracellular microenvironment and the interfacial charge property of the two lumens. According to the Nernst-Planck equation and Stokes-Einstein relation , the ionic diffusion coefficient relies closely upon the solution viscosity (Eqs. S1–S10), indicating that the ionic current is controlled by the medium viscosity. In the present case, as illustrated in Figure 1 (b), the essential resistance (RE), consisting of those of nanopipettes, electrodes, and electrolyte, was kept unchanged throughout the study, whereas the resistance of the cytosol around the nanotips (RC) was the only variate during the measurement, suggesting the variation of the acquired ionic signal solely originated from the varied RC associated with the local viscosity. Figure 1 (c) depicts the side-view (upper) and top-view (bottom) scanning electron microscopy (SEM) micrographs of the as-used laser-pulled double-barrel quartz capillaries with θ -type nanopores. The two compartmentalized semielliptical orifices possessed similar dimensions of 104 ± 6 nm , which were separated by a quartz septum of 32 ± 3 nm in width. Incidentally, to ensure better performance, similar devices were preferred, which could be easily screened via the SPV-induced ionic responses, as reflected in and discussed with Figures S1–S2 , respectively. The practical applicability of the nanotool was then in vitro studied. Previous reports had revealed the potential-directed delivery or sampling functions of nanopipettes , the function of which was initially investigated using HEPES milieu containing saturated fluorescein. As shown in Figure 2 (a), such an effect was verified by 20 min electroosmosis under -1.0 V, which led to obvious green fluorescence of fluorescein within the nanotool. By contrast, as shown in Figure 2 (b), the SPV test under the same conditions could not induce the appearance of the fluorescence, indicating the absence of fluorescein within the nanotool. This was due to the very short SPV time, i.e., 400 ms, and the rapid application of inverse voltage. Next, the responses of the nanotool were acquired in specific glycerin-HEPES milieus of different viscosities ranging from 1.0 to 320 cP (Table S1 ), which were set in advance with the assistance of a commercial viscometer. As shown by the differentiable i - t curves in Figure 2 (c), the generated ionic signals were gradually inhibited from ca. 6.7 to ca. 1.0 nA, which could be attributed to the reduced ionic diffusion coefficient with the increase in viscosity. Figure 2 (d) shows the derived linear relationship between the ionic signals and the corresponding viscosities. Incidentally, the effects of possible ionic variation (Figure 2 (e)), pH change (Figure 2 (f)), and adsorption (Figure 2 (g)) were also excluded. As shown in Figure 2 (e), the HEPES milieu of 60 cP containing 130 mM K + and different Na + concentrations of (i) 2.5 mM, (ii) 10 mM, and (iii) 15 mM were tested, and hardly any signal variation could be observed. Besides, to study the possible pH variation effect, as shown in Figure 2 (f), HEPES milieu of 60 cP with pH of 6.6, 7.4, and 7.8 had almost no effect on the response of the nanotool . To study the possible adsorption effect, the nanotool was sequentially immersed into the cell lysate for 10 min and then tested for three repeated times. As shown in Figure 2 (g), the nearly identical signals indicated that there was little adsorption on the nanotool. Besides, the small droplet experiments further confirmed not only its capability to distinguish different viscosities but also its stability during the repeated measurements . Its capability for in vivo spatial-resolved studies was then studied through targeting specific subcellular regions. Exemplified by three individual A549, MCF-7, and HeLa cells, three specific subcellular regions, i.e., the lysosome-dense, mitochondrion-dense, and the near-nuclear ones, were chosen. As shown in Figures S4–S6 , it was observed that the penetration into the cytoplasm by the nanotool could not induce the change of the cellular morphologies. Our earlier staining studies had indicated the minimal destructiveness of such nanotools without affecting the cytomembrane integrity and cellular viability , which was also confirmed in the present case of double-barreled nanopores . Three different dyes, i.e., Lyso-Tracker Green, MitoGreen, and Hoechst 33342, were used to locate lysosomes, mitochondria, and nucleus, respectively. The abovementioned three subcellular locations of three different cells were then targeted as represented in Figures 3 (a)– 3 (c) and Figures S12–S14 , with 25 cells per group. The corresponding current during the nanotip penetrated throughout the cell membrane As shown in Figure S15 and Figure 3 (d), the ionic responses of these three positions could be, respectively, measured with the derived average viscosities of ca. 58 cP, 55 cP, and 93 cP for A549 cells, of ca. 55 cP, 68 cP, and 99 cP for MCF-7 cells, and of ca. 55 cP, 57 cP, and 99 cP for HeLa cells. Although significantly biased lysosomal viscosities of ca. 65-130 cP and mitochondrial viscosities of ca. 62-120 cP [ 15 , 30 – 32 ] had been reported by respective optical methods, the medium viscosities in the mitochondrion-dense and lysosome-dense regions are largely unknown. Our statistical results revealed the less deviated viscosities around these organelles. In particular, as compared to those of the two subcellular regions, our results further disclosed the highest viscosities ranging from ca. 80 to 130 cP in the near-nuclear region. This phenomenon may be due to the dense distribution of the cytoskeleton next to the nucleus. Tubulin, the main component of the cytoskeleton, has been reported to have great influence on the local cytoplasmic viscosity . The nanotool was then implemented for in vitro temporal-resolved studies via subjecting the representative HeLa cells to glucose deprivation and heat shock experiments, respectively. Intracellular deprivation of glucose was initially performed to induce the enhanced cytoplasmic viscosity . Experimentally, after 12 h culture in glucose-containing Dulbecco’s modified Eagle medium (DMEM), the HeLa cells were treated with glucose-free DMEM for 30 min for glucose deprivation, followed by collection of the ionic responses every 15 minutes within the cytoplasm. As shown in Figure 4 (a), the targeted cell well maintained its morphology during the 30 min detection. As compared to the case of normal HeLa cells with the viscosity fluctuation around ca. 60 cP , Figure 4 (b) records the obvious varied viscosity responses with increase from ca. 7 to 106 cP with 10 treated HeLa cells per group . Stress granules (SGs) are a membraneless organelle composed of protein-wrapped RNA, which undergo a nucleated assembly mechanism under stress conditions (like oxidative stress, heat shock, and ultraviolet irradiation) . Formation of SGs is closely related to many diseases, e.g., tumor apoptosis and Alzheimer’s disease; hence, exploration of SG-associated physiological indexes is of great significance [ 37 – 39 ]. Heat shock experiment was then conducted with treatment at 42°C for 70 minutes and then 37°C for 30 minutes to induce the formation of SGs , which was proven via an indirect immunofluorescence assay, with the assistance of Hoechst 33342 to stain the nucleus . As compared in Figure 4 (c), no green fluorescence in the normal HeLa cell indicated the absence of SGs in the cytoplasm, whereas the appearance of green fluorescence around the blue nucleus indicated the assembly of intracellular SGs (white arrow). After 60 min recovery, the disappearance of the green granules indicated the disassembly of SGs [ 18 , 37 – 40 ]. As shown in Figure 4 (d) and Figure S18 , decreased viscosity of ca. 40 cP was measured after the formation of SGs from heat shock. With the increase in time, the increase in the viscosity was gradually observed and recovered to normal level of ca. 60 cP within 60 minutes. This phenomenon could be attributed to the removal of stress conditions and thus the depolymerization of SGs, which was also confirmed with dark field observation. As recorded in Figure S19 , there were many bright particles in the heat-shocked cells, which then gradually disappeared within the same 60 minutes. To conclude, we have devised a high spatiotemporal iontronic single-cell viscometer based on the potential-controlled electroosmotic manipulation of ionic flow confined within the θ -type nanopores. The miniaturized θ -type nanotip permits cytomembrane penetration under physiological conditions with cell-context preservation and allows the reversible ionic motion within the two adjacent nanopores; the generation of the varied ionic currents could be intimately correlated with the altered intracellular viscosities. Significantly, practical spatial studies disclosed not only the less deviated medium viscosities of the subcellular lysosome- and mitochondria-dense regions than those of the organelles themselves but also the highest viscosities in the near-nuclear region among the studied three subregions. Exemplified by the glucose deprivation and heat shock experiments, changes of cellular viscosity were further temporally revealed. As compared to current implementations of θ -type nanopores (Table S2 ), this work presented a new application scenario, which should potentially contribute to the futuristic investigation of single-cell viscosity, single-cell diagnostics, and assessment of specific drugs and chemotherapies. All ionic current measurements were recorded using a Multiclamp 700B amplifier (Axon Instruments, USA) in voltage-clamp mode with the Digidata 1550 digitizer (Molecular Devices) and a PC equipped with pCLAMP10.5 software (Molecular Devices). The current-voltage ( I - V ) curves were recorded by sweeping the voltage from -1.0 V to +1.0 V and recorded with a sampling frequency of 5 kHz. A three-dimensional MP-225 micromanipulator (Sutter Instrument, Novato, CA) equipped with an inverted microscope (Ti2-E, Nikon, Japan) was applied for the precise control of the nanopipette to insert into cells under observation. The I - V and I - t recordings were plotted with Clampfit 10.5 software and OriginLab. Scanning electron microscopic (SEM) characterization was performed on a JSM-7800F instrument (JEOL, Japan), equipped with Stage Top Incubator (STX-EN-01, Tokai Hit Co., Ltd). Quartz theta (O.D.: 1.2 mm, I.D.: 0.90 mm; 7.5 cm length) was purchased from Sutter Instrument and were laser-pulled by using a P-2000 pipette puller (Sutter Instrument, Novato, CA, USA) with a two-line program containing the following parameters: line 1: heat = 900 , Fil = 4 , Vel = 30 , Del = 180 , and pull = 40 , and line 2: heat = 950 , Fil = 3 , Vel = 20 , Del = 180 , and pull = 120 . To ensure the reproducibility of nanotip geometry, the variation of pulling time was controlled within 0.2 second. Prior to electrochemical viscosity measurement, silver wires were immersed into the commercially 84 disinfectant for 25 min at room temperature to fabricate home-made Ag/AgCl wire electrodes. Then, two Ag/AgCl electrodes were, respectively, served as the working electrode and counter electrode as well as reference electrode inserted into two pores of the θ -nanopipette, which were then backfilled with NaCl 5 mM, KCl 120 mM, MgCl 2 4.5 mM, and HEPES 10 mM ( pH = 7.4 ). HeLa cells and A549 cells were cultured with DMEM in the presence of 10% FBS and antibiotics (penicillin and streptomycin) and maintained at 37°C in 5% CO 2 /95% air, while MCF-7 cells were cultured with normal RPMI 1640 medium in the presence of 10% FBS and antibiotics (penicillin and streptomycin) and maintained at 37°C in 5% CO 2 /95% air. HeLa cells were digested using the trypsin enzyme and then centrifuged with 1000 rpm for 5 min. Next, the sediment was diluted with 1 ml 1X PBS, and the cell density was then calculated via countess II (Life, the USA). Finally, the cell lysate was obtained by treatment with the diluted cell solution at 0°C using an ultrasonic cell crusher noise isolating chamber (Anxiu, China), with the program as follows: 35% power, 2 min and rod 6, and ultrasound performed for 5 s and paused for 5 s. The cellular vitality tests were performed via the dye staining experiments with the usage of propidium iodide (PI) and Hoechst 33342 , respectively. To locate lysosomes, Lyso-Tracker Green was diluted to 1/9250 ( V / V ) with DMEM, and A549, MCF-7, and HeLa cells were washed three times with 1x PBS. Next, the cells were incubated in the diluted Lyso-Tracker Green solution at room temperature for 30 minutes. To locate mitochondria, MitoGreen was diluted to 1/1000 ( V / V ) with PBS, and A549, MCF-7, and HeLa cells were washed three times with 1x PBS. Next, the cells were incubated in the diluted MitoGreen solution at room temperature for 15 minutes. To locate the nucleus, the cells were washed three times with 1x PBS and then stained with Hoechst 33342 at room temperature for 10 min . TIA-1 is an RNA-binding protein, which is regarded as playing a key role in SG assembly . Thus, SG degradation is able to be observed via tracking the statement of TIA-1 when the stress conditions alleviated. Experimentally, HeLa cells growing on the cell culture dish were fixed with 4% paraformaldehyde in PBS for 10 min at room temperature and then permeabilized with 0.3% TritonX-100 for 10 min, followed by incubation in blocking buffer (5% BSA and 0.1% Tween-20 in PBS) for 1 h before the addition of primary antibodies. A primary antibody of rabbit monoclonal anti-TIA-1 and goat anti-rabbit secondary antiserum lgG H&L were, respectively, diluted to 1 : 300 ( V / V ) and 1 : 1000 ( V / V ) using the blocking buffer. To characterize the stress granules, the heat-shocked HeLa cells were incubated in the diluted primary antibody solution at 4°C overnight. Then, after washing several times in PBS, the cells were incubated in diluted solution of the secondary antibody for 1 h in the dark. Then, the cells were rinsed three times with PBS and stained with Hoechst 33342 for 5 min to locate the nucleus . Heat-shocked HeLa cells (42°C for 70 minutes and 37°C for 30 minutes) were observed and photographed by inverted microscopy (IX71, Olympus) coupled with a true-color digital camera (Olympus DP80, Japan) in 10% FBS 1x PBS solution . | Review | biomedical | en | 0.999996 |
PMC11697698 | Pharmaceuticals are essential in healthcare for treating various conditions, but they must be administered carefully to avoid adverse effects. Concerns about environmental contamination have risen as pharmaceuticals and their metabolites, classified as “emerging contaminants” for over 15 years, are frequently detected in wastewater, eventually entering natural water sources through industrial discharge and human excretion . Developed nations often face issues with lifestyle drug misuse, while developing countries struggle with counterfeit life-saving drugs . Pharmaceuticals in wastewater, with concentrations from micrograms to nanograms per liter, pose risks as current treatment systems fail to remove them entirely, allowing these compounds and their by-products to reach drinking water sources . Moreover, organic solvents in pharmaceutical analysis contribute significantly to environmental and health issues, especially low-boiling-point solvents like n-hexane, toluene, methylene chloride, benzene, chloroform, methanol, ethanol, and acetonitrile, which have known toxic effects . For instance, benzene can cause anemia, hexane is a neurotoxin, and prolonged exposure to chloroform can damage the liver and kidneys. Recognizing the severe impact of these solvents, some are classified as “red solvents” by Pfizer due to their high toxicity . Fluorimetric techniques offer an efficient solution to reduce medication toxicity and solvent use. They require minimal sample preparation and solvent consumption. Simple instruments can detect fluorescent compounds at concentrations up to a thousand times lower than absorption spectrophotometry. By dissolving samples in a suitable solvent, chemical changes can make non-fluorescent compounds detectable, and applicable to both organic and inorganic substances. Medicines are essential for treating and preventing diseases, and for maintaining public trust in healthcare systems. However, all medications carry a risk of side effects, making it crucial to monitor both intended and unintended outcomes to balance risk and efficacy. Detecting impurities in both active ingredients and formulations is therefore vital . Early identification of unexpected substances, especially in new drugs, is important to reduce health risks. Pharmaceuticals, even at low doses, can be highly active, with contraceptives at nanograms/Liter (ng/L) levels potentially disrupting endocrine systems in municipal water. Excretion and improper disposal release pharmaceuticals into the environment, highlighting the need for effective detection to minimize their environmental and animal toxicity [ , , ]. As pharmaceutical research advances, the development of novel and highly selective analytical techniques is crucial for faster, more efficient methods that offer cost savings and reduce solvent consumption. This work evaluates recent quantitative analytical methods and their applications in pharmaceutical analysis, which are essential for quality control and require quick, reliable, and clear results. Key techniques used for the quantitative analysis of pharmaceutical compounds include capillary electrophoresis (CE), high-performance liquid chromatography (HPLC), Ultraviolet–Visible (UV/Vis) spectrophotometry, fluorimetry, titrimetry, voltammetry (in electroanalytical methods), thin-layer chromatography (TLC), gas chromatography (GC), and vibrational spectroscopies . Fig. 1 Traditional analytical techniques. Fig. 1 UV/Vis spectrophotometry is a widely used method in pharmaceutical analysis, measuring how much ultraviolet (UV) (190–380 nm) or visible (380–800 nm) radiation a chemical absorbs. It works by assessing the relationship between two beams of UV light, where absorption occurs when the energy matches the electronic transitions in the molecule. Combining UV and Vis spectrophotometry offers convenience due to its rapid analysis and ease of use . Fluorimeters and spectrofluorometers, which measure fluorescence (FL), offer higher sensitivity compared to other absorption methods. The limitations of FL spectroscopy arise from the sensitivity of detectors to different wavelengths and variations in energy intensity. Infrared (IR) spectroscopy, covering near IR (0.8–2 μm), mid IR (2–15 μm), and far IR ranges, provides detailed structural information of organic and inorganic compounds, with the fundamental 2–15 μm range being most informative for chemical structure analysis . Nuclear Magnetic Resonance (NMR) spectroscopy examines molecular structure at the atomic level, particularly using the 1 H and 13 C isotopes, providing detailed structural data through molecular vibrations . High-Performance Liquid Chromatography (HPLC) is an advanced technique that improves upon traditional column chromatography by enhancing separation speed, resolution, accuracy, and sensitivity. HPLC offers benefits like small sample size, customizable tests, and precise data generation, making it a valuable tool in pharmaceutical analysis. Spectrofluorimetry, the most sensitive method, can detect substances at the femtogram level, unlike other methods that require microgram-level sample preparation . This review differs from existing ones by providing a detailed exploration of the synthesis, surface functionalization, and photoluminescent properties of carbon dots (CDs), with a particular emphasis on their emerging applications in photocatalysis, energy, and sensing. Unlike other reviews, we focus on the potential of CDs as safer alternatives to conventional fluorescent compounds, particularly in in-vivo analysis. The specific aims of this review are to offer a comprehensive overview of recent advancements in CDs, highlighting their diverse applications and exploring their potential as substitutes for toxic compounds, as well as their role in electron storage and transport when exposed to light. Furthermore, this review addresses key gaps in the current literature, including the need for alternative materials that can efficiently remove unfiltered contaminants in wastewater treatment and the underutilized potential of CDs in nanomaterial-based solutions. By examining these aspects, we aim to uncover new avenues for the application of CDs in both biotechnological and environmental fields. Based on the recent advancements in the field of CDs, this review hypothesizes that CDs, due to their unique photoluminescent properties, surface functionalization, and environmental compatibility, can serve as safer and more efficient alternatives to traditional fluorescent compounds in various applications, including sensing, photocatalysis, and environmental monitoring. Furthermore, it is hypothesized that CDs have the potential to overcome the limitations of conventional wastewater treatment systems by offering innovative solutions for contaminant removal, thus providing significant benefits in both biotechnological and environmental fields. Nanomaterials are materials that have at least one dimension in the nanometer (nm) range and range in size from approximately 1 to 100 nm . According to the literature, most drug identification methods are based on chromatographic techniques. These techniques are well-established and accepted by regulatory authorities. However, it has several drawbacks related to relatively high cost, analysis time, and pretreatment steps . The incorporation of nanomaterials has enabled the development of novel and effective sensor platforms . The use of nanomaterials for biosensor development has attracted a lot of interest, and carbon nanomaterials (CNMs) are at the forefront. CNMs are composed of sp 2 -bonded graphitic carbon and are mainly classified into fullerenes (0-dimensional), carbon nanotubes (CNTs) (1-dimensional), and graphene (2-dimensional) . CNMs offer superior electrical conductivity, chemical stability, biocompatibility, and strong mechanical strength because of special characteristics including surface-to-volume ratio . As anticipated, these characteristics can affect the stability and selectivity of nanomaterials, as can the capacity to form hydrogen bonds, stacking, dispersion forces, dative bonds, and hydrophobic interactions . A quick search of the Web of Science database over the past five years for the terms “fullerenes,” “CNTs,” and “graphene” yields over 12,000, 32,000, and 98,000 papers, respectively . The facile functionalization and modification of such nanomaterial-based biosensors facilitated improved efficiency in the detection of antibiotic residues and narrow therapeutic index (NTI) drugs . Additionally, nanoparticles (NPs) can be used for targeted drug delivery and high drug-loading capacities . They also show great potential in cancer therapy by improving drug performance, reducing systemic side effects, and increasing therapeutic efficacy . However, there are complexities, difficult and lengthy synthetic processes, and the toxicity of heavy metal quantum dots (QDs) may hinder their application in biosensing . Therefore, nanomaterials can be used to determine drugs . However, some of them are toxic to humans. A common mechanism by which metal oxide NPs cause toxicity is a combination of the NPs properties and their propensity to generate reactive oxygen species [ROS] and cause toxicity in cells, genes, and neurons. Quasi-spherical particles having a diameter of less than 10 nm, known as fluorescent CDs were first identified in 2004 . CDs are a new class of nanostructures made of carbon (C) that have intriguing characteristics and small sizes. Surface-functionalized carbonaceous NPs known as CDs have extraordinary properties, including adjustable FL . CDs were reported to have good biocompatibility, less toxicity , high photoluminescence (PL) intensity , high chemical stability , and possess excellent biological, physical, and chemical properties, thus having great potential in various applications . Only a few of the benefits associated with their luminescence are their outstanding water solubility, biocompatibility, non-toxicity, high sensitivity to the environment, and apparent electron-donating and receiving capacities . CDs exhibit appealing optical characteristics such as size-dependent PL , photo-induced electron transfer (PET), up-conversion luminescence, chemiluminescence, and electrochemiluminescence (ECL) . These dots possess an inner sp 2 and outer sp 3 hybridized structure that frequently contains oxygen-containing functional groups. The surface of CDs imparts several traits to them, including effortless electron transfer, which can confer anti- or pro-oxidant behavior . In addition, CDs can be easily functionalized with hydroxyl, carboxyl, carbonyl, amino, and epoxy groups on their surfaces. This additional advantage enables them to bind readily with both inorganic and organic moieties. The functional groups present on the surfaces of CDs allow them to adopt either hydrophilic or hydrophobic properties, providing the necessary thermodynamic stability in different solvents, particularly in water . Moreover, CDs have exceptional sensing properties like multiplex, selective, and specific detectability. Abundant functional groups (such as amine, carboxyl, hydroxyl, etc.) or polymer chains on the surface of CDs make them highly soluble in aqueous solutions and easily functionalized with other nanomaterials . Because of their extremely sensitive responses to target molecules and tunable surface functional groups, these characteristics make CDs particularly appealing in sensing applications. Another fascinating property of CDs is their tunable emission, characterised by multiple FL colors under various excitation wavelengths . Without a crystal structure, CDs are always spherical and split into carbon NPs. All CDs have linked or altered chemical groups, like chains made of oxygen, amino acids, polymer etc., on their surfaces. X-ray diffraction (XRD), Raman spectroscopy, and high-resolution transmission electron microscopy (HRTEM) are the direct characterization techniques for the carbon core XRD. NMR, X-ray photoelectron spectroscopy (XPS), Fourier transforms infrared (FTIR), and matrix-assisted laser desorption ionisation time-of-flight (MALDI-TOF) are used to assess the grafting of chemical groups. These luminous CDs are therefore not made of “pure” C compounds. Various properties of CDs are depicted in Fig. 2 . The PL behavior of these CDs is mostly determined by the hybridization and coupling between the C core and surrounding chemical groups . In the UV spectral region, CDs typically exhibit considerable absorption with an extension into the visible spectrum. Sometimes, at a wavelength that is significantly longer than the UV absorption peak, a shoulder or a weak peak is also seen. Longer wavelength UV absorption, which typically takes the form of a shoulder or a weaker peak between 300 nm and 400 nm, is attributed to n → π∗ transitions of C=O. Shorter wavelength UV absorption, which is roughly between 200 nm and 350 nm, is attributed to π → π∗ electronic transitions of C=C and C=N . The UV–Vis spectra of Yellow-Green CDs (YG-CDs) give several absorption bands at 270 nm and 382 nm, just like Blue-CDs (B-CDs) do. However, YG-CDs have a higher absorption intensity than B-CDs, which may be due to the latter's higher degree of carbonization in a more acidic hydrothermal environment . The PL emission, comprising excitation-dependent and excitation-independent PL, which is caused by core-related and surface state-related emissions, is one of the CDs most fascinating characteristics . Further remarkable FL characteristics of CDs include tunable PL emission, excitation wavelength-dependent PL emission, strong FL stability, and effective photobleaching resistance. Some CDs can have up-conversion PL (UCPL) emission characteristics, which means that the emission wavelengths of carbon quantum dots (CQDs) are shorter than their excitation wavelengths, in contrast to standard PL emission. The most common explanations for the up-conversion FL phenomenon are often two-photon excitation and anti-Stokes PL emissions . CDs exhibit ECL capabilities and are useful for metal ions detection, optical devices, and biosensors . CDs have a protracted, intense FL emission (up to a year). As CDs can withstand a wide pH range (from 3 to 12), they exhibit excellent photobleaching impedance. Excitation following direct oxidation, amplification, or inhibition of luminescence are all ways that CDs might produce chemiluminescence . Fig. 2 Properties of CDs. Fig. 2 There are currently many different synthetic approaches that can be used to make CDs. The majority of studies focus on easy, affordable, size-controllable, or large-scale synthetic ways to produce CDs of superior quality. Chemical and physical processes are used to create synthetic materials, respectively, depending on the characteristics of the transformations of C sources to final products. Moreover, two classes of top-down and bottom-up may characterise existing synthetic techniques when taking into account the link between the sources and products . The synthesis process may also require additional purification of the end products using techniques like centrifugation, electrophoresis, dialysis, etc. One study by Zhu et al., for instance, used three cycles of concentration/dilution to separate CDs from other reactants . For this class, CDs are produced through oxidation, laser ablation, arc discharge, and electrochemical release on somewhat macroscopic C structures including CNTs, graphite columns, graphene, suspended C powders, etc . In bottom-up processes, CDs are made from a variety of small molecule precursors; most of them enable the manufacture of functionalized CDs in a single step. It consists of pyrolysis, microwave synthesis, ultrasound-assisted synthesis, and hydrothermal synthesis . Several C sources are utilized, including amino acids, citric acid, sugar, and even food waste. Fig. 3 Schematic diagram showing various sources and methods for synthesis of CDs. Fig. 3 Chemical oxidation frequently involves oxidising the substrate with an oxidative reagent. Chemical oxidation is also a quick and efficient approach to large-scale production. Coal, wood, and coconut activated carbons-all readily available in the marketplace-were the C sources. Nitric acid made it simple to etch CDs out of the amorphous structure of activated carbons. Chemicals with amine ends were then used to carry out the passivation process. The three C sources' end products had a constrained size distribution and spectacular luminosity [ , , ]. Because a huge volume of fluorescent CDs can be created in a short period, the laser ablation including solid targets in liquid (LASL) approach for producing fluorescent CDs can be very rapid and efficient. For the synthesis of high-quality or smaller-sized nanostructures, other synthesis procedures, that include the ablation of suspended micro particles or powders, need a longer ablation period. In this technology, greater control of the size distribution can be achieved by carefully controlling heat dissipation via wavelength and intensity change. In comparison to microscale-ablated particles, the size of the emitting NPs generated in the study is in the nanoscale range . Top-down electric arc or arc-discharge synthesis was used to create the first luminous CDs. In order to extract fluorescent C from raw small wall nanotubes (SWNTs), soot, and other materials using a preparative electrophoretic method, it was shown that the fluorescent materials PL quantum yield (PLQY) could reach 1.6 percent at 366 nm excitation wavelength. The benefit of this approach is that the CDs may emit multiple fluorescent colors under UV light without undergoing any surface alteration, and it just needs a simple purification procedure. It is also portable and environmentally friendly. The formation of mixes, low yield, difficulties in surface and compositional modification, and difficulty in these alterations are also drawbacks . A C material, such as graphite, CNTs, or C fibre electrodes, is chemically cut using the electrochemical method, which is influenced by an electric field. The method's low cost and simplicity make it useful. Interestingly, by altering process variables like the temperature, electric field, CNT diameter, and concentration of the supporting electrolyte, CDs made electrochemically can have their sizes adjusted . A simple bottom-up hydrothermal approach has been reported to make CDs. The colorless solution first needs to close in a Teflon-coated stainless-steel autoclave and then be kept at a definite temperature for a particular time. The autoclave was left to naturally cool to room temperature following the reaction. The solution is either centrifuged or passed through dialysis tubing of a certain molecular weight to remove the precipitation and to eliminate leftover small molecules, the CDs were dialyzed against ultrapure water at room temperature using a membrane . Depending on the frequency and strength of the applied field, ultrasound is a typical laboratory instrument that can be used to emulsify mixtures, drive chemical processes, and nebulize liquids into fine mists . Microwaves are electromagnetic wave types with a broad wavelength range of 1 mm (mm) to 1 m (m) that are frequently employed in daily life and science. The microwave can also deliver high energy to break the chemical bonds in the substrate, just like a laser can. The synthesis of CDs is thought to be more energy-efficient when done in a microwave, and the reaction time can also be significantly reduced. The substrate is typically pyrolyzed and surface functionalized during microwave-assisted synthesis . The sensing mechanism of the CDs depends upon the chemical bonding of the fluorescent probe. Various mechanisms of CDs have been available for sensing the analyte, these include Förster resonance energy transfer (FRET) , inner filter effect (IFE) , photoinduced electron transfer (PET) , intramolecular charge transfer (ICT) , static quenching (SQ), dynamic quenching (DQ) , aggregation caused quenching (ACQ), aggregation-induced emission (AIE) . Fig. 4 The sensing mechanism of CDs. Fig. 4 In FRET, energy is transferred non-radiatively from an excited donor molecule (D), typically a fluorophore, to a close-by ground-state acceptor molecule (A) via long-range dipole-dipole interactions. For efficient FRET, the acceptor must absorb energy at the donor's emission wavelength (s), though it does not need to emit this energy fluorescently. The efficiency of FRET is significantly influenced by three main factors: the degree of spectral overlap between the donor's emission and the acceptor's absorption, the relative orientation of the transition dipoles, and most importantly, the distance between D and A, usually in the range of 10–100 Å (Å), which is comparable to the size of many biological macromolecules . The IFE involves an absorber in a sensor system that modulates the excitation or emission of light from a fluorescent molecule. In an IFE-based sensing system, both an absorber and a fluorescent molecule must coexist. The absorber's absorption spectrum overlaps with the excitation and/or emission spectra of the fluorophore. This overlap enables the absorber to control and influence the FL emission. The system requires the absorber to exhibit a selective response to the analyte concentration, while the FL intensity of the fluorophore remains unaffected by the analyte, ensuring its role as an indicator in the sensor system . PET is a mechanism in which a fluorophore is linked to a recognition receptor via a spacer. FL quenching in PET occurs due to intramolecular electron transfer between the receptor and the fluorophore. Two types of PET processes are recognized: (1) Acceptor-PET (A-PET): Electron transfer from the receptor to the fluorophore occurs when the receptor's Highest Occupied Molecular Orbital (HOMO) energy level is higher than that of the fluorophore. (2) Donor-PET (D-PET): Electron transfer from the excited fluorophore to the receptor's Lowest Unoccupied Molecular Orbital (LUMO) level occurs during D-PET. The PET process is inhibited when the receptor binds to a target analyte, leading to restored FL emission . ICT involves electron transfer from a donor (D) to an acceptor (A) within a fluorescent probe upon light stimulation. When the donor interacts with an analyte, its electron-donating ability decreases, increasing the HOMO-LUMO energy gap and causing a blue shift in FL emission. Conversely, the interaction of the acceptor with the analyte decreases the energy gap, resulting in a red shift. Thus, both FL intensity and emission wavelength can be used for analyte detection. The varying dipole moments between the ground and excited states of ICT probes make them sensitive to different solvent environments, making them ideal for detecting changes in solvent conditions . SQ occurs when a non-fluorescent ground-state complex forms between the quencher and the CDs. This complex reduces the FL intensity of the CDs and alters their absorption spectrum. Temperature increases can destabilize this ground-state complex, reducing the quenching effect. DQ involves the interaction between an excited-state fluorophore and a quencher, resulting in energy or charge exchange that returns the fluorophore to its ground state. Unlike SQ, DQ affects only the excited state and does not alter the absorption spectrum of the fluorophore. The FL lifetime of the fluorophore is reduced in the presence of a quencher. Higher temperatures typically enhance the DQ effect, increasing the rate of quenching interactions . The reduction in FL intensity due to quenching can be quantified using the Stern-Volmer equation: F 0 /F = 1 + K[Q] = 1 + k q τ 0 [Q] Where K is the Stern-Volmer quenching constant, [Q] is the quencher concentration, τ 0 is unquenched lifespan, and k q is the bimolecular quenching constant, and F and F 0 are FL intensities in the presence and absence of the quencher, respectively. In ACQ high concentrations or solid-state fluorophores often exhibit diminished FL due to self-quenching. This effect arises from interactions such as hydrophobic effects, stacking, and hydrogen bonding, leading to non-radiative decay pathways. In contrast to ACQ, AIE materials show enhanced FL in high concentrations or solid forms. In dilute solutions, non-radiative energy dissipation occurs via intramolecular rotation. However, in the aggregated state, this rotation is restricted, leading to suppressed non-radiative decay, and enhanced FL emission organization groups similar principles together, improving the clarity and logical flow of the discussion on various FL mechanisms associated with CDs . Doped-CDs are carbogenic NPs with an average size of less than 10 nm that have atomic impurities added like nitrogen (N), sulfur (S), phosphorus (P), boron (B), etc. during the production process to enhance their optical, electrical, and chemical capabilities. One can distinguish between single- and multiple-doped CDs based on how many atomic impurities have been added to the structure of the CDs. One of the most well-known options is N since it has five valence electrons, a similar atomic size to C, and can form bonds with C atoms. When the N content in the CDs rises, the FL peak has shown that it will move to a longer wavelength under the same stimulation. The observation's justification is based on the N-doped material forming new FL origins. In other research, N is also seen to improve the efficacy of emissions as opposed to displacing them. The mechanism is thought to involve electrons in the conduction band and the occurrence of an upward shift in the Fermi level. Hence, various polymers used as a precursor material for C and N sources are dodecyl-grafted-poly(isobutylene-alt-maleic-anhydride) , hydrosoluble chitosan , dried shrimp shells , citric acid, and urea , dried prawn shell , oolong tea , monkey grass , folic acid (FA) and phosphoric acid (H 3 PO 4 ) , lysine and ortho-phosphoric acid , p-phenylenediamines and ammonia water , gum ghatti and ethylenediamine , Borassus flabellifer (B. flabellifer) and aq. Ammonia , m-phenylenediamine , Ru(bpy)2(phen-NH 2 ) , 4-aminophenol , ethylenediamine, and Kentucky bluegrass , β-resorcylic acid and ethylenediamine , black soya beans , glucosamine and ethylenediamine , citric acid and diethylenetriamine , highland barley as C source and ethanediamine as N resource , biomass bacterial cellulose , tartaric acid and urea , adenosine , dried chrysanthemum buds, and ethylenediamine . The band-gap energy of photoexcited electrons may be modified by the S atom's density of states or emissive trap states, giving functionalized CDs additional favorable properties [ , , , , , , , , , ], sodium citrate solution, and sodium thiosulfate , blackstrap molasses, H 2 O 2 , and garlic powder , mercaptosuccinic acid , sulphuric acid (H 2 SO 4 ) . Fig. 5 Doped CDs sources. Fig. 5 Fig. 6 Single, multi, and co-doped CDs. Fig. 6 Thus various polymers used as precursor material for C and S sources are sodium thiosulfate and sodium citrate . With only three valence electrons, B (one less than C). As a result, adding a B atom to a C cluster causes the generation of p-type carriers inside CDs, altering their electrical structures and optical characteristics [ , , , , ]. Thus various polymers used as precursor material for C and B sources are phenylboronic acid , boric acid, urea and citric acid , glucose and boric acid , L-ascorbic acid and boric acid , citric acid, H 2 SO 4 , ammonia water, boric acid, and isopropyl alcohol , boric acid and ethylenediamine , citric acid monohydrate, thiourea, and boric acid . Moreover, citric acid, rhodamine B, methylene blue,1, 2-diboranyethane, N, N-dimethylformamide (DMF, anhydrous, 99.8 %) , etc. The P atom can have a significant impact on the chemical and electrical structures of semiconductors since it is a major electron donor in semiconductors, which are endowed with promising uses in photovoltaic devices and catalysts. The bigger size of the P atom compared to the C atom directly contributes to an increase in disorders inside the P-doped graphite carbon backbone [ , , , ]. The various polymers used as precursor materials for C and P sources are L-threonine and phosphorous pentoxide , sucrose and H 3 PO 4 , dextrose and disodium phosphate (Na 2 HPO 4 ) , beta-cyclodextrin and sodium pyrophosphate , lactose, and concentrated H 3 PO 4 , orthophosphoric acid and glucose . Nitrogen, Sulfur co-doped carbon dots (N, S-CDs), one of the heteroatom-containing CDs, display their remarkable FL behaviors . Example: thiourea and glutathione , p -aminobenzenesulfonic acid , DL-malic acid, ethanolamine and ethane-sulfonic acid , citric acid and L-cysteine , ammonium persulfate, glucose, and ethylenediamine , citric acid and thiourea , sodium citrate and thiourea , methionine and citric acid , basic fuchsin and sulfosalicylic acid , 4-aminophenylboronic acid hydrochloride and 2, 5-diaminobenzenesulfonic acid , ethylenediamine, L-cysteine and malic acid , citric acid and dithiooxamide , garlic , citric acid monohydrate and cysteamine , urea and H 2 SO 4 , citric acid and thiosemicarbazide , trisodium citrate dehydrate and L-cysteine , H 2 SO 4 , methionine and acrylic acid , polyvinylpyrrolidone and poly(4-styrenesulphonic acid) sodium salt , citric acid and arginine (A), histidine (H), lysine (L), cysteine (C), and methionine (M) . In addition, other combination includes heating egg white, egg yolk, pigeon feathers or pigeon manure , garlic and alfalfa , thiomalic acid and diethylenetriamine , rose petals, ethylenediamine, L-cysteine , para-benzoquinone and L-cysteine , cigarette and H 2 SO 4 , thioctic acid and triethylene tetramine . Heteroatoms like N and B have been created to enhance the optical performance of CDs. The luminous characteristics of these CDs are fascinating, including temperature- and pH-dependent FL responses and excellent quantum yield . Solanum betaceum ( S. betaceum ) fruit extract , trisodium citrate, urea, and boric acid , adenine and aminobenzene boronic acid monohydrate , citric acid, boric acid and ethylenediamine , 2,5-diaminobenzenesulfonic acid and 4-aminophenyl boronic acid , petroleum coke , 3-aminophenylboronic acid , citric acid, boric acid, and tris base , citric acid, urea, and boric acid, etc. are the various precursors used for the synthesis of N, B -doped CDs . On the periodic table, N and P sit next to C and serve as important tracking elements in the field of biomedical imaging. These two components change the optical and electronic characteristics of CDs and advance our fundamental knowledge of their PLQY. Moreover, this may result in multifunctional applications in photothermal therapy (PTT) and photoimaging. Some precursors used for the synthesis of N, P-doped CDs are ethylenediamine and N-phosphonomethylaminodiacetic acid , 1,4-naphthalenedicarboxylic acid and urea , glucose, ammonia, and H 3 PO 4 , adenosine 5′-mono phosphoric acid disodium salt dehydrate , diethylenetriaminepenta and m-phenylenediamine , glucose, polyethyleneimine and H 3 PO 4 , phytic acid and L-arginine , 1,2-ethylenediamine, phthalic acid and H 3 PO 4 , lily bulb , citric acid, tris (2-aminoethyl) amine and orthophosphoric acid , sodium citrate and diammonium phosphate , maize and urea , alendronate sodium , polyethylene glycol (PEG) (C source), and H 3 PO 4 . Various sources used for the synthesis of N, Cl-doped CDs are o-phenylenediamine, hydrochloric acid (HCl) and selenourea , cetylpyridinium chloride , 4-chloro-1,2-diaminobenzene and dopamine , p-phenylenediamine, melem hydrazine, and aluminum chloride hexahydrate , glycerine, choline chloride and urea , chloranil and triethylenetetramine , glucose, concentrated HCl and 1,2-ethylenediamine . B, N, and S-doped were synthesized by 2, 5- diaminobenzene sulfonic acid, and 4- aminophenyl boronic acid hydrochloride . The N, S, P, and Cl-doped were synthesized by glucose and ethylenediamine, and then H 2 SO 4, concentrated H 3 PO 4 , and concentrated HCl were added orderly into the beaker to get the CDs . As CDs are reported best for sensing pharmaceuticals. Fig. 13 illustrates a variety of CDs applications along with drug identification in pharmaceuticals. So far, some of the drugs listed in Table 1 are detected by carbon nanodots (CNDs) through a different mechanism of CDs. The study investigated the impact of deoxygenation and radical scavengers on the FL intensity of the C-dots-Ni(IV) system. It was proposed that when Ni(IV) reacts with dissolved oxygen in an alkaline solution, it produces the superoxide radical (•O 2 − ). Both Ni(IV) and •O 2 − can interact with C-dots, generating C-dot• + and C-dot• − , which then undergo electron transfer annihilation, forming an excited-state C-dot∗ as the final emitter. The target analyte was found to inhibit chemiluminescence by competitively reacting with Ni(IV). This led to the development of a flow-injection chemiluminescence method, which proved to be a fast, simple, sensitive, and robust technique for detecting trace levels of reducing compounds . In a separate experiment, CDs were synthesized using sodium citrate and ammonium bicarbonate through a hydrothermal process. The FL intensity of the CDs was found to be pH-dependent, increasing with pH up to 6.0, before stabilizing between pH 7.3 and 7.7. The study also tested the effects of various interfering substances, revealing that D-penicillamine (D-PA) caused significant quenching of the FL, while increasing concentrations of Hg 2+ also reduced the FL intensity, with near-total quenching occurring at 4 × 10⁻⁵ mol L⁻ 1 of Hg 2+ . Table 1 Pharmaceutical applications of fluorescent CDs-based sensors. Table 1 Sr. No. Drug name Wavelength excitation(λ ex ) Wavelength emission(λ em ) Linearity range Mechanism Ref 1. Paracetamol 360 nm 445 nm 4.48x10 −7 -8.96 x10 −5 μM Ni(IV) reacted with CDs produced could form an excited state C-dot∗ which interacts with the target analyte due to its competitive reaction with Ni(IV). 2. D-penicillamine 358 nm 442 nm 2–24 μmol/L Fluorescent switch sensor 3. Kaempferol 370 nm 490 nm 3.5–4.9 μM Non-radiative energy transfer (FRET) 4. Amantadine 335 nm 446 nm – FRET 5. Chloramphenicol 400 nm 525 nm – FRET 6. Hyperin 300 nm 540 nm 0.22–55 μM Non-radiative energy transfer 7. Sumatriptan 380 nm 446 nm 1–20 μM The fluorescent quenching observed is caused by the formation of hydrogen bonds between the nitro groups of DNB-CDs and the aliphatic -NH group on the phenyl aromatic ring of SUM. 8. 6-Mercaptopurine 370 nm 445 nm and 565 nm 1.0–100.0 μmol L −1 FRET 9. Gentamicin 340 nm 385 nm 0 to 2.9 × 10 −4 mol/L FRET 10. Gentamicin and kanamycin 390 nm 482 nm 200–2000 nM FRET 11. α-glucosidase 410 nm 510 nm 100 μL IFE 12. Iron 370 nm 432 nm 0–1.5 mM IFE 13. Hematin 314 nm 362 nm 0–75 μM IFE 14. Cefixime 350 nm 455 nm 0.2 × 10 −6 M to 8 × 10 −6 M IFE 15. Tetracycline 345 nm 435 nm 0.5–25 μM IFE 16. Chlortetracycline 370 nm 447 nm 0.4–20 μg/mL IFE 17. Tetracycline 350 nm 420 nm 0–50 μM IFE 18. Tetracycline 370 nm 440 nm 0.5–60 μM Internal filtering effect 19. Tetracycline 360 nm 440 nm 0.1–4.0 μg mL −1 IFE 20. Tetracycline and oxytetracycline and chlortetracycline 381 nm 456 nm TC (1–60 μM), OTC (1–40 μM) and CTC (1–80 μM) IFE 21. Nimesulide 330 nm 408 nm 0–100 μM IFE 22. Curcumin 380 nm 467 nm 0.1–35 μM IFE 23. Clioquinol 360 nm 440 nm 40.0–400.0 μmol L −1 IFE 24. Berberine hydrochloride 370 nm 452 nm 0.5–30.0 μmol/L IFE 25. Diacerein 325 nm 412 nm 2.5–17.5 μg/mL IFE 26. Captopril 520–550 nm – 1–50 μM IFE 27. Daunorubicin and doxorubicin – 450 nm DAN DOX IFE 28. Mitoxantrone 560 nm 655 nm 0.096–30 μM IFE 29. Gefitinib 320 nm 410 nm 0.1–20 μg/mL IFE 30. Doxorubicin 491 nm 591 nm 1–30 μM IFE 31. Nitazoxanide 340 nm 418 nm 0.25–50.0 μM IFE 32. Myricetin 360 nm 436 nm 0.2–112 μM IFE 33. Levocetirizine and niflumic acid 319 nm 392 nm (levocetirizine − 1.0–100 μM and niflumic acid − 0.5–100 μM) IFE 34. Vitamin B 12 545 nm 595 nm 1–65 μM/70–140 μM Internal filtration effect 35. Metronidazole 350 nm 425 nm 0.5–22 μM PET 36. Thiamine 367 nm 430 nm 10–50 μM Quenching due to Cu 2+ ion and regain after addition of thiamine. (Turn on) 37. Cephalosporin 300 nm 689 nm 0.2–80 μmol/L IFE 38. Prilocaine 400 nm 485 nm 2.3–400 nmol L −1 Quenching assay/Turn off the sensor 39. Enrofloxacin 368 nm 452 nm 1–15 μg/mL The FL was suppressed by Cu 2+ ions and then restored by the addition of ENR, resulting in an off-on switch of the FL. 40. Chlortetracycline 360 nm 438 nm 1–70 μM The FL of CDs is quenched by the addition of QDs, while increases linearly with the concentration of CTC resulting in an turn off–on switch of the FL 41. Glutathione 360 nm 430 nm 20–400 μM The FL of CDs was quenched with the addition of Cu 2+ , by the formation of CDs-Cu 2+ and recovered rapidly with the addition of GSH, due to the stronger interaction between GSH and CDs-Cu 2+ , resulting in an turn off–on switch of the FL. 42. Pentachlorophenol (PCP) 360 nm 440 nm 10–300 μM The FL of NS-CDs was significantly quenched by the addition of H 2 O 2 and HRP and induced FL quenching. While after the addition of PCP provided a reducing environment that can protect the active group of NS-CDs from oxidation. Thus, resulting in an turn off–on switch of the FL. 43. Ampicillin 340 nm 427 nm 6.6–200 ppm Fluorescent quenching 44. Nitrofurantoin 340 nm 470 nm 0–500 μM IFE and SQ 45. Phenobarbital 340 nm 410 nm 0.4–34.5 nmol L −1 Fluorescent quenching 46. Clonazepam 340 nm 430 nm 5x10 −8 x10 −6 M Fluorescent quenching 47. Acetaminophen 350 nm – 1–80 μM Fluorescent quenching 48. Atorvastatin 290 nm 423 nm 0.025–50 μM SQ 49. Doxorubicin 360 nm 441 nm 0.5–6.5 μM SQ 50. Imatinib 345 nm 415 nm 1.0–15.0 mg/mL SQ 51. Curcumin 360 nm 440 nm 0.339–136.0 μM SQ 52. Tetracycline 330 nm 520 nm 0–27.27 μM SQ 53. Folic acid 360 nm 450 nm 10–100 μg/mL DQ 54. Isoniazid 270 nm 447 nm 4–140 μM IFE and SQ 55. Quercetin 350 nm 450 nm 0.003–80 μmol/L IFE and SQ 56. Tigecycline 365 nm 500 nm 0.005–20 μg/mL IFE and SQ 57. Tetracycline 360 nm 520 nm 0.5–40 μM IFE and DQ 58. Nitrotyrosine 420 nm 679 nm 20–105 μM SQ and DQ 59. p-benzoquinone 350 nm – 0–25 mmol/L DQ 60. Methotrexate 355 nm 450 nm 0–50 μg/mL Fluorescent quenching 61. Doxorubicin 350 nm – 0.5–25 μg/mL Fluorescent quenching 62. Chondroitin sulfate 488 nm 520 nm 0.05–2 μg/mL Electrostatic interaction 63. Flutamide – – 0.05–590 μM – 64. Cytochrome C 475 nm 530 nm 0.5–25 μM IFE 65. Zoledronic acid 340 nm 430 nm 0.1–10 μM ZA could not quench the FL intensity of the N-CDs without Fe 3+ , once ZA was added to the N-CDs-Fe 3+ system, the formation of a complex between ZA and Fe 3+ ions occurred and the FL of N-CDs becomes on. (Turn on) 66. Glucose 520 nm 582 nm 0.5–1500 μM SQ The CDs were synthesized by adding distilled deionized water to diphosphorus pentoxide (P 2 O 5 ) in glacial acetic acid, conducted in a fume hood. After cooling, a dark brown solid was obtained. The FL intensity of the CDs was pH-dependent, peaking at pH 6.0 before decreasing with higher pH levels. The CDs showed high selectivity for kaempferol (KAE), which significantly quenched their FL in the presence of other substances, indicating strong sensitivity towards KAE . A method was developed to produce polyethyleneimine (PEI)-functionalized blue emissive CDs (b-CDs) using citric acid and PEI via a one-step hydrothermal process. These b-CDs were used to create a fluorescent immunoassay for simultaneously detecting chloramphenicol (CAP) and amantadine (AMD) in skinless chicken breasts. The b-CDs, along with green emissive CDs (g-CDs), featured numerous amino groups and distinct FL emission peaks. The study employed haptens of CAP and AMD as energy donors in a FRET system. Two-dimensional WS2 nanosheets (NSs) were used as energy acceptors, modified with antibodies specific to CAP and AMD. This selective antigen-antibody interaction facilitated the attachment of hapten-functionalized CDs to the WS2 NSs, resulting in FL quenching due to FRET. The immunoassay demonstrated potential for rapid detection of drug residues in food . The CDs were synthesized using a self-catalysis method, with FL intensity decreasing rapidly in the first minute and stabilizing after 10 min, which was chosen for further experiments. The study assessed the impact of interfering substances like metal ions, biomolecules, and co-existing compounds, testing the method's effectiveness for real sample analysis. The results showed that the CDs were highly selective for hyperin (Hyp), with only slight changes in FL intensity at low concentrations and a significant decrease as Hyp concentration increased. The method was successfully applied to quantify Hyp in real samples, including fufangmuji granules and human serum . The researchers developed a selective and sensitive fluorescent probe using zein biopolymer, functionalized with 3,5-dinitrobenzoyl chloride (DNB) to create DNB-CDs for detecting sumatriptan (SUM). The DNB-CDs were synthesized via direct pyrolysis of zein without additional reagents. Using the standard addition method for analysis, the DNB-CDs showed a strong selective response to SUM, unaffected by potential interfering substances. The study suggests that these CDs can be effectively used for determining SUM levels in human samples . A one-step aqueous synthesis method was used to create Zn-doped CdTe quantum dots (ZnCdTe QDs), which were combined with B-CDs to develop a ratiometric fluorescent probe for detecting 6-mercaptopurine (6-MP). The probe exhibited different FL responses from the yellow emission of ZnCdTe QDs and the blue emission of CDs when exposed to 6-MP. After adding 6-MP, the FL intensity ratio remained stable for up to 20 min, with the probe showing the strongest response at pH 8.7. Using FRET, the FL of ZnCdTe QDs was selectively quenched, while the FL of CDs remained unaffected. The probe successfully detected 6-MP in human serum, offering a rapid method for 6-MP analysis in biological samples. Fig. 7 presents a schematic illustration of ratiometric FL sensing for 6-MP . Fig. 7 Schematic illustration of ratiometric fluorescence sensing for 6-MP. Fig. 7 The researchers developed a sensor using N-C quantum dots (QDs) by combining a CQDs solution with sodium hydroxide, dried wheat straw powder, 2,2,6,6-tetramethylpiperidine amine, and 4,5-imidazole dicarboxylic acid. The FL intensity of the N-CQDs increased gradually at pH 7 as the gentamicin (GEN) concentration increased. The sensor's selectivity for GEN was evaluated using GEN analogs and metal ions, with metal ions causing only a slight decrease in FL intensity (around 5 %). This detection method was proven effective for accurately detecting GEN in water samples, offering a fast and simple approach with significant potential for detecting GEN residues in aqueous solutions . A unique two-in-one sensor based on Au@CQDs nanocomposites (NCs) was developed for detecting gentamicin (GENTA) and kanamycin (KMC). In the synthesis, CQDs acted as reducing agents to create Au@CQDs NCs. Initially, the FL intensity of CQDs decreased due to energy transfer after the formation of Au@CQDs NCs. However, the FL intensity recovered in the presence of the antibiotics, indicating that the sensor could effectively detect both GENTA and KMC through qualitative/colorimetric and quantitative/fluorimetric methods. When tested with spiked milk and eggs, the sensor showed excellent recovery, demonstrating its potential for food safety applications . In this study, CDs were synthesized using 4-hydroxybenzoic acid and ethylenediamine, with 4-nitrophenyl-α-D-glucopyranoside (NGP) serving as the substrate for the enzyme α-glucosidase. The enzyme catalyzed the release of 4-nitrophenol from NGP. Acarbose, a common anti-diabetes drug, was used as an inhibitor in the assays. The addition of acarbose inhibited α-glucosidase activity, leading to a recovery of FL intensity, as shown by an increase in FL when inhibitors were added. The sensor exhibited minimal interference from other substances, demonstrating its high selectivity and applicability for detecting α-glucosidase activity . CDs were synthesized using diammonium hydrogen citrate and PEG-400 via a microwave-assisted method. The resulting CDs were evaluated for cytotoxicity and cell imaging, with their FL intensity showing a pH-dependent behavior. The study also investigated the FL-quenching effects of various metal ions, finding that Fe 3+ had the most significant impact. This quenching was attributed to energy transfer processes within the excited CDs. Additionally, the CDs were successfully used for cellular imaging in BGC-823 and CT26.WT cells, showcasing their potential as optical nanoprobes for cell imaging applications due to their high FL brightness . Mg-doped carbon dots (Mg-CDs) were synthesized using a simple, one-pot microwave-assisted method by dissolving citric acid in a magnesium chloride (MgCl 2 ) solution and mixing it with ethylenediamine. The resulting Mg-CDs nanoprobes (Mg-CDs-0.1) demonstrated high selectivity and sensitivity for detecting hematin due to the strong IFE between Mg-CDs and hematin. Specificity tests revealed that Mg-CDs-0.1 showed a significant FL intensity reduction only in the presence of hematin, with no impact from other molecules or metal ions. Similar results were observed with Mg-CDs-0.5, confirming their high reproducibility and selectivity for detecting hematin in human red cell hemolysate . Carbon dots (CDs) were synthesized from environmentally friendly pomegranate juice using a simple hydrothermal method and tested as sensitive indicators for detecting cefixime (CEF). The detection method relies on the interaction between palladium ions (Pd (II)) and CEF in an acidic buffer (pH 4). The excitation spectra of the synthesized CDs and the Pd (II)-CEF complex were found to overlap, leading to a decrease in FL intensity when both CEF and Pd (II) were present, enabling quantitative detection of CEF. The technique showed minimal interference from other ions and biomolecules. The method was successfully applied to analyze urine samples from a healthy individual and pharmaceutical formulations, demonstrating its potential for rapid CEF detection in real samples . A rapid and efficient microwave-assisted method was used to synthesize glycerol-urea CDs (GUCDs). The ability of GUCDs to detect antibiotics in urine samples was tested by adding various compounds, including antibiotics, to GUCDs solutions. The FL signal of GUCDs remained mostly unaffected by the presence of other molecules, except for tetracycline (TC) antibiotics (TC, doxycycline, and oxytetracycline (OTC)), which significantly altered the responses. The FL intensity of GUCDs was pH-dependent, with the highest emission observed at pH below 4.0. This pH was chosen to maximize the FL signal and prevent TC degradation. As a result, a simple, low-cost method based on the reduction of GUCDs FL was developed to detect TC in urine samples, indicating the high selectivity of GUCDs for TC antibiotics . In a study focused on the development of a fast FL sensor, CDs were synthesized using hawthorn, and the sensor was designed to detect chlortetracycline (CTC) in pork samples. The results showed that substances like CAP, sulfanilamide, and florfenicol did not interfere with the sensor's reaction system. However, TC antibiotics (such as TC, doxycycline, and OTC) did affect the FL response, with TC having the strongest impact. This highlighted that the N-CDs were particularly selective for detecting TC antibiotics, making them useful for detecting harmful substances in food . The europium doped carbon dot (Eu-CDs) were synthesized using citric acid, melamine, and Europium (III) Nitrate Hexahydrate (Eu(NO₃)₃·6H₂O) in a one-pot hydrothermal method, with Eu serving as a TC-binding site and providing FL. Formaldehyde was added as a passivating agent to modify the nitrogen's chemical state in the Eu-CDs. The study found that the maximum FL intensity ratio for both TC and Al³⁺ occurred at pH 8.0, which was identified as the optimal pH. The Eu-CDs exhibited stable FL and color, unaffected by biological ions or other antibiotics. Notably, only TC caused a significant FL enhancement at 620 nm and a color shift from blue to red, demonstrating the Eu-CDs' high selectivity for TC detection . In a separate study, B-CDs were synthesized with the help of salicylic acid, melamine, and distilled water, incorporating zinc nitrate hexahydrate and 2-methylimidazole. The CDs were then modified into a hierarchical mesoporous zeolitic imidazolate framework-8 (HZIF-8) using hydrogel as a template. The specificity of the CDs@HZIF-8 system for TC was evaluated by introducing various potential interfering substances, such as metal ions, amino acids, vitamins, and antibiotics. The results showed that the sensor maintained excellent selectivity and anti-interference capacity for TC, confirming its robustness for real-world applications . A novel FL sensor was developed using nitrogen-doped carbon dots (N-CDs) embedded in zinc-based metal-organic frameworks and molecularly imprinted polymer (ZIF-8&N-CDs@MIP). This sensor, optimized at pH 7, demonstrated high sensitivity and selectivity for TC, with minimal interference from its structural analogs, such as CAP, OTC, and CTC. The sensor showed excellent selectivity, improved sensitivity, and fast response times for detecting TC . N-CDs were synthesized using pitaya peel and 1,2-ethylenediamine via a hydrothermal method. The N-CDs exhibited a “turn-off” FL response when interacting with TC and OTC, while showing a “turn-on” FL response with CTC, making them highly sensitive for CTC detection. Selectivity tests demonstrated that the FL intensity of N-CDs was minimally affected by other potential interfering substances, including metal ions, antibiotics, amino acids, and anions. The sensor proved to be a versatile, dual-mode platform for detecting TC with high sensitivity and adaptability. For example, the schematic illustration of the synthetic process of N-CDs and their application as a multifunctional nano-sensor for sensing TC, OTC, and CTC is shown in Fig. 8 . Fig. 8 Schematic illustration of synthetic process of N-CDs and application as a multifunctional nano-sensor for sensing of TC, OTC, and CTC. Fig. 8 In this study, CDs were synthesized using trimesic acid and 4-amino acetanilide hydrochloride through a one-step hydrothermal process. This method involved combining glucose solutions of varying concentrations with glucose oxidase (GOx) in a Britton-Robinson buffer at 37 °C for 40 min to produce hydrogen peroxide (H 2 O 2 ). When CDs were added to the mixture, their FL was quenched by H 2 O 2 , with the quenching intensity being directly proportional to the H 2 O 2 concentration, enabling glucose detection. The method showed good sensitivity and selectivity when tested with biological samples, including human serum and urine from diabetic and healthy individuals. Additionally, B, N-doped CDs were synthesized using ammonium citrate and bis(pinacolato)diboron via a hydrothermal method. Interference studies revealed that nimesulide significantly quenched the FL, while other substances had little to no effect. This demonstrated the high sensitivity and selectivity of the sensor. Furthermore, the B, N-CDs proved useful for fluorescent staining and anti-counterfeit applications. The method was successfully applied to pharmaceutical samples for detecting Nimesulide . N and Cl-functionalized CDs (N, Cl-CDs) were synthesized quickly and environmentally using glucose, ethylenediamine, and HCl. The FL intensity of the N, Cl-CDs was rapidly quenched by curcumin (Cur), indicating its effective interaction with the CDs. Selectivity tests showed that Cur was the only substance among the investigated compounds that significantly reduced the FL intensity, while other potential interfering substances had little to no impact. This demonstrates that N, Cl-CDs exhibit high selectivity for Cur and are unaffected by interference from other drugs, making them a promising tool for Cur detection . A study developed a label-free probe for detecting cytochrome c (Cyt C) using nitrogen and fluorine co-doped carbon dots (N, F-CDs), which were easily synthesized through a solvothermal method using 3,4-difluorophenylhydrazine as a precursor. The results demonstrated that the N, F-CDs probe exhibited excellent anti-interference properties, making it a promising candidate for detecting Cyt C. Additionally, the probe showed potential as a temperature sensor with higher sensitivity, suggesting its broad application in biosensing . In this study, CDs were synthesized using ammonium citrate and ammonium thiocyanate through a hydrothermal process. The addition of Cu 2 ⁺ initially increased the FL intensity of the CDs. However, when clioquinol (CQ) was introduced, its high affinity for Cu 2 ⁺ displaced Cu 2 ⁺ from the CDs surface, leading to FL quenching due to IFE. The study examined the effects of pH, ionic strength, and UV exposure time on the FL intensity. The difference in FL intensity between the CDs + Cu 2 ⁺ system and the CDs + Cu 2 ⁺ + CQ system was greatest at pH 8.0. Cu 2 ⁺ adsorbed on the CDs surface, stabilizing them and preventing non-radiative recombination, which increased FL intensity. A new UV absorption peak confirmed the formation of the Cu 2 ⁺-CQ complex. Interference studies showed that CQ could significantly quench the FL of CDs, while other metal ions had negligible effects. This system was proposed as a fluorescent sensor for detecting CQ . Silicon-doped carbon quantum dots (Si-CQDs) were synthesized through a straightforward one-pot hydrothermal method using 3-aminopropyltrimethoxysilane and H 2 SO 4 . The pH had no significant effect on the FL behavior of the Si-CQDs. The study also examined how the FL intensity of Si-CQDs was influenced by the addition of various metal ions, amino acids, saccharides, and other substances that could be found in urine samples. Among these, only berberine hydrochloride (BH) caused a significant reduction in FL intensity, while other drugs had minimal effects. These results demonstrate that the developed sensor has high sensitivity and selectivity for BH detection . Chitosan was utilized as a C and N source in the carbonization process to develop a fluorescent sensor. The FL of the resulting probe was effectively quenched by diacerein (DIA), producing rapid and stable FL responses. The sensing system demonstrated high precision and accuracy, allowing for the selective detection of DIA in its tablet dosage form, even in the presence of co-formulated medications . Rhodamine B was dissolved in ultrapure water and used in a solvothermal process to synthesize CDs. Various chemicals, including sugars (glucose, sucrose), inorganic ions (K + , Na + , Ca 2+ , Cl − , NO 3 − ), amino acids (histidine, alanine, threonine, phenylalanine, arginine), and medications (paracetamol, artemisinin), were tested for their impact on the sensor. When these substances were added individually without captopril (CP), there was no significant change in the FL and absorbance signals. However, the addition of CP led to a substantial enhancement in both FL and absorbance signals. These results demonstrated the high selectivity and robustness of the CP sensor, which was able to tolerate interference from coexisting chemicals . Using a one-pot green synthesis method, a green bell was chosen to produce CDs due to its high-quality contents, including carotenoids, ascorbic acid, carbohydrates, and other carbonaceous organic compounds. To create MSA-CdTe quantum dots (QDs), cadmium chloride (CdCl 2 ), mercaptosuccinic acid (MSA), sodium tellurite (Na 2 TeO 3 ), and a borate-acetic acid buffer solution were combined. Then, Si@CdTe QDs were synthesized using MSA-CdTe QDs, 3-Aminopropyl triethoxysilane (APTES), and ethanol. The ideal pH for detecting doxorubicin (DOX) and daunorubicin (DAN) was found to be pH 7. Sodium chloride (NaCl) did not cause significant changes in the signal intensity, which was used to assess the impact of potential interferents like glutamine, fructose, and glucose in biological samples. These results demonstrated that the ratiometric fluorescence (RF) sensors were highly effective in detecting both DAN and DOX . Red-emission carbon dots (R-CDs) were synthesized using a solvothermal process involving formamide, N, N-dimethylformamide, and citric acid. The optimal pH for the reaction system was found to be 6.0. When used as a fluorescent probe for detecting mitoxantrone (MITX), R-CDs exhibited excellent selectivity and resistance to interference, as the presence of interference compounds had little to no effect on either the R-CDs or the R-CDs-MITX system's FL. The developed label-free fluorescent nanoprobe proved to be highly effective for the rapid and accurate detection of MITX in human serum samples . The green tea leaf residue was used as the carbon source for synthesizing carbon dots (named as T-CDs) employing a combination of high-temperature pyrolysis and oxidation with concentrated H 2 SO 4 . The FL intensity of the T-CDs was found to decrease as the concentration of gefitinib increased, with the FL quenching effect being proportional to the drug concentration. Selectivity tests revealed that no other substances at a concentration of 20 μg/mL significantly affected the FL of T-CDs, except for gefitinib, indicating the high specificity of T-CDs for gefitinib detection. The method utilized the IFE of gefitinib to quench the FL of T-CDs, and it was successfully applied to detect gefitinib in urine samples . A study developed a FL probe for detecting DOX using plum-based carbon quantum dots (PCQDs) synthesized via a simple bottom-up method. This ratiometric FL probe was tested on urine and serum samples, which may contain interfering substances. The results showed that, apart from DOX, the absorption peaks of interfering chemicals did not overlap with the emission peak of the PCQDs. Only DOX significantly increased the I591/I491 ratio of the PCQDs, indicating a strong response. The probe exhibited high selectivity for DOX over other drugs, such as cytarabine, cytoxan, 5-fluorouracil, and methotrexate, which had no effect on the I591/I491 readings. This demonstrated that the presence of other substances did not interfere with the detection of DOX, making the probe a reliable and selective tool for DOX detection in complex samples . Plant-based sulfur and nitrogen self-co-doped carbon quantum dots (S, N-CQDs) were synthesized in an environmentally friendly, cost-effective, and rapid one-pot process using onion and cabbage juices and distilled water. The optimal pH for the procedure was determined to be 8.0. The applicability of the synthesized S, N-CQDs for FL sensing of nitazoxanide and hemoglobin (Hb) was evaluated by testing their response to various potential interfering substances, including glycine, glucose, sucrose, citric acid, sodium benzoate, sodium citrate, urea, lysine, nicotinamide, glutathione, and various ions (Ca 2 ⁺, K⁺, Na⁺, Mg 2 ⁺, Cl⁻, SO₄ 2 ⁻). The results demonstrated that these green, safe, affordable, and sustainable S, N-CQDs have significant potential for use in pharmaceutical and biological applications . N-CDs were synthesized using diethylenetriamine and citric acid through a hydrothermal process. The FL intensity of the sensor decreased linearly with an increase in myricetin concentration. The sensor showed high selectivity for myricetin, as its signal intensity was unaffected by various structural analogs such as warfarin, KAE, and luteolin, among others. Additionally, the sensor exhibited strong anti-interference properties, with minimal impact from potential interfering ions or compounds, ensuring reliable detection of myricetin. The schematic diagram of the preparation process of N-CDs and the detection principle for myricetin is shown in Fig. 9 . Fig. 9 Schematic diagram about the preparation process of N-CDs and detection principle for myricetin. Fig. 9 Water-soluble carbon dots (SD-CDs) were synthesized using a microwave-assisted approach with 5-sulpha anthranilic acid (SAA) and 1,5-diphenylycarbazide (DPC) as precursors. The selectivity of SD-CDs towards levocetirizine and niflumic acid was investigated in the presence of various metal ions (Na + , Ni 2+ , Mn 2+ , Cd 2+ , K + , Ca 2+ ), anions (Br − , SO 4 2− , Cl − , NO 3 − ), and drugs. The results showed that the FL intensity of SD-CDs was enhanced exclusively by levocetirizine, even in the presence of other interfering substances. On the other hand, niflumic acid caused a significant FL quenching when mixed with other chemicals, demonstrating a high selectivity of SD-CDs for these two compounds. The addition of metal ions, anions, and other drugs did not result in FL enhancement or quenching, confirming the robustness and specificity of SD-CDs. These findings indicate that SD-CDs could serve as a reliable analytical platform for detecting levocetirizine and niflumic acid in real-world samples . Orange-emission fluorescent multifunctional carbon dots (O-CDs) were synthesized using safranine T and ethanol through a one-step hydrothermal process. These O-CDs exhibit excitation-independent FL properties, making them suitable for applications in biosensing, cellular labeling, and photovoltaic materials. When tested for selectivity, O-CDs showed significant FL quenching only in the presence of vitamin B 12 (VB 12 ), indicating their high selectivity for VB 12 analysis. The O-CDs also demonstrated a longer FL emission duration compared to other fluorescent techniques for VB 12 analysis, making them highly effective for detecting VB 12 in biological samples. The mechanism of FL quenching was attributed to IFE, confirmed through UV–Vis absorption spectra and lifetime measurements, showing that VB 12 absorbed the excitation spectrum of O-CDs, leading to FL quenching. This specificity was further validated as the FL did not change in the presence of amino acids or other vitamins . In a study using a high-pressure microwave method, biomass CDs were synthesized from longan peels. To enhance the selectivity and sensitivity of the fluorescent probes, CDs were integrated with molecularly imprinted polymers (MIPs) and restricted access materials (RAM-MIPs), resulting in the creation of CDs@RAM-MIPs. The results showed that CDs@RAM-MIPs exhibited excellent selectivity for metronidazole (MNZ), with no significant interference from other drugs. The unique recognition cavities of the CDs@RAM-MIPs selectively bind MNZ, allowing for precise detection of the target molecule. The method's applicability was also evaluated by measuring MNZ levels in serum samples, demonstrating its potential for accurate detection in complex biological samples . Fluorescent CDs were synthesized by a microwave-assisted method using a mixture of coconut water and ethanol (1:1 v/v). The resulting CDs displayed luminescent properties, which were influenced by the reaction temperature and microwave exposure time during the synthesis. The study showed that pure coconut water itself did not exhibit any FL, but the CDs produced during the microwave reaction displayed strong FL. These CDs were then used as a sensing platform for thiamine detection. The FL of the CDs was quenched by the addition of Cu 2+ ions. However, when thiamine was added to the mixture of CDs and Cu 2+ , the FL intensity was restored to its original value. This restoration of FL intensity was directly proportional to the concentration of thiamine, allowing for the quantification of thiamine in real samples such as blood and urine. The method proved highly selective for thiamine, with minimal interference from other potentially coexisting molecules and ions. This makes the CDs a useful tool for thiamine analysis in biological samples, demonstrating a practical and sensitive approach to thiamine detection . In a separate study, N-CDs were synthesized using a hydrothermal method to detect zoledronic acid (ZA) in blood serum. The N-CDs exhibited a FL intensity change when ZA was added. Specifically, the FL was quenched when Fe 3+ ions were present, but it was restored upon the introduction of ZA, forming a complex with Fe 3+ . The optimal pH for this FL sensing system was found to be 4.0, using an acetate buffer. This method showed high selectivity for ZA, as there were no significant FL changes when other molecules or ions, even in excess, were added. The study also compared the FL response of ZA to other substances like fludarabine (FLD), a chemotherapy drug, and found that FLD did not interfere with the sensing process. This demonstrates that the N-CDs-Fe 3+ complex can be effectively used for selective and sensitive detection of ZA in blood serum, highlighting the potential of N-CDs for chemical sensing and medical applications . Saffron was used as a precursor to synthesize CDs through a hydrothermal method, offering a novel and cost-effective approach. The fluorescent properties of these CDs enabled the sensitive detection of prilocaine in human plasma. Additionally, the synthesized CDs were applied for bioimaging, specifically for imaging olfactory mucosa cells and bone marrow cells. The results demonstrated excellent imaging capabilities, suggesting that these saffron-derived CDs are promising candidates for bioimaging applications, particularly in cancer cell imaging. This highlights their potential for medical and diagnostic use . The researchers synthesized N-CDs using DL-malic acid and glycine. Initially, adding enrofloxacin (ENR) to the N-CDs caused no change in the FL intensity or shape, indicating no interaction between the two. However, the introduction of Cu 2+ restored the FL of the N-CDs, suggesting that Cu 2+ interacts with both ENR and the N-CDs. Additionally, the concentration of N-CDs in the solution increased with the addition of Cu 2+ , likely due to the aggregation of some N-CDs or the formation of larger complexes between Cu 2+ and ENR. This approach was shown to selectively detect ENR in tap and river water samples, demonstrating its potential for environmental monitoring . In a study, the FL intensity of CDs increased with rising concentrations of CTC, with a blue shift in the emission peak. This sensor showed excellent selectivity for CTC over other TC antibiotics, structurally similar drugs, various ions, and naturally occurring amino acids. The sensor was also effective in detecting CTC in milk and tap water, indicating its application in food safety and environmental testing . Moreover, N, S-CDs were synthesized using a one-pot ionothermal method with deep eutectic solvent and microcrystalline cellulose (MCC) as solvents and dopants. These N, S-CDs were used to detect glutathione (GSH), where the FL quenching caused by the formation of a CD-Cu 2+ complex was reversed by adding GSH. The interaction between GSH and Cu 2+ restored the FL, and as GSH concentration increased, the FL became more intense. This work demonstrated that GSH is selective for the recovery of CD FL, and the study also evaluated the impact of interfering substances like amino acids and small reducing molecules. The findings suggest broad applications for biomass-derived carbon dots in selective FL sensing. The schematic illustration of the preparation of N, S-CDs and their detection capabilities for Cu 2 ⁺, and GSH is shown in Fig. 10 . Fig. 10 Schematic preparation of N, S-CDs and its detection for Cu 2+ and GSH. Fig. 10 Food-derived crawfish shells were utilized as green precursors to synthesize N, S-CDs via a hydrothermal method. The FL of the N, S-CDs was significantly quenched upon the addition of H 2 O 2 and horseradish peroxidase (HRP). This quenching occurred because the hydroxyl radicals generated by the reaction between H 2 O 2 and HRP oxidized the surface groups (N, sulfur, and oxygen) of the N, S-CDs, altering their surface states and causing FL suppression. However, when pentachlorophenol (PCP) was introduced to the NSCDs@HRP/H 2 O 2 system, the FL was restored. PCP provided a reducing environment that prevented the oxidation of the active groups on the N, S-CDs, thus reactivating the FL. This fluorescent sensing method proved highly effective for detecting PCP in real samples, highlighting the potential of this approach for environmental and analytical applications . In one study, CDs were synthesized through hydrothermal treatment using glucose, ampicillin, 2-phenyl glycine, and (+)-6-amino penicillanic acid. The β-lactam subunit within ampicillin, with its N and S atoms, was identified as the key factor contributing to the unique absorption and emission properties of the resulting CDs. Unlike a glucose solution without hydrothermal treatment, the product solutions containing varying amounts of ampicillin exhibited distinct FL, showcasing the significant role of ampicillin in the FL properties of the CDs . In a study involving co-doped CNDs containing N and phosphorus (P), the synthesis was achieved using FA through a single-step solvothermal method. The interference study showed that the FL intensity of the CNDs remained almost unchanged when exposed to common excipients like lactose, L-ascorbic acid, talc, magnesium tartrate, and starch, suggesting that these substances did not interfere with the analytical method. This indicates the reliability of the CND-based sensor. Furthermore, in vivo studies revealed that the CNDs had no significant cytotoxic, hematological, or biochemical effects on animals, supporting their biocompatibility . In another study, Cedrus was used as a green source for producing CDs through a hydrothermal method. These CDs were then modified with MIPs using reverse micro-emulsion technology, resulting in MIPs-Green Source CDs (MIPs-GSCDs). The sensor was optimized in terms of pH, temperature, and response time. The MIPs-GSCDs exhibited high sensitivity and selectivity for phenobarbital in human blood plasma, demonstrating the practical application of this sensor for real sample analysis . A separate study used citric acid and ammonia to synthesize CDs through pyrolysis at high temperatures. These CDs were tested for their selectivity in plasma and pharmaceutical formulations, particularly in the presence of potential interfering substances. While most compounds had little effect on the FL of CDs, clonazepam caused a significant quenching of FL, likely due to interactions between its nitro group and the surface functional groups of the CDs. This high selectivity allows for the precise detection of clonazepam in low concentrations, making the sensor valuable for monitoring trace amounts in pharmaceutical and plasma samples . Additionally, red-emitting copper nanoclusters (CuNCs) were synthesized using DNA as a template and combined with blue-emitting CDs to form a self-assembled complex, DNA-CuNC/CDs. This complex was tested as a ratiometric FL nanoplatform for detecting arginine (Arg) and acetaminophen (AP) in human serum samples. Upon the addition of AP, the FL of the complex shifted from purplish-red to blue, and the FL of the CDs was restored, demonstrating the ability to detect AP. The system showed excellent selectivity and sensitivity, with minimal interference from other substances. This ratiometric FL nanoplatform also highlighted the potential for building “INHIBIT” logic gates at the molecular level, offering exciting prospects for early disease diagnosis and real-time clinical monitoring . A microwave-assisted method was used to synthesize CDs from glucose and deep eutectic solvents (DESs), composed of choline chloride and urea mixed with deionized water. The CDs were tested for FL properties by mixing them with various ions and molecules at a concentration of 3 μM. The FL intensity remained mostly unaffected by most species, except for atorvastatin, which caused significant quenching, indicating the sensor's high selectivity for atorvastatin. FL quenching occurred quickly at room temperature after adding atorvastatin. The FL intensity was pH-dependent, increasing between pH 3–8, with a peak at pH 7.4, which was chosen as the optimum pH for the sensor. The FL intensity decreased significantly with increasing atorvastatin concentration, demonstrating the CDs sensitivity for atorvastatin detection. Nitrogen and chloride-doped carbon dots (N/Cl-CDs) were synthesized using the same method to enhance the sensor's selectivity and sensitivity. These N/Cl-CDs were successfully used as a fluorescent nanosensor to detect atorvastatin in blood, showcasing their potential for monitoring atorvastatin in biological samples . Hydrothermal synthesis was used to create nitrogen and phosphorus co-doped carbon dots (N, P-CDs) with intense blue FL using alanine and diammonium phosphate. The FL intensity remained stable with an increase in pH until DOX was introduced, which caused a significant decrease in the FL intensity, indicating that the N, P-CDs were sensitive to DOX. The FL intensity reached its peak at pH 7.0, and with increasing concentrations of DOX, the FL intensity gradually reduced. When tested against various common metal cations and biomolecules, only DOX caused a marked “turn-off” effect, demonstrating the high selectivity of the N, P-CDs for DOX detection . Thiosemicarbazide and citric acid were utilized in a one-step hydrothermal method to synthesize nitrogen and sulfur-doped carbon quantum dots (N, S-CQDs). These N, S-CQDs demonstrated strong FL and were used for the detection of imatinib (IMA) in pharmaceutical and biological samples. As the concentration of IMA increased, the FL intensity of the N, S-CQDs decreased, providing a simple and sensitive approach for IMA detection, with the added benefits of low cost and ease of use. The systematic detection process of imatinib is depicted in Fig. 11 . Fig. 11 Outline of the synthesis process and applications of N,S-CQDs. Fig. 11 Similarly, a fluorescent sensor for Cur detection was developed based on N, S-CQDs and MIPs. The CQDs with blue FL were synthesized using citric acid and o-phenylenediamine. The sensor, composed of N, S-CQD@MIP, showed higher quenching efficiency for Cur compared to the N, S-CQD@NIP composite, highlighting its superior sensitivity. Even when the concentration of interfering drugs was ten times higher than Cur, the FL quenching efficiency remained largely unaffected, demonstrating excellent selectivity for Cur. Further tests with various ions and proteins confirmed that the sensor was highly selective and specific for Cur detection . Highly luminous CDs were synthesized from wild lemon leaves using one-step microwave pyrolysis. The selectivity of the CDs was tested by introducing a variety of foreign substances, including biomolecules, metal ions, and antibiotics. The CDs demonstrated exceptional selectivity for TC detection, as they remained highly fluorescent even in the presence of various interferents. This biocompatible, label-free nanoprobe was successfully used to detect TC in environmental water samples, showing good recovery rates and promising potential for environmental monitoring . Researchers synthesized Aconitic acid-based carbon dots (AA-CDs) using aconitic acid (AA) and 1,2-ethylenediamine via a hydrothermal method. They found that the addition of FA could effectively quench the intrinsic FL of AA-CDs without requiring additional surface modifications or passivation. The AA-CDs were successfully used for detecting FA in food and pharmaceutical samples. Additionally, the researchers demonstrated the potential of FA-AA-CDs for targeted imaging, as they were able to distinguish cancer cells based on varying levels of folate receptor's (FRs) expression. This showed the feasibility of using turn-on FL for imaging cancer cells with overexpressed FRs indicating a promising application for cancer diagnostics and imaging. Fig. 12 presents a schematic illustration of ratiometric FL sensing for FA . Fig. 12 Schematic illustration for the detection of folic acid. Fig. 12 Fig. 13 Various applications of CDs. Fig. 13 A new type of fluorescent CDs was synthesized by pyrolyzing folic acid. These fluorescent CDs exhibit excitation-independent FL, allowing for rapid and sensitive detection of isoniazid through a combination of IFE and SQ effects. This makes them promising for drug testing applications, potentially offering new methods and technologies for detecting pharmaceutical compounds . Another study focused on the synthesis of fluorescent nitrogen-doped carbon quantum dots (N-CQDs) using a hydrothermal process involving natural osmanthus fragrans, without the use of harmful substances or surface chemical modifications. In tests for potential interference, the FL intensity of N-CQDs remained stable when mixed with various metal cations, indicating that they are highly selective for quercetin (QT) sensing. These N-CQDs can serve as a reversible, sensitive platform for detecting QT, making them suitable for clinical diagnostics and other biomedical applications . Additionally, Fluorine, Nitrogen-doped carbon dots (F, N-CDs) were synthesized using tetrafluoroterephthalic acid and tetraethylenepentamine. These F, N-CDs exhibited a rapid decrease in FL intensity as the concentration of tigecycline (TIGE) increased. The FL quenching effect was highly specific, as other potential interferences showed minimal impact. The method demonstrated strong anti-interference properties, making it suitable for real-time drug monitoring. The approach was successfully applied for detecting labeled TIGE and TIGE injections in human plasma, highlighting its potential for precise and reliable drug monitoring in clinical settings . A sensor was developed by coupling ovalbumin (OVA) and 3-aminophenyl boronic acid (3-APBA) with CDs through the specific interaction of cis-diol bonds in glycoproteins and borate groups on the CDs surface. This CDs-functionalized OVA acted as both a stabilizing and reducing agent during the synthesis of gold nanoclusters (AuNCs), forming a CDs-AuNCs nanocomposite. The sensor demonstrated the highest ratiometric FL efficiency at pH 8.5, producing a bright yellow color. The sensor was tested for TC detection, showing high selectivity and minimal interference from other antibiotics, amino acids, and potential contaminants. This triple-emission sensor is highly selective for tetracyclines (TC's) and has potential for practical applications in antibiotic detection . In a single step, CDs were synthesized using GSH and formamide via a microwave-mediated process. Interference studies demonstrated that the CDs were highly selective for detecting nitrotyrosine (nTyr), even in the presence of other biologically significant compounds like tyrosine (Tyr) and phosphotyrosine (pTyr). Without nTyr, the FL emission of the CDs was only slightly reduced (by 10 %) in the presence of these interferents, indicating that the CDs were specifically designed for nTyr detection with minimal interference . The CDs were synthesized using a hydrothermal method with citric acid as the carbon source and ethylenediamine as the co-reactant. These CDs were employed as probes to detect dopamine (DA) and DOX in human serum solutions. In the serum, DA effectively quenched the FL of the CDs, establishing a strong linear correlation. Similarly, DOX was also detected with a good linear relationship in the serum solution. The results indicate that CDs are highly suitable for detecting DA and quinone-based drugs, such as DOX, in human serum due to their excellent stability, eco-friendliness, and anti-interference properties. The method was also successfully applied to quantitatively identify DOX and MITX, both of which share a typical quinone structure. This approach provides a solid foundation for further biological applications, offering reliable and efficient detection capabilities in clinical and pharmaceutical settings . Fluorescent CDs were synthesized using citric acid and L-glutathione to create a platform for detecting chondroitin sulfate (CS). The positively charged N-doped CDs (P-NCDs) were combined with FAM-labeled random-sequence single-stranded DNA (F-ssDNA), creating a sensitive and simple ratiometric homogeneous assay through competitive electrostatic interactions. The process of CS detection was confirmed by measuring the zeta potential, which decreased due to the electrostatic interaction between negatively charged CS and P-NCDs/F-ssDNA. The zeta potential decreased significantly when CS was added, showing a stronger interaction between P-NCDs and CS (−12.5 mV) compared to P-NCDs and F-ssDNA. Further validation was achieved by comparing the zeta potential of P-NCDs with CS (−4.5 mV) to that of their mixture (−19.75 mV). The system was successfully applied to detect CS in actual joint fluid, demonstrating its potential for clinical use. This study highlights new insights into the development of fluorescent nanomaterials and DNA for biosensing applications . CDs were synthesized using CO(NO 3 ) 2 .6H 2 O and cetrimonium bromide (CTAB), followed by dispersion with multi-walled carbon nanotubes (MWCNTs) via ultrasonication. The FL intensity of the CDs was found to be pH-dependent, with the highest redox peak current observed at pH 7.0 for the drug molecules flutamide (FLU) and nitrofurantoin (NF). The sensor was evaluated for practical use in human urine samples, showing its capability for the simultaneous detection of these drug molecules. This study demonstrates a straightforward method for synthesizing a hybrid nanocomposite and highlights its potential for drug detection applications . Many pharmaceutical drugs have been detected using HPLC, HPTLC, UV–Vis, and Fourier transform infrared spectroscopy (FT-IR) to date. Analytical experiments (sampling, sample preparation, instrumental analysis, data processing, and data interpretation) present distinct problems in achieving the goal of enhancing the existing state of pharmaceutical analysis and, more broadly, functionalization. To overcome these challenges CDs can be used for detection just by converting the non-fluorescent substances into fluorescent substances and sensing through a different mechanism. Pharmaceutical residues of antibiotics, nonsteroidal anti-inflammatory drugs (NSAIDs), lipid-regulators, hormones, β-blocker, anticonvulsants, steroids, opiate drugs, antidepressants, and anti-diabetics in the environment have recently been discovered as a hazard, with their presence in the aquatic environment being particularly critical . Experiments conducted in both laboratory and natural settings have revealed that oral contraceptives are leaving traces in water bodies and causing the feminization of fish and amphibians, while psychiatric drugs are leaving residues that alter fish behavior. Furthermore, the excessive use of antibiotics has led to a rapid rise in antimicrobial resistance, which has become a major global health crisis . Previous studies have found that common antidepressant medications are accumulated in the brain tissue of fish that inhabit water downstream from wastewater treatment plants (WWTPs) . Numerous techniques have been proposed for eliminating antibiotics, including advanced oxidation, reverse osmosis, membrane filtration, electrochemical methods, and biological treatments. However, most of these methods are expensive, produce by-products, or are not as efficient. Removal of antibiotics by using various adsorbents (sawdust, activated carbon, NPs, sludge biochar) has been reported. Antibiotics such as gatifloxacin have been removed by more than 90 % using NPs and 68.5 % and 64 % of ciprofloxacin by using kandira stone and sawdust as an adsorbent . The Fenton oxidation process combined with ultrafiltration (UF), sand filtration, reverse osmosis (RO), and nanofiltration (NF) resulted in recovery above 90 %. In another study, the electro-Fenton process was used to remove diclofenac sodium where about 80 % drug removal was observed . Recently, various techniques have been developed to remove antibiotics and antibiotic resistance genes. These techniques include the use of Fe 3 O 4 /red mud NPs, 3D hierarchical porous-structured biochar aerogels, calcined layered double hydroxides, co-doped UiO-66 NPs, Cu@TiO 2 hybrids, bioelectrochemical systems, and aerobic granulation process. The results of studies have shown that most of these methods are effective in removing antibiotic residues and antibiotic resistance genes, with removal rates ranging from 85 % to 95 % . A recent study found that a mix of domestic and livestock sewage in rural wastewater contained five steroid hormones [androsta-1,4-diene-3,17-dione (ADD), 4-androstene-3,17-dione, 19-norethindrone (19-NTD), testosterone (T), and progesterone ] and four biocides [N, N-diethyl-3-methylbenzamide (DEET), triclosan (TCS), carbendazim (CBD), and methylparaben (MP)]. However, the results showed that an integrated constructed wetland (ICW) was able to significantly reduce the levels of the detected hormones and biocides, with a reduction rate of 97.4 ± 0.09 % and 92.4 ± 0.54 %, respectively . Due to the physical and chemical complexity of the NSAIDs compounds, there is no single method that is sufficiently effective against all types of contaminants . Evidence reveals that pharmaceutical chemicals, physicochemical properties, and structural complexity make them difficult to remove completely in traditional WWTPs, highlighting their unintentional persistence in the environment. There is a toxic effect of pharmaceutical compounds on the human body at low concentrations. Hence, a more sensitive method is required to detect the pharmaceutical compounds at low concentrations, and for this; CDs are the best alternative method to detect the pharmaceutical compounds at the femtogram level. Nowadays, nanomaterials can be used for the treatment of wastewater and purification. Materials such as graphene oxide-based NPs (GONPs), mesoporous Mn x Co 3-x O 4 NPs, etc., have been used for the removal of ibuprofen, ciprofloxacin, and levofloxacin from water and aqueous solution by the mechanism of adsorption mainly due to electrostatic interactions, accelerated electron transfer and by photocatalytic degradation. The percentage removal of ibuprofen, ciprofloxacin, and levofloxacin was found to be 98.2 %, 100 %, and 95 %, respectively [ , , ]. CDs with an optimum size of 5 nm–10 nm and modifying the surface functionalization of CDs can be used for various applications apart from bio-sensing. It includes tumor theranostics (cancer-targeted drug delivery, gene delivery, PTT, photodynamic therapy (PDT)), bio-imaging biomarkers (cell membrane, cytoplasm, for self-targeted bioimaging and diagnosis of tumor cells), catalysis, detection of small molecules, photoacoustic imaging , light-emitting diode (LED) device , FL ink , in agriculture , etc. This review highlights the recent advancements in CDs, focusing on their synthesis, surface functionalization, photoluminescent properties, and diverse applications across fields such as photocatalysis, energy, and sensing. CDs have emerged as promising alternatives to traditional fluorescent compounds, particularly in in-vivo analysis, due to their safer nature and exceptional FL properties. The unique physical and optical characteristics of CDs, including their efficient electron storage and transport capabilities when exposed to light, present vast untapped potential. Looking forward, the development of more reliable and efficient synthesis methods will likely drive further exploration of innovative applications. The versatility of CDs positions them as impactful materials in biotechnology and environmental remediation, offering safer substitutes for conventional analytical techniques. Given the complexity of contaminants that conventional wastewater treatment plants cannot fully eliminate, there is an increasing need for advanced materials like CDs to address these challenges. With their exceptional electron transfer abilities and light-harvesting efficiency, CDs hold significant promise in photovoltaic and photocatalytic applications. Additionally, their potential extends to areas such as bio-sensing, tumor theranostics, bio-imaging, LED technology, small molecule detection, and agriculture. The continued research and development of CDs are expected to unlock new applications, broadening their utility in various scientific and industrial fields. | Review | biomedical | en | 0.999998 |
PMC11697699 | Statistical analysis was performed in Python (v 3.9) and JASP software . Basic descriptive statistics were used to assess differences in math anxiety in Russian and Chinese samples. Confirmatory Factor Analysis (CFA) was performed to assess the factor structure of AMAS for the two samples. The DWLS (Diagonal Weighted Least Squares) estimator was used. One-factor, two-factor, second-order (two factors), and bi-factor (two factors) CFA models were compared. TLI, CFI, RMSEA, and SRMR metrics were used to assess model fit. Structural Equation Modeling (SEM) analysis was performed to measure invariance, and the Cronbach Alpha coefficient was used to assess the internal consistency of AMAS and subscales for two samples. Descriptive statistics for total AMAS and two subscales with frequency histograms across the sample are presented in Supplementary Table 4 and Supplementary Figure 3 . It can be seen that regardless of country, the LMA score distribution is skewed to the low values, while the MEA score distribution is closer to normal distribution. MEA and LMA show moderate Pearson correlations for all samples: r = 0.54, p < 0.001 for the joint sample, r = 0.52 for the Russian sample, and r = 0.66 for the Chinese sample. | Study | biomedical | en | 0.999997 |
PMC11697701 | As the life expectancy of the global population gradually increases with the advancement of medical treatment and the improvement of living standards, the problem of population aging is becoming increasingly serious, and how to face population aging positively has become the most important medical and social issue in the world ( 1 ). One of the major challenges facing an aging population is the increasing prevalence of age-related frailty, which is a state of reduced ability to cope with stimuli due to age-related declines in the physiological reserve capacity and function of multiple systems and organs ( 2 ). Several prospective cohort studies have shown that frailty is strongly associated with poor health and that frail older people are more likely to experience death ( 3 ), disability ( 4–6 ), falls ( 7 ), and hospitalization ( 2 ) than non-frail older adults. Although frailty poses a significant risk of adverse health outcomes in older adults, it is a dynamic and reversible disease, which means that it is preventable and controllable and that early recognition and interventions of frailty can halt its progression ( 2 , 8 ). Early identification and diagnosis of frailty will help to maximize the reversal of its further progression, alleviate or delay underlying symptoms, control adverse clinical health outcomes such as recurrent hospitalization and death, maintain their functional status, and enhance their quality of life. Several studies have shown that timely recognition and intervention of frailty in the clinical setting or daily life can contribute to benefits for older adults ( 9 , 10 ), and may even delay the onset of death in 3 to 5% of older adults ( 11 ). Clinical practice guidelines developed by the International Conference of Frailty and Sarcopenia Research (ICFSR) also recommend that adults 65 years old and older should be screened for frailty using a simple, validated, and rapid screening tool appropriate for the specific scenario and that all older adults considered to be frail or pre-frail should be further assessed for frailty ( 12 ). However, there are no standardized criteria regarding the selection of screening and assessment tools for frailty. Therefore, the development of efficient and practical screening and assessment tools for frailty should be a top priority in the field of frailty research. This paper provides an overview of the current state of research on screening and assessment tools for age-related frailty and the characteristics of commonly used frailty scales, with the aim of helping clinical researchers and practitioners to accurately judge and fine-tune the management of frail older adults. The search database was PubMed. The retrieval time node ranged from January 2000 to December 2023. The retrieval strategy was optimized with the use of Boolean logical operators. The retrieval formula is ((“Frailty/diagnosis”[Mesh] OR “Frailty/epidemiology”[Mesh]) OR ((Frailties[Title/Abstract]) OR (Frailness[Title/Abstract])OR (Frailty Syndrome[Title/Abstract]) OR (Debility[Title/Abstract]) OR (Debilities[Title/Abstract]))) AND (). Then, we imported the transcript of all retrieved literature into Endnote Application. After briefly reading the title and abstract, articles not related to the screening or assessment of frailty were excluded. The specific inclusion criteria were defined as follows: (1) papers covered a population of older adults aged ≥60 years; (2) the paper’s main topic was screening and assessment of frailty; (3) the full text was accessible; (4) paper was presented in English. The exclusion criterion was that the paper was on the pathogenesis or interventions for frailty or its association with other diseases. The process of screening the literature was done independently by two researchers, followed by cross-checking. In case of disagreement between the two researchers, a third person was consulted to assist in the judgment. Next, we read the remaining literature and used an Excel sheet to record the screening or assessment scales addressed in each paper, selecting those that appeared ≥150 times. Finally, we read the literature pertaining to the above scales carefully and used another Excel sheet to document the content, focus, measurement patterns, and application scenarios of each scale for subsequent categorization and summarization. At present, studies on screening and assessment tools for frailty have mostly focused on two areas: frailty-related biological markers and frailty-related scales. The development of frailty involves multiple complex pathophysiological processes such as chronic inflammatory responses, imbalances in energy metabolism, nutritional deficiencies, immune disorders, oxidative stress, and so on ( 13 ). In older frail patients, the levels of biological factors involved in these pathophysiologic processes are altered accordingly and can be biomarkers of frailty. To date, biomarkers of age-related frailty can be categorized as inflammatory response-related biomarkers (C-reactive protein, interleukin-6, tumor necrosis factor) ( 14–17 ), metabolism-related biomarkers (muscle growth inhibitor, 25-hydroxyvitamin D, insulin-like growth factor 1) ( 18–21 ), immune-related biomarkers (neutrophils/lymphocytes ratio, platelet/lymphocyte ratio and systemic immune-inflammatory index) ( 22 ), Oxidative stress-related biomarkers (8-dihydro-2′-eoxyguanosine, reactive oxygen species, and superoxide dismutase) ( 23 , 24 ), and nutrient-related biomarkers (docosahexaenoic acid and vitamin B12) ( 25 , 26 ). However, biomarkers are susceptible to a variety of factors, making these indicators potentially less stable. For example, the circulating level of insulin-like growth factor 1 can be affected by nutritional levels and genetic factors, and the ratio of neutrophil/lymphocyte is susceptible to factors such as acute illnesses and infections. In addition, some frailty biomarkers have gender specificity, such as C-reactive protein, interleukin-6, and muscle growth inhibitors, which makes it necessary to take gender into account when selecting biomarkers. Therefore, although the changes in biomarkers precede the appearance of the organism’s phenotype, and the objectivity and sensitivity of biomarkers are superior, their specificity, precision, stability, and reliability are weaker than those of frailty-related scales ( 27 ). In fact, due to the lack of definitive laboratory tests and appropriate clinical testing techniques and equipment ( 28 ), the frailty-related scales have become the most commonly used clinical tool for the identification and assessment of frailty ( 29 ). The frailty scales are mostly based on the three conceptual models of frailty and are established by incorporating the clinical symptoms, signs and subjective feelings of the patient, which have the advantages of simplicity, time-saving, validity, economy and feasibility, such as: Fried Frailty Phenotype (FFP), Frailty Index (FI), Groningen Frailty Indicator (GFI), and so on. Three of the conceptual models described above are the Biological Phenotype, the Cumulative Health Deficit Model, and the Frailty Integral Model. In 2001, Fried et al. ( 30 ) proposed the concept of the Fried Frailty Phenotype (FFP) based on the theory of the Biological Phenotype, stating that frailty is a syndrome that meets three or more of the five phenotypic criteria, which is mostly centered on physical deterioration, together with a decrease in physical performance and muscular strength. The Cumulative Health Deficit Model suggests that the more health deficits are accumulated, the more severe the degree of frailty. Based on the Cumulative Health Deficit Model, Rockwood et al. ( 31 ) proposed the concept of the Frailty Index (FI) in 2005, which considered frailty as a complex unity of physiological, psychological, and social functioning, and a risk condition that develops as a result of the accumulation of multiple disorders due to multiple factors. The above two models are commonly used in existing studies and have been generally confirmed. Based on these two models, Gobbens et al. ( 32 ) proposed the Integral Model of Frailty (IMF), which further defines the operational definition of frailty as a dynamic and continuous process that includes somatic, psychological, and social aspects. Recently, WHO has proposed a new concept of “intrinsic capacity” based on healthy aging, which emphasizes the physiological and psychological dimensions of the individual, and is a longitudinal assessment that follows a trajectory rather than the traditional assessment of frailty at a cross-section or cut-off point. In a sense, intrinsic capacity evolves from frailty, and frailty is one of the components of the decline trajectory of intrinsic capacity ( 33 ). Another hybrid concept analysis of frailty described frailty as a dynamic and fluctuating inability to manage biopsychosocial and environmental stimuli that involves a decline in functioning and life changes, leading to a loss of autonomy and motivation, or poor health outcomes ( 34 ). Based on the above concepts, the frailty scales should address multiple dimensions such as somatic, psychological as well as social conditions. Frailty scales can be categorized as screening scales and assessment scales, and the two are often conflated in clinical practice. In fact, screening tools are not identical to assessment tools, and the emphasis of the two is not the same. The frailty screening scales focus on their operationalization, efficiency, and high sensitivity in order to screen older patients at risk of frailty or in a stage of frailty in a very short period of time, while the frailty assessment scales is more complex, focusing on high precision and support by reasonable biological indicators in order to determine more precisely the stage of frailty in which they are placed, and then to develop different treatment plannings according to their stages and risks ( 35 ). Besides, the different screening and assessment tools count the different prevalence rates of frailty ( 36 ). A Meta-analysis showed that the prevalence of frailty within the same group was 12% using the FFP versus 24% using the FI ( 37 ). Another cross-sectional study among older Brazilians showed that the prevalence of frailty was 0.3% when assessed only in the physical domain of the Tilburg Frailty Indicator (TFI), 2.9% when assessed in both the physical and social domains, and 52% when assessed in a combination of all three domains: physical, social and psychological ( 38 ). Although a variety of geriatric frailty scales have been developed, there is still no recognized gold scale for assessing geriatric frailty, and translation from research to clinical practice remains a challenge in the future ( 39 ).Next, this article summarizes and analyzes the existing commonly used frailty scales in terms of their screening and assessment roles, and classifies each scale according to its content, focus, measurement mode, and application scenarios, so that clinicians or researchers can use the most appropriate frailty scales according to their characteristics, thus achieving the goal of early screening, early assessment, and early intervention of frailty, and reducing a series of adverse outcomes. The purpose of frailty screening is primarily to make a quick diagnosis, which is performed in all groups of older people, to identify those who are at high risk of frailty or already in a state of frailty through the use of simple tests. Here, we summarize and generalize the advantages and disadvantages of some commonly used screening scales for age-related frailty. The FS is a clinically applicable self-screening scale for frail older adults proposed by the experts of the International Academy on Nutrition and Aging (IANA) ( 40 ). FS is a simple patient self-reported questionnaire containing only 5 items as follows: fatigue, increased sense of resistance, decreased activity, multimorbidity co-morbidity, and weight loss. It quickly categorizes the state of an older person into 3 types, among which those who meet 3 or more items are frail, those who meet 1 to 2 items are pre-frail, and those who do not have any of the 1 items are in a healthy state ( 41 ). FS can be easily mastered by healthcare professionals and has a high degree of maneuverability and screening efficacy, which has been translated into many languages and widely used worldwide ( 42–44 ). In addition, FS has high specificity and sensitivity. A survey of the Chinese version of the adaptation of FS among 1,235 Chinese community-dwelling older adults showed that the sensitivity of FS was as high as 86.96% while the specificity was as high as 85.64% ( 42 ). Another study, which conducted on 308 Chinese older patients aged 60 years and above, showed that FS had a sensitivity of 85.9% and a specificity of 72.5%, and noted that FS was convenient and time-saving, which could be used for the initial screening of frail patients to improve work efficiency ( 45 ). A cross-sectional study conducted by Aprahamian et al. ( 46 ) in a geriatric outpatient clinic showed that the sensitivity of FS was 54% and the specificity was 73% and suggested that FS could be selected as a screening tool for frailty because of its significant time and cost benefits. FS can be used not only to screen for frailty but also to predict adverse outcomes in older adults. FS is a valid predictor of mortality in older adults over 10 years, according to a cohort study among older adults aged 65 years and older ( 47 ). In a longitudinal study of women’s health in middle age in Australia, FS predicted the incidence of disability in women from middle age to old age over the next 15 years ( 48 ). FS is entirely self-reported, without any objective measures, and can even be completed by telephone without face-to-face inspection, which makes it simple and easy to administer ( 49 ). The simplicity and ease of FS increases the convenience and completion rate of frailty screening, reduces the cost of screening, helps to carry out the development of frailty review, and is worthy of clinical application. However, FS suffers from a certain amount of information bias due to its complete reliance on patient self-reported outcomes, and special attention should be paid to this point in clinical applications. The CFS is a frailty screening tool developed in 2005 by Rockwood et al. ( 31 ) for use in the Canadian Health and Aging Study. The original CFS contained 4 dimensions: physical activity, mobility, physical function, and energy status, which categorized older adults’ health status into 7 levels. With further research on geriatric frailty, the scope of CFS was expanded to co-morbidities, functional status, and cognitive ability domains, increasing the classification to 9 ( 50 ). The specific levels of CFS are as follows: Very Fit, Fit, Managing Well, Living with Very Mild Frailty, Living with Mild Frailty, Living with Moderate Frailty, Living with Severe Frailty, Living with Very Severe Frailty, and Terminally Ill, wherein levels 5 and above are defined exactly as a frailty state. A follow-up study of 210 acutely hospitalized older patients with adverse health outcomes found that both CFS and FS could identify older adults at risk for hospitalized adverse health outcomes and could be used as an easy screening tool for frailty; however, CFS demonstrated higher sensitivity than FS (89.6% vs. 54.6%) ( 51 ). Another cross-sectional study conducted in China also confirmed that the sensitivity of CFS was superior to FS as a screening tool for age-related frailty, both in all patients (94.1% vs. 63.0%) and in patients from different wards (91.8–98.5% vs. 58.0–65.7%) ( 52 ). In their study of the association between CFS and in-hospital mortality in patients with Corona Virus Disease 2019 (COVID-19), Sablerolles et al. ( 53 ) found that the in-hospital mortality was significantly higher in frailty patients (CFS 6–9) than in healthy patients (CFS 1–3) and that the grade of CFS was negatively correlated with the health status of the patient. A meta-analysis indicated that CFS could predict in-hospital mortality in acutely ill older patients and is a reliable predictor of short-term mortality in older patients presenting to the emergency department ( 54 ). CFS combines clinical judgment with objective measures and can be used not only to predict the need for institutional care or the incidence of death, but also to assess specific domains including co-morbidities, functioning, and cognition, making it a widely used screening tool for frailty ( 55 ). Because of its simplicity, rapidity, and accurate ability to predict adverse outcomes, CFS is often considered the most desirable tool for geriatric frailty screening in emergency medicine ( 56 ). In addition, CFS is highly sensitive to symptoms associated with frailty syndrome, which makes it also useful for assessing and stratifying the management of frailty ( 57 ). However, the completion of CFS needs to be based on clinical diagnosis combined with the interpretation of clinical parameters, which requires that the user should be a medical staff with a certain medical knowledge base, which limits the popularization and application of CFS to a certain extent. The EFS is a multidimensional screening scale for frailty developed by ROLFSON et al. ( 58 ) for non-specialists without specialized training in geriatrics based on the traditional frailty phenotype. The EFS consists of 9 dimensions and 11 entries as follows: (1) Cognitive function (unable to complete the clock drawing test successfully); (2) Functional performance (needing help from others in daily activities); (3) General health (self-assessment of health and the number of hospitalizations in the last year); (4) Independence (unable to complete manual labor alone, unable to walk 2 flights of stairs or walk 1,000 m); (5) Social support (unable to seek outside support successfully when encountering problems); (6) Medication status (being on 5 or more prescription medications at the same time, forgetting to take medication); (7) Mental status (depression); (8) Nutritional aspects (unintentional and significant weight loss recently); (9) Self-control (urinary and fecal incontinence). The EFS has a maximum score of 17, with a score of 0 to 4 indicating no frailty, 5 to 6 indicating sensitive individuals prone to frailty, 7 to 8 indicating mild frailty, 9 to 10 indicating moderate frailty, and 11 or more indicating severe frailty. EFS covers all domains of frailty and is highly correlated with other frailty scales ( 59 ). A study of the differences in the prevalence of frailty calculated by five frailty screening tools showed that the prevalence of frailty screened by the EFS was 25.2%, which was most similar to the prevalence of frailty of 27.6% after integrating the five frailty screening tools mentioned above, suggesting that the accuracy of EFS screening was high ( 52 ). In addition, the above study found that the EFS had the highest specificity for the assessment of frailty in surgical wards at 98.1%. EFS is commonly used in the identification of geriatric frailty prior to surgery and helps to stratify the risks and identify potentially modifying factors, which makes it have a higher feasibility rating in the surgical setting ( 60 ). A prospective study in people aged 70 years and older undergoing major abdominal surgery showed that the EFS has good reliability and validity and can be used as a preoperative assessment tool to predict the risk of surgical complications in older adults ( 61 ). In a study conducted by McIsaac et al. ( 62 ), it was found that although the accuracy of assessments of postoperative risks using the modified Fried Index (mFI) and the EFS was similar, the EFS had the advantage of a shorter time-consuming and greater patient acceptance, and should be recommended for clinical use. EFS can be completed within 5 min, with high acceptance by both investigators and respondents. It is easy to operate and can be used by professionals or even non-professionals in multiple departments. It has a wide range of applications venues, which can be used in medical settings such as emergency, outpatient, and hospital wards, as well as in non-medical settings such as the community and the home, making it a reliable screening tool for geriatric frailty ( 63 ). However, EFS uses only one question to assess the specifics of the social support domain, disputing the comprehensiveness of the social frailty screen. FFP is a phenotype derived by Fried et al. ( 30 ) from observing and tracking the follow-up to validate adverse outcomes in 5317 older adults aged 65 years or older who participated in the U.S. Longitudinal Cardiovascular Health Study, which explains why FFP was also called the Cardiovascular Health Study Index (CHS). The entries of FFP consist of 5 self-reported symptoms combined with biologically measured signs. The details of its entries are as follows: significant loss of body mass (unintentional weight loss of more than 4.5 kg or more in the past 1 year), weakness (low grip strength in both hands), fatigue (self-reported to be more easily fatigued in the last 6 months), slowness of the body (significant slowing of the walking speed), and physical inactivity (sedentary and physically inactive). Among them, those who fulfill 3 or more are defined as frail, those who have 1 or 2 are defined as pre-frail, and those who do not have any of the above 5 are defined as non-frail. FFP can not only measure the physical frailty status of older adults but also reflect the mental health status of the older adults and obtain more objective and accurate data, which is the most popular and widely used frailty measurement tool in the clinic ( 12 ). Results of a survey conducted among cancer patients showed that FFP had a sensitivity of 92% and a specificity of 41% for screening for frailty ( 64 ). FFP is widely applicable and its reliability and validity have been validated many times ( 65 ). FFP has now been shown to have predictive value for adverse health outcomes in a diverse range of older adults, including hospitalized and general older adults ( 66 ). FFP is excellent for initial stratification for risks of the older population based on different characteristics (i.e., robust, pre-frail, and frail), without the need for an initial clinical assessment, and can be applied at the first patient contact ( 67 ). However, FFP focuses on the physiological level of assessment, lacks social, psychological, environmental, and multiple disease factors, and the implementation of some items (e.g., grip strength, step speed, etc.) requires trained personnel and specialized tools ( 68 ). The characteristics of FFP described above make it inappropriate for older adults with cognitive impairment, psychiatric disorders, impaired functioning, or in the acute phase of illness, which also make its applicability limited to hospitals, communities, and nursing facilities. In addition, the 5 phenotypic criteria of frailty allow for different ways of measuring them, and many previous studies have adapted their measurements, which have been confirmed to lead to differences in measurement effects ( 69 ). Therefore, future studies should report all the details about how the phenotypic criteria of frailty are measured in order to facilitate the interpretation of the results. In recent years, more and more frailty screening tools have been developed as a result of the progress of frailty research. For example, in 2007 Ensrud et al. ( 70 ) found that data collected using the Study of Osteoporotic Fractures (SOF) Index was independently associated with FFP-predicted frailty-related adverse health outcomes, and subsequently, the SOF index became one of the screening tools for frailty; the Kihon Checklist (KCL) was proposed by the Japanese government for the implementation of the long-term care insurance system ( 71 ); and the Simple Self-Assessment Screening Tool—the Vulnerable Elders Survey-13 (VES-13) was created by Saliba et al. ( 72 ). The characteristics of common frailty screening scales are summarized in Table 1 and compared from nine perspectives, including entries, time required, content, and so on ( 30 , 31 , 40 , 58 , 70–76 ). Rapid screening should be followed by further precise assessment of all older adults in pre-frail and frail states. An accurate assessment to evaluate which state of frailty a frail older adult is in can help to predict poor health outcomes better and facilitate the development of individualized treatment and management plans for frailty patients. Recently, there have been a large number of studies devoted to the development of objective quantitative frailty assessment tools ( 77 ). Since these assessment tools are not limited to questionnaires and there are differences in the consistency of the assessment tools, places of application, populations administered, and dimensions assessed, the results of the assessment of frailty cannot yet be judged uniformly. Therefore, we will next summarize the characteristics of the commonly used assessment scales for age-related frailty. FI is a classic tool for assessing frailty in older adults developed by Mitnitski et al. ( 78 ) based on the cumulative deficit model. It covers multiple dimensions such as physical, psychology, cognition, and social functioning, and contains 30 to 70 evaluation items, the specific content of which is variable. Since there are no standardized criteria for the content of its items, researchers can choose their own entries according to their own research purposes. Although FI lacks specific variables that are uniformly standardized, the stability of FI is supported by the fact that FI consisting of different numbers and types of deficient items yields similar assessment results in different populations or research settings. Generally speaking, when FI ≥ 0.25, it implies frailty; when FI < 0.12, it implies non-frailty; when FI is between 0.12 and 0.25, it implies that the older adults are in the pre-frail stage ( 79 ). The sensitivity and specificity of FI in identifying frailty was 94.8 and 87.0% in all patients, 96.4 and 88.8% in patients on the cardiology ward, 95.9 and 81.1% in patients on the non-surgical ward and were 89.6 and 89.5% on the surgical ward ( 52 ). FI can be used not only for the screening of debilitation but also for the assessment of debilitation. FI is the first tool to successfully quantify the frailty state of older adults and has been widely used in several countries due to its good reliability and validity ( 80 ). FI is strongly associated with negative health-related outcomes (including mortality) and with deterioration in disease-specific health status, which makes it a good predictor of clinical prognosis ( 81 , 82 ). It has been found that FI can be utilized to evaluate the role of musculoskeletal disorders on frailty, rather than just being a categorical variable ( 83 ). In addition, FI has important applications in reflecting health service needs, public health management, and interventions. During the period of the COVID-19 pandemic, the use of an electronic version of the FI to assess frailty helped clinicians make decisions by identifying patients most likely to require ICU (intensive care unit) admission and those with a poor prognosis ( 84 ). By focusing on the cumulative number of individual health deficits and integrating multiple complex health information into a single indicator, FI breaks through the limitation of a single variable describing the functional status, and can better assess the overall health status of older adults. With its advantages of multidimensionality, continuity, and objectivity, FI is suitable for frailty assessment in almost all environments. However, the establishment of FI requires a large amount of clinical information, while obtaining a large amount of clinical information is laborious, extremely cumbersome, and time-consuming, which is a major challenge in the use of FI for the assessment of frailty. CGA was conceptualized for the development of a scientific rehabilitation training program for older adults in the 1940s by Marjory Warren ( 85 ). As the population ages, the application of CGA continues to extend and becomes a common method of assessing and treating older patients with frailty or loss of function ( 86 ). CGA focuses on comprehensive assessments of somatic function, cognitive function, psychology, and social/environmental factors in older adults, thereby identifying and quantifying the degree of frailty, and providing the basis for subsequent frailty intervention strategies and comprehensive care, which is conducive to the early reversal of frailty, the slowing down of the deterioration process of frailty, and the improvement of health outcomes ( 87 ). A systematic evaluation that included 22 studies involving 10,315 patients showed that patients in the group that took interventions based on CGA had a lower likelihood of death or worsening of their condition and a higher likelihood of cognitive improvement compared to the group that took conventional medical care ( 88 ). Lee et al. ( 87 ) found that a CGA-based intervention program for a frail population could potentially promote healthy aging in community-dwelling older adults, with sustained health benefits of up to 1 year for them. Mazya et al. ( 89 ) conducted a trial of dynamic geriatric assessment-frailty intervention, in which the control group of the study received conventional treatment and humanistic care while the intervention group received dynamic assessment by CGA and multidisciplinary team interventions (including medication adjustments, exercise, and dietary advice, etc.) in addition to conventional care. After the 24-month intervention, the proportion of patients in the intervention group who were pre-frail was significantly higher than in the control group, suggesting that more patients with chronic diseases or co-morbidities in the intervention group moved from frailty to pre-frailty or strong than that in the control group. With the help of CGA, a comprehensive and scientific assessment of frailty can be made and a personalized medical intervention plan for the older adults can be developed to slow down the process of frailty by healthcare professionals. CGA can accurately judge the health status of older adults, assess the degree or stage of frailty, identify its causes or triggers, and provide suggestions for its preventive or therapeutic measures, which can help in making risk stratification and clinical decisions for frailty. However, as the CGA is a multidimensional and interdisciplinary diagnostic and therapeutic process that emphasizes a multidimensional and comprehensive risk factor exploration and assessment, it requires a large amount of manpower, energy, and time, which is inconvenient in practice and is only applicable to the hospital healthcare environment ( 90 ). Furthermore, despite the fact that many studies have shown a large advantage of CGA for the assessment of frailty, most of these studies were conducted on small samples of older adults within a single institution or region, resulting in poor accuracy of the results of these studies, which still need to be further validated ( 91 ). GFI is a widely used frailty screening tool developed by Steverink et al. ( 92 ) in 2001. The GFI consists of the physical dimension (mobility, multiple health problems, physical fatigue, vision and hearing), the psychological dimension (depressed mood and anxiety), the cognitive dimension (cognitive dysfunction), and the social dimension (emotional isolation), with a total of 15 entries. The 15 entries of the GFI are all dichotomous questions, with each score set at 0 or 1. Higher scores on the GFI indicate more severe frailty, with those scoring ≥4 diagnosed as moderately or severe frailty ( 93 ). A study comparing the ability of four frailty screening tools to predict frailty-related adverse outcomes showed that the sensitivity of the GFI in predicting the frailsty adverse outcomes of death and hospitalization was 76.2 and 63.9%, respectively, and the specificity was 42.1 and 50.3%, which suggests that the GFI has a higher sensitivity and a poorer specificity ( 94 ). When the Chinese version of the GFI was used to screen for pre-frailty and frailty in 350 Chinese community-dwelling older adults, it demonstrated good internal consistency, with a Cronbach’s alpha coefficient of 0.87, a re-test reliability of 0.87, and the concurrent validity between the GFI and the Fried frailty phenotype of 0.76. suggesting that the GFI is a reliable and valid tool for pre-frailty and frailty screening in community-dwelling older adults ( 95 ). In addition, GFI can accurately predict total healthcare costs for the following year and can help healthcare professionals allocate healthcare resources ( 96 ). As the prevalence of frailty increases, more and more frailty assessment tools are being developed to make individualized and precise interventions for frail patients. For example, the Comprehensive Frailty Assessment Instrument (CFAI), which was first included in environmental assessment, was developed ( 98 ) and the Rapid Geriatric Assessment (RGA), which improves on the cumbersome assessment process of the CGA, was also created ( 99 ). A comparison of various common frailty assessment scales is shown in Table 2 ( 78 , 86 , 92 , 98–101 ). Frailty is an emerging global health burden with significant implications for clinical practice and public health. With the rapid growth of an aging population, the prevalence of frailty increases year by year. Frailty is a dynamically changing clinical state, with the pre-frailty phase showing potential reversibility. The early recognition of frailty and appropriate interventions for it can help slow or even reverse the process of it and reduce the risk of adverse outcomes. In order to achieve “healthy aging” centered on wellness, we need to improve the rate of the early identification of frailty for its early precise intervention. Although there are approximately 67 screening and assessment tools for frailty internationally and and there is a trend toward an increase in the number of such tools ( 77 ), different screening and assessment tools have more significant differences in conceptual basis, clinical utility, program content, and place of application. As a result, there is still considerable debate as to what is the best scale for screening and assessing frailty. Strictly speaking, frailty screening scales and assessment scales have different requirements and should not be confused. Screening scales need to be simple, quick, and highly sensitive to frailty, which allows clinicians to recognize frailty quickly. Assessment scales require high accuracy, utility, and support of sound biological theories, which allows clinicians to identify the stages of frailty in older adults accurately and predict the occurrence of adverse health events in frail older adults, such as falls, cognitive deficits, loss of mobility, and death. Through reading a large amount of literature, we have differentiated and reviewed the most common frailty scales for screening and assessment in order to help researchers as well as healthcare professionals to select the most appropriate frailty scales for its identification and assessment. Based on our summaries and generalizations, we roughly design the process of screening and assessment for the frailty in different types of older people, which is shown in Figure 1 . The high-risk groups and older people without frailty symptoms are screened out through a rapid screening scale, and the high-risk groups should go to the hospital for treatment. Older people without frailty symptoms should adopt self-screening and regular community screening once every 6 months to prevent the emergence of frailty. For older hospitalized patients, high-risk groups are screened out through the doctor’s simple judgment (whether it needs to be evaluated directly), and then a detailed and comprehensive evaluation can make them quickly benefit from the follow-up personalized intervention treatment. The development of frailty can be slowed or even reversed by early recognition, accurate assessment, and timely interventions for frailty, which will reduce the strain of frailty on healthcare systems around the world and promote healthy aging of the global population. Although we have provided insights into potential solutions for early identification and assessment of frailty by reviewing a large body of literature, however, there are some limitations to our study. Firstly, we searched only one database and limited our search to one language, which may have led to the omission of relevant articles. Secondly, our review lacked a critical assessment of the included articles, which resulted in the variable quality of the articles we included. Finally, we only summarized scales that have been studied frequently and are relatively well-established, which is somewhat one-sided. In the future, we still need to explore objective, simple, time-saving, effective, economical, and feasible scales that can accurately identify and assess frailty in clinical practice. | Review | biomedical | en | 0.999997 |
PMC11697712 | Antibiotics are extensively used in livestock production to prevent and treat diseases [ , , ]. In 2010, global antibiotic use in livestock production reached over 63 Gg . Approximately 30–90% of these antibiotics are excreted in an unchanged active form during livestock excrements . Annual antibiotic consumption in livestock production worldwide is projected to reach approximately 106 Gg by 2030 . This may increase the use of antibiotics in the environment from livestock production. China requires a thorough evaluation of how livestock production impacts antibiotic pollution in rivers and groundwater. China is one of the largest global users of antibiotics in livestock production globally . The total use of antibiotics for livestock production was approximately 15 Gg in China in 2010 . This amount is twice that of the United States . This is because China had many more livestock species and hardly had regulations to control antibiotic use compared to the United States [ , , ]. Today, China still has a higher livestock population than countries such as the United States , which may lead to more antibiotic use. Manure management differs from the past. Historically, livestock manure has been used as an organic fertilizer, especially in crop production . Since the 1990s, large amounts of manure were not used in crop production . Synthetic fertilizers largely replaced manure. As a result, considerable amounts of manure were dumped into surface waters. Since the 2000s, the national government has introduced various environmental policies to avoid water pollution by directly discharging manure through more manure recycling on the land . Farmers start using manure more often as an organic fertilizer. Because of the incomplete absorption and partial metabolism, unchanged active forms of antibiotics are excreted from the bodies of livestock species in the manure . Some amounts of antibiotics in manure can directly enter rivers through direct discharges of manure [ , , ]. In contrast, some antibiotics can enter agricultural land through manure application. From fertilized land, antibiotics can enter rivers through runoff and soil erosion and leach through deeper soil layers into groundwater . Antibiotics have been widely detected in soils and waters in China . Antibiotics in the environment can disturb biological and microbial communities and promote the transition and spread of antibiotic-resistant genes . Several models have been used to quantify antibiotics-related water pollution in China . For instance, a Level-III fugacity model is applied to China . This model allows for the spatially explicit analysis of 58 basins of China, which enables quantifying the contribution of livestock species to rivers in 2013 . This model does not quantify antibiotic leaching into groundwater or consider the diffusion of antibiotic inputs to rivers in 2020. The model only considers the antibiotic usage by pigs, chickens, and others (total weight of meat of sheep, cows, and fish) in China . River sub-basins are crucial for better understanding spatial variability in pollution levels, particularly in large basins like the Yellow and Yangtze Rivers. Existing models hardly focus on sub-basin scales for the whole of China. An exception is the family of the MARINA models (Model to Assess River Inputs of pollutaNts to seAs). These models run at the sub-basin scale for nutrients, plastics, and chemicals [ 10 , , , , ]. The models have been widely applied globally, nationally (China and Europe), and in individual lakes [ 10 , , , , , , ]. However, such models do not exist for antibiotics. Many antibiotics are used in Chinese livestock production. Among them, 24 antibiotics are extensively used in livestock production and frequently detected in China . These 24 antibiotics are grouped into six categories, i.e., Sulfonamides, Tetracyclines, Fluoroquinolones, Macrolides, β-lactams, and Lincosamides . Antibiotics used for livestock production accounted for 52% of the total antibiotics in China in 2013 . Zhang et al. reported that these 24 antibiotics contributed to over 55% of the total usage of antibiotics in China in 2013 based on survey data. Thus, there is a lack of quantitative information at the sub-basin scale on the effects of livestock distribution and manure management on antibiotic water pollution in China. In recent decades, the Chinese government has exhibited increased proactivity and assertiveness in addressing diffuse source pollution caused by agriculture. Agricultural Green Development (AGD) was proposed as a national strategy for sustainable development in China at the 19th National People’s Congress in 2017 . AGD aims to increase environmental quality while satisfying food demand to achieve sustainable agriculture. However, to the best of our knowledge, we still lack knowledge on simultaneously assessing livestock production relocation, improving manure management, and the resulting impacts on antibiotic water pollution during the period of 2010–2020. The period from 2010 to 2020 is important when looking at the history of agricultural policies in China. Before 2010, agricultural policies for manure management were generally limited in China . From 2010 until 2020, various agricultural policies have been introduced to facilitate more manure recycling on land to avoid direct discharges of manure to rivers and, thus, reduce river pollution [ , , ]. Some policies aim to shift livestock production from the south to the north [ , , ]. Examples are the ‘14th Five-Year National Agricultural Green Development Plan’ , ‘Livestock and Poultry Manure Utilization Action Plan ’ , and China’s livestock relocation policies . These policies may influence water pollution with antibiotics in China. However, the period of 2010–2020 has not been well studied for water pollution with antibiotics but needed to support the formulation of water pollution control strategies to achieve AGD policies in China as well as to support the United Nations' Sustainable Development Goals 6 (clean water) and 12 (sustainable food production) . Sub-basin analyses may help better understand pollution hotspots and their sources to formulate sustainable AGD solutions for clean water in China and elsewhere. Our main research objective is to estimate the flow of 24 antibiotics from seven livestock species into rivers and leaching into groundwater in 395 sub-basins in China and to examine changes in antibiotic water pollution between 2010 and 2020. The spatially explicit MARINA-Antibiotics (China-1.0) model (Model to Assess River Inputs of pollutaNts to seAs for Antibiotics in freshwater) is developed and evaluated for this study. Our model results can be used to prioritize sub-basins, livestock species, and antibiotic groups in water pollution control. Other countries that experience antibiotic pollution from intensive livestock production can use our model as a tool to better understand which antibiotic group (e.g., Sulfonamides), where (sub-basins), and from which livestock species (e.g., cattle) enter rivers and groundwater. This could raise the attention of the public, policymakers, and other stakeholders on the need to consider antibiotics in national water quality policies and monitoring programs. This study will contribute to developing AGD strategies to reduce antibiotic use in Chinese waters and elsewhere. MARINA-Antibiotics is short for Model to Assess River Inputs of pollutaNts to seAs for Antibiotics in freshwater. We developed the first version for antibiotic loadings into rivers and groundwater (via leaching) from livestock manure and applied it to 395 Chinese sub-basins. Our MARINA-Antibiotics (China-1.0) model was based on an existing sub-basin-scale MARINA-Multi (Global-2.0) modeling approach and integrated chemical approaches . Our model quantified antibiotics in rivers and groundwater using consistent model inputs (e.g., livestock density) in space (e.g., sub-basins) and time (e.g., annual). Our selection of the years 2010 and 2020 was justified for two primary reasons: (1) the implementation of key Chinese agricultural policies during this period [ , , , , , 42 ] and (2) the availability of data for these years (see details in Supplementary Materials). Thus, we applied the model to 2010 and 2020 to demonstrate the potential effect of the implemented national agricultural policies on water pollution with antibiotics in China. Fig. 1 Overview of the MARINA-Antibiotics (China-1.0) model, focusing on livestock manure. MARINA-Antibiotics is short for Model to Assess River Inputs of pollutaNts to seAs for Antibiotics in freshwater. The model considers seven livestock species: pigs, buffaloes, cattle, chickens, goats, sheep, and ducks. ‘A’ denotes antibiotics. Twenty-four antibiotics from six groups are considered in this model: Sulfonamides, Tetracyclines, Fluoroquinolones, Macrolides, β-lactams, and Lincosamides. For details on model inputs, see Tables S1–S4 in the Supplementary Materials. Sources: the MARINA-Antibiotics (China-1.0) model (Section 2.1 ). Fig. 1 The MARINA-Antibiotics (China-1.0) model used a lumped approach to quantify annual antibiotic inputs to rivers and leaching to groundwater from seven livestock species. The livestock species included pigs, buffaloes, cattle, chickens, goats, sheep, and ducks. In our study, we selected 24 antibiotics from six main categories : (1) Sulfonamides (sulfaquinoxaline, sulfathiazole, sulfamethoxazole, sulfamethazine, sulfameter, sulfamonomethoxine, sulfaguanidine, sulfadiazine, and sulfachlorpyridazine); (2) Tetracyclines (oxytetracycline, doxycycline, tetracycline, and chlortetracycline); (3) Fluoroquinolones (pefloxacin, ofloxacin, norfloxacin, fleroxacin, enrofloxacin, difloxacin, and ciprofloxacin); (4) Macrolides (tylosin and roxithromycin); (5) β-lactams (penicillin-G); (6) Lincosamides (lincomycin). We considered the direct discharge of manure to rivers, degradation (persistence) of antibiotics in manure during storage, soil degradation and adsorption of antibiotics, runoff, soil erosion, and leaching to determine the fate and transfer of antibiotics from livestock production to rivers and groundwater ( Fig. 1 , equations (1) , (2) , (3) , (4) , (5) , (6) , (7) , (8) , (9) ). The input data were processed using ArcGIS, and many model parameters differ among sub-basins (see Tables S1 and S9 in the Supplementary Materials for details). Below, we presented calculations for the inputs of antibiotics to rivers and groundwater from livestock production. In the MARINA-Antibiotics (China-1.0) model, the top 60 cm of the soil was regarded as the root zone. The 60–200 cm region of the soil was considered the last soil layer before antibiotic leaching into groundwater, following the definition from Poggio et al. and Arrouays et al. . In the top 60 cm of the soil, antibiotics in soil particles and solutions could be transported to rivers via soil erosion and runoff. Below 200 cm of the soil, we estimated the antibiotics that remain in the soil solution after runoff losses, and these antibiotics could further leach into groundwater. The depth of 200 cm was chosen based on its relevance to the scope of the study. This depth captured both relevant processes in the soil profile and considered the potential vertical movement of antibiotics in deeper soil layers. In existing studies [ , , , , ], 200 cm was used as a common depth for examining soil properties, leaching patterns, and transport of pollutants. For example, Arrouays et al. indicated that 200 cm is pragmatic for soil sampling, providing reliable observations of soil properties, even in thick soil. For assessing pollutants leaching into groundwater, Zhang et al. explored the impacts of agricultural fertilization on nitrate in soil and leaching into groundwater, including the movement of agricultural nitrate to a depth of 200 cm. This depth also typically included the root zone of most crops (e.g., corn, wheat, alfalfa, and soybean) and reached the subsoil . Contaminants moving to this depth may potentially entered groundwater or deeper soils. The model distinguished between diffuse and point sources of river pollution. The annual inputs of antibiotics into the rivers from point and diffuse sources were calculated using equation (1) . Point sources of antibiotics in rivers resulted from the direct discharges of livestock manure from storage systems (equation (2) ). Diffuse sources of antibiotics in rivers resulted from runoff and soil erosion after the application of manure to agricultural land, which is corrected for degradation processes in the soil. Antibiotic inputs to rivers from diffuse sources were calculated as a function of antibiotic inputs to agricultural land from storage and grazing systems, soil retention, erosion, and runoff (equations (3) , (4) , (5) , (6) , (7) , (8) ). (1) R S A , i , j = R S d i f A , i , j + R S p n t A , i , j (2) R S p n t A , i , j = S s s , A , i , j × f r d , i , j (3) R S d i f A , i , j = R S s r A , i , j + R S e s A , i , j (4) R S s r A , i , j = S s o l A , i , j × F E s r j (5) R S e s A , i , j = S p a r A , i , j × F E e s j Antibiotics in soil solution and soil particles after adsorption and degradation were calculated as a function of various processes (equations (6) , (7) ) as follows: (6) S p a r A , i , j = W S d i f A , i , j × F S p a r , A , j × F S d e , A , j (7) S s o l A , i , j = W S d i f A , i , j × F S s o l , A , j × F S d e , A , j where, F S p a r , A , j and F S s o l , A , j are the adsorption fractions ( FS ) of antibiotic ( A ) in the soil particle ( par ) and solution ( sol ) in the sub-basin ( j ), respectively (0–1). The adsorption fractions of antibiotics were calculated as a function of the linear adsorption constant (K d value) of antibiotics (L kg −1 ) and the maximum water-holding capacity of soil based on the soil textures. The adsorption fractions of antibiotics in the soil particle and solution varied among sub-basins. Adsorption processes reflect the amount of antibiotics attached to soil particles and in solution. Physical (e.g., the maximum water-holding capacity of soil-related antibiotic adsorption) and chemical (e.g., Kd value, the linear adsorption constant) processes influence the adsorption of antibiotics in the soil. The maximum water-holding capacity of the soil depends on soil texture. Here, we distinguished the dominant soil textures among sub-basins based on the data from the National Earth System Science Data Center (NESSDC) . According to Pan and Chu and Geohring et al. , we considered the dominant maximum water-holding capacity based on soil textures among sub-basins. The degradation of antibiotics in the soil is influenced by physical (such as soil temperature and soil saturated water content, which relates to antibiotics degradation), chemical (such as soil pH, soil organic carbon content, and the half-life of antibiotics-related degradation), and biological (such as biological responses to soil changes associated with antibiotic degradation) processes. These processes were incorporated into our model according to the approach of Tang and Maggi and Wöhler et al. (details are in Tables S1–S4 of the Supplementary Materials). Each sub-basin has different model inputs to represent the physical, chemical, and biological processes in the soil for the degradation of antibiotics. Antibiotic inputs to agricultural land from grazing and storage systems were calculated using equation (8) . Livestock manure was considered an organic fertilizer for agricultural land. In grazing systems, manure-containing antibiotics entered the land directly. For storage systems, the input of antibiotics to agricultural land was calculated as a function of manure production, livestock number, and the degradation (persistence) rates of antibiotics during the manure management practices (e.g., storage, composting, and anaerobic digestion) according to the livestock species and antibiotics. This implied that manure was collected during storage before being applied to agricultural land and was also corrected for direct discharges of manure to rivers. Antibiotic losses during storage were associated with, for example, the degradation of antibiotics during manure management practices in storage systems. (8) W S d i f A , i , j = W S d i f A , s g , i , j + W S d i f A , s s , i , j W S d i f A , s s , i , j is the application of antibiotic ( A ) to agricultural land from the manure of livestock ( i ) from the storage system ( ss ) after correcting for the direct discharges of manure to rivers and manure management practices (e.g., storage, composting, and anaerobic digestion) in the sub-basin ( j ) (kg antibiotics per year). The model input was calculated using the following equation: W S d i f A , s s , i , j = S s s , A , i , j × ( 1 − f r d , i , j ) . See more details in Supplementary Materials Tables S1–S4 . The export fraction of antibiotics that enter rivers in the soil solution due to surface runoff in the sub-basin in 2010 was derived from Li et al. . The Variable Infiltration Capacity hydrological model provided data for precipitation and natural river discharges for the period up to 2020 . Then, we calculated the average annual precipitation per sub-basin by statistically averaging 30-year annual runoff per sub-basin. This was calculated based on the 30-year average annual natural river discharge in the sub-basin divided by their drainage area. Finally, we calculated the export fraction in 2020 based on the 30-year averaged runoff divided by the 30-year averaged precipitation per sub-basin following the approach of Li et al. . See more details in Supplementary Materials Fig. S2 and Tables S1–S4 , and S9. This model parameter was different per sub-basin. This export fraction reflected land use change and pollutants transported from land to rivers through surface runoff. Sub-basins with higher export fractions received more pollutants entering their rivers through surface runoff than those with lower export fractions. We defined ‘pollution hotspots’ for the antibiotic input into rivers and groundwater following the approach of Li et al. . In descending order, we ranked sub-basins based on the inputs of antibiotics per km 2 of the sub-basin areas. Consequently, we have inputs ranging from Level I (lower inputs) to Level V (higher inputs). For rivers, the input of antibiotics ranged from 0 to 0.2 g km −2 year −1 (Level I), 0.2–2 g km −2 year −1 (Level II), 2–32 g km −2 year −1 (Level III), 32 to 700 (Level IV), and 700 to 4468 (Level V). For groundwater, antibiotic inputs ranged from 0 to 0.1 g km −2 year −1 (Level I), 0.1–0.2 g km −2 year −1 (Level II), 0.2–2 g km −2 year −1 (Level III), 2–3 g km −2 year −1 (Level IV), and 3–31 g km −2 year −1 (Level V). The top 25% of sub-basins were considered pollution hotspots, for which inputs of antibiotics in rivers and groundwater fall under Levels IV and V. We modeled that approximately 8354 and 3424 tonnes of all 24 antibiotics entered Chinese rivers from livestock production in 2010 and 2020, respectively . This implies that the total input of antibiotics into rivers decreased by approximately 59% from 2010 to 2020 for China as a whole. This was largely due to decreased direct manure discharges into rivers (fewer manure point sources). In 2010, the contribution of direct manure discharges to the total input of antibiotics in all rivers in China was 8137 tonnes. This decreased to 3219 tonnes in 2020 (Supplementary Materials Tables S6–S9 ). The ‘14th Five-Year National Agricultural Green Development Plan’ has called for an increase in the use of livestock manure on land to 76% in 2020 in China as a whole . This could be attributed to the fact that more manure was recycled on land to avoid its direct discharge into rivers, which is the potential effect of the introduced agricultural policies in 2020 compared to 2010. Our results showed that fluoroquinolones accounted for 55% and 56% of rivers' antibiotics in 2010 and 2020, respectively . In 2010, pig and cattle manure were the dominant contributors to antibiotic pollution in rivers. In 2020, antibiotic inputs to rivers were mainly pig and chicken manure. Fig. 2 Annual flows of antibiotics from livestock manure to rivers ( a , b ) and groundwater ( c , d ) in China in 2010 ( a , c ) and 2020 ( b , d ) (tonnes of antibiotics per year). Sources: the MARINA-Antibiotics (China-1.0) model (see Section 2.1 for the model description). Fig. 2 River pollution varied largely among the 395 Chinese sub-basins . We distinguished between five pollution levels (see Section 2.3 for the definition). In 2010, river sub-basins in Levels I–II received less than 2 g of antibiotics per km 2 of sub-basin area annually . This resulted in approximately 3 tonnes of all selected antibiotics entering rivers in these sub-basins. The contribution of runoff in this river pollution was 45%, and the contribution of soil erosion was 55% (diffuse sources). Most of the Level I–II sub-basins were in the western region of China, covered 38% of the total surface area, and accommodated 2% of the total human population in China in 2010 . Between 2010 and 2020, the total antibiotic input to all rivers in these sub-basins increased by 18%. However, changes in antibiotic pollution during 2010–2020 ranged from −49% (decrease) to +270% (increase) among sub-basins of Levels I–II . This implies that rivers in some western sub-basins became cleaner (decreased pollution), whereas rivers in other sub-basins in the north and northwest became more polluted (increased pollution) during 2010–2020. The livestock numbers of northern sub-basins in Levels I–II increased by 3%–226% between 2010 and 2020 among sub-basins . Moreover, the application of antibiotics to livestock manure on agricultural land increased by 33% between 2010 and 2020 (Supplementary Materials Table S9 ). The combined effects of the changed spatial distribution of livestock production and the application of manure resulted in the northern rivers in Level I–II sub-basins receiving more antibiotics by 2020 . Fluoroquinolone and Sulfonamide groups were more responsible for river antibiotic pollution in these sub-basins in 2020 than in 2010 . They mainly originated from cattle and sheep manure . Fig. 3 River pollution by antibiotics in 395 Chinese sub-basins according to pollution levels and by livestock species in 2010 and 2020. The bar graphs show antibiotic inputs from livestock manure (tonnes year −1 ) to rivers. The pie charts show the share of antibiotic groups in the total antibiotic inputs to rivers from livestock manure (0–1). Levels I–V refer to the pollution levels of total river antibiotic inputs (see Section 2.3 for the definition). Fig. S19 in the Supplementary Materials shows the changes in antibiotic inputs into rivers (%) between 2010 and 2020. Sources: the MARINA-Antibiotics (China-1.0) model (see Section 2.1 for the model description). Fig. 3 Fig. 4 Antibiotics leaching into groundwater in 395 Chinese sub-basins according to pollution level and by livestock species in 2010 and 2020. The bar graphs show antibiotic leaching from livestock manure (kg year −1 ) into groundwater. The pie charts show the share of antibiotic groups in the total leaching into groundwater from livestock manure (0–1). Levels I–V refer to the pollution levels of the total antibiotic leaching into groundwater (definition see Section 2.3 ). Fig. S20 in the Supplementary Materials shows the changes in antibiotic leaching into groundwater (%) between 2010 and 2020. Sources: the MARINA-Antibiotics (China-1.0) model (see Section 2.1 for the model description). Fig. 4 Rivers in Level III sub-basins received 31 tonnes of antibiotics in 2010, mainly from diffuse sources in which the share of soil erosion was considerable . Most of the Level III sub-basins were in northeastern and southwestern China. These sub-basins covered 22 % of the total surface area. They accommodated 13 % of the total population in China in 2010 . Between 2010 and 2020, the total antibiotic inputs to all rivers in these sub-basins decreased by 11%. As a result, 27 tonnes of antibiotics from livestock production entered rivers in the Level III sub-basins in 2020. Most northeastern Level III sub-basins experienced increased antibiotic pollution in rivers . Most southern sub-basins of Level III were estimated to have decreased by more than 25% in total antibiotic river pollution by the year 2020 . These changes could be attributed to the greater increases in livestock numbers and manure recycling in the northern sub-basins than in the southern sub-basins in Level III by 2020 . In addition, the Level III sub-basins generally had moderate surface runoff and soil erosion, which could facilitate the antibiotics transport through the topsoil layer into rivers . Fluoroquinolones were the predominant antibiotic group, accounting for 58% of the total antibiotic input to river sub-basins in Level III for the years 2010 and 2020 . Cattle manure was the main source of Fluoroquinolones and Tetracyclines in rivers of these sub-basins in both 2010 and 2020 . Over 90% of antibiotics in the rivers originated from 40% of the basin area in China . These sub-basins were identified as pollution hotspots (Levels IV and V) in this study . Most of this amount was from point sources that accounted for over 94% of total antibiotics in rivers of these sub-basins in 2010 and 2020. In 2010 and 2020, rivers in hotspot sub-basins received 8321 and 3393 tonnes of total antibiotic inputs, respectively. These sub-basins were in central and southern China , which accommodated approximately 84% of the total population in 2020 . Between 2010 and 2020, the antibiotic inputs to rivers of hotspot sub-basins decreased by 59%. However, the changes in antibiotic inputs into rivers ranged from −75% (decrease) to +62% (increase) among Levels IV–V sub-basins during this period. For hotspot sub-basins located in central and southeastern China, our model estimated a decrease of over 50% in river pollution during 2010–2020 . Rivers in some southwestern hotspot sub-basins also became cleaner, with a decrease of more than 25% in total antibiotic inputs by 2020 compared to 2010 . Total antibiotic inputs to rivers in a few hotspot sub-basins in southwestern China increased by 0–25% between 2010 and 2020 . This may also be relevant to the implication of agricultural policies for forbidden direct discharges of livestock manure to rivers and the changed spatial distribution of livestock production during 2010 and 2020. Higher pollution levels in hotspot sub-basins may also largely be associated with higher surface runoff and soil erosion compared to those in the other sub-basins . The proportion of antibiotic groups in rivers varied considerably among sub-basins . Fluoroquinolones contributed more than 40% of the total antibiotic inputs to rivers in hotspot sub-basins between 2010 and 2020 . Pig and cattle manure were the dominant contributors to river pollution with antibiotics from livestock manure in 2010 . Compared to 2010, the contribution of chicken manure to total river pollution in hotspot sub-basins increased by 46% in 2020 . Hotspot sub-basins received approximately 2400 tonnes of antibiotic inputs to rivers from pig and chicken production in 2020. Our model estimated that 10 and 12 tonnes of antibiotics were leached into groundwater nationally in 2010 and 2020, respectively . This implies that the total leaching of antibiotics into groundwater increased by 15% during 2010–2020 for China as a whole. This could be a result of more recycled manure on land and changed spatial distribution of livestock species in 2020 compared to that in 2010 . For example, in 2010, 1587 tonnes of antibiotics in manure were applied on land in northern sub-basins of China. This amount increased to 2183 tonnes in 2020 (Supplementary Materials Table S9 ). Increased manure recycling was facilitated by existing agricultural policies to avoid direct manure discharges from 2010 to 2020 . Our results showed that Sulfonamides contributed to over 90% of the total antibiotic leaching into groundwater during 2010–2020 . One of the important reasons for this was the good solubility of Sulfonamides. This indicated that Sulfonamides were more easily transported with soil solutions than other groups. Pig manure was the main contributor to the total antibiotic leaching into the groundwater in 2010 . In 2020, the total antibiotic leaching into groundwater was mainly from pig and sheep manure . However, there was large spatial variability among sub-basins . In Level I and II sub-basins, less than 0.2 g of antibiotics per km 2 per year were leached into groundwater from livestock production in 2010 . This resulted in approximately 1 tonne of antibiotics entering groundwater. Most of these sub-basins were located in southwestern China , accommodated approximately 9% of the Chinese population, and covered 39% of the total sub-basin area in China in 2010. The share of the sub-basins in total groundwater pollution in China reached 32% in 2020 . This implies that fewer sub-basins were identified as belonging to pollution Levels I–II in 2020 than in 2010. Between 2010 and 2020, changes in antibiotic leaching into groundwater from 2010 to 2020 ranged from −57% (decrease) to +147% (increase) among Level I–II sub-basins . Some sub-basins in the southwest and northeast became cleaner in 2020 than in 2010 . However, the northwest Level I and II Chinese sub-basins became more polluted in 2020 than in 2010 . This was largely associated with the effects of climate change (lower surface runoff) and the relocation of livestock production activities in Level I and II sub-basins during 2010–2020. Sulfonamides constituted the predominant antibiotic group in the total antibiotic groundwater pollution in those sub-basins for both 2010 and 2020 . Compared to 2010, the contributions of sheep manure to antibiotic groundwater pollution became more dominant in 2020 . Fig. 5 Shares of livestock species in river ( a ) and groundwater ( b ) pollution with antibiotics in hotspot sub-basins (Levels IV and V, %). Levels IV and V sub-basins are considered pollution hotspots (definition see Section 2.3 ). The shares of livestock species in the river and groundwater pollution in Levels I–III are in Supplementary Materials Fig. S8 . Sources: the MARINA-Antibiotics (China-1.0) model (see Section 2.1 for the model description). Fig. 5 Groundwater in Level III sub-basins received 4 tonnes of antibiotics in 2010 . These sub-basins covered 52% of the national area, accommodated 58% of the total population in China , and were mainly located in northeastern and southern China in 2010 . By 2020, the total antibiotic leaching into groundwater increased by 28% in Level III sub-basins compared to that in 2010. As a result, more than 5 tonnes of antibiotics from livestock production leached into groundwater in Level III sub-basins in 2020. Approximately 64% of the total population lived in the Level III sub-basins in 2020. The decreases in antibiotic leaching into groundwater from 2010 to 2020 ranged from 28% to 48% among some southwestern and northeastern sub-basins . These decreases were largely associated with climate change (more surface runoff) and the relocation of livestock species in 2020. Most central and southern sub-basins experienced increased antibiotic leaching into groundwater, ranging from 25% to 161% by the year 2020. Sulfonamides were the dominant antibiotic group in the groundwater of the Level III sub-basins . Compared to 2010, pigs and sheep remained the main contributors to antibiotic groundwater pollution in 2020 . The contributions of chicken and goat manure to groundwater pollution increased by 94% and 25% by 2020, respectively . These increases in antibiotic leaching into groundwater could be attributed to the combined effects of livestock production migration (increased livestock density of chicken and goat) and increased recycling of livestock manure as organic fertilizer between 2010 and 2020 . Fig. 6 The conceptual framework for the ‘building trust’ approach for the large-scale water quality model. This framework is modified based on Strokal et al. . Details on using the ‘building trust’ approach to evaluate the MARINA-Antibiotics (China-1.0) can be found in Section 3.3 . Fig. 6 Fig. 7 Overview of lessons from the MARINA-Antibiotics (China-1.0) model for manure management. Details can be found in Section 3.4 . Fig. 7 Approximately 10% of the Chinese sub-basin areas were identified as pollution hotspots for groundwater pollution (Levels IV and V) for the years 2010 and 2020 . The hotspot sub-basins received 5 tonnes of total antibiotic leaching to groundwater in 2010. These sub-basins were in central and northern China. They accommodated around 31% of the total Chinese population in 2010 . The total antibiotic leaching into groundwater in hotspots nationally increased by 12% between 2010 and 2020 . This resulted in 6 tonnes of antibiotic leaching into groundwater in hotspot sub-basins in 2020. By 2020, changes in antibiotic leaching into groundwater were estimated to vary from −69% (decrease) to +70% (increase) for hotspot sub-basins . In 2010, pigs were the dominant contributor to antibiotic contamination of groundwater in the hotspots . Between 2010 and 2020, the contributions of chicken and sheep manure to the total antibiotic leaching into groundwater increased by 40% and 67%, respectively . Sulfonamides remained the main antibiotic group in groundwater pollution in 2020. In 2020, β-lactams from chickens, pigs, and ducks accounted for more than 10% of the total antibiotics leaching into groundwater . We applied four model evaluation options following a widely used approach to build trust in the models (see Section 2.2 ). For Option 1, we compared the following model inputs with other datasets: soil pH, soil temperature, soil organic carbon content, and soil saturation. The selected model inputs were plotted on a 1:1 line. We assessed the model performance using two statistical indicators: Pearson’s coefficient of determination ( R P 2 , from 0 to 1) and the Nash-Sutcliffe efficiency (NSE, from −∞ to 1). According to the performance rates from Moriasi et al. , the differences between our model inputs and independent datasets were acceptable: R P 2 > 0.8 and NSE >0.6 . Option 2 showed that our model outputs for Tetracyclines, Fluoroquinolones, and Sulfonamides as the dominant contributors to antibiotic pollution in rivers in China were in line with those of existing studies . We modeled that pig manure contributed approximately 1106 and 517 tonnes of antibiotic inputs to the Yangtze River basins in 2010 and 2020, respectively. Chen et al. used the level Ⅳ fugacity model to evaluate the emission, multimedia fate, and risk of antibiotics in the entire Yangtze River basin. Their model results indicated that, between 2013 and 2021, approximately 514–903 tonnes of antibiotics were introduced into the Yangtze River from pig production. Our model results were slightly higher than those of their study because we were more complete regarding the number of antibiotics used (24 in our study and 18 in Chen et al. ). Zhang et al. also indicated that pigs accounted for over 40% of river antibiotic inputs in 2013. This was demonstrated by our main livestock species responsible for antibiotic river pollution in 2010. Option 3 focused on comparing the spatial variability of pollution hotspots with those of other studies . For instance, the river pollution hotspots in central and northern China in 2010 and 2020 were in line with the findings of previous studies . Generally, these sub-basins (e.g., Hai and Yangtze River sub-basins) had high livestock densities and high manure inputs per km 2 both in 2010 and 2020. This was in line with previous studies (e.g., Refs. ). Other studies have shown that areas with higher direct manure discharges received more pollutants from rivers in 2010 . This was also demonstrated by our hotspot sub-basins in 2010. Examples were sub-basins in southern China (e.g., Yangtze River sub-basins) with higher direct discharges of livestock manure to rivers than those in northern China (e.g., Songhua River sub-basins) in 2010. Other studies have indicated that antibiotics in rivers were generally higher in central and eastern China than in western China . Huang et al. indicated that antibiotic groundwater pollution was higher in the Hai River Basin than in the Yangtze and Pearl River Basins, which was consistent with our study for both 2010 and 2020. For Option 4, we reflected on our modeling approach. Our model was an integrated modeling approach that combined the existing knowledge and literature on the soil processes of antibiotics that can be transported to rivers and groundwater. First, we reviewed the literature on the physical, biological, and chemical processes that can affect the degradation and adsorption of antibiotics in the soil and soil erosion . We developed an approach for the degradation and adsorption of antibiotics in soil particles and solutions based on expert knowledge of field experiments. We also used expert knowledge of soil erosion supported by literature to estimate the antibiotics in soil particles transported from agricultural land to rivers. The four aforementioned options helped us to better understand the performance of our model (inputs, outputs, and approach) and to better interpret the results. However, our model did not account for sources such as antibiotics in sewage systems or antibiotic manufacturing and processing. Thus, the river and groundwater pollution levels may have been underestimated. Extreme events (e.g., heavy rainfall) were not considered, which may have resulted in more antibiotics entering rivers through runoff and soil erosion and leaching into groundwater at certain moments. We focused on 2010 and 2020. Thus, the trends in our pollution levels during 2010 and 2020 may not consistently decrease but could initially increase and then decrease. Our model results were under- or overestimated in sub-basins, depending on their agricultural development. Future studies may need to quantify the processes and dynamics of antibiotic inputs to rivers and groundwater in more detail and consider the by-products that emerge during degradation processes for more years. However, this study focused on livestock and manure production. We quantified the flows of 24 antibiotics from livestock into rivers and groundwater. We considered important sources of antibiotics in livestock production: pigs, cattle, chickens, ducks, goats, sheep, and buffaloes. Our selection of seven livestock species was justified by three main reasons. First, these livestock species are of high economic importance for the whole of China . The selected livestock species contributed largely to China’s agricultural production and food systems . These livestock species could reflect those most widely produced across the whole of China, thus covering diverse production systems . Second, livestock species with potentially varying environmental impacts were included to explore how different manure management systems (e.g., storage vs. grazing) affected inputs of pollutants into rivers and leaching into groundwater between 2010 and 2020 . Third, we recognized that the main livestock species may vary geographically, as in northern vs. southern China, where environmental and agricultural factors influenced species prevalence . However, the selections of these seven livestock species reflected the broader national significance rather than focusing on region-specific species. They were important across multiple regions, even though their relative importance varied by location. Thus, the uncertainties associated with missing sources did not affect our conclusions. Future studies could use our modeling tool and add missing sources to better understand the contribution of other sources to antibiotic pollution. In this study, we developed a steady-state, large-scale water quality model to estimate antibiotic loadings input into rivers and leaching into groundwater at the sub-basin scale. We estimated loadings of antibiotics into waters (not concentrations). Our mode did not consider local-scale factors, such as the exact distance between farms and nearby rivers. The current model inputs were all at the sub-basin scale. Thus, our model may not be directly used for local analyses (e.g., specific farms or watersheds). However, our modeling approach was integrated and more process-oriented than the existing models . Our model was run annually to examine changes in river and groundwater pollution from antibiotics in livestock production in 2010 and 2020 at the sub-basin scale. Our model can also be used for other years if data are available. We considered the different pathways that contribute to antibiotics in rivers and groundwater, including surface runoff, soil erosion, and leaching. Seven dominant livestock species in China were considered. Our approach can simultaneously quantify antibiotic river and groundwater pollution from 24 antibiotics and seven livestock species, which has not been done previously for over 300 sub-basins in China. Because our model was integrated, process-based, and uncalibrated, it offers an opportunity to conduct future analyses and account for climate changes, technological developments, and food production drivers. All datasets used in our study were widely used and accepted by the scientific community, and they were freely available for download [ 1 , , , , , , 25 , 44 , 57 , 69 ]. Our analyses drew four lessons regarding antibiotic pollution in rivers and groundwater. First, our study helped identify the contributions of livestock species to river and groundwater pollution in 2010 and 2020. This study was conducted in China. We showed that pig and cattle manure were the dominant contributors to river pollution with antibiotics in 2010 . In 2020, pig and chicken production became the dominant sources of antibiotic-related river pollution. Other countries can use our new modeling tool and increase their understanding of the species and efforts required to reduce livestock-production-related river and groundwater pollution. From our analyses, we learned that, for non-hotspot sub-basins (Levels I–III), a reduction of water pollution with antibiotics was needed from pig, sheep, and cattle production. For hotspot sub-basins (Levels IV–V), future water pollution strategies should focus more on managing chicken and pig manure. This information supported the formulation of livestock-specific manure management. Our model can be a useful tool for projecting future antibiotic rivers and groundwater and supporting decision-makers for specific livestock species that would be prioritized in manure management policies in the future. Second, our study provided a better understanding of the antibiotic groups from livestock production toward rivers and groundwater. We showed that Sulfonamides were important for river pollution in Level I sub-basins, and this pollution resulted mainly from the manure of sheep and cattle, both in 2010 and 2020. Fluoroquinolones and Sulfonamides were the most important river pollutants in Levels II–V sub-basins and mainly originated from chicken and pig manure in 2020. This study focused on seven livestock species due to their economic significance (e.g., most widely farmed and consumed livestock in China ) and data availability. Future research can consider expanding to include more livestock species based on our model. We also realized that, in reality, there may be even more antibiotics present in Chinese waters, but this model can be easily extended to other antibiotics. In other countries, other antibiotics may dominate in the use for livestock; for example, Tetracyclines were predominantly used in livestock production in the United States , Cyprus, Bulgaria, and Portugal , whereas in Australia, Macrolides were the main antibiotic groups used in livestock production . Applying our modeling tool could help those countries understand which livestock species can contribute to water pollution. Third, our results supported existing agricultural policies for better groundwater and river pollution control with other pollutants in China. Our results indicated less antibiotic river pollution and more groundwater pollution during 2010–2020 in China as a whole . Manure contains other pollutants, such as nutrients and pathogens . With the implications of agricultural policies, for example, ‘Livestock and Poultry Manure Utilization Action Plan ’ , ‘14th Five-Year National Agricultural Green Development Plan’ , and China’s livestock relocation policies , etc.), more manure recycling on agricultural land may not only affect water pollution with antibiotics in China as we estimated in our study. These may also reduce nutrient or pathogen pollution in rivers. This has also been demonstrated by existing studies on other pollutants (e.g., nutrients). Recycling manure was shown to be the most cost-effective option for reducing future coastal eutrophication . However, there may be a trade-off between policies facilitating more manure recycling and groundwater pollution, as indicated by our study's changes in antibiotic groundwater pollution . This implies that future agricultural policies are required to avoid the trade-off between the recycling of manure on land and groundwater pollution. Fourth, our results provided policymakers with a better understanding of the development and implementation of clean water strategies for manure management in a spatially explicit manner. Sub-basin analyses can help identify pollutants' origin and sources to formulate effective solutions for agriculture-related pollution. Our model results showed that antibiotic river pollution in some sub-basins increased, whereas in others (e.g., southern sub-basins in China) decreased from 2010 to 2020. This may be associated with increased livestock production in China’s northern and southern sub-basins between 2010 and 2020. This implied that future manure management policies for these sub-basins need to be combined with better treatment to avoid more pollutants in rivers. We also found that avoiding the direct discharges of manure could considerably decrease antibiotic inputs to rivers in the southern sub-basins of China. However, our results indicated that antibiotic leaching into groundwater increased by 11% from 2010 to 2020 in these southern sub-basins. This implied the importance of considering the potential trade-off between manure recycling and groundwater for developing future water pollution controls. As we indicated in the Introduction section, our study could raise the attention of policymakers, the public, and other stakeholders on the importance of considering antibiotics in national water quality policies and monitoring programs in the future. This study was the first attempt to account for antibiotics from livestock production in rivers and groundwater in China at the sub-basin scale. The MARINA-Antibiotics (China-1.0) model was developed and evaluated to quantify the flow of 24 antibiotics into rivers and leaching into groundwater from seven livestock species in 395 Chinese sub-basins and to examine changes in antibiotic water pollution between 2010 and 2020. In 2010 and 2020, 8364 and 3436 tonnes of antibiotics entered rivers and groundwater, respectively, causing antibiotic pollution. 50–90% of the antibiotic losses to rivers and groundwater originated from 40% of the basin areas in China between 2010 and 2020. The total river antibiotic inputs decreased by 59% during 2010–2020 because of fewer manure point sources. In contrast, total antibiotic leaching into groundwater increased by 15% nationally, which was largely because of increased manure recycling. Fluoroquinolones were responsible for 55% of the antibiotics in Chinese rivers in 2010 and 2020 and mainly originated from pigs, cattle, and chicken manure. Sulfonamides were responsible for over 90% of groundwater antibiotics, mainly from pig and sheep manure. Our study supports future agriculture-related policy designs in China. | Study | biomedical | en | 0.999996 |
PMC11697714 | The Orobanchaceae family encompasses a diverse array of mostly parasitic plant species, which, after major rearrangements in the Lamiales, includes the genus Castilleja , commonly known as Indian Paintbrush. 1 , 2 With a distribution spanning the Americas and some species documented in Asia and Russia, Castilleja comprises over two hundred annual and perennial herbaceous species. 3 Host dependence within the Orobanchaceae family exhibits a wide spectrum. Striga species, for example, are obligate hemiparasites that rely on hosts for survival while retaining photosynthetic capabilities. In contrast, Orobanche species exemplify holoparasitism, entirely dependent on hosts for sustenance due to the loss of photosynthetic function. 4 Phtheirospermum japonicum , Triphysaria, and Castilleja , on the other hand, are facultative parasites, capable of transitioning between autotrophic and hemiparasitic lifestyles based on environmental conditions. Root-parasitic plants have developed a specialized organ, the haustorium, to establish connections with the host’s vascular system. 5 , 6 The haustorium allows for the transfer of water and nutrients from the host to the parasitic plant, enabling their exploitation of host resources and successful adaptation to nutrient-deficient environments. Within the life cycle of Orobanchaceae, the detection and utilization of host-derived signaling molecules play a key role in the parasitic lifestyle: Notably, strigolactones, a group of carotenoid-derived plant hormones, are well-known for their role in inducing germination of seeds from obligatory parasitic Orobanchaceae species. 7 , 8 , 9 Many parasitic Orobanchaceae have evolved a large clade of diversified KAI2 proteins (KAI2d) as receptors for exogenous strigolactones. The diversification of these proteins is regarded as the main driver of parasitism for these plants. 10 While the host specificity of Castilleja species and the structure of the Castilleja haustorium have been extensively described, 11 , 12 , 13 , 14 , 15 , 16 it is unclear if these plants can perceive strigolactones and whether strigolactones have any biological role in the parasitic lifestyle of Castilleja . As a matter of fact, it has been demonstrated that Castilleja seeds are not impacted by the hemiparasitic nature of Castilleja , but that germination occurs after a several weeks or months long moist chilling period. 17 Here we show that strigolactones are perceived by multiple proteins of the diverged clade of KAI2 proteins and trigger the formation of haustorium-like structures in Castilleja . We investigated the potential biological role of strigolactones in the life cycle of Castilleja species. To this end, we germinated stratified seeds from Castilleja wightii , Castilleja affinis , and Castilleja foliolosa under two different conditions: in the presence of H 2 O and in the presence of the chemical strigolactone analog GR24 5DO. Despite the different conditions, germination efficiency did not vary significantly between the two environments . However, we noted marked morphological differences in the emergent seedlings. Seedlings that germinated in H 2 O developed a robust primary root system accompanied by an array of lateral root hairs . In contrast, the seedlings exposed to GR24 5DO presented a slenderer root structure devoid of lateral root hairs . This divergence in morphology was consistent across the three tested Castilleja species. We quantified the percentage of germinated seedlings that developed such root structure after exposure to different strigolactones but found no significant difference . When we examined the terminal root structure more closely, we noticed that GR24 5DO grown plants had a smaller root cap and smaller cells at the tip of the structure compared to water grown plants . We used Rhodamine 123, which serves as an indicator for mitochondrial transmembrane potential, to confirm the viability of these cells . In summary, the structures grown in the presence of GR24 5DO more closely resemble the elongated state characteristic of root-parasitic plants such as Striga than they do typical roots. Figure 1 GR24 triggers the formation of haustorium-like structures in Castilleja (A) Germination assay showing percentages of germinated C. wightii and C. affinis seeds when germinated on H 2 O vs. GR24 5DO. Error bars represent the standard deviation of three independent measurements. (C and E) Heterotrophic root phenotypes and (B, D, and F) possibly parasitic phenotypes of C. wightii and C. affinis after germination on GR24 5DO. These observations suggest that while strigolactones may not function as a germination signal for Castilleja species, they do effectively mimic a host plant, participating in triggering a parasitic state in the seedlings immediately post-germination when present. Therefore, strigolactones appear to play a crucial role in activating post-germination modifications in Castilleja , preparing for a parasitic lifestyle. Considering the impact of strigolactones on the morphological development of Castilleja , we set out to identify the strigolactone receptors within Castilleja foliolosa and aimed to characterize these proteins. To investigate the potential presence of strigolactone receptors in Castilleja , we sequenced the genome of Castilleja foliolosa , also known as Woolly Indian Paintbrush. The assembly spanned 551 contigs, adding up to a total length of approximately 752 Mbp with an N50 of 11,150,047 bp and an N90 of 2,884,952 bp ( Table S1 ). The haploid genome length, as determined through a K-mer distribution analysis, 18 measured approximately 555 Mbp, and a Smudgeplot analysis 18 indicated that the genome is likely diploid , which is in agreement with a previous study. 19 BUSCO analysis against the eudicots_odb10 lineage dataset 20 showed that 87.5% of the BUSCOs were complete ( Table S2 ). We compared the genome of Castilleja foliolosa to the published genomes of other species within the Orobanchaceae family, namely Phtheirospermum japonicum , 21 Striga asiatica , 22 and Orobanche cumana . 23 As anticipated, a substantial number of syntenies were found between Castilleja foliolosa and these other parasitic plant genomes, with the highest count of 21,204 syntenies noted with Phtheirospermum japonicum , followed by 15,251 syntenies with Orobanche cumana and 12,476 syntenies with Striga asiatica . Figure 2 Strigolactone receptors in the genome of Castilleja foliolosa (A) Circos plot showing synteny between C. foliolosa and 3 other parasitic plants: The facultative hemiparasite P. japonicum , the obligate hemiparasite S. asiatica , and the holoparasite O. cumana . The scale has been adjusted for genome size and is in Mbp. The number of syntenies to C. foliolosa are 21,204 with P. japonicum , 15,251 with O. cumana , and 12,476 with S. asiatica . (B) Phylogenetic tree of D14, KAI2c, and KAI2d proteins from C. foliolosa , P. japonicum , S. asiatica , and O. cumana . Proteins in black boxes were used for differential scanning fluorimetry (DSF) as shown as follows: (C–F) DSF of C. foliolosa KAI2d8, KAI2d14, KAI2d15, and KAI2i1, showing their destabilization upon presence of the chemical strigolactone analog GR24 5DO. DSF curves are shown as mean values of three independent measurements. Utilizing a BLAST search, potential strigolactone receptors were identified. We discovered 1 gene encoding for DWARF14, 4 genes encoding KAI2c paralogs, 2 genes encoding KAI2i proteins, and 15 genes encoding KAI2d proteins . This high number of paralogs in the KAI2d protein clade is in line with other parasitic plants in the Orobanchaceae, suggesting a potential function as strigolactone receptors in Castilleja as well. We collected bulk RNA from Castilleja affinis and Castilleja foliolosa seedlings and conducted PacBio Iso-Seq to identify full-length transcripts. Additionally, we analyzed a publicly available RNA-seq dataset from Castilleja miniata . In all three species, we were able to detect the presence of transcripts from several kai2d orthologs, including those that we analyzed from Castilleja foliolosa . To further investigate if these predicted KAI2 proteins serve as strigolactone receptors, we heterologously produced five of them, namely KAI2d8, KAI2d9, KAI2d14, KAI2d15, and KAI2i1, in Escherichia coli . We were, unfortunately, unable to produce sufficient amounts of the other orthologs from E. coli . By conducting differential scanning fluorimetry (DSF), we observed a clear, concentration-dependent destabilization of KAI2d14, KAI2d15, and KAI2i1 in the presence of the chemical strigolactone analog GR24 5DO. A weaker but discernible destabilization was noted with KAI2d8, strongly suggesting that these KAI2 proteins are indeed strigolactone receptors . We were, unfortunately, unable to obtain sufficient amounts of KAI2d9 for the DSF assay. To obtain additional insight into the strigolactone perception in Castilleja , we solved the crystal structure of KAI2d15 at a resolution of 1.8 Å ( Table S3 ). As expected, the protein adopted a canonical α/β hydrolase architecture, with a 4-helix lid domain covering the substrate binding pocket containing a Ser/His/Asp catalytic triad at its base. The substrate binding pocket was quite voluminous, measuring 908 Å 3 , which surpassed the 861 Å 3 found in the strigolactone receptor KAI2d4 from Orobanche minor , 24 but was smaller than the 1111 Å 3 detected in the highly sensitive SL receptor HTL7 from Striga hermonthica . 25 Figure 3 Structural and biochemical analysis of C. foliolosa KAI2d15 (A) Crystal structure of C. foliolosa KAI2d15, showing the substrate binding pocket in red, and highlighting the residues of the catalytic triad (S95, D217, H246), and the methionines surrounding the entrance to the pocket (M142, M153, M157). (B) Rotated and zoomed in view of the methionines surrounding the pocket entrance. Note that an alternative side-chain conformation of M142 is shown. (C) Chemical structures of the strigolactone molecules used in the following DSF assay. (D and E) DSF assay comparing the destabilization of C. foliolosa KAI2d15 wild type and a M142V M153V M157V variant upon the presence of different strigolactone molecules. Concentrations closest to the equilibrium are highlighted in bold for easier comparison. DSF curves are shown as mean values of three independent measurements. We identified three methionine residues at the entrance to the binding pocket . To study the effect of these methionine residues on the receptor’s substrate specificity and sensitivity, we created a protein variant, replacing the three methionines with valines (KAI2d15 M142V M153V M157V). We carried out separate DSF experiments with four different strigolactone molecules, testing both the variant and the wild-type protein. The mutant protein showed destabilization at lower ligand concentrations when GR24 or 5-deoxystrigol were present, while the wild-type protein destabilized at lower concentrations of strigol or orobanchol . We examined the enzymatic properties of Castilleja foliolosa of KAI2d8, KAI2d9, KAI2d14, KAI2d15, and KAI2i1 in Michaelis-Menten experiments using the large fluorescent strigolactone analog Yoshimulactone Green (YLG). All investigated proteins turned out to be efficient high-turnover enzymes, with turnover numbers ranging between 600 s −1 and 2780 s −1 and catalytic efficiencies between 4.2·10 8 s −1 M −1 and 2.6·10 9 s −1 M −1 . We specifically compared Michaelis-Menten parameters between the KAI2d15 wild type and the mutant, in which three of the methionines at the substrate pocket entrance had been replaced with valines (KAI2d15 M142V M153V M157V). While the turnover numbers (k cat ) of these proteins showed no significant difference, we found a 3-fold decrease in the Michaelis constant (K M ) for wild-type KAI2d15 as compared to the mutant version (0.8 μM in wild-type KAI2 vs. 2.4 μM in KAI2d15 M142V M153V M157V) . We further noticed a proportional relationship between the Michaelis constants and the turnover rates of these proteins , suggesting a trade-off between substrate turnover and affinity in the catalytic reaction. KAI2d15 wild type was a notable exception, maintaining both high k cat and low K M . Strikingly, the KAI2d15 M142V M153V M157V version of the protein lay well within the trendline shared with the other KAI2d proteins, as a result of the higher K M value compared to wild type. These results suggest that the properties of the methionine residues located at the entrance to the substrate binding pocket allow KAI2d15 to overcome this trade-off between substrate turnover and affinity that appears to be characteristic of the other investigated KAI2d proteins from Castilleja foliolosa . Figure 4 Michaelis-Menten assays of C. foliolosa KAI2d proteins with the fluorescent strigolactone analog Yoshimulactone Green (YLG) as substrate (A–E) Comparison of Michaelis-Menten values between different C. foliolosa KAI2 proteins (The chemical structure of YLG is shown in A). (F) Correlation between turnover rates and Michaelis constants of different C. foliolosa KAI2 proteins. Error bars represent the standard deviation of three independent measurements. Strigolactones have long been recognized as germination signals for root-parasitic plants from the Orobanchaceae family. Beyond this, they can also function as chemoattractants for host tropism. 26 While strigolactones serve as potent germination stimulants for obligate parasites such as Striga and Orobanche , their role is different in the facultative parasite Phtheirospermum japonicum , where they stimulate germination only in the absence of nitrate ions. 27 In our study, we discovered a role for strigolactones in Castilleja , another facultative parasite. Here, strigolactones do not cause germination but instead act post-germination. Castilleja can adopt a parasitic lifestyle when their root system encounters a host organism – a behavior well documented in adult plants, which form lateral haustoria upon exposure to a host. 13 However, there is no data supporting that strigolactones are involved in the parasitic transition. We demonstrate that the presence of strigolactones during germination suppresses the development of a typical root system and instead leads to the formation of a structure reminiscent of the elongated stage known from other root-parasitic plants, possibly representative of a parasitic state in Castilleja . This adds a layer of complexity to our understanding of the functions of strigolactones within the Orobanchaceae family. The results of our syntenic block analysis and sequence comparison provide insights into the genomic relationships among the four species of plants studied, which include Orobanche cumana , Castilleja foliolosa , Striga asiatica , and Phtheirospermum japonicum . All these species belong to the Orobanchaceae family and display parasitic lifestyle traits; however, the degree and nature of parasitism vary among them. Castilleja foliolosa and Phtheirospermum japonicum , both facultative hemiparasites, shared the highest number of syntenic blocks, indicating a closer genomic relationship, contrasting with Orobanche cumana and Striga asiatica , which have different parasitic strategies. When we examined the strigolactone receptor sequences, a family of genes known for their rapid evolution and critical role in plant parasitism, we found that the receptors in Castilleja foliolosa are most closely related to those in Phtheirospermum japonicum . This provides additional evidence of a closer evolutionary relationship between these two species, and it further suggests that their shared facultative parasitic lifestyle has them placed closer to their common ancestor than obligatory parasites in the Orobanchaceae, which are assumed to have evolved later. 4 We identified 15 paralogs of the diverged clade KAI2 proteins (KAI2d) encoded in the genome of Castilleja foliolosa , one of which, KAI2d15, showed characteristics previously not observed in a strigolactone receptor. We found 3 methionine residues in the crystal structure of KAI2d15 that surround the entrance to the protein’s substrate binding pocket, including an alternative side-chain conformation of M142. We speculate that there might be a role of the branched, flexible methionines in the more favorable accommodation of strigol and orobanchol, which are slightly larger than GR24 and 5-deoxystrigol. The methionines also appear to optimize substrate affinity and turnover. In many enzymes, achieving a high affinity for a substrate often comes at the cost of efficient substrate turnover. This is largely due to the binding dynamics: a tightly bound substrate might be converted efficiently but released slowly, leading to a longer turnover cycle. 28 We observed a linear trade-off between these two catalytic parameters in all investigated KAI2 proteins, except KAI2d15, and remarkably, the trade-off was restored in the KAI2d15 M142V M153V M157V mutant. Flexible methionine residues at the pocket entrance might aid substrate translocation, resulting in a lower K M . However, their flexibility could also play a role in destabilizing the enzyme-product interaction post-catalysis, making product release more efficient and maintaining a higher k cat . Thus, a possible dual role of the methionines in both enhancing substrate binding and promoting product release might be the molecular basis for KAI2d15 mitigating the typical trade-off observed in enzyme kinetics. The pocket entrance methionine residues might also affect the affinity for GR24 specifically. The sulfur atom in methionine is capable of engaging in various types of interactions with aromatic moieties, including van der Waals forces and sulfur-π interactions. However, a comprehensive understanding of these interactions would necessitate studying a broader variety of strigolactones to discern the specificity and affinity nuances. The presence of methionines in the entrance to the SL binding pocket has been previously reported in the case of Striga hermonthica HTL4. 25 While two of the methionines in the pocket entrance in Castilleja foliolosa KAI2d15 align with methionines in ShHTL4, the third methionine (M157) corresponds to a phenylalanine in ShKAI2d4 (F157) . It is also noteworthy that none of the Striga hermonthica or Striga asiatica KAI2d proteins contain methionine at this position. A different methionine in ShKAI2d4 (M154) is located deeper inside the pocket, rather than at its entrance. In Striga hermonthica , the presence or absence of a crucial hydrogen bond between F150/Y151 and L178 determines substrate specificity. 25 This structural basis, however, does not seem to be applicable to the protein we investigated. Castilleja foliolosa KAI2d15 features a histidine instead of a phenylalanine/tyrosine at this position. In addition, this histidine is about 4.7 Å away from the corresponding leucine, likely too distant for hydrogen bonding . A notable observation is the generally high turnover rates exhibited by Castilleja foliolosa ’s KAI2 proteins. While the turnover rates of SL receptors vary, especially D14 proteins are characterized by slow or even single-turnover rates. 29 , 30 , 31 The proteins in this study demonstrated substantial catalytic efficiencies, the highest achieved by KAI2d15 at 2.6·10 9 s −1 M −1 on YLG, which is in the range of a diffusion-limited enzyme. While it is not immediately clear whether there are advantages to high-turnover SL receptors, it seems intuitive that such a receptor, which rapidly hydrolyzes strigolactones, would depend on constant strigolactone input to maintain its activation and availability for binding to the F box protein MAX2, the next step in the strigolactone signaling pathway after SL perception by its receptor. 8 Diverse strigolactone turnover rates through different receptors could possibly enable the plant to map out strigolactone gradients in its surroundings. Another reason for the elevated catalytic activity observed in certain KAI2d proteins could be a decrease in strigolactone sensitivity. This could be through either the degradation of the host’s ligand before signal transduction begins or by necessitating elevated levels of strigolactone for a prolonged signaling event to take place. Additionally, it might be beneficial for a hemiparasite to either eliminate or disregard its own strigolactone production in the root area. In summary, our research reveals that Castilleja possesses a large clade of KAI2d proteins, acting as strigolactone receptors, participating in activating a parasitic state in the plant. Notably, one of these receptors operates as a diffusion limited enzyme that achieves specificity for larger strigolactones and high catalytic efficiency through a cluster of methionine residues at the pocket entrance. Hopefully, in the future, transgenic Castilleja plants will be available to study the function of these receptors in isolation and in planta . We hope that our findings will lay the groundwork for future studies investigating the role of strigolactones in Castilleja . Our study reveals important insights into strigolactone perception in Castilleja and opens avenues for future research. The development of a reliable transformation system for Castilleja species would allow the validation of our findings through genetic approaches, such as knockout studies of individual kai2d genes. Among the fifteen identified KAI2d paralogs, we successfully characterized five through biochemical analyses, leaving opportunities to explore the properties of additional family members as expression systems are optimized. While we observed clear morphological changes in seedling root structures upon strigolactone treatment, future studies can further illuminate the cellular and molecular nature of these structures and their relationship to parasitic states. These considerations highlight promising directions for future studies that will further expand our understanding of strigolactone signaling in facultatively root-parasitic plants. REAGENT or RESOURCE SOURCE IDENTIFIER Bacterial and virus strains BL21-CodonPlus (DE3)-RIL competent cells Agilent 230245 Chemicals, peptides, and recombinant proteins GR24 5DS Strigolab EN1010 Rhodamin 123 Sigma-Aldrich 6030951 Isopropyl β-D-1-Thiogalactopyranosid, IPTG, Isopropyl β-D-Thiogalactosid (IPTG) Sigma-Aldrich I6758 Tris(2-carboxyethyl)phosphin -hydrochlorid (TCEP) Sigma-Aldrich C4706 HRV 3C Protease Takara 7360 SYPRO Orange ThermoFisher S6651 Critical commercial assays Nanobind plant nuclei kit PacBio 102-207-800 SMRTbell Express Template Prep Kit 2.0 PacBio 100-938-900 Deposited data Genome sequence of Castilleja foliolosa GenBank JAVIJP000000000 Crystal structure of Castilleja foliolosa KAI2d15 Protein Data Bank 8TMX Recombinant DNA pGEX-4T-1 Castilleja foliolosa kai2d8 Genscript (this study) pGEX-4T-1 Castilleja foliolosa kai2d9 Genscript (this study) pGEX-4T-1 Castilleja foliolosa kai2d14 Genscript (this study) pGEX-4T-1 Castilleja foliolosa kai2d15 Genscript (this study) pGEX-4T-1 Castilleja foliolosa kai2d15 M142V M153V M157V Genscript (this study) pGEX-4T-1 Castilleja foliolosa kai2i1 Genscript (this study) Software and algorithms ZEN 3.7 Zeiss Hifiasm 0.16.0 https://github.com/chhylp123/hifiasm Cheng et al. 27 Helixer v0.3.3 https://github.com/weberlab-hhu/helixer Stiehler et al. 28 InterProScan 5.55-88.0 https://github.com/ebi-pf-team/interproscan Jones et al. 29 EggNOG-mapper 2.1.12 https://github.com/eggnogdb/eggnog-mapper Cantalapiedra et al. 30 SynMap2 https://genomevolution.org/SynMap.pl Haug-Baltzell et al. 31 R 4.2.0 https://www.r-project.org/ R-Core-Team 32 MEGA11 https://www.megasoftware.net/ Tamura et al. 33 XDS https://xds.mr.mpg.de/ Kabsch 34 Phenix 1.20.1 https://phenix-online.org/ Zwart et al. 35 WinCoot 0.9.8.1 https://bernhardcl.github.io/coot/ Emsley et al. 36 Escherichia coli strain BL21 (DE3) cells were used for heterologous expression of Castilleja KAI2 proteins. Cells were cultured in Lysogenic Broth (LB) medium and induced with 0.1 mM IPTG at an optical density (OD600) of 0.6, followed by overnight incubation in Terrific Broth (TB) medium at 18°C. Seeds of all Castilleja species in this study were stratified on wet Whatman filter paper at 4°C in the dark for six weeks, after which they were transferred to a growth chamber maintained at 21°C under a 16-hour light/8-hour dark cycle. In GR24-treated experiments, seeds were exposed to a 1 μM concentration of GR24 5DO. Castilleja seeds sourced from San Diego County, California were stratified for 6 weeks on wet Whatman filter paper at 4°C in the dark. After, they were transferred for germination into a growth chamber at 21°C and a 16 h light cycle. For GR24-treated seeds, a 1 μM concentration of GR24 5DO DO (StrigoLab, Turin, Italy) was used. Castilleja seedlings were directly observed on Whatman filter paper without any fixation. Images were captured using a Zeiss Axio Zoom.V16 microscope in brightfield mode, employing a 2x optovar and a 0.63x camera adapter. Images were acquired at a resolution of 2464 x 2056 pixels. Adjustments for brightness and contrast were applied using the ZEN 3.7 software (Carl Zeiss AG) to enhance clarity and detail. To assess cell viability, we used Rhodamine 123, which selectively stains mitochondria in living cells. 37 The plants were submerged for 30 minutes in a Rhodamine 123 staining solution, prepared to a final concentration of 0.02% (w/v). Root structures and cells were then examined using a Leica Stellaris 8 confocal microscope. Genomic DNA was isolated using the Nanobind plant nuclei kit (PacBio). 15 μg of genomic DNA was isolated from 2 g of a single Castilleja foliolosa flower. Sequencing libraries were prepared using the PacBio SMRTbell Express Template Prep Kit 2.0, following the manufacturer’s guidelines. Sequencing was conducted on the PacBio Sequel IIe system. The raw HiFi reads generated from the PacBio Sequel IIe platform were assembled into contigs using Hifiasm. 32 Default parameters were used for the assembly, and quality control metrics were examined to ensure the fidelity of the assembly. Castilleja foliolosa gene prediction was performed using HELIXER 38 and functional annotation was carried out using InterProScan 39 and EggNOG-mapper. 33 Pairwise synteny analyses were done using SynMap 34 with a minimum number of aligned pairs set to 20. The 'tidyverse' package 35 in R 36 was used to transform aligncoords files: The data file was imported, lines commencing with '#' were filtered out, and chromosome IDs were extracted from the data. Genome fasta files were transposed into segment data files using the 'Biostrings' package 40 : Lengths of the sequences were computed and a data frame containing chromosome names, start positions, and end positions were created. Link and segment data were adjusted to the actual genome sizes by applying scaling factors. The 'OmicCircos' package 41 was then used to generate a Circos plot, and links indicating associations between segments were added. A phylogenetic tree of KAI2 protein sequences was built using the Maximum Likelihood method and JTT matrix-based model. The tree with the highest log likelihood is shown. The final tree was displayed using MEGA11. 42 For heterologous protein production, all genes were synthesized codon-optimized for Escherichia coli and cloned into a pGEX 4T1 expression vector to produce GST fusion proteins. Genes were designed to encode an N-terminal site for HRV3 protease, leaving two amino acids (Gly-Pro) as N-terminal cloning artifact. Escherichia coli strain BL21 (DE3) cells were transformed and subsequently cultivated overnight in Lysogenic Broth (LB) medium. A fresh culture was initiated in Terrific Broth (TB) medium the next day using a 1:100 dilution. Cultivation was maintained at 23°C until an optical density (OD 600 ) of 0.6 was reached, after which induction was performed with 0.1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) at 18°C and left overnight. The cells were harvested, and lysis was achieved through sonication. Centrifugation at 75,000 g for 45 minutes was then used to separate cell debris, after which the supernatant was transferred onto a glutathione affinity column. This column was flushed with a buffer of 50 mM TRIS-HCl, 150 mM NaCl, 5% glycerol, and 1 mM TCEP, with a final pH of 7.7, until no protein flow-through was detected by UV absorption. HRV3 protease was subsequently introduced to the column and left overnight. The cleaved target protein was eluted using the same buffer and further purified to homogeneity through size exclusion chromatography on a GE Healthcare HiLoad 16/60 Superdex 75 column, prepared with 20 mM TRIS-HCl, 30 mM NaCl, and 1 mM TCEP-HCl buffer, with a final pH of 7.7. The proteins were concentrated to a minimum of 10 mg/ml and flash-frozen in liquid nitrogen. Differential scanning fluorimetry experiments were performed in a CFX Opus 384 system (Biorad). Sypro Orange (Life Technologies) was used as reporter. 10 μg of protein was heat-denatured using a linear 25°C to 95°C gradient at a rate of 1°C per minute. The denaturation curve and its derivative were obtained using the CFX manager software. Reaction mixtures were prepared in 20 μl volumes in triplicates in 384 well white microplates. Reactions were carried out in 20 mM TRIS-HCl, 30 mM NaCl, 1 mM TCEP-HCl, final pH 7.7. A final 3x concentration of Sypro Orange was used. Crystals of Castilleja foliolosa KAI2d15 were obtained from a 200 nl sitting drop, prepared with a protein-to-reservoir ratio of 1:1 and a chemical composition of 0.9 M diammonium phosphate, 0.1 M sodium acetate at pH 4.5, and 0.01 M barium chloride. To these crystals, 1.8 M sodium malonate was applied as a cryo-protectant. X-ray data were collected at beamline 8.2.2 of the Advanced Light Source at Lawrence Berkeley National Laboratory and subsequently processed with XDS. 43 The structure of KAI2d15 was solved with Phaser through molecular replacement, utilizing chain A of PDB structure 4IH1 ( Arabidopsis thaliana KAI2) as the model. For the calculation of R-free, five percent of the data were flagged. Initial models were constructed using AutoBuild and refinement of the models was performed using phenix.refine, all of which are part of the Phenix suite. 44 Manual correction and finalization of these models were accomplished with Coot. 45 Data for seed germination, DSF destabilization, and Michaelis-Menten kinetics are presented as mean values with standard deviation (SD) across three or more independent experiments and calculated with R for seed germination and with Origin (OriginLab) for DSF and Michaelis-Menten kinetics. Detailed sample sizes (n) and specific conditions are noted in each figure legend, along with any variation among replicates, with error bars representing the standard deviation where applicable. | Study | biomedical | en | 0.999997 |
PMC11697716 | Lesions in the primary visual cortex (V1) trigger degeneration and volume loss in the lateral geniculate nucleus (LGN), both in human and non-human primates . The degeneration involves projection neurons in the magno- (M), parvo (P), and koniocellular (K) neurons, as well as corticogeniculate fibers . However, amount of degeneration may vary according to the species, lesion extent and survival time . V1 lesions in primates also cause cortical blindness, a condition that may reflect not only the immediate loss of cortical tissue, but also the secondary loss of LGN and retinal cells . Despite degeneration and volume loss caused by the lesion, there are a number of surviving neurons in the LGN, offering the potential for plasticity and recovery . One recently revealed form of plasticity are changes in protein expression in the surviving neurons . Neurons in the normal LGN show specificity in their expression of calcium-binding proteins, with M and P neurons exclusively expressing parvalbumin (PV) and K neurons expressing calbindin-D28K . In animals with long-term V1 lesions, this specificity is disrupted with many M and P neurons co-expressing CB and PV . It has been found that some of the neurons undergoing this neurochemical change form projections to the middle temporal area (area MT) , one of the key cortical areas hypothesized to mediate residual visual capacities following V1 lesions . These findings reinforce the view that surviving LGN neurons may play a role in the residual visual abilities that remain within scotomas associated with V1 lesions. Retrograde degeneration following V1 lesions is likely to be a gradual process . However, data on the exact pace of the degeneration remains sparse, particularly in the first weeks after V1 lesions. In addition, little is known about the time course of changes in calcium-binding protein expression. Here we address this knowledge gap by studying the cellular anatomy of the LGN in marmoset monkeys in the first weeks or months following V1 lesions. Marmosets are non-human primates which have been gaining prominence as a translational model for studies of human biology and disease . Understanding the rate of progress of post-injury degenerative processes may provide important clues for future work aimed at minimising cell loss, and potentially greater preservation of residual visual abilities. The present study was performed in a cohort of 21 marmoset monkeys ( Callithrix jacchus ), of which 14 received a unilateral V1 lesion in adulthood and 7 were non-lesioned controls. The animals were of either sex as detailed in Table 1 . Eleven of these animals (4 lesioned, 7 controls) were part of other experiments involving fluorescent tracer injections or electrophysiology experiments, not reported here. Their health condition and well-being were monitored throughout the experimental period. The experiments were conducted based on the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes. All experiments were approved by the University Animal Ethics Experimentation Committee. The V1 lesion surgery was conducted following an updated procedure based on the technique introduced by Rosa et al. . This procedure involves an occipital lobectomy along a vertical plane across the border between V1 and the second visual area , resulting in a complete loss of the representation of the visual field up to 10° eccentricity along the vertical meridian, and 20–30° along the horizontal meridian. Reconstructions of lesions and visual field defects created with this procedure have been reported previously . The animals were pre-medicated with oral meloxicam (Metacam; Boehringer Ingelheim, 0.1 mg/kg, i.m) and cephalexin (Ibilex; Alphapharm P/L, 30 mg/kg, i.m) 24 hours before the surgery. Atropine (Atrosite; Ilium, 0.2 mg/kg) was administered 30 minutes before anaesthesia, which was accomplished by inhalation of isoflurane (Isorrane; 4–5% in oxygen, Baxter). Dexamethasone (Dexason; Ilium, 0.3 mg/kg, i.m) was also administered. During the surgery, the animals were positioned in a modified stereotaxic head holder while their heart rates, body temperatures, and body oxygenation levels (PO 2 ) were continually monitored. The anaesthetic condition was adjusted continuously (isoflurane, between 2 and 5%) to ensure the animals showed no spontaneous muscle activity and had no withdrawal reflexes. After craniotomy and durectomy the occipital pole was removed using a fine-tipped cautery, following a vertical excision along a plane extending from the dorsal surface of the occipital lobe to the cerebellar tentorium, across its entire mediolateral extent. After the removal of tissue, the resulting cavity was filled with haemostatic microspheres (Arista AH, BARD Davol Inc.) until the bleeding stopped. The surface of the wound was covered with ophthalmic film (Gelfilm, Pfizer Inc.), and the cavity was filled with Gelfoam (Pfizer Inc.). The skull flap was repositioned and secured with cyanoacrylate (Vetbond, 3M), followed by skin suture with polyglactin thread (5-0 Vicrly, Johnson & Johnson). The animals then were placed in an infant incubator (Atom Medical) for recovery and reintroduced to the home cage after recovery of mobility. During recovery, postoperative analgesia (oral meloxicam 0.05 mg/kg for adults, 3 d) and antibiotic (oral cephalexin 30 mg/kg, 5 d) was given. The animals demonstrated normal movement abilities including precise grasping, holding, jumping between branches, and obtaining food without assistance already on the day following surgery, and throughout the recovery period. With the exception of animals tested at very short survival times (<1 month), they were kept within family groups and lived in large cages with access to both indoor and outdoor environments. The short survival animals were kept in a facility that allowed close monitoring, in visual and auditory contact with other marmosets. Following variable survival periods ( Table 1 ) the animals were anaesthetised with Alfaxan (Ibilex, 30mg/kg i.m.) and then overdosed with a pentobarbitone sodium injection (100 mg/kg i.v.). Following cardiac arrest, they were perfused with 0.1 M heparinised phosphate buffer (PB; pH 7.2) and 4% paraformaldehyde (PFA) in 0.1 M PB. The brain was removed and post-fixed for 24 hours in the same solution, after which cryoprotection was performed by immersion in 4% buffered PFA solutions with increasing concentrations of sucrose (10%, 20% and 30%). The brain was then snap-frozen and cut into 40 μm coronal sections using a cryostat . Table 1 Experimental details for all subjects. Table 1 Subject (Sex) Survival (∼months) Shrinkage ratio (% ipsi/intact) Age at lesion (∼months) Age at perfusion (∼months) Analysed LGN WA9(M) 39 50.84% 19 58 Both WA14 a (F) 31 52.30% 25 57 Both WA13 a (M) 28 68.61% 29 57 Both WA16 b (M) 23 57.36% 32 55 Both WA15 a (F) 12 43.17% 26 38 Both WA25(F) 6 71.53% 26 32 Both WA22(F) 3 85.11% 42 45 Both WA23(F) 3 85.98% 37 40 Both WA20(M) 2 75.87% 36 38 Both WA21(F) 2 84.02% 44 46 Both WA27(M) 1 78.83% 39 40 Both WA32(F) 2 weeks 91.54% 30 30 Both WA24(F) 3 days 96.65% 32 32 Both WA26(M) 2 days 101.58% 45 45 Both CJ227 a (M) – 102.51% – 37 Both CJ217 a (M) – 99.37% – 34 Both CJ200 a (F) – 98.39% – 44 Both F1741 a (F) – – – 42 Right CJ174 b (F) – – – 32 Right CJ170 b (M) – – – 29 Right CJ167 b (F) – – – 28 Right a Animals that received fluorescent tracer injections for other projects. b Animals that participated in electrophysiology experiments for other projects. CJ217 and CJ227 were only used for volume analysis. (M) Male; (F) Female. For NeuN staining, every fifth section was washed in a PB solution (0.1 M). The sections were incubated with a blocking solution (0.3% Triton-X100 and 10% horse serum in 0.1 PB) for 1 h at room temperature, and then treated with NeuN primary antibody for 46–48 h at 4 °C. This was followed by incubation with biotinylated horse anti-mouse IgG secondary antibody for 30 min at room temperature. The sections were then incubated with ABC reagent (100μl of solution A and 100μl of solution B in 5 ml of 0.1M PB) for 30 min at room temperature. After incubation, sections were stained with DAB substrate working solution for 30 min at room temperature, followed by three washes with PB (0.1M). The mounted sections were dried for 48 h before being coverslipped. For fluorescence staining of calcium-binding proteins another series of sections (1 in 5) was incubated with blocking solution for 1 h at room temperature, followed by 46–48 h incubation in primary antibodies for CB and PV . The secondary antibodies [1:600; Alexa Fluor® 488 and Alexa Fluor® 647 ] were applied for 60 min at room temperature, followed by coverslip using antifade mounting medium (Vectashield, Vector Laboratories). These antibodies have been validated previously and here we have provided further evidence in Extended Data Fig. 1 . Fig. 1 Design of sampling for stereological analysis. A–M: NeuN-stained coronal sections from WA25 covering the extent of lateral geniculate nucleus , on which location and number of sampling frames (100 × 150 μm 2 ) are shown. A represents the most posterior part of LGN. N: Inset in E. Red dashed lines separate the lesion projection zones (LPZ) from the rest of LGN. The boundaries of LPZ is determined according to cell size and the variation in neuronal density. The counting frames were placed in a radial dimension covering the different cellular layers in LGN. Evidence of no cellular staining in the absence of NeuN primary antibody is shown in the supplementary Fig. 1 . Scale bar: 1mm. Fig. 1 For NeuN-stained brain sections, slides were scanned with an Aperio Scanscope AT Turbo microscope (Leica Biosystems) at × 20 magnification under the resolution of 0.50 μm/pixel, and analysed using Aperio Image Scope software. Estimation of volume was done using Cavalieri estimator based on the area of LGN in equally spaced sections throughout the anteroposterior extent of this nucleus . For neuronal density, counting frames (150 × 100μm 2 ) were placed systematically on each LGN section covering both degenerated and undegenerated sectors. In each section, 2–13 of counting frames were placed in a radial direction across all layers . Only neurons with clear staining were counted, regardless of shape. Every neuron that was fully located inside the frame or that intersected the top or right edges was included. Data of all sections were combined for analysis. The cell counts were converted to densities (cells/mm 3 ) by taking into consideration the section thickness and a shrinkage factor of 0.801 . The fluorescence immunostained sections were scanned using confocal microscopes (Nikon C1 invert, 20x magnification, filters 488, 647) and analysed with ImageJ software (Fiji, USA). The LGN was sampled in fixed sized square frames (638 × 638μm 2 ) from the middle sections of the LGN, where the placement of the counting frames covered most of LGN . Only neurons with clear fluorescent signal were counted using the cell counter plug-in. For analysis of CB expression in M and P neurons, we manually marked all PV-immunoreactive neurons within the M and P layers of LGN. We then counted CB-expressing neurons from the population of marked neurons, and calculated the percentage of double-labelled neurons. All statistical analysis was performed using Prism 9 (GraphPad software, USA). Data were analysed using student's t-test, one- or two-way ANOVA and linear regression where applicable and presented as mean ± standard error of the mean (SEM). Statistical results with a p value < 0.05 were considered statistically significant. In the animals perfused 2–3 days post lesion, NeuN staining of the LGN ipsilateral to the lesion revealed no changes in volume, lamination and neuronal size/density in comparison with the contralateral LGN . In all other cases a pale staining region corresponding to the lesion projection zone (LPZ; the sector of LGN that corresponds topographically to the V1 lesion) was obvious in the ipsilesional LGN, similar to previous observations . Higher magnification views of neurons within and outside the LPZs are shown in Fig. 4 A, showing that the pale staining regions were characterised by obvious reduction in neuronal density. In one animal (WA32), which survived only two weeks after V1 lesion, the LPZ was only partially evident, particularly at higher magnifications, by virtue of morphological changes such as reduced cell size and the loss of definition in the contours of neuronal nuclei . In this case, the volume of the LGN ipsilateral to the lesion was 8% lower than that on the unaffected side, which slightly surpasses the typical variability in LGN volume estimates between hemispheres, further indicating an ongoing degenerative process. Fig. 2 No visible cellular loss is observed in the lateral geniculate nucleus (LGN) three days (A) or two weeks (B) after lesion in the primary visual cortex (V1). Top: NeuN-stained LGN in the lesioned (left) or intact hemispheres (right). Dashed lines show layer boundaries. PE: parvocellular external layer, PI: parvocellular internal layer, K3: Koniocellular 3, MI: magnocellular internal layer, ME: magnocellular external layer, K1: Koniocellular 1. Scale bar: 1 mm. Black arrows point to the limits of degenerated zone in B. Bottom: Boxed areas in the expected lesion projection zone (parvocellular layers) and a corresponding region of the contralateral hemisphere are shown in higher magnification. Scale bar = 100μm. Fig. 2 Fig. 3 Lesions of the primary visual cortex (V1) trigger retrograde degeneration in the lateral geniculate nucleus (LGN). A: Representative images of NeuN-stained coronal sections through the LGN (interaural +4.80mm), obtained from four animals with different post-lesion survival times. In all cases the LGN ipsilateral to the V1 lesion shows a well-defined lesion projection zone (LPZ, identified by dashed line/asterisk), while the contralateral LGN shows normal lamination and shape. Scale bar: 1mm. Fig. 3 Fig. 4 Lesions of the primary visual cortex (V1) cause significant neuronal loss in the lateral geniculate nucleus (LGN) within the first month. A: Representative images from the lesion projection zone (LPZ) and non-LPZ regions in control (CJ167), short survival (WA27) and long survival (WA13) cases. Scale bar = 100 μm. B–D: Mean ± SEM of neuronal density of LGN for the short and long survival animals in LPZ (B) and non-LPZ (C) as well as control animals (D). Different post-lesion survival times are identified with different symbols in the short survival group (two weeks to 6 months post lesion). Density values for the case of two weeks survival (WA32) are marked on A and B for comparison purposes, but they were not included in the averaging and statistical analysis. E: Variability of neuronal density across LGN layers for all cases. Each circle represents a sampling frame as depicted in Fig. 1 . Small lines represent mean, also indicated in B-D. Fig. 4 For quantitative analysis, the cohort of animals was divided into short survival (<6 months, excluding animals that only survived 2–3 days) and long survival (12–39 months) groups, the latter corresponding to time points already explored . As shown in Fig. 4 B, the neuronal density in the LPZs dropped to nearly half of the values observed outside the LPZs in the same animals, or in control animals. This drop was evident in all cases that survived at least 1-month post lesion, indicating a rapid neuronal loss following V1 lesion. In contrast, in the case with a 2-week survival the neuronal density in the LPZ was similar to the values observed outside the LPZ or in control animals , indicating that the apoptotic process had not been completed. This is despite the surviving neurons displaying more variable degrees of size, shape and definition compared to the healthy-looking neurons of the equivalent area in the contralateral LGN , suggesting initial stages of degeneration. Density values for this case are shown in Fig. 4 for comparison purposes, but were not included in the statistical analyses below. The observed neuronal loss was similar among all cases in the short survival group . Comparison with those in the long survival group also did not reveal any differences for neuronal density in the LPZ, indicating no further degeneration [ Fig. 4 B and C, two-way ANOVA; survival time: F (1, 20) = 2.15, p = 0.16, zone: F (1, 20) = 52.71, p < 0.0001, interaction; F (1, 20) = 0.0027, p = 0.96, post hoc; LPZ vs. non-LPZ; short survival , long survival (18.59 ± 0.54 vs. 35.02 ± 1.03 × 10 3 /mm 3 , p < 0.001)]. The mean neuronal density outside the LPZ was similar to that found in the LGN of non-lesioned control animals, both in the short and long survival groups [ Fig. 4 C and D, One-way ANOVA, F (2, 14) = 0.49, p = 0.62), control; 39.65 ± 1.90 × 10 3 /mm 3 ]. Neuronal densities acquired from all sampling frames in each LGN has been shown in Fig. 4 E, suggesting a variable neuronal density in different LGN layers, although this variability remained relatively similar across cases. The average LGN volume loss (ratio of ipsilesional LGN volume to contralesional LGN volume) was more extensive in the long survival group compared to the short survival group . To account for the possibility of variations in lesion sizes, we ran a separate analysis comparing the percentage loss of volume in the LPZ and non-LPZ regions, compared to the contralesional LGN volume. This analysis showed that the difference between the two groups originates mainly from changes in the LPZ volume . Although the analysis of non-LPZ volume shown in Fig. 5 C indicates some variability across cases, the results did not reach statistical significance (non-LPZ; 36.52 ± 6.12 vs. 48.40 ± 2.919%, p = 0.15). In summary, the result indicates a significantly smaller volume loss in the short survival group. Fig. 5 Lateral geniculate nucleus (LGN) volume loss correlates with the post-lesion survival time. A–C: Mean ± SEM of volume of the LGN for animals with short (2 weeks–6 months, n = 7) and long (12–39 months, n = 5) post-lesion survival period. Volume is presented as percentage of ipsilateral LGN (A) or its sectors, lesion projection zone (LPZ, B) and non-LPZ(C) compared to the contralateral LGN. Different post-lesion survival times are identified with variable symbols in the short survival cases. ∗∗p < 0.01. D–I: Percentage mean volume of LPZ (D, F, H) or non-LPZ (E, G, I) sectors are plotted against the post-lesion survival time. Data obtained in the short survival and long survival groups are shown separately (D–G) or combined (H–I). R2 represents the significance levels for the slopes of simple linear regressions for each condition. Fig. 5 To further understand the differences in volume loss in relation to survival times, we plotted percentage volume (i.e. the ratio of the volumes in the LPZ vs. contralesional LGN) against the survival time post-lesion in Fig. 5 (D–I). A negative linear correlation was observed for LPZ volume . When the correlations were conducted separately for the short and long survival group, only the short survival group demonstrated a statistically significant negative correlation . This finding is consistent with the previous study that the shrinkage of LGN stabilised within six to seven months following V1 lesions . The normalised volume of non-LPZ (as compared to contra-lesion LGN), on the other hand, did not show statistically significant changes in both short and long survival groups . Thus, the results indicate a substantial rapid loss of neurons occur first, followed by a gradual shrinkage of the LGN after lesion, and argue against differences in lesion extent being a confounding factor. Previous studies have demonstrated that, in addition to degeneration, V1 lesions elicit neurochemical changes in the LGN M and P neurons . In particular, these neurons, which normally only express PV , also show co-expression of CB, including in M neurons projecting to area MT . Here we explored the timing of CB expression in M and P neurons following V1 lesions. The CB positive neurons were quantified from populations of PV-expressing neurons (M and P) in the non-LPZ in the short survival group. This confirmed the presence of CB in M and P cells in all cases, ranging from 2 weeks to 6 months post-lesion . Example images of CB expression in M and P neurons are shown in Fig. 6 A–B. Regression analysis revealed a trend towards greater CB expression in M and P neurons with the survival time, although this did not reach a statistically significant level . While degeneration is exclusive to the ipsilesional LGN, a minimum of about 11% CB co-localisation with PV was also detected in the contralateral LGN , confirming that this neurochemical change triggered by the unilateral lesion occurs in both hemispheres . The average values obtained from all lesioned animals showed that the proportion of CB expressing M and P neurons was significantly higher in the ipsilesional LGN . Lack of CB expression in M and P cells in nonlesioned animals is indicated as an average of three cases in Fig. 6 D. These data, obtained using the same methodology, have been published previously as individual cases . Higher percentage of M neurons showed CB expression in comparison with P neurons only in the contralateral LGN [ Fig. 6 E; two-way ANOVA; interaction, F (1, 102) = 0.24, P = 0.62; hemisphere, F (1, 102) = 3.19, P = 0.077; Cell type, F (1, 102) = 11.03, P = 0.0012, post hoc: M vs. P: Ipsi; 27.54 ± 1.94 vs. 21.94 ± 2.09, P = 0.17, Contra: 24.98 ± 2.43 vs. 17.41 ± 1.24, P = 0.048]. Fig. 6 Calbindin (CB) immunoreactivity in parvalbumin (PV)-expressing neurons of lateral geniculate nucleus (LGN) is present after two weeks following lesions of the primary visual cortex (V1). A: Representative images showing CB expression in PV-expressing magnocellular (M) and parvocellular (P) neurons for 3 animals which survived two weeks (WA32), three (WA22) and six (WA25) months post-lesion. Yellow arrows point to individual neurons expressing both CB and PV. Scale bar: 100μm. B: Locations of individual images within the LGN. ME: magnocellular external layer, MI: magnocellular internal layer, PE: parvocellular external layer, PI: parvocellular internal layer. C: Mean percentage of CB expression in the ipsilesional M and P neurons is plotted against post-lesion survival time. R 2 indicates the significance level for the slope of a simple linear regression. D: Percentage mean ± SEM of CB expression in M and P neurons of the LGN for control and lesioned cases (the latter shown as individual cases and also averaged). Black circles represent individual sections analysed. E: Averaged data from lesioned cases (shown in D) is indicated separately for M and P layers. ∗p < 0.05. Evidence of no cellular staining in the absence of primary antibodies for CB and PV is shown in Supplementary Fig. 1 . Fig. 6 The present study quantified changes in the structure and neurochemistry of the marmoset monkey LGN across an extended time span following V1 lesions. Our study builds on the prior knowledge of degenerative changes in primate LGN, by employing quantitative, comprehensive assessment of neuronal density, volume and neurochemistry. Using specific methods for the labelling of neurons, we found that the vast majority of the changes in neuronal density in the LPZ occurred between 14 and 30 days post lesion, with no further changes up to 3 years post lesion. However, the shrinkage of the LPZ happened according to a slower timescale, not being completed until 6 months post lesion. In parallel to these degenerative changes, we found that CB expression could be detected in surviving M and P neurons as early as 2 weeks post lesion. V1 lesion induced fast retrograde degeneration, which resulted in a sharp decrease of neuronal density in the LGN within just 1 month of the lesions. The fast nature of degeneration is in agreement with previous studies in other primate species , which used the Nissl stain to assess neuronal loss. In squirrel monkeys, neuronal degeneration is reported to be completed within 42 days, with early signs of retrograde degeneration being evident only 5–8 days after lesions . This time course is compatible with that suggested by our data, where no cell loss was detected 3 days after lesion, while there were indications of changes in cell morphology and size after 2 weeks, possibly as signs of degenerative process. Despite changes in morphologies, our quantification suggests that there is no overt neuronal loss at this time point. The small variability in the estimates of neuronal density in the LPZ among cases with survival times of a month or more indicates that the degeneration process has been largely completed within a month, supporting previous data in macaque monkeys , who reported completion of degeneration within 3–6 weeks. The present study, employing quantitative assessment of neuronal density with a specific neuronal marker, extends previous knowledge with additional significant details, over a wider time scale, and to a different primate species (marmoset). In the context of previous literature, these results are significant in two ways. First, they demonstrate that the time course of degeneration is similar despite large differences in brain size (and hence axonal length) among primate species. This species similarity indicates that a comparable time course is likely to apply to the human brain. Second, they identify a time window during which possible future interventions to reduce neuronal loss in the LGN need to apply. Long-term occipital lesions cause loss of function not only due to the direct loss of V1 cortex, but also subsequent degenerative changes in the LGN and retina . Preventing or minimising the secondary losses in the afferent visual pathway may be relevant for future therapeutic interventions aimed at retaining or improving residual vision. Previous studies in macaque have suggested a potentially more significant cell loss in the LGN following V1 lesions, compared to observations in marmoset . In this context, the variability in lesion sizes, involving different parts of extrastriate visual cortex, can significantly contribute to variations in cell loss . In the present study, the lesions consistently damaged the foveal and near-peripheral representations of V1 in one hemisphere, largely avoiding V2 except for near the boundary with V1 . The difference may also be partially due to technical differences, including the use of Nissl staining in earlier studies, rather than the more specific NeuN stain, and, in some cases, qualitative assessment . The possibility also exists that there are differences in the pattern of projections from LGN neurons to extrastriate areas between marmosets, macaques and humans, which could affect neuronal survival by allowing preservation of different numbers of synaptic targets outside the lesioned zones. However, currently there is no evidence of such differences , warranting further studies. Our observation of a variable distribution of neuronal loss across LGN , is partly reflective of the natural variability as we have reported previously , however it may also reflect differential neuronal loss across LGN layers that requires further studies. Atrophy of the LGN is a critical pathological indication for V1 lesions which can be quantified with non-invasive brain imaging . Whereas volume reduction is clearly influenced by neuronal loss, these two aspects of degeneration can occur independently. For example, volume changes without concomitant loss of neuronal cell bodies have been observed in the pulvinar complex following V1 lesions, an observation that has been interpreted as indicating that volume reduction was likely due to changes in the neuropil . In the present study, the volume loss in the LGN ipsilateral to the lesion increased with the survival time, despite the level of neuronal loss being similar across all cases. This observation was normalised for the lesion size (a factor which is likely to affect LGN shrinkage) by separate estimation of percentage volume loss for LPZ and non-LPZ. This correlation with survival time was not observed among long survival animals. This is consistent with previous observations that volume loss in the marmoset LGN does not progress beyond 6–7 months post lesion. The delayed time scale for volume loss, compared to neuronal degeneration, may be due to the time required for debris removal , neuropil loss, or even remodelling of the capillary bed due to reduced energy requirements following apoptosis of LGN neurons. Moreover, cortico-geniculate axons and terminals fill much of the space between LGN neurons , and their loss may also contribute to volume changes. Although evidence suggests that the time course and magnitude of retrograde and Wallerian (anterograde) degeneration are similar , there may be differences due to the differential site of injury along the axons or heterogeneity of axons and their compartmentalisation . Although it has been reported that early signs of cortico-geniculate terminal degeneration precede the retrograde degeneration of LGN neurons in squirrel monkeys , the biological complexity of degenerative processes may create difficulty in pinpointing the precise timing of events. What is clear is that the physical collapse of LGN volume is a more prolonged process than neuronal death. Recent studies have shown that neurochemical changes are part of long-term plastic changes in the primate LGN following V1 injury . Under normal conditions neurons in the M and P layers only express PV . Following V1 lesions they also co-express CB, a fact that we find evident even two weeks after lesion. However, the percentage of M and P neurons expressing CB following short survival times is lower (17–27%) than that observed after longer survival times , further emphasising the gradual nature of the emergence of CB expression in M and P neurons. The present observations are in line with previous data that indicate changes in the neurochemistry of M and P neurons also occur in the LGN of the hemisphere contralateral to the lesion . It has been suggested that V1 lesions could lead to reduced cortical feedback onto surviving LGN neurons , creating imbalanced activation of these neurons by the retina, which in turn would trigger CB expression . Underpinning this assumption, M neurons, with the most significant CB overexpression after V1 lesions , have larger than normal receptive fields and the least degeneration of their retinal afferents . Moreover, the more prevalent CB expression in M cells may suggest their higher potential to form new connections to the cortex . CB expression has been highlighted as a protective mechanism linked to neuronal resilience under stress . Thus, concomitant with the ongoing degeneration in LPZ, the neurons in non-LPZ undergo neurochemical changes that may prepare them for better survival, or regenerative processes . Indeed, our observations are consistent with this hypothesis, as alterations in neurochemistry occur prior to the onset of neuronal loss. As recently demonstrated, the neurochemical change involves remodelling of the projections of M neurons, which form projections to extrastriate cortex . Whether the emergence of the CB expression in M/P neurons within the first few months after lesion coincides with the process of remodelling their projection to extrastriate areas remains to be tested. Changes in the neurochemistry of projection neurons, including expression of GABA , might lead to better survival rates. We have also previously shown that after adult V1 lesions the percentage of GABAergic neurons is increased to almost sevenfold in the LPZ of LGN, suggesting that the cell loss due to retrograde degeneration is more prominent among projection neurons . The progressive CB expression among projection neurons after lesion suggests that other neurochemical changes may also be linked with survival time, and thus a differential ratio of surviving excitatory neurons to interneurons in the LPZ is likely to happen over time. The present study focuses on the changes occurring in LGN following V1 lesions in young adult marmosets. However, there is evidence that the age at which injury happens could affect the extent and pace of the degeneration process . Thus, additional work focused on different stages of life is advisable in order to obtain a full picture of the effects of V1 lesions. Earlier work comparing lesions in young (2–6 weeks postnatal) and adult marmosets revealed modest physiological differences in the surviving LGN neurons . The extent of the damage to the extrastriate visual cortex can also influence estimates of degeneration . In the present experiments, the lesions were designed to be largely contained to V1, although involvement of parts of the second visual area near the border is inevitable. More extensive lesions resulting in damage to rostral extrastriate areas would likely result in more extensive loss due to the removal of projection targets of neurons that would otherwise survive the degeneration process. However, the few surviving neurons in the LGN continue to receive retinal projections even following complete unilateral hemispherectomies . Whether the surviving neurons following larger lesions undergo biochemical changes remains an open question. In the present study, none of the animals showed damage extending to rostral extrastriate areas such as the homologues of V4, or MT Our study demonstrates that the neuronal loss in the LGN proceeds rapidly in the marmoset, being largely completed within one month of V1 lesions, in agreement with previous work in other non-human primates. These findings support the view that the marmoset is a valuable animal model to study the anatomical and physiological plasticity processes involved in the aftermath of V1 lesions . At the same time, the results indicate that the volume loss and biochemical changes in the LGN are slower processes, highlighting the complexity of the processes triggered by such lesions. This information has broader implications for our understanding of traumatic brain injury and future efforts towards minimising visual loss, and promoting regenerative processes which may improve the quality of residual vision in primates, including humans. | Study | biomedical | en | 0.999997 |
PMC11697718 | The term asbestos refers to a group of naturally occurring fibrous minerals that are used in various industrial processes mainly in the asbestos cement, construction and engineering industries ( International Agency for Research on Cancer (IARC), 2012 ). The health risks associated with asbestos exposure depend on several factors, including the concentration of fibres in the air, the length of exposure, the type and size of the fibres . The scientific community has extensively studied the relationship between asbestos exposure and the development of respiratory diseases, including asbestosis, lung cancer, and pleural mesothelioma . In addition, the International Agency for Research on Cancer (IARC) has identified links between asbestos exposure and other types of cancer, which have been considered asbestos-related diseases (ARDs), such as pharyngeal, laryngeal, oesophageal, stomach, colorectal and ovarian cancers . Data were analysed in aggregate form. Quantitative data were reported as medians and interquartile ranges (IQR) based on their distribution. Categorical variables were expressed as counts and percentages. To assess statistical significance, Mann-Whitney test was applied for comparisons between two groups while Kruskal-Wallis test was used for comparisons across multiple groups regarding quantitative data. For categorial data, associations were tested using Chi-square test, Fisher's exact test, or Cramer's V. The internal consistency of the Awareness section (Q15-Q19) was assessed using Cronbach's alpha. A two-sided p -value <0.05 was considered statistically significant. All statistical analyses were performed using IBM SPSS® version 25 for Windows. Regarding the knowledge section (see Table 1 ), 59.3 % of GPs had a good level of knowledge and only 18 % an optimal level. The median knowledge score was 63 % (IQR 51–72). There was a statistically significant difference in knowledge levels among the seven districts ( p = 0.002), and difference became noticeable when comparing Casale Monferrato district to the others ( p < 0.001). GPs practicing in Casale Monferrato district exhibited a higher median knowledge level of 72 % (IQR 60–78), with 42.6 % achieving an optimal level of knowledge. Table 1 Descriptive statistics related to GPs' overall knowledge indexes in ASL AL districts emerged from the survey filled out during the EAT meetings . Table 1 TOTAL ASL AL DISTRICTS p ⁎ Casale Monferrato Other ASL AL Districts ASL AL p ⁎⁎ Alessandria Casale Monferrato Valenza Tortona Novi Ligure Ovada Acqui Terme ( n = 216) ( n = 64) ( n = 47) ( n = 17) (n = 21) ( n = 37) ( n = 11) ( n = 19) (n = 47) ( n = 169) N % N % N % N % N % N % N % N % N % N % Knowledge scarce 5 2.3 2 3.1 0 0.0 0 0 1 4.8 2 5.4 0 0 0 0 0.002 0 0.0 5 3.0 <0.001 sufficient 44 20.4 15 23.4 4 8.5 3 17.6 11 52.4 5 13.5 3 27.3 3 15.8 4 8.5 40 23.7 good 128 59.3 39 60.9 23 48.9 14 82.4 8 38.0 25 67.6 6 54.5 13 68.4 23 48.9 105 62.1 optimal 39 18.0 8 12.6 20 42.6 0 0 1 4.8 5 13.5 2 18.2 3 15.8 20 42.6 19 11.2 Median (IQR) 63 (51–72) 62 (49–71) 72 (60–78) 65 (56–70) 48 (39–63.5) 62 (52.5–69) 64 (47–71) 59 (51–71) 72 (60–78) 61 (49–70) ⁎ Kruskal-Wallis test. ⁎⁎ Mann-Whitney test. Regarding the evaluations for the knowledge-related questions, all items assessed showed a statistically significant association with the overall score level (see Table 2 ). However, a few items did not meet this criteria, including questions about personal protective equipment (Q4f), asbestos-related diseases (Q5b, Q5e), radiological signs of asbestosis (Q9, excluding answers related to pleural plaques), and anatomical structures where mesothelioma does not originate (Q13c). Knowledge evaluations for each ASL AL district are shown in supplementary table 2. Table 2 Knowledge of asbestos risk and ARDs, along with the association of overall scores to corresponding questions. Table 2 KNOWLEDGE TOTAL (n = 216) Association with knowledge score (p) ⁎⁎ Yes (knowledge) No (no knowledge) N % Overall Index No No answer Overall Index median score IQR N % N % median score IQR Q1) Asbestos is dangerous to human health 203 94.0 64.0 53–73 12 5.5 1 0.5 36.0 22.5–47 <0.001 Q2) Exposure to asbestos in living or working environments increases the risk of developing mesothelioma 200 92.6 64.0 54–73 16 7.4 – – 38.0 25–44 <0.001 Q3) The following types of asbestos exposure can influence the onset of malignant mesothelioma: a) Occupational exposure 182 84.3 64.5 55.75–73 31 14.3 3 1.4 43.0 30–55 <0.001 b) Family exposure 93 43.1 71.0 64–79 105 48.6 18 8.3 55.0 45–64 <0.001 c) Environmental exposure 147 68.1 66.0 59–74 62 28.7 7 3.2 51.0 39–64 <0.001 Q4) What types of personal protective equipment are effective in reducing the risk of occupational exposure to asbestos? a) Disposable coveralls 187 86.6 64.0 53–73 29 13.4 – – 55.0 36.5–63 0.001 b) Surgical masks 179 82.9 64.0 51–73 37 17.1 – – 58.0 46–67.5 0.040 c) Filtering face pieces 174 80.6 65.0 57–73.25 42 19.4 – – 47.0 33.75–55.5 <0.001 d) Washable rubber boots 95 44.0 70.0 59–76 121 56.0 – – 59.0 47–67 <0.001 e) Hearing protectors 187 86.6 62.0 49–72 29 13.4 – – 67.0 62–74 0.007 f) Heat resistant clothing 204 94.4 62.0 51–72 12 5.6 – – 69.5 60.5–72.75 0.182 Q5) Which of the following diseases are associated with asbestos exposure? a) Pulmonary asbestosis 207 95.8 64.0 52–72 9 4.2 – – 39.0 29.5–55.5 <0.001 b) Meningioma 209 96.8 63.0 51–72 7 3.2 – – 59.0 34–67 0.287 c) Pericardial mesothelioma 166 76.9 65.0 55.75–74 50 23.1 – – 49.5 36.25–62 <0.001 d) Mesothelioma of the tunica vaginalis testis 111 51.4 70.0 62–77 105 48.6 – – 54.0 44–64 <0.001 e) Non-Hodgkin lymphoma 194 89.8 63.0 51–72.25 22 10.2 – – 63.5 51.75–67.75 0.657 Q6) Which of the following are methods of exposure to asbestos? a) Domestic exposure, which refers to those living with someone who has been professionally exposed to asbestos 121 56.0 70.0 62–76.5 95 44.0 – – 51.0 40–62 <0.001 b) Environmental exposure, which refers to those who live in geographical areas contaminated by asbestos 189 87.5 64.0 54–73 27 12.5 – – 40.0 30–59 <0.001 c) Occupational exposure, which refers to those who carry out a professional activity in which asbestos is present 206 95.4 64.0 53–72.25 10 4.6 – – 37.5 22–45.25 <0.001 Q7) Based onthe literature, what is the primary symptom of asbestosis that is useful for directing the diagnosis? c. Exertional dyspnoea, which may progress to dyspnoea at rest 90 41.7 68.5 57.75–77.25 109 50.4 17 7.9 59.5 47–68 <0.001 Q8) When a worker is diagnosed with asbestosis, what does the GP need to report online? a. The physician submits a report online to INAIL 109 50.5 66.0 55.5–76.5 99 45.8 8 3.7 61.0 46–68 <0.001 Q9) What are the radiological signs of pulmonary asbestosis? a) Pleural plaques 99 45.8 61.0 47–69 117 54.2 – – 65.0 54.5–74 0.004 b) Fine basal reticular patterns 42 19.4 62.0 52.5–72 174 80.6 – – 63.5 51–72 0.727 c) A diffuse reticular-nodular pattern 137 63.4 64.0 51–73 79 36.6 – – 62.0 51–71 0.385 d) Air bronchograms 211 97.7 63.0 51–72 5 2.3 – – 57.0 22–64.5 0.159 e) Increased intercostal spaces 214 99.1 63.0 51–72 2 0.9 – – 47.5 n.c. ⁎ 0.165 Q10) From the literature, what is the average latency period for mesothelioma? e. Over twenty-five years 115 53.2 68.0 58–77 100 46.3 1 0.5 58.0 47–66 <0.001 Q11) Which of the following investigations is most suitable for the diagnosis and staging of pleural mesothelioma? b. Computed Tomography 165 76.4 65.0 55.5–73.5 45 20.8 6 2.8 51.0 40–63 <0.001 Q12) Which statement regarding pleural mesothelioma is correct? d. Asbestos exposure and tobacco smoking are synergistic risk factors 128 59.3 66.5 58.25–74.75 85 39.3 3 1.4 55.0 42.5–65 <0.001 Q13) From which of the following anatomical structures does mesothelioma NOT originate? a) Lymph nodes 99 45. 8 68.0 58–78 117 54.2 – – 59.0 45–66.5 <0.001 b) Meninges 112 51.9 65.0 55.25–75 104 48.1 – – 59.0 48.25–70.75 0.003 c) Pericardium 210 97.2 63.5 51–72 6 2.8 – – 57.0 41.25–61.25 0.152 d) Myocardium 85 39.4 66.0 55–78 131 60.6 – – 61.0 49–70 0.002 e) Vaginal tunic 183 84.7 65.0 54–73 33 15.3 – – 50.0 38.5–58.5 <0.001 Q14) According to Law 257/92 which regulations relateto the cessation of asbestos use? Asbestos exposure is permitted, with appropriate safety measures for workers, in the following activities d. Disposal and/or remediation of areas or artefacts containing asbestos 167 77.3 65.0 55–74 41 19.0 8 3.7 50.0 38.5–64 <0.001 ⁎ Not calculable because of the low number of responses in the group. ⁎⁎ Mann-Whitney test. Although there was a high and widespread knowledge about the health hazards of asbestos (94 %) ( Table 2 , Q1) and the increased risk of mesothelioma due to this exposure (92.6 %, Q2), significant disparities were evident both in the variation of knowledge among different districts and when comparing Casale Monferrato district with the others. The greatest variations in knowledge were particularly noted in the following areas: types of asbestos exposure, procedures for reporting occupational diseases, and understanding of mesothelioma, including latency, diagnostic methods, and risk factors. Regarding the types of asbestos exposure linked to mesothelioma (Q3), the rates were 84.3 % for occupational exposure 84.3 %, and 68.1 % and 43.1 % for environmental and domestic exposure, respectively. Domestic exposure had the highest rate of missing responses (8.3 %) and knowledge for this type of exposure (Q3b) showed significant differences among the districts ( p = 0.039), with Casale Monferrato reporting the highest awareness (53.2 %). Statistically significant differences were also observed when comparing Casale Monferrato to the other districts ( p = 0.008). Furthermore, when evaluating types of asbestos exposure (Q6), the one least identified by GPs was domestic exposure (Q6a); only 56 % of them recognising it as a mode of exposure, compared to environmental (87.5 %) and occupational exposure (95.4 %). Statistically significant differences in knowledge were found both among the different districts ( p = 0.003) and between Casale Monferrato and the other districts ( p = 0.001). Finally, statistically significant differences were found among the districts in correctly identifying the relationship between asbestos exposure and mesothelioma (Q12), specifically that exposure to asbestos and tobacco smoke act as combined risk factors ( p = 0.004). The analysis of awareness index ( Table 3 ) revealed a moderate/high overall level of 62 % (median: 54 %, IQR: 46–62). Only 4.6 % of GPs reported a high level of awareness. There were no statistically significant differences in awareness levels when comparing the seven districts or between Casale Monferrato and the other districts ( p > 0.05). Table 3 Descriptive statistics related to GPs' overall awareness indexes in ASL AL districts emerged from the survey filled out during the EAT meetings . Table 3 TOTAL ASL AL DISTRICTS p ⁎ Casale Monferrato Other ASL AL Districts p ⁎⁎ Alessandria Casale Monferrato Valenza Tortona Novi Ligure Ovada Acqui Terme (n = 216) (n = 64) (n = 47) (n = 17) (n = 21) (n = 37) (n = 11) (n = 19) (n = 47) (n = 169) N % N % N % N % N % N % N % N % N % N % Awareness 0.065 0.060 inadequate 3 1.4 1 1.6 0 0.0 0 0.0 0 0.0 1 2.7 0 0.0 1 5.3 0 0.0 3 1.8 poor 79 36.6 30 46.9 14 29.8 4 23.5 5 23.8 14 37.8 4 36.4 8 42.1 14 29.8 65 38.5 moderate 124 57.4 30 46.9 29 61.7 12 70.6 15 71.4 22 59.5 6 54.5 10 52.6 29 61.7 95 56.2 high 10 4.6 3 4.6 4 8.5 1 5.9 1 4.8 0 0.0 1 9.1 0 0.0 4 8.5 6 3.5 Median (IQR) 54 (46–62) 51 (43–59.5) 57 (49–65) 57 (50–64) 62 (50–68) 52 (45.5–60.5) 57 (48–69) 52 (45–60) 57 (49–65) 52 (45.5–62) ⁎ Kruskal-Wallis test. ⁎⁎ Mann-Whitney test. Regarding the awareness items ( Table 4 ), all associations between the overall score and the individual areas were statistically significant. Awareness evaluations for each ASL AL district are reported in supplementary table 3. Table 4 Awareness/competence on asbestos risk and ARDs and association of overall score with the corresponding questions. Table 4 AWARENESS TOTAL (n = 216) Association with awareness score (p) ⁎ Yes (aware) No (not aware) Overall Index No No answer Overall Index N % median score IQR N % N % median score IQR Q15) Which of the following actions related to occupational diseases fall within your scope of responsibility? a) Diagnosis 175 81.0 57.0 48–63 35 16.2 6 2.8 43.0 34.5–52 <0.001 b) Complaint/ formal notification 140 64.8 57.0 49–65 66 30.6 10 4.6 49.0 40.50–57 <0.001 c) Compilation of medical certificates 142 65.7 57.0 49–65 57 26.4 17 7.9 48.5 41.5–57 <0.001 What factors contribute to the difficulty in reporting an occupational disease? a) Inexperience with bureaucratic procedures 17 7.9 72.0 62.5–80.5 184 85.2 15 6.9 52.0 46–60 <0.001 b) Challenges in carrying out bureaucratic procedures 12 5.6 71.0 62.25–77.75 191 88.4 13 6.0 52.0 46–60 <0.001 c) Lack of understanding of diagnostic criteria 76 35.2 62.5 57–71 117 54.2 23 10.6 49.0 43–57 <0.001 d) Lack of time 86 39.8 60.0 52–68.25 105 48.6 25 11.6 50.0 43–58 <0.001 e) Inadequate professional updating (ECM, etc.) 53 24.5 63.0 57.5–72 147 68.1 16 7.4 51.0 43–58 <0.001 f) Complexity of the List of occupational diseases for which reporting is mandatory 29 13.4 68.0 61–73 170 78.7 17 7.9 52.0 45–58 <0.001 Q17) Do you feel that your current level of scientific and professional knowledge on asbestos-related diseases is sufficient to address patient inquiries regarding occupational diseases and workplace accidents?” 55 25.5 58.0 49–71 160 74.0 1 0.5 52.0 46–60 <0.001 Q18) Do you find the Continuing Medical Education (CME) in the Piedmont Region to be adequate in covering asbestos-related diseases? 38 17.6 59.0 48.75–72 177 81.9 1 0.5 52.0 46–62 0.004 Q19) Are you well-informed about the occupational activities of your patients? 136 63.0 57.0 49–65 79 36.5 1 0.5 49.0 42–57.75 <0.001 Q20) In the last 12 months, have you followed Continuing Medical Education courses (ECM) that covered or included topics on asbestos-related diseases? 12 5.6 61.5 57–73.25 203 93.9 1 0.5 52.0 46–62 0.010 Q21) In the past 12 months, have you seen or treated patients with asbestos-related conditions? 65 30.1 58.0 49–68 151 69.9 – – 52. 0 45–60 0.001 Q22) In the last 5 years, how many occupational disease reports related to asbestos exposure have you submitted? 53 24.5 57.0 51–70 161 74.6 2 0.9 52.0 45–60 0.002 ⁎ Mann-Whitney test. In terms of barriers for reporting occupational diseases, statistically significant differences were found for the lack of knowledge of diagnostic criteria (Q16c), both among districts ( p < 0.001) and compared to Casale Monferrato ( p = 0.001). Differences were also observed for lack of time (Q16d) and the complexity of the occupational diseases (Q16f) among districts ( p = 0.036 and p = 0.049 respectively), but not when compared to Casale Monferrato. Conversely, this difference was evident for the item on inadequate professional updating, but not among districts (p = 0.036). Additionally, 69.9 % of GPs (1.4 % who failed to recall) reported they had not evaluated ARD cases in the previous year (Q21). Significant differences were observed between districts and in comparison with Casale Monferrato. Notably, 78.7 % of GPs in Casale Monferrato, reported their experience in visiting patients with ARD ( p < 0.001). The median number of patients visited was 2.50 (IQR 2–6.25) in Casale Monferrato, compared to 1 (IQR 1–2) in the other districts (p < 0.001). | Review | biomedical | en | 0.999997 |
PMC11697735 | Amiri et al., conducted a systematic review and meta-analysis that provided data regarding the attitudes, knowledge, and skills of medical, dental, and nursing students concerning artificial intelligence, evaluating 5789 participants from 24 studies. Overall, 44% of students showed a medium to high level of knowledge of AI applications and principles. Most students, however, knew very little about artificial intelligence. Although students demonstrated moderate knowledge, they generally had positive attitudes towards AI, as 65% of all students agreed with the use of AI in medicine and had a favorable view. Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) version 22.0 statistical analysis tool. Data normality was tested using the Kolmogorov–Smirnov test. The normally distributed data were compared using the independent samples t-test and one-way analysis of variance. The chi-square test was used to assess the categorical data. The analysis's results were shown as frequency (percent) for categorical data. For quantitative data, the analysis's results were shown as mean ± standard deviation. A significance level of P < 0.05 was chosen. There was an insignificant statistical difference between knowledge about AI and gender, type of attending university, and the education year with p values (0.924, 0.442, and 0.105), respectively. Also, an insignificant statistical difference was observed between awareness of AI usage in dentistry and gender, type of attending university, and the education year with p values (0.978, 0.459, and 0.701), respectively. Table 1 . Table 1 Responses to the questionnaire's second section in relation to demographic data Variable knowledge about AI Awareness of AI usage in dentistry Number (N) Percentage (%) Yes No Chi-square Yes No Chi-square Gender Male 160 42% 76 84 0.924 81 79 0.978 Female 224 58% 111 113 111 113 Attending University Cairo university 241 63% 121 120 0.442 124 117 0.459 Egyptian Russian university 143 37% 66 77 68 75 Dental education year 3rd year 83 21% 45 38 0.105 44 39 0.701 4th year 99 26% 54 45 51 48 5th year 202 53% 88 114 97 105 It was extensively agreed that AI could be used for the diagnosis of dental caries, periodontal diseases, soft tissue lesions, and pathologies in the jaws radiographically (58%, 56%, 49%, and 51% agreement respectively) in contrast to (10%, 9%, 19% and 15% disagreement respectively). It was generally agreed that AI could be used for the diagnosis of dental caries, periodontal diseases, soft tissue lesions, and pathologies in the jaws radiographically (58%, 56%, 49%, and 51% agreement, respectively). This was in accordance with Turkish , Saudi , and Indian students , who thought that AI could be used for radiographic diagnosis of tooth caries, periodontal diseases, and the diagnosis of soft tissue. In addition, the majority of participants (58%) think that AI could be useful in diagnosis and treatment planning in dentistry. Close responses were reported in Turkey (57.2%), Saudi Arabia (44.2%) , and much higher in India (88.69%). According to the systematic reviews of Khanagar,2021 and Khan, 2024 , AI is revolutionary in terms of delivering reliable data for forensic scientific decision-making. AI models can be useful tools for identifying victims of mass disasters and as an additional aid in medico-legal situations while concurrently reducing the time required and the risk of human error. By minimizing the effect of human factors, AI can contribute to more reliable and reproducible outcomes. In this study (44%) of participants agreed on AI's usefulness in forensic dentistry. This was in accordance with studies by Yüzbaşıoğlu, 2020 (52.1%), Khanagar et al., 2021 (38.3%), and Murali et al., 2023 (90.43%). The majority of the students (96%) in Elhijazi and Benyahya, 2023 , (85.43%) in the Murali et al., 2023 , and ( 80%) in Kalaimani et al., 2023 studies wished to receive more in-depth training in AI applied to dentistry. Also, (79.80% and 74.60%, respectively) of participants in the Yüzbaşıoğlu, 2020 study granted that subjects about AI should take part in undergraduate and postgraduate dental education. Moreover (59%) of students in Aldowah et al., 2024 and almost half of the participants in Jeong et al., 2024 (49%) studies believed that AI should be included in dental curricula. | Review | biomedical | en | 0.999996 |
PMC11697764 | Recently, there has been a surge in HIV cure research aimed at either eliminating the virus from the body (HIV eradication, clearance cure) or ART-free durable control of the virus (functional cure, HIV suppression). 1 While much of this research focuses on biomedical, translational, and clinical sciences, the International AIDS Society (IAS) also emphasizes the importance of social engagement, particularly the engagement of affected communities, including people with HIV, their partners, and other key populations including GBMSM. 1 The emphasis on community engagement reflects a broader trend within health psychology over the past 25 years, emphasizing social justice. 2 Community engagement prioritizes strategies that draw from communities’ strengths and needs, fostering co-learning environments where individuals gain knowledge, skills and influence, empowering them to play an active role in research and decision-making. 2 Recognized for its effectiveness in reducing health inequities and improving health outcomes, community engagement has become an important factor in health promotion and other health and social science disciplines. 2 , 3 Evidence from the early HIV epidemic illustrates how people with HIV gained a “seat at the table” by educating themselves and blending lived experiences with scientific reasoning to advocate for their rights [ 4 pp284]. Successful community engagement has prompted health authorities, including the WHO, to revise guidelines, such as expanding early access to ART, and has been essential in HIV vaccine development by managing expectations and enhancing trial acceptance. 5 Contrastingly, inadequate community engagement in pre-exposure prophylaxis (PrEP) research has been linked to the premature termination of several clinical trials in the early 2000s, resulting in delays in implementation. 6 , 7 A meta-analysis further emphasized the importance of community involvement in PrEP adaptation, revealing that concerns about its efficacy and potential side effects were significant barriers to adoption. Additionally, it highlighted that awareness and knowledge are crucial enablers of PrEP uptake. 8 This underscores how overlooked community perspectives can hinder not only the progress of development but also play an important role in the adaptation and implementation of biomedical advances. Given these precedents, we contend that community engagement is also essential for the successful development and implementation of an HIV cure. 9 Health behavior models can offer valuable insights into how affected communities can be effectively engaged in HIV cure research. 10 The engagement process can be understood using stage models like the Precaution Adoption Process Model (PAPM), which outlines stages from being ‘unaware of the issue’ to ‘aware but not personally engaged,’ progressing to ‘engaged and deciding what to do,’ and moving toward ‘planning to act,’ ‘acting,’ and ‘maintaining.' 11 Inspired by the first three stages, this study is one of the first to assess the current engagement with HIV cure research among affected communities in the Netherlands by evaluating their awareness, interest, and information-seeking behaviors. While most studies focus on hypothetical scenarios, [e.g. Refs. 12 , 13 , 14 , 15 , 16 , 17 , 18 ] such as trial participation, few have explored actual community engagement in HIV cure research. 19 To our knowledge, only one study from Hong Kong explicitly assessed awareness of ART-free durable control. That study found that less than half of people with HIV included in their study were aware of ART-free durable control, with treatment-experienced participants being more likely to be aware. 20 Research from the United States and Brazil indicated that people had heard about HIV cure research, knew a cure was not yet available, or wanted to know more. 14 , 21 , 22 Furthermore, our qualitative research in the Netherlands among people with HIV, their partners, and GBMSM without HIV revealed a general awareness of HIV cure research, though active engagement was uncommon. 23 , 24 It also remains unclear who is aware, interested, and actively seeking information about HIV cure research, as engagement likely varies by age, gender, education, and context. 10 , 19 Recent research also suggests that HIV-related illness perceptions – encompassing views on illness effects, control, symptoms, concerns, understanding, duration, and causes – may influence engagement. 19 , 23 , 24 , 25 Therefore, in addition to evaluating current engagement, our study investigated the extent to which participant characteristics and HIV-related illness perceptions are associated with different stages of engagement. Lastly, we aimed to identify the specific characteristics and HIV-related illness perceptions that influence HIV cure research engagement among distinct communities, namely people with HIV and key populations without HIV. Eligible participants included people with HIV and key populations without HIV (partners of people with HIV and GBMSM), aged 18 or older, residing in the Netherlands. Recruitment combined online (e.g., Instagram®, Grindr®) and offline strategies (magazines, community events, clinics), supported by stakeholders such as Hiv Vereniging , HIV care providers, Hello Gorgeous (part of national HIV alliance), Volle Maan (health communication agency), Amsterdam Municipal 10.13039/100018696 Health Services (GGD), and Maastricht University's HIV Lab. The final sample comprised 1077 participants from affected communities who completed sections on HIV cure engagement, participant characteristics, and HIV-related illness perceptions, including 499 people with HIV and 578 key populations (533 GBMSM and 45 partners). For people with HIV, additional data included the year of diagnosis, disclosure status (someone; no one), and current ART use (yes; no). We measured the frequency of ART-related side effects and missed doses over the past month on a 5-point scale (1 = never, 5 = very often). Other HIV-related characteristics included whether people with HIV had switched or stopped their ART regimen (yes; no), latest viral load (undetectable; detectable; unknown), recent CD4 T-cell count (<500; >500; unknown), AIDS diagnosis history (yes; no; unknown), and prior participation in HIV-related medical research (yes; no). For key populations, we gathered data on PrEP use (yes; no) and HIV testing frequency (every 3 months; 6 months; every year; less than once a year; never). HIV-related illness perceptions were measured using a modified version of the Brief Illness Perception Questionnaire (Brief IPQ), 26 adapted for people with HIV and key populations ( Table S1 ). This scale evaluates seven dimensions: perceived consequences, perceived personal control, perceived treatment control, perceived identity, experienced concerns, experienced emotions, and perceived comprehensibility. The duration dimension from the Brief IPQ was excluded due to HIV's chronic nature. Participants responded on a 5-point scale (1 = not at all, 5 = very much). Higher scores for consequences, identity, concerns, and emotions indicated more threatening perceptions, while higher scores for control and comprehensibility reflected less threatening perceptions. A principal factor analysis revealed two factors for people with HIV and three for key populations ( Table S1 ). For both groups, the primary factor centered on the perceived impact of HIV, including dimensions such as consequences, concerns, identity, and emotions. Notably, the identity dimension was integrated into this factor for people with HIV only. Internal consistency for this factor was strong (people with HIV: Cronbach's α = 0.82; key populations: Cronbach's α = 0.79). The second factor, which captured perceived control and comprehensibility, demonstrated low internal consistency (people with HIV: Cronbach's α = 0.41; key populations: Cronbach's α = 0.45). A third factor, identified only for key populations, focused solely on perceived identity. Due to the divergent factor structures between people with HIV and key populations and the unsatisfactory Cronbach's alpha for the second factor, all items were analyzed individually. Guided by the first three stages of the PAPM, 11 we measured HIV cure engagement across three dimensions: awareness, interest, and information frequency. Awareness and interest were rated on a 5-point scale (1 = not at all, 5 = very much), while information-seeking frequency was reported on a 5-point scale (1 = never, 5 = often). We conducted descriptive analyses to describe participant characteristics, HIV-related illness perceptions, and levels of engagement. Differences between people with HIV and key populations were examined using chi-square and ANOVA tests. Pearson correlation coefficients were calculated to explore relationships between awareness, interest, and information-seeking frequency for all affected communities and separately for people with HIV and key populations. To evaluate how participant characteristics and HIV-related illness perceptions influence awareness, interest, and information-seeking frequency, we conducted three multivariable linear regression analyses. Bivariate linear regressions identified correlations among participant characteristics, HIV-related illness perceptions, and the three outcomes. To ensure inclusivity, all variables with a p < 0.10 from these analyses were incorporated into a standard multiple regression model. This process was repeated for people with HIV and key populations separately. Variables having p < 0.05 were deemed statistically significant. Since participants were not required to answer all questions (except for informed consent and bot detection), missing data were handled through listwise deletion. All analyses were conducted using SPSS version 29.0.0.0. The mean age was 47 years ( SD = 14.14), with people with HIV significantly older ( M = 54, SD = 12.51) than key populations ( M = 42, SD = 13.34) ( Table 1 ). Most participants identified as cisgender men (91.8 %), but people with HIV had a higher proportion of non-cisgender men (13.0 %) compared to key populations (4.0 %). Approximately 64.0 % held a bachelor's degree, with key populations having a higher rate (69.9 %) than people with HIV (57.1 %). About 25 % reported a migration background, with similar rates across both groups. Roughly 80 % identified as gay or lesbian, with a higher proportion among key populations (84.4 %) than people with HIV (78.0 %). Approximately half reported having one or more steady partners, with no significant differences between groups. Table 1 Participant characteristics and HIV-related illness perceptions across people with HIV and key populations without HIV. Table 1 All affected communities People with HIV (N = 499) Key populations without HIV (N = 578) Chi-square or ANOVA results N (%) M(SD) N (%) M(SD) N (%) M(SD) χ 2 (df) or F (df), η 2 ₚ p-value Participant characteristics Age (in years) 47.45 (14.14) 53.54 (12.51) 42.18 (13.34) F = 205.77, η 2 ₚ = 0.161 <0.001 Gender χ2 (2) = 51.96 <0.001 Cis men 989 (91.8) 434 (87.0) 555 (96.0) Cis women 67 (6.2) 59 (11.8) 8 (1.4) Other 21 (1.9) 6 (1.2) 15 (2.6) Bachelor's degree χ2 (1) = 18.65 <0.001 No 385 (35.7) 212 (42.5) 173 (29.9) Yes 689 (64.0) 285 (57.1) 404 (69.9) Migration background 0.284 No 799 (74.2) 380 (76.2) 419 (72.5) First generation 106 (9.8) 42 (8.4) 64 (11.1) Second generation 171 (15.9) 77 (15.4) 94 (16.3) Sexual identity χ2 (3) = 70.53 <0.001 Gay/Lesbian 877 (81.4) 389 (78.0) 488 (84.4) Heterosexual 81 (7.5) 72 (14.4) 9 (1.6) Bisexual 79 (7.3) 24 (4.8) 55 (9.5) Other 40 (3.7) 14 (2.8) 26 (4.5) Steady partner(s) χ2 (1) = 6.11 0.013 No 486 (45.1) 214 (42.9) 292 (50.5) Yes 519 (52.7) 283 (56.7) 285 (49.3) HIV-related illness perceptions Consequence 2.84 (0.99) 2.73 (1.00) 2.95 (0.96) F = 11.28, η 2 ₚ = 0.011 <0.001 Personal control 3.62 (0.91) 3.51 (1.02) 3.73 (0.78) F = 16.16, η 2 ₚ = 0.015 <0.001 Identity 2.67 (0.99) 2.22 (0.97) 3.08 (0.80) F = 247.64, η 2 ₚ = 0.193 <0.001 Treatment control 4.26 (0.82) 4.54 (0.69) 4.02 (0.84) F = 116.68, η 2 ₚ = 0.099 <0.001 Concern 2.58 (1.00) 2.23 (0.96) 2.88 (0.94) F = 123.70, η 2 ₚ = 0.104 <0.001 Emotions 2.58 (1.07) 2.37 (1.04) 2.77 (1.07) F = 39.14, η 2 ₚ = 0.036 <0.001 Comprehensibility 3.29 (0.73) 3.56 (0.67) 3.06 (0.70) F = 130.13, η 2 ₚ = 0.109 <0.001 Note. Since participants were not required to answer all questions (except for informed consent and bot detection) degrees of freedom may differ for across variables. Most people with HIV were diagnosed between 1996 and 2020 (82.0 %), with 8.1 % diagnosed after 2020 and 9.9 % before 1996. Nearly all people with HIV (99.4 %) were on ART and demonstrated high adherence ( M = 4.58, SD = 0.73), with 98.8 % achieving an undetectable viral load. Additionally, 94.4 % had disclosed their HIV status to someone. Among key populations, 62.9 % did not take PrEP, and most tested every 3 or 6 months (55.8 %). HIV-related illness perceptions differed significantly between people with HIV and key populations ( Table 1 ). Participants had a mean consequence score of 2.84 ( SD = 0.99), with people with HIV perceiving consequences as less threatening ( M = 2.73, SD = 1.00) compared to key populations ( M = 2.95, SD = 0.96). The mean personal control score was 3.62 ( SD = 0.91), with people with HIV reporting lower personal control ( M = 3.51, SD = 1.02) than key populations ( M = 3.73, SD = 0.78). For identity, the overall mean was 2.67 ( SD = 0.99), with people with HIV exhibiting less threatening perceptions ( M = 2.22, SD = 0.97) compared to key populations ( M = 3.08, SD = 0.80). Treatment control scores averaged 4.26 (SD = 0.82), with people with HIV reporting more control ( M = 4.54, SD = 0.69) than key populations ( M = 4.02, SD = 0.84). Concern levels averaged 2.58 ( SD = 1.00), with people with HIV showing less concern ( M = 2.23, SD = 0.96) than key populations ( M = 2.88, SD = 0.94). The mean score for emotions was 2.58 ( SD = 1.07), with people with HIV reporting less intense emotions ( M = 2.37, SD = 1.04) compared to key populations ( M = 2.77, SD = 1.07). Lastly, comprehensibility averaged 3.29 ( SD = 0.73), with people with HIV scoring higher ( M = 3.56, SD = 0.67) than key populations ( M = 3.06, SD = 0.70). Participants reported a mean awareness of 3.08 ( SD = 0.99) and a higher mean interest of 3.67 ( SD = 0.85), while the mean frequency of information-seeking was lower at 2.33 ( SD = 0.97) . People with HIV scored higher than key populations in awareness ( F = 42.67, η 2 ₚ = 0.038, p < 0.001), interest ( F = 76.76, η 2 ₚ = 0.067, p < 0.001), and information-seeking ( F = 55.89, η 2 ₚ = 0.049, p < 0.001). Fig. 1 Mean and standard error for the level of HIV cure engagement. Fig. 1 Table 2 shows a weak positive correlation between awareness and interest across all affected communities ( r = 0.060, p < 0.05), which was not significant for people with HIV and key populations. In contrast, awareness had a strong correlation with information-seeking frequency across all communities ( r = 0.500, p < 0.001), as well as for people with HIV ( r = 0.433, p < 0.001) and key populations (r = 0.517, p < 0.001). Additionally, interest significantly correlated with information-seeking frequency across all communities ( r = 0.321, p < 0.001), with slightly stronger correlations among people with HIV ( r = 0.336, p < 0.001) than key populations ( r = 0.228, p < 0.001). Table 2 Pearson correlations for engagement variables. Table 2 1 2 3 All affected communities People with HIV Key populations All affected communities People with HIV Key populations All affected communities People with HIV Key populations 1. Awareness – – – 2. Interest 0.060* 0.006 0.027 – – – 3. Information-seeking frequency 0.500*** 0.433*** 0.517*** 0.321*** 0.336*** 0.228*** – – – p <.05 ∗∗p < .01 ∗∗∗p<0.001 Awareness among affected communities was significantly higher in older participants ( B = 0.007, SE = 0.002), non-cisgender men ( B = 0.187, SE = 0.078), and those with greater perceived personal ( B = 0.095, SE = 0.034) and treatment control ( B = 0.078, SE = 0.038) over HIV, as well as higher perceived comprehensibility of HIV ( B = 0.474, SE = 0.043) ( Table 3 ). Table 3 Bivariate and multivariate regression analysis of the level of HIV cure awareness, interest, and information seeking among all affected communities. Table 3 Awareness Interest Information seeking Bivariate Analyses Multivariate analysis (N = 993) Bivariate Analyses Multivariate analysis (N = 993) Bivariate Analyses Multivariate analysis Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Participant characteristics HIV-status Ref. People with HIV −0.389 (0.059)∗∗∗ 0.010 (0.077) −0.378 (0.051)∗∗∗ −0.432 (0.063)∗∗∗ −0.502 (0.057)∗∗∗ −0.357 (0.074)∗∗∗ Age (in years) 0.012 (0.002)∗∗∗ 0.007 (0.002)∗∗ −0.001 (0.002) 0.007 (0.002)∗∗∗ 0.005 (0.002)∗ Gender Ref. cisgender male 0.189 (0.084)∗ 0.187 (0.078)∗ 0.150 (0.072)∗ 0.096 (0.068) 0.242 (0.082)∗∗ 0.014 (0.080) Bachelor's degree Ref. No 0.064 (0.063) −0.005 (0.054) −0.124 (0.062)∗ −0.157 (0.061)∗ Migration background Ref. No −0.011 (0.040) 0.147 (0.034)∗∗∗ 0.089 (0.034)∗∗ 0.170 (0.039)∗∗∗ 0.123 (0.039)∗∗ Sexual identity: Ref: gay/lesbian −0.044 (0.039) −0.040 (0.034) 0.083 (0.038)∗ 0.055 (0.038) Partner(s): Ref. No 0.196 (0.060)∗∗ 0.098 (0.058) 0.135 (0.052)∗∗ 0.124 (0.050)∗ 0.145 (0.059)∗ 0.118 (0.057)∗ HIV-related illness perceptions Consequence −0.081 (0.031)∗∗ −0.032 (0.038) 0.125 (0.026)∗∗∗ −0.008 (0.033) 0.087 (0.030)∗∗ −0.032 (0.037) Personal control 0.164 (0.033)∗∗∗ 0.095 (0.034)∗∗ −0.063 (0.029)∗ −0.002 (0.030) 0.018 (0.033) Identity −0.143 (0.031)∗∗∗ −0.065 (0.034) −0.044 (0.027) + −0.022 (0.030) −0.102 (0.030)∗∗∗ −0.080 (0.034)∗ Treatment control 0.267 (0.036)∗∗∗ 0.078 (0.038)∗ 0.094 (0.032)∗∗ 0.033 (0.033) 0.137 (0.036)∗∗∗ 0.009 (0.037) Concern −0.141 (0.030)∗∗∗ −0.008 (0.040) 0.139 (0.026)∗∗∗ 0.150 (0.035)∗∗∗ 0.110 (0.029)∗∗∗ 0.190 (0.040)∗∗∗ Emotions −0.081 (0.028)∗∗ 0.033 (0.035) 0.154 (0.024)∗∗∗ 0.125 (0.031)∗∗∗ 0.102 (0.028)∗∗∗ 0.090 (0.035)∗ Comprehensibility 0.544 (0.038)∗∗∗ 0.474 (0.043)∗∗∗ 0.192 (0.035)∗∗∗ 0.116 (0.038)∗∗ 0.331 (0.040)∗∗∗ 0.292 (0.043)∗∗∗ p < 0.10 + , p < 0.05∗, p < 0.01∗∗p < 0.001∗∗∗. Note. Since participants were not required to answer all questions (except for informed consent and bot detection), missing data were handled through listwise deletion resulting in varying participant numbers per outcome. Bold means statistically significant in the multivariate model. Awareness R 2 = 0.205; Interest R 2 = 0.160; Interest R 2 = 0.181. For people with HIV, greater awareness correlated with higher perceived personal control ( B = 0.157, SE = 0.044), increased HIV-related comprehensibility ( B = 0.440, SE = 0.052), and better self-reported adherence ( B = 0.149, SE = 0.054) ( Table 4 ). Among key populations, greater awareness was associated with older age ( B = 0.008, SE = 0.003), increased perceived comprehensibility ( B = 0.440, SE = 0.049), and higher treatment control ( B = 0.154, SE = 0.049). Key populations who tested more regularly ( B = −0.051, SE = 0.022) and reported fewer concerns about HIV ( B = −0.091, SE = 0.042) also showed greater awareness ( Table 5 ). Table 4 Bivariate and multivariate regression analysis of HIV cure awareness, interest and information seeking among people with HIV. Table 4 Awareness Interest Information seeking Bivariate Analyses Multivariate analysis (N = 486) Bivariate Analyses Multivariate analysis (N = 474) Bivariate Analyses Multivariate analysis (N = 478) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Participant characteristics Age (in years) 0.002 (0.003) −0.007 (0.003)∗ 0.001 (0.003) −0.001 (0.004) Gender Ref. cisgender male 0.174 (0.110) 0.043 (0.096) 0.169 (0.117) Bachelor's degree Ref. No 0.009 (0.085) −0.040 (0.075) −0.073 (0.091) Migration background Ref. No −0.081 (0.057) 0.154 (0.049)∗∗ 0.077 (0.053) 0.151 (0.060)∗ 0.096 (0.060) Sexual identity: Ref: gay/lesbian 0.019 (0.061) 0.052 (0.053) 0.218 (0.064)∗∗∗ 0.190 (0.063)∗∗ Partner(s): Ref. No 0.095 (0.085) 0.061 (0.075) 0.133 (0.091) Years of diagnosis 0.003 (0.005) 0.013 (0.004)∗∗ 0.009 (0.005) 0.014 (0.005)∗∗ 0.007 (0.005) On treatment Ref. Yes 1.053 (0.543) 0.800 (0.475) 1.407 (0.577)∗ 1.079 (0.542)∗ Side effects −0.023 (0.037) 0.049 (0.032) 0.044 (0.040) Adherence 0.152 (0.057)∗∗ 0.149 (0.054)∗∗ 0.064 (0.050) 0.036 (0.061) Viral Load Ref. undetectable −0.002 (0.316) −0.014 (0.275) −0.024 (0.337) CD4-count Ref. Less than 500 cells/mm 3 −0.076 (0.066) 0.049 (0.058) −0.090 (0.071) AIDS Ref. Yes −0.042 (0.097) 0.073 (0.085) 0.135 (0.103) Disclosure Ref. To someone −0.417 (0.182)∗ −0.241 (0.173) 0.022 (0.160) −0.334 (0.194) Participation in Medical trial Ref. Yes −0.222 (0.084)∗∗ −0.148 (0.080) 0.145 (0.073)∗ 0.023 (0.076) −0.034 (0.090) HIV-related illness perceptions Consequence −0.056 (0.042) 0.181 (0.036)∗∗∗ 0.095 (0.051) 0.109 (0.045)∗ −0.018 (0.59) Personal control −0.218 (0.052)∗ 0.157 (0.044)∗∗∗ −0.021 (0.036) 0.073 (0.044) + 0.096 (0.047)∗ Identity −0.088 (0.043)∗ −0.039 (0.049) 0.102 (0.038)∗∗ −0.023 (0.047) 0.062 (0.046) Treatment control 0.099 (0.040)∗∗∗ 0.049 (0.062) −0.078 (0.053) −0.062 (0.065) Concern −0.090 (0.044)∗ 0.063 (0.053) 0.226 (0.037)∗∗∗ 0.102 (0.050)∗ 0.227 (0.046)∗∗∗ 0.244 (0.060)∗∗∗ Emotions −0.078 (0.040) + −0.007 (0.049) 0.206 (0.034)∗∗∗ 0.093 (0.050) 0.150 (0.043)∗∗∗ 0.049 (0.057) Comprehensibility 0.494 (0.059)∗∗∗ 0.440 (0.062)∗∗∗ 0.019 (0.055) 0.289 (0.065)∗∗∗ 0.336 (0.065)∗∗∗ p < 0.10 + , p < 0.05∗, p < 0.01∗∗p < 0.001∗∗∗. Note. Since participants were not required to answer all questions (except for informed consent and bot detection), missing data were handled through listwise deletion resulting in varying participant numbers per outcome. Bold means statistically significant in the multivariate model. Awareness R 2 = 0.174; Interest R 2 = 0.111; Interest R 2 = 0.153. Table 5 Bivariate and multivariate regression analysis of HIV cure awareness, interest and information seeking among key populations without HIV. Table 5 Awareness Interest Information seeking Bivariate Analyses Multivariate analysis (N = 534) Bivariate Analyses Multivariate analysis (N = 553) Bivariate Analyses Multivariate analysis (N = 526) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Unstandardized Beta (SE) Participant characteristics Age (in years) 0.011 (0.003)∗∗∗ 0.008 (0.003)∗ −0.008 (0.003)∗∗ −0.006 (0.003)∗ 0.000 (0.003) Gender Ref. cisgender male 0.090 (0.124) 0.156 (0.103) 0.172 (0.109) Bachelor's degree Ref. No 0.140 (0.091) 0.136 (0.075) + 0.033 (0.076) −0.039 (0.080) Migration background Ref. No 0.060 (0.055) 0.155 (0.045)∗∗∗ 0.087 (0.046) 0.205 (0.048)∗∗∗ 0.131 (0.049)∗∗ Sexual identity: Ref: gay/lesbian −0.078 (0.050) −0.097 (0.042)∗ −0.089 (0.041)∗ 0.006 (0.044) Partner(s): Ref. No 0.230 (0.083)∗∗ 0.089 (0.080) 0.123 (0.069) + 0.164 (0.069)∗ 0.086 (0.073) PrEP Ref. Yes −0.350 (0.085)∗∗∗ −0.066 (0.104) −0.262 (0.071)∗∗∗ −0.178 (0.090)∗ −0.237 (0.075)∗∗ −0.080 (0.096) HIV testing Ref. every 3 months −0.078 (0.018)∗∗∗ −0.051 (0.022)∗ −0.045 (0.015)∗∗ 0.000 (0.019) −0.060 (0.016)∗∗∗ −0.027 (0.020) HIV-related illness perceptions Consequence −0.067 (0.043) 0.115 (0.036)∗∗ −0.102 (0.046)∗ 0.119 (0.038)∗∗ −0.023 (0.049) Personal control 0.159 (0.054)∗∗ −0.015 (0.053) −0.064 (0.045) 0.021 (0.048) Identity −0.049 (0.054) −0.017 (0.044) −0.053 (0.047) Treatment control 0.248 (0.048)∗∗∗ 0.154 (0.049)∗∗ 0.084 (0.041)∗ 0.038 (0.042) 0.112 (0.043)∗ 0.081 (0.044) Concern −0.085 (0.044) + −0.091 (0.042)∗ 0.222 (0.036)∗∗∗ 0.178 (0.051)∗∗∗ 0.199 (0.038)∗∗∗ 0.120 (0.054)∗ Emotions −0.023 (0.040) 0.185 (0.032)∗∗∗ 0.104 (0.040)∗∗ 0.155 (0.034)∗∗∗ 0.075 (0.042) Comprehensibility 0.527 (0.56)∗∗∗ 0.440 (0.058)∗∗∗ 0.202 (0.049)∗∗∗ 0.132 (0.052)∗ 0.211 (0.052)∗∗∗ 0.131 (0.054)∗ p < 0.10 + , p < 0.05∗, p < 0.01∗∗p < 0.001∗∗∗. Note. Since participants were not required to answer all questions (except for informed consent and bot detection), missing data were handled through listwise deletion resulting in varying participant numbers per outcome. Bold means statistically significant in the multivariate model. Awareness R 2 = 0.196; Interest R 2 = 0.142; Interest R 2 = 0.099. Interest in HIV cure research among affected communities was significantly higher among people with HIV ( B = −0.432, SE = 0.063), those with a migration background ( B = 0.089, SE = 0.034), and individuals with steady partners ( B = 0.124, SE = 0.050). Participants expressing greater concerns ( B = 0.150, SE = 0.035), negative emotions about HIV ( B = 0.125, SE = 0.031), and a higher comprehensibility of HIV ( B = 0.116, SE = 0.038) also showed increased interest ( Table 3 ). Among people with HIV, only those with greater concerns ( B = 0.102, SE = 0.050) demonstrated significantly higher interest ( Table 4 ). In key populations, younger individuals ( B = −0.006, SE = 0.003), gay or lesbian individuals ( B = −0.089, SE = 0.041), those without partners ( B = 0.164, SE = 0.069), and those on PrEP ( B = −0.178, SE = 0.090) exhibited greater interest. Additionally, key populations who perceived less consequence from HIV ( B = −0.102, SE = 0.051), had more concerns ( B = 0.178, SE = 0.051), and higher HIV-related comprehensibility ( B = 0.132, SE = 0.052) were more interested ( Table 5 ). Among affected communities, information-seeking frequency was significantly higher among older ( B = 0.005, SE = 0.002), people with HIV ( B = −0.357, SE = 0.074), individuals without a bachelor's degree ( B = −0.078, SE = 0.061), those with a migration background ( B = 0.096, SE = 0.039), and those with one or more partners ( β = 0.080, SE = 0.057). It was also associated with a stronger HIV identity ( B = −0.082, SE = 0.034), greater concerns ( B = 0.190, SE = 0.040), negative emotions ( B = 0.090, SE = 0.035), and increased HIV comprehensibility ( B = 0.292, SE = 0.043) ( Table 3 ). For people with HIV, information-seeking was notably higher among non-gay/lesbian participants ( B = 0.190, SE = 0.063), those with more concerns ( B = 0.244, SE = 0.060), higher perceived personal control ( B = 0.096, SE = 0.047), and greater HIV comprehensibility ( B = 0.336, SE = 0.065). Participants not on treatment ( B = 1.079, SE = 0.542) were also more likely to seek information about HIV cure research ( Table 4 ). In key populations, greater information-seeking was found among individuals with a migration background ( B = 0.131, SE = 0.049), those expressing more concerns ( B = 0.120, SE = 0.054), and those with higher perceived comprehensibility ( B = 0.131, SE = 0.054) ( Table 5 ). This research is one of the first to assess the non-hypothetical engagement of affected communities in HIV cure research by evaluating their awareness, interest, and information-seeking behavior, inspired by the early stages mapped out in the (PAPM). 11 We also investigated which affected communities, participant characteristics, and HIV-related illness perceptions were associated with these three stages. Lastly, we examined if associated characteristics differed between the two groups of affected communities included in this research: people with HIV and key populations. Our findings indicate that engagement with HIV cure research in affected communities is largely passive, characterized by low active information-seeking despite notable interest levels. Correlations between awareness and interest were weak across all communities and not significant among people with HIV and key populations, challenging the PAPM's assumption that greater awareness leads to higher interest. 10 , 11 However, higher interest was associated with more frequent information-seeking, indicating that survey participation may have increased awareness and stimulated interest in HIV cure research. Alternatively, the lack of a clear definition of HIV cure research may have led to uncertainty or underconfidence in participants' self-reported awareness, consistent with previous research in the Netherlands that highlighted uncertainty between general awareness and detailed understanding. 24 , 27 Future research should assess awareness of specific HIV cure developments for clearer insights. People with HIV showed greater engagement across all stages compared to key populations, but HIV status did not significantly influence awareness. Instead, awareness was linked to individuals' perceived comprehensibility and control over HIV, which also influenced their interest and information-seeking frequency. This may be due to individuals who perceive higher comprehensibility and control over HIV having a stronger sense of agency and a desire to manage their health, prompting them to seek and retain more information about ongoing advancements in HIV cure research. 28 Awareness of HIV cure research was also influenced by age and gender; however, older individuals and women did not show a corresponding increase in interest. This discrepancy may stem from their greater involvement in community settings, 2 , 29 which enhances overall knowledge, including HIV cure awareness. For instance, the women's group 'Posidivas' has recently gained attention, 30 and since 2022, the Hiv Vereniging has been chaired by a cisgender woman, 31 fostering engagement and knowledge among women. Similarly, the ‘Long Term Survivors’, those diagnosed before 1996 and an active group within the community, 32 likely keeps members informed. This aligns with research showing that HIV activists are often more willing to participate in cure trials. 15 Further research should explore how community connectedness relates to HIV cure awareness and engagement. Subgroup analyses indicated that ART adherence among people with HIV and frequent testing among key populations were linked to greater awareness. This suggests that individuals who manage their health, 28 and engage regularly with healthcare professionals are more aware, a finding supported by studies highlighting the critical role of healthcare professionals in promoting HIV cure engagement. 20 , 33 , 34 , 35 In contrast to awareness, both interest and information-seeking frequency were significantly higher among people with HIV. Individuals with greater concerns and negative emotions about HIV, a migration background, or steady relationships also demonstrated increased interest and information-seeking, suggesting that a heightened perceived need for an HIV cure drives these behaviors. People with HIV likely feel a stronger need for a cure due to its significant impact on their lives, 24 , 36 as do those with heightened concerns and negative emotions. 24 Individuals from migration backgrounds may face increased stigma, 37 leading to poorer psychosocial and clinical outcomes, 38 , 39 which heightens their perceived need for a cure. 24 For those in steady relationships, a cure may be desired as it may alleviate transmission concerns. 23 , 36 Future research should explore how perceived needs influence interest and information-seeking behavior. Additionally, interest levels among key populations were influenced by younger age, identifying as gay, and PrEP use. Previous research suggests that the potential for an HIV cure to enhance sexual freedom, by eliminating transmission risk and reducing HIV-related anxieties, may drive this interest. 23 , 36 Further investigation is needed to assess the extent to which the prospect of increased sexual freedom impacts interest in HIV cure research, including potential concerns about the risk of reinfection post-cure. Our study is among the first to evaluate non-hypothetical engagement in HIV cure research across diverse affected communities. However, several limitations should be noted. Although we highlighted the importance of HIV-related illness perceptions, our measurement tool showed inconsistent factor loadings and low internal consistency, likely due to high participant diversity and varied responses. For example, identity emerged as a distinct factor for key populations, rather than being part of the perceived impact factor, as it was for people with HIV. This may be because key populations have indirect experiences with HIV, leading to more varied and potentially unrealistic or threatening perceptions. 23 , 41 Future studies should investigate how HIV-related illness perceptions form and the factors that shape them to refine the adapted Brief IPQ for both people with HIV and key populations. Despite our sample's diversity, we may not have fully captured the range of experiences within the people with HIV and key populations in the Netherlands, particularly because partners of people with HIV were notably underrepresented. This limits our ability to draw specific conclusions about their perspectives, which likely differed from GBMSM. 24 Future research should address this gap. Although we received input from advisory boards and used inclusive materials like information videos, the abstract nature of HIV cure research may have hindered recruitment, especially among individuals without bachelor's degrees. Future studies should consider assisting underserved communities in completing surveys to ensure more diverse participation. Additionally, while the Dutch context may resonate with experiences in other high-income countries, these findings may not be applicable in settings lacking access to ART or PrEP. Future research should explore these dynamics in diverse global contexts to enhance the broader applicability of the findings. In conclusion, while moderate awareness exists—driven by knowledge, community ties, and healthcare interactions—engagement remains passive, with limited information-seeking. However, significant interest, fueled by the perceived need for a cure, is evident. To foster the active and meaningful engagement advocated by the IAS HIV cure agenda, HIV communities, researchers, and healthcare providers must enhance communication efforts to make information on HIV cure development more accessible, especially for those not connected to community organizations. | Review | biomedical | en | 0.999996 |
PMC11697765 | Pubertal development is controlled by the hypothalamic pituitary gonadal (HPG) axis . The HPG axis initiates development of the reproductive system at mid-gestation, until after the first year when it becomes inactive . The HPG axis is then reactivated when pubertal maturation begins . This timetable of changes suggests possible prenatal intervention in the reawakening of the HPG axis during puberty. The pubertal development process may be affected by environmental stimuli and cellular metabolites including those of sex hormones . In animals, growth and age of pubertal onset can be manipulated in ways that include changing diets and injecting hormones during the prenatal period. Evidence from such studies suggests that fetal nutrient and/or hormonal exposures may affect fetal development. For example, there is an increased risk of fetal growth restriction (FGR) in suboptimal environments , such as those with poor maternal nutrition. This may lead to low birth weight and small-for-gestational age (SGA). If there is catch-up growth (reflecting increased growth rate after a period of growth delay during early childhood after such exposures, the growth and its related metabolic alterations may expedite the reactivation of the HPG axis and pubertal onset . Several nutrients such as fatty acids, iron, iodine, zinc, and B-vitamins play important roles in nerve tissue functions and metabolic processes , which may affect the activity of the HPG axis and hence, pubertal development. The brain is lipid rich with its structural components dominated by long-chain PUFA such as DHA . Therefore, insufficient intake of the essential fatty acids alpha-linolenic acid and linoleic acid, which can be converted into DHA and arachidonic acid, respectively, may contribute to impaired brain function . Prenatal malnutrition may alter the expected timeline and function of the HPG axis, and consequently shift pubertal development. In low-income settings, cereals and tubers are common staples consumed by pregnant and lactating females; these staples are of low nutrient quality . Multiple micronutrient (MMN) deficiencies, associated with consumption of diets dominated by these foods, are associated with poor maternal and child outcomes such as FGR and poor growth and development among children [ , , ]. The International Lipid-based Nutrient Supplements (iLiNS) Project team developed small-quantity lipid-based nutrient supplements (SQ-LNS) (20 g/d) for enriching the usual diets of pregnant and lactating females and infants with micronutrients, essential fatty acids, and small amounts of energy (118 kcal/d) and high-quality protein (2.6 g/d) during the first 1000 d. At least 16 trials have been conducted in 10 countries in which versions of SQ-LNS were provided either to infants only during the period 6–23 mo of age [ , , , , , , , , , , , ], or to both mothers and infants [ , , , ] during pregnancy, lactation, and infancy. In meta-analyses [ , , ], the provision of SQ-LNS to infants aged 6–24 mo was associated with positive anthropometric outcomes, including a reduction in the prevalence of stunting/severe stunting, wasting/severe wasting, and underweight. There was a lower prevalence of anemia, when compared with the provision of no intervention. Early pubertal timing has been associated with development of metabolic syndrome and cancers of the reproductive system in later life, whereas late pubertal timing has been linked to psychosocial difficulties . In Sub-Saharan Africa, previous studies have shown associations of stunting and underweight with later pubertal development [ , , ]. On the other hand, there is the increasing prevalence of earlier onset of puberty over the past 2–3 decades, parallel to the global increasing prevalence of obesity. In Ghana, prior studies have shown a decline in age at menarche by 9–15 mo with obesity and high socioeconomic status as predictive factors among urban schoolgirls . These suggest that nutritional factors and their corresponding endogenous metabolic and hormonal signals play relevant roles in pubertal development , which is consistent with studies showing positive associations of anthropometric measurements with pubertal development . In the iLiNS-DYAD trial in Ghana, we demonstrated that SQ-LNS provided during the first 1000 d positively impacted birth size as well as subsequent growth and socioemotional outcomes. In this study, we assessed whether there were intervention group differences in pubertal status at 9–11 and 11–13 y. We hypothesized that the SQ-LNS group would be more advanced in pubertal development than the non-LNS group at these ages . We also tested whether sex, household asset index, or birth order moderated the effect of the intervention on pubertal stage. We did not set a priori hypotheses for effect modification. Finally, we investigated whether the effect of the intervention on pubertal stage was mediated by birth weight for gestational age z-score or BMI z-score (BMIZ) at 4–6, 9–11, and 11–13 y. The iLiNS-DYAD Ghana study was a randomized, partially double-blind, controlled trial that compared 3 nutrient supplements. The trial was conducted in peri-urban settlements in the Yilo Krobo and Lower Manya Krobo districts of the Eastern region of Ghana, ∼70 km north of Accra (the capital of Ghana). Primary caregiver written informed consent was obtained prior to data collection. Each caregiver was visited at home and visited our project office, whereas we collected data on sociodemographic information using interviewer-administered questionnaires. Details of the trial have been previously reported elsewhere and are summarized herein. In the main iLiNS-DYAD randomized controlled trial (RCT), females were eligible to participate if they were ≥18 y of age and ≤20 wk gestation. Females were excluded for the following reasons: antenatal card indicated HIV infection, asthma, epilepsy, tuberculosis, or any malignancy; known milk or peanut allergy; not residing in the study area; intention to relocate within the next 2 y; unwillingness to consent to participate, receive home visits from fieldworkers or take the study supplement; and participation in another trial. Pregnant females attending antenatal clinics in 4 main health facilities in the study area were recruited from December 2009 to December 2011. Eligible females were visited at home and those who met the inclusion criteria and provided informed consent were scheduled for a clinic visit for baseline assessments. Enrolled females were randomly assigned to 1 of 3 supplementation groups ( Table 1 ) : 1 ) daily 60 mg iron and 400 μg folic acid (IFA) during pregnancy, and 200 mg calcium (Ca) only during the first 6 mo postpartum, with no supplementation for offspring during infancy; 2 ) daily MMN (1–2 Recommended Dietary Allowance of 18 vitamins and minerals) during pregnancy and the first 6 mo postpartum, with no supplementation for offspring during infancy, and 3 ) daily 20 g SQ-LNS during pregnancy and the first 6 mo postpartum (SQ-LNS during pregnancy and lactation contained similar vitamin and mineral content as the daily MMN, plus calcium, phosphorous, potassium, magnesium, and essential fatty acids), with SQ-LNS for offspring (20 g with 22 vitamins and minerals with concentrations based on Recommended Nutrient Intakes for infants) from 6 to 18 mo of age. Participants were aware whether they received SQ-LNS or a capsule but were blind to whether they received IFA or MMN. Field workers who assessed outcomes and collected other data were blind to the intervention groups. TABLE 1 Nutrient and energy contents of supplements used in the iLiNS trial in Ghana. TABLE 1 Ration per day IFA MMN Maternal SQ-LNS Child SQ-LNS 1 capsule 1 capsule 20-g sachet 20-g sachet Total energy (kcal) 0 0 118 118 Protein (g) 0 0 2.6 2.6 Fat (g) 0 0 10 9.6 Linoleic acid (g) 0 0 4.59 4.46 α-Linolenic acid, (g) 0 0 0.59 0.58 Vitamin A (μg RE) 0 800 800 400 Vitamin C (mg) 0 100 100 30 Vitamin B-1 (mg) 0 2.8 2.8 0.3 Vitamin B-2 (mg) 0 2.8 2.8 0.4 Niacin (mg) 0 36 36 4 Folic acid (μg) 400 400 400 80 Pantothenic acid (mg) 0 7 7 1.8 Vitamin B-6 (mg) 0 3.8 3.8 0.3 Vitamin B-12 (μg) 0 5.2 5.2 0.5 Vitamin D (mg) 0 10 10 5 Vitamin E (mg) 0 20 20 6 Vitamin K (μg) 0 45 45 30 Iron (mg) 60 20 20 6 Zinc (mg) 0 30 30 8 Copper (mg) 0 4 4 0.34 Calcium (mg) 0 0 280 280 Phosphorus (mg) 0 0 190 190 Potassium (mg) 0 0 200 200 Magnesium (mg) 0 0 65 40 Selenium (μg) 0 130 130 20 Iodine (μg) 0 250 250 90 Manganese (mg) 0 2.6 2.6 1.2 Abbreviations: IFA, iron and folic acid capsule; iLiNS, international lipid-based nutrient supplement; MMN, multiple micronutrient supplement capsule; SQ-LNS, small-quantity lipid-based nutrient supplement. Information from table previously published . Using participants’ addresses and telephone contacts from the original iLiNS-DYAD-Ghana trial, caregivers with surviving children were traced and invited to participate in this study. We obtained ethical approval for the current follow-up study from the Institutional Review Board of the University of California Davis and the Ghana Health Service Ethical Review Committee . Child written informed assent was obtained prior to data collection. Each child visited our project office while we collected data on pubertal stage using interviewer-administered questionnaires. At the 4–6-y follow-up, weight and height measurements were taken by trained anthropometrists as reported previously , and these measurements were repeated at 9–11 and 11–13 y. Pubertal status was assessed at 9–11 y and 11–13 y of age. Our targeted enrollment of 966 provided 80% power to detect a small effect size, that is, a mean difference between 2 groups of >0.2 SD for continuous outcomes at a significance level of P < 0.05. Participants responded to each item on the Petersen Pubertal Development Scale (PDS) score using a 4-point Likert scale that described the stage of 5 pubertal milestones each, for boys and girls. On the scale, 1 was defined as pubertal development not yet begun, 2 as barely started, 3 as definitely underway, and 4 as completed. In girls, we determined PDS score by self-report of breast development, menarche, growth spurt, skin changes, and body hair. In boys, we determined pubertal development by self-report of voice break, facial hair, growth spurt, skin changes, and body hair. A total score ranged from 5 to 20. We conducted a pilot study of 30 caregiver-child dyads in our study sample to assess test-retest reliability of the PDS score. Our pilot study showed that the 1-wk test-retest reliability ( N = 30; child report: r = 0.72; caregiver report: r = 0.70) of the PDS was good . We selected the child report, which had a higher test-retest reliability score, for assessing pubertal status. In addition, we adjusted the PDS score by age in a regression model, computed residuals and standardized to calculate age-adjusted PDS z-score (aPDSZ) as our outcome. We posted a statistical analysis plan to Open Science Framework ( https://osf.io/7j368 ) before conducting analyses. All analyses were performed using R version 4.3.1 . All tests were 2-sided, at 95% confidence interval (CI) and a 5% level of significance following a complete case intention-to-treat framework. We planned to compare SQ-LNS compared with non-LNS (IFA + MMN) groups. Nevertheless, we first performed a sensitivity analysis comparing the IFA and MMN groups to confirm that combining the groups was reasonable. We examined whether children in the SQ-LNS and non-LNS groups were similar in baseline characteristics using descriptive statistics for each intervention group. To evaluate potential bias in the sample, we compared baseline characteristics between the sample included in the analyses and the sample enrolled in the main trial but lost to follow-up, using t tests for continuous variables and χ 2 tests for categorical variables. To test our hypothesis that the SQ-LNS group was more advanced in pubertal stage than the non-LNS group at 9–11 and 11–13 y of age, we examined the difference between the SQ-LNS and non-LNS groups using analysis of covariance (ANCOVA) in minimally adjusted (with child sex) and covariate adjusted models. The prespecified covariates listed above were tested for associations with the aPDSZ. Significant ( P < 0.10) covariates were included in the adjusted ANCOVA model. Prespecified covariates were identified from previous studies and included sociodemographic background characteristics at baseline: maternal age, years of maternal education, maternal marital status, household asset index, household food insecurity, household improved water, household toilet facility, maternal prepregnancy BMI (kg/m 2 ), maternal height, maternal hemoglobin concentration, season of birth, birth order, and parity. In exploratory analyses, we tested potential effect modification by 3 prespecified variables: baseline household asset index, child sex, and birth order. We tested the interaction between each potential effect modifier and intervention groups in ANCOVA models. Significant interactions ( P < 0.10) were further examined with stratified analyses and regions of significance in stratified analyses . We tested potential mediation of exposure effects by: 1 ) birth weight measured in grams and used to calculate birth weight for gestational age z-score, based on INTERGROWTH norms, to account for variations in birth weight by gestational age and child sex; 2 ) childhood nutritional status indexed by BMIZ at ages 4–6, 9–11, and 11–13 y, standardized using WHO norms. Potential mediators that were associated with SQ-LNS or aPDSZ at P < 0.10 were included in causal mediation analyses using the mediation package in R . Out of 1320 mothers enrolled during the main trial, 1228 (93%) children were born live. A total of 1217 children were eligible to participate in the follow-up study at 9–11 y; 966 children participated in the pubertal development assessment including 635 in the non-LNS group and 331 in the SQ-LNS group. At 11–13 y, 919 children participated in the pubertal development assessment including 608 in the non-LNS group and 311 in the SQ-LNS group . The 966 and 919 included in the analysis were similar to the 354 and 401 enrolled in the main trial, respectively, but lost to follow-up regarding most background characteristics, such as baseline maternal education, food security, and BMI ( Supplemental Table 1 ). FIGURE 1 Flowchart of child’s eligibility, enrollment and data collection. FIGURE 1 Sensitivity analysis showed no difference between IFA and MMN groups ( P > 0.10) at both 9–11-y and 11–13-y follow-up; hence, we proceeded to analyze by a 2-group comparison, SQ-LNS and non-LNS groups (IFA+MMN). Table 2 presents selected maternal and child characteristics. The 2 groups generally did not differ significantly in the 11 maternal baseline characteristics presented except for baseline household asset index z-score. At 9–11 y ( P = 0.019) and 11–13 y ( P = 0.019), the SQ-LNS group had lower asset index z-score than the non-LNS group. TABLE 2 Selected maternal and child characteristics at 9–11-y follow-up. TABLE 2 Variable SQ-LNS ( n = 331) Mean (SD) or % ( n /total) Non-LNS ( n = 635) Mean (SD) or % ( n /total) Maternal characteristics at baseline Age (y) 27.0 (5.5) 26.8 (5.4) Education (y) 7.6 (3.8) 7.7 (3.6) Married or cohabiting, % ( n / N ) 91.8 (304/331) 94.0 (596/634) Household asset index z-score 1 –0.07 (0.97) 0.09 (0.97) Household food secure, % ( n / N ) 60.3 (199/330) 56.98 (359/630) Household improved water source, % ( n / N ) 99.1 (328/331) 98.1 (621/633) Household toilet facility, % ( n / N ) 97.6 (322/330) 97.6 (618/633) Height (cm) 159.1 (5.4) 158.8 (5.9) Prepregnancy BMI 2 (kg/m 2 ) 24.9 (4.5) 24.3 (4.4) Hemoglobin concentration (g/L) 111.5 (11.2) 111.4 (12.6) Primiparous, % ( n / N ) 33.7 (102/303) 31.7 (201/635) Child characteristics Dry season 3 , % ( n / N ) 50.5 (167/331) 48.9 (310/634) First born, % ( n / N ) 30.8 (102/331) 31.7 (201/634) Females, % ( n / N ) 51.7 (171/331) 51.7 (328/634) Child age at 4–6-y follow-up (y) 4.9 (0.6) 4.9 (0.5) Child age at 9–11-y follow-up (y) 9.9 (0.5) 9.9 (0.5) Birth weight for gestational age z-score –0.41 (0.97) –0.61 (0.95) Child BMIZ at 4–6 (y) –0.55 (0.81) –0.58 (0.81) Child BMIZ at 9–11 (y) –0.43 (1.18) –0.50 (1.16) PDS score 7.5 (1.1) 7.5 (1.1) Abbreviations: non-LNS, iron and folic acid + multiple micronutrient groups; PDS, Petersen Pubertal Development Scale; SQ-LNS, small-quantity lipid-based nutrient supplement. 1 Proxy indicator for household socioeconomic status constructed for each household based on ownership of a set of assets (radio, television, etc.), lighting source, drinking water supply, sanitation facilities, and flooring materials. Household ownership of this set of assets is combined into an index (with a mean of 0 and SD of 1) using principal components analysis. Higher value represents higher socioeconomic status. 2 Estimated prepregnancy BMI was calculated from estimated prepregnancy weight (based on polynomial regression with gestational age, gestational age squared, and gestational age cubed as predictors) and height at enrollment. 3 Born during the dry season. At 9–11 y, SQ-LNS (mean ± SD) did not impact pubertal status in the minimally adjusted (0.01 ± 0.95 compared with non-LNS: −0.01 ± 0.98, P = 0.748) or fully adjusted [mean difference (SE): 0.00 (0.06), P = 0.958] models. At 11–13 y, the SQ-LNS group was marginally more advanced in the minimally adjusted model (mean ± SD = 0.07 ± 1.04 compared with non-LNS: –0.04 ± 0.98, P = 0.078), and the group difference was significant in the fully adjusted model [mean difference (SE): 0.13 (0.07), P = 0.049]. At 9–11 y of age, sex and household asset index z-score did not modify the effect of SQ-LNS on pubertal development ( P -interaction > 0.10). At 11–13 y of age, the effect of SQ-LNS on pubertal stage differed by sex and household asset index z-score . More advanced pubertal stage was observed in the SQ-LNS compared with non-LNS group among females (mean difference = 0.30 SD; 95% CI: 0.08, 0.52; P = 0.007) but not among males ( P = 0.167). More advanced pubertal stage was found for SQ-LNS compared with non-LNS groups in the first asset tertile (mean difference = 0.36 SD; 95% CI: 0.13, 0.60; P = 0.002) but not in the second tertile ( P = 0.436) or highest asset tertile ( P = 0.332) subgroups of household asset index z-score. Birth order did not modify the impact of SQ-LNS on pubertal stage at either age ( P -interaction > 0.10). FIGURE 2 SQ-LNS effect modification by sex at 11–13-y follow-up. aPDSZ, age-adjusted pubertal development scale z-score; non-LNS, iron and folic acid + multiple micronutrient groups; SQ-LNS, small-quantity lipid-based nutrient supplement. FIGURE 2 FIGURE 3 SQ-LNS effect modification by asset index z-score by tertile groups at 11–13-y follow-up. aPDSZ, age-adjusted pubertal development scale z-score; non-LNS, iron and folic acid + multiple micronutrient groups; SQ-LNS, small-quantity lipid-based nutrient supplement. FIGURE 3 Mediation analysis showed that SQ-LNS impacted aPDSZ partially through birth weight for gestational age z-score (total effect = 0.12 SD, P = 0.074; direct effect = 0.11 SD, P = 0.098; indirect effect = 0.01 SD, P = 0.096; proportion mediated = 7%, P = 0.150). Child BMIZ at 4–6, 9–11, and 11–13 y were not associated with SQ-LNS intervention group ( P > 0.10) and thus were not found to be mediators. To our knowledge, this is the first RCT to investigate the impact of early life SQ-LNS during the first 1000 d of life on pubertal development. We found that adolescents who received SQ-LNS from 6 to 18 mo of age after maternal SQ-LNS consumption during pregnancy and 6 mo postpartum had greater mean aPDSZ at 11–13 y than those in the non-LNS group who received no direct supplementation after maternal IFA or MMN during pregnancy, and placebo or MMN during 6 mo postpartum. Pubertal development is influenced by an interplay of hormones, central neurotransmitters, and environmental factors including nutrition, that induce the maturation of the HPG axis, although the mechanism of HPG axis reactivation is unknown . Thus, the activity of the endocrine axes may be altered during the first 1000 d of life by inadequate nutrient supplies to the fetus, decreased nutrient bioavailability, and restricted growth, possibly dysregulating the HPG axis and consequently impacting pubertal status. Because SQ-LNS is a nutrient-dense supplement, it may have contributed to the timely and appropriate regulation of the HPG axis during pregnancy and infancy, preparing the child for the reactivation of the HPG axis during puberty when the neural circuitry established during early life and infancy by gonadal steroids becomes functional again. Increase in weight is known to be associated with increases in leptin, insulin, IGF-1, and cortisol levels . Elevated insulin and IGF-1 levels promote growth in lean body mass, muscle mass, and fat mass, triggering a growth spurt . Leptin and IGF-1 also contribute to skeletal development by stimulating bone mineral accretion and bone maturation . Increased leptin also stimulates hormonal activity in the hypothalamus, pituitary, adrenals, and gonads, expediting pubertal development . However, it is unlikely that the observed group difference in mean aPDSZ at age 11–13 y was a result of group differences in obesity, as we found no intervention effect on BMI at 4–6, 9–11, or 11–13 y. Hence, BMI was not a mediator for pubertal development. Although BMI may not be an adequate proxy for fat mass, in this cohort BMI at 11–13 y was strongly correlated with triceps skinfold (rho = 0.80) and mid-upper arm circumference (rho = 0.94), which suggests that there were no intervention group differences in fat mass at 11–13 y, as was the case at 4–6 y and 9–11 y . Regarding effect modification, we found greater effects of SQ-LNS on aPDSZ among females and among adolescents in households with lower asset index z-scores. This agrees with previous findings indicating that the effect of SQ-LNS on social-emotional difficulties at 4–6 y was greater among children from disadvantaged homes , and the effect on height at 9–11 y was greater among females . During infant and child development, males and females show differences in their responses to hormonal changes . Furthermore, reactivation of the HPG axis during adrenarche and gonadarche begins earlier in girls than in boys . Therefore, females may have had greater potential to respond to early life SQ-LNS with regard to pubertal development at this age, compared with males. Because males typically exhibit later pubertal development than females , we may see an effect of SQ-LNS on this outcome among males at older ages, that is, >13 y. Regarding significant effect modification in lower socioeconomic households, it is possible that children with lower asset index scores with higher risk of malnutrition and poor cognitive functions tended to respond better to SQ-LNS compared with children from average and higher socioeconomic households. This hypothesis is supported by a prior individual participant meta-analysis wherein greater effects of SQ-LNS was found on language, motor, and executive function among children in lower socioeconomic households. We did not find birth order to be a significant modifier of SQ-LNS impact on pubertal status in this study, even though effects of SQ-LNS on stunting and underweight were greater in later-born than first-born children in individual participant data meta-analyses . Higher birth weight for gestational age z-score partially mediated the effect of SQ-LNS on aPDSZ. Previous studies have investigated birth and early life influences on puberty using anthropometric indices like birth weight as exposures . Most of these studies have focused on the Tanner stages or a few of the milestones on the PDS , and not the total PDS score. For example, in the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) Study , it was reported that children who weighed between 2500 and <3000 g at birth were ∼7 mo younger at age at take-off of the pubertal growth spurt than children who weighed ≥3000 g. This appears to conflict with our findings showing that SQ-LNS impacts aPDSZ partially through higher birth weight for gestational age. However, it is difficult to compare our findings with those of the DONALD study as we assessed how birth weight for gestational age, not birth weight, is associated with pubertal development. In this trial, we found that maternal SQ-LNS increased birth weight compared with the IFA group and reduced the risk of SGA compared with the MMN group . Therefore, the mediation by birth weight for gestational age observed herein is plausible, although it explained only 7% of the total effect of SQ-LNS on aPDSZ. Our study has several strengths to note. First, this was an RCT, and the SQ-LNS and the non-LNS groups at 9–13 y did not differ significantly in baseline characteristics except for household asset index z-score, suggesting that the intervention groups remained balanced over time. Second, we had large sample sizes with good retention rates at both the 9–11 y (79%) and 11–13 y (75%) follow-up. Third, we used the PDS (for assessing pubertal stage) that has shown high validity in previous research . There are some potential limitations, however. Because we did not assess pubertal development at 7 y, we may have missed cases of precocious puberty in our study cohort. Consequently, we are unable to determine whether the SQ-LNS induced earlier onset of pubertal development than the non-LNS group. Nevertheless, the results show no record of precocious puberty assessed by menarche among females by self-report, and the mean aPDSZ does not suggest an earlier onset of pubertal development. Another limitation is that participants included in these analyses differed significantly in maternal parity ( P < 0.05) compared with those lost to follow-up. However, it is unlikely that this led to bias in the results because parity was not associated with pubertal status. Provision of SQ-LNS in this setting during the first 1000 d of life was associated with more advanced pubertal stage among females at 11–13 y, but not at 9–11 y. Because we did not observe more advanced puberty in the younger age group, the results suggest that the advancement of puberty at 11–13 y was not an adverse outcome. This finding provides additional evidence that SQ-LNS influences both physical and behavioral development. More research is needed to understand the potential impact of early life SQ-LNS exposure on pubertal development among males, and the consequences of more advanced pubertal development among females. | Study | biomedical | en | 0.999997 |
PMC11697769 | The prevalence of ulcerative colitis (UC) is higher in Western countries compared to India. Changing dietary habits and lifestyles are projected to make India one of the countries with the highest disease burden of UC in the world . About 20% to 30% of patients with UC require surgery at some point . Restorative proctocolectomy with ileal pouch-anal anastomosis (IPAA) is the preferred surgical treatment for UC, offering disease-free bowel function, reducing the cancer risk without a permanent stoma, and lowering long-term costs. Nevertheless, IPAA is not free from complications that affect long-term outcomes. Reported early complications are pelvic sepsis, bleeding, and surgical site infection, among others. The long-term complications include pouchitis, pouch-anal anastomosis stricture, pouch vaginal fistulae, and fecal incontinence. Many studies have reported acceptable complication rates, good functional outcomes, and good quality of life (QoL) after IPAA for UC . Most of these studies are from Western countries. There are only a few studies from India on the outcomes of IPAA in UC patients . This study was conducted at a tertiary care referral center in north India to identify the early and late postoperative complications of IPAA for UC and the associated predictors. All patients with UC who underwent IPAA in the Department of Surgical Gastroenterology at Sanjay Gandhi Postgraduate Institute of Medical Sciences between 1995 and 2018 were included in this study. Ethical approval for this study was obtained from the Institutional Bioethics Committee . Information about demographics, clinical parameters, operative details, and early and late postoperative complications were retrieved from a prospectively maintained hospital-based electronic database. Patients undergoing IPAA with diagnoses other than UC, Crohn’s disease, indeterminate colitis, or familial adenomatous polyposis were excluded. The extent of the disease was classified as ulcerative proctitis (E1), left-sided UC (E2), or pancolitis (E3) according to the Montreal Classification . The indications were recorded as elective indications, which comprised failed medical therapy (steroid-refractory UC, steroid-dependent UC), dysplasia, and malignancy. Emergent indications were acute complications such as toxic megacolon, perforation, and acute severe ulcerative colitis (ASUC). ASUC was defined using the Truelove and Wits criteria . The early complications were defined as complications within 30 days following IPAA. Early complications were graded according to the Clavien-Dindo classification . Pelvic sepsis was defined as “Any infective process in the peri pouch area, detected during the investigation of clinical symptoms. This includes all abscess formations associated with or without anastomotic leak or purulent drain output.” Late complications were those that occurred during the follow-up period before or after stoma closure. Late complications included small bowel obstruction, pouchitis (diagnosed endoscopically and/or by histopathology), abscess, anastomotic stricture, and pouch failure. The anastomotic stricture was defined as a clinically significant stricture that requires endoscopic or surgical dilation. Pouch failure was defined as the need to construct a permanent ileostomy with or without excision of the ileal pouch . A subjective scoring system on a scale from 0 to 10 was employed to evaluate patient satisfaction following surgery. Patients with a score of 9-10 were classified as very satisfied, those with a score of 6-8 as satisfied, and those with a score below or equal to 5 as poorly satisfied with surgery. All patients were referred to surgery by a medical gastroenterologist after initial medical management with steroids, immunosuppressants, and biologics. Most patients were tapered off steroids before pouch construction. The two-stage procedure was defined as a proctocolectomy with the creation of the IPAA and covering loop ileostomy as the first stage, followed by the ileostomy closure. The three-stage procedure involves a subtotal colectomy with end ileostomy and distal mucosal fistulae creation during the first stage, followed by restorative proctocolectomy with IPAA and covering loop ileostomy, and, finally, ileostomy closure. The IPAA was performed either by the stapled or handsewn technique. All IPAA procedures were performed using the open technique. SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA) was used for statistical analyses. Continuous variables are presented as mean and standard deviation (SD) or median (interquartile range (IQR)) depending upon normality status, while categorical variables are reported as raw values with percentages. Univariate analysis was performed using Pearson’s chi-square and Fisher’s exact tests. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using univariate logistic regression analysis to assess the relationship between the outcome and potential influencing factors. Variables with a p-value <0.05 in the univariate analysis were selected for multivariate analysis. Statistical significance was set at p-values <0.05. Risk factors for early complications were analyzed using univariate analysis. Various late complications such as pouchitis, anastomotic stricture, and pouch vaginal fistulae were separately analyzed using univariate analysis. A total of 101 patients underwent IPAA for UC. The median age of the patients was 32 years (range = 14-67 years; IQR = 26-44 years). The median preoperative disease duration was 48 months (range = 1.0-260.0 months; IQR = 23.3-118.8 months). The median follow-up period was 64 months (range = 6.0-273.0 months; IQR = 35.5-136.0 months). The median length of hospital stay after IPAA was 12 days (IQR = 9-18 days). The demographic and clinical characteristics of patients are described in Table 1 . The indications and types of surgery are described in Table 1 . A three-staged procedure was performed in 57 (56.4%) patients, while a two-staged procedure was performed in 44 (43.6%) patients. Most patients who underwent the three-stage procedure were steroid-refractory (n = 29, 50.8%) or had acute emergencies (n = 15, 26.3%). The two-stage procedure was primarily performed for steroid-dependent UC (n = 30, 68.1%). J-pouch was formed in 91 (90.1%)patients, while a W-pouch was formed in 10 (9.9%) patients. We performed a subgroup analysis to examine the complications rate between the J-pouch and W-pouch and could not find any significant difference. Most patients underwent IPAA using a double stapling technique (n = 74, 73.2%), while hand-sewn anastomosis was performed in 27 (26.7%) patients. A diverting loop ileostomy was performed for all patients following pouch-anal anastomosis. Three patients underwent a laparoscopic first-stage procedure (subtotal colectomy). The analysis revealed that in the initial five years, the number of pouches was high and decreased afterward, but it remained in a similar range from 2009 onward. The year-wise trend is shown in Figure 1 . In total, 72 (71.3%) patients experienced early postoperative complications (Table 2 ). The majority had Clavien-Dindo grade 1 (n = 27, 26.7%) and grade 2 morbidities (n = 24, 23.7%). Grade 3 and 4 morbidities were encountered in 17 (16.8%) and 3 (2.9%) patients, respectively. The most common complication was pelvic sepsis, observed in 26 (25.7%) patients. Of the 26 patients with pelvic sepsis, 15 had collections in the peri-pouch area, five had demonstrable pouch leaks, and six patients had purulent drain output. Of the 26 patients, seven required percutaneous drainage of collections, three required re-exploration, and the remainder were managed conservatively. Unfortunately, one patient died after IPAA in the immediate postoperative period due to anastomosis leak and sepsis. Overall, 10 (9.9%) patients required re-exploration in the early postoperative period for missed enterotomy (five cases), pelvic sepsis (three cases), and bleeding (two cases). Various risk factors analyzed to see their association with early complications are shown in Table 3 . None of the factors were significantly associated with early complications on univariate analysis. Late complications were observed in 59 (58.4%) patients. The late complications were as follows: pouchitis (n = 37, 36.6%), anastomotic stricture (n = 27, 26.7%), pouch-vaginal fistula (n = 11, 10.9%), pouch-perianal fistulae (n = 3, 2.9%), small bowel obstruction requiring re-exploration (n = 17, 16.83%), incisional hernia (n = 6, 5.9%), impotence (n = 1, 0.9%), and urinary retention requiring clean intermittent self-catheterization (n = 2, 1.9%). We analyzed various long-term complications separately, as outlined below. Table 4 highlights the risk factors analyzed to assess their association with pouchitis. On univariate analysis, none were found to be significant in our study. Of the 37 patients with pouchitis, 34 responded to conservative management, such as oral antibiotics, hydrocortisone enema, and mesalamine suppositories. Three patients required pouch excision. Of the 27 patients with anastomotic stricture, 19 (70.4%) had stapled anastomosis, and eight (29.6%) had hand-sewn anastomosis. The various risk factors analyzed to assess their association with anastomotic stricture are depicted in Table 4 . The presence of pelvic sepsis was significantly associated with the development of anastomotic stricture (OR = 2.704, 95% CI = 1.041-7.022, p = 0.041). All patients were subjected to a graded dilatation program. Overall, 24 (88.8%) patients underwent successful dilatation by endoscopic or surgical methods. The average number of dilation sessions in the present study was four. Three patients had unsuccessful dilatation; one of them required pouch excision due to severe pouchitis and coexisting pouch-vaginal fistulae. Two other patients were still living with a stoma at the time this study was completed. In total, 11 (10.9%) patients developed a pouch-vaginal fistula during the follow-up. While in five patients it was reported before the stoma closure, six patients found it after stoma closure. The various risk factors analyzed to assess their association with pouch-vaginal fistula are depicted in Table 4 . Hand-sewn anastomosis was the only factor significantly associated with the development of pouch-vaginal fistula in univariate analysis (OR = 3.943, 95% CI = 1.093-14.229, p = 0.036). Four of these patients required surgical repair, while two patients were managed with diversion ileostomy followed by restoration of continuity. One patient later had recurrent pouch-vaginal fistulae with severe pouchitis, leading to pouch excision. Two patients are still living with a stoma. In the long term, 11 (10.9%) patients suffered from pouch failure. Various causes of pouch failure included pouchitis (three patients), pouch-vaginal fistulae (three patients), pouch peri-anal fistulae (two patients), anastomotic stricture (two patients), and fecal incontinence (one patient). Three patients had their pouches excised with end ileostomy. In one patient, the pouch-anal anastomosis was dismantled, and the end of the pouch was made into a stoma. In seven patients, the stoma could not be reversed. Follow-up data were collected from outpatient visit records, personal interviews, and telephone conversations. On analyzing the long-term outcomes, the average stool frequency was 6.18 ± 1.87/day. Daytime frequency was 4.05 ± 1.27/day, and nighttime stool frequency was 2.05 ± 2/day. Of the study population, patient satisfaction could only be assessed in 65 (64.3%) patients, who were contacted either in the outpatient department or via direct telephone conversation. Using a subjective scoring system, they were asked to rate their satisfaction with the surgery after IPAA on a scale of 0-10. Overall, 49 (75.3%) patients were either very satisfied or satisfied with the IPAA. Common reasons for dissatisfaction in the remaining patients were recurrent abdominal pain, increased stool frequency, and nocturnal incontinence. Despite the emergence of biologics and improved medical management, IPAA remains the surgical treatment of choice for UC when surgery is warranted. IPAA offers a one-time solution for UC, potentially providing significant relief for this vulnerable patient population. The demographic parameters in our study were similar to those reported in other studies. The median age at the time of surgery was 32 years, similar to Pal et al. from India . Of the study population, 16 (15.8%) patients had extraintestinal manifestations, but none presented with primary sclerosing cholangitis (PSC). Previous studies from Western countries have demonstrated that the prevalence of UC with PSC ranged from 13.3% to 21.8%, whereas it is uncommon in India . Studies by Kedia et al. and Singh et al. from India reported lower frequencies of PSC at 0.40% and 0.39%, respectively. In the present study, medically refractory UC was the most common indication for surgery, accounting for 81.1% of cases. Similarly, in studies by Lim et al. and Zittan et al. , medically refractory UC was also the primary indication for surgery, comprising 83% and 74.9% of study populations, respectively. The initial first five-year higher number of patients reflects increased referral and non-availability of medical treatment for better control and remission such as biologicals, antimetabolites, and immunosuppressants. In our study, the majority of patients underwent three-staged procedures. In the study conducted by Zittan et al. , 37.6% of patients underwent three-stage procedure, 31.3% underwent a modified two-stage procedure, and 29.4% underwent two-stage procedure. The J-pouch was preferred in our study, although 10 patients had a W-pouch performed in the first few years. The J-pouch is the preferred type of reconstruction in other studies as well, as it is technically easier and has widely acceptable functional outcomes. In their meta-analysis, Lovegrove et al. demonstrated similar outcomes for all three types of pouches. In the present study, 74 (73.2%) patients underwent stapled anastomosis, while the remainder underwent hand-sewn anastomosis. Similarly, in a study conducted at the Cleveland Clinic, 87% of patients received stapled anastomosis, while 13% underwent hand-sewn anastomosis . In this study, there were no significant differences in long-term outcomes between stapled and hand-sewn anastomosis, except for pouch-vaginal fistula, which was more commonly associated with hand-sewn anastomosis than with stapled anastomosis. In the largest study (n = 2,959) by Fazio et al. , the mortality rate in the perioperative period (<30 days) was 0.1%. However, in the present study, it was 0.9%, which seems higher and may reflect the small sample size. In the present study, 72 (71.8%) patients experienced early morbidity. The early morbidity rate in various studies ranged from 20% to 50% (Table 5 ). Although we observed a higher rate of overall early complications, only 21% were Clavien-Dindo grades 3 and 4, while the remaining 51% were Clavien-Dindo grades 1 and 2 complications. McCombie et al. observed grade 3-5 complications in 7.4% of patients. In the present study, a missed enterotomy was the most common cause of re-exploration (n = 5, 4.9%). In contrast, in the series by Kampka et al. , the most common reason for reoperation was an intra-abdominal/pelvic abscess (n = 6, 42.9%). Unlike in Western studies, the majority of patients with pelvic sepsis did not require re-exploration and were managed with percutaneous drainage. Despite the increased morbidity, the mortality rate was low and comparable with the published series. Our study did not find any significant factors associated with developing early complications. In contrast, various studies have reported that older age and preoperative steroid use are linked to the development of early complications . An increased rate of early complications in our study can be multifactorial. Although all patients in our study were tapered off steroids before pouch construction, many studies indicate that steroids are a significant risk factor for early complications following IPAA. Another factor contributing to the increased rate of early complications in our study was the patients’ general status. Although serum albumin levels were optimized before pouch construction, other factors, such as sarcopenia and American Society of Anesthesiologists grade, were not considered during the analysis. Many of our patients had compromised nutritional reserves that were not accurately reflected by serum albumin values. Additionally, although most patients underwent elective procedures, many can be accurately considered semi-elective procedures. Late complications were observed in 59 (58.4%) patients. Comparisons of late complications observed in different studies are outlined in Table 6 . On long-term follow-up, pouchitis was encountered in 37 (36.6%) patients. This rate is consistent with findings reported in other studies . Several studies have identified various factors linked to the development of pouchitis, such as extraintestinal manifestations, pancolitis, a history of PSC, and prior treatment with anti-tumor necrosis factor before colectomy . In the present study, we found no significant risk factors associated with pouchitis on univariate analysis. Only three (2.97%) patients required pouch excision due to severe pouchitis and associated fistulae, similar to the findings published in a previous study . The rate of anastomotic stricture reported in various studies ranges from 2% to 38% . In the present study, 27 (26.73%) patients developed anastomotic stricture. In our research, pelvic sepsis was significantly associated with anastomotic stricture, consistent with the findings of Lewis et al. . Other studies have demonstrated an association between female gender, pelvic sepsis, and hand-sewn anastomosis with the development of anastomotic stricture . Most of our patients responded to endoscopic or surgical dilation, with only three patients experiencing treatment failure. Similar results were observed in other studies, demonstrating a good response of anastomotic strictures to dilation . Our results indicate that most anastomotic strictures, which are typically non-fibrotic, can be successfully treated with dilation programs. The overall risk of pouch-vaginal fistula following IPAA ranges from 4% to 16%. Pouch failure is observed in 21% to 30% of patients with pouch-vaginal fistula . It is challenging to manage and is an essential factor leading to pouch failure . In the present study, 11 (10.8%) patients had pouch-vaginal fistulae. In the present study, hand-sewn anastomosis was found to be a risk factor for pouch-vaginal fistula although the CI was wide. Mallick et al. found that pouch-vaginal fistulas are also most common in patients with a history of pelvic sepsis. In the present study, 9 out of 11 patients (75%) with pouch-vaginal fistulas required some form of intervention, either repair or stoma creation, similar to findings reported in the literature . The present study showed pouch failure in 11 (10.9%) patients. The incidence of pouch failure was reported as 5.1% by the Cleveland Clinic, 7.7% by the UK Pouch Study Group, and 5.9% by Chapman et al. . Heuthorst et al. showed that pouch failure was significantly correlated with fistulae and pelvic sepsis. Similar results were also observed in our study, with all patients with pouch failure having pelvic sepsis in the immediate postoperative period, and 6 out of 11 patients had pouch-related fistulae. The average stool frequency was 6.18 ± 1.87/day, Daytime frequency was 4.05 ± 1.27/day, and nighttime stool frequency was 2.05 ± 2/day. In a study conducted at Cleveland Clinic, average daytime and nighttime frequencies were 5.06 ± 1.8 and 1.36 ± 1.3, similar to our results . In a study conducted in India by Rao et al. , the mean stool frequency was 7.2 stools per 24 hours. In Another study by Raviram et al. from India, the median stool frequency was 7 per 24 hours. Due to the study’s retrospective design, we devised a subjective scoring system ranging from 0 to 10 to assess satisfaction after IPAA. In our study, 75% of patients were satisfied or very satisfied after surgery. Similarly, in a study by Rao et al. , 85% of patients reported being very satisfied with the procedure. In another study conducted in India, Somashekar et al. reported a mean stool frequency of 7, with 84% of patients expressing satisfaction with the surgery. Our study has some limitations. First, its retrospective design results in bias and misclassification as many follow-up data were extracted from follow-up records. Second, the number of patients included in our study was smaller compared to the Western series. Third, the study period spanned over 30 years, during which changes in patient management, surgical techniques, and perioperative care could have influenced the results. Despite these limitations, this is the largest study conducted in India to date that describes various complications and analyzes risk factors associated with IPAA in patients with UC. In our study, both early and late complications were significantly high. The majority of early complications were minor except pelvic sepsis. Major late complications were pouchitis, strictures, pouch-vaginal fistulae, and pouch failure. No factors were found to be significantly associated with early complications. However, pelvic sepsis and hand-sewn anastomosis were significant predictive factors for the development of anastomotic stricture and pouch-vaginal fistulae, among late complications. By keeping the predictive factors in mind, surgeons may anticipate complications, which may help in performing prompt and judicious interventions to deal with these complications to improve the outcome. These factors may also help to take preventive measures during surgery to minimize morbidity. The result of our study will guide surgeons to be aware of possible complications of IPAA in UC and how to minimize and manage them. | Study | biomedical | en | 0.999996 |
PMC11697778 | Autologous “free flap” breast reconstruction (ABR) is a highly regarded option for women undergoing breast cancer surgery due to its natural feel, aesthetic appeal, and ability to maintain sensory function. The positive psychosocial benefits of this approach have been extensively acknowledged, making it a valuable option for a multitude of patients. 1 , 2 In addition, ABR is a durable option that can endure the effects of aging, without the risk of implant-related complications or necessity for prothesis replacements. 3 ABR procedures are now common in healthcare clinics worldwide, with various surgical methods described in the literature. 2 , 4 , 5 Ongoing research into new imaging modalities and recovery optimization techniques aims to enhance the efficacy of these procedures. 6 , 7 However, there is no centralized international consensus on ABR preparation and perioperative care, resulting in a dependence on nonstandardized national or intramural guidelines. Therefore, this study aimed to gain insight into the global practice patterns of ABR experts worldwide, focusing on flap choice, imaging modalities, monitoring devices, perioperative care, and additional surgical possibilities such as direct contralateral symmetrical reduction and surgical lymphedema treatment. The purpose of this study is to provide novice and experienced plastic surgeons with a global perspective on ABR practices, based on the collective insights from surgeons worldwide. This can serve as a reference point when implementing ABR in their hospitals or clinics, encouraging critical assessment, informed decision-making, and potential refinements of their local ABR guidelines. This research is a descriptive quantitative study conducted by the Department of Plastic Surgery at Radboud University Nijmegen in the Netherlands. The aim was to gather information worldwide using a questionnaire consisting of 42 multiple-choice and 10 open-ended questions. The survey covered topics such as donor site selection, surgical approaches, imaging modalities, and perioperative care. Information about the participating surgeons’ details, practice settings, and experience were also collected. The survey questions are available in the appendix. The researchers extensively searched the PubMed and SciELO databases to identify plastic surgeons involved in articles related to ABR. A total of 280 subjects and 39 international societies of plastic and reconstructive surgery were contacted by mail for inclusion in this study. They were asked to complete an online survey using the LimeSurvey application (version 2.06+). Nonresponders were sent reminder emails 2 and 4 weeks after the initial email. If no response was received, a phone call was made to inquire about their interest in participating. This research targeted plastic surgeons who are directly involved with ABR procedures and those affiliated with healthcare centers where these procedures are performed. The objective of this study was to collect data through an online questionnaire with subquestions directed to the respondents on the previous answers, assisting in providing an individual-centered questionnaire. Consequently, different sample sizes were taken for carrying out the analyses on specific segments of the data. The data were coded anonymously to ensure confidentiality and the analysis was carried out using IBM SPSS Statistics 27. Moreover, frequency analysis and crosstabulation tests were used to assess the demographics and relationship between multiple variables. Further, the Chi-squared test of independence was employed to determine the relationships between the categorical variables in the study. Although all responses were reviewed and analyzed, we prioritized presenting only the results with sufficient response rates. Data with low response rates or incomplete answers were excluded from the final analysis when they did not contribute meaningfully to the overall findings. This selective approach allowed us to focus on presenting reliable and representative trends, ensuring a more robust and comprehensive overview of global practices in autologous breast reconstruction. Data from 60 respondents were analyzed to understand the prevalence and preferences of imaging techniques used in the preoperative phase. Among these respondents, approximately 75% (n=44) used preoperative imaging routinely, with most of them preferring CTA (82%, n=36). No association was observed between not using imaging and the unavailability of imaging modalities at the respondents’ healthcare centers. Among the 16 respondents who reported not using preoperative imaging, the majority (80%, n=12) stated that imaging would not make any difference in the outcomes. Among the respondents who used imaging modalities preoperatively, 52% (n=23) used a combination of modalities. Sixty-one percent (n=14) used CTA with a handheld Doppler as their preferred combination, primarily to confirm the location of perforators and reduce intraoperative identification time. The use of a single imaging modality and a combination of modalities were evenly distributed across all healthcare centers: academic, nonacademic, and private clinics. Regarding intraoperative planning for ABR, data from 60 respondents were analyzed. Most respondents preferred to use abdominal donor site flaps, with the DIEP flap being the most commonly used (85%, n=51), followed by the transverse rectus abdominis myocutaneous (TRAM) flap (13%, n=8), and superficial inferior epigastric artery (SIEA) flap (2%, n=1). Among the 51 respondents who indicated having an alternative option, the SIEA- and the transverse upper gracilis flap were the most commonly used second-choice flaps, each used by 22% (n=13) of the respondents, followed by the TRAM flap in 17% (n=10) of the cases ( Table 1 ). Table 1 Respondents’ preferential donor site and their second choice donor site preferences in case their first choice was unavailable/unsuitable. Table 1 Donor site Preferential donor site (%) (n=60) Second choice (%) (n=51) Deep Inferior Epigastric Pedicle Flap (DIEP) 85% (n=51) 7% (n=4) Transverse Rectus Myocutaneous Flap (TRAM) 13% (n=8) 23% (n=14) Superficial Inferior Epigastric Artery Flap (SIEA) 2% (n=1) 22% (n=13) Latissimus Dorsi Flap (LD) 0% 2% (n=1) Thoracodorsal Artery Perforator Flap (TDAP) 0% 2% (n=1) Superior Gluteal Artery Perforator Flap (SGAP) 0% 5% (n=3) Inferior Gluteal Artery Perforator Flap (IGAP) 0% 3% (n=2) Transverse Upper Gracilis Flap (TUG) 0% 22% (n=13) Profunda Artery Perforator Flap (PAP) 0% 7% (n=4) The internal mammary artery was the most commonly used recipient vessel (90%, n=54), followed by the thoracodorsal artery (82%, n=49) in case the internal mammary artery was unsuitable. Data from 58 respondents were analyzed to examine the utilization trends of intraoperative imaging and their confidence levels in performing ABR. Approximately one-third (33%, n=19) used imaging to locate perforator vessels during surgery, with the majority (89%, n=17) using handheld Doppler. During ABR, 60% (n=35) of the respondents reported always feeling confident, while the remaining respondents indicated usually feeling confident during free flap harvesting. Notably, all respondents who reported feeling confident without intraoperative imaging usually used an imaging modality preoperatively. The respondents also reported their confidence in flap harvesting without preoperative imaging. The confidence levels of the 43 respondents who completed the subsection on flap harvesting without preoperative imaging were analyzed and the majority reported moderate (28%, n=12) to very high (35%, n=15) levels of confidence. Respondents who reported always feeling confident in performing ABR used more frequent preoperative imaging (48%, n=28) than those who reported usually feeling confident (25%, n=15). However, no significant association was found between the confidence level during flap harvesting and preoperative imaging usage (p=0.21). The respondents’ confidence levels were self-estimated in this survey. Data from 60 respondents were analyzed to identify the current intraoperative surgical methods in ABR surgeries. These procedures included contralateral symmetrical breast reduction, lymphedema treatment, and direct nipple reconstruction. Contralateral symmetrical breast reduction during initial ABR is not a standard procedure. One-third of the respondents (33%, n=20) never performed contralateral symmetrical breast reduction during the initial ABR, whereas approximately a quarter (23%, n=14) performed this in less than 25% of the cases. Approximately one-fifth (18%, n=11) always performed breast reduction in case of asymmetry. Furthermore, 8% (n=5) of the respondents performed it in more than 75% of the cases, 17% (n=8) in 25%-75% of the cases, and 3% (n=2) did so rarely or not at all. In academic and nonacademic settings, approximately half of the respondents performed contralateral reductions during the initial ABR, indicating a similar trend. The intraoperative preventive measures for perfusion issues were based on data from 77 respondents. Intraoperative preventive actions were reported by 43% (n=33) of the respondents. The most commonly reported actions included the application of warmth (42% n=14), followed by the administration of anticoagulant such as LMWHs (33%, n=11) and mono-antiplatelets (18%, n=6). Popular combinations included LMWH plus warmth application (18%, n=6) and LMWH plus mono-antiplatelet therapy (18%, n=6). Intraoperative challenges were evaluated based on the data from the 58 respondents, highlighting the most challenging aspects of ABR. Respondents were asked to rate the challenges on a scale of 1 to 5, with 1 indicating “not a challenge” and 5 indicating “very challenging.” Among the 35 respondents who experienced challenges during ABR, achieving symmetry was rated as the most challenging, with a mean score of 2.8. Achieving proportional body dimensions in bilateral reconstructions was the second most challenging, with a mean score of 2.6, followed by donor site wound healing, which had a mean score of 2.4 . Figure 1 Level of reported difficulty in ABR in academic, nonacademic, and private clinics. The difficulty level is rated on a scale of 1 (not a challenge) to 5 (very challenging). Figure 1 Flap failure and its management were analyzed based on the data from the 58 respondents, providing insights into the frequency and types of anastomotic revisions performed postoperatively. Among the 58 respondents, 79% (n=46) acknowledged experiencing postoperative flap failure to varying extents. In cases where anastomotic revisions were deemed necessary, 91% (n=42) of the respondents indicated the need to revise the venous anastomoses, while 76% (n=35) reported the need to revise the arterial anastomoses at various frequencies. The specific percentages and distribution of these revision frequencies are detailed in Table 2 . Table 2 Frequency with which venous or arterial anastomosis revisions were required when revision was deemed necessary. Table 2 Revision likelihood Venous revisions Arterial revisions Rarely (1%-5% of cases) 28 (61%) 31 (68%) Occasionally (6%-10% of cases) 7 (15%) 1 (2%) Sometimes (11%-20% of cases) 0 (0%) 1 (2%) Often (>20% of cases) 7 (15%) 2 (4%) Total respondents 42 (91%) 35 (76%) Notably, most respondents who experienced flap failure did not take preventive actions for perfusion problems preoperatively (60%, n=35) or intraoperatively (72%, n=42). However, no significant correlation was found between the incidence of flap failures and absence of preventive actions taken preoperatively (p=0.73) or intraoperatively (p=0.49). Postoperative preventive actions and their implications were assessed based on the data from 77 respondents. Among these respondents, 75% (n=58) reported taking postoperative preventive actions. Medicinal treatments were predominantly administered in the postoperative phase, with 67% (n=39) administering LMWHs and 28% (n=16) using mono-antiplatelet therapy. The most common combinations of preventive actions were LMWHs with warmth application (10%, n=6) or LMWHs with mono-antiplatelet therapy (10%, n=6). Flap viability assessment was analyzed postoperatively based on the data from 72 respondents, offering insights into the frequency, methods, and personnel involved in the assessments. Flap viability was primarily monitored by ward personnel such as nurses alone in 86% (n=62) of the cases, together with a plastic surgeon in 19% (n=14), and alongside a plastic surgeon and resident in 15% (n=11) of the cases. Monitoring was conducted hourly (67%, n=48) on the first day, every 2 h (49%, n=35) on the second day, and every 4 h (46%, n=33) on the third day after surgery. The viability of the flaps was assessed based on the following parameters: temperature (85%, n=61), color (97%, n=70), capillary refill (92%, n=66), and edema (53%, n=38). Most plastic surgeons (85%, n=61) used monitoring devices to assess flap perfusion. Handheld Doppler (74%, n=53) was the most commonly used device, followed by local SpO 2 sensors (14%, n=10), implantable Dopplers (11%, n=8), and temperature sensors (11%, n=8). A preoperative vascular map assists in selecting the appropriate perforator. Most plastic surgeons use CTA imaging as it has been shown to be the most accurate and precise method for visualizing perforators. 8 Furthermore, recent studies have revealed that CTA imaging can also assess the suitability of flap tissue for mobilization. 9 However, for patients who need to minimize their exposure to radiation or contrast, alternative imaging techniques such as MRA and handheld Doppler are valuable alternatives. 10 In this study, most respondents (89%, n=17) used handheld Doppler alongside preoperative imaging to locate perforator vessels during surgery. The literature further identifies several real-time perforators imaging techniques, including duplex, fluorescence near-infrared angiography, and dynamic infrared thermography. Additionally, techniques such as image-guided stereotactic navigational systems and 3D-printed anatomical models are reported to enhance precision when paired with volumetric imaging. 6 The reported usage rate of intraoperative imaging modalities was 33% (n=19), which was lower than initially anticipated. 10 This discrepancy could be due to certain complementary techniques, such as the handheld Doppler, which were described and categorized as imaging modalities. Although the handheld Doppler is frequently used as a complementary technique to identify perforator vessels and assess anastomosis patency intraoperatively, it is technically not an imaging modality. Consequently, the reported use of “complementary techniques” might be understated compared to their actual usage. Consistent with the findings of previous studies, this study's findings reaffirm that the abdomen remains the predominant donor site for microvascular ABR, with the DIEP flap often considered as the gold standard. 3 A secondary benefit of selecting the abdominal donor site is the more aesthetic abdominal contour observed postoperatively. 11 Notably, as with several studies, the choice of donor site remains a highly individualized decision, contingent on a patient's unique needs, circumstances, and body dimensions. Regarding intraoperative challenges, muscle preservation during ABR was reported as a significant challenge, ranking it as the fourth most challenging aspect. Despite proper soft tissue management, this remains a critical issue. 7 Although the literature claims limited donor site morbidity, clinically significant bulges or hernias are reported as complications of DIEP flap reconstructions. 12 A promising robotic approach to DIEP flap harvest has been described in the literature, aiming to minimize abdominal wall disruption and optimize muscle preservation. 7 Another major challenge in ABR is achieving proportional body dimensions and symmetry. Although the current techniques largely depend on the surgeon's judgment, a study by Hummelink et al. proposed a virtual flap planning method using 3D stereophotogrammetry and CTA, potentially assisting surgeons in accurately harvesting the correct flap volume. 5 Regarding preventive actions against perfusion problems occurring postoperatively, 67% (n=39) of the surgeons administered LWMHs whereas 28% (n=16) used mono-antiplatelet therapy intraoperatively; a combination of both was observed in 10% (n=6) of the cases. LMWH has been described in the literature as an essential thromboprophylactic measure during surgery. 13 Although LWMH is recommended, the use of a combination of LMWH and mono-antiplatelet therapy is debatable, especially for low-risk cardiovascular patients. Enajat et al., in their retrospective review, found no significant difference in the incidence of microvascular complications between patients who received both medications and those who received only LMWH perioperatively. 14 Moreover, considering the known risks and significantly higher incidence of hematoma in patients receiving both medications, they recommended discontinuing the administration of mono-antiplatelet therapy postoperatively. In cases of flap failure, venous anastomoses were revised more frequently (91%, n=42) than arterial anastomoses (76%, n=35), reflecting a higher likelihood of venous thrombosis over arterial occlusion as the cause of flap failure. Interestingly, a study conducted by Masoomi et al. revealed that venous thrombosis has a higher rate of successful treatment upon re-exploration compared to arterial occlusion. 15 Given the urgency of timely interventions, 85% (n=61) of plastic surgeons use devices for postoperative neo-mamma monitoring, predominantly the handheld Doppler (74%, n=53), followed by local SpO 2 sensors, implantable dopplers, and temperature sensors. With ongoing advancements, there is growing interest in innovative flap assessment methods. In recent literature, promising techniques such as near-infrared spectroscopy and implantable Doppler have been described for flap assessment, providing continuous objective physiological data on tissue perfusion. 16 , 17 Regarding supplementary surgical interventions, a minority of respondents reported performing intraoperative lymphedema treatment using VLNTs. The limited implementation of this procedure may be due to it primarily being performed in academic settings and the logistical challenge of the additional operation time required. Nevertheless, the incidence of lymphedema following breast cancer treatment is relatively high, ranging from 24% to 49% after mastectomy. 18 Research has shown that combining VLNT with DIEP flap breast reconstruction can significantly improve lymphedema-related quality of life rate. 19 Regarding this supplementary procedure, this study focused solely on whether lymphedema treatment was performed during initial surgery. A further in-depth analysis of lymphedema treatment was not conducted. Postoperatively, patients typically stay hospitalized for an average of 5 days. Prior research, including the one by Frey et al., associate microsurgical ABR with longer operative times and extended hospital stays. 20 To address this, an enhanced recovery after surgery protocol has been introduced to optimize recovery of patients after DIEP flap reconstructions. 21 , 22 To ensure data accuracy for each subsection, all individual question responses were considered for each subsection, regardless of the survey completion status. Although this approach improved the understanding across topics, it led to varying sample sizes across the data subsections. To provide clarity, the specific sample sizes have been explicitly stated for each subsection. Additionally, the customized design of this study enabled respondents to answer only the questions that were applicable to them, contributing to the variability in sample sizes. Cross-continental comparisons were hindered by limited samples from different continents. This uneven distribution of data across continents highlights where ABR research or practices are the most common and also suggests potential geographical biases in the dataset. Accuracy in calculating the response rate was compromised by emails sent to individual plastic surgeons and societies of unknown sizes. Consequently, a reliable response percentage could not be determined and is therefore not reported. This study provides valuable insights into the current practices of ABR worldwide, serving as a comprehensive overview for novice and experienced plastic surgeons. By broadening horizons beyond the local methodologies, it will aid in making well-informed decisions during the preparation and perioperative care of ABR, and underscores the potential areas for innovation in breast surgery. Although not a definitive guide, this study provides a state-of-the-art portrayal of how ABR is performed worldwide. | Study | biomedical | en | 0.999997 |
PMC11697783 | 3′-Phosphoadenosine 5′-Phosphosulfate (PAPS) is the universal sulfuryl donor. PAPS is synthesized by bifunctional PAPS synthase (PAPSS) that contains N-terminal APS kinase (APSK) and carboxy terminal ATP sulfurylase (ATPS) domains . First the carboxy terminal ATPS domain of hPAPSS1 (residues ∼220–623) condenses the sulfuryl group from inorganic sulfate on to adenylyl moiety of ATP to form Adenosine 5′-Phospho Sulfate (APS) and PPi. In the second step the 3′-hydroxyl of APS is phosphorylated by the amino terminal domain (residues ∼1–268) by APS kinase using ATP to form PAPS and ADP . In humans there are three isoforms referred to as hPAPSS1, hPAPSS2a, hPAPSS2b . hPAPSS1 and 2 are ∼77 % identical and the hPAPSS 2b differs from 2a by possessing an extra pentapeptide sequence GMALP . All three hPAPSS isoforms contain typical N-terminal APSK domain and C-terminal ATPS domain deduced by linear amino acid sequence that contains conserved ATP binding motifs . The exact regions that are necessary for APS-kinase and ATP sulfurylase domain is not clear for isoforms especially the 2a and 2b. However, previous study with hPAPSS1 indicated residues between 220 and 265 (referred to as linker sequence) is a must, for both APS kinase and ATP sulfurylase activities . ATPS is a alpha-beta ATP bond splitting enzyme in contrast to the most common beta-gamma bond cleaving kinases. On the other hand, APSK is a beta-gamma phosphoanhydride ATP bond splitting enzyme that has the P-loop with a typical Walker A motif (GxxGxxK) [ , , ]. The transfer of gamma-phosphoryl from ATP onto 3′-OH of APS is purported to require Mg 2 + that interacts with residues of the D 87 GD 89 N phosphotransferase motif. Mutation of both aspartic residues (D 87 ,D 89 ) abolished APSK activity without any drastic consequences for ATPS activity . Phosphoadenosine 5′-phosphosulfate (APS) binds to ATPS and APSK. In ATPS domain the APS is a product and for APSK it is a substrate. D 87 GD 89 N is absent in ATPS but present in APSK. Mutation of both aspartic residues (D 87 ,D 89 ) into alanine did not hamper the ATPS but abolished APSK activity . ATP sulfurylase lacks the GxxGxxK motif for ATP binding, instead it contains conserved H 425 NGH 428 residues coined here as Venk motif. Previously, site selected mutations of the H 425 and H 428 single mutant into alanine completely knocked out the ATPS activity . ATPS being a adenylyl-sulfotransferase, transfers sulfate group onto adenylyl moiety of ATP which then releases the products APS and PPi. The released PPi is hydrolyzed by ubiquitous pyrophosphatase into 2Pi. This drives the thermodynamically unfavorable ATPS reaction forward. In contrast most kinases (gamma-phosphoryl transferases) are beta-gamma bond splitting enzymes that binds ATP and drives the reactions thermodynamically in the forward direction . Whereas ATPS enzyme is unfavorable in the forward direction, is driven forward with the help of separate pyrophosphatase enzyme . Thus, PAPSS is the only bifunctional enzyme that we know of that uses ATP in two different manners. ATPS domain is a alpha-beta phosphoanhydride bond cleaving type and APSK is a beta-gamma phosphoanhydride bond cleaving activity domain. In this paper we report that the dual mutation of H 425 A-H 428 A makes the ATP binding nearly impossible with high energy required compared to wild type (WT). In addition, this is the first report to show the advantage of basic residue at position 426. In hPAPSS1, 2a and 2b the residue 426 is an asparagine (N). When N426 is mutated to K it improves the catalytic efficiency of hPAPSS1 by 3–4 folds. ATPS from various organisms Riftia symbiont , Saccharomyces cerevisiae , Penicillium Chrysogenum , Aquifix aeolicus , and Thermus thermophilus contained R instead of N in the HNGH region of hPAPSS . The in silico binding kinetics of N 426 –K mutant of hPAPSS1 exhibit much less energy than WT for ATP binding. In this paper we also report the kinetics of individual C-terminal domain (polypeptide 220–623) and N 426 –K mutant which are quite different and fascinating. The conserved motif (H 425 NGH 428 )) located in the COOH-terminal region of human PAPS synthase was used for site selected mutagenesis experiments. Amino acid substitutions were carried out corresponding to the H 425 NGH 428 sequence. Using the quik change mutagenesis kit from Strategene, mutations were performed by following instructions from the manufacturer. For example, oligonucleotides containing the respective base substitutions corresponding to the selected mutated codons were synthesized. Mutations were performed by PCR using the wild type hPAPS synthase expression vector (plasmid pET-19b) as template and substituted oligonucleotides as primer. Pfu DNA polymerase was used for DNA amplification. PCR consisted of 12 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 1 min, and synthesis at 68 °C for 12 min. Once the PCR is done, Dpn I restriction enzyme was used for degrading the methylated parent template plasmid. Transformation was performed using XL1-Blue supercompetent cells and circular dsDNA. Colonies were isolated and used for fetching plasmids that possessed the correct sequence which was confirmed by DNA sequencing. Confirmed plasmids with mutations were used for transformation by using protease deficient bacteria (pLyz BL21-DE3). Further, plasmids were isolated and the sequence was verified for the presence of the appropriate mutation. Full-length hPAPS synthase (wildtype) and mutant hPAPS synthase plasmids were PCR amplified using primers containing Bam HI restriction sites. They were then cloned into Bam HI digested pET-19b vectors that contained proprietary 122 base pair Nco I- Nde I cassette (Veritas, Potomac, MD). The vector contained sequences for calmodulin binding site of calcineurin followed downstream by histidine tag and an enterokinase cleavage site. Transformation into competent DH-5α Escherichia coli was done as before using CaCl 2 . Colonies were isolated, miniprepped, and plasmids were sequenced for correct orientation of the initiator codon with respect to the T7 promoter sequence. Transformation was performed with expression host cells from Stratagene (BL21-DE3 plyz) using the pET-19b vectors containing the correct colned inserts with proper orientation. Bacteria was grown in LB broth containing ampicillin. Once the turbidity reached to A595 nm of 0.5, IPTG (1 mM) was added to the culture for inducing protein expression for 3h. After 3 h cells were centifuged to obtain the pellet. Then, 150 ml of lysis buffer (20 mM Tris-HCl, pH 7.5, containing 50 mM KCl, 1 mM dithiothreitol, 10 % glycerol, and 1.2 mg/ml lysozyme) was used for resuspending the pellet. Resuspended cells were transferred into centrifuge tubes. The original tube was then washed with100 ml of lysis buffer and the wash was added to the original cell suspension. Cell lysis was carried out at 25 °C for 7 min. The lysates were then centrifuged for 15 min at 10,000 g at 4 °C. The clear supernatants were purified by Ni + coulumn. Further purification was done by gel filtration. The purified preparations were used for enzyme assays. Reactions were performed in a total volume of 10 μl. The enzyme reaction consisted 3 μl of reaction buffer (150 mM Tris-HCl, pH 8.0, 50 mM KCl, 15 mM MgCl 2 , 3 mM EDTA, and 45 mM dithiothreitol), 1 μl of 50 mM ATP, 3 μl of sample. The reaction was strated by adding 3 μl of inorganic [ 35 S]SO 4 (∼3.4 μCi). Reactions were carried out for 30 min at 37 °C. The reaction was stopped by placing the tubes in boiling water for 5 min. After cooling 1 μl was placed onto PEI-TLC plates. The PEI-TLC plates were chromatograped using 0.9 M LiCl solvent. Dried PEI-TLC plates were exposed overnight on to x-ray film. The respective radioactive spots corresponding to PAPS, APS, and SO 4 were cut out, and the liquid scintillation activity was measured by standard counts per minutes (cpm). Given that the crystal structures of human PAPSS1 bound to various ligands have been resolved by X-ray diffraction to a high resolution of up to 1.75 Å (PDB entries: 1x6v, 1xjq, 1xnj, 2qjf), we can utilize atomistic molecular dynamics simulations and ligand-docking techniques to gain deeper insights into the structural and functional roles of specific residues within the conserved HXGH motif, which forms part of the substrate binding pocket in the ATP sulfurylase domain. In the initial phase of this work, we used homology modeling with YASARA to address missing loop regions, conduct in silico mutagenesis, and prepare the ATP-Sulfurylase domain (ATPS) of human PAPSS1 for molecular docking studies. We used a 2.2 Å resolution crystal structure of the ATP-Sulfurylase domain of human PAPSS1, complexed with Adenosine-5′-Phosphosulfate, obtained from the Protein Data Bank (PDB ID: 2qjf ). The structure comprises two identical chains, labeled A and B. While both chains were included in all docking experiments, chain B was specifically chosen for positioning the simulation cell and serving as the receptor site for ligand docking. To prepare the structure for docking, all ligands, phosphate, and water molecules were removed, and hydrogen atoms were added following the standard procedure in YASARA and then the entire structure underwent energy minimization. The Mg-ATP ligand was prepared using structures from the Protein Data Bank, with YASARA's AutoSMILES feature employed for automatic force field parameter assignment. AutoSMILES utilizes SMILES strings to identify known molecules and applies the AM1BCC and GAFF . (General AMBER force field) methods for molecules not recognized. AM1BCC charges were further refined by incorporating known RESP charges of similar molecular fragments and by calculating semi-empirical AM1 Mulliken point charges , which involved geometry optimization using the COSMO solvation model . For molecular docking, YASARA employs AutoDock-Vina , which uses an iterated local search global optimizer algorithm to predict binding poses and a semi-empirical scoring function to evaluate and rank them. To ensure an unbiased search for binding pockets, a simulation cell was defined to extend at least 5 Å beyond all atoms of domain B. We conducted 100 docking runs in the global search mode of AutoDock Vina, as implemented in YASARA, to dock the ligands to the ATP-sulfurylase domain of human PAPSS1. The resulting conformations were grouped into clusters, each differing by a minimum heavy atom RMSD of 5.0 Å. From each cluster, the conformation with the highest binding energy was selected as the representative structure. Throughout the docking process, the protein structure was maintained in a fixed position, while the ligand was permitted full flexibility to explore potential binding poses. To validate our docking methodology, we initially re-docked Adenosine-5′-Phosphosulfate to verify that the binding site was accurately predicted as per the crystallographic data. Following this, MgATP was docked, with the conformation exhibiting the highest binding affinity selected as the representative structure within the cluster. We also performed in silico mutagenesis to generate single-point mutations (G427A and N426K) and a double mutant (H425A-H428A). These mutations were introduced by substituting the respective side chains within YASARA, followed by a brief 100 ps energy minimization, where non-mutated residues were constrained to avoid local structural distortions. These mutations were applied to each monomer of the ATPS domain of human PAPSS1, and energy minimization was conducted using the Amber 03 force field . to maintain consistency with the force field used during the docking studies. This detailed approach enables us to examine the structural and functional effects of these specific mutations in the ATPS domain. To analyze the system's dynamics, classical molecular dynamics (MD) simulations were performed with Gromacs version 4.6.5 , utilizing the AMBER99SB force field . The system was placed in a cubic simulation box with dimensions of 10 × 10 × 10 nm and solvated using the TIP3P explicit water model . To maintain electrostatic neutrality, Na + ions were added to replace randomly selected water molecules. The system then underwent steepest descent energy minimization with a maximum step size of 0.05 Å to address unfavorable van der Waals interactions in the initial configuration. Long-range electrostatic interactions were calculated using the Particle Mesh Ewald (PME) method with a 10 Å cutoff distance, and bond lengths were constrained with the LINCS algorithm employing a fourth-order expansion . The parameterization of ATP ligands was carried out using the standard RESP procedure in Antechamber, with the partial charges for free Mg2+ ligands obtained from HF/6-31G∗ calculations in Gaussian03 . Histidine residues were modeled with double protonation at the ND1 and NE2 positions, whereas arginine and lysine residues were protonated according to Gromacs default settings. After energy minimization, each of the six systems was equilibrated by restraining the heavy atoms under constant temperature conditions with the V-rescale thermostat at 300K for 1 ns. This was followed by pressure equilibration using the Parrinello-Rahman barostat for an additional 1 ns at each temperature. Initial velocities were assigned according to a Maxwell-Boltzmann distribution at the corresponding temperature, and neighbor lists were updated every 10 fs using a group-based cutoff scheme. The minimized and equilibrated configurations were used as the starting points for all subsequent unrestrained MD simulations, which were performed for 100 ns each in the isothermal-isobaric (NPT) ensemble. The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) method, implemented in Gromacs as g_mmpbsa , was used to calculate the receptor-ligand binding free energy from the trajectories. Each trajectory was reduced to 500 snapshots, and the g_mmpbsa tool was utilized with default settings for the calculations. ATP can possibly be reacted nucleophilically in four different ways. The possible nucleophilic attack of the molecule is described in Fig. 1 . Fig. 1 Mechanism of ATP use in various reaction types. ATP sulfurylase is a adenylyl transferase (Type II) and APS kinase is a Type I phosphoryl transferase. Fig. 1 Most of the kinases are beta-gamma hydrolases. APS kinase is a beta-gamma hydrolase that splits the phosphoryl and transfers it to the recipient molecule that gets phosphorylated. In contrast the nucleophilic attack on alpha phosphoryl of ATP releases pyrophosphate (PPi) as the by product and the adenylyl portion gets added onto the attacking nucleophile. ATP sulfurylase binds inorganic sulfate and the sulfate oxy anion reacts with alpha-phosphoryl of ATP to form 5′-phosphoadenosine phosphosulfate (APS) and PPi. Thus, as classified in Fig. 1 , ATPS falls under the category of adenylyl transfer (type II) reaction. In mammals ATP sulfurylase and APS kinase are fused gene products. Human PAPSS1 (hPAPSS1) contains NH 2 -terminal APS kinase domain and COOH-terminal ATP sulfurylase domain connected by a common linker region (residues 220–265) as shown in Fig. 2 . Fig. 2 Linear amino acid sequence of hPAPSS1. Residues ∼1–265 encompasses APSK domain and residues ∼265–623 encompasses ATPS activity domain. The highlighted residues are common between two domains and mandatory for bifunctional (ATPS-APSK) activities of hPAPSS. Fig. 2 Without the linker region both APSK and ATPS activities are negligible . The COOH-terminal domain constructed by including residues 220–623 of hPAPSS1 was cloned, expressed, purified and characterized for incresing substrates of ATP with fixed sulfate concentration and increasing ATP with fixed sulfate concentration . Line-Weaver Burk reciprocal plot (insets) indicates Km of 2.2 mM for ATP and 0.53 mM for sulfate. Fig. 3 COOH-terminal (aa 220–623) ATPS domain activity. Fig. 3 A shows increasing concentration of ATP plotted against velocity (pmol APS formed/min/mg). The corresponding inset is the Lineweaver-Burk plot of 1/s vs 1/v showing the Km value of 2.2 mM for ATP. Fig. 3 B shows increasing concentration of Sulfate plotted against velocity (pmol of APS formed/min/mg). The corresponding inset is the Lineweaver-Burk plot of 1/s vs 1/v showing the Km value of 0.53 mM for sulfate. Fig. 3 ATPS being an alpha-beta ATP hydrolase doesn't contain typical Walker motif (GxxGxxK) as seen with APSK. APSK is a typical beta-gamma hydrolase which contains canonical Walker-A motif and ATPS instead contains conserved HNxH motif as shown in Fig. 4 below. Fig. 4 HPAPSS1 structure. The top ribbon diagram represents the dimeric structure of hPAPSS. Top Green and Silver ribbon diagram depicts APSK structure. The green ribbon structure also shows APS substrate (in red color) bound to it. The bottom yellow and silver ribbon diagram represents dimeric domain of ATPS. The circled part on the yellow ribbon possesses sequences that contains conserved HXGH motif. Fig. 4 The full length construct containing 1–623 amino acid residues of hPAPSS1 cloned, expressed purified and characterized. The kinetics with various sulfate concentrations are represented below for wild type (WT) top panel . As can be seen for overall PAPS production (ATPS + APSK combined activities) hPAPSS1 shows typical Michaelis-Menton kinetics. The APS formation by ATPS trails APSK product PAPS at a slower rate. The bottom panel represents the kinetics exhibited by N 426 K mutant. As compared to the WT, Km for overall activity of PAPS formation (ATPS + APSK combined activities) for N 426 K mutant was ∼4X higher whereas the Km value towards sulfate was similar to that of WT. Fig. 5 Increasing substrate concentration of sulfate versus reaction velocity of APS formed by full length WT (5A) and N 426 K mutant (5B) of hPAPSS1. Fig. 5 A and B shows increasing concentration of sulfate plotted against velocity (pmol APS and PAPS formed/min/mg). Overall PAPS formed in WT and N 426 K mutant exhibited typical hyperbolic Michaelis-Menton kinetics. However the APS formed in WT reached saturation, N 426 K mutant was linear. Fig. 5 In Fig. 6 A (the top panel) shows the response of WT enzyme towards varying concentration of ATP. The PAPS formation (solid circle) exhibits biphasic curve with first phase becoming flattening between 3 and 4 mM ATP and the second phase plateaus ∼ between 6 and 7 mM ATP with a highest Vmax reaching up to ∼120 pmol/min/mg. The N 426 K mutant enzyme also exhibits bimodal response towards ATP however the Vmax was significantly higher reaching up to 300 pmol/min/mg which is ∼3 fold higher than WT. The intermediate APS accumulated was nearly similar between WT and N 426 K reaching up to ∼120 pmol/min/mg. Fig. 6 Kinetics of full length WT and N 426 K mutant enzymes against ATP. The overall PAPS formed exhibited bimodal curve evinced both in WT and N 426 K. Fig. 6 Since hPAPSS exhibited biphasic response towards ATP a half maximal estimate was determined to represent an approximate Km value. WT showed K0.5 of 4.3 mM and N 426 K mutant showed 3.7 mM, Km value that is slightly lower for N 426 K towards ATP. Contrary to ATP response, both WT and N 426 K mutant exhibited typical Michalis-Menton kinetics (a hyperbolic response) with a calculated Km (Lineweaver-Burk plot inset) of 0.33 mM for WT and 0.44 mM for N 426 K towards sulfate. The Vmax for N 426 K was 3–4 fold higher for ATP and sulfate compared to WT. The overall catalytic efficiency (Vmax/Km) was about 3 folds higher for N 426 K compared to WT ( Table 1 ). Table 1 Kinetic parameters exhibited by WT and N 426 K towards ATP and inorganic sulfate. Table 1 Construct K 0.5 ATP mM (half maximal estimate) Km SO 4 mM Vmax ATP pmol/min/mg Vmax SO 4 pmol/min/mg Vmax/K 0.5 ATP Vmax/Km SO 4 Wild Type 4.3 0.33 120 118 28 357 N426K 3.7 0.44 310 470 84 1068 Using high-resolution crystal structures of hPAPSS1 domains complexes of wild-type and mutant hPAPSS1 with bound ATP were created and molecular dynamics simulations were performed to describe movements of the atoms over time. As can be seen in Fig. 7 in WT the two histidine's (H 425 and H 428 ) side chain imidazole ring exhibited pi-pi interaction with anti-oriented purine ring structure of ATP and the overall ATP binding energy for hPAPSS1 as calculated by MM/PBSA is ∼ -40 kJ/Mol ( Table 2 ), indicating good binding of ATP for the WT active site pocket. In contrast, when the two histidine's (H 425 and H 428 ) were replaced with alanine the R group methyl is not in close proximity for closer/fine interactions. Table 2 describes the overall binding energy differences between WT hPAPSS1 and mutants (N 426 –K, G 427 -A, and H 428 -A-H 425 -A) of the HNGH motif. As can be seen the double mutant exhibited an unfavorable binding energy for ATP. The ATP molecule which was artificially docked into the binding pocket to measure the binding energy thus would probably not stay long enough in the binding pocket, making the reaction thermodynamically impossible to take place during the intracellular time needed. The N426K mutant shows an even more favorable binding energy (−70 kJ/Mol) than WT, while the G-A mutant exhibited an unfavorable binding energy (218 kJ/Mol) for ATP. This correlates well with the experimental findings that even single mutants of the two histidine render the enzyme into a totally inactive enzyme as measured by overall PAPS assay . Fig. 7 ATP docked into the binding pocket of the sulfurylase domain of PAPSS1. ATP is depicted as a ball and stick model in green. The histidine residues H425 and H428 and their respective mutations to Alanine are shown as a stick model in red, Left panel: WT, right panel:H425A-H428A double mutant. Fig. 7 Table 2 MM/PBSA binding energies of MgATP in complex with hPAPSS1. Table 2 WT (ATP) kJ/mol G427A (ATP) kJ/mol N426K (ATP) kJ/mol H425A_ H428A (ATP) kJ/mol MM-PBSA Binding energy −40 218 −70 528 In WT the ATP (depicted with silver sticks) is closer to Mg 2 + (blue ball with Yellow Font on top). Whereas in N426K mutant, after 100 ns of MD simulation the ATP phosphates were found to be significantly closer with looped structure forming even better contacts with cationic (Mg 2 + ) (Blue Ball with Yellow Font on Top). From this we predict that lysyl-ε−amino group positive charge neutralizes the repulsive forces of anionic charges and stabilizes the active site complex for nucleophilic attack by inorganic sulfate that can happen with ease. . Fig. 8 In Fig. 8 shown below the active site pocket of WT (with asparagine at position 426) is compared with the mutant enzyme replaced with basic residue lysine. Position of Mg-ATP after 100 ns of molecular dynamics simulation, left panel: WT, right panel: N426K mutant. The magnesium ion is depicted as a blue sphere while ATP is shown in grey as a stick model. Fig. 8 To further test the above idea, docking of inorganic sulfate was done using autodock VINA. As can be seen in Fig. 9 there are two arginine's (proximal R421 and distal R522) that are in closer proximity at a respective distance of 1.93 and 1.96 Å. Out of the four oxygens of the inorganic sulfate, two oxyanions normally bears electro negative charges. One oxyanion had to engage in nucleophilic attack and the other neutralized. As can be seen in Fig. 9 , one of the oxyanions is about 3.01 Å distance perhaps poised for nucleophilic attack with alpha phosphoryl group of ATP. Fig. 9 Sulfate docked into the ATP-PAPSS1 complex. Sulfate is depicted in green as a stick model. Fig. 9 PAPSS synthase (PAPSS) is a bifunctional enzyme in many organisms . PAPSS produces 3′-phosphoadenosine 5′-phospho sulfate (PAPS) in two steps. In the first step inorganic sulfate reacts with ATP to form PPi and adenosine 5′-phosphosulfate catalyzed by ATP sulfurylase (ATPS) . In the second step APS kinase (APSK) uses APS and ATP to form 3′-phosphoadenosine 5′-phosphosulfate (PAPS) and ADP . PAPS is the universal sulfuryl donor. Sulfotransferases use activated sulfuryl group from PAPS to transfer, onto recipient hydroxyl containing molecule to form sulfate-ester The sulfurylated molecule receives a negative charge that changes the physicochemical properties of the molecule . Thus, PAPS production and its regulation is crucial for all biological sulfonation and consequent cell integrity and metabolism. We used human PAPSS 1 (hPAPSS1) as a model system to study the structure function. As reported earlier, the hPAPSS1 has two domain activities ATPS and APSK. Previous study delineated the regions of ATPS activity domain and APSK . As shown in Fig. 1 the ATP molecule can participate in four different ways. Most of the serine/threonine and tyrosine kinases are type I that transfers gamma-phosphoryl of ATP on to the recipient amino acid residues that gets phosphorylated . Many small molecules, one for example glucose gets phosphorylated inside the cell by hexokinases using ATP to form glucose 6-phosphate which are also Type I phosphoryl transferase. ATP sulfurylase (ATPS) is a type II that transfers adenylyl portion of ATP onto the recipient sulfate molecule to form APS and PPi . In contrast to ATPS, CTP:phosphocholine cytidylyltransferase uses CTP to transfer cytidyl group on to phosphocholine to form CDP-choline . Though type III is a possible mechanism in biological system to use ATP as the moiety for ADP transfer, thus far no known report is available to support the existence of such transferase (Venkatachalam proposed). Nonetheless, ADP transfer is a common occurrence in biological system in wide variety of organisms, as for example histone ADP ribosylation which use NAD + as the ADP donor instead of ATP . The type IV transfer involves the transfer of adenosyl group from ATP onto a recipient molecule. Methionine adenosyl transferase (MAT) is the only enzyme that falls on to the type IV category that transfers the adenosyl group from ATP on to sulfur of methionine to form the universal methyl group donor s -adenosylmethionine (SAM) . In MAT catalyzed reaction it is quite interesting that all three phosphates are released as PPi and Pi . Thus, the interplay of phosphate and sulfur metabolism is well established across organisms . PAPSS is the one and only enzyme that is known thus far to use ATP in two different mechanisms. ATPS domain activity of PAPSS is a type II and APSK domain of PAPSS is a type I transfer entities . Type I phosphoryl transfer seen with protein and small molecule kinases use typical Walker motif . APSK and putative APSK domain of all PAPSS possess typical Walker A motif GxxGxxK . In all three hPAPSS (1, 2a and 2b) the ATP binding P-loop motif contains conserved GLSGAGKT on the N-terminus. In hPAPSS1 G 59 LSGAGKT 66 is between residues 59–66 . In hPAPSS2a and 2b G 49 LSGAGKT 56 is between residues 49–56 . ATPS being a type II transfer type doesn't contain P-loop with Walker motif [ , , ] instead it contains HNxH motif . In all hPAPSS it is HNGH residues (Venk motif) that is purported to bind ATP. In hPAPSS1 “HNGH” is between residues 425–428. In hPAPSS2a it is between residues 415–418 and in hPAPSS2b it is between residues 420–423. It is interesting that the hPAPSS isoforms are about 5–10 residues apart from each other on the linear primary sequence. In this paper we present the experimental studies on the mutational effects of the HNGH motif of the hPAPSS1. In addition, the in silico studies are performed on hPAPSS1 on the HNGH motif. With hPAPSS1 the residues H 425 NGH 428 were experimentally studied for kinetic parameters. The N 426 K mutant exhibited higher Vmax and catalytic efficiency (Vmax/Km). The residue next to histidine in many organisms is substituted with arginine and the physiological significance is unknown. Perhaps in organisms which reduce sulfate to sulfide and subsequent reaction of hydrogen sulfide with O-acetylserine to form cysteine and homocysteine followed by methionine synthesis may require higher amounts of PAPS synthesized . The reductive pathway of activated sulfate PAPS into sulfite by PAPS reductase is in high demand in plants and is totally absent in mammals. This warrants a less active PAPS synthase since the product PAPS in mammals is used only for limited and specific sulfotransferase reactions . Thus, we speculate that the conversion of N into K of WT in hPAPSS leads to gain of function. In other words, nature had done the mutation to convert a more basic residue (R) seen in certain organisms (bacteria, yeast etc.) into a neutral residue as seen with mammals. This change perhaps evolved according to the PAPS demands, dictated by the respective organism. The mutation of both histidine's (H 425 and H 428 ) into alanine completely abolished its activity. The G 427 to A mutation resulted in loss of activity in the forward direction of PAPS formation and the backward reaction of PPi combining with APS to form ATP and sulfate assessed by radioactive 35 SO4 formation was much faster a paradox that we can't explain with available data (data not reported). When the C-terminus (residues 220–623) was separated from the crucial parts (1–219) of APSK domain it exhibited typical Michalis-Menton (MM) kinetics with hyperbolic response with both ATP and sulfate. On the other hand, the WT full length (residues 1–623) exhibited a bimodal/biphasic response with ATP and typical M-M response with sulfate. This response is true with full length N426K mutant as well exhibiting a bimodal/biphasic response with ATP and typical M-M response with sulfate. Thus, in fused gene product (ATPS-APSK) of PAPSS the two domains are coordinately influencing each other's activities in overall PAPS synthesis controlled perhaps by ATP availability. Our experimental data is fully supported by in silico data in terms of ATP binding energies. That is the H 425 -A-H 428 -A double mutant possessed no activity and it is corroborated by high energy required for ATP binding presented in the results section. Thus, the double mutant is highly thermodynamically unfavorable to drive the reaction. We tested this hypothesis with conserved HNGH motif of hPAPSS2b H 420 -A-H 423 -A double mutant. The mutation of two histidine's (H 420 –H 423) drastically increased binding energy for ATP a response that was less severe with hPAPSS2b compared to hPAPSS1. This means the ATP binding pocket is less stringent with hPAPSS2b compared to hPAPSS1 for ATP binding and in part explains the two isoforms are structurally different which reflects in the functional differences. Finally, when we looked at the active site binding of sulfate the reactive oxy anions are neutralized by two arginine's (R 421 and R 522 ). This makes the sulfate fixed nucleophile with one reactive oxyanion poised for nucleophilic attack of the alpha-phosphoryl of ATP to form APS and PPi. In N 426 –K mutant the loop structure in the active site pocket is drastically different making the sulfate closer to ATP than seen with WT which additionally explains the higher activity exhibited by the N 426 –K mutant. In addition, the tradeoff between substrate binding (a parameter of Km) and the product release is an orchestrated balanced event in that both WT and N 426 –K mutant exhibits similar Km for sulfate. However, Km for ATP for N 426 –K is slightly lower which in essence could alter the energetics of the product formation along with sulfate binding for product APS release. Whether APS release is the part where the observed ∼3x higher velocity seen with N 426 –K differs from WT is one of the questions that needs to be answered with reference to higher catalytic efficiency seen with N 426 –K. | Study | biomedical | en | 0.999997 |
PMC11697825 | In Bavaria, a state within the Federal Republic of Germany, a novel agreement (Bavarian Drug Agreement, German: Wirkstoffvereinbarung, in following WSV) was introduced in 2014 . While other states focus on absolute costs for prescribed drugs, the WSV requires the prescription of preferred drugs . The subject matter of prescriptions are DDDs , which present the average maintenance daily dose for adults (WHO) . The number of DDDs contained in each pack is calculated according to the number of tablets and the amount of drug. In the WSV 2.0, for 31 indication areas, prescribing targets consisted of proportions prescribed for recommended drugs or generics . For example, orthopaedists must prescribe 63% (WSV 2.0) defined daily doses (DDDs) of generics in ‘drugs for the treatment of bone diseases’ . ‘Recommended drugs’ are usually well-established drugs supported by evidence regarding their safety and effectiveness. They are recommended by current guidelines and frequently available as generics or biosimilars. In 2018 and 2019, for example, when conducting our interviews with gastroenterologists, 39% of their prescribed DDDs within ‘TNF-alpha-inhibitors’ had to be for the following four recommended drugs: Benepali® = etanercept, Flixabi® = infliximab, Inflectra® = infliximab, and Remsima® = infliximab . JG (female, pharmacist) developed an interview guide covering participants’ practice philosophy, relationship with their patients, prescribing behaviour, professional learning and staying up-to-date, experiences with the WSV, and opinions on prescribing surveillance (see supplement, S1: interview guide). JG and NL (female, social scientist) pretested the interview guide with physicians of the research teams in Marburg and Erlangen. The interviews started with more general topics and gradually approached the contentious area of drug prescribing. JG and NL jointly conducted the interviews. The interviews took place in participants’ practices between December 2018 and July 2019 and were conducted in German. JG made fieldnotes (including experiences and impressions) during and after the interviews, which were subsequently discussed between JG and NL. The interviews were recorded and transcribed by an external service provider. JG coded the transcripts using MAXQDA. JG developed a code tree, which was continuously modified in discussion with another researcher who in parallel coded 13 interviews. With the improved tree JG coded the second 13 interviews, resulting in only very little further modifications of the code tree. The resulting final code tree was used by JG for the final coding of all 26 interviews. We used an inductive-deductive procedure, which is oriented towards the qualitative content analysis, according to Mayring and Fenzl . Deductive codes evolved from the interview guide, and inductive codes resulted from the contents of the interviews. Coding and data analyses started after conducting the first interviews. We presented and discussed results continuously to the research team. For choosing the quotations for the result section we first built the main massage of the single code and then identified the most pertinent citations. We undertook further selection according to brevity and fit with other utterances. The authors translated the German quotations by participants into English. A professional service undertook language editing of the paper with additional checks of quotations by authors. Patients with osteoporosis should preferably receive generic drugs, and bisphosphonates are available in generic form. However, most of the participating orthopaedists state missing their prescribing target by often preferring denosumab (only available as Prolia®). These physicians consider Denosumab to be ‘actually almost unrivalled’ and ‘a real advance’ . They feel that it prevents bone loss and promotes bone formation, while bisphosphonates only prevent further bone loss. Furthermore, they mention very low bone mineral density (BMD) as an indication for choosing denosumab for its better ‘mechanistic evidence’. Likewise, because of the limited duration of therapy for bisphosphonates, physicians feel the necessity of switching to another drug. Guidelines recommend Adalimumab (at the time only available as Humira®) and infliximab for treating IBD. The WSV, however, recommends only infliximab biosimilars. Participants justify preferring adalimumab for IBD instead of the biosimilar infliximab with patient comfort due to independent subcutaneous application at home, while infliximab is administered intravenously: ‘[Humira® is] Much more comfortable, because they don’t have to come to the practice every six or eight weeks and be monitored for four hours, they just do it themselves. The effectiveness is about the same, maybe even a little better here and there.’ . Other authors demonstrated physicians’ prescribing behaviour to be influenced by fear of litigation , especially switches to biosimilars . That’s one reason for not switching biologicals to biosimilars and preferring biologicals. While our interviews were conducted, the context for this decision changed. In 2018, a phase III study showed the equivalence of adalimumab biological and biosimilars regarding efficacy, safety, and immunogenicity in patients with psoriasis, where adalimumab is approved as well . This research addresses the scepticism of physicians and patients and provides a basis for argumentation and reducing the fear of litigation. When the WSV was introduced, preferential prescribing of VKA was recommended, with DOACs encouraged only for selected indications. Later, a second and lower-ranking recommended drug target within prescribing DOACs (recommended drugs: apixaban, edoxaban) was introduced . However, even after this update, participants still missed their drug targets regarding DOACs. This case demonstrated the dilemma of surveillance systems such as the WSV: when including new drugs, they can be ‘too quick’ by permitting drugs with limited evidence base and high cost or ‘too slow’ by preventing effective and safe new drugs from reaching patients. Various drugs are available for the therapy of MS. In particular, therapy with glatiramer acetate and interferon beta 1b has long been seen as first-line therapy, and therapy started with them . Accordingly, these substances were also found as recommended drugs within the framework of the WSV, while the monoclonal antibodies alemtuzumab and natalizumab were not . Younger patients with a high number of relapses benefit from early use of a high escalation level . For these cases, monoclonal antibodies, such as natalizumab or alemtuzumab, are recommended by the German guidelines for MS . Physicians who sub-specialise in MS treat many young patients with multiple relapses and/or many patients with advanced MS and, therefore, prescribe a high proportion of drugs not recommended. Despite different postulated mechanisms, bisphosphonates and denosumab show comparable effects on fracture risk . In addition, the fracture risk-reducing effect of bisphosphonates does not seem to correlate with an increase in bone density . Guidelines, therefore, restrict BMD measurements to a few exceptions . Still, participants felt a need to monitor their patients’ BMD, thus producing ‘treatment failures’ . Infliximab-biosimilars are recommended by the WSV. However, they are administered intravenously over two hours, and patients have to stay in practice for one to two hours for observation regarding allergic reactions [ 31 – 34 ]. After three infusions, the infusion duration could be shortened to one hour [ 31 – 33 ]. The corresponding adalimumab (Humira®) can be self-administered at home . After approval of a subcutaneous form in 2019 for rheumatic arthritis, since 2020, intravenous infliximab can be switched to subcutaneous infliximab in IBD as well . However, the possibility of switching came to light after conducting our interviews. Gastrointestinal side effects with bisphosphonates should not motivate a switch to denosumab. Often, they result from incorrect intake and can be reduced through better patient education . The parenteral application appears more convenient to some patients. One study with a cross-over design showed compliance to be lower with oral bisphosphonate once a week than with parenteral administration of denosumab every six months . In our view, this underlines the importance of effective patient education. As the example of denosumab shows, expensive medicines are not automatically better than cheaper ones (see above). Exposure to the advertising of novel drugs by the industry may lead to increased drug costs . A systematic review describes that physicians with low exposure to pharmaceutical promotion show an increased prescribing of generic drugs . Furthermore, this review suggests that primary care physicians with more exposure to pharmaceutical promotion show less adherence to guidelines and prescribe a wider range of drugs . If pharmaceutical representatives are used as a source of information and represent one of the main sources of information, this influences beliefs and the physician’s prescribing behaviour. This will also be the case for pharma-sponsored journals and conferences. For prescribing drugs, physicians have to consider not only the indication, comorbidities, and simultaneous treatments but also requests formulated by their patients, expectations by their profession, current guidelines, and the results of clinical trials. All measures addressing the economy of drug prescription thus increase the cognitive and, potentially, the financial burden for prescribing physicians. Since all drugs with a market authorisation in Germany are covered by the SHI, additional measures to contain costs are needed, such as the WSV and comparable surveillance policies. In essence, this leads to bedside rationing [ 44 – 46 ]. A restriction on the range of drugs covered by the SHI has been attempted in the past (‘Positivliste’) but was stopped by the federal government, not least because of pressure from the pharmaceutical industry [ 47 – 50 ]. The only regulation concerning the healthcare system is prices for novel drugs. Only if the joint committee decides that a new drug has benefits compared to established treatments can manufacturers obtain a higher price, which must be negotiated with SHI. To understand and support physicians, especially outliers, and still achieve the objectives of the surveillance policies, we have the following suggestions: surveillance policies should be designed and function in a way to avoid unnecessary complexities; information about surveillance and feedback procedures must be available in an easily accessible form; efforts must be made to understand prescribing behaviour, barriers against rational prescribing, and compliance with regulations, such as the WSV, to help develop specific interventions targeted at the mechanisms identified in our study; strengthen drug information and prescribing independent of commercial interests; a system-wide decision regarding the covering of drugs by SHI would be a relief for physicians and reduce the burden of bedside rationing. This, however, will place the onus on central institutions, such as the joint committee and/or the federal government, to introduce appropriate legislation; conflicts of intersectoral (hospital and ambulatory sector) can only be avoided by establishing surveillance procedures for the hospital sector in parallel to ambulatory care; moreover, surveillance should differentiate between first prescribers of problematic drugs and practices issuing repeat prescriptions; and to support physicians, additional information campaigns aimed at the general public and patients should be developed. | Study | biomedical | en | 0.999998 |
PMC11697834 | Lung cancer currently has the second highest incidence rate and the highest cancer-related death rate globally. In China, it ranks first for both. In 2020 alone, there were 820,000 new lung cancer cases and 720,000 lung cancer-related deaths in China. Thus, lung cancer poses the greatest threat to human health and life, while also imposing an immense economic burden on patients . The diagnosis and staging of lung cancer increasingly rely on minimally invasive interventional procedures, including imaging-guided lung mass biopsies, bronchoscopic brush biopsies, bronchial biopsies, and lung peripheral nodule sampling biopsies under magnetic levitation navigation. Given the rapid development of personalized lung cancer treatments, pathologic typing and molecular pathology results are essential to guide therapeutic decisions. Consequently, obtaining adequate and representative specimens on-site is of utmost clinical importance, as it can reduce unnecessary repeat procedures and expenses, thereby conserving medical resources. Currently, on-site judgments primarily utilize Diff-Quik staining. Compared to traditional Hematoxylin and Eosin (HE) staining and Papanicolaou staining, the Diff-Quik process significantly shortens staining time but provides less nuclear detail. Most domestic pathologists are unfamiliar with this technique and require specialized training to gain proficiency. Additionally, full-time cytologists, whether domestic or international, typically have high workloads, and there is a shortage of qualified personnel [ 3 – 5 ]. As a result, at least 30 min is required for an expert to provide on-site judgment of a single case, which is both inefficient and costly. Some hospitals resort to quick training programs to enable interventional clinicians to screen films on-site or employ remote cytology consultations via the internet after scanning films [ 6 – 8 ]. However, the former leads to lower diagnostic accuracy, while the latter necessitates significant capital investment and consumes valuable time and resources from superior hospitals. With advancements in digital imaging and machine learning, artificial intelligence-assisted cytology shows promise for rapid on-site diagnoses while reducing human subjectivity and increasing repeatability. ResNet-18 is a convolutional neural network with an 18-layer architecture, comprising 17 convolutional layers and one fully connected layer. It can be trained by inputting pre-processed images and then used to classify new, unknown images. ResNet-18 has been successfully applied in medical imaging diagnostics, including radiology, histopathology, and cytopathology [ 9 – 12 ], though it has not yet been used for on-site cytopathology of respiratory specimens. This study aimed to assess the performance of an improved ResNet-18 classification model as an aid in on-site determinations of respiratory cytopathology samples. A total of 739 respiratory specimens with on-site diagnoses were collected at our hospital from January 2022 to March 2023. Positive cases included those with biopsy or cell block confirmation and immunohistochemical verification of diagnosis. The sample comprised 96 squamous cell carcinoma cases, 64 adenocarcinoma cases, 6 non-small cell carcinoma cases (which could not be definitively categorized as squamous cell carcinoma, adenocarcinoma, or small cell carcinoma by morphology and immunohistochemistry), 50 small cell carcinoma cases, 1 carcinoid case, and 20 normal cytology cases, where biopsy, clinical data, and imaging showed no malignancy. A MAGSCANNER KF-PRO-005 digital pathology slide scanner (Zhejiang Ningbo Jiangfeng Bioinformation Technology Co., Ltd.) scanned Diff-Quik stained slides at 200X magnification to generate whole slide images (WSIs), automatically focusing on the smears. Two senior cytologists then captured 400X magnification images with a 2048 × 1024 pixel resolution. In cases of disagreement, another chief physician arbitrated and selected consensus images. Generally, 3–20 + images were captured per case. Images were de-identified before model testing. To illustrate the diversity and diagnostic challenges of the dataset, we included representative images from each diagnostic category. These images were selected based on their quality and the presence of characteristic features relevant to the diagnosis. The selected images represent carcinoid, normal cells, adenocarcinoma, squamous cell carcinoma, non-small cell carcinoma, and small cell carcinoma. Each image was annotated with its corresponding diagnosis and used in the training and testing of the ResNet-18 classification model (Please refer to supplementary Data – Sample Information file). To visualize the process of rapid on-site diagnosis in respiratory cytology, we developed a comprehensive workflow that outlines the steps from sample collection to diagnostic conclusion, incorporating both AI and pathologist evaluations. The workflow integrates deep learning models to assist pathologists in making real-time diagnostic decisions. The figure below presents the step-by-step procedure, demonstrating the clinical application of our diagnostic system . Images were randomly selected from six categories: 8 images of carcinoid, 5 images of non-small cell carcinoma, 16 images of small cell carcinoma, 33 normal images, 20 images of squamous cell carcinoma, and 34 images of adenocarcinoma. These images were mixed and assigned random numbers. Three cytopathologists, each from a different hospital, along with an AI model, were tasked with diagnosing the individual images. The 116 test images were presented in a multiple-choice format, with the following answer options: precancerous, non-small cell carcinoma, small cell carcinoma, normal, squamous cell carcinoma, and adenocarcinoma. In cases of disagreement between the diagnoses of the physicians and the AI model, the correct diagnosis was selected as the final joint diagnosis. The three diagnosticians included a deputy chief physician with 28 years of experience, an attending physician with 16 years of experience, and a senior resident with 9 years of experience, all from different hospitals. Each of the physicians was qualified in cytological diagnosis. In our study, we adopted a consensus approach to evaluate the combined diagnostic potential of AI and cytopathologists. Each of the 116 digital images was independently assessed by three cytopathologists and the AI model. Final diagnoses were determined based on consensus or specific criteria: a consensus diagnosis was recorded when all four entities agreed; if two or more cytopathologists agreed with the AI’s diagnosis, that was considered final; if the AI’s diagnosis aligned with the most experienced cytopathologist’s (deputy chief physician) diagnosis, it was deemed conclusive; and in cases of disagreement between the AI and cytopathologists, the diagnosis of the most experienced cytopathologist was selected to emphasize the value of human expertise in complex cases. This methodology is utilized for the combined diagnostic accuracy data with the full dataset provided as a supplementary file. Sensitivity = TP / (TP + FN) × 100%. Specificity = TN / (TN + FP) × 100%. Positive Predictive Value (PPV) = TP / (TP + FP) × 100%. Negative Predictive Value (NPV) = TN / (TN + FN) × 100%. Accuracy = (TP + TN) / (TP + FN + TN + FP) × 100%. Errors were counted as incorrect tumor type predictions or instances where a diagnosis could not be made. Multi-class classification metrics were calculated using the macro-average method, and multi-class ROC curves were generated in Python . Table 1 presents the diagnostic ability evaluation of the test for the three human doctors and the AI model. The four multi-class ROC curves provide a direct comparison of their respective diagnostic capabilities. Due to the experimental design, evaluating the diagnostic performance of human-AI joint diagnoses is challenging. However, in an ideal scenario where human and AI diagnoses differ, selecting the correct diagnosis would improve accuracy by 4.44%, 13.51%, and 11.79% for each physician, respectively (see Table 2 ). Table 1 Statistical results of AI and human cytopathologist interpretation Sensitivity Specificity Positive Predictive Value Negative Predictive Value Accuracy P1% 70.27 92.80 69.84 92.98 88.36 P2% 19.63 86.04 29.72 85.27 77 0.01 P3% 38.21 88.85 40.45 89.18 82.04 AI% 59.95 89.89 52.90 89.40 84.05 To further assess the agreement between the AI model and human experts, we computed Pearson correlation coefficients, which are visualized in the correlation heatmap . This heatmap illustrates the correlation between the diagnostic outcomes of the AI model and the three human experts, as well as the combined results of the AI model and each human expert. The color gradient ranges from blue (negative correlation) to red (positive correlation), with white indicating no correlation. The heatmap reveals strong positive correlations across all comparisons, indicating a high level of agreement between the AI model and human experts in their diagnostic assessments. The heatmap further highlights the strong positive correlations across all comparisons, suggesting a high level of diagnostic consistency between the AI model and the three physicians (P1, P2, P3). Combined results between the AI and each expert show particularly high agreement, underscoring the potential of joint diagnosis to improve overall accuracy. We observed that the AI’s diagnostic performance was comparable to that of human experts, with human accuracy being influenced by years of experience. Therefore, joint diagnosis proved to be significant. The accuracy of cytological diagnosis is heavily influenced by the pathologist’s years of experience, accumulated expertise, and working conditions. It also depends on the adequacy of lesional cell quantity and quality, as well as proper slide preparation. After considering these confounding factors, the cytopathologist makes a holistic judgment based on background, cell arrangement, morphology, size, nuclear features (morphology, size, chromatin quality, nucleoli), nuclear membrane irregularity, N/C ratio, and cytoplasmic characteristics. These principles also apply to rapid on-site cytological diagnosis of respiratory specimens. In clinical practice, cytopathologists strive to differentiate tumor types, but poorly differentiated squamous cell carcinoma and adenocarcinoma often exhibit overlapping morphological features, necessitating immunohistochemical confirmation for definitive categorization. As a result, misclassification between these two types may occur. The workflow outlined in this study was specifically designed for the rapid on-site diagnosis of lung cancer, focusing on subtypes such as squamous cell carcinoma, adenocarcinoma, and small cell carcinoma. However, we acknowledge that the principles behind this workflow—such as integrating AI-assisted cytological analysis, real-time diagnostics, and human expertise—could potentially be applied to other lung diseases, including interstitial lung diseases (ILD) and idiopathic pulmonary fibrosis (IPF). While these conditions are distinct from lung cancer, they present unique diagnostic challenges that could benefit from AI support. It is important to note that the cytological features of ILD and IPF differ significantly from those of malignant lung tumors, requiring potential adaptations in the workflow for accurate diagnosis. At present, the workflow has not been validated for these diseases, and further research is needed to assess its applicability and diagnostic accuracy in non-cancerous conditions. Future studies could explore how the workflow might be adjusted for diseases like ILD or IPF, incorporating disease-specific features while maintaining the diagnostic efficiency of AI and human collaboration. The suboptimal human diagnosis results in this study can primarily be attributed to the use of Diff-Quik staining for on-site diagnoses. This technique is unfamiliar to most cytopathologists in China, with few pathology labs routinely employing it. Compared to HE or Pap staining, Diff-Quik stains exhibit less nuclear detail and poorer cell cluster contrast, even with microscope adjustments. Thus, improving human on-site diagnosis requires specialized training and experience with this methodology. Additionally, the test provided only one image per question with six answer choices, a setup that differs from real-world conditions. The relatively low physician performance may not accurately reflect their capabilities in routine practice, as pathologists typically examine multiple slides and adjust focus when analyzing suspicious areas. Therefore, single-image testing cannot fully replicate physicians’ true diagnostic proficiency. ResNet is a deep convolutional neural network architecture originally designed for image classification, and it has been applied to histopathology and cytopathology. Its use for on-site diagnosis of respiratory cytology samples is novel. The improved ResNet-18 model performed exceptionally well in this study. As shown in Table 1 , its diagnostic capability on images reached the level of human experts. Compared to human physicians, the AI model has the advantage of faster processing, taking only about 0.3 s to diagnose each image. Additionally, the AI’s performance is more objective and repeatable. A well-trained AI can detect subtle morphological changes that cytopathologists might overlook, given the considerable subjectivity and poorer reproducibility in human diagnoses (both intra- and inter-observer). For instance, in Fig. 4 , an adenocarcinoma case was missed by all three pathologists because, without control cells as a background, they could not assess the size and shape of the cells properly, potentially overlooking enlarged nucleoli and thickened nuclear membranes. However, the AI identified the case as squamous cell carcinoma, which was closest to the gold standard. In Fig. 5 , the AI correctly identified small cell carcinoma, while the human pathologists either erred or could not provide a definitive diagnosis. Retrospective analysis revealed normal bronchial epithelium in the upper left and a near-naked tumor cell in the lower right, which resembled normal bronchial cells and was easily overlooked by humans. Moreover, while humans can adjust the microscope focus to discern differences in morphology and arrangement, AI faces difficulties in diagnosing complex, high-density cell groups and accurately interpreting cellular arrangements. Advanced algorithms are needed to address these limitations . Currently, AI algorithms in cytopathology are still in early development and cannot fully replace human expertise. They may make mistakes or overlook critical cytological features. For example, in Fig. 6 , both the AI and one pathologist called the case normal, while the senior pathologist diagnosed it as non-small cell carcinoma. This discrepancy likely occurred because the cells resembled reactive epithelium, though their nuclear features differed from those of normal cells. AI-assisted diagnosis, where the correct diagnosis is selected when human and AI diagnoses differ, has been shown to improve accuracy, as reflected in the literature . However, human judgment remains essential in clinical practice. When discrepancies arise, professional knowledge and experience are required to assess whether the AI’s conclusion is correct and whether it should be adopted. Ultimately, humans must make the final cytological interpretation . Fully relying on AI also introduces ethical, legal, and governance issues regarding its future application. Nonetheless, AI’s role in cytopathological diagnosis is likely to grow as machine learning and algorithms continue to evolve. Study limitations include using images from only one hospital. To evaluate real-world AI performance, multi-center participation and model training/validation on more cases are necessary to ensure the accuracy and reliability of the models. The ultimate goal is for AI to provide an integrative interpretation of entire digital slides, achieving diagnostic capabilities comparable to cytopathologists. In our study, we used minimally invasive techniques such as fiber optic bronchoscopy, magnetic navigation bronchoscopy, and CT-guided transthoracic needle aspiration to obtain respiratory cytology samples. These methods are effective in providing diagnostic value while minimizing patient trauma. However, sampling bias, influenced by factors such as lesion location, heterogeneity, and operator experience, may impact the results. While efforts were made to ensure sample diversity and representativeness, the potential for bias remains a concern. Additionally, the morphological differences in tumor samples—such as the distinction between central necrotic areas and peripheral atypical squamous cells in squamous cell carcinoma or the comparison between marginally invasive cells and central mature tumor cells in adenocarcinoma—are significant but beyond the scope of this study. Furthermore, varying case exposure among cytopathologists at different hospital levels directly affects their diagnostic experience, with younger doctors more likely to consider preliminary diagnoses and more experienced doctors considering a wider range of differential diagnoses. Our deep learning model, based on ResNet-18, addresses this experience gap by assisting in diagnostic accuracy, demonstrating its potential to serve doctors across various hospital levels. Moving forward, we plan to expand our research to include a broader range of respiratory diseases, conduct multi-center studies, explore tumor heterogeneity, and optimize the model for better clinical application, ultimately enhancing AI’s role in cytological diagnosis and improving diagnostic services for patients. While this study focuses on the Chinese context, particularly with the use of Diff-Quik staining, the findings have global relevance. In many resource-constrained settings, including both developing and developed countries, Diff-Quik staining offers a fast, cost-effective, and accessible alternative to more complex staining methods like HE or Pap. In regions where specialized training and advanced diagnostic tools are limited, this simple yet effective staining method can support timely and accurate on-site cytological diagnoses. Moreover, AI-assisted cytology, as demonstrated in this study, holds promise worldwide. While developed countries may have the infrastructure to adopt these technologies, AI can also be instrumental in improving diagnostic capabilities in developing countries, where access to trained pathologists may be limited. The integration of AI into cytopathology could reduce diagnostic variability and enhance the accuracy of diagnoses in diverse healthcare settings. As AI and staining techniques become more accessible globally, these methods could significantly improve diagnostic outcomes, particularly in areas with limited healthcare resources. In essence, AI and human cytopathologists form a dynamic partnership that amplifies each other’s strengths. By collaborating, they unlock the potential for improved diagnostic outcomes. AI lightens the workload and boosts diagnostic precision, while human expertise provides critical, high-level judgment, ensuring diagnoses of unmatched accuracy and reliability. | Review | biomedical | en | 0.999993 |
PMC11697875 | A central theme in biology is the idea that function is shaped by structure. Biological tissues, for example, often comprise stereotyped organizations of specific cell types that together enable proper function of the tissue. Formation of these structures during development is orchestrated by intrinsic gene regulatory networks and extrinsic cell-cell interactions. Therefore, analysis of cellular architecture of tissues can provide insight into both developmental processes that generate them and mechanisms that govern their function. Recent technological developments in spatial omics have enabled researchers to map the position of cell types in complex tissues . Developing methods to analyze and interpret the resulting cellular maps is an active area of research [ 3 – 5 ]. Reported developments can roughly be classified into three groups. First, methods that examine spatial distribution of one cell type label relative to itself. This includes spatial autocorrelation and Ripley’s spatial statistics that determine whether cells with a given label are clustered, dispersed, or randomly distributed in space. Second, methods that quantify association between pairs of cell type labels based on proximity. Analysis of this kind, which is implemented in popular tools such as and also utilized in several other studies [ 9 – 13 ], can reveal enrichments or depletions in cell-cell interactions by comparing frequency of pairwise interactions in the samples with a random configuration. Third, methods that identify distinct cellular neighborhoods or microenvironments [ 14 – 21 ]. These methods typically cluster cells based on an embedding that represents the types and abundances of their neighboring cells. Variations of this approach have been used to study immune tumor microenvironment in colorectal cancer and reorganization of local tissue architecture in response to acute kidney injury . Methods have also been developed to analyze higher-order assembly of cellular neighborhoods, such as constructing “Tissue Schematics” or to employ concepts from natural language processing, like “bag-of-words” idea in Spatial-LDA, to identify distinct microenvironment “topics” . Despite the remarkable diversity of existing methods for spatial analysis, to our knowledge, none of them capture patterns in sequential arrangement of cells, as they focus on composition of cell types in each region regardless of their order. Spatial ordering of cell types within tissue is crucial for understanding organizational principles. Stereotypical sequential arrangements appear in many tissues, for example in the intestinal crypts along the crypt-villus axis, in airway epithelial cells of bronchioles, and in layers of skin epidermis. But their intricacy and significance is perhaps most evident in the central nervous system, where sequential arrangement of cell types enables precise signal processing and thus is directly related to tissue function. While some simple patterns in the spatial arrangement of cells are readily recognizable, the vast diversity of cell types revealed by spatial omics, combined with the complexity of biological tissues, necessitates a systematic statistical approach to uncover many of the underlying spatial patterns. Here, we introduce a strategy for identifying “Spatial Motifs” that reveal statistically overrepresented spatial arrangements in complex tissues. Spatial omics data are often modeled as neighborhood graphs. Our approach focuses on paths in these graphs to directly capture ordered arrangements that may be overlooked by methods focused solely on regional composition. To extend the concept of motifs to spatial maps of cell types, we first developed an algorithm for enumeration and uniform sampling of paths in neighborhood graphs. Each path consists of a sequence of nodes, labeled by the cell types they represent. Paths along which the physical distance monotonically increases capture arrangements of cell types in the sample. Identifying overrepresented patterns in linear sequences, such as DNA, has long been a cornerstone of bioinformatics, with methods extensively refined and optimized over the years. To identify recurring patterns of cell types in sampled paths, we adapted the STREME algorithm, originally developed for motif discovery in nucleic acid sequences, enabling us to build on a history of highly optimized techniques . Our new algorithm, called Spatial MOtif REcognition (SMORE), introduces crucial modifications to accommodate input from spatial graphs rather than one-dimensional sequences. It also integrates motif discovery with differential gene expression analysis to compare cells within spatial motifs to those of the same type located elsewhere in the tissue. Fig. 1 Overview of the spatial motif discovery algorithm. a Schematic of the procedure for finding motifs on a set of spatially distributed nodes, each labeled by a color and a letter for illustration purposes. (1) A set of spatially distributed nodes in 2D space. (2) Delaunay triangularization is used in this case to generate the graph. (3) URPEN is used to sample a set of length-4 radial paths. Control data is created by shuffling the observed graph node cell types. (4) SMORE is used to extract most significant spatial motifs. (5) PWM logos can be used to represent the output motifs. b An example of a radial and a non-radial path. Radial path sampling ensures nodes that are farther from each other in the sample are also farther from each other in physical space. c A simplified example for the SMORE pipeline. Input samples are converted to nmers from length 3 up to length of the motif and p values of the seeds evaluated based on negative binomial test with Bernoulli probability computed based on total number of distinct seeds in primary and control graph. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {nEval}=3$$\end{document} nEval = 3 most significant seeds are input to refine and enrichment where ZNIC p values are used to refine and enrich candidate seeds. Hold-out scoring is performed to compute the p value for the output motif. Nodes of the seeds involved in the motif are erased from the graph and the process is repeated again. More detailed description of each block is provided in the “ Methods ” section. Letters and sequences are chosen for demonstration purposes and do not correspond to specific cell types We tested the sensitivity, specificity, and accuracy of SMORE by recovering motifs that were embedded at specified frequencies within synthetic graphs. We then analyzed published datasets of mouse retinal bipolar cells, hypothalamic preoptic region, and embryonic tissue to identify a variety of spatial motifs. Our results revealed that gene expression of cells in some spatial arrangements can differ significantly from other cells of the same type, providing clues to the functional significance of the spatial motifs. We also demonstrate SMORE’s remarkable versatility and scalability by applying it across diverse spatial transcriptomics datasets, spanning both 2D sections and 3D volumes, imaging- and sequencing-based techniques, and a whole mouse brain dataset with nearly 4 million cells. Together, this work presents a novel and broadly applicable approach for identifying patterns in spatial data that go beyond pairwise associations and regional composition. Our algorithm for finding spatial motifs in neighborhood graphs consists of two main components: first, a method to uniformly sample paths from the graph, and second, a procedure to find motifs in the obtained samples. Each path provides a sequence of cell type labels that occur near each other in space. Generating sequence samples from the neighborhood graphs reduces identification of spatial patterns into finding overrepresented sequences of labels. Despite some important differences, this task is similar to identifying motifs in nucleic acid or protein sequences. Therefore, we generalized existing methods of motif discovery in genomic sequences for our application on graphs. The sampling algorithm takes a graph G and returns an unbiased sample of all paths inside the graph. In a graph, a path is a walk that does not intersect itself. Our selected paths are also constrained to be “radial.” Radial condition in a spatially embedded network is defined as the requirement that physical distance along a path monotonically increases along the sequence of edges in the path. Radial condition ensures that the sequence of labels in a sampled path corresponds to a spatial arrangement of cell types in space, so that labels that are farther from each other in the path are also farther from each other in physical distance . Therefore, the radial requirement simplifies interpretation of the output motifs. After sampling, the motif discovery algorithm identifies sequences of a given length that are statistically overrepresented in an iterative process. In each step, a significant recurring pattern of cell types is identified and is subsequently refined by considering sequences similar to the initial pattern or seed . The algorithms for path sampling and motif discovery are described below. For more details, refer to the extended “ Methods ” section. The time required for analysis of the experimental datasets in this study is summarized in Additional file 1 : Table S1. The code for sampling and motif discovery algorithms is available at: SMORE: Spatial Motif Recognition. The first step in our approach involves uniform sampling of neighborhood graphs. We have developed an algorithm for Uniform Random Path Enumeration (URPEN) based on the Rand-ESU algorithm . The Rand-ESU method involves enumerating all potential subgraphs within a given graph, incorporating a probability element to uniformly sample a subset of these subgraphs. In our modification, we have adapted this method to exclusively sample paths as opposed to subgraphs. Paths differ from subgraphs in that they cannot intersect themselves, and each node, excluding the initial and terminal nodes, is only linked to its preceding and succeeding nodes in the path sequence. This contrasts with subgraphs where nodes can be connected to an arbitrary number of neighboring nodes. This distinction is crucial when selecting the next neighbor to expand the growing sample. Similar to ESU-tree, the PEN algorithm’s structure can be visualized as a tree structure. The tree structure for an example graph is demonstrated in . This tree has 18 leaves which correspond to the 18 size-3 paths of the graph. We can use this tree to sample paths uniformly without bias. The PEN algorithm systematically traverses its associated PEN-tree. In situations where a full traversal is impractical, we can perform a partial exploration of the PEN-tree such that each leaf is reached with equal probability. To achieve this, a probability is introduced for each depth \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1 \le d \le k$$\end{document} 1 ≤ d ≤ k in the path (or each depth in the PEN-tree), and the subsequent node rooted at a node at depth d is traversed with probability \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_d$$\end{document} p d . This is implemented by calling the ExtendPath function at lines 3 and E6 of the PEN algorithm (Algorithm 1) with probability \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_d$$\end{document} p d . This new algorithm is called Uniform Random Path Enumeration, URPEN. It can be observed that URPEN visits each path with equal probability of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p =\prod _{d=1}^k p_d$$\end{document} p = ∏ d = 1 k p d . The method is tested in the “ URPEN enables efficient and unbiased sampling of neighborhood graphs ” section on a random graph to validate its accuracy numerically. Fig. 2 Unbiased sampling of paths from neighborhood graphs. a An example of the PEN sampling tree. b URPEN returns each path at a frequency corresponding with the sampling level, whereas Radial Random Walk (RRW) results in biased sampling of the graph. Examples of RRW upsampled and downsampled paths are shown in the top right corner of the 20% sampling panel. The sampling probability in URPEN is set to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p=(1, 1, \ldots , 1, 0.2)$$\end{document} p = ( 1 , 1 , … , 1 , 0.2 ) for 20% sampling, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p=(1, 1, \ldots , 1, 0.6)$$\end{document} p = ( 1 , 1 , … , 1 , 0.6 ) for 60% sampling. This test was performed 1000 times. c Sampling quality with URPEN and RRW on the bipolar cell type graph and a random graph with 12000 nodes. d Sampling speed for non-radial and radial sampling on the bipolar and random networks Applied to a neighborhood graph, URPEN returns sequences of cell types that are observed near each other. Similar to DNA sequences, we can search through these cell type sequences for motifs using unsupervised learning techniques. The SMORE method is developed to detect motifs within the sampled sequences. Our approach uses as its basis the recently developed STREME method which the author has demonstrated to be more accurate, sensitive, and comprehensive than several widely used motif discovery algorithms. STREME is developed to find motifs within sequence-like samples; SMORE on the other hand finds motifs in a network-based dataset. The algorithm follows a series of steps to accomplish its objectives. Construct the graph from spatial data: To construct a graph from the spatial coordinates of cell types, Delaunay triangularization is employed, forming a graph with nodes as cells, labeled with their respective cell types. Given that each dataset may comprise multiple tissue sections or animal IDs, separate graphs are generated for distinct sections and IDs. For generating control data, each section or animal ID is shuffled independently. Additionally, besides Delaunay triangularization, the method offers options to construct the graph using arbitrary K nearest neighbors or epsilon graph approaches. Sampling the graph and generating control data: SMORE uses URPEN for uniform sampling of the input graph. Control samples are generated using one of two methods: shuffle or kernel. The shuffle method produces control data by shuffling node cell type labels within samples (e.g., tissue sections and animal IDs). On the other hand, the “kernel” method establishes a kernel around each cell and swaps the cell’s label with that of a cell within its kernel . In the experiments detailed in this paper, kernels with a radius of K neighbors are used, where K is specified for each experiment. K = 1 means only first neighbors within the graph are considered for shuffling. The degree of randomness in the control data is controlled by adjusting the number of neighbors considered for shuffling. There are certain scenarios where specific cell types associated motifs are readily apparent. In such cases, these cells can be fixed between primary and control data, meaning that their cell labels are not shuffled. We generate nTrain control data and seed numbers for control data are the total number of that specific seed within these nTrain samples. NScore independent control data is generated for output motif scoring, with the same settings as the training data. Convert to n-mers and count seeds: Input samples to the algorithm can be of an arbitrary length. These samples are converted to W-mers, where W is the desired motif length. These W-mers are input to the count seed modules. Three strategies have been proposed for counting motif instances: counting all occurrences, counting those with no shared edges, and counting those with no shared nodes . We adopted the third approach, which assumes that motifs share no common nodes, to prevent overrepresentation due to overlapping occurrences. This approach, referred to here as the Zero Node in Common (ZNIC) model ensures that each counted motif instance is structurally independent. Motif counts under ZNIC model are referred to as ZNIC counts. By using the ZNIC model, we avoid inflated frequency counts, allowing for a more accurate capture of the network’s true structural patterns. ZNIC counts of unique seeds, along with their associated nodes, are then passed to the next module for evaluating initial seeds. Initial seed evaluation: The significance of each initial seed is obtained by the negative binomial test (see the “ Methods ” section for details). The justification for this specific test is argued in the extended “ Methods ” section. The first nEval seeds with this criterion are passed to the next stage of refinement and enrichment. The default value for nEval is 25. Refinement and nested seed enrichment: The refinement and seed enrichment both use the same process of enrichment, except that refinement is only one iteration, and seed enrichment is nEnrich iterations, with the default value of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {nEnrich}=20$$\end{document} nEnrich = 20 . Enrichment groups similar path samples together and compares ZNIC counts of the grouped sequences with the control data. nEval motifs from the initial evaluation step are first enriched for one iteration and top nRef (nRef = 4 as a default) motifs are further refined in seed enrichment block for nEnrich iterations or until p value does not improve. During each iteration, the Position Weight Matrix (PWM) is calculated from the sequences participating in the motif. Likelihood ratio scores are then computed for all samples, using this PWM matrix, and the samples are arranged in descending order based on their PWM scores. In the event of an equal PWM score, the samples are further sorted based on their p values obtained in the initial evaluation block. Subsequently, ZNIC counts for the ordered samples are determined, and the PWM score threshold that minimizes the p value is identified. This process is iterated if the p value obtained is more significant than the previous iteration. Motif scoring: In most cases, tissue samples are different from each other and it is not optimal to take sections of the samples as hold-out for scoring. In our experiment, the same samples are used for finding motifs and scoring the output motif, with the scoring part iterated over NScore times with different shuffled networks to avoid false positives. Implementation results on synthetic data with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {nScore}=50$$\end{document} nScore = 50 shows that the false positive rate is negligible. Furthermore, tests on real data with randomly shuffled cell type labels did not result in any significant output motif. The ZNIC counts for the seeds involved in the output motif are computed in the primary and nScore randomly generated control data and the 95 percentile least significant p value is considered as the output p value of the scoring module. Motif node erasing: The respective nodes for the seeds involved in the motif are erased (i.e., their cell type is set to 0) from the graph in primary and control data along with their reverses. The previous steps are then repeated until output motifs are not significant anymore, or a specified number of output motifs have been discovered. The sequence of cell types along a path in the neighborhood graph captures their local spatial arrangement. A collection of these sequences can be used for identifying overrepresented patterns in the graph, only if it represents an unbiased sample of all possible paths. Furthermore, the sampling algorithm should be able to handle the large number of cells in typical spatial transcriptomics datasets. To confirm that URPEN sampling is unbiased, we generated a graph by applying Delaunay triangularization on a spatially uniform distribution of 120 two-dimensional Cartesian points. We then used URPEN to sample radial paths of length 5 from this random graph at three sampling levels; 20%, 60%, and 100%. If sampling is unbiased, we expect each path to appear in the sample with a probability equal to the sampling level. We repeated these tests 1000 times. So, for the case of 20% sampling, we expect each path to appear on average 200 times in the output results. This number is 600 for 60% sampling and 1000 for complete 100% sampling. As a comparison, we also sampled the same graph with the commonly used random walk method. Random walk starts with a randomly chosen node and subsequent nodes are selected from the neighboring nodes with equal probability, until a path of the desired length is obtained. While URPEN returned paths at the expected frequency, random walk sampling showed significant bias for certain paths . The distribution of the URPEN counts is also consistent with a set of identical independent binomial distributions with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = p_d$$\end{document} p = p d , confirming that paths were sampled with equal probability . Similar results were obtained for other path lengths and for paths not constrained by the radial condition . The sampling bias of random walk can be mitigated by using unbiased estimators . However, that incurs complexity and leads to a penalty in speed performance . We also evaluated the sampling quality of URPEN compared to random walk . Sampling quality is defined as the percentage of path types for which the number of extracted paths has at most 20% error relative to the exact counts, similar to the measure used previously . Path samples with at least 5 counts were considered for quality evaluation. Sampling quality was computed for two cases: A random graph with 12,000 nodes and 35,823 edges and a graph based on spatial distribution of bipolar cells in a section of mouse retina . Sampling quality for the URPEN method increases with increasing sampling probability, reaching one at complete sampling. In contrast, sampling quality for the random walk peaks at around 0.5. We then assessed how sampling speed scales with the path length . The two graphs of Fig. 2 c was sampled by URPEN at 10% level. The speed appeared to be independent of the path length for non-radial paths. However, when the radial constraint was applied, the speed decreased with path length, because the proportion of paths that meet the radial condition decreases with increasing path length. Consistent with this explanation, the speed of sampling radial paths is not independent of graph architecture, as it appears to be the case for the non-radial paths. Together, our results demonstrate that URPEN avoids the shortcomings of random walk sampling and offers a robust method for uniform path sampling. Evaluating the performance of SMORE requires datasets with known ground truth. Since the presence and frequency of overrepresented spatial patterns in existing experimental data is unknown, we created synthetic data by embedding patterns within random graphs at known frequencies. Each graph has 12,000 nodes, 35,823 edges, and 12 cell types. The embedding percentage indicates the proportion of nodes used for pattern embedding. For example, 2% embedding indicates that 2% of the nodes (i.e., 240 nodes) were used for embedding patterns. In the case of a length 4 motif, this results in 60 sequence patterns. Other nodes in the graph were labeled randomly. Embedded patterns included one variable position, which was filled with one of two cell types with equal probability. Other positions in the pattern were assigned one predefined cell type. Although more complex patterns can also be considered, we chose our patterns so that they are sufficiently complex while still providing insight into the algorithm’s performance. The algorithm was run 100 times for each embedding frequency, generating 10 output motifs per run. The samples for length 4 motif included all possible radial paths inside the graph. For the length 5 motif, the graph was downsampled by URPEN and 65% of all radial paths were used. The accuracy of a motif discovery algorithm describes its ability to recover accurate versions of overrepresented patterns . We assessed the accuracy of SMORE by measuring the similarity of output motifs to embedded patterns . In each run, SMORE returns 10 motifs that are sorted based on their statistical significance. At embedding frequencies above 0.5%, the most significant output motif tends to be the one most highly correlated with the embedded pattern. At 1% and higher embedding frequency, the Pearson correlation coefficient between the extracted motifs and the embedded patterns was higher than 0.9 in all 100 tests. Based on these results, we expect SMORE to be able to accurately identify length 4 and 5 motifs even when they only occur at low frequencies in a sample. Fig. 3 Evaluation of SMORE’s performance on synthetic data with known ground truth. a Accuracy of SMORE, defined as the best correlation coefficient between the embedded motif and extracted motifs for length 4 motifs with varying embedding frequency ( f ). This figure represents the average results from 100 algorithm runs. The highlighted curves indicate the best correlation among the n = 10 output results. Faded curves demonstrate the results when considering only n = 1, 2, or 3 of the best output motifs, instead of all 10. Examples of motifs with different levels of correlation are shown on the top. b Specificity results for length 4 motifs, measured by false positive rate and true positive rates against log p value threshold and ( c ) against each other. d Sensitivity of SMORE measured by successful motif recovery rate against embedding frequency and ( e ) enrichment log p value To assess the specificity of SMORE, we examined the true positive rate (TPR) and false positive rate (FPR) against output p values (see the “ Methods ” section for details). Ideally, both TPR and FPR should be high at high p values and decrease as the p values decrease, with FPR decreasing at a faster rate to allow for correct motif identification. Accordingly, we observed that FPR curves reached zeros at around log p value of –10, while TPR performance gradually increases with the embedding frequency . The corresponding ROC curves confirm close to perfect classification performance of SMORE at embedding frequencies higher than 1%. To evaluate the sensitivity of SMORE, we defined success rate to be the proportion of output motifs that are statistically significant, at a given p value, and have a Pearson correlation coefficient of at least 0.95 with respect to the embedded pattern. With a log p value threshold of –10 , success rate for identifying length 5 motifs exceeds 90% at embedding frequencies above 1%. Success rates for length 4 motifs increase more gradually with embedding frequency, exceeding 90% only at 3% embedding. At each embedding frequency, success rate appeared to drop sharply beyond a specific p value threshold . As expected, this threshold decreases with increasing embedding frequency. Retina contains a rich diversity of neuronal cell types, organized into three layers of cell bodies. These cell types have different features and frequencies and are tiled across the retina in a stereotypical manner that supports the overall function of the tissue. Cell type diversity and individual variability make it difficult to identify recurring patterns in the cellular architecture of the retina. Spatial motif analysis can reveal higher order associations between retinal neurons and provide insight into their development and wiring. We applied SMORE on a dataset of the mouse bipolar interneuron subtypes containing more than 30,000 cells . These subtypes were differentiated using co-detection of 16 gene markers by SABER-FISH, allowing the classification of all 15 bipolar subtypes. Bipolar interneurons bridge all visual circuits, establishing the link between sensory rod and cone photoreceptors and the output neurons. Bipolar cells also do not migrate from their birthplace, providing a spatial map between their final location and the location of their progenitors . Rod bipolar cells (RBCs) constitute the majority of retinal bipolar cells in mice and their cell bodies are mainly organized together, further out in the inner nuclear layer (INL) compared to cone bipolar cells (CBCs) . SMORE evaluates the statistical significance of cell type arrangements in the experimental data against control data, which are generated by shuffling cell type labels. When shuffling is done for all cells across the whole graph, relatively obvious structures, like separation of RBCs, are identified as highly significant motifs . To reveal other motifs involving RBCs, besides this trivial case, we can fix the position of RBCs in the control data . This would eliminate any motif whose significance stems from RBCs and elucidate the relationship between RBCs and other cell types . Fig. 4 Spatial motif analysis of mouse retinal bipolar cells. a A retinal section with classified bipolar subtypes (left) and examples of control data generated from this section using different randomization methods, global shuffling (middle) and shuffling with fixed RBCs (right). b The top five output motifs, identified using the global shuffling method, are displayed in order from top to bottom with their respective log p -values. Their positions within a section of the retina are shown by highlighting the nodes associated with each motif, using the color code in the bottom right corner. Two example regions, marked by rectangles, are enlarged for a closer view. c Same as b , but for motifs obtained when RBCs are fixed. Annotations for the cell types involved in the motifs are listed at the bottom. d Schematic for primary pathway for rod-driven signals involving rods, rod bipolar cells, AII amacrine cells, OFF or ON (cone) bipolar cells, and OFF or ON ganglion cells. e Absolute log p value versus difference in gene expression medians (delta median) for cells in a spatial motif versus cells of the same type that are not in a motif arrangement. The results for a random selection of cells are shown in red. f Selected cases and genes with absolute log p value greater than 7. The heatmap is colored with delta median values. The motif cases, specified by motif number-position-cell type, are sorted by cell type on the vertical axis. Values in each cell show absolute log p value for comparison between cells within a given motif and overall cells of the same type Our analysis revealed several highly significant spatial motifs among retinal bipolar cells. These motifs can be investigated in the context of retina development, anatomy, and physiology. For example, when RBC positions are fixed, the most significant motif involves a type 2 OFF CBC followed by two RBCs and a type 6 ON CBC . Overrepresentation of this cellular arrangement can be understood in the context of a primary rod pathway that enables scotopic vision . Within this pathway, the signal originating from a single rod cell is primarily directed to a select few AII amacrine cells through two RBCs . The AII amacrine cells establish connections with nearly all bipolar cell types to gather scotopic signals originating from RBCs (denoted as O in our motif notation). These signals are then distributed to both ON and OFF CBCs through gap junctions and inhibitory synapses, respectively . However, the number of connections varies depending on the bipolar cell types . Type 2 (C) cells account for 69% of the total number of OFF bipolar chemical synaptic contacts with AII amacrine cells, while type 6 (K) cells contribute 46% of the total area of ON bipolar gap junctions with AII amacrine cells . Both type 2 and type 6 cells not only have the highest access to AII amacrine cell signals but also share these signals with other types of bipolar cells through interconnected gap junctions in the network. These findings support the central role of type 2 and type 6 cells in conveying the most sensitive scotopic signals to the postsynaptic ganglion cells. Furthermore, AII amacrine cells are characterized by their narrow-field dendrites. Typically, a bipolar cell receives more inputs from AII amacrine cells that are in its close proximity . This suggests that bipolar cell types involved in scotopic vision should be spatially close to each other. Given these considerations, we hypothesize that the COOK motif is associated with the primary rod pathway for scotopic vision in mice. SMORE is specifically designed to identify overrepresented sequential arrangements of cell types. Therefore, its objective and output differs from previous methods that search for spatial neighborhoods or microenvironments based on local cell type composition, regardless of order of cells. To clarify this distinction, a side-by-side comparison between SMORE and two such methods, HistoCAT and ImaCytE , is included in the Supplementary Information . Spatial context of the cells often influences their function. With recent advances in spatial gene expression profiling, there has been an increased interest in systematically characterizing spatial variability of gene expression [ 32 – 38 ]. Spatially variable genes may explain functional distinctions between cells in different regions or demarcate spatial domains [ 32 – 34 , 37 ]. If spatial motifs represent functional units made of various cell types that together play a distinct role, we can expect cases with distinct gene expression signatures. Specifically, cells within some spatial motifs may exhibit unique gene expression profiles compared to cells of the same type that lie outside these specific spatial arrangements. We assessed differential gene expression between cells of each type that are involved in a spatial motif and the ones that are not. For each gene, the median expression value among the cells of a specific type that are involved in the motif was subtracted from the median of all the cells of the same type. The p values for the observed delta medians were obtained through theoretical computation by analyzing the distribution of delta median values for a random subset of cells (see the “ Methods ” section for details). We performed this analysis for motifs obtained by shuffling with fixed RBC cells for the 16 genes profiled in the retinal bipolar dataset . Several cases of highly significant differential gene expression were observed in the motif cells . In contrast, control samples where a random subset of cells were selected, with the same size and type of the corresponding motif case, showed much higher p values. This observation is consistent with functional specialization of cells in spatial motifs. The heatmap in Fig. 4 f illustrates the absolute log p values across all cases for 20 output motifs. Each motif can consist of multiple cell types in different positions. For example, the second motif in the fixed shuffling case of Fig. 4 c comprises 10 cells, 4 cells in each of the positions 1 and 4, and one cell in positions 2 and 3. Here, we consider each cell type in each position of each motif as a separate case. Genes with absolute log p values greater than 7 are highlighted in Fig. 4 f. Among genes that were significantly upregulated or downregulated in the motifs, Grm6 stands out because it shows the most extreme differential expression in both directions. Grm6 is upregulated in type 1b OFF bipolar cells (B) in OBBO motif, where O represents an RBC, ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{delta}\;\text{median}=45,-\log(p\text{value})=30.31$$\end{document} delta median = 45 , - log ( p value ) = 30.31 ) and downregulated in type 5b ON bipolar cells (H) in HBBI motif ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {Delta} \; \text {median} = -12, -\log (p\text{value}) = 14.67$$\end{document} Delta median = - 12 , - log ( p value ) = 14.67 ). Grm6 encodes the metabotropic glutamate receptor 6 (mGluR6) which is localized to the dendritic tips of ON bipolar cells . It plays a crucial role in triggering depolarization of ON bipolar cells in response to light-induced hyperpolarization of photoreceptors . Mutations in Grm6 gene in humans lead to autosomal recessive congenital stationary night blindness (arCSNB) . We observed that enrichment of Grm6 in type 1b cells in motif 11 (OBBO) can be explained by their position along the radial axis of the retina. Grm6 is expressed at higher levels in type 1b cells whose cell body is closer to the photoreceptor level . Since RBCs (O) are concentrated in this outer region, type 1b cells in OBBO motif also tend to be in radial positions where Grm6 expression is higher . In contrast, downregulation of Grm6 in type 5b cells (H) of motif 4 (HBBI) seems to be explained by their proximity to type 1b (B) cells rather than their radial position . Type 1b cells lack dendrites connecting them to photoreceptors . Therefore, the mechanism of their function is not well understood . Our observation suggests that type 1b cells may influence signal processing in the retina by altering expression of key postsynaptic receptors, like Grm6, in nearby ON bipolar cells. Fig. 5 Grm6 shows motif specific expression patterns. Expression of Grm6 versus radial position of cell in the mouse retina for ( a ) type 1b and ( b – d ) 5b cells. Cells within specific spatial arrangements are highlighted in each panel. The radial position was computed by considering the outer extreme points as the maximum radius points and computing the radial position for other nodes relative to the nearest outer point To explore the utility of SMORE at identifying non-trivial recurring patterns in a significantly more complex sample, we applied it to a spatial transcriptomics dataset of the mouse hypothalamic preoptic region . This dataset profiles about 1 million cells and has identified about 70 neuronal populations with distinct signatures and spatial organizations. As we showed before, the approach used to generate control data affects the output motifs. This can be used to tune the algorithm to different anatomical features. Here in addition to fixing specific cell types, we introduce local kernel shuffling . Shuffling cell labels across the entire sample can result in the emergence of relatively straightforward structural motifs, such as regional boundaries. On the other hand, local shuffling maintains cell type frequencies within cellular neighborhoods. Therefore, if a sample is compartmentalized to regions with distinct cellular compositions, local shuffling is more likely to identify patterns that are overrepresented within each compartment. Control data generated by local shuffling maintains a higher degree of similarity with respect to the original experimental data. Therefore, the motifs obtained with global shuffling tend to have more significant p values compared to the output motifs of local shuffling. In both shuffling methods, if certain cell types form obvious structures (e.g., Fig. 6 a Ependymal cells (blue)), their positions can be fixed in the control data. Fig. 6 Spatial motif analysis of mouse hypothalamic preoptic region. a The graph of the preoptic region of the hypothalamus from five adult male mouse brains at Bregma − 0.29. b Examples of control data generated from the 5th tissue using different randomization methods: global shuffling, kernel shuffling with 6 neighborhood depth, and kernel shuffling with 4 neighborhood depth. c Control data obtained by applying the same randomization methods as ( b ) but with the positions of non-neuronal cell types fixed. d The top five motifs, identified using global shuffling (top), kernel shuffling with depth 6 (middle), and kernel shuffling with depth 4 (bottom). The position of motifs within a tissue section is shown to the right, along with their log p -value, ordered from top to bottom. To show the position of the motifs, nodes associated with each motif are highlighted using the color code in the bottom right corner. e Same as d , but for motifs obtained when non-neuronal cell types are fixed. Annotations for the cell types involved in the motifs are listed at the bottom We applied SMORE to cellular maps of the hypothalamus preoptic region from five adult male mouse brains at Bregma − 0.29 to identify length 4 spatial motifs. The sections used in our analysis comprise 28,866 cells. We tried both global shuffling and local shuffling with kernel sizes of 4 and 6 . In another set of experiments, we also fixed the position of non-neuronal cell types between the primary and control data . When non-neuronal cell types were not fixed, the most significant motif consists of a group of four interconnected ependymal cells . This pattern is immediately visible in the graphs because ependymal cells form a layer that lines the ventricles. As expected, fixing the position of non-neuronal cells removes this motif as well as the motif made entirely of mature oligodendrocytes . Instead, other motifs emerge, some of which are combinations of ependymal and neuronal cells (e.g., motif 3 of fixed global shuffling). The first five motifs generated through the global shuffling method correspond to discernible patterns within the fifth tissue section shown in Fig. 6 a. In addition to the first motif of ependymal cells, motif 2 comprises astrocytes, E-9, and E-14 cell types, which are enriched in the PVA nuclei of the hypothalamus . E-14 and E-9 cells also show similar gene expression patterns . Motif 3 represents a pattern of mature oligodendrocytes known to be enriched in the anterior commissure and the fornix. Motif 4 is a pattern of I-2 and I-13 cells which are indicated to be enriched in BNST-p and StHy nuclei. I-2 and I-13 are both aromatase-enriched clusters and express both androgen receptor (Ar) and estrogen receptor alpha (Esr1) . Motif 5 is primarily composed of cell types I-11, I-12, and I-14, collectively enriched in the MPN and StHy nuclei. Motifs obtained from kernel shuffling methods capture less obvious patterns. For instance, motif 5 in the case of “fixed kernel 6,” represented as hSci sequence which is equivalent to a radial path of 4 excitatory neuronal cell types, E-15, E-8, E-12, and E-17, occurs 4 times in the primary data (2 occurrences in each of animal IDs, 10, and 11) and 3 times in the total of 50 generated control data. This motif consistently appears in kernel shuffled tests, with different ranks. Interestingly, there is a similar motif, BSic, with B representing astrocytes, that appears as motif 11 of the not fixed kernel 4 experiment and appears at the opposite side of the brains of the same Animal IDs. The functional explanation of identified spatial motifs is not the focus of this study. But it is reasonable to expect that such patterns hint to either functional relationships between the cell types involved or specific developmental programs that generate them. Therefore, they can help generate hypotheses for future studies. For instance, motif 5 in the non-fixed global shuffling experiment primarily consists of cell types I-11, I-12, and I-14. In the case of the fixed shuffle, motif 4 is predominantly composed of cell types E-8, E-15, I-34, and I-15 in the first position, while I-11 occupies the remaining positions. I-15, I-2, I-11, I-14, I-33, E-8, and E-15 cells display sexually dimorphic cFos enrichment in male mating . Interestingly, a similar motif exists in female sections , where I-15 replaces I-11. The Esr1-enriched cluster I-15 exhibits significant enrichment in female animals and is preferentially activated in females, with lesser activation in males after mating . We also performed gene expression analysis for the motifs obtained by global shuffling with fixed non-neuronal cells. Many cases of highly significant differential gene expression were observed in the motif cells . The heatmap in Fig. 7 b illustrates the absolute log p values across all cases for 40 output motifs. The majority of statistically significant cases for genes imaged using combinatorial smFISH measurements are concentrated in the first 10 output motifs. In contrast, genes measured through sequential FISH rounds, typically genes with higher expression levels, exhibit a higher prevalence of significant cases in this analysis. This difference is probably related to the fact that gene expression values for sequential genes are generally under dispersed , resulting in the upregulated or down regulated genes being more significant. Genes with absolute log p values greater than 20 are highlighted in Fig. 7 c. In most cases, the significance of differential gene expression varies depending on the position in the motif. For example, Vgf is upregulated in motif 2, position 1, cell type 26 (i.e., 2-1-26 case). But its differential expression is not significant in position 3 of the same motif. This may indicate that within a spatial motif, cells of the same type in different positions can have differences in their gene expression and potentially distinct roles. Fig. 7 Motif specific gene expression analysis for hypothalamus motifs. a Absolute log p value against difference in median gene expression in motif and non-motif cells. Blue markers show experimental results; red markers are the results for one random selection of cells. The expression values are not normalized. b Heatmap of absolute log p values for genes (on the horizontal axis) and motif cases (on the vertical axis). Out of 155 genes, 135 were measured by combinatorial FISH and 20 were measured in sequential rounds of FISH. These two groups are separated with a dashed line. c Selected cases and genes with at least one absolute log p value greater than 20. The motif cases are sorted by cell type. The gene names and their motif address (motif number-position-cell type) are included on the x - and y -axis, respectively So far, spatial transcriptomics has mostly been applied to thin tissue sections or monolayer cultured cells. In these cases, obtained measurements are typically projected on a two-dimensional plane. However, recent advances have made it possible to map gene expression in thicker tissue slices, resulting in 3D datasets [ 45 – 48 ]. This is an important step in development of spatial methods because it enables profiling of cells in their native context, which in many tissues of interest is inherently three-dimensional. Since our method operates on a neighborhood graph, it should be able to identify spatial motifs in 3D datasets as well. To test this, we applied SMORE on the cellular map of a 200-µm-thick slice of mouse anterior hypothalamus, encompassing over 78,000 cells . The cells in this dataset are classified into 21 excitatory neuronal clusters, 26 inhibitory neuronal clusters, and 7 non-neuronal cell subclasses based on the expression of 156 genes . Fig. 8 Spatial motif analysis of a 200 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document} μ m slice of mouse hypothalamus. a The graph of 3D MERFISH dataset with classified cell subtypes shown as dots colored by subtype. The perpendicular views from different angles are shown alongside the 3D view for the primary graph. Epen, ependymal cells; ASC, astrocytes; OGC, oligodendrocytes. Excitatory and inhibitory subtypes started with E and I letter, respectively. b The first 20 spatial motifs obtained using global shuffling to generate the control data. c The first 5 output motifs along with their highlighted nodes on the tissue graph, and their respective log p values. Annotations are the same as Fig. 6 for the shared cell types Figure 8 b illustrates the top 20 output motif nodes highlighted within the graph. Comparing this figure with the equivalent view from Fig. 8 a (lower left) indicates the similarity of the highlighted motifs with the visual structure of tissue and agrees with our expectation that global shuffling is able to derive discernable patterns, along with other motifs that are less obvious. Figure 8 c highlights the first 5 motifs along with the logos representing the identified motifs. Overall, our results demonstrate that SMORE can be used for analysis of both 2D and 3D spatial data. The cells within the 3D graph of tissue structure tend to have a greater number of neighbors on average compared to those in 2D datasets. For instance, while the average number of neighbors for the 2D mouse hypothalamus dataset in Fig. 6 a is 5.9, it is 15.2 for the 3D hypothalamus dataset. Additionally, the 3D dataset comprises more than twice the number of cells in the 2D hypothalamus dataset (78,229 compared to 28,866). Consequently, the number of paths within the 3D dataset is substantially higher. Here, we used URPEN to sample 10% of the radial paths in this 3D tissue graph. Despite this downsampling, the number of radial paths for the 3D dataset was 1,838,093, while it was 696,141 for the specific tissue sections analyzed in Fig. 6 , which were not downsampled. Due to the larger sample size, the log p values for the 3D dataset exhibit greater significance. To further demonstrate scalability of our approach, we applied SMORE on a spatial atlas of the whole mouse brain (Allen Brain Cell Atlas) , which includes about 4 million cells profiled by MERFISH . Thus far, we have focused on spatial maps of cell types in neural tissues obtained through imaging-based methods. The complexity of neural tissues, with numerous intermingled cell types, and the single-cell resolution of imaging-based techniques make these datasets particularly well-suited for spatial motif analysis. However, SMORE is a general framework, agnostic of the methodology used for spatial profiling. Here, we demonstrate the broader utility of our method by analyzing transcriptomic maps of mouse embryos at embryonic day (E) 8.5 and 9, generated using Slide-seq . Our analysis includes data from two E8.5 embryos, with 15 and 17 sagittal sections collected at 30 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document} μ m intervals, and one E9 embryo with 26 sagittal sections collected at 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu$$\end{document} μ m intervals. In total, the dataset encompasses 256,487 cells with gene expression profiles spanning 27,554 genes. Following the original study, we used 29 cell states as labels, each assigned by computationally mapping beads to a pre-existing single-cell reference. This approach effectively mapped cell states to their expected spatial domains, as shown for 10 selected cell states Fig. 9 a. Fig. 9 Spatial motif analysis of a slide-seq mouse embryo dataset. a Cell type distributions in two E8.5 embryos (replicates) and one E9.0 mouse embryo. Three example sagittal sections from each embryo are shown. b First 10 output motifs obtained using global shuffling to generate the control data are highlighted on the tissue graph. Obtained motifs are represented by logos in the bottom. Annotations for the cell types involved in the motifs are either listed in the legend for panel ( a ) or at the bottom of panel b . Each motif is indicated by a different color in the highlighted tissue graph, with the colors noted on top of the motif logos. c Clustergram obtained based on correlation of the frequency of first 30 motifs in different embryo tissues As expected, due to the coarse-grained classification of cell states, most of the top motifs were composed of repeating patterns of a single cell state . These homotypic motifs represent regions of the sample that are broadly labeled, for example as brain or heart. Among the top 10 motifs, some also represent the boundary of two tissues, for example presomitic mesoderm and neuromesodermal progenitors . Although these motifs lack the complexity seen in neural tissues, they still reveal statistically significant patterns. Therefore, we asked if motif frequency could be used to cluster structurally similar samples. Indeed, clustering based on the frequency of the top 30 motifs successfully grouped the two E8.5 samples together, separating them from the E9 embryo . An advantage of sequencing-based spatial transcriptomics methods, like Slide-seq, is their ability to capture a comprehensive, unbiased view of gene expression across the tissue. We performed differential gene expression analysis, comparing cells within motifs with the cells of the same type elsewhere in the samples, and found numerous highly significant cases . A notable example is the lower expression of retinoic acid pathway members Crabp1 and Crabp2 in brain cells positioned within a sequence of anterior neuroectoderm cells . This observation aligns with previously reported anterior-posterior expression domains of these genes and may suggest region-specific modulation of RA signaling. Overall, among the 181 unique significant genes (log(p) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$< -80$$\end{document} < - 80 and absolute delta median \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$> 0.5$$\end{document} > 0.5 ), 40 overlapped with the 352 top enriched genes along the anteroposterior and dorsoventral axes reported in the original study . This overlap, observed from a pool of 27,554 genes, is statistically significant ( p -value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$= 8.99e-38$$\end{document} = 8.99 e - 38 ). Together, this analysis demonstrates SMORE’s versatility in identifying spatial motifs across diverse tissue types and transcriptomics platforms. It also highlights the scalability of our motif-specific gene expression analysis for genome-wide, sequencing-based data and showcases clustering of tissues based on frequency of their spatial motifs. Fig. 10 Motif specific gene expression analysis of mouse embryonic samples. a Selected cases and genes with at least one absolute log p -value greater than 100 and absolute delta median greater than 1. The gene names and their motif address (motif number - position in motif - cell type - embryo number; MPCE) are included on the x - and y -axis, respectively. Embryos are numbered as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1\text {:E8.5\_rep1,} 2:\text {E8.5\_rep2, and} 3:\text {E9}$$\end{document} 1 :E8.5\_rep1, 2 : E8.5\_rep2, and 3 : E9 . b Motif logos for the motifs that are not included in Fig. 9 but are present in the panel ( a ) heatmap. Cell type annotations are shown at the bottom. c Expression level of Crabp1 in all “brain” cells (cell state A) in the E9.0 embryo viewed from the top along the z -axis (left) compared to brain cells in motif #25 (right). d same as c for Crabp2 expression level. Both Crabp1 and Crabp2 are identified as significantly downregulated in brain cells within motif #25 compared to other brain cells, as highlighted in the panel ( a ) heatmap Spatial transcriptomics is a rapidly growing field. We have seen significant innovation in the field over the past few years, resulting in a multitude of techniques and consistent improvements in their efficiency and scalability. Concurrently, application of spatial transcriptomics has expanded beyond specialized groups to the broader community of biomedical researchers. There are already several commercial platforms available to researchers, and it is expected that additional options will become available in the near future. Therefore, the need for innovative computational methods to extract biologically relevant information from this type of data is on the rise. In this work, we introduce a method for identifying patterns of cell type arrangements with arbitrary length. There are two major contributions that make this task possible: a method for unbiased sampling of paths from a graph (URPEN) and a method for identifying motifs in such samples (SMORE). Our approach is general and can be applied to any system with sufficient complexity profiled with any spatial omics method. This includes solid tumors and organoids, where greater heterogeneity increases the need for statistical analysis. It is also not limited to two-dimensional maps and can be readily adapted for three-dimensional data. In addition to the application presented here, individual components of our method can be independently employed in a range of other contexts that are modeled as a graph. For example, path sampling via random walk has been used in applications ranging from network representation learning to estimation of similarity measures . Unbiased path sampling using URPEN can offer benefits over random walk in such applications. Detailed evaluation of performance improvement with URPEN needs further investigation. Spatial motifs can be explained in terms of their functional significance or developmental mechanisms that generate a specific cell type arrangement. Therefore, they can be used to generate hypotheses and further our understanding of tissue biology. We have provided a few examples in this study. This includes association of RBCs with type 2 and 6 cone bipolar cells that can be involved in the primary scotopic pathway and downregulation of Grm6 in type 5 b ON bipolar cells near type 1b cells that offers a possible mechanism for the function of these atypical bipolar cells. Interpretation of each spatial motif at this point can only be done on a case-by-case basis, in the context of what is known about the cell types involved. This can begin with length 2 motifs, which are easier to interpret, and progress to higher lengths in a stepwise manner. For motifs that consist of more than one seed, each seed can also be investigated separately. Our findings also underscore the potential of spatial motifs as powerful tools for systematically characterizing tissues based on their cellular architecture. It will be interesting to explore more systematic ways of utilizing spatial motifs for characterizing tissues. For example, the set of all spatial motifs in a sample can provide a quantitative representation of the tissue structure. These representations can be valuable in classifying tissues with subtle differences in their cellular architecture, such as various cancer subtypes. In this study, we showcase an example of such classification for mouse embryonic tissues from E8.5 and E9 stages. To enhance this utility, further analysis is required to optimize key factors, including the normalization of high- and low-frequency motifs, selection between motifs, incorporation of features beyond motif frequency, and identification of the optimal motif length for best performance. Our analysis here is constrained by certain technical limitations of the existing data. In datasets we analyzed, each cell is represented by a point in the tissue, which is typically the center of its nucleus. This ignores variation in size of the cells and their receptive fields. Position of the cell body may also not be a good indicator of neuronal connectivity. Furthermore, our motifs are currently confined to short range local neighborhoods surrounding each cell. Expanding the method to incorporate longer range interactions could be an interesting next step, either by clustering identified motifs or by permitting gaps in the motif sequence. Gene expression analysis can be informative as shown here. However, gene panels in imaging based spatial transcriptomics datasets are often selective, focusing on known cell type markers. In contrast, sequencing-based spatial methods offer expansive genomic coverage, but their measurements can be more noisy and sparse. As more, scalable and multimodal spatial methods become available, SMORE has the potential to discover more intricate relationships between the spatial positioning of cells and their functional characteristics, including gene expression. In conclusion, SMORE provides a novel and robust method for identifying spatial motifs in complex tissue structures. By capturing the sequential order of cells, SMORE fills a critical gap and complements the rapidly growing suite of spatial analysis methods. We present substantial algorithmic developments that enable efficient, unbiased sampling of paths from neighborhood graphs and discovery of motifs in the resulting cell type sequences. By rigorously investigating the performance of each component of our method, we quantitatively demonstrate their accuracy, specificity, and sensitivity. The application of SMORE to spatial maps of the mouse retina, brain, and embryonic tissue illustrates its utility in uncovering previously unrecognized patterns of cell type organization and contextualizing their biological significance through gene expression analysis. We further demonstrate the versatility and scalability of our method by analyzing samples spanning a wide range of cell and cell type counts, profiled using different spatial transcriptomics platforms. Together, our results highlight the capability of SMORE to illuminate the substantial complexity of neural tissues, provide novel insight even in well studied models, and generate experimentally testable hypotheses. In order to apply the SMORE method on the spatial structure of the cell types, spatial transcriptomics dataset is imported and the neighborhood graph based on cell positions is created. Control data is generated using one of two methods: global shuffling and kernel shuffling. As an example, Additional file 1 : Fig. S1 illustrates a kernel for the center node (highlighted in magenta). In this figure, K is set to 4 and 6, indicating that all nodes within K neighborhoods of the center node are part of the kernel. nTrain instances of shuffled labels are generated. Labels for fixed nodes are not shuffled and labels for the other nodes are not shuffled with the fixed nodes. The graph is sampled with URPEN with the specified sampling frequency and the labels for the sampled paths are imported from either the original cell type labels or the set of nTrain shuffled labels created for the control data. After incorporating the reverse paths into the dataset, both primary and control data are fed into the SMORE method to identify motifs. The algorithms for path sampling and motif discovery are described in detail below. The topological relations inside the spatial data can be represented as graphs. A graph is defined as an ordered pair G = ( V ; E ) consisting of a nonempty set of vertices V and a set of edges E of two-element subsets of V . In the following, we are dealing with undirected graphs, but the path sampling algorithm can equally be applied to directed graphs as well. Vertices in V are assumed to be uniquely indexed by the integers \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1, \ldots , n$$\end{document} 1 , … , n , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n=|V|$$\end{document} n = | V | is defined as the size of the graph. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v> u$$\end{document} v > u is used to indicate that the index of a vertex v is larger than that of a vertex u . For a vertex \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v \in V_0$$\end{document} v ∈ V 0 , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_0$$\end{document} V 0 is a subset of vertices, its forward neighborhood with respect to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V_0, N_{frw}(v, V_0)$$\end{document} V 0 , N frw ( v , V 0 ) , is defined as the set of all vertices from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\setminus V_0$$\end{document} V \ V 0 which are adjacent to v . The neighborhood of a vertex is simply its forward neighborhood with respect to the empty set, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\emptyset$$\end{document} ∅ . The developed algorithm for finding motifs in graphs consists of two main components; first a method to uniformly sample the graph, and second, a procedure to find motifs inside obtained sequences. The sampling algorithm takes a graph G and all paths inside the graph are sampled uniformly. In a network, a path is a walk that does not intersect itself. Selected paths are also constrained to be radial. Radial condition in a spatially embedded network is defined as the requirement that actual physical distance along a path monotonically increases along the sequence of edges in the path. The graph on spatial dataset is created based on the distance between nodes, therefore, the radial requirement enables us to better interpret output motifs in our experiment on the real dataset. The significance of each initial seed is obtained by the negative binomial test. The Poisson distribution arises naturally in the study of data taking the form of counts. If a data point y follows the Poisson distribution with rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document} θ , then the probability distribution of a single observation y is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y\sim \text {Poisson}(\theta )$$\end{document} y ∼ Poisson ( θ ) . The Poisson model for data points \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathbf {y_v}=[y_1, y_2, \ldots , y_n ]$$\end{document} y v = [ y 1 , y 2 , … , y n ] can be extended to the form \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_i\sim \text {Poisson}(w_i\theta )$$\end{document} y i ∼ Poisson ( w i θ ) , where the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$w_i$$\end{document} w i values are known positive explanatory values proportional to the population, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document} θ is the unknown parameter of interest. Seed number in each graph can be modeled as a Poisson distribution where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_i$$\end{document} y i is the number of paths of some specific type (seed) in the graph, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document} θ is the underlying rate in units of seeds per graph. To perform Bayesian inference, we need a prior distribution for the unknown rate. We use a gamma distribution as prior, which is conjugate to the Poisson. With prior distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text {Gamma} (\alpha , \beta )$$\end{document} Gamma ( α , β ) , the resulting posterior distribution is obtained as 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \theta | \mathbf {y_v}\sim \text {Gamma} \left(\alpha + \sum\limits_{i=1}^n y_i, \beta + \sum\limits_{i=1}^n w_i \right) \end{aligned}$$\end{document} θ | y v ∼ Gamma α + ∑ i = 1 n y i , β + ∑ i = 1 n w i The known form of the prior and posterior densities can be used to find the marginal distribution for a single observation, which has a predictive distribution as 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} p(y_0|\mathbf {y_v})=\frac{p(y_0|\theta ) p(\theta |\mathbf {y_v})}{p(\theta |y_0, \mathbf {y_v})}=\text {NegBin}(\alpha _n, p_n), \end{aligned}$$\end{document} p ( y 0 | y v ) = p ( y 0 | θ ) p ( θ | y v ) p ( θ | y 0 , y v ) = NegBin ( α n , p n ) , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha _n=\alpha + \sum _{i=1}^n y_i$$\end{document} α n = α + ∑ i = 1 n y i , and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_n=\frac{w_0}{(\beta _n +w_0)}$$\end{document} p n = w 0 ( β n + w 0 ) with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta _n =\beta + \sum _{i=1}^n w_i$$\end{document} β n = β + ∑ i = 1 n w i . Assuming that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_0$$\end{document} y 0 is the seed number in the primary graph, p value for some specific observation is obtained as 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} P=\sum _{k=y_0}^\infty {n +k-1 \atopwithdelims ()k} p_n^k(1-p_n)^n, \end{aligned}$$\end{document} P = ∑ k = y 0 ∞ n + k - 1 k p n k ( 1 - p n ) n , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_0$$\end{document} y 0 , and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_i$$\end{document} y i , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i=1, 2, \ldots , n$$\end{document} i = 1 , 2 , … , n are the number of ZNIC sites of the specific seed in primary and control data, respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$w_i$$\end{document} w i numbers are the total number of ZNIC seeds in the respective graph. In our experiments, prior \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} α and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta$$\end{document} β values are assumed to be equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y_0$$\end{document} y 0 , and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$w_0$$\end{document} w 0 , respectively. The first nEval significant seeds according to this criterion are passed to the next stage of refinement and enrichment. The refinement and seed enrichment both use the same process of enrichment, except that refinement is only one iteration, and seed enrichment is NREFIter iterations. nEval motifs from initial evaluation step are first enriched for one iteration and top NREF motifs are further refined in seed enrichment block for NREFIter iterations or until p value does not improve. At each iteration, all seeds with positive likelihood ratio scores with respect to the PWM matrix obtained from the previous iteration are sorted with their score and p values obtained in the initial evaluation block and their ZNIC counts are computed. More specifically, it’s computed how many ZNIC samples each seed contributes to the previous samples. These counts are used to obtain significance with the same negative binomial test obtained in 4 . For the negative control data, sum of the counts over nTrain control data are used for significance computation. The PWM score that minimizes p value (maximizes absolute log p value) is selected to create the PWM matrix for the next iteration. There is an option in the algorithm to use differential enrichment where seeds are added to the PWM until the p value is decreasing. For example, if there are four PWM score thresholds with ZNIC log p values, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[-10, -11, -9, -13]$$\end{document} [ - 10 , - 11 , - 9 , - 13 ] , the default mode will consider lowest score threshold which is equivalent to log \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p{\text{value}}=-13$$\end{document} p value = - 13 , but differential p value will consider the threshold corresponding to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\log-p\mathrm{value}=-11$$\end{document} log - p value = - 11 . The differential p value option will generally lead to a simpler motif structure. Default mode is used for bipolar and 3D hypothalamus dataset, and differential p value is used for the preoptic area of mouse hypothalamus. Maximum likelihood estimation is used to estimate a new version of the motif PWM matrix for the next iteration. This step is iterated until the p value is decreasing or the maximum number of NREFIter is performed. In order to perform maximum likelihood estimation, let us assume that we have L distinct cell types in our dataset, encoded as integer numbers from 1 to L . Assuming that the initial seed consists of W letters, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S = s_1, s_2, \ldots , s_W$$\end{document} S = s 1 , s 2 , … , s W , the PWM matrix, M , is a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L \times W$$\end{document} L × W matrix where W is the length of the searched motif. Elements of the PWM matrix in the first iteration are obtained as follows, 4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \textbf{M}(i, j)= \left\{ \begin{array}{ll} {1+\rho \textbf{b} (s_j)}/{1+\rho }& \text {if}\ s_{j} = i\\ \rho /{1+\rho } & {\text {otherwise}} \end{array}\right. \end{aligned}$$\end{document} M ( i , j ) = 1 + ρ b ( s j ) / 1 + ρ if s j = i ρ / 1 + ρ otherwise where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho$$\end{document} ρ is the Dirichlet prior set to 0.01 in our experiment, and b is the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L \times 1$$\end{document} L × 1 vector of background probabilities of the cell types. For the subsequent iterations, the maximum likelihood estimation for M matrix is obtained as follows , 5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \hat{\textbf{M}}=\sum _{i=1}^{K} {P_i Z_i/N_i \textbf{I}_i } +\rho \textbf{b} \end{aligned}$$\end{document} M ^ = ∑ i = 1 K P i Z i / N i I i + ρ b The PWM matrix, M , is obtained by normalizing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\textbf{M}}$$\end{document} M ^ through the columns. K is the number of seeds involved in the motif, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textbf{I}_i$$\end{document} I i is the indicator matrix of ith seed, which is equivalent to PWM matrix of 4 with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho$$\end{document} ρ set to 0. Zi , Ni , and Pi are the incremental ZNIC counts, total ZNIC counts, and log p value of the ith seed involved in the motif, respectively. Total ZNIC counts are the number ZNIC sites of the ith seed and incremental ZNIC counts are the fraction of these sites that don’t have any node in common with previous seeds, up to i ’th seed. The background frequency in synthetic data experiment is assumed to be near uniform, with 6 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} {\textbf {b}}_F=[1, 1/2, 1/3, \ldots , 1/12]^{1/4}, \end{aligned}$$\end{document} b F = [ 1 , 1 / 2 , 1 / 3 , … , 1 / 12 ] 1 / 4 , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textbf{b}_F$$\end{document} b F is the background frequency and it is assumed to be known to the algorithm. Cell type labels, other than the ones for the motifs, are distributed randomly among available nodes according to their frequency. This specific form of background frequency is arbitrary and is designed to have at most twofold difference in the cell type frequency to avoid potential unwanted repeating cell type patterns like AAAA. Output motifs are represented by sequence logos. Some output motifs are simple in structure and some are more complex, consisting of multiple cell types in each position. Simple logos like AAAA indicate a repetitive pattern of A type cells interconnected in the graph within that particular tissue. In more complex motifs like (AB)AAA, the first position can be occupied by either A or B. Embedded length-4 patterns in the synthetic data follow a format like (A/B)CDE, where one variable position can be filled by either cell type A or B, each with equal probability. The remaining positions in the pattern (CDE) are fixed to specific cell types. For a 2% embedding, the nodes corresponding to 60 random path samples from the total length-4 sampled paths are labeled as ACDE and another 60 as BCDE, while all other nodes in the graph are randomly labeled. In Fig. 3 a, the Pearson correlation coefficient (PCC) between the extracted motifs and the embedded motifs is shown. The Tomtom method is employed to identify the best enriched matches among the 10 output motifs from SMORE. Tomtom searches for the best match by considering all possible shifts of the query motif with respect to the target motif (the embedded motif in this case). The matching position weight matrices (PWMs) are aligned using the obtained offsets and overlaps from Tomtom, and the PCC is computed and plotted to evaluate the pipeline’s accuracy. Each embedded motif word corresponds to a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$12 \times 4$$\end{document} 12 × 4 PWM matrix, employing a Dirichlet prior with a weight of 0.01. The Dirichlet prior is a uniform distribution of the cell types. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{TPR}$$\end{document} TPR is computed as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{TPR}=\textrm{TP}/(\textrm{TP}+\textrm{FN})$$\end{document} TPR = TP / ( TP + FN ) , where TP (true positive) denotes the number of output motifs with a correlation exceeding the threshold (0.95 in our context) and a p value below the specified threshold. FN (false negative) is the number of instances with a correlation surpassing the threshold but a p value greater than the specified threshold. Thus, TP + FN represents the overall number of cases with a correlation exceeding the threshold. FPR is defined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{FPR}=\textrm{FP}/(\textrm{FP}+\textrm{TN})$$\end{document} FPR = FP / ( FP + TN ) , with FP (false positive) indicating the number of output motifs possessing a correlation below the threshold and a p value below the specified threshold. TN (true negative) is the number of output motifs with a correlation below the threshold and a p value exceeding the specified threshold. Consequently, FP + TN denotes the total number of cases with a correlation below the threshold. One potential method for deriving a functional interpretation of these motifs involves examining gene expression disparities between the cells participating in the motif and those that are not involved. This analysis is carried out in the “ Cells within spatial motifs exhibit gene expression differences compared to other cells of the same type ” section. Each motif is composed of multiple positions, and each position includes one or more cell types. For example, the motif (AB)CDE comprises cell types A and B in its first position. Among all cells labeled as A ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_A$$\end{document} N A ), only a subset, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_{AM}$$\end{document} N AM , takes part in the initial position of this particular motif. Each cell has a gene expression profile. For each gene, the delta median(dMedian) is calculated by subtracting the median expression of the gene in the subset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_{AM}$$\end{document} N AM cells involved in the motif from the median expression of that gene across all \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_A$$\end{document} N A cells of the specific type. The significance for this delta median value is then computed against a random selection of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_{AM}$$\end{document} N AM cells from the total \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_A$$\end{document} N A cells of that type. Assume that there are \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_{A}$$\end{document} N A cell types of A, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_{AM}$$\end{document} N AM of these cell types is in motif (AB)CDE, with dMedian expression \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x_0$$\end{document} x 0 . One sided p value (significance) for these cells is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p(\text {dMedian} \ge x_0$$\end{document} p ( dMedian ≥ x 0 ). The probability that dMedian expression of these \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_{AM}$$\end{document} N AM cells is larger than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x_0$$\end{document} x 0 is the probability that at least \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_0=\text {floor}(N_{AM}/2+1)$$\end{document} N 0 = floor ( N AM / 2 + 1 ) of these cells have expression greater than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x_0$$\end{document} x 0 , 7 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} p_0=\frac{\sum _{k=N_0}^{N_{AM}} {N_H \atopwithdelims ()k}{N_L \atopwithdelims ()N_{AM}-k} }{{N_A \atopwithdelims ()N_{AM}}} \end{aligned}$$\end{document} p 0 = ∑ k = N 0 AM N H k N L N AM - k N A N AM Where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_L$$\end{document} N L is the number of cells (out of all \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_A$$\end{document} N A cells) that have lower than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x_0$$\end{document} x 0 delta expression, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N_H$$\end{document} N H is the number of cells that have higher than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x_0$$\end{document} x 0 delta expression. | Review | biomedical | en | 0.999995 |
PMC11697876 | Non-traumatic subarachnoid hemorrhage (SAH) is a life-threatening emergency caused by bleeding in the subarachnoid space caused mainly by a ruptured aneurysm . Despite recent advances in subarachnoid hemorrhage treatment, overall mortality and severe disability remain high, and this represents a major public health concern since the population affected is younger compared with other kinds of stroke. As shown in the Swiss SOS study, of all patients affected by SAH, 1 in 10 patients survived in a dependent state at 1 year, while 22% of patients died within 1 year . Furthermore, among SAH survivors with good functional outcomes, quality of life is often impaired. The pathophysiology of brain injury in SAH patients is extremely complex and not fully understood. The main determinants of brain injury are early brain injury (EBI) and delayed cerebral ischemia (DCI) . EBI encompasses several disorders occurring within the first 72 h following the aneurysm rupture. Extravasation of blood into the subarachnoid space causes an increase in intracranial pressure (ICP) and consequent reduction of cerebral blood flow and transient global cerebral ischemia. Moreover, hydrocephalus could represent a life-threatening emergency in the acute phase and could aggravate the injury. Other mechanisms leading to poor outcome are vasospasm and DCI . Even though cerebral vasospasm has been considered the main determinant of DCI, recent data show that pathophysiology is far more complex than once thought; in fact, cortical spreading depression, microthrombosis, and impaired collateral circulation could contribute to DCI and consequent brain tissue infarction . Predicting outcomes among critical care patients is difficult: Advances in medicine have reduced short-term mortality from critical illness despite an increasing number of older patients, but this poses many questions about the cost of survival. As widely reported, patients surviving critical illness develop long-lasting impairments affecting physical, cognitive, and/or mental health status . Furthermore, among neurocritical care patients, the burden of injury can be significant on long-term outcomes, and this should be considered since the dichotomy survived/death cannot fully describe the impact of the disease on patients. Long-term outcome in SAH is mainly inferred from randomized control trial cohorts, and even fewer data are available from patients with poor grade SAH admitted to intensive care unit (ICU). A recent French study showed as only two-thirds of patients survive at one year, and only one-third of them has a good outcome . As highlighted by several organizations, including WHO, disability is just a partial description of the health status of a patient ; the patients’ experiences of disability, her/his adaptation, resilience, and the family background contribute defining patient’s quality of life . Glasgow Outcome Score (GOS), its extended form (GOSE) and modified Rankin Scale (mRS) are the most used tools to evaluate disability after SAH, while Short Form (36) Health Survey and EuroQoL are the most used to evaluate the quality of life in this group of patients . The long-term outcomes of SAH patients have been evaluated in several studies . However, to our knowledge, there are no studies that evaluate the relationship between clinical presentation and long-term disability and quality of life in SAH patients admitted to the ICU, a restricted group of patients who often exhibit distinctive outcomes compared to others. Our pilot study aims to describe the long-term outcome and quality of life of patients affected by SAH admitted to our ICU and to establish if there is a relationship between outcome and initial clinical presentation or DCI. The following data were extracted from clinical records: Baseline data: age, sex, history of smoking, chronic arterial hypertension, diabetes mellitus, WFNS scale, HH scale, mFS, GCS at admission Aneurysm treatment: endovascular vs neurosurgical, requiring of an external ventricular drain (EVD) Complication occurred during ICU stay: early (< 24 h) intracranial hypertension, hydrocephalus, late (> 24 h) intracranial hypertension, seizures, central neurological fever, hyponatremia, nimodipine interruption due to hypotension, increased middle cerebral artery (MCA) mean flow velocity, vasospasm location, impaired CT perfusion, induced hypertension therapy, endovascular vasospasm treatment Outcome measures: hospital length of stay, 12-month Glasgow Outcome Scale Extended (GOSE), 12-month EuroQoL-5D-3L Index (EQ-5D Index), and EQ-VAS Aneurysm exclusion was performed as soon as possible. The type of securing procedure was decided after a multidisciplinary discussion between neuroradiologists, neurosurgeons, and intensivists. EVD was placed in case of acute hydrocephalus, HICP, or for surgical needs. All patients with GCS ≤ 8, delayed awakening after procedure or critical medical condition were admitted to ICU. In all patients, a CT scan was obtained within 24 h after aneurysm exclusion. Nimodipine was administered orally 60 mg q4h. If patients showed poor tolerance to nimodipine developing hypotension, nimodipine administration was suspended. Sedation was suspended as soon as possible in all the patients without HICP, delirium, hemodynamic instability, or acute respiratory failure, while TCD monitoring through the acoustic transtemporal window was performed daily to detect ultrasonographic vasospasm . In case of clinical deterioration or ultrasonographic vasospasm, supported by clinical judgment, a CT angiography/CT perfusion or cerebral arteriography was obtained. Patients with vasospasm/DCI were treated with blood pressure augmentation. In case of refractory vasospasm/DCI, endovascular rescue therapy was considered. Complications were defined as follows: High intracranial pressure (HICP): This was defined as intracranial pressure > 20 mmHg sustained for a period > 5 min . If it occurred within 24 h from admission, it was defined early HICP; if it occurred after this period, it was defined as late HICP. Seizures: EEG were performed as early as possible in comatose patients. Electrographic and electroclinical seizures, electrographic status epilepticus, and electroclinical status epilepticus were diagnosed according to the current American Clinical Neurophysiology Society guidelines . Seizures were treated with antiepileptic drugs and, in most severe cases, with deep sedation. EEG during sedation holiday was performed to evaluate efficacy of antiepileptic therapy. Central neurological fever: central neurologic fever was defined on clinical judgment accordingly to the criteria reported by Hoker et al. . Hyponatremia: patients requiring sodium implementation to maintain serum level within normal range. Symptomatic vasospasm and delayed cerebral ischemia: accordingly with Rass et al., we used the following definition : ◦ Clinical deterioration: New focal neurological deficit OR GCS 2 points OR NIHSS 2 points once excluded other causes (e.g., fever, hyponatremia, hydrocephalus). ◦ Angiographic vasospasm was defined as a reduction in arteries diameter of at least one-third, measured on either CTA or digital subtraction angiography (DSA). ◦ Delayed cerebral infarction (DCIn) was defined as an infarction on CT scan or MRI scans performed within 6 weeks after SAH, absent from the scan performed between 24 and 48 h after aneurysm occlusion, and not attributable to another cause: aneurysm-securing procedure or EVD placement. Telephone interviews were performed between 12 and 15 months after initial bleeding by two operators to evaluate outcome. When patients were unable to reply to the survey, the patient’s caregiver was interviewed. Degree of disability and survival were measured using the GOSE . Favorable outcome was defined as GOSE ≥ 4 while patients with GOSE ≤ 3 as unfavorable outcome . Quality of life was evaluated using EQ-5D-3L . The EQ-5D Index scores were calculated from the vectors by using the UK tariff . The nominal range of the EQ-5D Index scores is 0 to 1, but negative scores as low as − 0.59 are possible for health states deemed to be worse than death. The EQ-VAS, instead, records the patient’s self-rated health on a 0–100 scale where 0 represents “the worst health you can imagine,” while 100 is the “best health you can imagine.” When the patients are unable to answer, it is asked to caregivers to give the answer the patient would have given. Objectives our study were as follows: To describe the long-term outcome of patients admitted to ICU following SAH To describe quality of life of patients admitted to ICU following SAH Independent Student’s t test, Mann–Whitney U test, Pearson’s chi-squared test, Fisher’s exact test, and Kruskal–Wallis test were used to test any difference between the “favorable” and “unfavorable “groups. Secondly, to test correlations between different variables, we measured Spearman’s rank correlation coefficient. Table 1 summarizes main baseline characteristics. Table 1 Relation between baseline characteristics at admission and outcome Total Favorable* Unfavorable* p V alue Missing Female N (%) 24 (63.2) 18 (75.0) 6 (25.0) 0.081 - Male N (%) 14 (36.8) 6 (42.9) 8 (57.1) Current smoking N (%) 11 (28.9) 6 (54.5) 5 (45.5) 0.493 Chronic hypertension N (%) 21 (55.3) 13 (61.9) 8 (38.1) 0.859 - Type II diabetes N (%) 2 (5.3) 2 (100) - 0.267 - Age Median (IQR) 58 (20) 53.5 (17) 60 (19) 0.098 - Glasgow Come Scale N (%) 37 23 (62.1) 14 (37.8) 0.156 1 Median (IQR) 11 (8.25) 13 (5.5) 6 (11) WFNS scale 1 N (%) 10 (27.0) 6 (60) 4 (40) 0.078 1 2 N (%) 5 (13.5) 5 (100) - 3 N (%) 7 (18.9) 5 (71.4) 2 (28.6) 4 N (%) 5 (13.5) 4 (80) 1 (20) 5 N (%) 10 (27.0) 3 (30) 7 (70) Hunt-Hess scale 1 N (%) - - - 0.033 1 2 N (%) 11 (29.7) 7 (63.6) 4 (26.4) 3 N (%) 9 (24.3) 9 (100) - 4 N (%) 3 (8.1) 1 (33.3) 2 (66.7) 5 N (%) 14 (37.8) 6 (42.9) 14 (37.8) mFisher scale 1 N (%) - - - 0.165 - 2 N (%) 6 (15.9) 5 (83.3) 1 (16.7) 3 N (%) 3 (7.9) 3 (100) - 4 N (%) 29 (76.3) 16 (55.2) 13 (44.8) *Percentages in the columns “favorable” and “unfavorable” refer to the variable analyzed, not to the overall population Table 2 summarizes relation between clinical course in ICU and outcome. Table 2 Relation between clinical course in ICU and outcome Total Favorable* Unfavorable* p Value Missing Treatment Endovascular N (%) 19 (51.3) 11 (57.9) 8 (42.1) 0.582 1 Surgical N (%) 18 (48.6) 12 (66.7) 6 (33.3) Hydrocephalus N (%) 22 (57.9) 13 (59.1) 9 (40.9) 0.542 - Neurological fever N (%) 27 (71.0) 18 (66.7) 9 (33.3) 0.482 Early HICP (<24 h admission) N (%) 11 (28.9) 4 (36.4) 7 (63.6) 0.028 - Late HICP (>24 h admission) N (%) 10 (26.3) 4 (40) 6 (60) 0.077 - Hyponatremia N(%) 18 (47.4) 12 (66.7) 6 (33.3) 0.671 Seizures N (%) 5 (13.6) 3 (60.0) 2 (40.0) 0.569 - Nimodipine interruption N (%) 12 (31.6) 9 (75.0) 3 (25.0) 0.309 - Clinical deterioration N (%) 7 (18.4) 5 (71.4) 2 (28.6) 0.693 - TCD Vmean MCA > 120 cm/sec N (%) 16 (42.1) 10 (62.5) 6 (37.5) 0.942 - TCD Vmean MCA > 160 cm/sec N (%) 12 (31.6) 8 (66.7) 4 (33.3) 0.760 - TCD Vmean MCA > 200 cm/sec N (%) 8 (21.0) 6 (75.0) 2 (25.0) 0.434 - Vasospasm (MRA/CTA or catheter angiography) N (%) 13 (34.2) 9 (69.2) 4 (30.8) 0.931 Impaired CT/MR perfusion N (%) 4 (10.5) 2 (50) 2 (50) 0.409 Hospital length of stay (H-LOS) Median (IQR) 22 (12) 19.5 (13) 22.5 (8) 0.540 *Percentages in the columns “favorable” and “unfavorable” refer to the variable analyzed, not to the overall population Eleven (28.9%) patients developed early HICP, while 10 (26.3%) developed late HICP. Intracranial hypertension was treated implementing sedation, with hyperosmolar therapy, CSF withdrawal, normo-hypocapnia, and, in most extreme cases, surgical decompression. Figure 2 summarizes incidence of vasospasm and DCI. Seven patients (18.4%) showed clinical deterioration defined as new focal neurological deficit or loss of 2 points on GCS or 2 points on NIHSS. All of them had an increase in MCA mean flow velocities of at least 120 cm/sec. Fig. 2 Vasospasm and delayed cerebral ischemia detection Of the 16 patients with MCA mean flow velocities > 120 cm/s, CT angiography and/or arteriography were performed in fifteen patients (39.5%). Vasospasm was confirmed on CTA/arteriography in thirteen patients (34.2%). Twelve patients (31.6%) required induced therapeutic hypertension. Nimodipine was interrupted in twelve patients (31.6%) due to poor tolerance. Only four patients (10.5%) showed impaired perfusion on CT scan. Of the four patients with impaired CT perfusion, three patients underwent endovascular rescue treatment. Only one patient, the one with impaired CT perfusion who did not undergo to endovascular rescue therapy, developed delayed cerebral infarction (DCIn). Long-term neurological outcome of the 38 patients is displayed on Table 3 . Twenty-four patients (63.2%) had favorable outcome (GOSE ≥ 4). GOSE at 1 year is summarized on Table 3 . Table 3 GOSE at one year GOSE at 1 year N (%) Unfavorable outcome 1 9 (23.7) 2 1 (2.6) 3 4 (10.5) Favorable outcome 4 5 (13.2) 5 1 (2.6) 6 5 (13.3) 7 8 (21.0) 8 5 (13.2) Among 29 patients (76.3%) survived at 1 year, median EQ-5D Index was 0.743 (IQR 0.287), while median EQ-VAS was 74.79 (IQR 18.5). Median EQ-5D Index and median EQ-VAS were higher among patients with favorable outcome [EQ-5D Index 0.796 (IQR 0.2) vs − 0.331 (IQR 1.0) p = 0.037], [EQ-VAS 80 (IQR 20) vs 50 (IQR 37.5) p = 0.003]. Relations between GOSE and EQ-5D Index and EQ-VAS are shown in Table 4 and Fig. 3 . Table 4 Relation between GOSE and EQ-Index GOSEEQ-VAS 2 − 0.331 (0) p = 0.051 20 (0) p = 0.019 3 0.144 (1.2) 50 (30) 4 0.587 (0.45) 70 (7.5) 5 0.587 (0) 96 (0) 6 0.812 (0.38) 85 (18.75) 7 0.83 (0.18) 80 (15) 8 0.812 (0.22) 88 (16.25) Data are reported as median and interquartile range (IQR) Fig. 3 Distribution of EQOL-Index ( a ) and EQOL-VAS ( b ) in the group with favorable (blue) and unfavorable (red) outcome Finally, our higher incidence of good outcome could be related to a low cut-point on GOSE. There is no consensus on the cut-off point to define favorable outcome with GOSE in acute brain injury patients. As shown in a recent paper by Zuckerman et al. there is a wide range of cut-points on GOSE in patients with acute brain injury that ranges from 3 to 7 . Accordingly with the other authors , we included GOSE 4 among favorable outcome, recognizing functional independence for at least 8 h as a favorable outcome for patients and caregivers. Another major issue of discussion regarding outcome is the quality of life perceived by the patients. Median EQ-VAS varies significantly among GOSE grades ( p = 0.019), while median EQ-index is not significantly different. This could be related to a small sample-size, but also to a broader effectiveness of EQ-VAS in summarizing overall health that is closer to the patient’s perspective. As reported in Table 3 , the highest GOSE grades are not the ones with the highest values of EQ-5D Index and EQ-VAS. This is a phenomenon known as disability paradox . Several studies have shown that many patients, despite a severe disability after brain injury, enjoy a high quality of life . It is remarkable the broad range of EQ-VAS and EQ-5D Index in the GOSE 3 group in our cohort: In fact, in this group usually considered an unfavorable outcome in studies on acute brain injury, there are a patient with poor quality of life (EQ-5D Index -0.594 and EQ-VAS of 20) and a patient with good quality of life (EQ-5D Index 0.883, EQ-VAS of 80). Should this still be considered a poor outcome, or should we focus our attention on the perceived quality of life? It is a thought question that goes behind the purpose of this study, but that should foster a debate. In this study, of the most used severity scale, only HH scale seems to show a relation with poor outcome. This scale, introduced in 1968, was first used to predict the rate of mortality based solely on the clinical features in SAH patients; higher HH grades have been associated with poor outcomes also in more recent studies . However, since HH scale does not take in account the presence of reversible causes of coma such as hydrocephalus and seizures, we agree that this scale should not be used alone to define the prognosis of SAH patients. Consistently with other studies , GCS at admission is related to long-term outcome in our cohort. The level of consciousness in patients admitted with SAH could represent the epiphenomenon of the early brain injury that is developing after aneurismal rupture; the pathophysiology of this process is not completely understood and should be the object of further studies. Nonetheless, a poor GCS at admission could be associated with a good recovery as shown by Hoogmeoed et al. . Among SAH-related complications, only high early intracranial hypertension was related with poor outcome in our cohort. This finding could have different explanations. Early HICP could be related to a well-known cause such as hydrocephalus, but this was not related to poor outcome in our cohort. Possibly, early HICP could be a surrogate marker of EBI due to loss of autoregulation or cerebral edema . As highlighted in other papers, our data support the lack of relation between vasospasm detected on TCD and/or CTA and long-term outcome. The incidence of vasospasm is similar to other cohorts of patients and emphasizes the importance of distinguishing between vasospasm, DCI and DCIn . As highlighted by recent papers, DCI has a complex pathology in which vasospasm is just one of the determinants of brain injury. TCD and angiographic spasm seem to pose the patients at higher risk of DCI, but this is not related to long-term outcome [ 39 – 41 ]. DCI can be suspected in patients with clinical deterioration or in patients with impaired CT-perfusion . As remarked by the same authors that proposed the definition of clinical deterioration due to DCI in 2010, diagnosis of DCI can be tricky due to several confounders, especially in ICU patients . The presence of several confounders combined with the small sample size could explain the lack of relation between DCI and long-term outcome in our cohort. Another possible explanation is that DCI is not strictly related to DCIn. In fact, in our cohort, only 25% of patients who had impaired CT-perfusion developed DCIn. This seems to support the hypothesis that DCI detected on CT-perfusion still represents a reversible situation that requires the highest quality of care to prevent DCIn . This study describes long-term disability and quality of life in SAH patients admitted to ICU and their relation with clinical features. High HH grades and early HICP were related with unfavorable outcome. Among patients with unfavorable outcome, quality of life has a broad range of variability, and this result should be taken into account when reporting patient-centered outcomes. | Study | biomedical | en | 0.999998 |
PMC11697907 | ADHD is most commonly treated with stimulant and non-stimulant medications, which have been shown to be clinically effective . Extended-release (ER) stimulants provide the benefits of long-acting symptom control leading to greater treatment satisfaction compared to immediate-release (IR) stimulants . These long-acting medications reduce the need for repeated doses, thereby improving adherence and treatment response [ 3 – 5 ]. They also have a lower risk of misuse compared to short-acting IR stimulants . However, IR stimulants are still used to supplement once-daily medications when symptoms are not sufficiently controlled . Prescribers may advise using IR formulations to prolong and boost the therapeutic effects of an ER medication or to curb unwanted effects if the ER dose wears off . Formal treatment guidelines specifically for adults with ADHD have not yet been developed in the U.S . With approximately 30 different FDA-approved stimulants available for providers to choose from, first-line treatment decisions are often the result of trial and error [ 8 – 10 ]. Justifying an initial choice of prescription is further complicated by the variability in treatment responses between patients . Although stimulants are the most effective intervention for ADHD , some patients do not respond well to common frontline medications . A number of clinical factors contribute to heterogeneous treatment effects. Individuals who are older (amongst children), have milder symptoms, and have comorbid anxiety are the least likely to respond well to stimulants . Worse initial symptoms, including inattentiveness and disinhibition, are weak predictors of favorable responses . Treatment non-adherence is related to worse outcomes. Patients are less likely to adhere to treatment if they are younger (< 25 years old), have less than a secondary level of education, lack of family history of ADHD, have lower baseline symptom severity, and perceive lower medication efficacy . The rise of precision medicine in psychiatry has underscored the need to identify reliable predictors of treatment response, particularly the tendency to supplement daily medication with IR formulations . Previous studies have shown that ER medications are effective on the group level, but few have sought to find whether individual variability can be attributed to baseline patient characteristics . Variable clinical outcomes likely arise due to complex interactions between patient factors, including baseline psychological profiles and demographics . Demonstrating reduced IR supplementation with a particular medication, regardless of underlying individual variables, would provide a basis on which to make first-line treatment decisions. If ER efficacy is significantly altered depending on a specific patient variable, it would help inform individualized treatment plans. Dyanavel XR is an ER amphetamine with a targeted pharmacokinetic profile enabling rapid onset of action with continuous release that prolongs its active duration to allow once-daily dosing . Dyanavel XR leverages its unique technology to optimize the balance between fast onset of effect and maintenance of efficacy throughout the day . In adults, Dyanavel XR improves symptoms and has a safety profile comparable with other approved stimulants . In a large national sample representing 60% of all insurance claims in the U.S., Dyanavel XR was shown to be more frequently used as monotherapy compared to other ER medications . The abundance of individual factors with the potential to confound the overall effect of Dyanavel XR on monotherapy rates warrants investigation to determine whether any patient-level variable can explain the effect of Dyanavel XR on rates of IR supplementation. The purpose of the current study was to determine whether Dyanavel XR’s tendency to reduce IR supplementation could be explained by any other individual patient variable. Through a series of predictive analyses using retrospective data from 417 adult patients with ADHD from the Rochester Center for Behavioral Medicine (RCBM), the results aim to provide insights into potential predictive variables for ADHD treatment response. This study is a retrospective cohort analysis of medical treatment records obtained between November 2022 and June 2024. To be included, participants must have had a diagnosis of ADHD, have received treatment for ADHD, be at least 18 years of age or older at the start of treatment, received treatment of at least one extended release (ER) psychostimulant (amphetamine or methylphenidate preparations) for at least six months, and provided consent for secondary research use of their medical treatment data. Participants with a potentially confounding comorbid psychiatric condition, including bipolar spectrum disorders, alcohol and substance use disorders, or an initial Patient Health Questionnaire-9 (PHQ-9) score greater than or equal to 14, were excluded. Participants with a potentially confounding comorbid medical condition, including thyroid conditions, cancer or chemotherapy treatment, sleep disorders, or migraines, were also excluded from the study. A stratified sampling strategy was used, grouping patients by ER medication to target a total of 150 patients for each of the following ER formulations: Dyanavel XR, ER amphetamine (equal numbers of Adderall XR and lisdexamfetamine [Vyvanse]), and ER methylphenidate (equal numbers of Focalin, Concerta, and generic methylphenidate ER). The target sample size was calculated based on a power analysis for structural equation modeling to meet a power of 85% when the root mean square of error of approximation is 0.09 and approximated degrees of freedom is 20 . Patient records were selected randomly for each stratum. After reviewing patient records and excluding those who did not meet the study criteria, there were 143 Dyanavel XR, 131 ER amphetamine (65 Adderall XR and 66 lisdexamfetamine), and 143 methylphenidate (51 Focalin, 53 Concerta, and 39 generic methylphenidate ER) patient records meeting the inclusion criteria. If patients were missing any assessments, their data were included in analyses where possible, but values were excluded if missing. Participants were referred to RCBM prior to treatment by various mental health and medical professionals and completed intake forms prior to their intake appointment. Next, they completed a pre-visit survey prior to each visit through the online survey distribution software, Qualtrics. The pre-visit survey is hosted by RCBM’s Qualtrics platform and is connected with RCBM’s electronic charting program through the Qualtrics API. This API connection allows for a patient’s individual responses to be automatically filed in their chart for clinician review. Their responses are paired with information from the electronic medical record so the clinician can review the patient-reported information in tandem with their medical history and clinician-reported symptom severity ratings from visit to visit. The API also allows the researchers to extract the necessary variables for the current study’s analyses without accessing the electronic medical record system. This analysis compiled data from the pre-visit surveys at baseline and after 90 days of ER treatment. The follow-up time point of 90 days was selected because, in practice, most patients would have had their first follow-up appointment with their prescribing clinician within 90 days . Clinicians completed the Clinical Global Impression (CGI) for patients at each visit . The CGI was developed by the National Institute of Mental Health in collaborative pharmacology trials of schizophrenia to assess illness improvement. Since its origins, it has become a routine measure in psychiatric settings. The scale has three items: Severity of Illness (CGI-S), Global Improvement (CGI-I), and Efficacy Index. CGI-S is a single item rating on a seven-point scale from 1 (“normal”) to 7 (“extremely ill”) asking the clinician to rate the patient’s severity of illness based on their experience with individuals of the same clinical population. The CGI-I is also a single item rating on a seven-point scale from 1 (“very much improved”) to 7 (“very much worse”). The Efficacy Index is a rating of the effect of the therapeutic intervention from 1 (“none”) to 4 (“outweigh therapeutic effect”) . The CGI scale has established utility in the rating of schizophrenia, panic disorder, depression, obsessive compulsive disorder, and social anxiety disorder . It has good concurrent validity and sensitivity to change in patients with panic disorder and depression and performs similarly to other standard outcome measures, including the Health of the Nation Outcome Scales and the Brief Psychiatric Rating Scale . The change between CGI-S score at admission and discharge is highly correlated with the CGI-I at discharge, showing its reliability to interpret changes in disorders . Patients completed an online survey prior to every clinic visit. The pre-visit survey form includes three psychometric tools: the Patient Health Questionnaire-9 (PHQ-9) , the Generalized Anxiety Disorder-2 (GAD-2) , and the ADHD Symptom and Side Effect Tracking (ASSET) scale . To extract patient data for this analysis, researchers performed a query of patient data from the Qualtrics API. The query included the list of medications being actively managed by RCBM at the time of each visit, the type of treatment provider that the patient saw for the follow up visit and/or their prescribing clinician, and the diagnoses and/or clinical problems the clinician designated as the targets of treatment. Data from pre-visit surveys were also used to calculate the tendency of prescribers to pair an IR stimulant with an ER stimulant, as reported by the patients’ indication of their prescribing clinician and current medications. The Patient Health Questionnaire (PHQ)-9 is a nine-item self-administered screening tool for depression . Responses are rated on a Likert scale from 0 (“not at all”) to 3 (“nearly every day”) indicating increasing severity of symptoms with a maximum score of 27. Across 14 validation studies conducted in primary care, medical outpatients, and specialty services, the PHQ-9 has high sensitivity (0.80 [95% CI: 0.71–0.87]) and specificity (0.92 [95% CI: 0.88–0.95]) for major depression when scores are greater than or equal to 10 . The Generalized Anxiety Disorder (GAD) scale-2 is a 2-item shortened version of the GAD-7, a seven-item, Likert scale for identifying GAD, with items rated from 0 (“not at all”) to 3 (“nearly every day”) . The GAD-7 has been validated in large samples in primary care, with internal consistency, good test-retest reliability, and high sensitivity (89%) and specificity (82%) for GAD. The GAD-2 was developed as a truncated version of the full questionnaire, only presenting the two questions of the GAD-7 representing the core symptoms of anxiety. With a cutoff score of greater than or equal to three indicating GAD, the GAD-2 scale maintains high sensitivity (86%) and specificity (83%) . The ADHD Symptom and Side Effect Tracking Scale (ASSET) is a ten item self-report measure for ADHD symptom severity with a companion list of assorted side effects clinicians are advised to track throughout psychopharmaceutical treatment for ADHD. The scale asks the participant to rate the level of the impact on daily life functioning they may have experienced due to problems with the sign or symptom of ADHD referenced by the item (anchors: 1 = no problem present, 6 = severe impact). The ten items are split into two subscales. The Inattentive Subscale includes the items “attention span,” “forgetfulness,” “follow-through,” “trouble organizing tasks and activities,” “misplacing daily items,” and “productivity.” The Hyperactivity and Impulsivity Subscale includes the items “fidgetiness,” “trouble waiting turn/general impatience,” “anxiety,” and “mood”. The scoring of the baseline scale is a factor score calculated as a weighted sum of the ten severity items. A cut score of greater than or equal to 4.40 achieves high sensitivity (80%) and specificity (80%) . A factor score change of 0.75 indicates reliable change . The list of side effects included insomnia, generalized pain, fatigue, dry mouth, poor appetite, food binges, tics, anger, suspiciousness, restless legs, end of dose crash, return of symptoms as dose wears off, and unwanted changes in weight, and were rated on a Likert scale (1 = never, 5 = always). Baseline patient characteristics from the screening battery and pre-visit survey at their initial and 90-day visits, or the visit closest to 90 days since the start of their ER treatment, are reported. Descriptive results are stratified by whether the patient supplemented their treatment with an IR stimulant at 90 days. For continuous variables, mean, standard deviation, minimum, and maximum values were calculated, and frequency counts and percentages were calculated for categorical variables. The relationship between Dyanavel XR stimulant use and the tendency to supplement with IR stimulants was assessed by a crosstabulation with Z-tests for independent proportions. This analysis excluded patients who had used IR medications at baseline. Because Dyanavel XR was uniquely associated with a reduction in IR use at 90 days, the other medications (Adderall XR, Vyvanse, Focalin, and Methylphenidate ER) were collapsed into a single group, and Dyanavel XR use was coded into a binomial variable (Dyanavel XR = 1, other ER medication = 0). To account for IR stimulant use at baseline, an ANCOVA was conducted using Dyanavel XR as the predictor variable and IR at baseline as the covariate. Independent relationships between each patient variable and IR supplementation rate were assessed using point-biserial Pearson correlations for continuous variables and binomial regressions for categorical variables. To determine if any factors related to the patient, treatment, side effects, and treatment responses mediated this relationship between Dyanavel XR use and IR use at 90 days, backward stepwise linear regressions were modeled using variable groupings determined a priori in alignment with hypothesized predictive variables, and with IR use at 90 days as the outcome variable. The predictor variable groupings were: (1) side effects reported at baseline, (2) side effects reported after 90 days, and (3) change in symptoms from baseline to 90 days using assessment scale scores (ASSET, CGI-S, CGI-I, GAD-2, and PHQ-9). All variables in the group were included in the initial regression analysis. At each step, the variable with the lowest level of significance was removed, and the regression was performed again using the remaining variables. This process was repeated until all variables satisfied the significance condition ( p < .05). The analyses met the assumptions of linearity, independence, and normality of residuals. A linear regression was performed to determine the relationship between IR at baseline and the addition of IR supplementation at 90 days. Overall, IR use at baseline was a significant predictor of IR supplementation at 90 days ( R 2 = 0.552). Due to the overall effect of baseline IR, an exploratory analysis was conducted excluding patients who had used IR medications at baseline to determine if Dyanavel XR had a unique effect on the need to supplement with IR medication compared to other ER medications. Results of a cross-tabulation with Z-tests for independent proportions within each ER medication indicated that few patients ( n = 23) on any ER medication supplemented with an IR at 90 days. Dyanavel XR was the only ER medication that significantly reduced IR supplementation at 90 days (no IR added: n = 140; IR added:, n = 3; p < .05). Because Dyanavel XR was uniquely associated with a reduction in IR use at 90 days, the other medications (Adderall XR, Vyvanse, Focalin, and Methylphenidate ER) were collapsed into a single group, and Dyanavel XR use was coded into a binomial variable (Dyanavel XR = 1, other ER medication = 0). To account for IR use at baseline, an ANCOVA was conducted using Dyanavel XR as the predictor variable and IR at baseline as the covariate. As expected, the use of IR medication at baseline was associated with the addition of IR at 90 days of treatment ( F 1,413 = 5.86, p = .016). Controlling for IR use at baseline, there was a significant effect of Dyanavel on IR at 90 days ( F 1,413 = 4.67, p = .031), with Dyanavel XR being associated with reduced IR use. Of the patients who added an IR stimulant at 90 days, their prescribers had an average IR-prescribing tendency of 22.6%, and of patients who did not add an IR at 90 days, their prescribers had an average IR-prescribing rate of 27.3%. At baseline and at 90 days, patients took ASSET, GAD-2, and PHQ-9 tests, and their clinicians completed CGI-S and CGI-I scales (for descriptive statistics of assessment outcomes, see Table 2 ). Regardless of whether patients added an IR medication at 90 days, ASSET scores improved over the 90-day time period, but the effect was stronger in patients who supplemented with an IR medication (Table 3 ). Table 2 Descriptive statistics: patients with and without IR supplementation at 90 days a Variable n Min Max Mean SD Patients with IR supplementation at 90 days Time in Treatment Prior to ER (Days) 23 0.08 4761.07 746.11 1109.78 Baseline ASSET 23 0.98 5.44 3.76 1.21 PHQ-9 23 0 24 6.43 5.88 GAD-2 23 0.00 6.0 1.57 1.59 CGI-S 21 2 5 3.67 0.66 CGI-I 20 1 4 2.10 0.79 90 days ASSET 23 0.97 5.02 3.00 0.98 PHQ-9 23 0 23 5.61 5.37 GAD-2 23 0.00 6.00 1.91 2.11 CGI-S 21 3 4 3.57 0.51 CGI-I 20 1 4 2.15 0.67 Valid N (listwise) 17 Patients without IR supplementation at 90 days Time in Treatment Prior to ER (Days) 393 0.021435 4745.88 989.10 1164.93 Baseline ASSET 385 0.00 5.98 3.50 1.19 PHQ-9 392 0 21 7.08 4.68 GAD-2 393 0.00 6.00 1.93 1.55 CGI-S 371 2 6 3.83 0.76 CGI-I 360 1 6 2.81 1.13 90 days ASSET 385 0.00 5.88 3.12 1.08 PHQ-9 392 0 24 5.65 4.36 GAD-2 393 0.00 6.00 1.68 1.47 CGI-S 371 1 6 3.71 0.82 CGI-I 360 1 5 2.57 0.97 Valid N (listwise) 313 ASSET, ADHD Symptom and Side Effect Tracking; CGI-S, Clinical Global Impressions-Severity; CGI-I, Clinical Global Impressions-Improvement; ER, extended release; GAD-2, Generalized Anxiety Disorder 2-Item; PHQ-9, Patient Health Questionnaire-9 a The visit falling closest to 90 days after ER stimulant was prescribed Table 3 Magnitude of the change from baseline in psychometric assessments in patients with and without IR supplementation at 90 days a Change from baseline n Min Max Mean SD Cohen’s d b Patients with IR supplementation at 90 days ASSET 23 -1.68 2.91 0.7570 1.11 0.68 PHQ-9 23 -10.00 18.00 0.8261 5.04 0.16 GAD-2 23 -6.00 1.00 − 0.3478 1.47 0.24 CGI-S 21 -2.00 1.00 0.0952 0.62 0.15 CGI-I 20 -1.00 1.00 − 0.0500 0.51 0.10 Patients without IR supplementation at 90 days ASSET 385 -2.60 4.12 0.3858 0.98 0.39 PHQ-9 392 -14.00 15.00 1.4311 4.11 0.34 GAD-2 393 -6.00 5.00 0.2468 1.48 0.17 CGI-S 371 -2.00 3.00 0.1186 0.65 0.18 CGI-I 360 -3.00 4.00 0.2361 1.07 0.22 ASSET, ADHD Symptom and Side Effect Tracking; CGI-S, Clinical Global Impressions-Severity; CGI-I, Clinical Global Impressions-Improvement; ER, extended release; GAD-2, Generalized Anxiety Disorder 2-Item; PHQ-9, Patient Health Questionnaire-9 a The visit falling closest to 90 days after ER stimulant was prescribed b Indicating the magnitude of the change from baseline (small: Cohen’s d = 0.2, medium: Cohen’s d = 0.5, large: Cohen’s d ≥ 0.8) The binary variable indicating whether the patient was prescribed Dyanavel XR or another ER was significantly associated with IR supplementation at 90 days (χ 2 = 4.320, Nagelkerke R 2 = 0.039, p = .038). No other continuous (Table 4 ) or categorical (Table 5 ) variable was associated with IR supplementation. Table 4 Point-biserial Pearson correlations demonstrating the relationship between each continuous variable and the addition of IR medication at 90 days. Treatment responses representing a change in score between baseline and 90 days were Z-transformed prior to analysis Variable Group Variable Description Pearson correlation coefficient ( r ) R 2 p Demographics Age Age in Years 0.02 0.00032 0.716 ER Prescription Decision Tendency to Prescribe ER Integer Value: % of patients with an ER prescription − 0.10 0.0090 0.07 Time in Treatment Prior to ER Integer Value − 0.05 0.0023 0.33 Assessment scores at baseline ASSET ADHD symptoms in terms of daily life functioning impact at the time of visit 0.050 0.0025 0.31 PHQ-9 Severity of depressive symptoms in terms of frequency at the time of visit − 0.03 0.00096 0.53 GAD-2 Severity of anxious symptoms in terms of frequency at the time of visit − 0.05 0.0028 0.28 CGI-S Global severity of the overall presentation assessed by the clinician at the time of visit − 0.05 0.0023 0.34 CGI-I Global improvement of the overall presentation assessed by the clinician − 0.14 0.020 0.01 Assessment scores at 90 days a ASSET Severity of ADHD symptoms in terms of daily life functioning impact − 0.03 0.00063 0.62 PHQ-9 Severity of depressive symptoms in terms of frequency − 0.00 0.000004 0.96 GAD-2 Severity of anxious symptoms in terms of frequency 0.04 0.0013 0.47 CGI-S Global severity of the overall presentation assessed by the clinician − 0.04 0.0014 0.45 CGI-I Global improvement of the overall presentation assessed by the clinician − 0.10 0.0096 0.06 Treatment response (Change in clinical assessments from baseline to 90 days a) Change in ASSET Change of severity of ADHD symptoms in terms of daily life functioning impact 0.09 0.0074 0.08 Change in PHQ-9 Change of severity of depressive symptoms − 0.03 0.0011 0.50 Change in GAD-2 Change of severity of anxiety symptoms − 0.09 0.0083 0.06 Change in CGI-S Change of global severity of the overall presentation assessed by the clinician − 0.05 0.00086 0.34 ASSET, ADHD Symptom and Side Effect Tracking; CGI-S, Clinical Global Impressions-Severity; CGI-I, Clinical Global Impressions-Improvement; ER, extended release; GAD-2, Generalized Anxiety Disorder 2-Item; PHQ-9, Patient Health Questionnaire-9 a The visit falling closest to 90 days after ER stimulant was prescribed Table 5 Binomial regressions demonstrating the relationship between each categorical variable and the addition of IR medication at 90 days Variable Group Variable Wald statistic (χ 2 ) Nagelkerke R 2 p ER Prescription Number of ERs previously attempted 1.40 0.011 0.24 ER Prescription category (Dyanavel XR, AMP ER Stimulants, or MPH ER Stimulants) 2.40 0.017 0.12 ER Prescription category (pooled; Dyanavel XR or all other ER stimulants) 4.32 0.039 0.04 Side effects at baseline a Insomnia 0.06 0.000 0.81 Generalized Pain 0.06 0.000 0.80 Dry mouth 0.54 0.004 0.46 Poor Appetite 2.07 0.016 0.15 Food Binges 0.39 0.003 0.53 Tics 0.00 0.000 0.96 Anger 0.06 0.000 0.81 Suspiciousness 0.63 0.005 0.43 Restless Legs 0.28 0.002 0.60 End of Dose Crash b 2.12 0.005 0.15 Return of Symptoms as Medication Wears Off 3.55 0.057 0.06 Side effects at 90 days a, c Insomnia 0.12 0.001 0.73 Generalized Pain 0.33 0.002 0.57 Dry mouth 2.5 0.020 0.12 Poor Appetite 0.24 0.002 0.63 Food Binges 0.05 0.000 0.83 Tics 0.09 0.001 0.76 Anger 2.33 0.018 0.13 Suspiciousness 0.24 0.002 0.60 Restless Legs 0.67 0.005 0.41 End of Dose Crash b 2.22 0.015 0.14 Return of Symptoms as Medication Wears Off 2.90 0.046 0.09 ER, extended release a Patient-rated Likert scales of how often the side effect was experienced in the past two weeks b Answered only if on ADHD medications c The visit falling closest to 90 days after ER stimulant was prescribed The backward elimination stepwise regression started with 11 side effects reported at baseline (generalized pain, insomnia, fatigue, dry mouth, poor appetite, food binges, tics, anger, suspiciousness, restless legs, and end of dose crash) determined to be potential predictors of supplemental IR use at 90 days, as inadequate symptom management and side effects are key reasons for augmenting treatment in adults . The initial regression was not significant ( F 11,376 = 0.690, p = .748). After the backward elimination procedure, the model did not reach significance with any predictor variable. In another stepwise regression of the same 11 side effects reported at 90 days, the initial model was not significant ( F 11,376 = 0.841, p = .599). After backward elimination, the model trended to significantly predict IR supplementation at 90 days ( F 13,387 = 2.55, p = .055) when including the predictor variables dry mouth ( t = -1.61, p = .11), anger ( t = -1.44, p = .15), and end of dose crash ( t = 1.97, p = .050), with end of dose crash significantly predicting IR use at 90 days. Continuing the stepwise elimination resulted in a failure of any predictor to reach statistical significance (all p > .05). The regression including the change in CGI-S, CGI-I, ASSET, GAD-2, and PHQ-9 scores from baseline to 90 days was significant ( F 5,365 = 3.07, p = .010), indicating that the change in at least one assessment score affected IR use at 90 days. Change in CGI-S ( t = 0.59, p = .95), CGI-I ( t = -1.17, p = .25), and PHQ-9 ( t = 0.21 p = .83) were not significant predictors of IR supplementation at 90 days. Change in ASSET scores, indicating worsened ADHD symptom presentation, and change in GAD-2 scores, indicating worsened anxiety, predicted greater IR use at 90 days (ASSET: t = 3.01, p = .003; GAD-2: t = -2.38, p = .018). After identifying worsened ASSET and GAD-2 measures as significant positive predictors of IR use at 90 days, the next analysis sought to determine if Dyanavel XR could refine the predictive model. With the three predictor variables of change in ASSET, change in GAD-2, and Dyanavel XR, the model still significantly predicted IR use at 90 days ( F 3,404 = 4.81, p = .003), and all variables were significant predictors (ASSET change: t = 2.377, p = .018; GAD-2 change: t = -2.543, p = .011; Dyanavel XR: t = -2.112, p = .035). This demonstrates that together, improved ADHD symptoms and improved anxiety, in addition to being on Dyanavel XR, was associated with reduced IR use at 90 days. Structural equation models and path analyses were planned to determine the relationships between significant variables. However, the results did not yield sufficient independently significant variables to attempt an adequately-powered analysis. In lieu of a complete analysis, the results prompted an exploratory path analysis to determine whether the relationship between Dyanavel XR and reduced IR supplementation at 90 days was explained in part by the relationship between the end of dose crash and IR supplementation. The path analysis shows that Dyanavel XR independently reduced the occurrence of end of dose crash and reduced IR supplementation. However, end of dose crash was not significantly associated with IR supplementation, demonstrating that the effect of Dyanavel XR on IR supplementation is not explained by its tendency to mitigate end of dose crashes . Table 6 Path analysis showing relationships between Dyanavel, end of dose crash, and IR supplementation at 90 days Regression weights Estimate SE CR p End of dose crash a ← Dyanavel XR − 0.35 0.11 -3.30 < 0.001 IR supplementation ← End of dose crash a 0.01 0.01 1.22 0.22 IR supplementation a ← Dyanavel XR − 0.05 0.02 -2.03 0.04 CR, critical ratio; IR, immediate release; SE, standard error; XR, extended release a At the time of visit falling closest to 90 days after ER stimulant was prescribed With the tremendous heterogeneity in the patient population with ADHD, different treatments may interact with individual patient characteristics and modify the effect of therapies on patient outcomes . The present analyses aimed to determine whether a specific ER amphetamine stimulant, Dyanavel XR, was uniquely associated with the likelihood of patients supplementing ER treatment with an IR medication. The results show that over 90 days of treatment, compared to other ER amphetamines and ER methylphenidates, patients who were prescribed Dyanavel XR were less likely to supplement with IR formulations. Importantly, the association between Dyanavel XR and reduced IR supplementation was not explained by any other baseline patient variable measured here, but it was related to their change in ADHD and anxiety symptoms over 90 days. The analyses also showed that patients who used IR medications at baseline were more likely to supplement with an IR medication at 90 days, regardless of which ER medication they were prescribed. Dyanavel XR was distinct from the other ER stimulants because it maintained an association with reduced IR supplementation even when controlling for patients’ use of IR medications at baseline. Previous studies have shown that patients taking ER stimulants have greater treatment adherence and are less likely to switch to or augment with a medication of a different release method [ 3 – 5 , 37 ]. In a retrospective claims database analysis from 2010, patients taking ER amphetamines (Adderall XR, Dexedrine Spansules, or Vyvanse) had better treatment adherence and persistence than those taking ER methylphenidates (Concerta, Daytrana, or Focalin XR), but were equally likely to augment with IR medications . This is consistent with the present results showing that only Dyanavel XR reduced the tendency to supplement with an IR medication. These results distinguish Dyanavel XR from other ER amphetamines in its potential to be used as monotherapy. After confirming that Dyanavel XR reduced IR supplementation at 90 days, subsequent analyses investigated whether patient variables could better explain this association. The analyses did not reveal an impact of side effects reported at baseline on IR supplementation. Of the side effects reported at the 90-day visit, there was a trend to predict IR supplementation when the regression model was reduced to include only dry mouth, anger, and end of dose crash. In this model, the only significant predictor of IR use at 90 days was the frequency of experiencing an end of dose crash. Certainly, patients value medications with a longer duration of effect, shorter speed of onset, and reduced side effects . Although reducing the risk of an end of dose crash, or rebound effect after medication wears off, is less important to patients than reducing headaches, insomnia, and mood changes, it is still considered an important factor . Additionally, dose augmentation strategies are typically implemented when patients desire symptom management for longer than the typical 10–12 h achievable with ER medications and want to avoid rebound effects . The results here are consistent with the literature showing that some side effects, especially rebound after a dose wears off, would lead patients to augment their daily treatment . However, interpretation of this regression model is limited because the relationship was not strong and was driven by the effect of end of dose crash. Patients’ response to treatment regarding their anxiety and ADHD symptoms predicted their likelihood of supplementing with an IR medication. The present analysis found that patients who were taking Dyanavel XR whose anxiety and ADHD symptomatology improved over 90 days were less likely to supplement with IR medication. Given that anxiety is highly comorbid with ADHD, it is important to consider the effects one treatment may have on the presentation of both disorders . Some patients experience anxiety as a side effect of ADHD medications, and anxiety is one of the most common complications causing patients to discontinue their treatment . Long-term improvements in anxiety and ADHD, along with being on Dyanavel XR, reduces the tendency to supplement with IR medication, which supports the impact of Dyanavel XR on maintaining monotherapy and improving quality of life. The present results reflect the heterogeneous nature of ADHD in the general population and could thus be confounded by variability due to genetic and environmental factors that were not measured in this study. Differences in which symptoms manifest and persist, as well as their severity, have been linked to prenatal and postnatal experiences related to maternal health, stressors during pregnancy, and psychosocial childhood adversity . Exposure to harmful chemicals, toxins, and poor nutrition may also contribute to dysregulated neurobehavioral systems and give rise to ADHD-related symptoms . As such, the etiology of ADHD is complex and relies on an interaction between inherited genetic traits, divergent neurobiology, and environmental risk factors that could not be fully captured with the study design presented here. The clinical manifestation of ADHD may also vary over time within the same individual, as previous research has noted differences in which symptoms are likely to characterize the disorder depending on age . ADHD is traditionally thought of as a childhood disorder, but diagnoses amongst adolescents and young adults have become more common, in part due to the recognition that symptoms can fluctuate across development . For example, restlessness, aggression, and disruptive behaviors are more common in young children, whereas inattention tends to persist as people get older . Additionally, age has been linked to treatment patterns, including initiation, switching, and discontinuation . The present results showed that age at the start of the study was not significantly associated with IR supplementation at 90 days. Given the wide range of ages included from 18 to 81 years old, the age at which symptoms first appeared and age at first treatment initiation could have an effect, but the de-identified dataset precluded access to such historical data. Therefore, a direct investigation of how age at diagnosis, age at treatment initiation, and other demographic and environmental risk factors affect treatment responses would be a logical step for future research. Several additional limitations should be considered in the interpretation of these results. As an observational study, the patient population was sampled to achieve balanced groups of current ER medications, but could not account for the heterogeneity in patients’ medication history. The patient population also lacked diversity, with most patients being white and having obtained at least an undergraduate degree, limiting our external validity. Additionally, relatively few patients in this sample added an IR medication at the 90-day visit, limiting analytical power to detect smaller effect sizes of patient-level variables that could be influencing IR supplementation. Future studies would benefit from the inclusion of a control group of patients who did not receive an ER medication. Because this was a non-experimental study using observational data from a given timeframe, there was a paucity of data available for individuals who were not treated medically. Importantly, there was no significant difference in prescriber tendencies between the patients who did and did not add an IR medication, with both groups seeing clinicians who prescribed IR medications approximately one-fourth of the time. This rules out the potential that clinician biases were driving the results. However, longer-term follow-up would clarify whether Dyanavel XR prevents IR supplementation or delays it. Finally, these analyses relied on subjective psychometric tests for depression, anxiety, and ADHD, ratings of side effects, and clinician measures of improvement. Although these rating scales have been well-validated, psychiatry is always aiming to improve the reliability and validity of such measures . ER stimulants are a first-line option for adults with ADHD because they lead to better treatment adherence and reduce the risk of misuse compared to IR formulations . Still, some clinicians advise patients to supplement with an IR medication later in the day to ensure symptoms can be managed . For adults who have responsibilities throughout the day, effective ER medications that can be reliable as monotherapy are preferred. Dyanavel XR utilizes a unique mechanism of sustained release, which resulted in an efficacy duration of up to 13 h in the double-blind clinical trials in children and adults . While direct comparisons are not possible without head-to-head clinical trials, the statistical analyses presented here using retrospective patient data support the benefit of Dyanavel XR in reducing the need to supplement with an IR medication, regardless of IR supplementation at baseline. Despite its limitations, this study contributes to the growing literature demonstrating the value of precision medicine in ADHD treatment. Although treating ADHD is complicated by the wide range of symptoms and responses shown in patients, predictive analyses, such as those shown here, can be translated to clinical care. Additional real-world investigations should be conducted to determine whether individual variables can predict treatment efficacy to promote data-driven individualized treatment plans for ADHD. Adults with ADHD desire consistent and extended symptom management without the need for multiple, supplementary medications. The current research shows that Dyanavel XR is uniquely associated with reductions in the tendency to supplement daily ER treatment with IR medications. Dyanavel XR and reduced IR supplementation were also related to improvements in ADHD and anxiety symptoms over a 90-day period. Clinicians may consider these results when making treatment decisions with their adult patients with ADHD. | Review | biomedical | en | 0.999999 |
PMC11697941 | Direct pulp capping (DPC) is one of the most conservative vital pulp therapy (VPT) techniques aims at protecting the pulp tissue in healthy status in terms of maintaining the integrity of the vascular and neural supply in the dentin-pulp complex as well as supporting the immune defence mechanism of the dental pulp . DPC involves the application of a biocompatible dressing material over an injured pulp to prompt the regenerative power of human dental pulp cells (HDPCs) required for the healing process . The standards of optimal DPC material include biocompatibility, preventing microleakage, positive antimicrobial activity, and the bioinductive power of a calcified barrier with highly qualified properties . Several factors are affecting the decision-making of DPC procedures. According to the recent data gathered from a multinational survey from sixteen countries , pulp exposure resulting from carious lesions is the most significant determinant in clinical decisions regarding DPC. Other prognostic factors included the location of exposure, with dental practitioners showing a higher tendency to perform DPC for occlusally located caries compared caries located on the axial surfaces. Mineral trioxide aggregate (MTA) is still the benchmark capping agent because of its excellent physiochemical characteristics in terms of its effective potential for dentinogensis and its ability to cause less pulpal inflammation . Additionally, the high success rate results of several clinical trials boost the reliability of MTA as a capping material in VPT, especially DPC . However, there are some negative issues regarding the use of MTA. These drawbacks include prolonged duration required for setting, inadequate adhesion to the dentin, inconsistent mix and handling difficulties, the possibility of crown discoloration, toxicity due to bismuth leaching that has a negative impact on the cell viability, and the concerns that have been raised about the potential leakage of arsenic, its little bioinductive properties, and the high cost [ 9 – 11 ]. One of the major disadvantages of hydraulic calcium silicate cement such as MTA is the color change, especially in the medium and long term in contact with blood, because due to its radio-opacifier bismuth oxide, which is oxidized or reduced upon exposure to oxidizing agents including dentin collagen and sodium hypochlorite . Minimizing the diffusion of bismuth ions into the dentin tooth structure through dentinal tubule blockage using dentin bonding agents and applying a layer of glass ionomer linear can be effective in reducing coronal discoloration . Other strategies to reduce the crown discoloration include modifying the composition of gray MTA by eliminating metallic oxide components producing white MTA. However, tooth crowns still suffer from some degree of discoloration . Color alteration becomes worse in the presence of blood due to the higher blood absorption and subsequent hemolysis of MTA because it remains porous for a longer period . Therefore, MTA should be placed 2−3 mm below the cementoenamel junction . Another approach is modifying the powder of white MTA by the addition of zinc oxide or aluminum fluoride . The introduction of new hydraulic calcium silicate cement in which bismuth oxide is replaced with other radiopacifiers such as zirconium oxide in Biodentine and tantalum peroxide in bioaggregate . Accordingly, searching for capping material with higher qualities is still mandatory and represents a challenge for researchers. Sodium hexametaphosphate (SHMP) is a candidate of inorganic polyphosphates (poly[P]) . Poly(P) are linear polymers that are located in various body cells and consist of orthophosphate (Pi) residues connected with highly energetic bonds . Poly(P) has a significant role in the differentiation and maturation of osteoblasts, reflecting its osteoinductive potential and osseous calcification and prompting differentiation of human gingival fibroblasts . The odontogenic power of poly(P) compounds such as SHMP to induce proliferation and differentiation of odontoblast-like cells was postulated in two previous in vitro studies . The first tested the effect of two poly(P) candidates, SHMP and sodium triphosphate (STP), on the regenerative potential of HDPCs . The increase in cell count, alkaline phosphatase (ALP) activity, differentiation markers such as osteonectin, osteopontin (OPN), and osteocalcin (OCN), and the angiogenic factors in HDPCs indicated the capability of SHMP and STP in inducing HDPC proliferation and differentiation into odontoblast-like cells. The other study tested the role of matrix metalloproteinase (MMP)−3 on the growth and maturation of odontoblast-like cells . It was found that poly(P) induces odontoblastic biomarkers such as ALP, dentin sialophosphoprotein (DSPP), and dentin matrix protein-1 (DMP-1) mRNA that induce precipitation of osseous deposits. Accordingly, SHMP can be used in regenerative endodontic procedures. Until now, no in vivo study has been conducted to evaluate the biocompatibility and bioinductive potential of poly(P) on exposed dental pulp tissues. Therefore, the current study aimed to evaluate the histological and radiographic influence of SHMP as DPC agents in young permanent dogs' premolars. The null hypotheses ( H 0 ) of the current study suggested no histological (primary outcome) or radiographic differences (secondary outcome), respectively, between SHMP and MTA as capping materials in DPC immature dogs' teeth. Animals were housed in standard individual cages with an adequate daily amount of food and water ad libitum. Parasiticide of 1 mL/50 kg of Ivermectin (Ivomec supra® 1% injection, Merial, USA) administered subcutaneously. On the day of operation and after weighting each dog, an intravenous cannula (20 gauges) was placed in the recurrent tarsal (saphenous) vein. A split-mouth study design included a total of 36 premolars of three healthy 4-month-old Mongrel dogs (two males and one female) weighing approximately 7−9 kg. The number of teeth was calculated using a G*Power 3.1.9.4 after considering a medium effect size of the dependent means of the induced calcified bridge thickness after capping with the two materials. At a 5% alpha level of significance and a power of 80%, a total of 36 teeth were required. Teeth were assigned to either the right or left side of the jaw and were randomly treated with SHMP (intervention group, included 18 teeth) or MTA (control group, included 18 teeth) using a simple randomization approach. DPC was performed via traumatic exposure of sound immature maxillary and mandibular premolars of dogs (expect the fourth maxillary and mandibular premolars) with no apparent coronal or radicular developmental anomalies that have been confirmed with periapical radiographs. Food and water were held for 12 and 6 h before induction of anesthesia. Each dog was weighed to calculate the dosage of the drug, then a butterfly cannula (winged infusion set gauge 20) was inserted into the recurrent tarsal (saphenous) vein. Dogs were then premedicated by a slow intravenous administration of 1 mg/kg of xylazine HCl (Xyla-ject; ADWIA Co. New Cairo, Egypt) diluted in 3 ml saline. Ten minutes later, anaesthesia was induced by intravenous 10 mg/kg Ketamin HCl (Ketamine®; Hamelin, UK) and maintained by 24 mg/kg propofol (Diprivan® 10 mg/ml, Aspen Pharma, South Africa) at a dose of at a constant rate of infusion using an infusion pump. The dose of propofol was calculated and diluted in a net volume of 200 mg of sterile normal sterile saline and was infused at a rate of 100 mL per hour. Cefotaxime sodium (Cefotax® 500 mg vial, Abbott, USA) antibiotic was injected intramuscularly (IM) once daily for three days with a dosage rate of 50 mg/kg. Intramuscular injection of 7.5 mg of meloxicam (Mobitil® 15 mg ampule, Medical Union Pharmaceuticals, Ismailia, Egypt) were given twice a day as a non-steroidal anti-inflammatory and analgesic medication. Additionally, for three days, Povidine Iodine mouthwash and gargle (Betadine® Mundipharma, Dublin, Ireland) was administered twice a day as an oral antibacterial. Operated animals were followed up during and after the recovery period of anesthesia by well-trained animal caretakers at the veterinary medicine clinic of the local institution. The animals were examined on a regular basis to make sure they had a sufficient and healthy appetite and that all clinical parameters and vital signs (heart rate, pulse, temperature of the rectal area, breathing rate, urine, and excrement) were within normal ranges. After three months, a butterfly cannula (winged infusion set, gauge 20 gauge) was insteted into the recurrent tarsal (saphenous) vein for each dog, then they were euthanized by I.V. injection of xylazine at a dose of 1 mg/kg (Xyla-ject® 50 ml solution, ADWIA Co., new Cairo, Egypt). After that the dogs became less conscious, one-shot I.V. injection of thiopental sodium was injected (10 mg/kg; 25% solution) (thiopental-sodium® 500 mg vial, Epico, Egypt) which introduced the animals into deep irreversible anesthesia that ended by their death. Considering a split mouth design, each dog's maxillary and mandibular jaw halves were randomly treated with SHMP or MTA (18 premolars per group). Before starting the clinical procedures, all teeth were radiographically checked for maturity or any root abnormalities. After anesthesia and tooth isolation, a class V cavity was accessed from the buccal surface about 2 mm above the free gingival margin and parallel to the cementoenamel junction using a tungsten carbide pear-shaped bur, ISO #330 L. The access width was approximately 2−3 mm, and when the reddish pulp shadow was visible, a round carbide bur ISO # 1 (0.8 mm in diameter) was used to standardize the size of the pulp exposure to 0.8−1 mm. All cavity preparation procedures were performed at ultra-high speed with a copious water spray. To maximize cutting efficiency and preserve a sterile environment, each bur was used only once for each cavity . After rinsing the cavity with sterile saline and controlling the bleeding with a small cotton pellet under gentle pressure, the capping material was applied. In the experimental group, the crystalline SHMP was ground into fine powder. The powder was sterilized in a hot air oven before use. The powder with saline was mixed with saline in a 2 (powder):1 (liquid) proportion until a thick, workable mixture was obtained. The mix was applied using the MTA plug and condensed against the exposure site with a small, serrated amalgam condenser size¼ (HENRY SCHEIN®, USA) covered with a Teflon. In the control group, MTA (white MTA Angelus, Londrina, PR, Brazil) was prepared according to the manufacturer's guidelines and placed over the exposed pulp by an MTA carrier (Angelus, Londrina, Brazil). A moistened cotton pellet was placed over the MTA to permit its setting under the final restorative material. Finally, the access cavities of all teeth were sealed with a glass ionomer restoration (RIVA self-cure, SDI Ltd. Victoria, Australia). To assess the changes in root maturogenesis, 2D-digital periapical radiographs of the capped teeth were taken preoperatively and at the time of animal scarification. For standardization, all periapical images were taken using the paralleling technique (Vista Scan Mini Easy X-ray System, Bietigheim-Bissingen Germany). A PSP plate #2 (size of 3×4 cm) with a 100% active surface area was attached to a film holder that was mounted to a custom-made silicon-based index. For specimen preparation and histological analysis, dogs were euthanized by thiopental overdose at the predetermined interval. Surgical dissection was used to split the mandibular and maxillary jaws into halves along their midline. The specimens were decalcified in 10% EDTA for 3 months after being fixed for 7 days in 10% neutral buffered formalin. The specimens were dehydrated using ascending grades of ethyl alcohol, cleared in xylene, impregnated in soft paraffin, and finally embedded in hard paraffin. The capped teeth were extracted and washed thoroughly under running tap water for 3−4 h. To obtain serial sections of 5 μm thickness, extracted teeth were embedded in paraffin and cut buccolingually parallel to their vertical axis through the accessed cavities. Tissue sections were stained with haematoxylin and eosin (H & E) and Masson’s trichrome. The later was used to stain bone and collagen fibers and distiguish cells from the surrounding connective tissue . Representative photomicrographs were taken using a digital camera (LEICA, DFC290 HD system digital camera, Heerbrugg, Switzerland) connected to the light microscope using 4, 10, 20, and 40 objective lenses. Based on the criteria of the modified scoring system of Stanley (Table 1 ), the calcified bridge and inflammatory response of the pulp were scored. The mean thickness scores of the calcified bridge, predentin, and odontoblastic layer were measured using ImageJ software (version 1.50i; National Institutes of Health, Bethesda, MD, USA). Table 1 Distribution of histological criteria according to the modified scoring system of Stanley Histological parameters and scoring SHMP MTA P * Dentin bridge (DB) formation Absent (score 0) 0(0) 0(0) < 0.05 < 25% of DB formed (score 1) 0(0) 0(0) 26–50% of DB formed (score 2) 0(0) 0(0) 51–75% of DB formed (score 3) 0(0) 6(33.3) 76–100% of DB formed (score 4) 18(100) 12(66.7) Location of calcified bridge At the interface of exposure pulp (score 1) 18(100) 18(100) 1.00 Not at the interface of exposure pulp (score 2) 0(0) 0(0) Both (score 3) 0(0) 0(0) Quality of dentin formation in the bridge Absence of dentinal tubules (score 0) 0(0) 0(0) < 0.05 Dentinal tubules have regular pattern (score 1) 14(77.8) 8(44.4) Dentinal tubules have irregular pattern (score 2) 4(22.2) 10(55.6) Dentin chips Absent (score 0) 9(50) 13(72.2) > 0.05 Present (score 1) 9(50) 5(22.8) Connective tissue in the bridge Absent (score 0) 14(77.8) 9(50) > 0.05 < 25% (score 1) 4(22.2) 9(50) 26–50% (score 2) 0(0) 0(0) 51–75% (score 3) 0(0) 0(0) 76–100% (score 4) 0(0) 0(0) Pulpal inflammation Absent (score 0) 18(100) 16(88.8) > 0.05 Mild (score 1) 0(0) 2(11.1) Moderate (score 2) 0(0) 0(0) Severe (score 3) 0(0) 0(0) Abscess formation (score 4) 0(0) 0(0) Tissue necrosis (score 5) 0(0) 0(0) Pulp tissue reaction to the material No inflammatory cell infiltration (score 0) 17(94.4) 14(77.8) > 0.05 Mild inflammatory cell infiltration (score 1) 1(5.6) 4(22.2) Moderate inflammatory cell infiltration (score 2) 0(0) 0(0) Severe inflammatory cell infiltration (score 3) 0(0) 0(0) SHMP Sodium hexametaphosphate, MTA Mineral trioxide aggregate * Monte Carlo exact test Based on ImageJ software (version 1.50i; National Institutes of Health, Bethesda, MD, USA), the radiographic measurements included the following parameters : Root length (RL): a straight line from the CEJ to the apical foramen , apical foramen width (AFW) : a line extended between the mesial and distal root ends, and Root surface area (RSA): total root area minus the root canal space . Radiographic measures were estimated by an independent expert who was blinded to the used capping materials. Fig. 2 Radiographic measurements reflect root maturation: ( a ) root length (RL) line extended from the CEJ to the apical foramen; ( b ) apical foramen width (AFW) line extended between the mesial and distal root terminals; ( c ) radiographic root area (RRA) calculated by subtracting the total root area from the root canal space The histological specimens were examined independently by two experienced experts. The degree of agreement was checked using Cohen's Kappa (κ). Statistical Program for Social Sciences for Windows (SPSS), version 22 (IBM© Corporation, NY, USA) was considered. The measurements of radiographic (RL, RSA, and AFW) and histological data (Thickness of the calcified bridge, predentin, and odontoblastic layer) were tested for normality and variance homogeneity using Kolmogorov–Smirnov and Shapiro–Wilk tests. For histological and radiographic normally distributed quantitative measures obtained, the paired-sample t-test was considered. While the qualitative data of Stanley's scoring system criteria were analysed using the Monte Carlo exact test. The alpha level of significance was set at 5-percent ( P ≤ 0.05) and a 95% confidence interval ( CI ). The inter-examiner agreement of the histological records was 0.89. Although, the average thickness of the calcific barrier in SHMP specimens (624.44 ± 12.44 µm) was higher than that found in the MTA specimens (587.79 ± 11.34 µm), the difference between the two groups was non-significant ( P > 0.05). On the other hand, the average thickness values of the predentin and odontoblastic layers were significantly higher in the SHMP specimens compared to the average thickness values in the MTA specimens . Fig. 3 Mean thichness of induced dentin bridges, predentin, and odontablastic layrs formed in resonse to sodium hexametaphosphate (SHMP) and mineral trioxide aggregate (MTA) At the interface between the exposed pulps and both capping materials, the dentin bridge was fully calcified in 100% (18/18) of SHMP specimens, compared to 66.7% (12/18) of MTA specimens. Six MTA specimens (33.3%) showed partial formation of the dentin barrier. The difference between the two groups was statistically significant ( P < 0.05). The calcified barriers showed deeper H & E staining of in the SHMP group with more homogenous and regular patten of the dentinal tubules compared to the MTA group. Compared to MTA, the dentin bridge induced by SHMP showed a more palisading arrangement of the odontoblastic layer at the interface between dentin and pulp. There were numerous dentinal tubules within the developed hard tissue with homogenous well-arranged pattern similar to the dentin tubular structure (Table 1 ) and . The frequency of regularly arranged dentinal tubules in SHMP specimens [( n = 14 (77.8%)] was significantly higher compared to those shown in the MTA specimens [ n = 8 (44.4%)] ( P < 0.05). Fig. 4 Photomicrograph of the dog's premolars capped with sodium hexametaphosphate (SHMP) ( a , b , and c ) and mineral trioxide aggregate (MTA) ( d , e , and f ), stained with H & E. Both materials show that the dentin (D) and pulp (P) are lined with normal odotoblastic layer (arrowheads). The dentin bridge (DB) at the interface between the material (M) and dental pulp (P) is highly organized in the SHMP group ( a ) and less organized in the MTA group ( d ). The magnified black boxed area shows that the two capping materials have loose connective tissue with many normal blood vessels (V) with few chronic inflammatory cells (curved arrow) in the SHMP group ( b ) and many chronic inflammatory cells (curved arrow) in the MTA group ( e ). The magnified red boxed area shows that the calcified bridge (B) induced by the two capping materials are formed from dentin chips. In the SHMP group, the dentin bridge is completely calcified with regularly arranged dentinal tubules and continuous odontoblastic cell layer (Black arrows) ( c ). In contrast, the dentin bridge in MTA group is incompletely calcified and the arrangement of dentinal tubules is less regularly arranged with no demarcating line between the dentin bridge and pulp (Black arrows) ( f ) (Scale bars: ( a ) and ( d ) 500 μm; ( b ), ( c ), ( e ), and ( f ) 100 100 μm) Only two MTA specimens (11.1%) showed mild pulpal inflammation, while all SHMP specimens (100%) were free from pulpal inflammation ( P > 0.05). No inflammatory cell infiltration was observed in 94.4% and 77.8% of dental pulps in SHMP and MTA specimens, respectively ( P > 0.05). The photomicrograph of the pulp tissue of specimens stained with Masson’s trichrome showed that SHMP specimens had fewer chronic inflammatory cells and normal blood vessels compared to many inflammatory cells and congested blood vessels in the MTA specimens . Fig. 5 Photomicrograph of the dog's premolars capped with sodium hexametaphosphate (SHMP) ( a ) and mineral trioxide aggregate (MTA) ( b ), stained with Masson's trichrome. SHMP shows collagen bundles (black arrows) of the pulp (P) with few inflammatory cells (arrowheads) and normal blood vessels (V). MTA shows collagen bundles (black arrows) of the pulp (P) with many inflammatory cells (arrowheads) and congested blood vessels (V) (Scale bar 100 μm) After three months, the mean differencs in mesial and distal root lengths of premolars capped with SHMP and MTA were 0.42 mm with 95% CI of 0.13; 0.72 ( P = 0.008) and 0.33 mm with 95% CI of 0.03; 0.64 ( P = 0.048), respectively. Regarding the other radiographic parameters (RSA and AFW), both capping materials acted in a comprable manner with no statistically significant difference ( P > 0.05) (Table 2 ) and . Table 2 Mean scores of different radiographic indicators of root maturation Radiographic parameters SHMP MTA 95% CI of mean difference P * Root length (RL/mm) Mesial RL at baseline 4.18 ± 0.19 4.16 ± 0.10 -0.08; 0.13 0.66 Mesial RL after 3 months 6.53 ± 0.44 6.11 ± 0.44 0.13; 0.72 0.008 Distal RL at baseline 3.73 ± 0.25 3.86 ± 0.27 -0.35; 0.09 0.24 Distal RL after 3 months 6.46 ± 0.47 6.13 ± 0.46 0.03; 0.64 0.048 Root surface area (RSA/mm 2 ) Mesial RSA at baseline 1.83 ± 0.27 2.00 ± 0.29 -0.37; 0.04 0.18 Mesial RSA after 3 months 3.39 ± 0.42 3.47 ± 0.38 -0.29; 0.05 0.17 Distal RSA at baseline 1.84 ± 0.25 1.96 ± 0.20 -0.34; 0.17 0.5 Distal RSA after 3 months 3.83 ± 0.27 3.92 ± 0.19 -0.23; 0.04 0.16 Apical foramen width (AFW/mm) Mesial AFW at baseline 1.74 ± 0.27 1.86 ± 0.21 -0.28; 0.05 0.16 Mesial AFW after 3 months 0.41 ± 0.13 0.47 ± 0.08 -0.14; 0.02 0.14 Distal AFW at baseline 1.71 ± 0.18 1.74 ± 0.11 -0.13; 0.08 0.58 Distal AFW after 3 months 0.47 ± 0.11 0.44 ± 0.10 -0.06; 0.09 0.65 SHMP Sodium hexametaphosphate, MTA Mineral trioxide aggregate * Paired sample t test Fig. 6 Periapical digital radiographs of dog's premolars show the change in mesial and distal root lengths (RL), apical foramen width (AFW), and root surface area (RSA) after capping with SHMP ( a and b ) and MTA ( c and d ). Radiographic parameter measures at baseline ( a ) and ( c ) and Radiographic parameter measures at 3 months ( b ) and ( d ) The results of histological analysis showed the superiority of the calcific bridge properties of SHMP over the MTA in terms of complete calcific barrier formation, the qualitative properties of dentin formed within the bridge, and the quantitative measures including the thickness of predentin and odontoblastic layers ( H 0 was rejected). While the findings of other histological criteria, including the thickness of the dentin bridge and pulpal inflammation or tissue necrosis, in the experimental and control groups were comparable ( H 0 was not rejected). The search for a novel capping material that provides a better pulp response is crucial before assessing the material's success in clinical situations. Additionally, to overcome the shortcomings of traditional MTA, alternative calcium silicate cements such as Biodentine, pre-mixed TotalFill BC RRM Putty, and pulp capping material (PCM) showed comparable high shear bond strength (SBS) values after immediate placement of the final restoration . Xavier et al. reported comparable SBS to Biodentine™ and NuSmile® NeoMTA with higher SBS values obtained after applying an extra layer of hydrophobic resin over the adhesive and placing the composite resin restoration 7 days after applying the calcium silicate cement (delayed restoration). However, cytotoxicity of freshly prepared (i.e., before setting) Biodentine and TheraCal has been encountered . One of the important aspects of pulp tissue response and the quality of dentin bridge is the preoperative pulp status . According to Ricucci et al. histological analysis of the coronal and radicular pulp tissues was normal for teeth that have been clinically diagnosed with reversible or irreversible pulpitis with necrotic foci next to the pulp horn. Mild to moderate grade of inflammation modulates the regenerative power of the pulp tissues, while severe and/or chronic inflammation has a determinant influence on the pulp . Therefore, the radical shift towards treating teeth with irreversible pulpitis via the use of bioactive capping materials has become a focus of attention . Another aspect of DPC success is the ability of the capping material to protect the pulp in a healthy state and trigger mineralized barrier formation with superior quantitative and qualitative characteristics . The dentin bridge induced by SHMP was better than that produced by MTA. The high ability of SHMP to elicit calcified bridge formation could be attributed to its ability to increase the activity of the ALP enzyme by upregulating the expression of non-specific ALP and endopolyphosphatase genes . The expression of other genes of bone matrix proteins, including osteonectin, OPN, and OCN, could be upregulated by Poly(P) . These trigger the differentiation and proliferation of odontoblasts of HDPCs and the subsequent deposition of calcified niches . Furthermore, the increased expression of ALP mediated by the long chain of SHMP prompts higher production of polymeric phosphate (PPi) and monomeric phosphate (Pi) in an optimal balance . This permits the production of Pi at optimal levels, which induces calcified foci formation . Another possible mechanism of action of Poly(P) is triggering the expression of the matrix metalloproteinase (MMP)−3 gene in response to the injury of dental pulp and the subsequent release of inflammatory mediators . The results of a study conducted in rats on odontoblast-like cells (iPS-OD) suggested that upregulated MMP-3 genes elicited a hierarchy of activation of odontoblastic markers, including dentin sialophosphoprotein (DSPP) and dentin matrix protein-1 (DMP-1). As a result of the serially upregulated cascade of odontoblast markers mediated by MMP-3, the proliferation and osteogenic differentiation of odontoblast-like cells have increased . An integral part of pulp regeneration is the potential to organize a network of vascular capillaries (i.e., angiogenesis). SHMP, as a member of Poly(P), has the potential to elicit the migration and differentiation of HDPCs into endothelial cells through the evident elevation of the angiogenic markers, indicating an up-regulatory effect of Poly(P) on the angiogenic genes . The MTA-induced calcified barrier was evident in all specimens with a variable degree of mineralization, indicating that the mineralization process of the matrix was heterogeneous. This agreed with the findings of a previous histological animal model study . Interestingly, the current study findings showed that the degree of mineralization of dentin bridges formed in response to SHMP capping material was significantly superior to those organized by MTA. This observation might denote the faster rate of mineralization reaction of SHMP compared to MTA. The rate of calcification could be considered a determinant criterion for pulp capping materials . This observation was confirmed by the presence of connective tissue in half of the MTA specimens. This was in line with the findings of a prior histological analysis that connective tissue was detected in 40 percent of DPC samples. The results of the current study revealed that the newly formed odontoblast-like cell layer has a well-organized tubular system that could reflect the adequate surface adherence properties of the capping material. This prompts the better organization and differentiation of odontoblast-like cells . Depending on the formation of the dentin bridge exclusively as an indicator of capping material success is not enough to justify a healthy pulp status . There have been no previous reports regarding the pulpal inflammation response of SHMP. According to the findings of the present study, all specimens were free from inflammatory pulpal responses. The absence of pulp tissue inflammation and the highly qualitative and quantitative properties of the induced calcified tissue bridge refer to the superior bioinductive and biocompatible nature of the SHMP capping agent. Regarding the MTA-induced calcified bridge, the aqueous medium provides a suitable environment for calcium hydroxide formation . At the interface between the material and pulp tissues, calcite-like structures are precipitated . The developed crystals could attract the fibronectin required for cell adhesion and differentiation . The initial caustic pH level of MTA (pH of 12.5) at three hours following mixing . The high pH continues up to eight weeks following material hardening . The high alkaline pH provides an optimum microenvironment for hard tissue barrier induction through the regulated release of cytokines and controlled inflammatory processes . Radiographic analysis of the teeth capped with both capping agents permits root maturogenesis in terms of the increase in RL and RSA and the closure of the apexes of dogs' premolars. However, the mesial and distal roots of teeth capped with SHMP revealed a significant increase in their RLs compared to those treated with MTA. Other radiographic parameters showed comparable results between the two materials. The relative radiographic superiority of SHMP over MTA emphasized the advantageous findings of histological analysis. The relative advantage in radiography could be attributed to the better healing conditions in terms of better qualitative and quantitative calcified bridge formation and less induced inflammation obtained by SHMP at the site of pulp injury. This favorable environment may encourage a faster healing rate compared to the MTA group. However, this point should be examined deeply in further investigations, as the histological and radiographic examinations are carried out over more than one period. In the current study, a 3-month interval was adopted before considering histological and radiographic examination. This period was considered to ensure significant maturogenesis of the dog's roots. Moreover, this period provided a better chance for dentenogenesis to take place and permitted the maturation of the calcified dentin bridge. A similar interval was considered in a previous study , in which animals were sacrificed and specimens were assessed. The main advantages of SHMP were the cost compared to the MTA. The 500 gm of SHMP costs 78.35 US$. Approximately each tooth was capped with 250 mg powder of SHMP or MTA. Thus, each tooth capped with SHMP costed about 3.92 cents, while the tooth capped with MTA costed about approximately 4.12 US$. The other merits of the use of SHMP include the ability to apply the final adhesive restoration with no obvious crown discoloration compared to crowns of teeth treated with MTA. However, further investigations are required to elucidate the long-term effect of SHMP on the color change and mechanical properties. The main limitations of the present study are that it was performed in an ideal environment with sound dogs' teeth, healthy pulps with no previous inflammation, and standardized exposure sizes and sites. Therefore, it cannot guarantee that the response of the pulp tissues with preoperative inflammation will be similar . The reaction of the pulp is totally different according to the type of exposure (traumatic injury, mechanical stimulus, or caries) . Initially after the exposure, neuropeptides are released because of adjacent neural damage that is associated with an increase in vascular permeability . The predominant inflammatory cells in the initial phase are lymphocytes, plasma cells, and macrophages associated with polymorphonuclear leukocytes in the acute phase, then the condition becomes chronic . The chemotaxis of large numbers of neutrophils at the exposure site. This induces NETosis as a defensive mechanism which occurs because of the death of neutrophils. In NETosis, neutrophil extracellular traps destroy the invasive bacteria . Mild to moderate inflammation is associated with reactive or reparative dentinogenesis, while severe inflammation may destroy the odontoblasts . As previously mentioned, an early histological assessment might be required to provide a comprehensive perception regarding the inflammatory changes and the rate of healing of the SHMP capping material. Further studies are required to test the antimicrobial and sealing characteristics of SHMP and its sealing ability. Additionally, analysis of teeth with pulp exposure because of carious lesions is needed. Finally, assessment of the root maturogenesis depended on two-dimensional images. Therefore, future studies using 3D radiographs will be more beneficial. Additionally, further prospective clinical trials are required to confirm the effectiveness of SHMP and generalize the results of the present study. Within the limitations of the present study, it can be concluded that: There was no difference in some respects between SHMP and MTA. The histological evaluation showed that SHMP provided better bioinductive and biocompatible properties compared to MTA. Radiographically, both materials showed comparable root maturogenesis outcomes except for the increase in RL, which was significantly longer after DPC with SHMP. SHMP might be a suitable DPC alternative material in the treatment of immature permanent teeth. Further prospective randomized clinical trials are necessary to prove the findings of the current study. | Other | biomedical | en | 0.999998 |
PMC11697944 | Mutualistic fungi that live in symbiosis with plants play pivotal roles in the recycling of carbon, nitrogen, phosphorus, and other nutrients in the boreal forest ecosystem, many of which belong to ectomycorrhiza group. Ectomycorrhizal (ECM) fungi wrap around host lateral roots to form fungal mantle and form hartig net between epidermal and cortical root cells . Ectomycorrhizal fungi (ECF) that form symbiosis with trees has the capacity to influence growth limiting nutrient resources in forest ecosystem . The authors further noted that EMF composition was associated to a three-fold difference in tree growth and that fast tree growth was linked with EMF that harbored high inorganic nitrogen acquisition genes. ECM fungi have received in recent years heightened attention as key mediators which function within common mycorrhizal networks [ 3 – 6 ], which are associated with water and nutrient mobilization , plant growth in symbiotic performance [ 8 – 10 ], tree transcriptome in ectomycorrhizal symbiosis [ 7 , 11 – 15 ]. Rudawski et al. noted the dominance of several ECM ( Suillus luteus, Rhizopogon roseolus, Thelephora terrestris, Hebeloma crustuliniforme ) in Scots pine seedlings in forest nursery. Policelli et al. reported that ECM helps temperate and boreal forest trees to tolerate harsh environmental conditions such as restoration of sites degraded due to clearcut logging and wildfire, affected by soil erosion and contaminated with heavy metals as well as restoring sites invaded by non-native plant species. Unlike the main saprotrophic decomposers which dominate in freshly produced organic matter, ECM fungi dominate in deeper soil layers . They potentially act as decomposers, mobilizing N from the soil organic matter pool and making it available to their host plants . ECM fungi have nutrient transporters in their genome for nutrient mobilization . ECM fungi have a limited capacity to decompose plant litter as revealed by the analysis of genome sequences , including Laccaria bicolor , Tuber melanosporum and mycorrhizal Amanita species . Genome analysis on those ECM fungi indicated the loss of some genes encoding plant cell wall degrading enzymes such as glycoside hydrolases and peroxidases compared to their saprotrophic ancestors . The reductions and losses in specific protein families could be an indication of adaptation of ectomycorrhizal biotrophy in plant tissues . Host plants are the principal sources of metabolic carbon for ECM fungi, which in turn also benefit from ectomycorrhizal colonization. ECM fungi promote plant growth by enhancing water and nutrient uptake especially nitrogen and phosphorus . Root biomass and length of Populus nigra increased under ECM fungus inoculation . Several ECM fungi could change tree root architecture (including overall root growth, primary and lateral root length, lateral root number), in which plant hormones signaling and auxin pathways could be involved . ECM can also improve plant performance by inducing local and systemic defense responses which may be controlled by signaling networks involved in plant hormones salicylic acid (SA), jasmonic acid (JA), and ethylene, to confer broad-spectrum resistance during subsequent plant pathogen or herbivore attack . Suillus luteus , which is also called slipper Jack, is a common ECM fungus with pines, such as Pinus sylvestris , P. elliottii . S. luteus had the ability to help P. massoniana absorb phosphorus under nutrient deficiency . S. luteus promoted the growth of P. massoniana seedlings, producing phytohormones, especially SA and indole-3-carboxylic acid (ICA), methyl indole-3-acetate (ME-IAA), and indole-3-acetic acid (IAA) in mycelium . Stone pine without S. luteus symbionts could be more easily susceptible to H. annosum than plants with ectomycorrhizal symbiosis . Mycorrhizal inoculation of Suillus spp. increased chlorophyll a and b, carotenoids, and soluble protein in Scots pine young seedlings, and increased the activities of plant antioxidant enzymes CAT and POD, and plant beta-1,3-glucanase . Scots pine, dominating in boreal forest, is highly susceptible to infection by necrotrophic basidiomycete Heterobasidion annosum sensu stricto (Fr.) Bref. . Aerial basidiospores of the fungal pathogen fall on freshly cut stumps and form invasive hyphae, followed by spreading to nearby trees via root contact . The fungal pathogen grows necrotrophically to get nutrients from the living tree tissues and then turns to saprotrophic growth in dead wood cells , which may lead to reduced volume growth and tree mortality. Tree defense responses in conifer- Heterobasidion pathosystem demonstrated that the phenylpropanoid pathway, lignin biosynthesis and polymerization, flavonoids biosynthesis, terpenoid and stilbene pathways, and pathogenesis-related (PR) proteins are the principal responses at the gene level [ 39 – 46 ]. Jasmonic acid and ethylene signaling pathway plays a central role in tree defense responses to Heterobasidion infection, without antagonism of salicylate-mediated signaling pathway . Plant defense responses to Heterobasidion infection and beneficial microbe inoculation such as endophyte have been recorded . However, there are limited studies on plant response to ECM fungi in the presence of pathogenic fungi. The hypothesis is that pre-inoculation of plants with ECM fungi before fungal pathogen challenge would improve plant performance and help plant defense against pathogen infection. We investigated the similarities and difference in host responses between pathogenic and mutualistic fungal interaction, host responses towards pathogenic attack that were maintained under co-inoculation, and host responses towards pathogenic attack that are dampened by the presence of ECM fungus. Suillus luteus was obtained from the University of Helsinki Fungal Biotechnology Culture Collection (HAMBI/FBCC). Heterokaryotic Heterobasidion annosum 02034 was obtained from the culture collection of Kari Korhonen. Scots pine seeds were kindly provided by Natural Resources Institute of Finland (Luke). S. luteus was stocked on Modified Melin-Norkrans medium . H. annosum was maintained on malt extract agar . Scots pine seeds were surface sterilized with 30% H 2 O 2 for 15 min, rinsed several times with sterile water and stratified for 3–4 days in the dark at 4 ◦C. Rows of seeds were laid on 12*12 cm sterile square Petri dishes with 1% water agar and covered with moist, sterile filter paper. The Petri dishes containing seeds were sealed with parafilm, placed in a growth chamber for germination under a photoperiod of 16 h at 20 ◦C for 2 weeks. Dual cultures of H. annosum and S. luteus were set up on MMN media to investigate the impact of the ECM fungus on the pathogenic fungus growth. S. luteus agar plug (5-mm diameter) was put on the agar media 13 days before adding H. annosum agar plug, as the former grows slower than the latter. The distance between the two plugs was 6 cm on Petri dish. Self-pairing refers to two separate agar plugs of the same fungus on the culture plate and served as the control. The fungal cultures grew at 20°C in the dark. S. luteus was cultured on a 9-cm circular Petri dish with MMN agar media. One month later, agar plugs on the edge of the fungal culture with active mycelia were moved to a 12-cm square Petri dish with MMN agar media and cellophane membrane. S. luteus was growing on membrane agar plates for 17 days, and then the membrane with the fungal mycelia was moved into a new 12-cm square Petri dish containing MMN agar media. Two-week-old Scots pine seedlings were placed on the top of the fungal mycelia in the square Petri dish with MMN. Fungal mycelia and plant seedlings were grown together on Petri dish for one month to ensure successful inoculation and establishment of the ECM fungus on the plant. One month later, ECM fungus-inoculated seedlings were transferred into soil. The soil was peat-based substrate provided by Kekkilä Professional (Vantaa, Finland). Dry soil was mixed with water, and the moist soil was autoclaved at 121°C for one hour (two times) with an interval of 48 h. The autoclaved soil was put into the 12 cm-sterile square plates. Three or four infected seedlings were transferred into each soil plate. Seedlings were grown in the soil for one month before adding H. annosum . H. annosum used for the inoculation was pre-incubated in the malt agar media for 3 weeks. Ten agar plugs (0.5 cm in diameter) with active fungal mycelia were introduced into the soil next to the plant roots. The seedlings were watered every two weeks. In total, there were four groups of seedlings, including control seedlings without any inoculum (Ctr), Mutualistic fungus-inoculated seedlings (Sl), pathogen-infected seedlings (Ha), and co-inoculated seedlings with both the ECM fungus and the pathogen (SlHa, or co-inoculation). There were at least three Petri dishes for each group which served as biological replicates, with 3–4 seedlings in each dish. Seedlings were grown with H. annosum together for one month before sampling. At the time of sampling, Scots pine seedlings were three and half months old. By then, S. luteus had grown with the plants for three months, while H. annosum was with the plants for one month. The number of the lateral roots, the length of primary roots of the seedlings were counted and measured at termination of the experiment. Seedling roots were collected for RNA isolation as previously described . Transcriptome sequencing (paired-end, 101bp) was performed at CeGat (Germany). Sequencing libraries were generated by using TruSeq Stranded mRNA (Illumina). The library preparations were sequenced on Illumina NovaSeq 6000. Raw reads were preprocessed by CeGat. Demultiplexing of the sequencing reads was performed with Illumina bcl2fastq (2.20). Adapters were trimmed with Skewer (version 0.2.2). Trimmed raw reads were aligned to the annotated contigs of Pinus taeda v2.01 using the Illumina DRAGEN platform (software version 3.10.4). As Pinus sylvestris has presently no well annotated genome, thus we used P. taeda as the mapping genome. The P. taeda genome was also used for the analysis of P. sylvestris transcriptomic data partly because the P. taeda and P. sylvestris sequences showed high level of sequence similarity . The raw reads count was used to identify differentially expressed genes in Ha, Sl, and SlHa compared to Ctr using edgeR package in R v.4.3.3. Genes with at least 1 count per million in at least 3 libraries were utilized for further analysis. Normalized counts obtained by DESeq2's median ratios were transformed using rlog transformation to improve the distance or clustering for principal component analysis (PCA) and hierarchical clustering visualization. The cutoff values for defining differentially expressed genes (DEGs) were false discovery rate adjusted P -value (Benjamini and Hochberg’s approach) = 0.05 and |log2(fold change) |= 1. Coding sequences (CDS) of P. taeda were extracted from PlantGenIE. The CDS sequences of genes with at least 1 count per million in at least 3 libraries were blasted to Scots pine de novo transcriptome assembly fasta sequences constructed with Trinity . Since all Trinity assembly transcripts had been also annotated , we were able to gain the annotation information with nucleotide blast. The best hits were selected by bit scores. Gene ontology (GO) enrichment of DEGs was analyzed by clusterProfiler . R packages such as ggplots, enrichplot, pheatmap were utilized for dotplots, GO maps, and heatmaps. The two fungi were then inoculated either alone or together on roots of Scots pine seedlings. To ensure successful colonization of the roots, S. luteus was grown with plant seedlings for a month on artificial media before being transferred to the soil. However, mycorrhization marked by mantle formation or hartig net was not observed. A total of 88 seedlings were harvested (Additional file 2), including 22 seedlings from control (Ctr), 14 seedlings from H. annosum infection (Ha), 29 seedlings from S. luteus inoculation (Sl), and 23 seedlings from co-inoculation (SlHa) and used for phenotyping. While 38 seedlings were used for RNA sequencing as shown in Fig. 2 . Principal component analysis based on the primary root length and lateral root number showed that growth of Ha and Ctr could be different from Sl and SlHa . The median values of primary root length in Sl and SlHa were 13.1 cm and 17.2 cm, significantly higher than the values in Ctr and Ha (8.4 cm, 10 cm). The median values of the number of lateral roots in Sl and SlHa were 15 and 23, while the values in Ctr and Ha were 9 and 7.5, respectively . We found that seedlings in both Ha and Ctr grew poorly, suggesting pathogenic or other forms of stress in the two conditions. However, the plant root growth was improved significantly with the presence of S. luteus . Therefore, we reasoned that S. luteus as a beneficial fungus promoted plant root growth. Consequently, the term mutualistic or beneficial was utilized to qualify the interaction in this study. Fig. 1 The growth of Scots pine seedlings with fungal infection. A Principal component analysis based on the primary root length and lateral root number. B Box plot about primary root length of seedlings. C Box plot about lateral root number. Ctr: non-inoculated seedlings control; Ha: Heterobasidion annosum -inoculated seedlings; Sl: Suillus luteus -inoculated seedlings; SlHa: H. annosum -infected seedlings in the presence of S. luteus (co-inoculated plants). Asterisks indicate whether significant difference exists in the primary root length or lateral root number of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.001: ***; p < 0.01: **; p < 0.05: *) Fig. 2 Images of Scots pines seedlings used for RNA sequencing. C: non-inoculated seedlings control; Ha: H. annosum -inoculated seedlings; Sl: Suillus luteus -inoculated seedlings; SlHa: H. annosum -infected seedlings in the presence of S. luteus (co-inoculated plants). Numbers refer to the Petri dish ID in Additional file 2 About 73%—86% of sequenced fragments were mapped to Pinus taeda genome, and 35%-40% of the mapping fragments were used for expression quantification (Table 1 ). The results seem to show that the presence of S. luteus appeared to have a balancing buffering effect on the plant stress response in the presence of H. annosum . This is partly evident as principal component analysis (PCA) and hierarchical clustering revealed that the SlHa bio-replicates were much closer to Ctr and Sl, and far away from Ha replicates under 47% variance . Data for PCA and hierarchical clustering visualization was based on normalized counts as seen in Additional file 3. Table 1 RNA sequencing and mapping results to Pinus taeda genome. Scots pine seedlings without fungal inoculation (Ctr), and seedlings inoculated with H. annosum (Ha), S. luteus (Sl), and both (SlHa). Each treatment condition has three biological replicates Sample ID Number of fragments (in million) Number of bases (in Gb) Number of mapped fragments (in million) Proportion of sequenced fragments (in %) Number of fragments used for expression quantification (in million) Proportion of fragments used for expression quantification (in %) Ctr-4 34.944 7.042 29.7335 85.09 10.773 36.58 Ctr-6 34.812 7.025 29.9565 86.05 10.547 35.69 Ctr-7 40.255 8.119 34.1495 84.83 12.233 36.31 Ha-1 43.887 8.853 34.2225 77.98 12.494 36.97 Ha-3 28.883 5.81 21.246 73.56 8.024 38.11 Ha-4 34.919 7.034 26.603 76.18 10.08 38.25 Sl-4 51.438 10.275 42.967 83.53 17.068 40.19 Sl-7 33.48 6.741 28.2565 84.4 10.338 36.94 Sl-10 32.366 6.518 27.1815 83.98 9.731 36.15 SlHa-2 27.209 5.476 22.631 83.18 8.253 36.77 SlHa-3 35.059 7.076 29.53 84.23 10.173 34.83 SlHa-4 54.151 10.826 44.958 83.02 17.437 39.32 Fig. 3 RNAseq profiling of Scots pine in Ctr, Ha, Sl and SlHa. A PCA plot of rlog-transformed normalized counts obtained by DESeq2's median of ratios. B Heatmap of hierarchical clustering of normalized counts The expression of genes obtained by DESeq2's was shown in the heatmap (Additional file 4) using the Log2(1 + normalized CPM). As seen in the heatmap, Ha had a unique expression profile, largely different from Ctr, Sl, and SlHa . This validated the balancing buffer role and counteracting effect of S. luteus . We extracted the significant differentially expressed genes (DEGs) in Ha, Sl, SlHa comparing against Ctr with edgeR. About 440 DEGs were found in Ha, 403 in Sl, and 420 in SlHa . 31 common DEGs were found in Ha, Sl, and SlHa. Additionally, Sl shared 157 DEGs with SlHa, and most genes were downregulated . However, Ha shared only 32 DEGs with Sl and 47 DEGs with SlHa . The original data about normalized CPM, log2foldchange, and FDR value are shown in Additional file 5. Fig. 4 A 440, 403, 420 DEGs were found in Ha, Sl, SlHa relative to Ctr. 330, 183 and 185 DEGs were specific in Ha, Sl, SlHa respectively. 31 DEGs are common genes which were shared in Ha, Sl, SlHa. 32, 47, 157 DEGs were overlapped between Ha and Sl, Ha and SlHa, Sl and SlHa. B Hierarchical clustering of DEGs using log2foldchang based on Venn diagram. Specific genes, overlap genes between two conditions, and common genes shared in three conditions were illustrated in heatmap and each group can be divided into downregulated genes and upregulated genes. For example, 330 DEGs specifically in Ha in Venn diagram were grouped in upregulated genes and downregulated genes in the heatmap. Ctr: non-inoculated seedlings control. Ha: pathogen-infected seedlings. Sl: Suillus luteus -infected seedlings. SlHa: co-inoculated seedlings with both S. luteus and H.annosum . ‘ + ’ refers to upregulated, while -’ refers to downregulation. Ha.Sl.SlHa: + + + refers to DEGs that were upregulated in all treatments compared to Ctr. Ha.Sl: + + refers to DEGs that were upregulated both in Ha and in Sl. Ha.SlHa: +-refers to DEGs that were upregulated in Ha but downregulated in SlHa. Scale bar refers to log2foldchang. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *) The best hits with annotation information were shown in Additional file 6. The top 20 significant GO terms of molecular function were selected in Ha, Sl, and SlHa . The GO terms related to pinosylvin synthase activity and peroxidase activity were enriched in Ha and Sl, but not in SlHa. Many GO terms were enriched only in Ha (linoleate 9S-lipoxygenase activity, glucan endo − 1,3 − beta − D − glucosidase activity, endochitinase activity, chitin binding) or only in Sl (hydroquinone: oxygen oxidoreductase activity, manganese ion binding, magnesium ion binding, oxidoreductase activity, abscisic acid binding, terpene synthase activity, copper ion binding). The GO terms shared in the three different samples were flavin adenine dinucleotide binding, mandelonitrile lyase activity, oxidoreductase activity. Fig. 5 Go enrichment for DEGs–Top 20 significant GO terms of molecular function were selected in Ha, Sl, and SlHa. GeneRatio is calculated as "input gene number"/ "backgound gene number" Similar gene expression pattern was observed between Ha and Sl which was marked by the upregulation of genes related to leucine-rich repeat domain receptor-like kinases (LRR-RLKs), SWEET sugar transporters, xyloglucan endotransglucosylase/hydrolase (XTH), E3 ubiquitin ligases, UDP-glycosyltransferase (UGT), and downregulation of cinnamoyl-CoA reductase (CCR) and α-xylosidases . The expression pattern of these genes was still maintained in the co-inoculation (SlHa) . However, several DEGs encoding for L-type lectin-domain containing receptor kinase (LecRLKs), CML, UDP-glycosyltransferase (UGT), cytochrome P450, that were found in Ha and Sl, were not significantly expressed in co-inoculation (SlHa) . Fig. 6 Heatmap of DEGs using log2foldchange which commonly up- or down-regulated in three treatments, and DEGs which were up- or down-regulated both in Ha and Sl, and DEGs which were up- or down-regulated in both Ha and SlHa. Scale bar refers to log2foldchang. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *) Genes which were upregulated in Ha but downregulated in Sl included eight predicted peroxidases, nine pinosylvin synthases belonging to chalcone/stilbene synthases, three glucosyl hydrolases related to PR proteins, two laccases, ABC transporter, protein HOTHEAD, ACC oxidase . H. annosum infection had unique expression pattern on genes encoding pathogenesis-related (PR) proteins (peroxidases, chitinases, β -1,3-glucanases, thaumatin), phenylpropanoid pathway/lignin biosynthesis (PAL, 4CL, COMT, CCoAOMT, peroxidases, laccases, cytochrome P450s), flavonoid biosynthesis, chalcone/stilbene biosynthesis, ethylene signaling pathway, JA signaling pathway, cell remodeling and growth, transporters, fungal recognition (Additional file 7, Additional file 8). Fig. 7 Heatmap of DEGs using log2foldchange which were upregulated in Ha but downregulated in other conditions. Scale bar refers to log2foldchang. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *) Whereas Sl induced a smaller unique set of genes but had more downregulated genes than Ha . S. luteus specifically induced plant genes which are mostly implicated in root growth promotion. The Sl-specific upregulated genes were associated with nutrient uptake (transporters), terpene biosynthesis and lignin biosynthesis (terpene synthases and cytochrome P450s), fungal recognition (cysteine-rich receptor-like protein kinases), hormone signaling (LOX, α-dioxygenase, MAPK) . However, the Sl-specific downregulated genes were mostly encoding genes involved in plant defense responses, especially PR proteins, and other defense-related genes including laccases, chalcone/stilbene synthases, terpene synthases, cytochrome P450s, receptor-like protein kinases (RLKs) related to fungal recognition (Additional file 9). By contrast these defense related genes were not significantly expressed in SlHa co-inoculation. Fig. 8 Heatmap of DEGs which were specifically upregulated in Sl. Red indicates high level of upregulation, while blue indicates high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *) A set of DEGs found in Ha were also found to be maintained in SlHa . Many of the upregulated genes involved in sugar transporters, annexin Gh1 related to calcium-permeable transporters, RMR function in transporting storage proteins to protein storage vacuole, ethylene-responsive transcription factors and basic endochitinase CHB4. The downregulated transcripts included genes related to glycine-rich domain-containing protein, expansin-like A, disease-resistance locus receptor-like protein kinase, GH36, flavin monoamine oxidase, and Zinc-containing alcohol dehydrogenase . On the other hand, Sl shared many DEGs with SlHa (Additional file 10, Additional file 11), with major downregulated genes such as receptor-like protein kinases, glycosyl hydrolases, peroxidases, laccase, ABC transporters, calcium-binding proteins (Additional file 11). Plants pre-inoculated with S. luteus and subsequently challenged with H. annosum were not negatively affected in terms of root growth like the plants inoculated with S. luteus alone. Ha invested efforts in the induction of PR proteins and stilbene synthases under pathogen attack, while SlHa gene machinery was reprogrammed towards cell wall modification (e.g. XTHs, pectinesterases, chitinase-like protein, β-mannanase, GTs, UGTs, EXORDIUM-like protein), water and nutrient uptake proteins (aquaporins, aluminum-activated malate transporter 3, bidirectional sugar transporter SWEET3b, and aluminum-activated malate transporter), fungal recognition (LRR-RLKs, LecRLKs) (Additional file 12, Additional file 13). SlHa also induced a set of genes involved in phenylpropanoid/lignin biosynthesis/flavonoid biosynthesis (CcoAMT, ANR, two bifunctional pinoresinol-lariciresinol reductases, LACs, GGPP synthases, cytosolic sulfotransferase), auxin homoeostasis (WAT1-relaterd proteins, auxin-responsive protein SAUR32), and genes involved in hormone signaling (calcium uniporter protein, E3 ubiquitin ligases with U-box domain, abscisic acid receptor, LOX, Salicylic acid 3-hydroxylase) (Additional file 12). S3H can hydrolyze salicylic acid (SA) to 2,3-DHBA, a deactivated form of SA to prevent over accumulation of SA . SlHa had two S3Hs upregulated but four downregulated, while Ha induced four S3Hs. This could indicate that different hormone signaling pathways were utilized by plants in response to SIHa inoculation. DEGs specifically upregulated in SlHa were also involved in fungal recognition such as G-type LecRLKs and LRR-RLKs, disease resistance NB-LRR. By contrast some genes related to fungal recognition were found to be downregulated, including two cysteine-rich receptor-like protein kinases, three G-type LecRLKs, and three LRR-RLKs (Additional file 13). In this study, although no mantle or Hartig net was formed as a sign of mycorrhization during the short period of the experiment, the beneficial impact of S. luteus in promoting plant primary root growth was still evident. Other authors previously reported that S. luteus mycorrhiza enhanced plant growth by taking up a greater quantity of phosphorus than non-mycorrhizal roots of young seedlings of P. radiata . To ensure successful colonization of the roots, ECM fungus S. luteus mycelia and plant seedlings were left to grow together for a month on artificial media before transfer into the soil. No barrage or demarcation zone was observed in the dual culture of S. luteus and H. annosum , which suggested that S. luteus probably had no antagonism or antibiosis effect on H. annosum growth. Sillo et al. noted that Heterobasidion spp. isolates always completely overgrew S. luteus . The interactions between the Scots pine seedling roots with either the pathogen or the beneficial fungus were most likely regulated by pathogen or microbe associated molecular patterns (PAMP/MAMP). Most often plants deploy plasma membrane-localized pattern recognition receptors (PRRs) and nucleotide-binding (NB)-LRR proteins to recognize MAMPs/PAMPs, or effectors, eliciting MAMP/PAMP-triggered immunity (MTI/PTI), effector-triggered immunity (ETI) . Plants could differentiate symbiotic microbes from pathogens by receptor competition and inhibit MTI for symbionts . LRR-RLKs and LecRLKs probably served as PRRs [ 58 – 60 ]. In our study, the expression of gene encoding LRR-RLKs and L-type LecRLKs in both Ha and Sl may indicate the perception of conserved MAMPs in H. annosum and S. luteus . Chitin and β-glucan are typical fungal MAMPs . SWEET sugar transporters were found to be expressed in Ha and Sl in this study. SWEETs facilitate sugar flux across the cell membrane , and some SWEETs can bind bacterial effectors . The sugar efflux function of SWEET transporters probably help pathogens and symbionts for their nutrition . PtaSWEET1c, which was identified in Populus tremula × alba – Laccaria bicolor symbiosis, was localized in the host plasma membrane surrounding the Hartig net to unload glucose and sucrose to meet the nutritional demands of colonizing hyphae . Most DEGs in Ha were upregulated while most DEGs in Sl were downregulated in this study, which might suggest that beneficial fungus could elicit weaker gene expression changes compared to pathogen-induced responses . The results in this study also showed that defense-related genes were suppressed by S. luteus but were induced by H. annosum , indicating that S. luteus promoted mutualistic interaction by suppressing plant defense responses. H. annosum infection induced unique gene expression patterns in PR proteins, phenylpropanoid pathway/lignin biosynthesis, flavonoid biosynthesis, chalcone/stilbene biosynthesis, while ECM fungus inoculation repressed defense-related genes (peroxidases, laccases), receptor-like protein kinases, methyltransferases, germin-like proteins (GLPs). Methyltransferases work on cytosine methylation that influences gene expression and represses transcription . Plant GLPs are associated with enzymatic activities including oxalate oxidase (OxO), superoxide dismutase (SOD), and ADP glucose pyrophosphatase (AGPPase) , playing crucial roles in disease resistance and defense responses under biotic stress . GLP2 was induced in Norway spruce trees in response to Heterobasidion infection . Unlike in plant-pathogenic interaction wherein plant immunity can be triggered against pathogen infection, plant immunity is often suppressed in the plant-mutualistic interaction to facilitate successful colonization . For example, peroxidase activity in Norway spruce roots was induced in response to pathogenic Ceratocystis polonica but was evaded or suppressed during interaction with the ECM fungus L. bicolor . In P. sylvestris - L. bicolor interaction, genes involved in cell wall modification (XTH and β-xylosidase) were expressed and antifungal α-pinene synthase was downregulated . The support for our observation was previously noted in Populus and L.bicolor interaction where poplar JA-responsive defense gene expressions were blocked by symbiotic effectors (MiSSP7) from L. bicolor . This gene prevented the JA repressor PtJAZ6 degradation during symbiosis development . The symbiotic effector promotes the symbiotic interaction probably through maintaining the repression of transcription factor PtMYC2.1‐regulated genes . Overexpression of poplar MYC2s impaired the fungal growth and the formation of Hartig net in planta, activating the expression of defensive genes encoding terpene synthases, chitinases, GLPs, LRR-RLKs, β-glucosidase and ERF/AP2 . Furthermore, additional supporting evidence to our study is found in oak-ECM fungi interaction. In this system, oak growth was enhanced by three EMC fungi P. microcarpus , P. involutus and L. bicolor, with a common reduction of core DEGs in colonized roots including genes encoding proteins involved in carbon metabolism, defense responses, phenolic pathways and transport . Local host defenses may be activated transiently by MAMPs of ECM fungi like chitins, but plant defense genes could be repressed at the later developmental stage to benefit ECM fungal growth . In this study, S. luteus inoculation induced genes related to nutrient transporters, such as amino acids, nitrates, sugar, and magnesium. This may indicate the fungus could promote plant growth by enhancing nutrient uptake. During poplar— L. bicolor interaction, ethylene and jasmonic acid pathways were induced at the late stage of root colonization to limit fungal growth within roots . The induction of terpene synthases, cytochrome P450s, LOX, MPAK by S. luteus inoculation in our study may suggest that plant limited S. luteus growth in roots by terpene biosynthesis pathway and hormone signaling. Auxin signaling was activated in poplar during L. bicolor colonization, which could facilitate root growth . In our study, defense response-related genes and cell wall modification-related genes were induced by co-inoculation, suggesting plants had balancing buffering defense responses and growth under co-inoculation. Auxin homoeostasis-related genes (WAT1-relaterd proteins, auxin-responsive protein SAUR32) were upregulated in SlHa, which may contribute to the primary root growth. S. luteus promoted mutualistic interaction by suppressing plant defense responses. Pre-inoculation of Scots pine seedlings with beneficial fungus S. luteus prior to pathogen challenge promoted primary root growth, as well as had a balancing buffering role in plant defense responses and cell growth at transcriptome level. Additional file 1. Dual culture of S. luteus and H. annosum on MMN agar media. S. luteus had grown for 13 days prior to the inoculation of H. annosum . Photos were taken 7 days and 17 days after H. annosum colonization. Additional file 2. All seedlings that were recorded from all treatments. Information includes ID, primary root length, and lateral root number. NA refers to not available. Additional file 3. Normalized counts that were obtained by DESeq2's median of ratios. Additional file 4. Hierarchical clustering of DEGs using log2(1 + TMM-normalized CPM). CPM: Counts per million. CPM = raw count of each gene/lib.size of each sample. We keep genes that are expressed at least 1 CPM in at least 3 libraries in differential gene expression analysis using EdgeR. Ctr: control seedlings without any inoculum. Ha: pathogen-infected seedlings. Sl: Suillus luteus -infected seedlings. SlHa: co-infected seedlings with both S. luteus and H.annosum . ‘ + ’ refers to upregulated, while ‘-’ refers to downregulation. Ha.Sl.SlHa: + + + refers to DEGs that were upregulated in all treatments compared to Ctr. Ha.Sl: + + refers to DEGs that were upregulated both in Ha and in Sl. Ha.SlHa: +—refers to DEGs that were upregulated in Ha but downregulated in SlHa. The value on the scale bar refers to log2(1 + TMM-normalized CPM). Additional file 5. Summary of DEGs into common genes in three treatments, genes overlapped in two treatments, and treatment-specific genes based on the data from the Venn diagram in Fig. 4 A. The data in each file included Gene ID, TMM-normalized CPM in replicates of each treatment, logFC, FDR, protein family, and protein name. ‘1’ refers to upregulated, while ‘0’ refers to downregulation. 31_000 refers to DEGs that were upregulated in all treatments compared to Ctr. 32_11 refers to DEGs that were upregulated in both Ha and in Sl. 330_1 refers to DEGs that were specifically upregulated in Ha. Additional file 6. Best hit selected by bit score for each CDS sequence of DEGs which blast against to Scots pine de novo transcriptome assembly fasta sequences constructed with Trinity . Additional file 7. Heatmap of DEGs which were specifically upregulated in Ha. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *). Additional file 8. Heatmap of DEGs which were specifically downregulated in Ha. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *). Additional file 9. Heatmap of DEGs which were specifically downregulated in Sl. Red indicates a high level of upregulation, while blue indicates high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *). Additional file 10. Heatmap of DEGs using log2foldchange which were upregulated in both Sl and SlHa. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *). Additional file 11. Heatmap of DEGs using log2foldchange which were downregulated in both Sl and SlHa. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *). Additional file 12. Heatmap of DEGs which were specifically upregulated in SlHa. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *). Additional file 13. Heatmap of DEGs which were specifically downregulated in SlHa. Red indicates a high level of upregulation, while blue indicates a high level of downregulation. Asterisks indicate whether significant difference exists in gene expression level of treatment such as Ha, Sl, SlHa compared to that of Ctr. ( p < 0.05: *). | Study | biomedical | en | 0.999995 |
PMC11697948 | Parkinson’s Disease (PD) is currently the fastest-growing neurological disorder globally, second only to dementia . Worldwide prevalence has doubled over the past 25 years, now exceeding 8.5 million individuals . It is a progressive neurological disorder due to substantia nigra cell loss, reducing dopamine production . Dopamine deficiency leads to motor symptoms like tremors, bradykinesia, gait issues, and rigidity , and non-motor symptoms including speech problems, urinary issues, constipation, sleep disturbances, and neuropsychiatric symptoms . These worsen over time, impacting patients’ quality of life . Symptoms of PD, especially those affecting emotional expression and recognition such as facial masking greatly contributes to stigma . Stigma plays a significant role in the lives of individuals living with Parkinson’s disease as it is a complex social phenomenon encompassing stereotypes, prejudice, discrimination, and exclusion . It differs from shame, which is an emotional response characterised by a deep sense of personal inadequacy or dishonour . While stigma refers to the negative social perception imposed by others due to a particular condition, shame is more personal, stemming from internalised beliefs about one’s own worth or behaviour. Thus, while stigma is shaped by societal attitudes, shame is a self-directed feeling that can compound the challenges faced by individuals with PD . Stigma can manifest in several forms, each affecting individuals in unique ways. Felt stigma refers to the perception that others view a person negatively due to their condition, leading to feelings of anticipated discrimination or exclusion . Enacted stigma, on the other hand, involves actual discriminatory actions or behaviours directed at the individual based on their condition . Affiliated stigma affects caregivers who may experience stigma due to their association with someone living with a stigmatised condition . Lastly, self-stigma occurs when individuals internalise negative beliefs and judgements about themselves, leading to shame, guilt, and a diminished sense of self-worth . These different forms of stigma combine to create complex social and emotional challenges for those affected. One study exploring stigma in PD reports more than 50% of people with Parkinson’s disease conceal their diagnosis, mask symptoms or avoid appearing in public due to stigma. A major contributing factor to this stigma is the lack of public awareness and understanding of PD and its symptoms, such as tremors, slow movement, and facial masking . These symptoms are often misinterpreted by the public, leading to misconceptions about the capabilities and behaviours of those with PD . Within the context of this review, the public refers to individuals in society who are not directly affected by PD. As a result of these misunderstandings, individuals with PD and their caregivers frequently face social stigmatisation. This stigmatisation can manifest in various ways, such as being unfairly judged, socially isolated, or excluded from community activities. The impact of stigma on the lives of people with Parkinson’s disease is profound, affecting their quality of life and mental health . Research indicates that stigma in PD can lead to increased levels of depression, anxiety, and social isolation among those living with the disease . Another study highlights the wider health and social impacts of PD stigma, aggravating poverty due to loss of income, increasing severity of disability and sometimes resulting in mortality. Research on the stigma associated with PD remains limited, despite evidence indicating that stigma can profoundly affect the social and psychological well-being of individuals with Parkinson’s disease. While studies such as Maffoni et al. and Lubomski et al. have explored the experience of stigma in PD, their scope and methodologies reveal gaps. Maffoni et al. conducted a review focused solely on qualitative studies, providing insights into patients’ experiences of stigma but limiting broader generalisability and cross-study comparison. Similarly, Lubomski et al. highlighted stigma as a barrier to care, yet primarily within healthcare settings rather than the broader community. Most existing literature emphasises medical symptoms and treatment options for PD, leaving a lack of comprehensive research into the social and psychological dimensions, including the impact of public and community-based stigma. This review addresses these limitations by employing a mixed-methods approach to synthesise both quantitative and qualitative findings across diverse settings, with a focus on the stigma encountered in everyday social contexts. Filling this gap is essential to developing targeted, evidence-based interventions aimed at reducing stigma and improving the quality of life for individuals with PD and their caregivers. This systematic review aims to explore PD-related stigma, examining its impact on individuals and their caregivers, and identifying potential interventions to reduce stigma by synthesising existing literature. By understanding the stigma associated with PD, we can determine the extent of the problem and provide recommendations to address it. This will help raise public awareness and promote PD stigma as an important public health issue, ultimately improving the lives of those affected and fostering a more inclusive and supportive society. This will be achieved through the following objectives; first, to conduct a comprehensive systematic review and synthesise relevant empirical research on PD-related stigma, drawing from multiple databases. Second, to identify recurring themes, patterns, and trends in the literature concerning the experiences of stigma and its impact on individuals affected by PD. Finally, to provide evidence-based recommendations for future research and stigma-reduction strategies, offering targeted interventions to support individuals with PD in addressing and overcoming social stigma. These objectives aim to enhance understanding and inform future efforts to improve the well-being of those impacted by PD. A systematic review was conducted following the Joanna Briggs Institute methodology for Mixed-Methods Systematic Reviews guidance . It is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to enhance the quality and transparency of reporting. The review protocol was registered on PROSPERO register of systematic reviews on 13th November 2023 . The PRISMA 2020 Checklist can be seen in Additional File 1 . Six databases were searched up to August 2024. These were CINAHL, Medline, Embase, PsycINFO, Web of Science and the Cochrane Library. These databases were chosen as their literature focusses on nursing, medicine, psychology, and social sciences. A search of the following websites was also conducted to source relevant grey literature: EThOS, PLOS, NICE and WHO. Reference lists of all included studies were manually searched to identify additional studies not found in the initial search. Internet search engines Google ( https://www.google.co.uk/ ) and Google Scholar ( https://scholar.google.com/ ) were extensively searched for peer-reviewed publications. The search was supplemented by journal alerts from Parkinson’s specific journals such as Journal of Parkinson’s Disease and Parkinsonism and Related Disorders. Keywords used to conduct the search were based on three main dimensions: Parkinson’s disease, stigma (and related terms) and public perception. These keywords were carefully selected to capture a broad range of articles related to Parkinson’s disease and the associated stigma. Using variations of “Parkinson’s disease” ensured that different terminologies that may be used in academic literature were covered, while the terms related to stigma and discrimination were chosen to encompass the different dimensions of stigma that individuals with PD may experience. Terms related to public perception helped to identify articles that examined societal views and misconceptions, and discussed the social dimensions of living with PD. The truncation wildcard (*) was used on key words, allowing alternate forms of the word to be searched . All search terms and Boolean logic can be seen in Additional File 2 . In this review, research exploring the stigma experienced by people affected by PD was of interest. Research incorporating people with Parkinson’s disease and their carers were included. Carers often share in the challenges of PD, therefore they were included to provide a comprehensive view of stigma’s impact on both individuals with PD and those who support them. In studies where the experiences of people with Parkinson’s disease was examined alongside other conditions, data were extracted only about PD without limitations. No data limitations were put in place during database searches. A summary of the inclusion/exclusion criteria used is provided in Table 1 . The systematic review software Covidence was used to assist in the removal of duplicates, screening, and data extraction . Studies were imported and duplicates were removed. The inclusion and exclusion criteria were used to screen papers within Covidence and determine relevance to this review. Stage 1 of screening involved examining studies by title and abstract, with the primary screening conducted by SC. To enhance reliability, a sample of 25% of studies was also checked by GC, serving as a secondary reviewer to reduce potential bias and improve consistency in selection. The 25% sample size was chosen as a manageable proportion for cross-checking while providing a robust reliability measure across the dataset. Stage 2 involved screening of full text studies by SC and GC independently. At this stage, both researchers (SC & GC) screened 100% of full text studies. Conflicts throughout the screening process were resolved following discussion with GM. The quality of papers included in the review was appraised using the Joanna Briggs Institute (JBI) Critical Appraisal Tool. Qualitative studies were evaluated with the JBI checklist for qualitative research , while quantitative studies were assessed using the JBI checklist for analytical cross-sectional studies. Covidence software was used to manage and streamline the appraisal process, allowing reviewers to independently assess each study. In cases where there were disagreements between reviewers, a third reviewer was consulted to resolve discrepancies, ensuring an unbiased evaluation. To further enhance reliability, inter-rater agreement was calculated using Cohen’s kappa , which provided a quantitative measure of consistency between reviewers. Twelve studies were deemed to be of high quality [ 28 – 39 ] and the remaining ten were medium quality [ 19 , 40 – 48 ]. No studies were deemed as low quality, therefore were not excluded. This review adopts a mixed methods approach, encompassing both qualitative and quantitative studies. Narrative synthesis, as defined by Popay et al. , involves using language and text to summarise and present findings across multiple studies, adopting a textual or ‘storytelling’ approach. It is a robust scientific approach that facilitates the integration of both quantitative and qualitative data related to a specific phenomenon . Narrative synthesis was used for this systematic review and was conducted by the researcher (SC). In this systematic review, both qualitative and quantitative methods of analysis were employed to provide a comprehensive synthesis of the included studies. For the qualitative component Braun and Clarke’s thematic analysis was used to systematically identify and categorise recurring patterns and themes within the findings. This involved coding the qualitative data from each study, organising these codes into broader themes, and refining these themes using NVivo 11. For the quantitative studies, the findings were converted to comprise textual descriptions or narrative views of quantitative results, allowing for a more holistic understanding of the data. This conversion process enabled the integration of quantitative findings into the broader narrative framework of the review. The integration of qualitative and quantitative data was achieved through a mixed methods synthesis, following the JBI methodology for Mixed-Methods Systematic Reviews . This approach allowed the triangulation of the data, enhancing the robustness and validity of our conclusions. The review PRISMA flowchart is presented in Additional File 3 . A total of 5441 studies were imported into Covidence and 1190 duplicates were removed. The inclusion and exclusion criteria were used to screen papers within Covidence and determine relevance to this review. Firstly, 4252 studies were screened by title and abstract (SC) and a sample checked (GC). In total, 4206 studies were determined irrelevant for this review, leaving a remaining 46 studies for full text screening. In total, 24 studies were excluded due to providing outcomes not relevant to the review ( n = 15), interventions based on disease management ( n = 2), the population did not focus on PD ( n = 2), and not empirical research ( n = 2). Literature reviews ( n = 3) were also excluded however, reference lists were screened for papers that may not have shown during the database search that may be suitable for the review. During the reference list search, one study was determined suitable for the review after title, abstract and full text screening. A final total of 22 papers were deemed suitable for this review. A full PRISMA flow chart can be seen in Additional File 3 . This review included 22 research studies published between 2002 and 2024. Of these, 11 used a qualitative design, and the remaining 11 employed a quantitative approach. Eleven studies in the review had a cross-sectional design using questionnaires, one using a cohort design via a 3-year prospective study and 11 used a qualitative approach using interviews and focus groups. A table of study characteristics can be seen in Additional File 4 . The included studies took place in various geographical locations: seven studies in the USA [ 34 , 36 – 38 , 41 , 42 , 47 , 48 ], four in the UK , two in China , two in Brazil , and one each in Mexico , Israel , Sweden , Kenya , Tanzania , Jordan , Taiwan and France . The studies were conducted in diverse settings, including participant homes, clinics, cafes, PD support groups, hospitals, academic medical centres, and online platforms. The geographic diversity of these studies suggests that the results may be shaped by differences in healthcare systems, cultural beliefs, and participant characteristics, all of which could affect the outcomes and limit the generalisability of the findings. Sample sizes ranged from six to 362 participants with a combined total of 2,502 participants. Studies included samples of people with Parkinson’s disease [ 19 , 29 – 33 , 35 – 41 , 43 – 48 ], spouses/carers of people with Parkinson’s disease , healthcare workers and traditional healers . All studies included both female and male participants, apart from one study that only included female participants. The mean age of participants in the included studies was 65.1 years old, with the range being 30–94 years old. Mean disease duration of participants was 8 years. The variability in sample sizes across studies may introduce bias, as smaller samples could result in less reliable or less generalisable findings. Additionally, studies with larger samples may overrepresent certain characteristics, skewing the results. This variation was considered when interpreting the overall findings as it could influence the accuracy and applicability of the conclusions drawn from these studies. All 22 studies reported obtaining ethical approval from appropriate review bodies. In total, 21 studies included information about informed consent processes. One study by Hermanns did not include information about obtaining informed consent from participants for the study. This omission of informed consent can raise ethical concerns and lead to potential bias as participants may not fully understand their involvement in the study. Six studies reported conducting the studies in accordance with the Declaration of Helsinki , and six maintained confidentiality and anonymity throughout the study . Three studies reported voluntary participation . A thorough review of included studies in this review revealed no significant ethical concerns. Across the studies, 24 different outcome measurements were used. The Parkinson’s Disease Questionnaire (PDQ-39) was the most common instrument, being used by seven studies [ 32 , 36 – 39 , 47 , 48 ]. This questionnaire includes 39 items assessing how often people with Parkinson’s disease experience difficulties across eight dimensions of daily living including relationships, social situations and communication. It also assesses the impact of Parkinson’s on specific dimensions of functioning and wellbeing. Of these studies, six used PDQ-39 and one used PDQ-8 which is a shorter version with eight items. Both versions of the PDQ are validated instruments that have been widely used for assessing the quality of life for people with Parkinson’s disease. Through the process of data analysis and synthesis, five distinct themes emerged. The first theme, stereotypes in Parkinson’s disease , examines the common misconceptions, preconceived notions, and prevailing stereotypes surrounding individuals with PD within the broader societal context. The second theme, drivers and facilitators of stigma , explores the underlying societal, cultural, and psychological mechanisms that foster stigmatising attitudes and behaviours towards individuals living with PD. The third theme addresses the impact of stigma on mental health and well-being , including feelings of embarrassment and shame associated with the stigma of PD. The fourth theme, response and consequences of stigma , considers the strategies adopted by those with PD to cope with public stigma. Finally, the fifth theme, beyond stigma , sheds a more positive light on the impact of living with PD, offering a comprehensive understanding of the condition. One prevalent stereotype is the misconception that PD is defined solely by visible motor symptoms, particularly tremor . Participants expressed frustration over this oversimplification, emphasising the lack of awareness regarding other symptoms of PD, particularly non-motor symptoms. For instance, one study reported that participants noted the public’s limited understanding of non-motor symptoms such as speech disorders and muscle stiffness, which undermines the complexity of PD. “I think it (the perception of the illness) is overly related to the tremors. Nobody understands the suffering caused by muscle stiffness” ( p.3) The literature also indicated that difficulties with activities of daily living (ADLs) and motor symptoms were associated with higher levels of stigma in individuals with PD. One study found that ADL difficulties contributed to greater stigma, and another linked independence in ADLs to lower levels of stigma. Lin et al. demonstrated that impairment in ADLs, motor examination scores, and non-motor symptoms correlated with higher levels of self-stigma. Furthermore, Ma et al. noted that difficulties with facial expression, a common PD symptom, tended to increase feelings of stigma and reduce quality of life. Another study found significant associations between motor symptoms and both self-stigma and enacted stigma. Another common stereotype was the belief that PD exclusively affected older individuals . This ageist stereotype was perceived to lead to delayed diagnoses and misinterpretation of symptoms, particularly in younger individuals. Participants reported encountering disbelief or scepticism from others upon disclosing their diagnosis, often leading to concealment of their condition to avoid judgement . Healthcare professionals’ biases further contributed to perceived ageist stereotypes and delayed treatment . Media portrayals emphasising advanced-stage symptoms also were noted to skew public perceptions of PD. “Everyone stands there not understanding how such a thing can occur (the disease befalling a young person).” ( ; p. 3). Stigma experiences in individuals with PD were noted to be influenced by time since diagnosis and disease severity. One study found that as the number of years since diagnosis increased, individuals reported higher levels of felt, enacted, and total stigma, which suggested more pronounced stigma in later stages. Hou et al. also found disease duration to be significantly associated with stigma in PD with Stigma Scale for Chronic Illness (SSCI) score ( p = 0.003) and felt stigma (= 0.004), particularly in males (p = < 0.001). Additionally, Ma et al. found that all stigma measures correlated with disease severity, with more severe PD being linked to increased stigma and poorer quality of life (p = < 0.05). Conversely, Salazar et al. found no significant contribution of disease characteristics to stigma perception, indicating a complex and multifaceted experience of stigma in PD. Comorbidities, especially mental health issues, exacerbated stigma in PD patients. One study reported that 79% of PD patients had additional health conditions, such as thyroid disease, depression, and anxiety, which correlated with higher stigma levels and lower quality of life. They demonstrated that depression and anxiety showed strong positive relationships with stigma ( p < 0.001). Another study also found significant associations between anxiety, depression, and stigma with higher SSCI scores (p = < 0.001) and higher felt stigma ( p = 0.001). These results showed these emotional challenges are closely linked to how much stigma people feel. This study also measured affiliate stigma, which refers to the stigma experienced by individuals who are closely associated with someone who has a stigmatised condition, such as a caregiver . Mental health issues were significant predictors of affiliate stigma ( p = 0.187) , highlighting their impact on stigma in PD. A prominent driver of stigma identified in the review was the lack of awareness and education about PD within communities [ 19 , 29 , 42 – 44 ]. Participants noted that misconceptions and misinformation about PD led to stigmatisation. One study emphasised the absence of public information about PD, resulting in stereotypes and stigma. Another reported that many people were unaware of what PD entailed, perpetuating misconceptions and hindering early diagnosis and intervention. Similarly, one participant from Valcarenghi et al. remarked: “One thing I’ve learned is that many people do not know what Parkinson’s disease is.“( ; p.275). Participants in Fothergill-Misbah’s study reported cultural beliefs as drivers of stigma, with some attributing PD to supernatural causes. These beliefs, often linked to blame and punishment, notably impacted individuals with PD, leading to social isolation and discrimination. Shah et al. highlighted the role of cultural ideologies in stigmatising PD, with participants reluctant to disclose their condition due to associated shame or taboo. “And there is a stigma attached to the condition back home , where I come from. Wherein , no one tends to divulge–well actually it’s a private information , like my condition. So , nobody would like , even my family wouldn’t like to tell anybody else that I’m having this condition. So , it’s better off taking me as a drunk , rather than a PD patient.” ( ; p. 11). In summary, stigma surrounding PD is driven by factors such as time since diagnosis, disease severity, comorbidities, lack of public awareness, cultural beliefs, healthcare system limitations, and media representation. Addressing these issues through public education, improved healthcare training, and accurate media portrayal can help mitigate stigma and improve the quality of life for individuals with PD. The psychological impact of stigma on individuals with PD was profound, leading to feelings of shame, embarrassment, and low self-esteem . Participants described internalised stigma from societal beliefs about their capabilities, leading to feelings of inadequacy and worthlessness . Depression, anxiety, and loneliness were common, exacerbated by public stigma . Stigma-induced psychological distress hindered individuals’ ability to seek social support and engage in meaningful activities, perpetuating a cycle of social isolation and diminished well-being. Self-compassion plays a crucial role in mitigating the negative impact of stigma on mental health. One study found that higher levels of self-compassion were associated with lower levels of depression, anxiety, and stress, both independently and through a significant indirect effect on felt stigma (p = < 0.001). However, self-compassion did not moderate the relationship between enacted stigma and mental health outcomes (p = < 0.00). Emotional well-being was significantly associated with stigma ( p = 0.12) , and mental health symptoms contributed to experiences of stigma ( p = 0.187) . Individuals with PD who experience stigma tended to have more severe depression and poorer quality of life (p = < 0.05) . Similarly, one study also found higher stigma severity to be an associated factor of depression in both people with Parkinson’s disease ( p = 0.001) and their caregivers ( p = 0.006). Verity et al. highlighted the significant impact of stigma on health-related quality of life and depression ( p = 0.01), underscoring the complex interplay between stigma, self-compassion, mental health, and quality of life in individuals with PD. Research addressing the stigma faced by individuals with PD in various social contexts and cultural settings is relatively recent. Of the 22 studies included in the review, 17 were conducted in the last decade, suggesting an increasing focus on this issue. The findings reveal significant public stigma towards individuals with PD across various global contexts, including Tanzania, Jordan, the United States of America (USA), and the United Kingdom (UK). This widespread stigma demonstrates its universal nature, transcending cultural and socioeconomic boundaries. Understanding this global perspective is crucial for developing comprehensive strategies to address the pervasive impact of stigma. This has been demonstrated in one study who found raising awareness in various global contexts can combat stigma associated with COVID-19. One key aspect of the review highlighted by the inclusion of various countries is the diverse cultural beliefs and alternative explanations for PD which majorly contributes to stigma. Multiple studies highlighted that PD is often misattributed to aging, clumsiness, or supernatural causes. These misconceptions lead to shame, embarrassment, and reluctance to disclose the diagnosis, further exacerbating stigma. This includes the belief that causes such as nature spirits, poverty, personal hygiene, and violation of social taboos. Such beliefs, common in indigenous communities, may influence health-seeking decisions where individuals may prioritise traditional healing practices, which reflect deeply valued cultural perspectives on health, before considering medical care . This has been seen in other diseases such as cancer, where seeing healers may potentially affect timing of diagnosis and treatment in indigenous communities . A significant manifestation of PD stigma is the stereotype that it exclusively affects older individuals. This belief can lead to younger individuals feeling profound shame about their diagnosis, causing them to hide their condition and avoid seeking necessary support and resources . The age-related stereotype also affects healthcare professionals, who may attribute PD symptoms to aging rather than recognising them as disease manifestations. Such assumptions by healthcare providers perpetuate stigma and contribute to the mismanagement of the disease, negatively impacting patients’ health outcomes and quality of life. This attribution of disease to older age is also common in Multiple Sclerosis (MS), as one study found younger individuals living with MS encountered more stigma which indicates that disease stigma affecting younger individuals is prevalent across various conditions. A primary physical manifestation of PD stigma is the public’s tendency to focus on visible symptoms, such as tremors, while neglecting non-visible aspects like cognitive impairment and mood disorders . This narrow perception reduces the disease to its apparent features, leading to inadequate support and heightened emotional burdens for individuals with PD and their caregivers. Motor symptoms and difficulties with activities of daily living (ADLs) contribute significantly to public stigma, particularly for those with noticeable impairments like lack of facial expression . This stigma can exacerbate motor symptoms through increased psychological stress and social isolation, further burdening caregivers . Similarly, people living with MS frequently experience significant stigma due to the visible symptoms of the disease, as evidenced by two studies, highlighting the impact on their quality of life . Stigma also profoundly impacts the mental health of individuals with PD, causing feelings of shame, embarrassment, social withdrawal, and worsened mental health . This is similar across multiple conditions including dementia , cancer and MS , causing low self-esteem, isolation and poor mental health in these conditions. This can have detrimental impacts on the well-being of individuals and their caregivers, who face significant emotional and psychological stress due to the demanding nature of living with disease and caregiving in a stigmatised context . Stigma in the workplace was a common theme in this review, leading to a reluctance in disclosing the condition, resulting in diminished self-confidence, loneliness, and depression . This can severely impact careers and finances, creating additional stress for caregivers . This is also prevalent in other diseases like MS, where one study found 79.2% of people with MS have experienced workplace stigma . This resulted in people feeling greater stress, low self-esteem and lower QoL. The review has shown how stigma increases with the duration and severity of PD. Eccles et al. noted higher levels of stigma among individuals with longer disease durations. Additionally, Ma et al. found that as PD progresses, so does the stigma, particularly due to visible symptoms such as tremors, balance difficulties, and wheelchair use. These symptoms lead the public to focus on motor impairments, neglecting broader impacts like cognitive impairment and mood disorders. This is similar in other illnesses like cancer, where time since diagnosis and disease severity have shown to have a positive effect on stigma experienced by the public . This narrow perception is problematic as it fosters a one-dimensional view of PD, marginalising those affected. A significant factor in PD stigma is the lack of public education and awareness. Multiple studies [ 19 , 29 , 34 , 42 – 44 ] emphasised that poor understanding of PD reinforces stereotypes, hinders early diagnosis and treatment, and leads to social withdrawal and mental health issues. A well-informed public can better understand the full spectrum of PD, including both its visible and non-visible symptoms. By dispelling myths and correcting misconceptions, education can reduce the stigma associated with PD . This has been demonstrated in a study by Jagota et al. , where a public social media educational campaign showing videos of people with Parkinson’s disease had a positive impact in creating awareness about PD. Comprehensive awareness campaigns, such as those used within the aforementioned study , using various media platforms can disseminate accurate information about PD. Additionally, integrating PD education into school curricula and workplace training programs can normalise discussions about the disease and encourage inclusivity. These programmes can equip young people and professionals with the knowledge to support peers and colleagues with PD and have proven to be successful in previous research where education in schools has positively changed students’ knowledge about and attitudes towards dementia [ 72 – 74 ]. Utilising the influence of public figures such as Michael J Fox and ensuring accurate media portrayals can also help shift perceptions, presenting balanced views of PD across various stages and backgrounds. By addressing these factors, it is possible to reduce stigma and improve the overall well-being of those living with PD. A key strength of this systematic review is that, to our knowledge, it is the first to comprehensively explore the stigma experienced by individuals affected by Parkinson’s disease. The review used an extensive search strategy, covering a wide array of databases, including Google Scholar and reference lists, combined with broad search terms and well-defined inclusion and exclusion criteria. This approach maximised the capture of relevant studies to address the research question. However, a notable limitation is the restriction to English-language publications, which may have excluded valuable insights on stigma from non-English-speaking regions, potentially limiting the understanding of stigma experiences across different cultural contexts. This review highlights implications for policymakers, clinicians, and researchers. Policymakers should prioritise public awareness campaigns to correct misconceptions about PD and reduce cultural and age-related stigma. Clinicians should adopt a holistic, culturally sensitive approach that addresses both the physical and psychological effects of stigma in PD, offering support for mental well-being alongside medical treatment. Future research should focus on how cultural beliefs shape stigma, as well as on the resilience and advocacy demonstrated by those living with PD, to inform more empowering and inclusive healthcare strategies. This systematic review examined the multifaceted nature and far-reaching impacts of stigma surrounding PD. The review synthesised findings from diverse studies, and revealed that stigma manifests through negative stereotypes, misconceptions, social exclusion, and discrimination, affecting both individuals with PD and their caregivers. The underlying drivers include lack of awareness, cultural beliefs, and societal norms, which perpetuate misconceptions and hinder access to essential resources and support. The consequences are profound, leading to social isolation, reduced quality of life, and psychological distress, as well as challenges in employment, financial stability, and healthcare access. However, the review also highlighted resilience, advocacy, and positive interactions, underscoring the agency and strength of individuals with PD. These insights highlight the need for targeted education, awareness campaigns, and advocacy initiatives. Future research should focus on longitudinal studies assessing the effectiveness of stigma-reduction interventions and explore how cultural and societal factors influence stigma. Such studies could fill gaps in the current literature and provide evidence-based strategies to improve the well-being of individuals with PD and their caregivers. | Review | biomedical | en | 0.999995 |
PMC11697968 | Chronic hypertension among women of reproductive age is an important risk factor of hypertensive disorders of pregnancy, a leading cause of pregnancy-related mortality in the United States (US) [ 1 – 4 ]. Rates of perinatal mortality are significantly higher among pregnant women with chronic hypertension than those with normotensive pregnancies , and women who suffer hypertensive disorders of pregnancy have substantially higher risks of cardiovascular disease later in life . Rates of chronic and gestational hypertension are increasing in the US, due in part to increased prevalence of obesity and higher average maternal age . Consequently, there is growing awareness of the need for effective prevention and control of hypertension among women of reproductive age to improve women’s cardiovascular and maternal health outcomes . There are important demographic disparities in chronic hypertension risk among women across the US. Non-Hispanic Black and American Indian/Alaskan Native women are substantially more likely to develop chronic hypertension than White women , and women residing in rural areas are more likely to develop chronic hypertension than those living in urban environments . Sociodemographic factors represent additional disparities in hypertension prevalence, as women with lower income and education levels tend to have higher prevalence of hypertension than those with higher socioeconomic metrics . Efforts to address these disparities are necessary to mitigate increasing rates of hypertension in the US . North Dakota has large rural and substantial minority populations with severe disparities in health outcomes and healthcare access, which may contribute to disparities in hypertension awareness, prevention, and control [ 11 – 13 ]. Thirty-eight of the 53 North Dakota counties are designated by the Health Resources and Services Administration as primary care health professional shortage areas (HPSAs) . Residents of HPSAs have lower access to medical care, including outpatient and preventive care, than people living in non-shortage areas . North Dakota also has substantial minority populations. Non-Hispanic American Indians are the most abundant minority ethnic/racial group, comprising more than 5% of the overall population . The American Indian population in North Dakota faces severe disparities in general health status and adverse pregnancy outcomes relative to the general population, including higher infant mortality rates, lower access to satisfactory prenatal care, higher rates of gestational diabetes, and higher rates of adverse childhood experiences [ 20 – 24 ]. In the northern Great Plains region, which includes North Dakota, American Indian health outcomes tend to be substantially worse than among American Indian populations in the US overall, indicating that these racial disparities are heightened by conditions associated with this region . There have been limited studies of disparities and predictors of chronic hypertension in North Dakota, especially among women of reproductive age. Therefore, the objectives of this study were to investigate predictors of self-reported chronic hypertension among women of reproductive age. The results of this study can be used to guide efforts to control pre-pregnancy hypertension and reduce the burden of hypertensive disorders among women of reproductive age in North Dakota. The study population was women of reproductive age (18–44 years old) who responded to the question “Have you ever been told by a doctor, nurse, or other health professional that you have high blood pressure?”. Respondents who answered “Yes” were provided the follow-up question: “Was this only when you were pregnant?” Respondents who were never told they had high blood pressure, had borderline high blood pressure, or were only told they had high blood pressure during pregnancy were categorized as non-hypertensive. Those who said that they had been told they had high blood pressure were categorized as having hypertension. A conceptual causal model was developed based on biological knowledge and questions asked in the BRFSS survey . The variables that were considered as potential predictors of hypertension are listed in Table 1 and consist of a variety of demographic factors, behavioral risk factors, chronic conditions, and variables associated with healthcare access and utilization including: age, race/ethnicity, smoking status, binge drinking, physical activity, fruit and vegetable consumption, frequent mental distress, body mass index (BMI), residence in an HPSA, inhibitive medical costs, personal healthcare provider, time of most recent checkup, and health insurance. Fig. 1 Conceptual model of predictors of hypertension among women aged 18–44 years in North Dakota Table 1 Demographic factors, lifestyle characteristics, and chronic health status of women aged 18–44 years in North Dakota, 2017–2021 Category Characteristic Weighted Frequency (standard error) Percent Hypertension (95% Confidence Level) Demographics Age 18–34 91,876 (3,138) 2.4 (2.4, 5.0) 35–44 44,753 (1,750) 6.9 (6.9, 11.6) Race White 107,025 (3,021) 5.1 (3.9, 6.2) American Indian 7,314 (788) 8.9 (3.1, 14.7) Black 6,042 (951) 6.7 (0, 13.6) Other Race/Ethnicity 15,912 (1,462) 11.9 (5.7, 18.1) Behaviors Heavy Drinking > 7 drinks per week 10,268 (1,045) 4.4 (0, 9.8) ≤ 7 drinks per week 119,186 (3,290) 5.8 (4.5, 7.0) Smoking Status Current smoker 22,193 (1,518) 8.9 (5.6, 12.2) Non-smoker 110,211 (3,170) 4.9 (3.6, 6.2) Physical Activity Outside of Current Job in Last Month No physical activity 33,036 (1,850) 7.1 (4.2, 10.0) Some physical activity 99,177 (3,016) 4.9 (3.6, 6.2) Fruit and Vegetable Consumption < 5 per day 103,458 (3,083) 5.8 (4.4, 7.1) ≥ 5 per day 22,363 (1,490) 5.6 (2.3, 8.9) Chronic Conditions Frequent Mental Distress (FMD) ≥ 14 poor mental health days 27,505 (1,791) 8.0 (4.5, 11.6) < 14 poor mental health days 107,976 (3,081) 4.6 (3.5, 5.8) Unimputed Body Mass Index (BMI) Obese (BMI ≥ 30) 37,376 (1,875) 10.2 (7.2, 13.3) Overweight (25 < BMI ≤ 29) 31,441 (1,763) 4.5 (2.1, 6.9) Normal (18.5 < BMI ≤ 25) 49,988 (2,251) 3.0 (1.7, 4.4) Underweight (BMI ≤ 18.5) 2,555 (531) 0.6 (0, 1.6) Unknown/Missing 15,269 (1,204) 10.1 (5.2, 15.1) Imputed BMI Obese (BMI ≥ 30) 41,229 (1,916) 14.3 (11.1, 17.6) Overweight (25 < BMI ≤ 29) 34,388 (1,802) 8.6 (5.5, 11.7) Normal (18.5 < BMI ≤ 25) 55,046 (2,301) 4.8 (3.2, 6.4) Underweight (BMI ≤ 18.5) 2,736 (534) 0.5 (0, 1.5) Healthcare Access/Utilization Problems with Medical Cost in Last 12 Months Could not afford needed care 59,589 (2,325) 3.0 (1.6, 4.4) Afforded all needed care 76,672 (2,662) 7.4 (5.6, 9.2) Personal Doctor No personal doctor 27,940 (1,826) 6.2 (3.0, 9.5) Has personal doctor 108,445 (3,058) 5.4 (4.1, 6.6) Most Recent Checkup Longer than 1 year ago 43,216 (2,074) 4.3 (1.8, 6.7) Within last year (reference) 89,890 (2,887) 5.8 (4.5, 7.2) Healthcare Insurance No healthcare insurance 11,192 (1,105) 6.9 (2.2, 11.7) Has healthcare insurance 112,371 (3,329) 9.1 (7.8, 10.6) Primary Care Health Professionals Shortage Area (HPSA) Score Resides in HPSA 44,428 (1,884) 11.5 (8.9, 14.1) Does not reside in HPSA 92,202 (3,054) 7.7 (5.8, 9.5) All data cleaning and analysis were conducted using SAS Version 9.4 . Age was categorized into a binary variable of ages 18–34 and 35–44 years, corresponding with evidence that pregnancy-related morbidity and mortality is significantly higher in women ages 35 years and older . Race and ethnicity were categorized as non-Hispanic white, American Indian, non-Hispanic Black, or Other Race/Ethnicity. Frequent mental distress (FMD) was calculated based on respondents’ answer to the question “how many days during the past 30 days was your mental health not good?”, using the CDC definition of 14 or more bad mental health days to define FMD . Body mass index was calculated and categorized by CDC based on the height and weight of the respondent (BMI under 18.5 = underweight; between 18.5–24.9 = normal weight; 25.0–29.9 = overweight; 30.0 or greater = obese). Time of most recent checkup was categorized as having had a checkup within the last year or having a checkup more than one year ago. Residence in a primary care HPSA was dichotomized based on the primary care HPSA score of the county where the respondent resided, with any score > 0 being categorized as living in an HPSA. All other predictors were dichotomous, e.g., whether the respondent was a current smoker (yes/no), whether the respondent was a heavy drinker (yes/no). The percentage of missing data was investigated for each potential predictor variable. For any predictor with more than 5% missing data in the target population, the missingness was analyzed in relation to other predictor variables and the outcome variable using univariable logistic regression. BMI had 5.5% missing data and so was a candidate for imputation. If there was evidence of an association between a predictor’s missing data and the outcome or other predictor variables, the data were considered not missing completely at random. Responses that were not missing completely at random were imputed using the surveyimpute procedure in SAS using the hot-deck method with weighted selection . Imputation cells included covariates that were expected to be associated with the predictor being imputed based on the causal model and that had a simple association with hypertension (Table 2 ). Five donor cells were provided for each imputed response, and sampling weights for imputed values were divided by the number of donor cells. The imputation was implemented on the entire dataset, including males and people older than 18–44 years old. Table 2 Results of univariable binary logistic regression models investigating simple associations between hypertension and potential predictors among women aged 18–44 years in North Dakota, 2017–2021 Predictor Odds Ratio (95% Confidence Interval) p -value Age < 0.001 35–44 2.3 (1.6, 3.3) < 0.001 18–34 reference Race < 0.001 White 2.5 (1.4, 4.5) 0.001 American Indian 1.7 (0.6, 4.6) 0.3 Black 1.6 (0.9, 3.0) 0.1 Other Race/Ethnicity reference Heavy Drinking 0.2 > 7 drinks per week 0.7 (0.2, 1.9) 0.46 ≤ 7 drinks per week Smoking Status < 0.001 Current smoker 2.0 (1.3, 3.1) 0.002 Non-smoker reference Physical Activity Outside of Current Job in Last Month < 0.001 No physical activity 1.7 (1.1, 2.6) 0.01 Some physical activity reference Fruit and Vegetable Consumption 0.9 < 5 times per day 1.0 (0.6, 1.7) 0.9 ≥ 5 or more times per day reference Frequent Mental Distress < 0.001 ≥ 14 poor mental health days 2.2 (1.4, 3.4) < 0.001 < 14 poor mental health days reference Body Mass Index (BMI) < 0.001 Obese (BMI ≥ 30) 3.3 (2.2, 5.1) 0.005 Overweight (25 < BMI ≤ 29) 1.9 (1.1, 3.1) 0.01 Normal (18.5 < BMI ≤ 25) 0.1 (0.01, 0.8) 0.005 Underweight (BMI ≤ 18.5) reference Problems with Medical Cost in Last 12 Months 0.004 Could not afford needed care 1.7 (1.0, 2.8) 0.03 Afforded all needed care reference Personal Doctor < 0.001 No personal doctor 1.8 (1.1, 3.0) 0.03 Has personal doctor reference Most Recent Checkup < 0.001 Longer than 1 year ago 0.5 (0.3, 0.8) 0.005 Within last year (reference) reference Healthcare Insurance 0.3 No healthcare insurance 1.3 (0.6, 2.9) 0.5 Has healthcare insurance reference Primary Care Health Professionals Shortage Area (HPSA) < 0.001 Resides in HPSA 1.6 (1.1, 2.3) 0.01 Does not reside in HPSA reference Descriptive analyses were conducted using the surveyfreq procedure in SAS to account for the complex survey design of the BRFSS data . The sampling weight (_LLCPWT), strata (_STSTR), and cluster (_PSU) variables were specified in each analysis . The original sampling weights provided by the CDC were divided by three to account for aggregation of three years of surveys . Weighted frequencies and hypertension prevalence were calculated with standard errors and 95% confidence levels, respectively. The full BRFSS dataset was not filtered to the target population, but instead domain analyses were conducted by calculating stratified tables based on respondents’ sex and age to ensure accurate calculation of standard errors and confidence intervals of weighted estimates . Model building followed a two-step process. First, all potential predictors from the conceptual model were assessed for simple associations with hypertension in univariable logistic regression models. Predictors were retained for further analysis if the p -value of the likelihood ratio test comparing the univariable model to a null model was < 0.20 . All models accounted for complex survey designs by specifying the sampling weight, strata, and cluster variables using the surveylogistic procedure in SAS software version 9.4 . The variables that had p < 0.20 in the 1st step were then assessed in a multivariable model using a manual backward selection process to identify a final parsimonious model. Covariates were removed from the model one-by-one based on p -values until all remaining variables were significant at a critical p -value of 0.05. At each step, the coefficients of remaining variables were compared before and after the removal of a covariate. If the coefficients changed by 20% or more, the variable being removed was considered a confounder and was retained in the model regardless of its statistical significance . The predictive ability of the final model was assessed using a receiver operating characteristic (ROC) curve and area under the curve (AUC) . The weighted total number of women ages 18–44 years was 136,629 persons. Approximately 67% of the study population were aged 18–34 years, while the remaining 33% were aged 35–44 years. Most of the population was non-Hispanic white (78%), 5% was non-Hispanic American Indian, 4% was non-Hispanic black, and 13% were other races or ethnicities. Thirteen percent of the population were heavy drinkers, 16% were current smokers, 24% had no physical activity outside of work during the previous 30 days, 76% reported eating fruits or vegetables fewer than five times per day. Approximately 20% of the population reported frequent mental distress (FMD). After imputation of missing BMI data, most of the population was classified as either obese (33%) or overweight (25%). Approximately 43% of the population was unable to afford some needed medical care during the last 12 months, 20% had no personal healthcare provider, 32% did not have a healthcare checkup in the last 12 months, and 33% lived in a county designated as a primary care HPSA. The overall prevalence of self-reported hypertension was 8.9% (95% CI = 7.4–10.4), but the prevalence varied widely across categories of some potential predictors. Notably, hypertension prevalence was disproportionately high among women who were American Indian or Other Race/Ethnicity (i.e., not white or black), current smokers, had no regular physical activity, had FMD, and were obese (Table 1 ). Of additional interest was that hypertension prevalence was lower among women who afforded all needed medical care in the last 12 months compared to women who could not afford some needed healthcare. Hypertension prevalence was also lower among those without a personal doctor compared to those with a personal doctor, and among women whose most recent healthcare checkup was more than 1 year ago compared to those who had a checkup within the last year (Table 1 ). The following variables had significant univariable associations with hypertension, based on a critical p -value < 0.20, and were retained for assessment as potential predictors in the multivariable model: age, race, smoking status, physical activity, FMD, BMI, problems with medical costs, having a personal doctor, time of last recent checkup, and residence in a primary care HPSA (Table 2 ). Based on the final multivariable logistic regression model, significant ( p < 0.05) predictors of hypertension included age, FMD, BMI, time since last checkup, and residence in a primary care HSPA (Table 3 ). The odds of hypertension were higher among women who were aged 35–44 years, had FMD, were obese, and resided in a county designated as a primary care HPSA compared to women younger than 35 years, who did not have FMD, had a normal BMI, and did not reside in an HPSA, respectively. No confounders were identified. The AUC of the final model was 0.68. Table 3 Results of the final multivariable binary logistic regression model investigating predictors of hypertension among women aged 18–44 years in North Dakota, 2017–2021 Level Odds Ratio (95% Confidence Interval) p -value Age 35–44 2.3 (1.6, 3.4) < 0.001 18–34 reference Frequent Mental Distress ≥ 14 poor mental health days 2.0 (1.3, 3.3) 0.003 < 14 poor mental health days reference Body Mass Index (BMI) < 0.001 Obese (BMI ≥ 30) 2.6 (1.7, 4.1) < 0.001 Overweight (25 < BMI ≤ 29) 1.6 (1.0, 2.7) 0.08 Normal (18.5 < BMI ≤ 25) 0.1 (0.01, 0.8) 0.03 Underweight (BMI ≤ 18.5) reference Most Recent Checkup Longer than 1 year ago 0.6 (0.4, 0.9) 0.02 Within last year (reference) reference Primary Care Health Professional Shortage Area (HPSA) Resides in HPSA 1.8 (1.2, 2.6) 0.003 Does not reside in HPSA reference This study investigated predictors of self-reported hypertension risk among women of reproductive age in North Dakota using data from the 2017, 2019, and 2021 BRFSS and county-level HPSA designations. The results suggest that among women of reproductive age in North Dakota, those who lived in primary care HPSAs or had FMD had significantly higher odds of hypertension compared to their counterparts who did not live in HPSA or have FMD, respectively. Similarly, those who were obese or 35–44 years old had significantly higher odds of hypertension compared to those who were not obese or were 18–34 years old, respectively. The findings of this study are useful for understanding risk factors of chronic hypertension among women of reproductive age in North Dakota and can be used to guide the development and implementation of programs aimed at reducing hypertension in this population. The positive association between mental distress and hypertension is an important finding of this study. Self-reported mental distress is more prevalent among women and young adults than among men and older adults throughout the US, and the overall prevalence and disparities of FMD are increasing . Although mental health conditions, such as diagnosed depression, may have a direct biological association with the development of hypertension , the utility of self-reported mental distress in BRFSS data as a predictor of hypertension has not been adequately demonstrated. One study identified a univariable association between FMD and hypertension along with several other indicators of preconception health, but did not assess that association while controlling for other factors like BMI and age . However, there is evidence that FMD is associated with a variety of other health conditions, including diagnosed mental disorders, indicating that self-reported mental distress is associated with diagnosable mental and physical health problems . The results of this study indicate that among women of reproductive age in North Dakota, FMD is significantly associated with hypertension and may represent a modifiable risk factor. Although cardiovascular risk factor prevalence tends to be higher among people residing in HPSAs, there is some evidence that the association between HPSAs and cardiovascular disease risk factors is eliminated when controlling for underlying sociodemographic characteristics . Another study has reported that among uninsured populations, people residing in primary care HPSAs are less likely to achieve control of hypertension compared to those living in non-HPSAs . The present study shows that among North Dakota women aged 18–44 years, the effect of HPSA is robust to the control of risk factors related to demographics, behaviors, and healthcare access and utilization. Women who did not have a healthcare checkup in the last year were also less likely to report diagnosed hypertension, which might suggest lower detection and reporting rates of existing hypertension compared to women who had checkups in the last year. The associations between obesity, age, and hypertension are well documented [ 45 – 47 ]. Blood pressure naturally increases with age due to changes in arterial structure , and although women tend to have lower hypertension rates than men, blood pressure increases more rapidly in women after the age of 30 . It is important for women at later ages to reduce the risk of developing hypertension by engaging in behaviors that lessen the risk of developing hypertension or avoiding behavioral risk factors. Obesity is also an independent risk factor of hypertension, but obesity rates increase with age, compounding the increased age-associated cardiovascular health risks . Notably, women who were underweight in the present study had significantly lower odds of developing hypertension. This finding is consistent with those from other studies which reported that underweight was associated with lower risk of hypertension . However, there is evidence that underweight individuals have greater risk of cardiovascular disease than normal weight individuals, and one study reported that mortality rates among underweight people with severe hypertensions were significantly higher than among normal weight individuals . The unadjusted odds of hypertension were significantly higher among American Indian women than white women, but that effect was mediated by covariates in the multivariable model. This result is meaningful because of the health disparities that American Indian women in North Dakota face relative to the general population [ 20 – 24 ]. Based on this dataset, hypertension disparities among American Indian women of reproductive age are apparently mediated by some combination of demographic and behavioral risk factors, chronic conditions, and healthcare access or utilization. North Dakota public health officials should still consider racial disparities when designing programs to reduce hypertension in reproductive age women, but with the understanding that racial and ethnic disparities are mediated by other risk factors that were identified in this study. This is the first study to use BRFSS data to investigate predictors of hypertension among women of reproductive age in North Dakota, and the results provide an improved understanding of risk factors of hypertension in this population that are useful for guiding efforts to improve cardiovascular and maternal health in this population. The study provides novel evidence of a robust association between self-reported FMD and hypertension and strengthens our understanding of associations between primary care HPSAs and chronic hypertension in North Dakota. These findings are useful for the North Dakota Department of Health and Human Services to guide the implementation of programs to raise awareness and improve control of hypertensive disorders to improve health and reduce the risk of adverse pregnancy outcomes among women of reproductive age. The use of BRFSS data means that hypertension classification was based on self-reporting and not direct measurement and hence the estimate of hypertension prevalence may be biased. Additionally, although FMD was a risk factor of hypertension in this study, that estimate of mental health is not specific to any condition. Finally, although HPSA was treated as a dichotomous variable in this study, the severity of primary care HPSAs are quantified by a numerical score that ranges from 0 to 25 . In this study HPSA was used as a dichotomous variable to be consistent with other studies . Taken together, these limitations probably contribute to the relatively low predictive power of the final model. Suffice it to say that this study provides initial findings to guide future studies and provides useful information to guide health program planning. The odds of hypertension among women of reproductive age tend to be high among those with FMD, who reside in an HPSA, who are 35–44 years old, and who are obese. Women who did not have a checkup within the last year had lower odds of hypertension, probably indicating low detection and reporting rates in this segment of the population. These findings suggest that self-reported mental distress is a useful predictor of hypertension in this population, and further investigations should be conducted to determine whether reducing mental distress can reduce hypertension risk. The fact that high odds of hypertension were observed in HPSAs is important in guiding policy and public health service provision. Allocation of resources for public health programs intended to reduce chronic hypertension among women of reproductive age may need to prioritize HPSAs to reduce/eliminate these disparities and improve hypertension outcomes for all. | Review | biomedical | en | 0.999997 |
PMC11697977 | There are two broad categories of new drugs: small-molecule drugs and biologic drugs. Generic drugs are copies of branded small-molecule drugs while biosimilars are copies of branded biologic drugs. Small-molecule drugs are low-molecular weight products derived from chemical compounds, while biologics are high-molecular weight products produced by living cells in a bioreactor or fermenter. 8 Small-molecule drugs are chemically synthesized and are relatively easy and inexpensive to produce. By contrast, biologics are more complex in nature, and the methods of manufacture need to be tightly controlled to ensure consistent purity, potency and safety. The cost of biosimilar development is much greater than generic drug development. Generic drug development typically costs $3 million and takes ⁓3 years. By contrast, the development of a biosimilar typically costs over $150 million and takes over 8 years. 9 , 10 Biosimilar development costs are greater in part because of the high price of the reference product (needed to be purchased for comparison studies at list price), the complex nature of manufacturing the biosimilar product, and conducting expensive clinical trials. Biosimilar development cannot begin until after FDA approval of the brand biologic’s product, which the biosimilar firm must use as a reference product. This delay in development is created, in part, because the biosimilar firm requires samples of the reference product for analytical testing to determine the precise profile (for example, glycosylation profile, oxidation profile, etc.) of the drug to be copied. 14 These samples are not available until the brand biologic launches their product and makes it available for commercial purchase. The process of manufacturing a biosimilar is crucial since the method of manufacture contributes the final biosimilar product profile. For example, the primary amino acid sequence, cell culture media and bioreactor conditions determine the ultimate physiochemical properties of a biosimilar product, including its glycosylation profile. Equally, steps used in the purification process of the biosimilar product influence its charge profile and purity profile. Extensive process development and product characterization are required before entering phase 3 trials and are expected to be ‘locked’ before the drug is administered in human trials. This is because the FDA requires that the proposed biosimilar used in the phase 3 study be representative of the to-be-marketed biosimilar. 26 Changing the product or process after beginning the phase 3 clinical study is undesirable because biosimilar firms would likely need to generate extensive additional comparability data and/or additional clinical trials, adding significant delays and costs on their development program. 27 Therefore, biosimilar firms are already incentivized to avoid changing their product or process in a manner that could impact the patent dispute. It is noteworthy that biosimilar phase 3 studies are far more likely to succeed as compared to branded biologic phase 3 studies. This is because brand biologics use phase 3 studies to demonstrate the risk and benefit of an untested drug candidate and large phase 3 trials that are required to show efficacy and safety. By contrast, the purpose of a biosimilar phase 3 trial is rather to compare the biosimilar to the brand biologic. By the time the biosimilar reaches the phase 3 trial stage, it has already undergone extensive evaluation and is advancing steadily towards obtaining FDA approval, hence the phase 3 study is merely confirmatory. Further evidence that biosimilar phase 3 studies are primarily confirmatory in nature comes from the regulatory field, where considerable debate exists over whether biosimilar clinical trials are even needed at all. 28 The Biosimilar User Fee Act III states that the FDA may waive and omit phase 3 studies for biosimilars. Similarly, the European Medicines Agency (EMA) is considering phase 3 study waivers. EMA-associated scientists reviewed all approved biosimilars and concluded that patient-trials did not play a decisive role in decision making. 29 Others argue that there is enough evidence from analytical, functional and pharmacokinetic studies to show biosimilarity. 30 The UK regulatory agency, already provides an option for biosimilars to waive phase 3 studies. These regulatory changes all suggest that biosimilar phase 3 studies are confirmatory in nature, meaning that biosimilar development can be considered more or less finalized once it reaches the stage of phase 3 clinical studies. We used PharmaProjects 36 to collect data on biosimilar drugs (clinical and marketed) with phase 3 clinical trial studies conducted or partially conducted within the United States that measure biosimilarity with a brand biologic reference product. We collected the following data points: biosimilar name, reference product, and manufacturer. Rationales for phase 3 trial discontinuation were recorded. For example, if press releases stated that the trial was discontinued for commercial reasons or due to biosimilar bankruptcy. The median time from phase 3 to FDA approval for all biosimilars that received FDA approval from January 1, 2010 to December 31, 2022 was 4.7 years (IQR 3.45–5.9). Accordingly, when assessing whether a biosimilar may have failed to obtain FDA approval, we excluded those biosimilars that were less than three years after initiation of phase 3 trials because those biosimilars were likely still undergoing development prior to FDA approval. Because the median time between the start of phase 3 to approval was 4.7 years, the median time from phase 3 to FDA approval is 4.7, to favor the counter-viewpoint, we classified as failures those phase 3 trials that were submitted before April 1, 2021 (more than 3 years) and lacked information on clinicaltrials.gov . Among the 59 biosimilars that initiated phase 3 trials, 54 (92 per cent) resulted in enough information to begin litigation without wasting court resources. From 2010 to 2022, we identified 95 biosimilars associated with 25 brand biologics that reached the development stage of initiating phase 3 trials. When determining how many biosimilars may have failed to obtain FDA approval after initiating phase 3 studies, among the 95 biosimilars that started phase 3 trials, we included 59 and excluded 36. The reason we excluded 36 biosimilars: 29 (81 per cent) likely had ongoing clinical trials , 6 (17 per cent) publicly declared as discontinued for commercial reasons, and 1 (3 per cent) biosimilar bankruptcy . Most biosimilar patent cases are settled before a final judgment on the merits is reached. If a biosimilar settles the case, the litigation is resolved in 0.95 years after initiation of litigation. Following settlement, the biosimilar launch occurs a median of 1.8 years after initiation of the patent litigation. The median duration between the primary patent expiry and biosimilar launch is 2.3, 2.5, and 16.5 years when biosimilars win, settle or lose litigation. The median time between the start of phase 3 and primary patent expiration is 2.8, 3.0, −0.21 years when biosimilars win, settle, or lose. We note that when biosimilars lose litigation, they typically start their phase three trials after the primary patent expires. Therefore, we find that settlement allows biosimilars to enter the market earlier than if they had awaited the median duration of time for litigation to conclude (2.9 years or 4.2 years). We find that 92 per cent of biosimilars progress to submitting an application to the FDA after initiation of phase 3 study while only 8 per cent fail. Therefore, allowing earlier litigation would not result in wasted court resources. This aligns with the fact that biosimilar phase 3 studies are merely confirmatory in nature and a robust comparability data package is already complete by the time the biosimilar reaches this last stage in development. Furthermore, allowing litigation to begin at this earlier time point is unlikely to be premature. When a biosimilar initiates phase 3 clinical trials, they are incentivized to ensure that their product profile does not change in a way that would alter any patent dispute. Changing the product or process after starting the phase 3 clinical study is undesirable to the biosimilar firm because it would typically be required to produce extensive additional comparability data and/or additional clinical trials, which would add delay and cost to the biosimilar development program. | Other | biomedical | en | 0.999998 |
PMC11697979 | Plastics, predominantly derived from fossil fuels, have undeniably become a fundamental component of contemporary society . However, the accumulation of end-of-life plastics in the environment is causing a profound ecological crisis worldwide . In the pursuit of a circular economy, plastic waste is increasingly recognized as a valuable carbon resource that can be reintegrated into the chemical/material industry . Consequently, devising efficient methods for chemically transforming plastic waste into original precursors or a variety of functional and high-value chemicals is of paramount importance . Notably, chemical upcycling, which differs from traditional recycling techniques focused on monomer recovery, presents an advantageous route for enhancing plastic waste management by converting waste into value-added chemicals [ 7–10 ]. For example, polyolefins, known for their stability, are difficult to be transformed, but also versatile in their transformation capabilities. They can be converted into various mixture compounds such as mixture of alkanes , olefins , aromatics , or oxygenates . Notably, an elegant and innovative method has been recently reported which allows polyethylene (PE) to react with ethylene, producing a highly valuable single chemical, propene . Similarly, there has been substantial progress in developing new methods for the catalytic upcycling of different types of plastic wastes, as evidenced by various studies and advancements in the field [ 11–22 ]. Polyurethane (PU), containing urethane bonds and accounting for ∼6% of all plastic waste , is typically synthesized from isocyanates and polyols and comes in various forms, including foams, adhesives, and elastomers, depending on the monomers and additives used . Although PU can be relatively easily segregated from waste streams, the complexity of its monomers and its robust cross-linked structure make recycling or upcycling challenging. Techniques such as pyrolysis, hydrolysis, alcoholysis, aminolysis, acidolysis, and glycolysis have been explored for PU recycling [ 25–31 ], with methanolysis being a straightforward approach for depolymerization . Yet, the resulting low-value polyols and non-virgin monomers (methyl carbamates) from the cleavage of the –C(=O)–O– in urethane bonds make this method less attractive for further PU reproduction. An alternative, more effective method is catalytic hydrogenation, which breaks both –NH–C(=O)– and –C(=O)–O– bonds in an atom-efficient manner to recover basic monomers or derivatives. Several transformation systems have been successfully developed based on homogeneous catalytic hydrogenation [ 34–37 ], although their low thermal stability and the challenges in separating catalysts for reuse from reaction systems may limit their practical applications. We suggest that an effective upcycling strategy for PU should incorporate a carefully engineered heterogeneous catalytic system, which integrates methanolysis with hydrogenation processes. In this context, our proposed strategy involves using CO 2 /H 2 as the reaction medium along with a heterogeneous catalyst. This catalyst is uniquely designed to facilitate both the conversion of CO 2 to methanol and the hydrogenation of plastic depolymerization reaction intermediates. This comprehensive approach is anticipated not only to improve the depolymerization efficiency of PU waste and the utilization of CO 2 waste, but also enable a complete recovery of the designated components. Importantly, we aim to transform these components into valuable materials for the valorization of plastic waste. Here we introduce a novel two-step reaction construct to convert polyurethane (PU), containing urethane and ester bonds, into two valuable polymers: polyimide (PI, an engineering plastic) and polylactone (P(BL- co -CL), a biodegradable plastic), as illustrated in Fig. 1 . During the initial heterogeneous depolymerization step of PU, we employed a mixture of CO 2 /H 2 , a highly effective combination for the catalytic hydrogenative depolymerization of PU into diamines, diols, and lactones. This process achieved a total product yield of 86% using an inverse ZnO-ZrO 2 /Cu catalyst at 200°C. Subsequently, the produced 1,4-butanediol (BDO) was further converted into γ-butyrolactone (BL) using the same catalyst at 220°C. In the subsequent step, the obtained diamine and lactones were utilized to synthesize PI and P(BL- co -CL), respectively. Remarkably, from 5 g of waste tyre material, predominantly composed of PU, we successfully produced ∼2.2 g of PI films. These films demonstrated excellent energy-storage capabilities, functioning as dielectric capacitors with a discharge energy density ( U e ) of 6.0 J cm −3 at 150°C. Concurrently, we also generated ∼0.44 g of polylactone, exhibiting both satisfactory chemical recyclability and ductile properties. This innovative approach not only paves a new path for upcycling PU waste into a range of valuable and functional polymers but also contributes to the realization of a sustainable future. First, we synthesized an inverse ZnO-ZrO 2 /Cu catalyst using a method similar to a previously reported procedure , with the characterizations presented in Figs S1 and S2 ( Supplementary data , XRD and N 2 physisorption analysis). This catalyst was selected for testing the hydrogenative depolymerization of PU in a CO 2 /H 2 environment, motivated by the known efficacy of inverse Cu-based catalysts in converting CO 2 to methanol and hydrogenating polyesters . We then evaluated this catalyst for degrading two types of polyurethane: a synthetic PU1, composed of urethane bonds created from the reaction of 4,4'-methylenedianiline with triethylene glycol (TEG), and a commercial PU2, containing both urethane and ester bonds . These were chosen as model feedstocks, with PU1 representing a simpler urethane-only structure and PU2 being more representative of real-world plastics. Remarkably, both PU1 and PU2 were completely converted within 4 hours at 200°C under CO 2 /H 2 conditions (1/3, v/v, 3 MPa) over inverse ZnO-ZrO 2 /Cu catalyst, leaving no residual PUs as confirmed by Fig. 2a, b , and Figs S3–S6 . The catalytic depolymerization yielded three types of products: aromatic amines (4,4′-methylenedianiline ( a ); 4-(4-aminobenzyl)- N -methylaniline ( b ); 4,4′-methylenebis( N -methylaniline) ( c )), diols (TEG from PU1; BDO and dipropylene glycol (DPG) from PU2), and lactones (BL and ε-caprolactone (CL) from PU2), as shown in Fig. 2a . The product yields were impressive, at 89% for PU1 and 82% for PU2 under condition 1 . Additionally, methanol formation was observed, arising from the CO 2 hydrogenation reaction . This indicates that an effective heterogeneous catalytic system for the degradation of polyurethanes has been successfully established. The mass balance of the catalytic process was calculated through dividing the mass of obtained products related to monomers by the total mass input of polyurethane. Although the calculation does not exclude the involed mass of hydrogen and methyl group from CO 2 , the mass balance is still appropriate to evaluate the efficiency of recovered products from depolymerization of polyurethane. The lower yield of obtained products from depolymerization of polyurethane at higher temperature may be due to the formation of N -alkylated byproducts between diamines and diols. We envision that CO 2 plays a crucial role in generating methanol, which in turn accelerates the hydrogenative depolymerization of PU. To test this hypothesis, we conducted depolymerization studies under various reaction conditions . Initially, we assessed the catalytic depolymerization performance using either hydrogen (2.2 MPa) or a stoichiometric amount of methanol (2 mmol) alone . In both scenarios, we observed a significant reduction in product yields, underscoring the efficacy of the CO 2 /H 2 combination for efficient catalytic degradation. Notably, the yields in hydrogen (PU1: 51%; PU2: 36%) were substantially higher compared to those in methanol (PU1: 31%; PU2: 21%), suggesting hydrogen's primary role in the hydrogenative deconstruction of PU. Subsequently, we conducted a control reaction with both hydrogen (2.2 MPa) and a stoichiometric amount of methanol (2 mmol) . While the activities were comparable, the product yields (PU1: 81%; PU2: 76%) were slightly lower than under condition 1. However, it confirmed the pivotal role of methanol generated in-situ from CO 2 in the depolymerization process. Moreover, in experiments using only hydrogen or tetrahydrofuran (THF) solvent without the catalyst , only lactone products were obtained from PU2. This indicates that hydrogenative depolymerization predominantly occurs over the ZnO-ZrO 2 /Cu catalyst and that the ester bond in PU2 can be partially broken down in THF solvent. To further discriminate the catalytic active sites in the reduction of CO 2 to methanol, the methanolysis and hydrogenolysis reactions of PU, we carefully investigated the catalytic processes by changing different reaction conditions . In the CO 2 reduction process, Cu species were the main active sites, and a Zn additive promoted the catalytic reduction activity. In the catalytic hydrogenolysis processes, Cu species were the main catalytic active sites for the cleavage of urethane bonds. The efficiency of the depolymerization processes for PU1 and PU2 was thoroughly analyzed using gel permeation chromatography (GPC) to measure the materials recovered from the reactions . A distinctive observation was that, under CO 2 /H 2 reaction conditions , no polymer signals were detected. This highlights the exceptional capability of the designed catalytic system for effective PU degradation. Intriguingly, the process also yielded valuable methylated amines , which are known to play a crucial role in modulating biological and pharmaceutical activities in life science molecules . The methyl groups in these compounds are believed to originate from CO 2 , as evidenced by experiments under various reaction conditions . This suggests a sustainable carbon-fixation process, further emphasizing the environmental and industrial relevance of this catalytic method. To further understand the cleavage of urethane bonds during the hydrogenative depolymerization process, a small model compound (Model 1, propyl p -tolylcarbamate) was synthesized . Since the compound only contains –NH–C(=O)– and –C(=O)–O– bonds that can be cleaved, it can be effectively converted into aromatic amines (4-methylaniline (MA), N ,4-dimethylaniline (DMA), and 4-methyl- N -propylaniline (MPA)) and 1-propanol (Pol) with a total yield of 98% over ZnO-ZrO 2 /Cu in the presence of CO 2 /H 2 . The formation of DMA and MPA indicates that N-alkylation occurred during the process. Obviously, the total product yield decreased to 80% under the hydrogen atmosphere , suggesting the lower efficiency of catalytic hydrogenation of the ester bonds. In addition, an excessive amount of methanol was used to visualize its effect during catalytic hydrogenation of the urethane bond under an inert atmosphere. In contrast, the total product yield toward MA and Pol was only 47%. The process was achieved along with formation of other N -methylated products (DMA and N,N ,4-trimethylaniline (TMA)) and methanolysis products (methyl p -tolylcarbamate (MTC) and N - p -tolylformamide (TFA)) with a yield of 43% . The formations of MTC and TFA can present intermediates for insight into the reaction mechanism during catalytic depolymerization of polyurethane, demonstrating that the effective cleavage of –C(=O)–O– bond precedes the –NH–C(=O)– bond in the presence of methanol. Moreover, the generated N -methylated products suggest that excessive methanol in the catalytic system is unfavorable for producing primary amines. Considering that the complex composition and additives in commercial PU plastics could potentially affect the efficiency of our designed catalytic system, we evaluated its robustness using four different commercial PU products: a shoe sole, a tube, a tyre, and a safety strip . The catalytic degradation of each of these PU plastics (400 mg each) was assessed, and the results are displayed in Fig. 3b . The total product yields from the catalytic depolymerization were found to be 77%, 80%, 86%, and 82% for the shoe sole, tube, tyre, and safety strip, respectively . These results underscore the effectiveness of our catalytic method in hydrogenatively depolymerizing various commercial PU plastics, despite their differing compositions. Taking the PU tyre as a case study, which contained the highest proportion of urethane linkage ( Table S1 ), we observed consistent catalytic performance over six repeated cycles of transformation . This consistent performance is a testament to the excellent stability of our catalytic system, indicating its potential for practical applications in recycling and upcycling commercial PU plastics. We subsequently conducted a scaled-up deconstruction of a 5 g PU tyre sample to extract basic chemicals for further processing . This procedure was executed with exceptional catalytic efficiency, as shown in Fig. 3e . Following purification by flash column chromatography, we isolated 0.90 g of compound a , 0.71 g of compound b , 0.95 g of BDO, and 0.45 g of lactones (BL and CL) . Given the value of BL as a precursor for biodegradable plastics, we converted the obtained BDO into BL using a straightforward dehydrogenation method . This reaction employed the same ZnO-ZrO 2 /Cu catalyst at 220°C under a nitrogen atmosphere, resulting in a BL yield of 96% over 12 hours . This BL was then combined with the lactones obtained in the initial step, yielding a total of 1.3 g of lactones (BL/CL ratio of 11/1, mol/mol) . Additionally, both the fresh and spent ZnO-ZrO 2 /Cu catalysts underwent comprehensive characterization using techniques such as XRD, X-ray photoelectron spectroscopy (XPS), X-ray absorption near-edge structure (XANES) spectroscopy, extended X-ray absorption fine-structure (EXAFS), and transmission electron microscopy (TEM) . These analyses revealed no significant changes, confirming that the crystalline phase, chemical valence, coordination environment, and morphology of the ZnO-ZrO 2 /Cu catalyst remains highly stable throughout the catalytic processe. Aromatic diamines and lactones are key building blocks for diverse applications. In our strategy, the aromatic diamines and lactones obtained from the process were utilized to synthesize two high-value polymers: polyimide (PI) and polylactone (P(BL- co -CL)). As depicted in Fig. 1 , the 4,4′-methylenedianiline (0.9 g) derived from the PU tyre was reacted with dianhydrides—specifically, pyromellitic dianhydride (PMDA) and 4,4′-(hexafluoroisopropylidiene)diphthalic anhydride (6FDA)—to produce two types of PI films (PI1 and PI2, respectively) as shown in Fig. 4a and b . PI is a crucial engineering plastic, prized for its exceptional thermal stability, electrical insulation, and high mechanical strength . The successful synthesis of PI films was confirmed by infrared (IR) analysis, which revealed characteristic vibration peaks: at 710 cm −1 (imide ring), 1365 cm −1 (C–N stretch), 1715 cm −1 (C=O symmetric stretch), 1780 cm −1 (C=O asymmetric stretch), and between 2850 and 2950 cm −1 (C–H stretch associated with the –CH 2 – moiety) . The properties of the synthesized PI1, PI2, and commercial Kapton films were compared. All films demonstrated excellent solvent resistance, remaining insoluble in various solvents (DCM, NMP, DMF, THF, and DMSO) as shown in Fig. S20 . Their thermal stability was assessed using thermal gravimetric analysis (TGA) and dynamic mechanical analysis (DMA) . The findings showed that the 5% weight loss temperatures and glass transition temperature ( T g ) of all films were above 450°C and 300°C, respectively, indicating superior thermal resistance. Additionally, the dielectric properties of the films, including the dielectric constant and loss tangent, were evaluated at a high temperature of 150°C . These properties remained stable across a frequency range of 10 to 10 7 Hz, demonstrating excellent dielectric stability and endurance under high voltage conditions. The PI1 and PI2 films synthesized in our study show great promise as dielectric materials for high-temperature capacitors. In comparison to commercial Kapton, the synthetic PI1 and PI2 films demonstrated notably higher maximum discharge energy densities ( U e ) of 2.4 and 6.0 J cm −3 , respectively, with charge-discharge efficiencies ( ƞ ) exceeding 90% at 150°C . This superior performance is likely attributable to their larger band gap compared to commercial Kapton, as evidenced in Fig. S22b . Furthermore, the PI2 film maintained its excellent performance, with a U e of 2.6 J cm −3 and η >90%, even under more challenging conditions at 200°C . This resilience under extreme temperatures highlights the potential of PI2 for advanced applications. Thus, the aromatic diamine recovered from the catalytic degradation of PU plastic waste is not just recycled but significantly upgraded into PI films with competitive properties, as shown in Fig. S23b . This transformation represents a major stride forward in converting waste materials into high-performance products. In parallel to producing polyimide, we utilized the lactones (1.3 g, BL/CL = 11/1, mol/mol) derived from the PU tyre to synthesize the biodegradable copolyester, P(BL- co -CL), through a ring-opening copolymerization reaction . Notably, P(BL- co -CL) is a green alternative to petrochemical-based polyolefins , first introduced by Chen et al. . We employed an yttrium complex supported with tetradentate aminoalkoxy-bis-phenolate ligands ( Y-N ), a highly efficient catalyst for the ring-opening polymerization of cyclic esters , for the copolymerization of the relatively ‘nonstrained’ BL and the more ‘strained’ CL. The copolymerization was conducted at −30°C to minimize the effect of the − T Δ S term on the Δ G of the reaction. After 17 hours, the random copolymer P(BL- co -CL) (Mn = 56.3 kg/mol, polydispersity index (Ð) = 1.41) was obtained, with a 73.5% incorporation of BL, alongside a conversion rate of 26.8% for BL and 93.7% for CL . Its thermal properties, analyzed via TGA and differential scanning calorimetry (DSC), revealed a decomposition temperature at 5% weight loss ( T d ) of 226°C , along with a crystallization temperature ( T c ) of −20.9°C and a melting temperature ( T m ) of 17.9°C, characterizing it as a semicrystalline random copolymer . The synthetic P(BL- co -CL) exhibited excellent mechanical properties, with a toughness of 171 MPa and an impressive elongation at break ( ε B ) of over 1000%, as determined by tensile testing of dog-bone-shaped samples at 5°C . These values surpass those of the corresponding homopolymers, poly (γ-butyrolactone) ( ε B <400%) and poly (ε-caprolactone) ( ε B ≈700%) . The copolymer also displayed an ultimate tensile strength ( σ B ) of 25.0 MPa and Young's modulus ( E ) of 228 MPa , comparable to poly(ethylene terephthalate) and low-density polyethylene . Remarkably, the synthetic P(BL- co -CL) can be fully recycled back to its original monomers (BL and CL) with a near-quantitative conversion rate of over 98% by heating at 250°C for 12.5 hours in the presence of Y(CH 2 SiMe 3 ) 3 (THF) 2 (5 mol%) . The composition of the recycled BL/CL was ∼2.75/1, aligning with the initial composition in P(BL- co -CL). Therefore, the lactones sourced from waste PU tyre have been successfully transformed into P(BL- co -CL) with outstanding chemical recyclability and ductility. In conclusion, this study presents an efficient approach for the upcycling of PU waste, utilizing a novel catalytic process that transforms PU into important chemicals and then valuable polymers. By employing a heterogeneous catalytic system combining methanolysis and hydrogenation, we effectively depolymerized PU into aromatic diamines and lactones in CO 2 /H 2 media. These intermediates were then used to synthesize high-value polymers: polyimide (PI) for advanced engineering applications and polylactone (P(BL- co -CL)) as a biodegradable alternative to traditional plastics. The PI films demonstrated exceptional thermal and dielectric properties, while the synthesized P(BL- co -CL) exhibited remarkable ductility and recyclability. Our approach not only addresses the valorization of PU plastic waste but also offers a sustainable pathway for converting waste into high-performance materials, contributing to a circular economy. | Study | biomedical | en | 0.999998 |
PMC11697981 | Deoxyribonucleic acid (DNA) has recently received significant attention as a promising candidate for data storage media owing to its extended lifespan and inherent storage density [ 1–4 ], especially for cold data, which refers to data with low access frequency and reading speed requirements, but with large volumes that need to be stored and managed for the long term . Specifically, examples include three-dimensional medical imaging data , planetary science data monitoring changes in planetary states and meteorological data documenting weather fluctuations . These datasets typically require large-scale data volumes and persistent archiving for historical trend analysis and retrospective research, resulting in high storage costs when stored using conventional storage media. By examining the characteristics of such datasets, we observed that they commonly consist of multiple files generated at different time points, sharing the same format, and containing inherent continuous content. In the context of this study, we refer to them as time-series archival datasets. Besides, strict scientific standards require high accuracy in the recovery of such data. However, due to the unpredictability of biochemical reactions during the synthesis, manipulation and sequencing processes, the DNA data storage process is error prone . To enhance the reliability of DNA data storage, various concatenated codes have been proposed. In the process of inferring the order of disordered and duplicate DNA sequencing reads, the outer code typically assigns a unique index to each encoded oligonucleotide (oligo), enabling the identification of its location. Consequently, dropout errors can be treated as erasure errors. Erasure codes, such as the DNA fountain code [ 11–13 ], indexed Reed–Solomon (RS) code and Low-density Parity-check (LDPC) code , are proven effective in restoring the missing information. We refer to them as index-added encoding strategies. Meanwhile, several error-correcting codes, including the watermarker code , RS code , HEDGES code , DNA-Aeon code , Derrick code and SPIDER-WEB code are considered valid inner codes for checking and correcting nucleotide errors. While numerous studies have proposed many coding schemes for DNA data storage, there is still potential for designing an encoding method that better adapts to time-series archival datasets. Moreover, most studies compress the raw information before encoding, which can lead to complete failure of data recovery even with a minor error. This phenomenon has been found in many studies . Current compression algorithms are not tailored for DNA data storage and are not suitable for scenarios with high error rates. It inspires us not to use compression algorithms, but to focus on the characteristics of the raw information itself, so as to design an adaptive encoding method to improve information density and correct errors. To achieve this goal, considering the characteristics of time-series archival datasets, we proposed a novel coding method called the DNA palette code. The main features of our coding scheme are that it does not require indexing, and can achieve high information density (i.e. the ratio of input bit information to the number of synthetic DNA nucleotides, excluding primers and adapters ) and a low decoding sequence coverage rate (i.e. the number of reads required to recover 100 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} information divided by the number of encoded oligos). In scenarios where sequencing coverage is very low, leading to high dropout rates and byte error rates, the decoder is still capable of recovering partial information. Our coding scheme is resilient to residual byte errors, allowing it to recover partial information to prevent complete data loss even in the presence of such errors. We verified the performance of the DNA palette code by simulation and experimental validation. In our in vitro test, we encoded 11.28 MB of clinical brain magnetic resonance imaging (MRI) data into 255 248 oligos of 155-nt length (data payload only, no primers and adapters). The information was successfully recovered with 100 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} accuracy at a median average coverage of 4.4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times$\end{document} . When the sequencing coverage is 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times$\end{document} , the recovered pixel data can also provide medical information. Furthermore, we conducted simulations on a large public MRI dataset (10 GB) and two other applications (including observations of the Earth’s plasmasphere by the extreme ultraviolet camera on the Chang’E-3 Moon lander and daily melt results on the surface of the Greenland ice sheet). This illustrates the robustness and broad application of the DNA palette code. A brain MRI scan yields a significant volume of slice data, where each slice is stored as an individual digital imaging and communications in medicine (DICOM) file . Adhering to the DICOM format , we introduced a data pre-processing scheme based on dictionary transforms, called the DNA ladder code, which can utilize the structural information of the dataset to convert it into a form more conducive to the DNA palette code encoding . Subsequently, we presented a ‘bit-to-oligo’ mapping approach grounded in combinatorial theory, termed the DNA palette code . The decoder undertakes trace reconstruction and nucleotide error-correcting tasks, accommodating duplicate sequencing reads without the need for clustering or multiple alignments . The DNA palette code is the major innovation, so we use it to refer to the coding scheme proposed in this work. The fundamental idea of the DNA palette code is to establish a bijection between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {X}^N$\end{document} , the range of the raw information, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {O}$\end{document} , the family of oligo sets. Here, we use \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {X} = \lbrace 0,1\rbrace$\end{document} to denote the binary alphabet and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {D} = \lbrace \text{A},\text{T},\text{G},\text{C}\rbrace$\end{document} to represent four natural DNA nucleobases: adenine (A), cytosine (C), guanine (G) and thymine (T). The codeword corresponding to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{x} \in \mathcal {X}^n$\end{document} is a subset of the oligo set. Metaphorically, the DNA palette code regards each oligo as a pigment, distinguishing different binary strings by coloring them with distinct colors. It enables the mixing of different pigments to create new colors. Assuming that mixed colors are distinct, and pigments exhibit a total order relation, the resulting mixed colors will possess a lexicographical order defined by this relation. Similarly, we have also defined a total order on the range of the raw information. This allows us to establish a one-to-one mapping between binary strings and colors through these two total order relations. Consequently, the input can be uniquely encoded as a color. Since pigments are mixed without regard to the order or quantity added, the number of pigments used for encoding binary strings is not fixed and no index needs to be inserted for each pigment. When considering oligos as pigments, the mixing process of pigments can be viewed as sampling without replacement within the set of oligos. Here is a straightforward example. Let ‘001’ be the raw information, and let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $O = \lbrace \boldsymbol{o}_1, \boldsymbol{o}_2, \boldsymbol{o}_3\rbrace$\end{document} be the preset oligos. The range of the raw information is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {X}^3 = \lbrace 000, 001, 010, 100, 011, 101, 110, 111\rbrace$\end{document} , and the family of oligo sets is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {O}^3 = \lbrace O_0, O_1, \ldots , O_7\rbrace$\end{document} , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $O_0 = \emptyset$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $O_1 = \lbrace \boldsymbol{o}_1\rbrace$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $O_2 = \lbrace \boldsymbol{o}_2\rbrace$\end{document} and so on, up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $O_7 = \lbrace \boldsymbol{o}_1, \boldsymbol{o}_2, \boldsymbol{o}_3\rbrace$\end{document} . We can define a map to encode sequences in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {X}^3$\end{document} to oligo sets in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {O}^3$\end{document} , such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $000 \mapsto O_0$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $001 \mapsto O_1$\end{document} and so forth. The decoder can determine the raw information by identifying which oligo was received. Notably, the number of encoded oligos is not fixed due to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $|O_0| \ne |O_1|$\end{document} . This is a key feature of our encoding scheme, allowing fewer oligos/nucleotides than expected to represent the raw information. Specifically, using a typical transcoding method (e.g., 00 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\leftrightarrow$\end{document} A, 01 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\leftrightarrow$\end{document} T, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $10 \leftrightarrow {\rm G}$\end{document} , 11 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\leftrightarrow$\end{document} C), ‘001’ would be encoded as ‘AT’. In contrast, our method would encode ‘001’ into \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $O_1 = \lbrace \boldsymbol{o}_1\rbrace = \lbrace \text{A}\rbrace$\end{document} when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $O = \lbrace \text{A, T, G}\rbrace$\end{document} . For a longer binary information sequence (i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{x}\in \mathcal {X}^N$\end{document} ) and a preset oligo set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $O=\lbrace \boldsymbol{o}_1,\boldsymbol{o}_2,\ldots ,\boldsymbol{o}_n\rbrace \subseteq \mathcal {D}^m$\end{document} ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $n> N$\end{document} ), we designed a map f to encode this binary information into an oligo set. Firstly, the order ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $< $\end{document} ’ of these preset oligos is defined as follows: for any \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{o}_1 = (o_1^1, o_2^1, \ldots , o_m^1) \in \mathcal {D}^m$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{o}_2 = (o_1^2, o_2^2, \ldots , o_m^2) \in \mathcal {D}^m$\end{document} , if there exists i such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $o_1^1 = o_1^2, o_2^1 = o_2^2, \ldots , o_{i-1}^1 = o_{i-1}^2$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $o_i^1 < o_i^2$\end{document} then \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{o}_1 < \boldsymbol{o}_2$\end{document} . The order of nucleotides is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\rm A} < {\rm T} < {\rm G} < {\rm C}$\end{document} . For example, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\rm AAT} < {\rm AAG} < {\rm ATT}$\end{document} . It can be easily proved that this order is transitive, antisymmetric and strongly connected, thus constituting a total order on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {D}^m$\end{document} . Without loss of generality, we defined \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{o}_1< \boldsymbol{o}_2< \cdots < \boldsymbol{o}_n$\end{document} . Then we defined a total order on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {O}$\end{document} , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {O}$\end{document} is the family of the subsets of O : for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $A=\lbrace \boldsymbol{o}^{A}_1,\boldsymbol{o}^{A}_2,\ldots ,\boldsymbol{o}^{A}_{m}\rbrace \subseteq O$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $B=\lbrace \boldsymbol{o}^{B}_1,\boldsymbol{o}^{B}_2,\ldots ,\boldsymbol{o}^{B}_s\rbrace \subseteq O$\end{document} , (i) if there exists j such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{o}^{A}_m=\boldsymbol{o}^{B}_s$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{o}^{A}_{m-1}=\boldsymbol{o}^{B}_{s-1}$\end{document} ,..., \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{o}^{A}_{m-j+1} =\boldsymbol{o}^{B}_{s-j+1}$\end{document} , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{o}^{A}_{m-j}< \boldsymbol{o}^{B}_{s-j}$\end{document} , then \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $A< B$\end{document} ; (ii) otherwise, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $B< A$\end{document} . Next, we defined a map \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $f:\mathcal {X}^N \rightarrow \mathcal {O}^N$\end{document} such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $f(\boldsymbol{x})$\end{document} is the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $(\sum _{i=1}^N2^{i-1}x_i)$\end{document} th set in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {O}$\end{document} . Here, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {O}^N$\end{document} denotes the first \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $2^N$\end{document} sets of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\mathcal {O}$\end{document} . It is easy to prove that f is a bijection. According to the map f , the raw binary information \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\boldsymbol{x}$\end{document} will be encoded into \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $f(\boldsymbol{x})$\end{document} . The decoder can recover the raw information via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $f^{-1}$\end{document} , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $f^{-1}$\end{document} is determined when given O and N . The decoding algorithm of DNA palette code can be regarded as a trace reconstruction process aimed at recovering the correct set of oligos from duplicate sequencing reads . Specifically, each read undergoes an initial check for compliance with the VT structure, encompassing error-correcting and verification processes. Subsequently, the reads are grouped and selected by the majority vote algorithm. Following these two steps, a substantial portion of the encoded oligos can be successfully restored. Notably, our decoder takes duplicate sequencing reads as input, rather than using the central sequence obtained from clustering or multiple sequence alignment algorithms as input. The decoding complexity of the majority vote algorithm scales linearly with the number of sequencing reads. Additionally, in adherence to the total order sorting rules, bits/oligos can be directly placed in their respective positions without necessitating the comparison and exchange process. This results in linear encoding and decoding complexity relative to the number of bits/oligos. Upon recalling the encoding process of the DNA palette code, it is noted that the number of encoded oligos is related to the Hamming weight of the raw binary information. To this end, we proposed a data pre-processing method called the DNA ladder code to convert the raw information into a new form containing a large number of zeros. This algorithm encompasses three stages: label alignment, differential encoding and block RS encoding. The initial two stages modify the structure of the raw information without introducing redundancy, while the third stage incorporates parity-check bits to address errors. Specifically, residual errors in the DNA palette decoder and the occurrence of dropout oligos might lead to a partial loss of information in the decoded binary string, which can be recovered through the RS code. A comprehensive description of the DNA ladder code is available in Section S1.3 . In the in vitro experiment, we stored the medical imaging data from two brain MRI examinations of a patient with ischemic cerebrovascular disease conducted in November 2021 and October 2023. Each examination produced 21 DICOM files ( Data S1 ). The 42 DICOM files, totaling 11.28 MB, were encoded into 255 248 oligos, each with a length of 155 nt . The encoded oligos were synthesized by Twist Bioscience. The DNA pool was amplified through polymerase chain reaction, followed by a sequencing procedure on the Illumina sequencing platform. The mean coverage (i.e. the total number of reads divided by the number of encoded oligos) was 256 reads . During the decoding process, we randomly sampled sequencing reads and gradually increased the sampling coverage. The sampling command is shown in Section S3.4 . When the mean value of sampling coverage is 5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times$\end{document} , the dropout rate is 1.63 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} . As the average sequence coverage increases, the byte error rate (i.e. the byte error rate in the decoded output) decreases significantly, and the decoding time increases linearly . The minimum average coverage rates of the two examination files are 4.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times$\end{document} and 4.6 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times$\end{document} with a median of 4.4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times$\end{document} (Table 1 ). The format of the elements is the number of successful decoding times compared to the number of tests. The specific experimental results are shown in Tables S1–S4 . Images generated from decoded pixel information at different coverage rates are shown in Fig. 2c . In the presence of byte errors, mosaic artifacts may appear in the image; however, most of the information is still discernible. This shows that our decoder can recover part of the original data when only a partial sequencing read is received. It differs from compression algorithms such as DEFLATE, where even minor fragment loss can render the compressed data completely unrecoverable . The decoded data can be used for three-dimensional (3D) reconstruction of medical images, such as the maximum intensity projection image and 3D volume rendering image . Besides, the number of encoded oligos for the first MRI examination is less than the index-added method . Here, ‘without DNA palette encoding’ denotes the scenario where the raw information is sequentially encoded into oligos, and the oligo index is added. The redundancy of its error-correcting code is the same as that of the DNA palette code. This suggests that, for MRI data, the DNA palette code can encode information using fewer oligos. First, to evaluate the performance of our coding scheme under a large data scale, we collected 10-GB DICOM files from public MRI datasets . Simulations show that the DNA palette code has a stable effect on reducing the number of encoded oligos . When the coding redundancy is fixed, the number of encoded oligos can be reduced by approximately \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1/3$\end{document} through DNA palette coding. We further conducted a series of simulations on random data with sizes ranging from 10 MB to 10 GB. The encoding time and the decoding time of the DNA palette code are linear in the length of the input . This is consistent with the results of the theoretical analysis. Our coding scheme also performs well in error handling across different DNA error rates and the number of duplicate sequencing reads . Here, we assumed that the IDS error rates are equal, and the total error rate is \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} ${\rm pr}= p_{\rm ins}+p_{\rm del}+p_{\rm sub}$\end{document} . The dropout rate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $p_{\rm drop}=5\%$\end{document} and the duplication number M ranges in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\lbrace 1,3,5,10\rbrace$\end{document} . We also tested the byte error rates when there were only substitution, dropout, deletion and insertion errors . Experimental data analysis based on Twist synthesis and Illumina sequencing technology shows that the raw error rate of the DNA data storage system is less than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $1\%$\end{document} . Simulations indicate that our coding scheme can achieve error-free decoding at such an error rate. We further tested our coding scheme on many different data formats. The first is the Planetary Data System (PDS) data format, which is a standardized form used for the archiving and distribution of planetary science data. Files in Table 2 record the Earth’s plasmasphere observations acquired by the extreme ultraviolet camera onboard the Chang’E-3 lander ( Data S2 ) . The second is the NetCDF Network Common Data Form (NetCDF), which is a software library and self-describing machine-independent data format that supports the creation, access and sharing of array-oriented scientific data. Files in Table 3 are based on the threshold method of the microwave radiometer’s day and winter brightness temperature difference to extract the Greenland ice sheet surface melt from the downscaling results, and obtain the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $0.05^\circ$\end{document} daily melt results of the Greenland ice sheet surface in 1985, 2000 and 2015 ( Data S3 ) [ 29–34 ]. Encoding results show that our coding scheme works well for such time-series archival datasets. Compared with the DNA fountain code, our code can effectively reduce the number of encoded nucleotides . Here, ‘ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $r({b}/{n})$\end{document} ’ refers to the ratio of the number of bits in the binary form of the file divided by the number of encoded nucleotides. Simulations show that our coding scheme is capable of error-free decoding in a wide range of data formats (panels (e) and (f) of Fig. 3 ). In the DNA data storage system, short DNA strands are stored in a spatially disordered structure within a three-dimensional space. Decoders typically require additional information to determine the order of the DNA strands to restore the original bitstream. The DNA palette code is designed to accommodate this feature. It is based on a sampling without replacement method, using unordered combinations of oligos to indicate binary information. The encoded oligos do not contain indexes and are not fixed in number when encoding different binary strings of the same length. When the DNA palette code is combined with contextual transformation methods (such as our proposed DNA ladder code), we can encode time-series archival data with fewer oligos than expected. Additionally, rather than employing a compression algorithm, we developed a direct transcoding method that converts raw information bits to nucleotides. Our coding scheme demonstrates resilience in recovering information even in scenarios characterized by high dropout rates and byte error rates. Specifically, even when the received sequencing data are significantly insufficient and the error rate is high, such as when the dropout rate exceeds \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $15\%$\end{document} and the byte error rate exceeds 9% , our code can recover part of the raw information. In addition, our method can achieve a net information density of more than 2 bits/nt when encoding time-series archival data, which has the effect of a compression algorithm, while avoiding the problem of being unable to recover information when there are a small amount of residual errors in the decoded data due to the use of a compression algorithm (such as the DEFLATE algorithm). We verified the effectiveness of our code in DNA data storage systems through wet and dry experiments. In our wet experiment, we encoded 11.28-MB MRI data into 255 248 oligos with a length of 155 nt, whereas the expected oligo number is 397 972. As shown in Table 4 , compared with other studies, our wet experiment stored an average of 2.39 bits in one nucleotide and achieved 100 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\%$\end{document} data recovery at a median decoding coverage of 4.4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\times$\end{document} . In simulations, the reliability of our method has been verified on large data scales and diverse data formats. The decoding results from wet experiments clearly reveal the presence of an acute cerebral infarction in the right frontal lobe of the patient in the medical images from November 2021 . In the medical images from October 2023, this cerebral infarction has evolved into a liquefied lesion, and a new acute cerebral infarction has appeared in the left cerebellar hemisphere . This highlights the significance of our scheme in storing medical images for disease screening and tracking. The DNA palette code aligns with the spatial disorder of oligos, using unordered combinations of oligos as codewords. For binary input strings of the same length, the number of encoded oligos varies and does not require additional indexing. It performs well in both in vitro and in silico experiments and shows the potential to increase the information density and reduce decoding sequence coverage. The DNA palette code has the potential to expand new application scope for DNA data storage, offering robust support for historical trend analysis and retrospective studies. However, achieving the same high compression ratio as mature compression algorithms, such as the DEFLATE algorithm, presents a challenge for our encoder. This is an important direction for our next research. | Study | biomedical | en | 0.999997 |
PMC11697997 | Returning to sports after corrective surgery is a significant consideration in patients with adolescent idiopathic scoliosis (AIS). Despite the absence of standardized guidelines, current evidence indicates that patients can safely return to various sports, including those that require extreme spinal movements, without serious complications . On the other hand, there are reports that only 60% of AIS patients are able to return to sports at an equal or higher level of physical activity than before surgery; therefore, the actual situation is not clear . Adjacent segment disease (ASD) is a common complication in patients with long-term outcomes following spinal fusion surgery for AIS, with an incidence rate of approximately 26-32% . However, the new-onset spondylolysis after corrective fusion surgery for AIS is extremely rare . We report two cases of spondylolysis occurring in the caudal adjacent vertebrae of the lower instrumented vertebra (LIV) after posterior corrective fusion surgery for AIS (a 13-year-old girl and a 12-year-old girl). In both cases, no bony healing of the spondylolysis was observed. Here, we share our experience and consider the relationship between sports activity and lumbar spondylolysis after posterior corrective fusion surgery for AIS. A 13-year-old girl was referred to our hospital with a chief complaint of right back and left lumbar prominence. She had no back pain or lower limb symptoms, and all neurological examinations were normal. There were no significant findings related to her growth or medical history, and she was a member of a swimming club in junior high school. Plain radiographs of the standing whole spine showed a 40° structural curve from T11 to L4 (32° in left side bending position) and a 31° non-structural curve from T5 to T11 (21° in right side bending position) . Computed tomography (CT) revealed no developmental abnormalities in the spinal structure, including lumbar spondylolysis. Therefore, the patient was diagnosed with AIS based on Lenke classification type 5C. She had not reached menarche, and Risser grade 2. Four months later, after orthotic bracing, plain radiographs of the whole spine showed that the T11 to L4 curve had progressed to 47°, and surgery was planned . The spino-pelvic parameters were the following: Pelvic incidence (PI): 65°, Pelvic tilt (PT): 19°, Sacral slope (SS): 44°, and Lumbar lordosis (LL): 55°. Nine months after diagnosis, posterior corrective fusion surgery was performed from T5 to L3 . All dorsal laminae in the corrective fusion range were decorticated, and local bone obtained from the excision of the spinous processes and facet joints was grafted. The main thoracic curve at T5-T11 was non-structural, but the right-back prominence and low left shoulder were also the main complaints and were therefore deemed to be within the range of correction. The major lumbar curve from T11 to T4 was corrected to 9°, and the main thoracic curve from T5 to T11 to 5°. Postoperative spino-pelvic parameters were 66° for PI, 18° for PT, 46° for SS, and 67° for LL, indicating augmentation of lumbar lordosis. Preoperative and one week after surgery, CT scans show no abnormalities in the pars interarticularis (PI) of L4 . The postoperative course was uneventful, and the patient did not complain of lower back pain. She was instructed to wear a trunk orthosis for three months and to refrain from sports for six months after surgery. The patient was allowed to play sports from the seventh month after surgery, and she returned to swimming. Twenty-four months after the surgery, she joined her high school’s badminton club. She followed a routine of practicing for more than three hours on weekdays and participating in matches and tournaments on weekends. At 43 months after the operation (at age 17 years), she experienced severe lower back pain during a badminton match, which made it difficult for her to continue playing. She presented to our hospital, where CT revealed bilateral spondylolysis at the L4 . She was treated conservatively with cessation of sports activity, lumbosacral orthosis, and stretching was introduced to alleviate hamstring tightness. After three months of conservative therapy, CT was performed five months later to assess bony healing. Although bony healing of spondylolysis was not observed, the patient strongly desired a return to badminton competition. After returning to competition, she did not complain of lower back pain and could perform exercises at the pre-injury level. Twelve months after the onset of lumbar spondylolysis, the patient remained free of low back pain. We plan to conduct regular follow-up in the future. A 12-year-old girl visited a doctor with a chief complaint of shoulder-height asymmetry. She had no back pain or lower limb symptoms, and all neurological examinations were normal. There were no significant findings in her growth or medical history, and she had not yet begun menstruation. She had been a badminton club member since the age of 11 years. Plain radiographs of the whole spine showed a 50° structural curve at T5 to T12 (47° in right side bending position) and a 27° structural curve at T1 to T5 (26° in left side bending position), and her condition was diagnosed as AIS of Lenke classification type 2 with Risser grade 0. Five months later, after orthotic bracing, plain radiographs of the whole spine showed that the T5 to T12 curve had progressed to 64°, and surgery was planned . The spino-pelvic parameters were the following: PI: 56°, PT: 14°, SS: 42°, and LL: 46°. Ten months after diagnosis, posterior correction and fusion surgery were performed from T3 to L2 . All dorsal lamina in the corrective range were decorticated, and local autogenous bone was grafted. The major thoracic curve from T5 to T12 was corrected to 9°, and the proximal thoracic curve from T1 to T5 to 15°. Postoperative spino-pelvic parameters were 58° for PI, 15° for PT, 43° for SS, and 51° for LL, indicating augmentation of lumbar lordosis. Preoperative and one week after surgery, CT scans show no abnormalities in the PI of L3 . A significant number of patients with AIS have concomitant spondylolysis, with a prevalence of 7%. . However, to the best of our knowledge, there have been only three reports of new-onset spondylolysis or spondylolisthesis occurring after corrective surgery for scoliosis . Based on these reports, the two cases reported here are considered extremely rare. Table 1 shows a comparison of the background and course of development of lumbar spondylolysis in previous reports and the present cases. In all previous cases, the LIV was L4 or L5, and spondylolysis occurred in L5 vertebrae. All of these cases were discussed as ASD caused by mechanical stress. However, since the L5 vertebrae are also commonly affected in ordinary spondylolysis and spondylolysis that occurs concomitantly with AIS, it is possible that there was a contribution from the anatomical characteristics of L5. In contrast, in the present cases, the injured vertebrae were L3 and L4, which were adjacent to the LIV, and the same mechanism as that of ASD after spinal fusion surgery was considered. AIS is the most common form of scoliosis, affecting approximately 2-4% of adolescents . Of these, approximately 10% require some form of treatment, and up to 0.1% undergo surgery . The goal of treatment is to prevent curve progression and improve cosmetic and functional outcomes. Surgical treatment of AIS has evolved significantly over the past two decades. Between 1997 and 2012, posterior fusion became the mainstay of treatment, with the rate of posterior fusion increasing from 63.4% to 94.1% . Recent studies have shown that adolescents can safely return to sports after surgery for AIS, and the use of modern instrumentation has enabled earlier resumption of activities . Most surgeons allow non-contact sports three months postoperatively, contact sports after six months, and collision sports after 12 months . However, a few reports suggest that the average time to return to sports is approximately eight months . Furthermore, there are also studies reporting a range of 6-18 months for return to sports activities . Factors affecting early return include being younger, having a higher Lenke type, and having lower main curve severity . Although many patients can resume their preoperative activities, some move on to low-impact sports because of reduced spinal mobility and flexibility . Despite the lack of established guidelines, current evidence suggests that patients can safely return to various sports, including those that require extreme spinal motion, without experiencing serious complications . The incidence of ASD following spinal fusion surgery for AIS is approximately 26-32%. . The risk factors for ASD include a long follow-up period, fusion of 10 or more segments, and the extent of fusion, with the highest prevalence in East Asia . Age at the time of surgery and pre-existing disc degeneration were also significant risk factors . Most previous reports on ASD discussed disc degeneration in long-term postoperative cases, and reports on spondylolysis of the adjacent vertebrae are extremely rare . Lumbar spondylolysis is a common condition in adolescent athletes, and its prevalence varies depending on the sport. The risk of spondylolysis differed by sex, with baseball, soccer, and hockey having the highest prevalence in males and gymnastics, marching band, and softball for female athletes . Although there are no reports that badminton is a risk factor for spondylolysis, it is an overhead sport that involves repeated extension and rotation of the lumbar spine, and it is thought to be a risk factor for acute and overuse disorders of the lower back in young elite players. Recent biomechanical research has shown that thoracic flexibility affects the stress on the lumbar intervertebral discs and PI. In other words, it is possible that in our cases, the decrease in thoracic flexibility due to spinal fusion increased the mechanical stress on the PI, leading to spondylolysis . In these two cases, the presence of a horizontal fracture line on the bilateral PI suggests that the cause of the spondylolysis was the stress of overextension rather than the rotational stress of the lumbar spine . In particular, in Case 1, the main thoracic curve was non-structural, which may have prevented the development of spondylolysis by reducing the thoracic fusion level and preserving thoracic mobility as much as possible. Furthermore, in both cases, a comparison of preoperative and postoperative spinopelvic parameters showed an increase in LL postoperatively. The increased LL is thought to be one of the causes of spondylolysis. The two cases we encountered were patients with AIS who had undergone posterior corrective fusion surgery and participated in high-level badminton competitions as members of organized teams. In addition to practicing for more than three hours on weekdays, they were exposed to high physical loads due to their participation in weekend matches and championships. Patients such as these commonly develop spondylolysis in the caudal adjacent vertebrae of the LIV. To prevent similar outcomes in the future, preserving the maximum possible number of mobile segments during corrective fusion should be considered in patients who strongly desire to return to high-level competitive sports. Particularly, the LIV should be set more cranially to preserve the mobile segment . In addition, because spinal flexibility decreases following surgery, transitioning to low-impact sports that do not involve rotational forces or excessive lumbar extension should be considered. Improving lower limb muscle tightness and strengthening the trunk muscles, especially the lower spinal extensor, are useful for postoperative AIS patients to return to sports . If lower back pain occurs during sports, it is important to visit a medical institution at the earliest for a thorough examination. It is especially advisable to consult a physician who performs scoliosis surgery. If a physician does not understand the pathophysiology, the diagnosis may be delayed. Although ASD is not an uncommon condition, it does not necessarily lead to clinical symptoms . In other words, long-term follow-up and regular screening are essential for the early detection and management of ASD, including spondylolysis. In this report, we present two cases of lumbar spondylolysis that occurred after corrective fusion surgery for AIS. Both patients participated in high-level badminton competitions postoperatively. Returning to sports after AIS remains a significant challenge, as high-level sports activities increase mechanical stress on the fixed adjacent segment, increasing the risk of developing lumbar spondylolysis. Sports activities involving rotational force or excessive lumbar extension should be approached cautiously. If a patient develops severe back pain after corrective fusion surgery for AIS, an early diagnosis by a specialist is necessary. | Review | biomedical | en | 0.999996 |
PMC11697999 | Sump syndrome is a rare complication of biliary surgery, mainly choledochoduodenostomy (CDD) or choledochojejunostomy, that is now rarely seen in the Endoscopic Retrograde Cholangiopancreatography (ERCP) era, with a reported prevalence of between 0 - 9.6% . The CDD procedure may be performed when the common bile duct (CBD) drains improperly and has many indications, including strictures or obstruction of the bile duct and pancreas, biliary fistulas, recurrent bile duct stones, stenosis of the sphincter of Oddi, and choledochal cysts . Classically, in sump syndrome, the distal bile duct becomes obstructed after CDD, with the CBD segment between the anastomosis and the ampulla of Vater becoming a reservoir or sump. This likely occurs due to low filling pressure in the distal bile duct because the well-functioning anastomosis interferes with normal distal peristalsis and drainage. When debris accumulates in the sump, recurrent pain episodes, fever, cholangitis, pancreatitis, biliary obstruction, or hepatic abscesses may develop . A 61-year-old female with a reported history of cholecystectomy, type 2 diabetes, and alcohol use disorder presented to the emergency department with two weeks of right-sided abdominal pain, nausea, and anorexia. She was hypertensive with right upper quadrant tenderness on physical exam. Her lab studies (Table 1 ) revealed elevated alkaline phosphatase (147 U/L) with otherwise normal transaminases, elevated lipase , total hyperbilirubinemia (1.6 mg/dL), leukocytosis with a left shift (WBC 17.9/L), hyperglycemia (596 mg/dL), high anion gap metabolic acidosis (venous pH 7.14, CO2 7 mmol/L, anion gap 31 mmol/L. A computed tomography (CT) scan of her abdomen with contrast showed abnormal appearing thick-walled proximal small bowel and a malrotated duodenum with surrounding inflammatory changes extending to the pancreas suspicious of fistulous communication to the bile ducts where there was extensive pneumobilia. She received fluids, ceftriaxone, metronidazole, and insulin and was transferred to our hospital. Her complete medical record was obtained following her arrival at our facility and revealed that she had undergone an open cholecystectomy and side-to-side choledochoduodenostomy two years prior. Magnetic resonance cholangiopancreatography (MRCP) demonstrated persistent extrahepatic biliary ductal dilation with a fistulous tract from the proximal CBD to the proximal duodenum and edema along the pancreatic bed, suggesting pancreatitis . Endoscopic retrograde cholangiopancreatography (ERCP) revealed an opening at the second part of the duodenum, appearing classic for a functioning choleduodenal anastomosis. The ampulla of Vater was consistent with a prior sphincterotomy; the CBD was cannulated without stenting, and fibrous food debris was extracted until no evidence of filling defects remained. The findings were consistent with the sump syndrome. The patient’s post-operative course was unremarkable, and she was discharged home in stable condition. A sump is a pit or reservoir serving as a liquid drain or receptacle . In sump syndrome, a sump is formed as a complication of biliary bypass surgery, most commonly CDD, with an average timeframe to appear of 6 - 11 years postoperatively . The resulting anatomy after a CDD creates a potential space for sump syndrome to develop . The incidence of sump syndrome following a CDD is variable, with reported values ranging from 0 - 15.7% . This variation can be attributed partly to the length of time to presentation and the lack of a precise definition of the syndrome. Our patient presented two years after her CDD, making it an unusually early occurring case of sump syndrome. A combination of clinical and radiologic findings best characterizes the sump syndrome. Though not always present, clinical symptoms are often recurrent and include right upper quadrant pain, jaundice, fever, liver abscesses, pancreatitis, and signs of cholangitis. Ultrasound, CT, and MRCP are all imaging modalities that can suggest sump syndrome. Radiological signs of suspicion include debris or gallstones in the distal CBD and pneumobilia . Pneumobilia may indicate a functional anastomosis in a patient with an unknown surgical history. In a patient with a known history of CDD, this may be a normal finding. However, these radiographic findings warrant further investigation in conjunction with clinical symptoms or in a patient without detailed history. Despite clinical signs and symptoms indicative of sump syndrome, the diagnosis is confirmed with ERCP. Diagnosis and treatment may occur concomitantly because ERCP with sphincterotomy of the ampulla of Vater should be the first-line treatment in most instances of the syndrome . In a large retrospective cohort study of 70 patients with a history of CDD, Demirel et al. found that endoscopic sphincterotomy successfully diagnosed and definitively treated all 11 with sump syndrome . However, definitive surgery may be performed if endoscopic management fails or repeat intervention is required . Recurrence of the syndrome after endoscopic sphincterotomy is not uncommon, as indicated by Mavrogiannis et al. In a case series of 31 patients, nearly 20% experienced restenosis of the sphincterotomy opening . Surgical options are limited but include conversion to other biliary diversion options such as hepaticojejunostomy or distal gastrectomy with Roux-en-Y gastrojejunostomy . It is essential to consider sump syndrome in patients with cholangitis or pancreatitis who have a history of biliary surgical intervention, especially when their procedural details are unknown. The case presented affirms MRCP as a valuable diagnostic tool for sump syndrome and is notable as an early-occurring example of a typically late-presenting complication of CDD. Identifying the diagnosis of sump syndrome is critical for definitive treatment and avoiding future episodes of potentially life-threatening complications like cholangitis. | Other | biomedical | en | 0.999996 |
PMC11698019 | In 2011, Ti 3 C 2 Tx was synthesized for the first time in the MXene family, attracting great attention since its first synthesis. MXene is characterized by atomic layers with sandwich-like layered morphology . Due to its high conductivity, high hydrophilicity and large specific surface area, MXene is widely used in various research fields such as energy storage, wireless communications, and biomedical applications. In the field of biomedical applications, MXene has been widely used in bioimaging, biosensors, photothermal therapies, drug delivery and antimicrobial drugs . To determine whether MXene-based biomaterials are biocompatible, they must be tested for biocompatibility in general biomedical applications. Recent developments in MXene-infused biomaterials with desirable physicochemical properties have been observed. A study by Lin et al. has found that electroactive MXene hydrogels can accelerate the healing process of skin wounds by coupling ES (Electrical Stimulation) with MXene . In a study by Ye et al., conductive Ti 2c-frozen gel enhanced cardiomyocyte function and myocardial infarction repai . Hu et al. prepared electroactive hydrogels by MXene and regenerated filament factor (RSF) to promote effective bone regeneration, and demonstrated that MXene/RSF hydrogels provide a unique and promising strategy for promoting direct bone formation, regulating the immune microenvironment, and neovasculation under ES. In their study, Yu et al. developed a novel polyvinylpyrrolidone/phytic acid/MXene hydrogel which is biocompatible and can be used for SCI repair. In a rat model of complete spinal cord amputation, hydrogels significantly accelerated spinal cord regeneration by accelerating angiogenesis, myelin regeneration, axon regeneration, and calcium channel activation . Researchers Yang et al. developed a diabetic wound healing injectable hydrogel that combined hyaluronic acid-graft-dopamine (HA-DA) and polydopamine (PDA) . The electrospinning process is a general approach to preparing polymer fiber scaffolds ranging from microns to nanometers in diameter. These fibrous scaffolds mimic the natural extracellular matrix, and they are used to develop tissues, deliver drugs, coat scaffolds with polymers, and so forth in biomedical applications. There are several types of polymers that can be used in the production of nanofibers, including polycaprolactone (PCL), polylactic acid (PLA), polyglycolic acid (PGA), cellulose, chitosan, gelatin and silk fibroin (SF) . SF, as a natural fibrin, has become a promising polymer biomaterial for tissue engineering due to its extensive molecular structure, remarkable mechanical properties, controllable morphology, multifunctional processing capabilities, and surface modification options . The silk fibers of Bombyx mori are composed of silk fibroin (SF) coated with silk sericin (SS). The degummed silk obtained after boiling and degumming is silk fibroin . Electrospun SF fibers exhibit high surface area-to-volume ratios, high porosity, and high flexibility, which are highly desirable for biomedical and tissue regeneration applications. In the earlier studies, hydroxyapatite mineralized silk fibroin was synthesized from reclaimed silk fibroin and tussah silk fibroin for bone tissue engineering. In this study, it was found that silk fibroin composite scaffolds were biocompatible and conducive to cell attachment and growth . In a study on fibroin membrane by Liu et al. , it was found that fibroin membrane is an excellent biomaterial with good cellular compatibility and provides a framework for post-trauma repair in clinical applications. At present, electrospinning technology in addition to the preparation of single-component nanofibers, composite electrospinning nanofibers can also be modified by introducing different components to obtain the required physical, chemical and biological properties . Adding other fillers to composite nanofibers, such as in situ and ex-situ methods. Nanofiber composites are synthesized in situ by combining precursors of fillers with polymer solutions. A difference between in situ and ex-situ methods is that ex-situ methods mix the pre-synthesized particles directly into the polymer before electrospinning . By mixing different polymers with different fillers, such as carbon-containing materials (such as graphene, carbon nanotubes), nanoparticles (such as polymers and metals), biomedical materials have enhanced electrical, chemical, mechanical, and thermal properties . Additionally, MXene has recently been used to modify the surface of electrospun nanofibers for biomedical applications . As a coating nanomaterial for PLLA nanofibers, Zhu et al. proposed Ti 3 C 2 Tx MXene as a nanomaterial that provides a surface that is rich in functional groups, hydrophilic and rough, thereby supporting the adhesion and proliferation of NSCs. In this study, the MXene coating significantly increased NSC differentiation into neurons and astrocytes, providing PLLA nanofibers with multiple advantages, improving nerve regeneration in a synergistic manner . We therefore prepared SF-MXene composite fibers by electrospinning technology using an ex-situ method in this study. A physicochemical and biological evaluation of synthetic composite fibers was performed to understand the potential of two-dimensional materials in biomedicine. Regenerative medicine and tissue engineering will greatly benefit in the future from the properties required for composite electrospun fiber reinforcement materials (such as hydrophilicity, mechanical properties, degradability, protein absorption, biomineralization, and cell viability) ( Scheme 1 ). The bombyx mori cocoons were cut into small pieces and immersed in 0.02 M Na2CO3 aqueous solution, followed by heating at 100°C for 30 min with continuous stirring to degum the cocoons. The degummed bombyx mori cocoons were then washed with deionized water 4-5 times to remove residual sericin on the surface and dried in a 60°C oven. The degummed silk fibers were then dissolved in a pre-prepared 9.3 M LiBr aqueous solution and stirred vigorously at 60°C to form a pale yellow solution. A semi-permeable membrane (12 kDa MWCO) was used to dialyze the pale yellow sericin-LiBr solution against deionized water at room temperature for 72 h to remove excess LiBr salt. The dialyzed solution was centrifuged at 6,000 rpm for 10 min to remove residual impurities. The sericin solution was frozen overnight at −80°C and freeze-dried to completely remove solid water, yielding a white solid sericin powder that was stored at −20°C for future use. MXene of different quality was added to formic acid solution to obtain MXene of different mass fraction. After ultrasonic dispersion for 12 h, multi-layer clay Ti 3 C 2 Tx MXene was stratified into less/single layer until it was well dispersed in formic acid solution. SF (15 wt%) was then added to each prepared MXene dispersion and magnetically stirred overnight until the solution was mixed. The mass fractions of MXene in each solution were (0.1, 0.5 and 1 wt%). The other group prepared a pure SF solution by adding only SF (15 wt%) to the formic acid and slowly stirring it overnight. The prepared electrospinning solution was sucked into a 5 mL plastic syringe using a metal capillary tube (diameter = 0.51 mm) as a needle. The high voltage power supply (Dongwen Co., LTD., Tianjin) provides 21 kV voltage and is connected to the metal needle. The syringe is connected using a digital injection pump (Ximai Technology Co., Nanjing), and the solution is set at 0.5 mL/h. The distance between the needle and the drum collector with aluminum foil on the surface (Qingdao Nuokang Technology Co., LTD., Qingdao) is 10 cm. The speed of the drum was set to 1500 rpm/min. The prepared electrospinning film was placed in anhydrous ethanol for 10 min, then washed with pure water at 37°C and dried at room temperature. The nanofiber membranes prepared with different Ti 3 C 2 Tx MXene content solutions were labeled as SF, SFM0.1, SFM0.5 and SFM1, respectively. The surface morphology of the electrospun fibers was observed by field emission scanning electron microscopy . Fiber diameters were measured using ImageJ software (NIH, United States). The elemental composition of the electrospun fibers was confirmed by mapping with FESEM integration. Fourier transform infrared spectroscopy (FTIR; Thermo Scientific Nicolet iS20, United States), X-ray diffractometer (XRD; Panalytical Empyrean, Netherlands), has analyzed the chemical properties and crystal structure of the matrix. The hydrophobicity of electrospun fibers was measured using a contact Angle analyzer (JY-82C, China). Thermogravimetric analysis (TA Discovery TGA 550, United States) was performed at 10°C/min at 30–600°C under a nitrogen atmosphere. The mechanical strength of the nanofiber membrane was tested at a tensile speed of 5 mm/min using a universal material testing machine (MTS, China). To investigate the in vitro degradation rate of electrospun nanofibers, samples were immersed in PBS (PH = 7.0) for 14 days at 37°C. After different time points (0, 1, 3, 5, 7, 14 days), samples were collected from PBS and excess water was removed. The weight was recorded as Wd and compared with its original weight (Wo). The rate of degradation was calculated using the following equation: Degradation rate = Wo − Wd / Wo × 100 % For the purpose of determining the synthetic nanomaterial’s adsorption efficiency, bovine serum albumin (BSA) was used as a model protein. The fiber film, cut into a 10 mm diameter circle, was placed in a test tube and incubated with 15 mL of BSA solution (2 mg/mL) at 300 rpm for 24 h. The protein concentration in the sample was measured using the BCA (bicinchoninic acid) assay, which specifically quantifies protein levels based on the colorimetric reaction between BCA reagent and proteins. The concentration of adsorbed BSA was determined by measuring the absorbance at 562 nm. The protein concentration was calculated using the following formula: Protein concentration μ g / mL = A determination − A blank / × A standard − A blank * C standard where A determination is the absorbance of the sample, A blank is the absorbance of the blank solution, A standard is the absorbance of the BSA standard, C Standard: Standard concentration, 524 μg/mL. For the purpose of studying the effects of 2D MXene on SF fiber surfaces, biomimetic mineralization tests were conducted on electrospun fibers incubated with SBF. Electrospun SF and SF-MXene composite electrospun fiber pads were incubated in 5 mL SBF solution at 37°C. The fresh SBF solution is replaced every 24 h during the culture process. After incubation for 2 weeks, the sample was removed from the SBF and washed with deionized water. A FESEM analysis is conducted on the samples obtained at room temperature after they have been dried. In order to perform the experiment, the electrospun fiber pad was sterilized in the microwave for 20 min and then transferred to a 24-well plate under ultraviolet light for 12 h. Sterile samples were rinsed with phosphate-buffered saline (pH 7.4) and co-cultured with fibroblasts/osteoblasts in DMEM.2 × 104 cell and 1,000 μL medium were co-cultured with electrospinning fiber membrane of each group, and incubated at 37°C and 5% CO2. Change the medium every 1-2 days. Three parallel control groups were set up for each sample. Incubate in the incubator according to preset time nodes (1d, 2d, 3d). According to the operation method of the CCK-8 kit, 100μLCCK-8 solution and 900 μL medium were added to each well, and incubated at 37°C for 2 h away from light. The absorbance was then measured at 450 nm using an enzyme-labeler. Cell viability is calculated by the following formula: cell viability % = measured value − blank value / × control value − blank value × 100 % After the cells were incubated with the fibromembrane for 3 days, live and dead cells were labeled using a fluorescence-based live/dead cell toxicity kit according to the manufacturer’s protocol to assess cell viability. A 20-min incubation in the dark was performed on cells co-cultured with the membrane. An inverted fluorescence microscope (Nikon, Japan) was used to image staining samples after washing the cells with PBS. A software application called ImageJ was used to detect and count live cells (green fluorescence) and dead cells (red fluorescence). Cell viability is calculated using the following formula: cell viability % = number of live cells / × number of live cells + number of dead cells × 100 % Pre-osteoblasts (MC3T3-E1) were seeded on each electrospun fiber membrane, and alkaline phosphatase (ALP) activity was detected by alkaline phosphatase staining kit after 7 days of culture. Briefly, the medium from each well was carefully removed and the cells were washed three times with PBS. Then, BCIP/NBT staining solution was added and incubated at room temperature for 2 h, and the color reaction was terminated by washing once with distilled water. Alkaline phosphatase activity was semiquantitatively analyzed using ImageJ software (NIH, United States). Three parallel trials were performed for each experiment, and all data are reported as mean ± SD. All experimental results were compared using one-way ANOVA, and the data were statistically analyzed using GraphPad Prism 8.0 software. A statistical difference of P < 0.05 was considered significant. (*P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.001) Few/single-layer MXene nanosheets were obtained by ultrasonically exfoliating clay-like multilayer Ti 3 C 2 Tx MXene. Scanning electron microscope (SEM) images of the multilayer Ti 3 C 2 Tx MXene showed a distinct multilayer nanosheet structure . After 12 h of ultrasonic dispersion, the nanosheets exhibited an obvious few/single-layer structure under the SEM . The surface morphologies of SF and SF-MXene composite electrospun fibers doped with different weight percentages of Ti 3 C 2 Tx MXene are shown in Figures 2A–D . As the content of Ti 3 C 2 Tx MXene increased, the fiber morphology of the composite fibers did not change significantly. Compared with SF fibers, the diameter of the composite fibers decreased after the incorporation of Ti3C2Tx MXene, and as the MXene content increased, the diameter gradually decreased. The histograms of fiber diameters for each group are shown in Figures 2E, F . The average fiber diameter decreased with increasing MXene content: SF fibers had a diameter of 0.105 ± 0.02 μm, 0.1 wt% MXene was 0.097 ± 0.02 μm, 0.5 wt% MXene was 0.071 ± 0.01 μm, and 1 wt% MXene was 0.068 ± 0.02 μm. Statistical analysis using ANOVA was performed to evaluate the significance of these changes. The results showed that the fiber diameter significantly decreased with increasing MXene content, with p-values of <0.0001, indicating a statistically significant difference between the groups. This change is likely due to the inherent conductivity of MXene, which increases the conductivity of the spinning solution, leading to a more pronounced stretching of the Taylor cone under the same electric field strength. After doping, the fiber surface remained smooth. In addition, the presence of Ti elements in the SF-MXene composite fibers was confirmed by SEM-EDS, indicating that Ti 3 C 2 Tx MXene had been incorporated into the fibers, as shown in Figure 3 . As shown in the FTIR spectra , the SF fibers exhibit distinct absorption peaks at 3,288 cm^-1, 2,986 cm^-1, 1,645 cm^-1, 1,516 cm^-1, 1,233 cm^-1, 1,168 cm^-1, 1,055 cm^-1, and 542 cm^-1. The broad peak at 3,288 cm^-1 corresponds to the O-H stretching vibration in silk fibroin. The absorption peak at 2,986 cm^-1 is attributed to the C-H stretching vibration. The absorption peaks at 1,645 cm^-1, 1,516 cm^-1, and 1,233 cm^-1 are characteristic bands of amide I, amide II, and amide III of the protein in silk fibroin, respectively. The peaks at 1,168 cm^-1 and 1,055 cm^-1 are due to the C-OH and C-O stretching vibrations. These characteristic absorption peaks are consistent with the typical structure of silk fibroin . The presence of a prominent broad absorption band at 3,400 cm −1 in MXene indicates strong external water absorption on its surface . In SF-MXene composite electrospun fibers, the peak intensities at 542 cm -1 and 3,288 cm -1 are reduced. This attenuation is attributed to the formation of hydrogen bonds between MXene and silk fibroin molecules, which weakens the stretching vibrations of C-N and O-H bonds. The narrowing and reduced intensity of the characteristic peak at 1,645 cm -1 suggest that the incorporation of MXene destabilizes the β-sheet structure, leading to a transition towards an α-helix structure. We further conducted XRD tests on various nanofiber groups, as shown in Figure 4B . Previous studies on silk fibroin (SF) have indicated that the main diffraction peaks of silk fibroin I structure appear at 12.2°, 19.7°, 24.7°, and 28.2°, while those of silk fibroin II structure are located at 9.1°, 18.9°, 20.7°, and 24.3° . In this study, the XRD curve of the silk fibroin membrane exhibited typical peaks at 19.7°, 20.7°, 24.7°, and 28.2°, providing strong evidence for the presence of silk fibroin I structure along with a small amount of silk fibroin II structure. As the Ti 3 C 2 Tx MXene content increased, the diffraction peak at 26.6°in the composite fiber XRD curve became more prominent. This might suggest that the introduction of Ti 3 C 2 Tx MXene altered the crystal structure of the composite material. MXene possesses abundant surface functional groups (such as -OH and -F), which could interact with amino or carbonyl groups in silk fibroin through hydrogen bonding or electrostatic interactions, enhancing the hydrophilicity and structural stability of the composite material and thus inducing changes in the structure of silk fibroin. This structural change might lead to a transition from a β-sheet structure to an α-helix structure, manifested as an enhancement associated with the 26.6°peak in the XRD pattern. Additionally, with increasing Ti 3 C 2 Tx MXene content, the diffraction peak at 17.6°in the silk fibroin-MXene (SFM) curve gradually decreased. This phenomenon could be attributed to the overlap of the characteristic peak of Ti 3 C 2 Tx MXene at 14.5°(004) with silk fibroin, resulting in a left shift of the peak position. This shift indicates an increase in the unit cell constant, suggesting the successful incorporation of Ti 3 C 2 Tx MXene into the silk fibroin structure, which may have led to microscopic adjustments in the structure of the composite material. Thermogravimetric analysis (TGA) was performed on SF and SF-MXene composites, and the corresponding TGA curves are presented in Figure 4C . Both SF and SF-MXene composite fibers exhibited two distinct stages of mass loss during thermal degradation. The first stage was attributed to the loss of moisture from the fiber membranes. The second stage, occurring between 270°C–330°C, corresponded to the degradation of SF itself. This mass loss was due to the decomposition of SF polymer chains, primarily through the loss of volatile components such as small molecules and degradation products. Interestingly, as the weight percentage of Ti3C2Tx MXene increased, the residual mass of the composite fibers also increased. This suggests that MXene enhances the thermal stability of SF fibers, which may be related to the high thermal conductivity and stability of MXene nanoparticles. The increased residual mass confirms the successful incorporation of MXene into SF fiber membranes at different weight percentages . The presence of MXene particles serves as a thermal stabilizer, reducing the overall mass loss during thermal degradation. The hydrophilicity of materials plays a crucial role in their interaction with cells, as it directly affects the material’s ability to adsorb proteins and interact with the biological environment. To evaluate whether the addition of MXene nanoparticles improves the hydrophilicity of composite electrospun fibers, we measured the water contact angle (WCA), as shown in Figure 5 . The pure SF fibers exhibited a contact angle of 113.36° ± 2.84°, indicating their hydrophobic nature. However, with increasing proportions of Ti3C2Tx MXene, the wettability of the composite fibers improved, evidenced by a gradual decrease in the contact angle: SFM0.1 had a contact angle of 95.57° ± 3.63°, SFM0.5 showed 91.27° ± 1.83°, and SFM1 demonstrated 85.27° ± 3.16°.The enhanced wettability with increasing MXene content can be attributed not only to the hydrophilicity of hydroxyl and oxygen-containing functional groups on the MXene surface but also to the increased surface roughness and porosity of the electrospun fibers. As the MXene content increases, the fiber diameter decreases, leading to an increased specific surface area of the fiber mat. This increase in specific surface area creates more contact points between the fiber mat and water molecules, thereby improving its wettability. Furthermore, the increased porosity aids in capillary action, allowing the fibers to absorb water more effectively, further enhancing their hydrophilicity. Thus, the increased MXene content improves not only the surface chemical properties of the fibers but also their physical structure, both of which contribute to the observed enhancement in wettability and hydrophilicity. Good mechanical properties have better stability and ductility, which can play a better role in biological tissue engineering. To elucidate the effect of adding Ti 3 C 2 Tx MXene on the mechanical properties of the scaffolds, the tensile strength of various nanofibrous scaffolds was measured. Figure 6 shows the stress-strain curves of composite fibers without and with 0.1wt%, 0.5wt%, and 1wt% MXene. With the increase of MXene content, the mechanical strength of composite fiber at 0.5wt% and 1wt% was significantly higher than that of pure SF and 0.1wt%. This may be due to the interaction between MXene surface rich oxides or fluorides and a large number of amino acid residues in SF, especially hydroxyl groups and amino groups, through hydrogen bonding. Thus, the mechanical properties of the composite fibers are improved. The degradation performance of biomaterials is also one of the important characteristics of their applications, and excellent degradation performance will be conducive to their wider application in more scenarios. In this study, we investigated the degradation properties of SF and electrospun fiber membranes with different contents of Ti 3 C 2 Tx MXene within 2 weeks. As shown in Figure 7 , panel A shows the degradation rate of each group’s fibrous membrane at different time points within 14 days, and panel B shows the degradation rate of each group’s fibrous membrane at 14 days. As can be seen from the degradation curve in FIG. A, the degradation rate of all samples reached the maximum at 5–7 days and tended to be flat at about 14 days. The results of panel B show that the degradation rate of the samples with high content of MXene addition (0.5wt% and 1wt%) is significantly lower than that of the samples with low content of mxene addition (0wt% and 0.5wt%). This may be due to the introduction of mxene nanoparticles, the fiber structure becomes compact due to hydrogen bonding, electrostatic interaction and other forces, the mechanical strength increases, and the degradation rate phase is relatively slow. Bovine serum albumin (BSA) was used as a model protein to test the protein adsorption capacity of different materials. Figure 8 shows the amount of protein adsorbed by each group of materials over 24 h. The results showed that the protein adsorption rate of each nanofiber membrane group was significantly higher than that of the control group, indicating that both SF and MXene promoted the adsorption of proteins. With the addition of mxene nanoparticles, the protein adsorption rate of SF-MXene composite fiber materials also increased, and the protein adsorption rate of SFM1 group was significantly higher than that of SF and SFM0.1 groups. This outcome is likely due to the addition of Ti 3 C 2 Tx MXene, which provides various functional groups that offer more active binding sites for protein adhesion. The increased binding capacity of the fiber membranes for proteins is enhanced through electrostatic interactions, hydrogen bonding, and π-π interactions. Studies have shown that cell surfaces have numerous receptors that interact with proteins, which play a crucial role in the cellular response process . The excellent protein adsorption capability of the material allows for the rapid formation of a protein layer on its surface, providing more binding sites and thus enhancing cell adhesion and migration. Therefore, the increased protein adsorption capacity of the material significantly enhances its bioactivity. Our study results demonstrate that incorporating Ti 3 C 2 Tx MXene can significantly improve the protein binding ability of SF. This characteristic can also effectively facilitate the binding of scaffold materials with osteoinductive factors such as growth factors and drugs, thereby increasing the material’s application value. The mineralization capability of SF-MXene composite fibers was evaluated using an in vitro biomineralization assay. SF fibers are well-known matrices for tissue engineering due to their good biocompatibility; it has not yet been evaluated whether composites containing Ti 3 C 2 Tx MXene mineralize. Therefore, the study investigated SF-MXene composites to evaluate their ability to adsorb calcium phosphate minerals in SBF solution. After 14 days of soaking in SBF solution, FESEM images of electrospun SF and SF-MXene composite electrospun fibers are shown in Figure 9 . As expected, the SF-MXene composite fibers successfully deposited calcium phosphate minerals . The deposition of calcium and phosphate ions on the electrospun fiber membrane may be due to the high hydrophilicity of MXene . In contrast, no significant calcium phosphate mineral deposition was observed on the pure SF electrospun fibers after 14 days of soaking in SBF solution . Further confirmation of the presence of calcium and phosphorus in the SBF-treated SF-MXene fiber scaffolds was obtained through EDS analysis. The results demonstrated that the substantial in vitro biomineralization and calcium/phosphorus deposition on the SF-MXene composite scaffolds endowed them with sufficient bioactivity, indicating their potential for further application in bone tissue engineering. To determine how SF-MXene composite electrostatic spinning fibers respond to cells, fibroblasts (L929) and pre-osteoblasts (MC3T3-E1) were co-cultured with different electrospun fibers for 1, 2, and 3 days. The biocompatibility of the fiber scaffolds was assessed using the CCK-8 assay and live/dead cell staining. Cells seeded without fiber membranes served as the control group. Figure 10A shows the viability of L929 cells cultured on the electrospun fiber scaffolds for 1, 2, and 3 days. The viability of L929 cells on both SF and SF-MXene composite fibers remained at a high level. Figure 10B shows the cell viability of pre-osteoblasts (MC3T3-E1) cultured on electrospun fibers on the same days. For MC3T3-E1 cells, the viability on both SF and SF-MXene composite fibers with different MXene contents also remained at a high level. Figure 10C shows live/dead cell staining on the third day to determine the viability of L929 and MC3T3-E1 cells on different electrospun fiber membranes. The images from the live/dead staining were analyzed with ImageJ software to determine the ratio of red to green fluorescence, which was used to assess the cell viability on each group of fiber membranes. Figures 10D, E further indicate that the nanofiber electrospun membranes did not exhibit cytotoxicity to L929 cells, as all groups maintained high cell viability. This finding is consistent with the previous CCK-8 assay results. Alkaline phosphatase (ALP) is crucial in bone formation and mineral deposition; thus, assessing ALP staining intensity can provide an indirect measure of cellular mineralization capability and mineral deposition. In this study, the ALP activity was examined by culturing MC3T3-E1 cells on nanofiber membranes with varying amounts of MXene, followed by ALP staining after 7 days of culture. As illustrated in Figure 11A , cells cultured on pure SF exhibited lower ALP intensity, while increasing MXene content led to a marked enhancement in ALP staining intensity , with the highest intensity observed at 1 wt% MXene content. Semi-quantitative analysis of the stained images confirmed that higher MXene content significantly elevated ALP activity, suggesting that MXene promotes early osteoblast differentiation and enhances cellular mineralization and mineral deposition . In this study, we successfully demonstrated the electrospinning of silk fibroin (SF)-MXene composite fibers. The obtained electrospun fibers were characterized for their physicochemical properties and in vitro biocompatibility. Our results indicate that both SF fibers and SF-MXene composite fibers exhibit excellent biocompatibility with L929 and MC3T3 cells, underscoring their potential for biomedical applications. Compared to pure SF fiber membranes, the SF-MXene composite fibers displayed notable improvements in wettability, mechanical properties, biomineralization, and protein adsorption. These enhancements not only improve the fibers’ biocompatibility but may also broaden their applicability in tissue engineering and regenerative medicine. We attribute the unique properties of SF-MXene composite fibers to the abundance of active hydrophilic groups on the MXene surface, which bind effectively to the amino acid residues on the SF surface. Furthermore, MXene itself possesses valuable properties, including hydrophilicity, non-toxicity, and the ability to promote cell proliferation and biomineralization. These composite electrospun fibers show promise for biomedical applications such as wound dressings and bone tissue engineering. Additionally, the approach presented here provides a theoretical basis for the development of MXene-based polymer fiber composites, supporting future applications in tissue regeneration engineering. | Review | biomedical | en | 0.999998 |
PMC11698196 | The SCMV genomic is around 10 kb in size and is a single-stranded positive sense RNA . The polyprotein produced by SCMV was cleaved, co- and posttranslationally, into its component gene products, including the coat protein (CP) gene, by viral coded proteases . Since the CP is predominantly conserved among SCMV isolates, serological methods can be used to detect the virus . Traditionally, a polyclonal antibody is developed using a pure virion from infected plant material for immunodiagnostics . However, the traditional approach to producing polyclonal antibodies has two drawbacks: the minimum viral concentration in the infected plant tissue and the purity of the virion. The process of propagating a virus in an appropriate host and purifying it is laborious . Moreover, certain viruses, such as potyvirus and phloem-limited luteoviruses, are particularly difficult since they have low viral concentrations. In the present study, we utilized in silico methods to enhance the development of a diagnostic tool for SCMV detection. Computational analyses, including examination of the physicochemical properties, immunogenicity, and subcellular localization of the SCMV CP, were used to predict the protein’s behavior and optimize its antigenic properties . These in silico approaches provide valuable insights that guide the design of recombinant proteins and improve the sensitivity and specificity of immunodiagnostic assays. By integrating in silico predictions with experimental validation, we aim to develop a rapid and reliable diagnostic tool for SCMV. This approach not only enhances the accuracy of SCMV detection but also reduces the time and resources required for diagnostic procedures, offering a significant advancement over traditional methods . Recombinant CP-based antibodies have been utilized extensively in immunodiagnostics to address the problem. Recombinant CPs have been used in several investigations as antigens to produce polyclonal antibodies, including Cucumber mosaic virus , Citrus psoriasis virus , Sugarcane mosaic virus , and Onion yellow dwarf virus . According to a recent report , CP-based antisera can detect viruses in symptomless primary and secondary hosts. Although the application of CP-based antisera for virus detection in symptomless plants may not constitute a novel approach, it is important to take into account that this method has a rich history in scientific research. For decades, researchers have relied on antisera specifically designed to target the CP of purified virus particles. This long-standing tradition highlights the enduring effectiveness and practicality of CP-based antisera in the field of virus detection; thus, serological detection is an easy, quick, and affordable technique suitable for virus identification. Therefore, crop damage can be reduced, and customized treatments can be used to tackle the pathogens. In the present study, the CP gene of an SCMV isolate infecting sugarcane in Pakistan was overexpressed in a bacterial expression system ( E. coli ) and the recombinant fusion CP was purified and mobilized in mice for the development of polyclonal antibodies. In addition, the optimization, validation, and standardization of ELISA immunodiagnostics were reported herein. The CP gene sequence of the SCMV isolate was obtained from GenBank 1 and subjected to alignments by MUSCLE software, built into MEGA X v10.1.8 and phylogenetic analysis was performed by the maximum-likelihood (ML) method, using the codon-based alignments with 1000 bootstrap replicates. The physicochemical characteristics were meticulously computed using the widely recognized ExPASy ProtParam online tool 2 . A 3D structural model was generated using I-TASSAR 3 and SWISS-MODEL 4 , respectively. PyMOL-2.5.7 5 was used to illustrate the structure produced by the I-TASSER server’s PDB data . The presence of signal peptides and cellular localization was predicted using online web servers 6 like SOSUI series system 7 and PSORTb version 3.0.3 8 . Immunogenic analysis was performed using the immune epitope database (IEDB) 9 and the Optimum Antigen™ design tool (GenScript, Piscataway, NJ, USA) 10 and their potential B-cell epitopes were predicted with a default threshold of 0.1512 by BepiPred-3.0 Linear Epitope Prediction tool 11 . The final expression cassette (SCMVCpaj405) was commercially synthesized from Macrogen Inc. Korea in pUC57 and subsequently cloned in pET28 (a+) vector using restriction endonuclease sites Nde I and Hind III (Cat #R008S) to generate the 6×His-tagged SCMVCpaj405- pET28 (a+) plasmid, which was then used to transform DH5 α ( E. coli ) competent cells. Positive transformants were screened using restriction digestion and DNA sequencing before being used to transform E. coli BL21 CodonPlus competent cells for gene expression. The expression of the 6×His-SCMVCpaj405 fusion protein was induced by adding the synthetic inducer, IPTG, at concentrations of 0.1, 0.5, and 1 mM for 3 and 6 h at 37 °C. The bacterial cultures were centrifuged for 15 min at 4000 rpm and 4 °C. The resultant pellets were suspended in 50 mM Tris-HCl pH 8.0 buffer and sonicated for 30 min on ice with a pulse of 20-s on, 40-s off cycle to disturb them. The total protein extracts were separated into fractions (soluble and insoluble) by centrifugation at 12,000 rpm for 20 min at 4 °C prior to the large-scale production of recombinant protein, which was induced for 6 h at a concentration of 0.5 mM IPTG. The purification was carried out under native conditions (as a soluble protein) using Ni-NTA (resin Bio-Rad Laboratories, Inc. California, USA) affinity chromatography. To remove the imidazole, the purified recombinant protein fractions were mixed and dialyzed against a phosphate buffer saline (pH 8.0) overnight at 4 °C. The protein’s purity was verified on 12% SDS-PAGE. The Bradford assay was used to determine the protein concentration by using bovine serum albumin (BSA; Merck, Germany) as a standard protein. This recombinant protein was then utilized as an antigen to develop mouse-based polyclonal antibodies in the following phase. The animals were housed in a controlled environment (temperature, light, and 12-h light/dark cycle) at the School of Biological Sciences (SBS), University of the Punjab, Lahore, Pakistan, where they always had access to food and water. Mouse polyclonal antisera to fusion protein were raised in albino female mice (Swiss Webster white strain, 4–6 weeks old) upon the approval by the Ethics Committee of SBS according to the protocol outlined by Salem et al. . The mice were given an initial dose of 100 g of recombinant CP with the same volume of Freund’s complete adjuvant (Santa Cruz Biotechnology, Dallas, TX, USA), followed by four weekly intraperitoneal booster injections of 200 g without removing the 6×His-tag with incomplete adjuvants (Santa Cruz Biotechnology). The mice were given deep anesthesia with ketamine hydrochloride (50 mg/kg of animal body weight) and xylazine (5 mg/kg of animal body weight) through intraperitoneal administration prior to blood collection through the cardiac puncture . The blood was centrifuged at 4000 rpm after being incubated for 1 h at 37 °C and antisera were stored at −20 °C for further analysis. According to the standard protocol, three independent experiments were performed for the preparation and characterization of raised antibodies in mice. Raised antisera and antigen coating conc was optimized by serial dilution method as outlined by Darsono et al. with modification of a dilution factor extended from 50X to 10,000X to accommodate the specific requirements of our study. The antigen (200 μL; 100 ng/well) was prepared in borate buffer (50 mM KCl borate, pH 8.0) and used to coat the wells of a microtiter plate. The next day, the coating solution was removed and washed three times with 1X PBST buffer with continuous shaking for 5 min. The vacant binding sites were saturated with a blocking reagent to reduce nonspecific binding to the surface, and 200 μL of blocking buffer (5% (W/V) skim milk in TBS-T) was added to each well followed by incubation at 37 °C for 2 h. After incubation, the wells were cleaned as before and dried. To detect the immunoreactivity the anti-mouse HRP conjugated IgG antibody produced in rabbits was diluted (50,000X) in blocking buffer and added to each well (200 μL/well). After incubation at 37 °C for 1 h, the wells were washed three times with 1X PBST buffer with continuous shaking for 5 min (200 μL/well) and dried. The plate was incubated at RT for 15 min for blue color development. The reaction was stopped by the addition of 2 M H 2 SO 4 (100 μL/well). To analyze the antibody titer, the differential OD450/630 absorbance was measured at 405 nm using a microplate reader (HUMAREADER plus, human GMBH). All the mice’s serum samples were replicated twice for their immunoreactivity, the values were recorded, and averages were calculated. The standard curve was plotted for different concentrations of antigen, i.e. 10 pg, 50 pg, 100 pg, 200 pg, 500 pg, 1 ng, 5 ng, 50 ng, 100 ng, 200 ng, 500 ng, 1 μg, and 2 μg were used in the optimization process with immunized mice and negative controls. The rest of the ELISA procedure followed the same methodology as described before. In the indirect ELISA, the sensitivity of the raised antisera was evaluated by serial dilution . The effectiveness and specificity of the raised antiserum in detecting the SCMV in various test samples were evaluated using targeted purified fusion protein, bacterial cell extracts (both recombinant and nonrecombinant plasmids), and total proteins extracted from healthy and SCMV-infected sugarcane plants . Using TRI REAGENT ® (Cat. #TR118), total RNA was meticulously extracted from 0.1 g of plant leaf tissues sourced from the field, encompassing both healthy samples and those displaying characteristic mosaic symptoms suggestive of mosaic disease for subsequent RT-PCR analysis. For the confirmation of the SCMV-infected sugarcane sample, the CP gene-specific primers (SNd-Cp-F CATATGGCTG-GAACAGTCGATGC and SHd-Cp-R AAGCTTCTAGTGGTGCTGCTGCAC) were designed using Primer3Plus and used in QIAGEN OneStep RT-PCR analysis, according to the manufacturers’ instructions. Before the statistical analysis, the data were checked for whether they were normally distributed or not. Then with the help of one-way analysis of variance (ANOVA), the Tukey post-hoc test, and GraphPad Prism software 8.0 (USA), statistical analysis and inferences were carried out. According to the Tukey post-hoc test using the least significant difference test, comparisons between treatments were deemed statistically significant at p < 0.05. All experiments were replicated twice. Data from three independent experiments are presented as mean and standard error. The physicochemical characteristics of native and fused CP-SCMV protein sequences were compared using ExPASy ProtParam ( Table 1 ). The nucleotide sequence translation to amino acids showed that both the native and 6×His tag CP genes code for 314 and 321 aa proteins, respectively. The computational prediction of isoelectric point (pI) and molecular weight of the proteins showed that they have pI values approximately the same, with native CP having a pI of 6.80 and the 6×His tag CP having a pI of 7.00 with theoretical protein mass of 33.8 kDa (native CP) and 34.7 kDa (6×His tag CP). The predicted protein size for rCP is around 34.7 kDa since the pET 28a (+) vector adds a few amino acid linkers and a 6×His tag to the protein’s N-terminus, adding roughly ~1 kDa . Two different tools (PSORTb and SOSUI) were used to determine the subcellular localization of native and fused proteins in E. coli . The PSORTb result revealed an unknown subcellular localization for both proteins; however, the SOSUI analysis showed a periplasmic subcellular localization for both proteins. The soluble proteins were predicted by the tools SOSUI signal and Signal P 5.0 without a signal peptide . Furthermore, the antigenic determinants of CP-SCMV were located in eight distinct stretches: ETGSVTGGQRDKDV, MSKKMRLPKAKGKD, PQQQDISNTRATRE, KKEYEIDDTQMTVV, DGDEQRVFPLKPVI, YRNSTERYMPRYGL, EMNSRTPARA-KEAH, and NVGETQENTERHTA, which were present at the upper surface of the 3D structure ( Supplementary Table S3 ). A 40 kDa overexpressed protein band was observed in BL21 DE3 culture in setups transformed with the 6×His-SCMVCpaj405-pET-28a (+) vector and induced by IPTG (0.1, 0.5, 1.0 mM, 3 h and 6 h) at 37 °C, as shown in Figure 2a . When examining the intensity of the 40 kDa band size for 3 h and 6 h of incubation, there was an increase in band size. Protein was purified under native conditions. SDS-PAGE analysis of elution fractions (450 and 500 mM of imidazole) under native conditions using the Ni-NTA column confirmed that the eluted protein was SCMVCpaj405 tagged with 6×His . Protein elutions were mixed, and dialysis was used to eliminate extra imidazole. Using BSA as the standard, the resultant solution was subjected to the Bradford test to determine the eluted protein concentration. According to the findings, 2 mg of recombinant SCMVCpaj405 protein was produced per 200 mL of BL21 (DE3) culture . Initially, all three immunized and nonimmunized (control) animals’ antisera were tested for antibody production using indirect ELISA. The ELISA results for the immunized mice were positive and virtually comparable, in contrast with the nonimmunized mice, which showed negative results . To establish the optimal coating concentration for the raised primary pAb antibody, serial dilution was used with a fixed amount of pure SCMVCpaj405 fusion protein antigen (100 ng/well) . Antisera targeting the SCMVCpaj405 fusion protein effectively detected the antigen across a wide dilution range, from 50X to 10,000X. The mean absorbance at 405 nm of the SCMVCpaj405 fusion protein antigen exhibited a gradual decrease, ranging from 3.094 (50X) to 1.719 (10,000X). Notably, the absorbance values of the buffer and healthy control (0.183) were consistently lower than that of the antigen . The purified SCMVCpaj405 fusion protein antigen remained readily identifiable up to a 10,000X dilution, where the mean absorbance value of 1.719 surpassed the established threshold value (TV) of 0.312. A sample was deemed significant if its mean absorbance value exceeded the TV. The sensitivity of the antisera was further evaluated by using indirect ELISA analysis at dilutions ranging from 50X to 10,000X to crudely extracted proteins from both healthy and infected sugarcane leaves. The crude extracts’ dilution steadily dropped, from 2.0 (50X) to 0.028 (10,000X) dilution at A405 nm. The raised antibody generated the strongest signal to identify virus particles in plant sap that had been diluted 1:50 and 1:100 , with letters (a, b, c, d, etc.) showing statistically significant differences between variables. Indirect ELISA was also used to evaluate the effectiveness and specificity of the raised antiserum to various test samples. The raised antibodies reacted positively against purified fusion protein, total cell lysate from bacterial cells transformed with recombinant plasmid , and total extracted proteins from SCMV-infected sugarcane plant in the indirect ELISA. Nevertheless, no signal was detected against the nonrecombinant plasmid extract or in the healthy plant total extract ( Table 2 ). To quantitatively measure SCMV in plant tissues using indirect ELISA, a standard curve was established by serially diluting SCMVCpaj405 fusion CP (10 pg to 2 μg). The resulting linear regression between SCMVCpaj405 fusion CP concentration and OD450 absorbance was described by the equation y = 0.0922x + 0.0471 with a correlation coefficient (R 2 ) of 0.957. This analysis indicated a minimum detection limit of approximately 100 pg/mL for SCMVCpaj405 fusion CP. Furthermore, the linear regression model demonstrated a working range of 100 pg/mL to 500 ng/mL . Finally, PCR-based confirmation was performed with SCMVCpaj405-specific primers SNd-Cp-F and SHd-Cp-R using the same healthy and SCMV-infected sugarcane field-grown plant used in the ELISA. Total extracted RNA was used as a template to create 1st strand cDNA by reverse transcription, and one-step RT-PCR successfully amplified the full length SCMV CP gene from infected sugarcane field-grown plant but not in a healthy one, which confirmed the presence of SCMV . In the present study, we successfully subcloned the CP gene of SCMV and expressed it in E. coli BL21 (DE3) cells. CP is frequently utilized as an antigen for polyclonal antibody production because it is highly conserved across different virus strains. This conservation allows broad-spectrum detection of SCMV, whether the virus is in an active replication phase or in an inactive state . The pET28a (+) bacterial expression vector, equipped with a T7 promoter for protein expression in E. coli and an N-terminal 6×His tag for purification and detection, was chosen due to the anticipated nonimmunogenicity of the 6×His tag . As expected, the 6×His tag facilitated efficient affinity purification of the recombinant protein without compromising the diagnostic potential of the polyclonal antiserum. Induction of transformed E. coli BL21 (DE3) cells with 0.5 mM IPTG successfully yielded recombinant SCMV CP, with protein production increasing proportionally to the postinduction incubation time . Purification of the recombinant SCMV CP under native conditions revealed its predominant localization in the soluble fraction of E. coli whole-cell protein extract. The purified protein exhibited a molecular size of approximately 40 kDa, which was larger than the predicted 34.7 kDa. This observation aligns with previous findings by Jensen et al. that SCMV-induced CP size can vary among isolates in plant tissues, ranging from 34.4 to 39.7 kDa . Furthermore, it is consistent with studies on recombinant CPs from other plant viruses such as tomato spotted wilt tospovirus , apple stem grooving virus , and Citrus psorosis virus . The recombinant fusion protein was observed to be predominantly in the soluble phase. Evaluation of the antibody titer involved assessing the raw antiserum’s ability to recognize the target antigen in SCMV-infected sugarcane samples. The results indicated that the mouse anti-SCMV CP antibodies could successfully react to SCMV-infected sugarcane samples up to a dilution of 1:10,000 of raw antiserum . While cross-reactivity between antisera and their corresponding antigens is a potential concern, it is expected due to the frequent amino acid sequence and structural similarities among plant viral antigens within the same virus family, as documented in prior research . The primary objective of immunodiagnosis in asymptomatic sugarcane plants is to determine the presence or absence of viral infections, regardless of the specific SCMV strain. Precise viral identification, a distinct goal, is better achieved through more specialized diagnostic techniques like PCR, monoclonal antibodies, or nanobodies . The concordance of indirect ELISA and PCR findings to identify SCMV in sugarcane shows that the anti-SCMV CP is applicable for SCMV detection in field-collected samples. The present study highlights the advantages of recombinant DNA technology over traditional viral purification methods, offering efficient bulk antigen synthesis for immunization. While native viral proteins from isolated virus particles maintain their original conformation, recombinant proteins can also be produced with similar structural integrity. The 6×His tag, shown to be beneficial in previous research, can be retained while optimizing protein expression conditions like incubation temperature and IPTG concentration to enhance solubility in E. coli . Our findings and prior studies indicate that overexpressed proteins in E. coli often localize to the soluble phase. In silico predictions using SOSUI-GramN aligned with this observation, suggesting a periplasmic subcellular location for SCMV CP. Elfageih et al. reported that alkaline phosphatase facilitates the correct folding of proteins with disulfide bonds in the periplasm. Consequently, recombinant viral proteins expressed in E. coli can be reliably purified under native conditions, preserving their overall native conformation. However, our work demonstrates that recombinant DNA technology is a productive and economical method of producing polyclonal antibodies against SCMV CP. In order to enable efficient disease control within sugarcane plantations, the generated antisera against SCMV CP may be effectively employed for the detection of SCMV in asymptomatic sugarcane plants. Furthermore, future research that makes use of the preliminary results from the present study can address the problem of specificity to further distinguish or discriminate between SCMV strains-infected sugarcane. In the present study, the antibodies developed against the SCMV CP proved effective for detecting both the purified CP and the virus itself using ELISA. The availability of these antibodies enhances the ability to screen plant material in sanitation programs and supports research into the virus’s pathogenicity. | Study | biomedical | en | 0.999998 |
PMC11698197 | The uncontrolled multiplication of melanocytes, skin pigment-producing cells, is the cause of melanoma, a type of malignant skin cancer. Despite being generally known to develop on the skin, melanoma can also occur on mucosal surfaces inside the body or in other regions including the uveal tract, where neural crest cells migrate 1 . Skin cancer is the prominent cause of malignancy and invasive melanoma accounts for around 1% of all skin malignancies; however, it is responsible for the majority of mortalities 1 . An estimated 325,000 new cases of melanoma (174,000 males and 151,000 females) were identified worldwide in 2020, with 57,000 deaths . While early detection and surgical removal of tumors remain the cornerstone of therapy, metastatic melanoma remains difficult to treat with limited treatment options. Therapy strategies, such as chemotherapy, targeted therapy, radiation therapy, surgical operation, and immunotherapy vary depending on the stage of melanoma, the thickness and growth rate of the tumor, and the genetic type of the melanoma . The most effective treatments are immunotherapy and radiotherapy compared to chemotherapy with the Food and Drug Administration-approved drug for melanoma, i.e. dacarbazine, which is less frequently preferred . Despite its potent action, the therapeutic impact of dacarbazine in the treatment of melanoma cancers is diminished by its poor water solubility and brief half-life (41 min) in blood circulation . Moreover, the limitations of a single agent therapy were demonstrated by a depressing response rate of between 10% and 25% and less than 5% total cancer recovery . Therefore, there is a high demand for novel chemotherapeutic approaches in the treatment of melanoma. Recent studies have provided evidence of the promising biological activities of all-trans retinoic acid (ATRA), and sphingomyelin (SM) on melanoma . ATRA, a natural metabolite of vitamin A, has shown therapeutic potential against melanoma by inducing differentiation, apoptosis, growth arrest, and immune modulation in cancer cells . Specifically, ATRA mediates the transcription of many cell differentiation genes on retinoic acid receptors (RARs) in a variety of cancers such as acute promyelocytic leukemia and melanoma, resulting in the activation of ATRA-dependent RARs and retinoid X receptors in the nucleus, thereby providing differentiation in the cancer cells . ATRA treatment also leads to a decrease in DNA synthesis, alterations in cellular morphology, and extended doubling time, and the induction of cell cycle arrest, specifically in the G1 phase through the suppression of oncogenic signaling pathways . The current preclinical and clinical evidence provides support for its utilization in the context of melanoma treatment, which was approved under the name Vesanoid for the treatment of acute promyelocytic leukemia that does not respond to any chemotherapy . While ATRA has demonstrated significant efficacy in treating several forms of cancer such as melanoma, it is important to note that the use of ATRA as a standalone treatment can result in the development of resistance and subsequent relapse . Therefore, new strategies can offer valuable insights into the development of the advancement of strategies focused on enhancing the administration of ATRA as a standalone therapeutic agent or in combination with other treatments, as well as overcoming drug resistance. In addition, ATRA has an impact on the immune system through the differentiation of myeloid-derived suppressor cells (MDSCs) into macrophage and dendritic cells at the level of preclinical and clinical phase studies. In two clinical studies, ATRA was targeted in combination with immunotherapeutics to MDSCs in patients with cancer, including metastatic renal and lung cancers . Two clinical studies demonstrated that decreasing the quantity of MDSCs leads to improved patient survival. A phase 2 clinical study investigated the efficacy of combining ATRA with ipilimumab in individuals diagnosed with stage 4 melanoma . Furthermore, ongoing clinical trials are investigating the efficacy of combining the programmed death ligand 1 (PD-L1) inhibitor pembrolizumab (Keytruda) with ATRA (Vesanoid) as a treatment for metastatic melanoma . The clinical research assess the efficacy of the combination by decreasing the quantity and inhibitory function of immunosuppressive MDSCs in the peripheral blood of melanoma patients, hence halting the advancement of the illness. Considering the chemotherapeutic approach together with immunotherapy, the combinational treatment of ATRA and sphingolipids have been identified as potential anticancer agents that exhibit synergistic effects by inducing apoptosis and inhibiting cancer cell proliferation . SM, a specific class of sphingolipids found in cell membranes, has lately gained attention in cancer research owing to its potential anticancer effects as tumor-suppressing biologically active lipids . The modulation of numerous signaling pathways involved in melanoma progression, such as the mitogen-activated protein kinase and phosphoinositide 3-kinase/protein kinase B (AKT) pathways, has been engaged in the regulation of cell proliferation, differentiation, and apoptotic cell death in melanoma cells . Moreover, SM has been reported to inhibit the growth and spread of melanoma cells by inducing cell death and limiting the formation of new blood vessels in the tumor . The anticancer properties of SM have been linked with the accumulation of ceramide and induction of apoptosis . Over the SM cycle, the destruction of SMs and the production of phosphocholine and ceramide result in the activation of neutral sphingomyelinases (nSMase). Ceramides reduce protein phosphorylation, and expression of protooncogenes such as c-Myc, and modulate apoptosis . León et al. showed that C2-ceramide has a cytotoxic effect on the SK-MEL-1 melanoma cell line, which was consistent with the other studies linked to the antitumor effects of ceramide-based nano-formulations on melanoma cells . Additionally, it was stated that the combinational treatment of nano-liposomal ceramide and sorafenib synergistically inhibited melanoma and breast cancer cell proliferation to reduce the development of the tumor . On the other hand, the effect of ATRA on the SM cycle was researched through sphingomyelinase (SMase) expression on MCF-7 (ATRA-sensitive) and MDA-MB-231 (ATRA-resistant) breast cancer cells . Accordingly, an increase in SMase activity was observed in the MCF-7 cell line, but not in the MDA-MB-231 cells, which are dependent on ATRA-sensitivity. In another study by Clarke et al. , they reported that growth arrest in ATRA-induced MCF-7 breast cancer cells was mediated by the SMase expression. Estrogen receptor-positive MCF-7 cells were employed as a model system in order to investigate the involvement of nSMase 2 (nSMase2) and sphingolipids in the growth arrest produced by ATRA . The findings indicated that ATRA leads to an elevation in ceramide levels and induces growth arrest in MCF-7 cells by upregulating the expression of nSMase2. Furthermore, nSMase2 was identified as the primary enzyme in the sphingolipid network of MCF-7 cells that was regulated by ATRA. These previous findings put the combinational approach forward as a prominent therapy for tumor-resistant agents such as ATRA. With the implemented approach, the present study introduces the combinational therapy of ATRA and SM to synergistically improve activity and apoptosis, and hence attain a combinational anticancer effect on the in vitro B16F10 melanoma model, whereas determination of the safety of the treatment on immune cells was performed via cell viability studies on healthy macrophages. Also investigated was the overall effect of the combinational therapy on various cellular parameters, including cell viability, apoptotic cell death, and cell cycle, as well as gene expression levels to gain insights into the molecular interactions between the two active compounds and the B16F10 melanoma cells. The murine macrophage RAW264.7 (TIB-71) and murine melanoma B16F10 cells were obtained from the American Type Culture Collection (Rockville, MD, USA). The cells were cultured in Dulbecco’s modified eagle’s high medium supplemented with 10% inactivated fetal bovine serum (Invitrogen Life Technologies, Waltham, MA, USA). The medium also contained 2 mM of L-glutamine, as well as 100 U/mL of penicillin and 100 μg/mL of streptomycin (Gibco, Thermo Fisher Scientific Inc.). The cells were maintained at 37 °C in a controlled atmosphere containing 5% CO 2 . The RAW264.7 macrophages and B16F10 melanoma cells were subjected to incubation in the presence of ATRA (in ethanol), SM, and various combinations of both for 24, 48, and 72 h. The determination of cell viability was conducted using MTS assay. Accordingly, the RAW264.7 and B16F10 cells were cultured in 96-well plates at a seeding density of 10,000 and 5,000 cells per well, respectively. Following incubation for 24, 48, and 72 h, the cells were subjected to treatment with varying doses of ATRA and SM at concentrations from 5 to 200 μM, both individually and in combination. After incubation, the cells were incubated in a solution of 10% MTS with glucose and 1X phosphate buffered saline (PBS) for 1 h. The absorbance at 490 nm was read using a microplate spectrophotometer (ELx800; BioTek Instruments Inc., Winooski, VT, USA). Cell viability was calculated as percentages by setting the absorbance of the untreated control group to 100%. The data were then analyzed using GraphPad Prism 8.0.1 (San Diego, CA, USA) to establish the half-maximal inhibitory concentration (IC 50 ), and half-maximal cytotoxic concentration (CC 50 ). The selectivity index (SI) of free ATRA and SM was calculated using the following equation: SI = CC 50 in the RAW264.7 macrophage cell line / IC 50 in the B16F10 melanoma cell line. The distribution of the cell cycle in the B16F10 cells was assessed by flow cytometry at 24 h. The B16F10 cells were seeded in 12-well plates with a density of 60,000 cells per well and incubated for 24 h. Following incubation, the cells were treated with ATRA (123 μM), SM (136 μM), and a combination of both (123 μM ATRA+136 μM SM) for 24 h. After 24 h of incubation and fixation with 70% ethanol, the cells were mixed with 0.01% nonidet P-40, and 100 μg/mL RNase A for 30 min. The analysis was performed with a flow cytometer for the gating of 10,000 cells after 5 min of incubation with 5 μg/mL of propidium iodide (PI). The percentages of cell populations were determined in the gap 0 (G0)/G1, synthesis (S), and G2/mitosis (M) phases. Apoptotic cell death of the B16F10 cells was analyzed via the Annexin V FITC/PI assay using a flow cytometer. Briefly, the B16F10 cells were seeded on 12-well plates with a density of 60,000 cells per well and incubated for 48 h. After incubation, the cells were treated with ATRA (123 μM), SM (136 μM), and a combination of both (123 μM ATRA+136 μM SM). After 48 h of incubation, they were subsequently exposed to Annexin V-FTIC for 15 min, followed by PI for 1 min, in line with the manufacturer’s instructions. The samples were examined using a FACSCalibur instrument (BD Biosciences, San Jose, CA, USA). A minimum of 10,000 cell counts were obtained for each data file. The gating procedure was appropriately adjusted to remove cellular debris, doublets, and clumps. Primers for caspase 3 (CASP3) F: 5′ GGGAGCAAGTCAGTGGACTC 3′ and R: 5′ CCGTACCAGAGCGAGATGAC 3′; Bax F: 5′ TTGGAGCAGCCGCCCCAGG 3′ and R: 5′ CGGCCCCAGTTGAAGTTGCC 3′; programmed cell death-ligand 1 (PD-L1) F: 5′ TGGTCATTGTGCTGCTGCTA 3′ and R: 5′ TTACAGTTCGGCTGTCCACC 3′; cyclin-dependent kinase inhibitor 2A (CDKN2A) F: 5′ GAACTCGAGGAGAGCCATCTG 3′ and R: 5′ CCATCATCATCACCTGAATCGG 3′ were facilitated through the utilization of the Primer-BLAST online program, provided by the National Center for Biotechnology Information (Bethesda, MD, USA) and synthesized by Sentebiolab Biotech (Ankara, Turkey). The isolation of total RNAs from the samples was performed using TRIzol reagent. The cDNAs were synthesized using a cDNA-Synthesis Kit. iTaq Universal SYBR Green Supermix was employed in the qPCR analysis to measure the mRNA expression levels of the target genes. The gene ß-actin, a housekeeping gene, was employed to normalize the collected data. The iCycler reverse transcription (RT)-PCR machine (Bio-Rad) was utilized for all the RT-PCR assays. GraphPad Prism Software (version 8.0.1) was employed for the statistical analyses. Error bars were utilized to visually represent the standard error of the mean. The datasets underwent initial ordinary one-way analysis of variance (ANOVA), followed by two-way ANOVA, and were subsequently scrutinized using Tukey’s multiple comparison test. Statistical significance was established at the following thresholds: * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, and **** p ≤ 0.0001. The RAW264.7 macrophages and B16F10 melanoma cells were treated with ATRA, SM, and a combination of both at concentrations ranging between 5 and 200 μM for 24, 48, and 72h. In the combination treatments, the concentrations of ATRA (free) and SM (free) were based on their respective IC 50 values, as follows: IC 50 /2 ATRA + IC 50 /2 SM, IC 50 /2 ATRA + IC 50 SM, IC 50 ATRA + IC 50 /2 SM, IC 50 /3 ATRA+ IC 50 /3 SM, IC 50 /3 ATRA + IC 50 SM, IC 50 ATRA + IC 50 /3 SM, 200 μM ATRA+ IC 50 /2 SM, IC 50 /2 ATRA+200 μM SM. In the positive control group, the cells were treated with 0.1% (v/v) ethanol to track the impact of the presence of the solvent (i.e. ethanol). The cell viability data of the MTS assay are presented in Figure 1 . The cell viability data of the RAW264.7 cells as a healthy cell line indicated that treatment with SM led to a gradual decrease in the viability of the RAW264.7 cells as the SM concentration increased to 150 μM (from 20% to 17%) and 200 μM (from 6% to 4%) . On the contrary, there was no significant change in cell viability with 20 μM of SM (from 93% to 91%) at any of the time intervals. The viability of the RAW264.7 cells decreased insignificantly from 99% to 85% with 40 μM of SM, from 100% to 87% with 50 μM of SM, and from 91% to 80% with 75 μM of SM, within 24 and 72 h respectively. However, a significant decrease to 31% and 17% occurred when the RAW264.7 cells were treated with 150 μM of SM for 48 and 72 h, respectively . On the other hand, cell viability decreased gradually to 25% and 22% with 150 and 200 μM of ATRA at 72 h, respectively . No significant change occurred in the viability of the RAW264.7 macrophages when treated with 5, 10, and 40 μM of free ATRA at 24, 48, and 72 h. Treatment with SM up to 100 μM caused no significant alteration in the viability of B16F10 melanoma cells, while treatment with 150 and 200 μM led to a significant decrease to 8% and 5% in the B16F10 cell viability , respectively, at 24 h . Similarly, up to a 15% decrease in cell viability was observed at 48 h with 200 μM of SM, whereas at 72 h, cell viability of the B16F10 cells was 6% and 11% with 150 and 200 μM of SM, respectively . On the other hand, with 150 and 200 μM of ATRA, the cell viability decreased significantly below 15% at 72 h. With the 200 μM ATRA treatment, B16F10 cell viability decreased further down to 16%, 12%, and 8% at 24, 48, and 72 h, respectively (p ≤ 0.01) . On the other hand, treatment with 5–50 μM of ATRA led to an increase in cell viability above 100% with low concentrations (i.e. 5–20 μM) at 24 and 48 h . The growth inhibition in the B16F10 and RAW264.7 cells allowed the calculation of both the IC 50 and CC 50 values for the free ATRA and SM . While the IC 50 values for the free ATRA and free SM were 123 ± 16.23 and 136 ± 15.03 μM at 72 h, respectively, for the B16F10 cells, the CC 50 values for the free ATRA and SM were 99.54 ± 1.55 and 104.7 ± 2.05 μM, respectively, for the RAW264.7 macrophages. The selectivity index values for the ATRA and SM on the melanoma cancer model were 0.81 and 0.77, respectively. Considering the IC 50 and CC 50 values, among the effective concentrations on B16F10 melanoma cells, the optimum concentration of ATRA and SM for further combinational treatment studies (i.e. cell viability, apoptosis, cell cycle, and qPCR) was 123 and 136 μM, respectively, which was relatively safe in the healthy control RAW264.7 macrophage cells at 24, 48, and 72 h of incubation. As shown in the cell viability analysis of the RAW264.7 cells after the combinational therapy , there was no significant change in cell viability at 24 h with any of the concentrations, whereas 200 μM ATRA+68 μM SM and 61.5 μM ATRA+200 μM SM led to significant reductions in the macrophage cell viability at 48 and 72 h. Accordingly, the 200 μM ATRA+68 μM SM combination significantly decreased cell viability from 74% to 57% at 48 and 72 h. Likewise, the 61.5 μM ATRA+200 μM SM combination caused a significant decrease in cell viability to 25% at 72 h. On the contrary, the 123 μM ATRA+136 μM SM combination did not cause any significant change in the macrophage cell viability at 24, 48, and 72 h. Figure 1F shows the potential anticancer effect of the SM and ATRA combinational therapy, which was attributed to increasing the biological effect of the compounds on the B16F10 cells with the improved safety properties in the RAW264.7 macrophages. Combinations of 61.5 μM ATRA+68 μM SM, 123 μM ATRA+68 μM SM, 41 μM ATRA+136 μM SM, and 123 μM ATRA+45 μM SM did not lead to any significant alterations in the B16F10 cell viability at 24 and 48 h. While combinational treatments with concentrations up to 123 μM ATRA and 136 μM SM did not cause a significant decrease in the cell viability at 24 and 48 h, the values indicated a gradual decrease in B16F10 cell viability compared to the individual molecular treatments in a time-dependent manner. Furthermore, 72 h of incubation led to a significant decrease to 45.74% compared to the untreated group (p ≤ 0.01). When the B16F10 cells were treated with the 200 μM ATRA+68 μM SM combination, there was a significant decrease in cell viability (i.e. 17.81% and 25.79%) at 48, and 72 h, respectively . Similarly, the treatment of B16F10 melanoma cells with 61.5 μM ATRA+200 μM SM significantly decreased cell viability to 6.99% and 5.81% at 48 and 72 h, respectively . Accordingly, these results revealed that the coadministration of 123 μM of ATRA and 136 μM of SM enhanced the anticancer efficacy in the B16F10 melanoma cells by mitigating their cytotoxic effects on the RAW264.7 macrophages. Phase-contrast microscopy was employed to observe alterations in the morphological characteristics of the B16F10 cells treated with a combination of ATRA and SM . The untreated cells exhibited a flattened adherence to the surface , whereas 150 μM of SM , 150 μM of ATRA , as well as the 123 μM ATRA+136 μM SM combination , resulted in a significant alteration in the surface morphology and cell adhesion dramatically. The morphological characteristics of the flat surface underwent a transformation, resulting in a rounded spherical form, and they were separated from each other. The cell cycle progression in the B16F10 cells after the SM and ATRA treatments was determined using flow cytometry analysis . The cell population percentage in the G0/G1 and G2/M phases varied depending on the treatment, demonstrating that cell cycle arrest was induced in the G0/G1 and G2/M phases at 24 h of incubation . At the end of 24 h, the mean ± standard deviation of the G0/G1 phase for the control group was 64.39 ± 2.32%, whereas it was 80.70 ± 6.08% for 123 μM of ATRA, 92.76 ± 4.45% for 136 μM of SM, and 64.53 ± 7.71 for the 123 μM ATRA+136 μM SM combination . Accordingly, as shown in Figure 3B , 123 μM of ATRA significantly increased the cell population (p ≤ 0.01) in the G0/G1 phase to 80.70 ± 6.08% in the B16F10 cells at 24 h. Similarly, the cell population in the G0/G1 phase increased significantly to 92.76 ± 4.45% with 136 μM of SM , whereas the 123 μM ATRA+136 μM SM combination significantly increased the cell population to 34.06 ± 7.30% (p ≤ 0.01) in the G2/M phase at 24 h. Overall, these results indicated cell cycle arrest in the G2/M phases after the combinational treatment of ATRA and SM at 24 h. The apoptotic cell death in B16F10 cells following treatment with 123 μM of ATRA, 136 μM of SM, and the 123 μM ATRA+136 μM SM combination was assessed using Annexin V flow cytometry analysis, as illustrated in Figure 4 , after 48 h. Accordingly, the apoptotic population fraction of the cells was 37.98% with 123 μM of ATRA, 96.03% with 136 μM of SM, and 53.91% with the 123 μM ATRA+136 μM SM combination at 48 h . The early apoptotic population in B16F10 cells was 7.39% for the untreated control (NC), 21.81% for 123 μM ATRA, 34.19% for 136 μM SM, and 27.78% for the combined treatment. Meanwhile, the late apoptotic population rose from 5.32% in untreated cells to 16.17%, 61.84%, and 26.13% after exposure to 123 μM ATRA, 136 μM SM, and the combined treatment, respectively . Furthermore, apoptotic cell death increased to 37.98% and 96.03% with 123 μM of ATRA and 136 μM of SM alone when compared to the untreated group (i.e. 12.71%), and the 123 μM ATRA+136 μM SM combination led to a significant increase to 53.91% at 48 h (p ≤ 0.001) . On the contrary, the percentage of necrotic cells was negligible for all of the cell populations after incubation. After treatment of the B16F10 cells with 123 μM of ATRA, 136 μM of SM, and the 123 μM ATRA+136 μM SM combination, the relative CASP3, Bax, PD-L1, and CDKN2A expressions of the cells were analyzed with qPCR at 24 h . The results showed that 136 μM of SM and the 123 μM ATRA+136 μM SM combination led to 5- and 2-fold increases in the CASP3 gene expression, respectively . The B16F10 cells treated with 136 μM of SM significantly overexpressed the Bax gene by 6 times , whereas the 123 μM ATRA+136 μM SM combination resulted in a 5-fold increase in the Bax gene expression (p ≤ 0.001) . However, 123 μM of ATRA alone did not change the relative Bax gene expression . On the other hand, the 123 μM ATRA+136 μM SM combination led to 0.13-fold and 0.5-fold (p ≤ 0.001) decreases in the PD-L1 gene expression in the B16F10 cells, respectively . In addition, treatment of the B16F10 cells with 123 μM of ATRA, 136 μM of SM, and the 123 μM ATRA+136 μM combination resulted in 6-fold (p ≤ 0.01), 17-fold , and 13-fold (p ≤ 0.001) increases in the CDKN2A gene expression, respectively, as depicted in Figure 5D . ATRA has been widely studied as a potent inhibitor of growth in melanoma cancer cells and displays very strong antineoplastic activity through the inhibition of cell proliferation, and induction of apoptosis in the cancer cells . Although ATRA indicates the efficacy in melanoma cells, it has some limitations, including the decrease in the concentrations due to its very short plasma pharmacokinetic profile, high volume of distribution, and excessive binding to tissues, thus leading to pharmacological limitations in its clinical use in cancer . Similarly, SM has recently garnered much attention in the cancer investigations due to its potential anticancer effects as tumor-suppressing biologically active lipids . However, the fast metabolic interconversions, and signaling functions occurring inside the biological membranes of sphingolipids pose biochemical and biophysical complexities when attempting to determine the precise roles of ceramides in the regulation of apoptosis. Furthermore, additional research is needed to investigate the potential modifications in sphingolipid signaling that could enhance immune cell (specifically macrophages) antitumor capabilities to improve the efficacy of immunotherapy in combating cancer. To address the limitations, a unique therapeutic approach involving the combination of ATRA and SM was devised herein for the treatment of melanoma, which has not been previously reported. The results demonstrated that the concomitant administration of ATRA and SM led to a substantial augmentation in anticancer activity, as evidenced by a notable decrease in the viability of the B16F10 melanoma cells with relatively less toxicity on the macrophages. Cell viability data indicated that the combination of 123 μM of ATRA and 136 μM of SM significantly inhibited the cell proliferation of the B16F10 cells at 72 h in a time-dependent manner . Furthermore, the combinational therapy improved the safety features of the ATRA and SM on the macrophages, in which the RAW264.7 viability was as high as 85.42% at 72 h of incubation with the 123 μM ATRA+136 μM SM combination compared to the standalone treatment of ATRA and SM . At 48 h of incubation with 100 μM of ATRA, the viability of the melanoma cells was approximately 80%, indicating consistency with the value in the literature (i.e. 70%–75% and 85%) for MCF7 breast and HCT116 colon cancer cells, respectively . In addition, Bidad et al. reported that ≤10 μM of ATRA had no significant impact on cell viability and proliferation. The IC 50 (123 ± 0.02 μM) and CC 50 (99.54 ± 1.55 μM) values of the ATRA indicated a selectivity of 0.81 for the B16F10 cells compared to the RAW264.7 cells . Previously, the effect of ATRA on the SM cycle was researched through SMase expression on MCF-7 (ATRA-sensitive) breast cancer cells, in which an increase in SMase activity was observed in the MCF-7 cell line in an ATRA-sensitivity-dependent manner . Clarke et al. reported that growth arrest in ATRA-induced MCF-7 breast cancer cells was mediated by SMase expression, and estrogen receptor-positive MCF-7 cells were employed as a model system to investigate the involvement of nSMase2 and sphingolipids in the growth arrest produced by ATRA. The underlying anticancer mechanism of the SM could be explained as the breakdown of SM by the SMase to produce ceramide after ATRA stimulation . Moreover, ceramide can also be used as a substrate for the generation of complex sphingolipids via conversion to glucosylceramide by glucosylceramide synthase . In terms of the anticancer mechanism, ceramide, a bioactive lipid, is thought to induce death, growth inhibition, and senescence in cancer cells, where classic mitochondria-dependent apoptosis was reported to be triggered by endogenous and exogenous ceramide signaling via ceramide-activated Ser-Thr protein phosphatases (CAPPs) and the expression of tumor suppressor genes (e.g., p38, Bax) . In addition, the effect of ceramide on CAPPs resulted in the inactivation of the antiapoptotic kinase AKT via protein dephosphorylation, and thus the downgrading of AKT by several signals (protein phosphatase 2A, protein kinase Cζ, and p38) decreases the phosphorylation of B-cell lymphoma 2 in intrinsic apoptosis . In line with the cell viability analysis, distinct morphological alterations were detected in terms of cell shrinkage, acquisition of a spherical form, and aggregation, as indicated by previous research , suggesting the signs of apoptosis. Treatment with ATRA and SM resulted in the transformation of cells to round-shaped cell morphologies, which was attributed to the loss of integrity and detachment of the B16F10 cells induced apoptosis . Parallel to the cell viability and morphological analysis, the cell cycle results suggested that the higher concentrations of ATRA and SM (i.e. ≥100 μM), alone, could alter the cell cycle frequencies at 24 h of treatment, thus inducing G0/G1 arrest in the B16F10 melanoma cells . The increase in the cell population in the G0/G1 phase and the induction of the cell cycle arrest by ATRA and ceramides alone in the cancer cells were compatible with the literature . Similar to ATRA, another study stated that the addition of ceramide in human leukemic cell lines (e.g., MOLT-4) caused cell cycle arrest in the G0/G1 phase, and cell cycle progression, accompanied by an increase in ceramide levels resulting from the breakdown of SM . On the other hand, the contribution of ceramide in the G0/G1 arrest was closely linked to the role of nSMase2 in facilitating growth arrest triggered by confluence . Clarke et al. demonstrated that nSMase2, a key ceramide-producing enzyme involved in cellular stress responses, plays a role in ATRA-induced growth arrest induced by ATRA in MCF7 cells. The observed phenomenon was ascribed to the dephosphorylation of β-catenin through the involvement of a protein phosphatase 1-γ, indicating the presence of a signaling mechanism that mediates this particular action . Recent research has elucidated the role of dihydroceramide in regulating G0/G1 arrest triggered by cell confluence in neuroblastoma . On the other hand, codelivery of ATRA with SM reduced the cell population in the G0/G1 phase in terms of G0/G1-phase cell cycle arrest, while leading to increased G2/M phase cell populations at 24 h . Previously, it was reported that ATRA treatment stimulates the conversion of SM into ceramides, an anticancer metabolite, by the induction of the SMase enzyme . Since ceramide induces apoptosis, cell differentiation, and senescence, the combination of SM and ATRA could be used as an anticancer agent by stimulating the ceramide accumulation mechanism. In addition, ceramide may have an involvement in regulating the G2/M cell cycle checkpoint . Previously, it was observed that the introduction of threo-1-phenyl-2-decanoylamino-3-morpholino-1-propanol, an inhibitor of glucosylceramide synthase, resulted in an increase in the levels of ceramide in NIH 3T3 cells . The increase in ceramide levels was attributed to a decrease in the cyclin-dependent kinase CDK1 activity, thus resulting in G2/M cell cycle arrest . The observed phenomenon in the current study can be ascribed to the arrest of the cell cycle in the G2/M phase following the combinational treatment of 123 μM ATRA+136 μM SM. Similarly, the modulation of G2/M arrest induced by the chemotherapeutic drug paclitaxel was observed by Espaillat et al. , when β-glucosidase was knocked down, leading to the inhibition of ceramide formation . Elevation in ceramide levels has been documented to take place immediately during the G2/M transition in order to modulate the dephosphorylation of retinoblastoma (Rb) . Moreover, recent studies have indicated that ceramide could produce G2/M arrest in rhabdomyosarcoma cells. The observation of elevated levels of CASP3 expression was consistent with the data obtained from the Annexin V FITC/PI flow cytometry analysis , indicating the incidence of higher cell death when the B16F10 cells were treated with the 123 μM ATRA+ 136 μM SM combination . Likewise, the CDKN2A gene was upregulated in the B16F10 melanoma cells when treated with 123 μM of ATRA, 136 μM of SM, and the 123 μM ATRA+136 μM SM combination, indicating consistency with the literature, in which germ-line mutations in the CDKN2A tumor-suppressor gene were related to the pathogenesis of hereditary melanoma, specifically concerning the development of melanoma. In addition, the gene CDKN2A is responsible for producing a cell-cycle regulator by suppressing the actions of CDK4 and CDK6, which are protein kinases that subsequently phosphorylate the Rb protein . Herein, overexpression in the CDKN2A was correlated with apoptosis after the ATRA and SM combinational treatment in the melanoma cells, thus inducing tumor suppression and reduction in cell viability. On the other hand, the PD-L1 gene was downregulated in the B16F10 cells following treatment with 136 μM of SM and the 123 μM ATRA+136 μM SM combination , verifying the results of previous studies in which ATRA exerted a suppressive effect on the expression of the PD-L1 gene in cellular systems . Interestingly, 123 μM of ATRA alone did not lead to an alteration in the expression of the PD-L1 gene in B16F10 melanoma cells . Recently, there have been effective applications of novel medicines that specifically target the surface expression of programmed cell death-1 (PD-1)/PD-L1 in the treatment of various solid malignancies, such as melanoma . The expression of PD-L1 has been linked to the occurrence of tumor metastases, the advancement of tumors, and worse prognosis. The downregulation of the PD-L1 gene in the combinational treatments could be attributed to the increased tumor suppression and cell cycle arrest on melanoma cells. Overall, the findings of the cell cycle and apoptosis were supported by an investigation of the mRNA expression levels. The combinational ATRA + SM treatment had a significant effect on cell proliferation and cell differentiation in melanoma cells, hence triggering apoptosis and causing cell cycle arrest. The combinational use of of 123 μM ATRA + 136 μM SM reduced cell viability, induced apoptotic cell death, caused cell cycle arrest, and overexpressed the Bax, CDKN2A, and CASP3 genes . In summary, the hypothesis herein proposed that the concurrent use of two therapeutic components would augment the effectiveness of anticancer treatment in the treatment of B16F10 melanoma cancer cells, indicating the synergistic effects of this innovative treatment strategy. The findings provided evidence that the concurrent application of ATRA and SM results in a significant enhancement in anticancer efficacy, as indicated by a considerable reduction in the viability and proliferation of B16F10 melanoma cells with relative toxicity on RAW264.7 macrophages. The observed increase implies a possible interaction between the molecular pathways affected by ATRA and SM, resulting in a combined effect that exceeds the effects of each treatment on its own regarding the induction of apoptotic cell death, and cell cycle arrest on B16F10 cells. The promising efficacy of the ATRA and SM combination in an in vitro model demonstrated remarkable efficacy, indicating its potential for future in vivo and clinical investigations in anticancer therapy. It is important to underscore that this study represents the first report revealing the combination of ATRA and SM enhancing the anticancer therapy on B16F10 melanoma cells. | Review | biomedical | en | 0.999998 |
PMC11698199 | MEIS genes, part of the TALE superclass of homeodomain proteins, are transcription factors crucial for diverse biological processes, such as development and tissue-specific gene expression . The MEIS gene family includes members like Meis1, Meis2, and Meis3 with multiple isoforms through alternative splicing . In recent years, there has been a growing acknowledgment of the crucial role played by MEIS proteins and their associated counterparts in a wide array of biological processes, spanning regeneration , stem cell functionality , cellular metabolism , tumor development , and modulation of lifespan. Notably, MEIS1 is essential for cardiac development and hematopoiesis , MEIS2 plays key roles in limb and eye development with relevance to neurodevelopmental disorders , and MEIS3, while less explored, contributes to specific developmental contexts . MEIS proteins play essential roles in critical pathways like HOX , Wnt , and Hedgehog signaling , exerting influence over body segment identity, limb development, neural tube formation , heart development , and eye development . Grasping the multifaceted functions of MEIS proteins in these pathways and their therapeutic modulation is of substantial relevance for the field of regenerative medicine and provides valuable perspectives into the underlying causes of developmental abnormalities and congenital disorders. Our recent research has been dedicated to developing MEIS1 inhibitors, capitalizing on our specialized tools and expertise in MEIS1 biology . Two recently identified MEIS1 small-molecule inhibitors, namely MEISi-1 and MEISi-2, have demonstrated the ability to enter cells and produce dose-dependent effects. Importantly, they operate by disrupting the interaction between the MEIS1 homeodomain and target DNA, thereby impairing the activation of MEIS1-targeted gene expression, including Hif-1α, Hif-2α, and p21. Furthermore, they show promise in expanding and enhancing the self-renewal potential of human and murine hematopoietic stem cells in vitro and in vivo. In addition, recent knockout studies in animal models showed that the elimination of Meis1 in adult cardiomyocytes triggers an increase in cardiomyocyte proliferation . Research has revealed that Meis1 plays a crucial role in the transcriptional network governing cardiomyocyte cell cycle, hematopoietic stem cell maintenance, and cellular metabolism. These discoveries imply that Meis1 holds promise as a therapeutic target for a range of conditions, including modifying cancer metabolism, targeting cancer stem cells, expanding HSC populations, and promoting cardiac regeneration and possibly preventing cardiotoxicity . Cardiotoxicity encompasses a spectrum of adverse effects on the heart due to drugs or other agents, which can lead to heart damage , arrhythmias , and cardiomyopathy . In severe cases, it may result in heart failure . This condition is particularly important in clinical medicine and drug development, notably in oncology, where some treatments have cardiotoxic effects, necessitating careful patient monitoring and potential treatment limitations . The downregulation of MEIS1 is associated with the enhanced maturation of oxidative phosphorylation during perinatal cardiomyocyte development, while Meis1 exerts inhibitory effects on angiotensin II-induced cardiomyocyte hypertrophy. Additionally, the restoration of Meis1 expression leads to improved electrophysiological function in cardiomyocytes . Here we investigated MEIS1’s pivotal role in regulating cardiomyocyte cell cycle arrest as a promising therapeutic target. We aimed to provide a compelling pathway for enhancing cardiomyocyte renewal through MEIS1 inhibition. This is supported by investigations involving neonatal cardiomyocytes, wherein two novel small molecules, MEISi-1 and MEISi-2, are used to stimulate neonatal and adult cardiomyocyte proliferation and cytokinesis by downregulating MEIS target genes and cyclin-dependent kinase inhibitors (CDKIs). Additionally, the study included the effect of MEIS1 inhibition in early development via the cultivation and differentiation of human induced pluripotent stem cells (hiPSCs) into cardiomyocytes. These findings could underscore the potential of MEIS inhibitors as a key regulator of cardiac gene expression, emphasizing their promise as therapeutic agents in regenerative cardiology. This protocol was conducted as previously described . Hearts were extracted from 1- to 2-day-old rat pups after decapitation. To avoid contamination, the hearts were briefly immersed in ethanol within a sterile hood before being placed in an enzyme solution for digestion. Only the ventricular portion of the heart was dissected and it was placed in the enzyme solution. The enzyme solution consisted of 0.1% Pancreatin, and 50 mL of the solution was used for each batch. Subsequently, the hearts were incubated at 37 °C for 20 min with gentle agitation at 100–120 rpm, followed by centrifugation at 2000 rpm for 10 min. To remove fibroblast cells in the pellet, the isolated ventricular cardiomyocytes were seeded into a specialized cell culture medium and incubated at 37 °C for 2 h. After the cells were gently collected, they were filtered through a 70–100 μm cell strainer and seeded in a cell culture medium precoated with gelatin, consisting of myocyte medium (3:1 DMEM: M199, Pen/Strep, L-Glutamine (2 mM), 10% normal rat serum, 5% FBS) at a density of 500,000 cells/mL and placed in a 37 °C incubator with 5% CO 2 . This protocol was conducted as previously described . Cardiomyocytes were expanded to a density of 50%–70% (5 × 10 5 cells/6-well plates). They were treated with putative Meis1 inhibitors (0.1, 1, and 10 μM concentrations), and an increase in cardiomyocyte division rates was assessed by examining the levels of Ph3, AuroraB, and TnnT in the cells after 3–5 days. Immunostaining was conducted for this purpose. Following applications of putative Meis1 inhibitors, cardiomyocytes were fixed with 4% PFA (10 min at room temperature). Subsequently, the cells were permeabilized with 0.1% Triton X-100 for 15 min at room temperature. The cells were then blocked with 1% normal bovine serum (for 30 min) after washing. Next, the cells were incubated at room temperature with antibodies for phospho-histone H3 (PH3) (cell division marker, Ser10, 1:250 dilution, rabbit polyclonal, Millipore), Aurora B (cytokinesis marker, 1:250 dilution, rabbit polyclonal, Sigma), and cardiac troponin T (TnnT2) (cardiomyocyte marker, Thermo Scientific MS-295-P1, 1:200 dilution, mouse monoclonal). Detection was performed using secondary antibodies such as Alexa Fluor 488 donkey anti-mouse and Alexa Fluor 555 donkey anti-rabbit (Invitrogen, 1:400 dilution), along with Hoechst 33342 (Invitrogen) DNA dye. The number of Ph3+TnnT2+ and AuroraB+TnnT2+ cardiomyocytes was determined using the GE Cytell imaging system. To isolate adult ventricular cardiac cells, we followed a previously described method , starting with the treatment of adult cardiac tissue with collagenase. For the collection of pure and viable ventricular cardiomyocytes from adult mouse hearts, we utilized the EASYCELL-CM system from Harvard Apparatus. Following enzymatic perfusion of the heart, the cells were gathered and allowed to undergo gravity settlement. Within just 5 min, a pellet formed, which contained the cardiomyocytes. These cardiomyocytes were isolated and examined under a microscope. EASYCELL-CM and enzymatic processes allowed us to obtain both fibroblasts and cardiomyocytes. To separate the fibroblasts from the cardiomyocytes, we employed PRIMARI cell plates. The solution containing the fibroblasts was then seeded into two separate PRIMARI cell culture dishes (10 cm each), where the adhering fibroblasts were allowed to grow for 3–5 days in the cell culture. Cardiac fibroblasts were cultured in a 20% FBS DMEM medium and seeded in a 96-well plate with 5000 cells per well, allowing them to attach over 24 h. Subsequently, MEISi-1 and MEISi-2 compounds were introduced to the cells at about 5 μM for each well. The cells were then incubated at 37 °C for 72 h. Following the incubation period, MTS solution was introduced to the cell cultures and left to incubate for 2 h under standard culture conditions. Absorbance measurements were recorded at 490 nm using a microplate reader (Thermo Fisher Multimode Reader Varioskan Lux). To obtain accurate readings, the absorbance of the blank group was subtracted from the absorbance values of the samples. hiPSCs , generously provided by the Köse Lab at Yeditepe University, were cultured at 37 °C with regular medium changes. Cells were passaged every 3 to 4 days, maintaining 80%–90% confluence . Passaging involved aspirating the medium, adding Versene, and transferring detached cells to Matrigel-coated plates at a 1:13 split ratio, with daily medium changes. Matrigel coating was prepared at 1.2 mg/mL in cold RPMI-1640, and the coated plates/flasks could be stored for up to 3 weeks. In the hiPSC-cardiomyocyte differentiation, we began with CDM3 + 6 μM CHIR on day 0, followed by CDM3 + 2 μM IWP2 on day 2, and continued with CDM3 medium until day 13 when cardiomyocytes were ready for expansion. Replating of differentiated hiPSC derived cardiomyocytes, suitable for cardiac expansion, occurred between days 10 and 14. This involved incubating cells with TrypLE Select, counting and replating them at a 1:10–20 split ratio in the desired culture system. Incubation was at 37 °C, 5% CO 2 , 21% O 2 , and 90% humidity, with gentle movements for even cell distribution. To evaluate the quality and expansion rates of hiPSC-derived cardiomyocytes, the cells underwent a series of procedures. Initially, the cells were gathered, fixed, and subjected to immunocytochemical staining for the cardiac markers α-actinin and troponin-T. Subsequently, the cardiomyocytes were dissociated. Following cell counting, 100,000 cardiomyocytes were collected in 1.5-mL tubes and centrifuged at 200 × g for 3 min. The supernatant was then discarded, and 50 mL of 4% PFA solution was added to each tube for a 10-min incubation period. The cells were later resuspended at a concentration of 1 × 10 5 cells in 50 mL of permeabilization buffer containing 5% BSA and 0.3% Triton X-100, and incubated for 30 min at 4 °C. Afterwards, the resuspended cardiomyocytes were placed in 50 mL of flow cytometry buffer, along with α-actinin antibody (1:300 dilution), troponin-T antibody (1:300 dilution), and a negative control containing 1 × 10 5 cells in 50 mL of flow cytometry buffer. This mixture was incubated for 30 min at 4 °C. The cells underwent subsequent washing steps, including centrifugation at 200 × g for 5 min at 4 °C, with the supernatant being discarded and the washing process conducted twice. The final step involved resuspending the cells in 50 mL of flow cytometry buffer containing secondary antibodies (goat anti-mouse and goat anti-rabbit, both at 1:300 dilution) for analysis using a flow cytometer. Total RNA was prepared using the RNeasy Kit (Qiagen) and reverse-transcribed with the iScript cDNA Synthesis Kit (Bio-Rad) using random primers. qRT-PCR was performed using the i-Taq SBR Green Master Mix (Bio-Rad). The expression levels of the target genes were normalized to GAPDH levels ( Table 1 ). hiPSCs were grown in m-TESR1 complete medium supplemented with 10 mM Y27632 and were seeded in a 96-well plate with 5000 cells per well and left for 24 h to attach. MEISi-1 was dissolved in dimethyl sulfoxide (DMSO) as a 100 mM stock solution. The molecule was then delivered to the cells at the various increased doses of MEISi-1 (10 nM up to 5 μM) for each well and incubated at 37 °C for 72 h. After incubation, MTS solution was added to the cells and incubated for 2 h under standard culture conditions. Absorbance was measured at 490 nm using a microplate reader (Multimode Reader Varioskan Lux, Thermo Fisher). The absorbance of the blank group was subtracted from the absorbance of the samples. The hiPSCs were thawed and seeded onto Matrigel-coated six-well plates at a cell density of 0.8 × 10 4 cells/cm 2 in mTeSR1 medium for 4 days. Afterward, the hiPSCs were treated with 5 μM MEIS inhibitor (MEISi) for 18 days, with medium changes every 2 days. From day 10 onwards, the experiment was continued without further medium changes for an additional 8 days. On day 18, the cells were collected for qPCR experiments. In the short-term MEISi treatment method, hiPSCs are initially cultured on Matrigel-coated 6-well plates for 4 days using mTesR medium with the same density of cells. Subsequently, to induce mesoderm progenitor cell formation, a 6 μM GSK3 inhibitor (CHIR) is introduced in a differentiation medium containing RPMI, ascorbic acid, and albumin, and the cells are left to incubate for 48 h. After this stage, the differentiation medium (CMD3) is prepared, supplemented with WNT inhibitor (IWP2) and MEIS inhibitor1, and the cells are cultured for an additional 3 days to promote the differentiation of cardiac mesoderm and cardiac progenitor cells. Throughout the process, cellular changes are monitored with a qPCR experiment to investigate whether MEIS inhibitors play a role similar to WNT inhibitors in guiding mesoderm cells towards a cardiac mesoderm or cardiac progenitor cell fate. To assess the expression of ventricular cardiomyocyte cell cycle regulators in cardiac tissue, we employed real-time qPCR following MEISi-1 and MEISi-2 injections into the animals, as outlined in our previous study . Whole cardiac tissue samples were collected and half of them subsequently powdered in liquid nitrogen using a mortar. Half of the cardiac tissue was used for parafilm sectioning and TnnT/Ph3 immunohistochemistry studies as outlined previously (. RNA isolation was performed using the TRIzol method. RNA concentration was determined with a NanoDrop (Thermo Fisher). For each tissue sample, 5 μg of RNA was converted into cDNA using random primers and the ProtoScript II First Strand cDNA Synthesis Kit (NEB, Cat. No: E656). Subsequently, the samples were stored at −20 °C after dilution. Gene-specific primers ( Table 2 ) were selected using NIH primer depot ( http://mouseprimerdepot.nci.nih.gov ) and ordered from Sentebiolab in Türkiye. The desired gene regions were then amplified from the cDNAs using a Bio-Rad FX96 Touch Real-Time qPCR Detection System, following the cycling conditions of 95 °C for 10 min, 95 °C for 10 s, 60 °C for 20 s, and 72 °C for 30 s (30 cycles). The expression of each amplified potential modulator gene was normalized against the GAPDH content using the ΔΔCt method. The mice were anesthetized as previously reported . In brief, they were placed in an induction chamber (Hugo Sachs Electronik) and 2%–3% vol/vol isoflurane delivered by 95% oxygen (1 L/min) was administered. After establishing narcosis, the mice were transferred to a surgical platform (Kent Scientific), equipped with a temperature control module (Kent Scientific), and fixed in a supine position with a rectal probe inserted for temperature maintenance at 37 °C throughout the entire procedure. The isoflurane concentration was regulated at 1.5% vol/vol using a vaporizer (Hugo Sachs Electronik) to maintain narcosis. Fentanyl (0.05 mg/kg body weight) was injected intraperitoneally for analgesia in the beginning of the experiment; additional doses (0.025 mg/kg) were given every 45 min. Depth of anesthesia was evaluated by the toe pinch reflex. Subsequently, once a negative response in the toe pinch reflex was confirmed, we could initiate the following experiments. Echocardiographic studies were conducted using a VEVO-2100 Imaging ultrasonographic system (VisualSonic, Toronto, Canada) at a resolution of 100 dpi. After anesthesia induction, a 12.5 MHz transducer was applied to the left hemithorax and two-dimensional M-mode images from the short-axis view were acquired. LV end-diastolic and end-systolic diameters, as well as LV anterior and posterior wall thicknesses, were measured using the leading-edge convention of the American Society of Echocardiography. The LV ejection fraction (EF) percentage was computed as EF (%) = (LVIDd3-LVIDs3) / LVIDd3 × 100, where LVIDd3 and LVIDs3 represent LV end-diastolic and end-systolic diameters, respectively. Following anesthesia induction, three subcutaneous needle electrodes (29 G, AD Instruments) were inserted in both the upper and left lower limbs of the mice to allow recording of a Lead I ECG. To record and analyze ECGs, an amplifier (AD Instruments), a PowerLab system (AD Instruments), and LabChart Pro software (AD Instruments) were used. The ECG data were derived from 5-min recordings. The data are presented as the mean ± standard error of the mean (SEM). Significance levels were determined using a two-tailed Student’s t-test and one-way ANOVA. Statistical significance in the echocardiography data was evaluated using the two-sided Mann–Whitney test. Statistical significance was ascribed to results with p-values less than 0.05. To investigate the dynamics of cell division within cardiomyocytes, we employed rat neonatal cardiomyocytes (RNCMs) as a model system . Then cellular division events were meticulously examined using immunostaining techniques. Upon successful isolation of RNCMs, they were cultured in 96-well plates and subjected to a progressive gradient of MEIS1 inhibitors at concentrations of 0.1, 1, and 10 μM. Following a 3-day incubation period, the cardiomyocytes were fixed and subsequently subjected to immunostaining using antibodies targeting specific markers: TnnT2 for cardiomyocytes, Ph3 for proliferation, and AuroraB for cytokinesis. The research analysis was primarily centered on quantifying actively dividing cardiomyocytes, defined as TnnT2+Ph3+. In addition, an evaluation of cytokinesis rates was conducted by examining TnnT2+AuroraB+ markers . Our findings revealed a substantial augmentation in both the proportion and number of TnnT2+Ph3+ cardiomyocytes , demonstrating an increase of up to 2.5 times. Moreover, a similar enhancement, amounting to a twofold increase, is clearly observed in the population of cardiomyocytes undergoing cytokinesis subsequent to MEIS1 inhibitor treatment, as visually represented in Figures 1E–1G . In conclusion, our study using neonatal rat cardiomyocytes and MEIS1 inhibitors demonstrated a substantial increase in actively dividing cardiomyocytes and those undergoing cytokinesis. These findings shed light on the potential for regulating cardiomyocyte division and offer valuable insights into the potential mechanisms that could be harnessed for cardiac regeneration and repair in the adult heart. Cardiomyocytes in the adult heart often enter a quiescent state, displaying limited participation in the active cell cycle. To explore the dynamics of cell division within adult cardiac tissue, adult ventricular cardiomyocytes as well as noncardiomyocytes were isolated and cultured, and MEIS1 inhibitors at concentrations of 5 μM were applied. After a 3-day incubation period, immunostaining was performed using TnnT2 for cardiomyocytes and Ph3 for proliferation . Our findings demonstrate a significant increase in the number of noncardiomyocytes (TnnT2-Ph3+) and cardiomyocytes (TnnT2+Ph3+) post-MEISi-2 treatments only. MEISi-1 did not show any effect on the proliferation of primary ventricular cells . Next, we assessed the effect of MEIS1 inhibitors in cultured cardiac fibroblasts . Intriguingly, both MEISi-1 and MEISi-2 treatments reduced cardiac fibroblast proliferation in vitro . In conclusion, our research conducted with adult cardiac tissue and the application of MEIS1 inhibitors underscores a significant augmentation in actively dividing ventricular cardiomyocytes and suggests the potential inhibition of fibroblasts originating from adult tissue. Next we wanted to assess if MEIS expression plays a role in cardiac differentiation in hiPSCs. To induce cardiac differentiation in these cells, we employed a strategic approach utilizing GSK3 and WNT inhibitors, as delineated in Figure 3A . These cells were cultured on Matrigel-coated six-well plates and maintained in mTeSR1 medium for 4 days. The differentiation process commenced with the introduction of CDM3 medium and CHIR99021 over 48 h, leading to the emergence of Brachyury-expressing cells. Subsequently, we introduced the IWP2 inhibitor for an additional 48 h to guide the cells towards a cardiac fate. The culture medium was refreshed every 2 days, and functional contracting cardiomyocytes were observed after 13 days . This was evident with the proportions of TnnT+ and actinin+ cells in hiPSC derived cardiomyocytes . We conducted an in-depth examination of gene expression patterns through real-time reverse-transcription PCR (RT-PCR). Our findings were consistent with established differentiation strategies and embryonic development principles. We observed a rapid downregulation of pluripotency markers OCT4, TERT, and Sox2, concomitant with the upregulation of cardiac mesoderm markers ISL1 and Brachyury . Notably, both early and late cardiomyocyte markers Nkx2.5 and TNNT2 exhibited robust expression, as depicted in Figure 3D . Furthermore, it is noteworthy that Meis1 and Meis2 displayed significant upregulation . In summary, the findings suggest that the upregulation of Meis1 and Meis2 genes following cardiac differentiation of hiPSCs underscores their significant roles in cardiac development. We initially assessed whether inhibiting the MEIS pathway resulted in any cytotoxicity for iPSCs. To this end, we assessed hiPSCs’ cell viability and mitochondrial activity post-MEISi-1 treatments using the MTS tetrazolium assay. These experiments, which included a range of MEISi-1 doses, did not show significant changes in mitochondrial activity or cell viability compared to the control group treated with DMSO, the solvent for the MEIS1 inhibitor . Then the MEIS1 inhibitor treatment protocol was designed to explore the impact of MEISi-1 on hiPSCs and monitor gene expression alterations throughout the experiment . The hiPSCs were initially grown on Matrigel-coated plates and then subjected to MEISi-1 for an extended duration, with routine medium changes up to day 10. Following this, the medium remained unchanged for the subsequent 8 days. Cells collected on day 18 were employed for qPCR analysis, facilitating the assessment of gene expression profiles influenced by MEIS1i treatment. We observed that MEIS1 inhibition results in the upregulation of pluripotency markers, including TERT, OCT4, and Sox2 expression following MEISi-1 treatments in comparison to the control (DMSO). Additionally, cardiac mesoderm markers ISL1 and Brachyury exhibited increased expression, indicating increased mesodermal differentiation after MEISi-1 treatments . Conversely, later cardiomyocyte marker TNNT2 showed downregulation post-MEIS1 inhibition, suggesting a hindrance in terminal cardiomyocyte differentiation . Intriguingly, Meis1 and Meis2 exhibited a modest upregulation (16- and 47-fold increases, respectively) , which in turn is a significant downregulation compared to no treatment as seen in the previous study . In the short-term MEISi-1 treatment method, hiPSCs are initially cultured for 4 days. Subsequently, a 6 μM GSK3 inhibitor (CHIR) is introduced into a differentiation medium and the cells are incubated for 48 h to induce mesoderm progenitor cell formation . After this stage, the differentiation medium (CMD3), supplemented with WNT inhibitor (IWP2) and MEIS inhibitor 1 separately, is used for an additional 3-day culture to promote the differentiation from mesoderm stage to cardiac mesoderm . We analyzed the expression of ISL1 , Brachyury , and GATA4 after short-term MEIS inhibition. Interestingly, consistent findings for ISL1 gene expression were observed after short-term MEIS1 inhibition, indicating no significant changes. Another mesoderm marker, Brachyury expression, showed upregulation , highlighting the enhanced mesodermal differentiation under MEIS1 inhibition conditions. Furthermore, GATA4 expression was also notably upregulated, indicating that MEIS1 inhibition plays a pivotal role in promoting mesoderm formation and subsequent cardiac mesoderm commitment. These results collectively demonstrate the positive regulatory effect of MEIS1 inhibition on key cardiac mesoderm markers, underscoring its potential in influencing cardiac differentiation processes. Moreover, analysis of early (Nkx2.5) and late (Tnnt2) cardiac markers expression after short-term MEIS1 inhibition showed notable upregulation in their expression levels. This indicates that the MEIS1 inhibition during the differentiation process had a stimulatory effect on the activation of these cardiac markers. The increased expression of Nkx2.5 and Tnnt2 suggests that MEIS1 inhibition enhances the proper differentiation of cardiac mesoderm progenitor cells and their progression into more mature cardiac cell lineages. These findings highlight the regulatory role of MEIS1 in cardiac development and the potential for MEIS1 inhibitors to positively influence cardiac lineage commitment and maturation. The analysis of various markers, including ISL1, Brachyury, GATA4, Nkx2.5, and Tnnt2, offered insights into the effects of long- and short-term MEIS1 inhibition on hiPSC differentiation. Overall, our findings shed light on the intricate interplay between MEIS1 inhibition and the regulation of pluripotency and cardiac differentiation in iPSCs, providing valuable insights for future research in this field. These findings suggest that MEIS1 inhibitors may trigger cell cycle activation in ventricular cardiomyocytes, especially due to the decrease in the expression of CDKIs, which negatively regulate the cell cycle. To investigate this, we analyzed cardiac tissue after injection of MEIS1 inhibitors into mice . Studies involved mouse heart tissues to investigate the live imaging effects of Meis1 inhibitors on cardiomyocyte proliferation via immunohistochemistry and expression of CDKIs after serial MEISi-1 and MEISi-2 injections by qPCR . Immunostaining and quantitative analyses conducted in the left ventricle of mouse hearts following injections of DMSO (control), MEISi-1, and MEISi-2 distinctly demonstrate that MEIS1 inhibitors significantly enhance the proliferation of ventricular cardiomyocytes . Within the left ventricular regions, a remarkable increase in the number of cells labeled with TnnT2+Ph3+ (a proliferation marker) is observed. These findings indicate that MEIS1 inhibitors stimulate the division of ventricular cells, suggesting this process as a promising target for cardiovascular regeneration. Previous studies demonstrated that the expression of CDKIs decreases in Meis1 knockout experiments, and these inhibitors directly regulate the expression of genes such as p21, Hif-1α, and Hif-2α. Here we examined whether the MEIS1 inhibitors we developed trigger similar gene regulation in cardiac tissue. Following MEISi applications, we observed a reduction in the expression of genes targeted by Meis1, including Hif-2α, and several CDKIs including p16, p18, p19, p19arf, and p27 after both MEISi-1 and MEISi-2 injections . The efficacy of the Meis1 inhibitors MEISi-1 and MEISi-2 became apparent through the observed reduction in Meis1 as well as mRNA expression of the genes targeted by Meis1. In mouse heart tissue, we observed an increase in ventricular cardiomyocyte proliferation following MEIS1 inhibitor injections. These findings indicate that MEIS1 inhibitors have the potential to activate ventricular cardiomyocyte cell cycles by downregulating CDKIs. The reduced expression of CDKIs and Meis1-targeted genes further supports the effectiveness of these MEIS1 inhibitors, suggesting their promise for promoting cardiac cell division and cardiovascular regeneration. Given the above results implying a potential role for MEIS1 inhibition in activating cell proliferation in cardiomyocytes, we assessed the impact of MEIS1 inhibition on the heart in vivo. Compared to control mice, MEIS1 inhibitor-treated animals showed a trend towards an increased heart weight and heart weight/tibia length ratio . To investigate the effects of MEIS1 inhibitor treatment on cardiac dimensions and ejection fraction, echocardiography was performed. LV diameter and ejection fraction, however, were not affected by MEIS1 inhibition . In line with the gross anatomy findings, a nonsignificant trend towards an increased diastolic thickness of the interventricular septum was observed. To evaluate the potential effects of MEIS1 inhibition on cardiac conduction, ECGs were analyzed . None of the ECG parameters including heart rate, P wave duration, PR interval, QRS duration, or QTc interval was significantly altered after MEIS1 inhibition. Heart failure is a pervasive medical condition, affecting millions of individuals worldwide. At the heart of its pathophysiology lies the heart’s reduced contractile force, stemming from the replacement of deceased cardiomyocytes, following cardiac events such as ischemia, with noncontractile fibrotic tissue. Although the general mammalian heart is considered incapable of regeneration, a limited number of myocytes do exhibit cell cycling. However, this phenomenon falls short of achieving substantial functional recovery postheart attack. While the mechanisms behind cardiomyocyte cell cycling in adult mammalian hearts remain incompletely understood, the activation of cardiomyocytes is considered a promising strategy for cardiac regeneration. Research on the development of small-molecule compounds has primarily focused on revealing substances that could facilitate the transformation of various stem cells or progenitors into cardiac cells, rather than concentrating on regulators of the cardiomyocyte cell cycle . Notable among the small molecules discovered for this purpose are those such as SB-203580 , CHIR99021 , ERK , and CamKII inhibitors , which target stem cells and progenitors. These compounds exert their effects on signaling mechanisms like MAPK and GSK-3β . Additionally, there is knowledge that small molecules like NBI-31772 and bromoindirubin-30-oxime (BIO) have a proliferative effect in zebrafish and mammalian heart muscle. However, it is important to note that, thus far, these investigations have not yielded outcomes at the desired level in terms of heart regeneration. This limitation can be largely attributed to the absence of comprehensive tools for studying modulators of mammalian heart regeneration. The identification of novel cardiogenic factors, such as Meis1, has provided an innovative foundation for the development of treatments targeting the cardiomyocyte cell cycle. Our previous molecular studies have revealed that Meis1 exerts transcriptional influence, turning factors that typically exert a negative impact on the cell cycle, like the p21 and INK4b loci, into positive regulators and negatively affecting cell division in cardiomyocytes . Interestingly, these two gene families are capable of inhibiting the cell cycle at different stages, underscoring the role of cell cycle arrest as the fundamental mechanism in both cardiac cell renewal and the prevention of excessive proliferation in cardiomyocytes. The majority of studies suggest that in adult cardiomyocytes the levels of CDKIs increase and their activities are associated with positive cell cycle regulators such as cyclins and CDKs . Several studies have demonstrated that Cdk2 and c-myc serve as cell cycle regulators, contributing to the upregulation of CDKs like cyclin CDK4 and CDK4, thereby promoting cardiomyocyte growth . Moreover, the deletion of CDKIs p27Kip1 or p21Cip1 in cardiomyocytes leads to S-phase progression, consequently enhancing cardiomyocyte proliferation and increasing heart size . Recent research has shown that Meis1 deletion induces the upregulation of CDKs and downregulation of CDKIs like p16, p15, p19ARF, p21, and p57 . Additionally, Meis1 deletion results in increased downregulation of positive cell cycle regulators such as MCM3, Chek1, and Ccnd2, and an upregulation of negative cell cycle regulators like APbb1, TP53, and Gpr132 . Targeting Meis1 represents a viable mechanism for inducing cardiomyocyte proliferation. Research into small molecules that stimulate cardiomyocyte regeneration is advancing rapidly. Our study offers significant insights into the effects of MEIS inhibitors on cardiomyocyte proliferation and gene expression. In neonatal rat cardiomyocytes, MEIS inhibitors significantly increased the number of actively dividing and cytokinesis cells, suggesting their potential for cardiac regeneration. Similarly, in adult cardiac tissue, these inhibitors reactivated the cell cycle, enhancing cardiomyocyte proliferation, and reduced cardiac fibroblast proliferation, impacting noncardiomyocyte cells within the heart. Additionally, investigations with hiPSCs undergoing cardiac differentiation revealed upregulation of MEIS1 and MEIS2, underscoring their roles in cardiac development . Recent research further highlights MEIS2’s role in calcific aortic valve disease (CAVD), where its inhibition promotes osteoblastic transdifferentiation and reduces Notch1 and Twist1 expression, making MEIS2 a potential target for CAVD prevention. A recent study reexamined ISL1’s role in human embryonic stem cell-based cardiac development, revealing that ISL1 accelerates cardiomyocyte differentiation rather than stabilizing precursor cells . Depletion of ISL1 delays cardiac differentiation and alters cardiomyocyte identity, as ISL1 interacts with retinoic acid signaling and MEIS2, competing with the retinoic acid pathway and the atrial specifier NR2F1 for cardiomyocyte fate. These findings provide valuable insights for cardiac regeneration strategies. Although the neonatal heart has inherent regenerative potential through cardiomyocyte proliferation, this capacity diminishes after postnatal day 7. We previously demonstrated that deleting MEIS1 in mouse cardiomyocytes extends the postnatal proliferative period and reactivates cardiomyocyte mitotic activity in adult hearts. MEIS1 plays a crucial role in activating critical CDK inhibitors, such as p15, p16, and p21, marking it as a fundamental regulator of cardiomyocyte proliferation and a promising therapeutic target for heart regeneration . Our observations with mouse heart tissue further support the potential of MEIS1 inhibitors to stimulate ventricular cardiomyocyte proliferation, downregulate CDKIs, and enhance cardiovascular regeneration, highlighting their prospective role in developing regenerative treatment strategies. A recent study investigated the role of Meis1 in ischemic arrhythmias in mice . Meis1 overexpression was found to reduce ventricular arrhythmias and improve cardiac conduction velocity, partly by restoring the function of cardiac Na + channels. Additionally, the study revealed that E3 ubiquitin ligase CDC20 plays a role in this process, highlighting a new mechanism for Na V 1.5 channel dysregulation in infarcted hearts. Fascinatingly, through the utilization of a Cre-dependent CasRx knock-in mouse model enabling precise gene suppression, another research team effectively downregulated Meis1 and Hoxb13 expression in ventricular cardiomyocytes . This intervention spurred cardiac regeneration after a myocardial infarction while also inhibiting the lncRNA Mhrt. MEIS proteins have a well-established connection with cancer, being implicated in tumorigenesis, metastasis, and invasion. In different cellular contexts, they can act as either tumor suppressors or oncogenes, and their expression frequently becomes dysregulated in various cancers . Studies have indicated their upregulation in cancers such as leukemia, lymphoma, thymoma, pancreas, glioma, and glioblastoma, as well as downregulation in cervical, uterine, rectal, and colon cancers. It is noteworthy that, within each cancer type, at least one subtype exhibits elevated MEIS expression. Furthermore, research has identified the potential of MEIS proteins and their associated factors as diagnostic or therapeutic biomarkers for various diseases. There is a historical association between Meis1 and acute leukemia, a challenging hematological disease characterized by resistance to therapy and frequent relapses. Meis1 has been implicated in the pathogenesis of various cancers, with historical observations linking its overexpression to both acute lymphoblastic leukemia and acute myeloid leukemia . Elevated MEIS1 expression in leukemic blast samples is associated with resistance to conventional treatments. A recent study indicated that MEIS inhibitors have the potential to reduce the viability of leukemia stem cells by inducing apoptosis, suggesting their possible use in limiting leukemia relapse and overcoming chemotherapeutic resistance . These findings imply that MEIS inhibitors could be promising for the treatment of leukemia and potentially other disorders characterized by similar resistance mechanisms. In addition, we have recently shown that newly developed MEIS inhibitors selectively hinder the growth of prostate cancer cells with high MEIS expression, triggering apoptosis . MEIS inhibition effectively reduced the viability of various prostate cancer cell lines and increased apoptosis, particularly in cells with elevated MEIS levels. These findings suggested the potential use of MEIS inhibitors for targeting high MEIS-expressing prostate cancer, although further research is needed before clinical application. The observations in leukemia and solid cancer cases indicate the potential utility of MEIS inhibitors in the management of MEIS-dependent or MEIS-positive malignancies. It is intriguing that our findings reveal differential effects of MEIS inhibition on cardiac fibroblasts compared to cardiomyocytes. To understand why MEIS1 impacts the cell cycle of these cell types differently, it is crucial to recognize that the precise mechanisms by which MEIS1 and its cofactors regulate cardiomyocytes versus fibroblasts are not yet fully elucidated. MEIS1 plays a role in regulating gene expression programs specific to each cell type, but the exact mechanisms by which MEIS1 and its cofactors differentially influence cardiac fibroblasts and cardiomyocytes remain unclear. In fibroblasts, MEIS1 is part of a regulatory network that maintains fibroblast-specific gene expression and suppresses cardiomyocyte-specific genes . This function is crucial for preserving fibroblast identity and function. However, the impact of MEIS1 on the cell cycle and gene expression in cardiomyocytes is less well defined and may involve interactions with other transcription factors and regulatory elements that are unique to cardiomyocytes. Our current understanding is limited regarding how MEIS1 and its cofactors are expressed and function differently in cardiac fibroblasts versus cardiomyocytes. This gap in knowledge suggests that MEIS1’s role in these cell types could be more complex than previously thought, potentially involving distinct regulatory networks and mechanisms that influence cell cycle dynamics and differentiation. Further research is needed to elucidate these differences and understand how MEIS1’s actions in these cell types contribute to their unique behaviors and responses. Heart failure, a widespread medical condition marked by reduced cardiac contractility and limited cardiomyocyte regeneration, underscores the need for innovative strategies in cardiac regeneration. While the mammalian heart exhibits regenerative potential in neonatal stages, this capacity diminishes after postnatal day 7. Studies have identified Meis1 as a crucial regulator of the cardiomyocyte cell cycle, offering promise in enhancing cardiac regeneration. The use of MEIS1 inhibitors, which promote cardiomyocyte proliferation and gene modulation, may serve as a potential therapeutic approach to stimulate cardiac repair. In addition to their relevance in cardiac regeneration, MEIS inhibitors hold promise in the context of cancer. MEIS proteins are associated with tumorigenesis and research indicates that MEIS inhibitors have the potential to reduce the viability of leukemia stem cells and induce apoptosis, suggesting their utility in addressing chemotherapeutic resistance in leukemia. Moreover, newly developed MEIS inhibitors exhibit efficacy in selectively blocking prostate cancer cells with high MEIS expression, triggering apoptosis. These findings highlight the multifaceted potential of MEIS inhibitors in addressing cardiomyocyte regeneration and managing MEIS-dependent or MEIS-positive malignancies. | Review | biomedical | en | 0.999997 |
PMC11698223 | Total hip arthroplasty (THA) is a highly common and successful procedure in the US with over 460,000 surgeries performed annually and expected to grow significantly by 2030 . Complication rates remain low with the most common reasons for failure being dislocation, fracture, and infection . With improvements in implant technology, failure rates from component failure have continued to improve over time. Improved polyethylene annealing and sterilization in inert gasses have led to improved wear characteristics and decreased component failure rates . Bearing surfaces have continued to evolve over the years, decreasing the component failure rates. The most common types of bearing surfaces applied in THA are metal-on-metal (MoM), metal-on-polyethylene (MoP), ceramic-on-ceramic (CoC), and ceramic-on-polyethylene (CoP). The most common bearing surfaces used in the US are MoP and CoP. MoP has shown higher wear rates in studies and is still susceptible to problems with trunnionosis, especially in large head sizes . First-generation ceramic heads were made from alumina, and due to their brittle nature had issues with ceramic head fracture rates of 13.4% . Zirconia and other metals have been added to ceramic components for their many benefits: increased hardness, scratch resistance, and lack of debris production . One major drawback to the use of CoC bearings is the ceramic commonly breaks without plastic deformation due to its high elastic modulus . Production processes have become more advanced, including the addition of zirconia to the BIOLOX delta heads, leading to increased durability and decreased fracture rates . Methods and tools used to implant the prosthesis have also changed slightly over time, leading to increased survivability rates and decreased revision rates . Despite the advancements in implant technology, ceramic head fractures still occur, although typically in CoC-bearing surfaces. Herein we present a case of atraumatic ceramic head fracture from right THA in a CoP patient. To our knowledge, this is the first atraumatic case presented in the literature of a newer-generation ceramic head fracture with a polyethylene-bearing surface. The patient is a 73-year-old female who underwent an uncomplicated, anterior approach cemented right total hip arthroplasty. Intraoperatively there was no trauma and we had good exposure of the femur and trunnion. Before implantation of the femoral head, the trunnion was cleaned in a typical fashion. We implanted a 36+0 standard femoral head and the hip was stable through all ranges of motion. She was doing well and was ambulating with physical therapy without pain or complication. Early on while working with PT, she felt a clunk and noticed some shortening of the leg. Radiographs were taken and showed a fracture of the femoral head . She still had minimal pain, and there was no fall or trauma. She was subsequently taken back and revised with the femoral head and liner. The liner was destroyed by the trunnion buried into the polyethylene liner. We slowly removed all of the fragments from the ceramic head and pieced them back together to ensure all pieces were removed . We also confirmed this with intraoperative fluoroscopy. Once this was done we removed the polyethylene liner and replaced this component with a new one. We replaced the head with a new 36+3 femoral head. During dissection to remove all of the ceramic debris further soft tissue release was required necessitating an increase in neck length for stability. The patient did well with physical therapy and was subsequently discharged to home. We followed her for three months postoperatively with no further complications and full range of motion without pain. The rest of her postoperative course was uneventful. The components were packaged and sent back to the manufacturer for analysis. The patient has a two-year follow-up with no pain and no complications. Informed consent was obtained from the patient for the publication of this case report and its images. Ceramic head fracture following THA is a rare, but catastrophic complication. Rates have decreased following the switch from alumina to BIOLOX delta ceramic heads . Our case represents the only case of an atraumatic fracture of a BIOLOX delta ceramic on a polyethylene hip in the literature. One major category of ceramic materials used in hip arthroplasty is alumina (Al 2 O 3 ). Pure alumina heads are associated with a relatively high incidence of clinical fracture of 0.0201% when compared to the 0.0010% found with alumina matrix composite . Although both material’s fracture rates are extremely low, the reliability is even better with alumina matrix heads. Squeaking is a commonly associated problem with hard-on-hard bearing hip replacements and can be caused by improper fluid film lubrication in the bearing. Causes of this include stripe wear, edge loading, introduction of unwanted particles, and ceramic fracture . The rubbing of non-lubricated parts may produce sounds audible to humans, however, this complication has not been directly associated with pain. Stripe wear has also been found to play a role in the durability of ceramic bearings. Stripe wear is caused by a microseparation of the femoral head leading to impingement during different parts of the gait cycle. It has also been found that stripe wear may occur due to a lack of lubrication during edge loading . As stripe wear begins to form, it becomes more likely for the problem to progress faster with time. The wettability of the surface has been found to have an impact on the durability of prosthetic materials. Materials with high wettability allow a fluid to remain in contact with a surface. Ceramic materials are often harder and smoother than their metal alloy counterparts, allowing for better lubrication . High wettability is associated with the promotion of material-tissue interaction, but may also be associated with increased corrosion, therefore a material with well-balanced wettability characteristics must be used . Although ceramic’s hardness is what makes it so good for use as a material in prosthetics, it is also one of its largest downfalls due to the likelihood of fracture with little plastic deformation due to its high elastic modulus . Increased material hardness is desired when making a prosthetic, especially for younger patients, due to its linkage to better corrosion resistance and overall toughness . Due to ceramic’s naturally high modulus of elasticity, it is naturally poor at dampening impacts made to the material and can lead to lubrication and durability issues in the future. BIOLOX delta ceramic heads were first introduced in 2003 and the delta-delta hip bearing was introduced in 2010. The ceramics used were combined with zirconia and several other materials to improve material hardness and further decrease the possibility of crack formation . This new ceramic was also produced as an attempt to minimize the production of minute commutia following a fracture by increasing bend strength . Recent registry data reveals a fracture risk for BIOLOX delta of 0.1% . This rate may be even lower for ceramic-on-polyethylene bearings, considering the data used was solely based on ceramic-on-ceramic bearings . With the low fracture rate of fourth-generation ceramic-on-ceramic bearings, other components are being analyzed for their contribution to revision rates. Ceramic surfaces also minimize or even negate the risk of trunnionosis, which has been found to account for up to 3% of all revisions . Wear patterns associated with ceramic-on-ceramic bearings have even been found to have improved performance during stop-dwell-start motions when compared to ceramic-on-polyethylene and metal-on-polyethylene bearings . The literature contains many examples of traumatic ceramic head fractures in THA but does not contain many non-traumatic examples. This case is even more rare since the fracture occurred days following the procedure. In discussion with the company post-implant analysis, it was determined that this was likely a machining defect. There was either a slight defect in the ceramic head or a slight mismatch in the Morse taper leading to catastrophic failure. Another option would be a slight defect in the femoral stem taper but we had no further issues with the new ceramic head so that was excluded. It is also possible that the head was placed malaligned during impaction but there were no complications during surgery and we had a good fit intraoperatively. No other common causes of non-traumatic head fracture seem to be validated due to the paucity of these cases in the literature. This case study highlights a rare instance of atraumatic ceramic head fracture in a CoP-bearing surface following THA. Despite advancements in ceramic technology, including the development of BIOLOX delta heads that are engineered for increased fracture resistance, this case demonstrates that ceramic fractures remain a possible complication. Although the fracture may have resulted from a machining defect or mismatch at the trunnion interface, the underlying cause remains speculative due to the low incidence of atraumatic ceramic fractures in THA. This case underscores the importance of ongoing advancements in implant manufacturing processes and material quality control to further reduce these rare complications. Overall, while THA continues to demonstrate excellent outcomes with low complication rates, this case emphasizes that vigilance and refinement in implant technology remain critical for optimizing patient outcomes. | Review | biomedical | en | 0.999997 |
PMC11698264 | This was a community-based cross-sectional study, conducted in the field practice area of Dhanalakshmi Srinivasan Medical College and Hospital (DSMCH), a tertiary care teaching hospital in the Perambalur district of Tamil Nadu, India, between March 2023 and February 2024. The study protocol was approved by the Institutional Ethics Committee of DSMCH (approval number: IECHS/IRCHS/No. 392). Written informed consent was obtained from all participants after explaining the study objectives and procedures in their preferred language. Participant confidentiality was maintained throughout the study, and data were stored securely following standard research protocols. The sample size was calculated based on a previous study by Choudhary and Ahmed in India, which reported that 55% of working women demonstrated high levels of perceived PWB . The minimum required samples in the study were calculated using Cochran's formula for calculating sample size: n = Z 2 × p × (1-p) / d 2 , where p = 0.55 (prevalence) and d = 0.05 (5% absolute precision) at 95% confidence level (Z 2 =3.84). The calculation yielded a minimum required sample size of 380 participants. The study population comprised adult women aged 18 years and above from both urban and rural areas within the field practice area of DSMCH. Participants were recruited using convenience sampling methodology. Table 1 shows the internal consistency reliability of the PWBS-18 was assessed using Cronbach's alpha (α = 0.682, standardized α = 0.709). While this reliability coefficient is slightly below the conventional threshold of 0.70, it is considered acceptable for a short-form version of an established scale . Therefore, all items were retained for analysis to maintain the scale's validated structure and enable comparability with existing literature. The data analysis was performed using IBM SPSS Statistics for Windows, Version 26.0 . The descriptive statistics for the sociodemographic variables were presented as frequency and percentage for categorical variables, and for continuous variables it was presented as mean and SD. The overall PWB scores and individual domain scores were presented as mean, standard deviation, median, and IQR for both homemakers and employed women. To compare the overall PWB scores between homemakers and employed women, an independent t-test was conducted. To compare the domain-specific PWB scores between homemakers and employed women, the Mann-Whitney U Test was conducted. For comparing the six domains of PWB between the groups, a one-way multivariate analysis of variance (MANOVA) was performed. The assumptions of MANOVA, including multivariate normality and homogeneity of variance-covariance matrices, were checked using Box's M test. MANOVA showed significant differences (p < 0.05), and subsequent post-hoc analyses using Bonferroni correction were conducted to identify which specific domains differ between the groups. Table 2 shows the sociodemographic characteristics of study participants based on occupational status. The mean age of homemakers (44.7 ± 14.06 years) was higher than employed women (37.12 ± 9.96 years). Among married women, 143 (56.7%) were homemakers and 109 (43.3%) were employed women. For education status, most illiterate women were homemakers (n=21, 77.8%), while the majority of graduates were employed (n=84, 75%). In terms of family type, joint families had more homemakers (n=88, 71.5%), while nuclear families showed a more balanced distribution with 84 (46.2%) homemakers and 98 (53.8%) employed women. Rural residence was more common among homemakers (n=146, 72.3%), while urban residence was predominant among employed women (n=80, 75.5%). Regarding socioeconomic status, higher classes (Class 1 and 2) had more employed women (n=31, 73.8% and n=49, 69%, respectively), while lower classes (Class 4 and 5) had more homemakers (n=75, 78.1% and n=25, 92.6%, respectively). Family size distribution showed that medium-sized families (four to six members) were most common in both groups, with 104 (52.5%) homemakers and 94 (47.5%) employed women. Additional characteristics showed an equal distribution of spouse addiction between groups (n=36, 50% each). Vital events occurred in 21 (43.8%) homemakers and 27 (56.3%) employed women. Chronic illness was present in 33 (54.1%) homemakers and 28 (45.9%) employed women, while debts were reported by 82 (59%) homemakers and 57 (41%) employed women. Table 3 shows the comparison of overall and domain-specific PWB scores between homemakers and employed women. The overall PWB score was slightly higher among homemakers (mean: 69.35 ± 6.595) compared to employed women (mean: 68.21 ± 6.046). Among the six domains, autonomy scores were higher in employed women (mean: 13.44 ± 2.121; median: 14) compared to homemakers (mean: 12.88 ± 2.716; median: 14). Environmental mastery showed higher scores in homemakers (mean: 9.53 ± 3.879; median: 9) than employed women (mean: 8.88 ± 3.703; median: 9). Personal growth and positive relations with other domains showed slightly higher scores among homemakers (mean: 11.05 ± 2.423 and 12.2 ± 3.051, respectively) compared to employed women (mean: 10.65 ± 3.042 and 11.99 ± 3.607, respectively). Purpose in life was similar between groups, with employed women showing a marginally higher score (mean: 13.4 ± 2.715; median: 14) compared to homemakers (mean: 13.24 ± 2.207; median: 13.5). Self-acceptance was higher among homemakers (mean: 10.44 ± 1.488; median: 10) compared to employed women (mean: 9.86 ± 1.005; median: 10). Table 4 shows the comparison of overall PWB scores between homemakers and employed women. Homemakers demonstrated a marginally higher mean score (69.35 ± 6.60) compared to employed women (68.21 ± 6.05). Levene's test confirmed the homogeneity of variances (F = 1.410, p = 0.236). The independent t-test revealed no statistically significant difference in overall PWB scores between the groups (p = 0.121). Figure 2 shows the comparison of the distribution of PWB domain scores between homemakers (n = 172, shown in blue) and employed women (n = 136, shown in red) using a Mann-Whitney U test. The autonomy domain demonstrated similar median scores between groups, though homemakers showed greater score variability. Environmental mastery exhibited a broader distribution pattern across both groups, indicating diverse levels of mastery over their environments. Personal growth scores revealed comparable patterns between the groups, with subtle differences in their distributions. The positive relations with other domains showed similar central tendencies between homemakers and employed women, suggesting comparable social relationship qualities. Purpose in life scores display relatively symmetrical distributions for both groups, indicating similar levels of life purpose perception. Finally, the self-acceptance domain showed more concentrated scores among employed women compared to a wider distribution among homemakers, suggesting more varied levels of self-acceptance in the latter group. The mean rank values are displayed for each group across all domains, providing quantitative measures of central tendency. Table 6 presents the results of MANOVA comparing six domains of PWB between homemakers and employed women. Prior to the main analysis, Box's M test was conducted to examine the assumption of homogeneity of variance-covariance matrices. Box's M test indicated that the assumption of homogeneity of variance-covariance matrices was violated (M = 69.174, F = 3.22, p < 0.001). Given the relatively large and balanced sample sizes, we proceeded with MANOVA using Pillai's Trace as the test statistic due to its robustness to assumption violations. MANOVA revealed a significant multivariate effect of employment status on PWB domains (Pillai's Trace = 0.071, F (6, 301) = 3.827, p = 0.001, partial η² = 0.071). This result indicates that there are significant differences in PWB profiles between employed women and homemakers, though the effect size suggests these differences are modest in magnitude. Subsequent univariate analyses were conducted to examine differences between homemakers and employed women across six domains of PWB. The results revealed significant differences in two domains: self-acceptance and autonomy. Self-acceptance demonstrated the most robust difference between groups (p = 0.001, partial η² = 0.048), with homemakers reporting higher levels compared to employed women. The effect size indicates that group membership accounted for 4.8% of the variance in self-acceptance scores. Autonomy showed a marginally significant difference (p = 0.050, partial η² = 0.012), with employed women scoring higher than homemakers. The remaining four domains did not exhibit statistically significant differences between groups. The effect sizes for these non-significant domains were notably small, ranging from 0.001 to 0.007, suggesting minimal practical differences between the groups in these aspects of PWB. Our analysis revealed that homemakers demonstrated slightly higher overall PWB scores (69.35 ± 6.595) compared to employed women (68.21 ± 6.046), though this difference was not statistically significant (p = 0.121). This finding aligns with research by Choudhary and Ahmad, who reported similar patterns in their study of women in North Bihar . The lack of significant differences in overall scores suggests that employment status alone may not be the primary determinant of women's PWB, supporting Sinha's assertion that multiple roles and their successful management are more crucial for psychological health than employment status per se . The multivariate analysis revealed significant differences in PWB profiles between the groups (Pillai's Trace = 0.071, p = 0.001), particularly in the self-acceptance and autonomy domains. The higher self-acceptance scores among homemakers (p = 0.001, partial η² = 0.048) suggest that these women may have developed a stronger sense of identity and satisfaction within their chosen role. This finding resonates with research by Chaudhry and Chhajer, who emphasized the importance of role satisfaction in PWB . The findings of this study have significant implications for developing targeted mental health interventions and support systems for women across different occupational statuses. The identification of distinct PWB profiles between homemakers and employed women suggests the need for tailored mental health promotion strategies . For instance, the higher autonomy scores among employed women indicate that workplace mental health programs should focus on maintaining and enhancing this strength while addressing potential challenges in self-acceptance. Conversely, community-based mental health initiatives for homemakers could focus on enhancing autonomy while building upon their stronger self-acceptance . The socioeconomic disparities observed in our study, particularly the concentration of employed women in higher socioeconomic classes, highlight the need for public health policies that address barriers to employment and mental health support for women in lower socioeconomic groups . Additionally, the similar scores in positive relations and purpose in life across both groups suggest that community-based support networks and social engagement programs could be equally effective for both homemakers and employed women, potentially serving as a cost-effective public health intervention strategy. Healthcare providers and public health practitioners should consider these findings when designing preventive mental health services and wellness programs, ensuring that interventions are culturally sensitive and address the specific psychological needs of both homemakers and employed women in different socioeconomic contexts . The study demonstrated several notable strengths in its approach and execution. The research utilized the validated Ryff's PWBS-18, providing a comprehensive assessment of both overall and domain-specific well-being. The methodology was strengthened by relatively balanced sample sizes between groups and the application of appropriate statistical analyses, including MANOVA and Mann-Whitney U tests. The study's robustness was further enhanced by using Pillai's Trace to address violated assumptions in statistical analysis. Additionally, the research incorporated a detailed examination of socio-demographic variables, including various family-related factors such as family type, size, and spouse addiction, as well as health and financial considerations like chronic illness and debts. This comprehensive approach provided a rich context for understanding the relationship between occupational status and PWB. Several limitations should be considered when interpreting the study's findings. The cross-sectional design inherently limits causal inference, and the focus on a single geographic location (Perambalur district) may affect the generalizability of results to other populations. The study's measurement approach faced some challenges, including relatively low internal consistency (Cronbach's α = 0.682) and the use of a shortened version of the PWBS, which might not capture the full complexity of the PWB construct. There were also analytical limitations, such as the violation of the homogeneity assumption in MANOVA and limited control for potential confounding variables. The absence of analysis regarding interaction effects between demographic variables represents another limitation of the study's scope. This study provides valuable insights into the complex relationship between women's occupational status and PWB in the Perambalur district of Tamil Nadu. While the overall PWB scores were comparable between both groups, the study revealed significant domain-specific differences. Homemakers demonstrated higher self-acceptance, suggesting a more stable self-concept and greater acceptance of their life choices, while employed women showed higher autonomy, reflecting increased independence and decision-making opportunities. The absence of significant differences in environmental mastery, personal growth, positive relations, and purpose in life domains indicates that both groups find distinct ways to fulfill these aspects of PWB. These findings challenge common assumptions about the relative advantages of employment versus staying at home and suggest that PWB is more nuanced than previously understood, influenced by factors such as family support, personal choice, and social circumstances. Future research should prioritize longitudinal studies across diverse geographical and socioeconomic contexts to better understand the temporal dynamics of PWB among women. Healthcare providers and policymakers should develop targeted mental health interventions addressing the specific needs of both homemakers and employed women while implementing community support programs that validate women's occupational choices. Establishing support groups and counseling services, along with educational initiatives focusing on coping strategies, would benefit both groups. Workplace policies should be designed to promote work-life balance, while community awareness programs should focus on destigmatizing and supporting women's choices regarding employment and domestic roles. | Study | biomedical | en | 0.999997 |
PMC11698272 | Over the past few years, numerous research studies have focused on enhancing treatment approaches and transitioning from conventional methods to contemporary techniques to minimize harm to healthy cells and specifically target cells during drug delivery processes. 1 Nanomaterials based on drug delivery systems could improve drug transport and provide opportunities for active-targeting of drug delivery and controlled release. 2 Nanotechnology can facilitate delivering the correct drug dose to the right place at the appropriate time inside the body to help the drug concentration and effectiveness during treatment. 3 Magnetic nanoparticles are known as a new class of carriers for drug delivery systems. Paramagnetic iron oxide nanoparticles (MNPs) are unique due to their characteristics, including nano size, magnetic properties, large surface area, simple preparation process, and low toxicity. 4,5 MNPs possess the capability to bind with different compounds, enabling customization for specific uses such as magnetic separation, tissue regeneration, MRI, hyperthermia treatment, catalysis, molecular diagnostics, and drug delivery systems. 1,6 have been extensively researched for their ability to deliver drugs to specific organs within the body when subjected to an external magnetic field. 3 In recent years, polymer-based magnetic nanoparticles, especially cyclodextrins, have gained much attraction for targeting drug delivery. 7 Recently, polymer-based magnetic nanoparticles, particularly those incorporating cyclodextrins, have garnered significant interest for targeted drug delivery. For example, a folic acid-conjugated glycodendrimer featuring a magnetic β-cyclodextrin core has been developed as a pH-responsive system for the targeted delivery of doxorubicin and curcumin, 7 Additionally, pH-responsive β-cyclodextrin-assembled Fe 3 O 4 nanoparticles have been utilized for the selective loading and targeted delivery of stereoisomeric doxorubicin and epirubicin. 8 Furthermore, Fe 3 O 4 @SiN nanoparticles, where Fe 3 O 4 serves as the core and SiO 2 as the shell, have been functionalized with β-cyclodextrin for thymol drug delivery. Another innovative approach involves the fabrication of β-CD-PDA-MNPs through the surface coating of Fe 3 O 4 nanoparticles with polydopamine, followed by functionalization with 6-thio-β-CD and diclofenac as a model drug. 9,10 Lastly, CD-GAMNPs were engineered for targeted anticancer drug delivery of retinoic acid by grafting cyclodextrin onto Arabic gum-modified magnetic nanoparticles (GAMNPs) using hexamethylene diisocyanate as a linker. These developments illustrate the versatility and potential of magnetic nanoparticles in enhancing drug delivery systems, particularly in targeting specific tissues while minimizing side effects. 11 These developments illustrate the versatility and potential of magnetic nanoparticles in enhancing drug delivery systems, particularly in targeting specific tissues while minimizing side effects. Cyclodextrins (CDs) are natural compounds that are made up of starch by the glycosyl transferase enzyme in bacterial degradation (CGTase). 12 The most common natural CDs are alpha-, beta-, and gamma-cyclodextrin, which are obtained from six, seven, and eight units of glucose, which are composed of α-(1,4) glycosidic bonds. 13 CDs have truncated cone shapes with hydrophobic internal cavities and hydrophilic outer surfaces. 14 CDs can accept lipophilic guests with the correct size in the cavity and form inclusion complexes. 15 CDs can improve solubility and stability, prevent volatility, mask flavors, and control the release of the guests. 15–18 CDs can also improve various pharmaceutical qualities, such as drug bioavailability, stability, solubility, and dissolution rate. 19–21 CDs are prominent tools for innovation in drug delivery systems. 3 Among cyclodextrins, β-CD is the most popular compound because of its suitable cavity size, reasonable price, non-toxicity, availability, and biodegradability. 22,23 To date, many studies have been conducted to introduce a magnetic composite with a facile route of preparation and substantial targeting drug delivery benefits. Among many composites, magnetic chitosan, cellulose, and hydrogel have gained more attention over the last decades. 24–26 However, magnetic cyclodextrin composites with their extraordinary structure may be considered as the better drug carrier due to their ability to form non-covalent interactions with a wide range of hydrophobic drugs, which results in the improved solubility, stability, and transport of lipophilic drugs. However, CDs provide a more controlled release, which is so advantageous when their magnetic composites have been guided to a local site to release a specific dosage of a bioactive substance at a particular time. 27,28 Amantadine ( Scheme 1 ) is a white, odorless, crystalline powder of the adamantane class of drugs. 29,30 In October 1966, amantadine was approved by the US Food and Drug Administration (FDA) specifically for the treatment of Asian influenza, and in 1976, it received approval to prevent influenza A. 31 However, the FDA coincidentally discovered that amantadine can be used to improve Parkinsonian symptoms. Also, it can be used as monotherapy or in combination with levodopa to control symptoms of Parkinson's disease. 30 It is also known to augment dopamine release and delay its reuptake. 32,33 The existing literature lacks reports on the formation of an inclusion complex between adamantine and magnetic carboxymethylated β-cyclodextrin. This study introduces a novel magnetic nanocarrier system aimed at enhancing targeted drug delivery. We achieved this by functionalizing the surface of Fe 3 O 4 nanoparticles with carboxymethyl-β-cyclodextrin (CM-β-CD), creating a nanocarrier that demonstrates “smart” characteristics, allowing for precise control over drug loading and release. Amantadine was selected as a model drug to assess these functionalities. To thoroughly characterize the magnetic nanocarrier's structure and composition, we utilized a comprehensive suite of analytical techniques. Transmission electron microscopy (TEM) provided insights into particle size and morphology, while Brunauer–Emmett–Teller (BET) analysis assessed surface area. Fourier-transform infrared spectroscopy (FT-IR) was employed for functional group identification, and thermogravimetric analysis (TGA) evaluated thermal stability. Magnetic properties were examined using vibrating sample magnetometry (VSM), dynamic light scattering (DLS) analyzed size distribution in solution, and X-ray diffraction (XRD) determined crystal structure. The drug release profile of amantadine from the nanocarrier was meticulously investigated using UV-visible spectrophotometry, followed by the application of mathematical models to elucidate release kinetics and mechanisms. Furthermore, we evaluated the cytotoxicity of the developed nanocarrier against normal human umbilical vein endothelial cells (HUVECs) using the MTT assay, ensuring its biocompatibility. This research not only fills a significant gap in the literature but also contributes to advancing the field of targeted drug delivery systems through innovative magnetic nanocarrier technology. XRD patterns were recorded using an X-ray diffractometer (STOE, Germany) with Cu Kα radiation at a voltage of 45 kV and a current of 40 mA, scanning the diffraction angle (2 θ ) from 6° to 90° at a rate of 2° min −1 . The morphology of the samples was assessed using transmission electron microscopy (EM 208S) at an accelerating voltage of 100 kV. FT-IR spectra were obtained with an ATR PerkinElmer Spectrum IR Version 10.6.2 over the range of 400–4000 cm −1 . Specific surface areas and pore size distributions were measured using an ASAP 2010 analyzer (Micromeritics, USA). Thermal analysis was conducted with a thermal gravimetric analyzer (TG 209 F3) at a heating rate of 10 °C min −1 from 30 °C to 600 °C. The size distribution of nanoparticles was determined using a dynamic light scattering (DLS) instrument (Microtrac, USA). Magnetization measurements were performed with a vibrating sample magnetometer at room temperature, while UV-VIS analysis was carried out on a UV-Vis spectrophotometer in the range of 200 to 500 nm. An aqueous solution (50 mL) of ferric chloride (10 mmol) and ferrous chloride (4.26 mmol) was refluxed for 30 min under nitrogen at 85 °C. After 15 min from the zero-time, 7.5 mL of ammonia solution (27%) was poured dropwise for 3 minutes. The color was changed to black immediately. Eventually, an external magnet was applied to collect the precipitates and washed 3 times by deionized water and ethanol. Finally, the precipitates were dried at room temperature. 34 The carboxymethylated β-cyclodextrin was prepared according to a method that was reported previously. 35 β-CD (2.0 g, 1.76 mmol) and NaOH (1.87 g, 46.5 mmol) were dissolved in 7.4 mL of water at room temperature. Then, 5.4 mL of monochloroacetic acid solution (16.2%) was added to the mixture and stirred at 50 °C for 5 h. After reaching ambient temperature, the pH was adjusted (6–7) by adding HCl. Then, an excess amount of methanol was poured into a neutralized solution at room temperature to precipitate the yield. The white residues were filtered and placed under a hood to dry for 24 hours. The magnetic drug carrier was synthesized according to a reported method. 4 A 100 mg of Fe 3 O 4 was suspended in phosphate buffer (2.0 mL, 0.003 M phosphate, 0.1 M NaCl, pH 6) and put in an ultrasonic bath for 10 min at room temperature. Then, carbodiimide solution (0.5 mL, 0.025 g mL −1 in phosphate buffer, pH = 6) was added and sonicated for 15 min at room temperature. Next, CM-β-CD solution (2.5 mL, 150 mg in 3 mL phosphate buffer, pH = 6) was added and sonicated for an extra 90 min at RT. Finally, the resulting magnetic nanoparticles were separated by an external magnet, rinsed five times with phosphate buffer, and dried in a vacuum oven for 24 hours. The Mag/CM-β-CD/Amn complex was synthesized using a co-precipitation. 36 A 10 mL methanolic solution of amantadine (0.1 g, 0.66 mmol) was stirred for 15 min at RT. After reaching equilibrium, 100 mg of Mag/CM-β-CD was poured and shaken for 24 h at RT. Then, the mixture was sonicated for 30 min and refrigerated for 12 h. The precipitated magnetic complex was separated using an external magnet, and after washing with methanol, it was placed in a vacuum oven at 40 °C for 24 h to dry. 100 mg of Mag/CM-β-CD was dispersed in an amantadine solution (30 mg in 3 mL methanol), shaken for 24 h, and then placed in an ultrasonic bath for 30 min at RT. Next, the Mag/CM-β-CD/Amn were separated using an external magnet. The encapsulation efficiency (EE%) and loading content (LC%) of Mag/CM-β-CD/Amn were determined by analyzing the unloaded concentration of amantadine using UV-Vis spectroscopy at 286 nm and the following equations: 37 1 2 The releasing process of amantadine was performed according to a previous study. 38 For this purpose, a dialysis bag consisting of Mag/CM-β-CD/Amn (50 mg/5 mL PBS) was placed in the containers consisting of 200 mL PBS at different pH (5, 7.4, 8) stirred at 37 °C at 300 rpm. The solution absorbance was read at various times (1, 2, 4, 8, 12, 24, 48, and 72 h). For this purpose, 3 mL of the prepared mixture was withdrawn and replaced by the same volume of fresh PBS after measuring the absorbance at 267 nm. The experiments were performed three times. Mathematical studies were adopted to investigate the amantadine release mechanism from Mag/CM-β-CD/Amn. For this purpose, 60% of experimental data obtained from the drug release step was fitted with kinetic models (zero-order eqn (3) , first-order eqn (4) , Higuchi eqn (5) , and Korsmeyer–Peppas eqn (6) ), and their curves are plotted. 3 Q = K 0 t 4 Ln Q = Ln Q 0 − K 1 t 5 Q = K H t 1/2 6 Ln Q = n Ln t + Ln K P where Q is the percentage of released amantadine at time t ; n is the release exponent; K 0 , K 1 , K H , and K P are the rate constant of eqn (3) , (4) , (5) , and (6) , respectively. The cytotoxicity of Mag/CM-β-CD and Mag/CM-β-CD/Amn against HUVECs cell lines (human umbilical vein endothelial cells) was examined using the MTT assay. First, the cell lines were implanted in a 96-well plate and incubated under 5% CO 2 at 37 °C for 24 hours. After cells reached 80% confluence, Mag/CM-β-CD and Mag/CM-β-CD/Amn were added to the wells and incubated in the dark humidified environment along with DMSO (0.05% v/v) at 37 °C for 24 hours. Later, the cell layers were washed with PBS and incubated with 50 μL of the MTT solution (2 mg mL −1 ) for another 4 hours under the same conditions. Therefore, the cell cultures were centrifuged to remove extra supernatants. Finally, the blue-violet MTT crystals were dissolved by adding 200 μL of DMSO and 25 μL of Sorenson, respectively. The absorbance of each cell culture was measured spectroscopically at 570 nm. The absorbance of the control group was considered 100%. The treatments were assayed three times in independent experiments. The cell viability percentage was obtained from eqn (7) : 7 The capability of CDs in the formation of inclusion complexes is one of the most important aspects that makes the compounds desirable in the supramolecular chemistry field. CDs with different cavity sizes can form typical complexes, namely host–guest complexes, with various materials, including organic and inorganic compounds. 39 In this type of complex, the binding of the guest to the host molecules is not a covalent bond. 40 In addition to the importance of size, the binding strength of the complex depends on the interactions between the surface atoms of host and guest molecules. 41 The formation of Mag/CM-β-CD/Amn is presented in Scheme 1 . According to the scheme, the preparation of the Mag/CM-β-CD/Amn consists of three steps including (1) the formation of CM-β-CD by reaction of β-CD with NaOH and then by monochloroacetic acid; (2) the establishment of the linkage of CM-β-CD to Fe 3 O 4 , in this step, carbodiimide was used to activate the OH group of the carboxymethyl group of cyclodextrin to become a better-leaving group while the OH group of the surface of magnetite particles attacks for the formation of Mag/CM-β-CD; (3) the loading of Amn in the carrier using the co-precipitation method. Fig. 1a presents the FT-IR spectra of β-CD, CM-β-CD, Fe 3 O 4, and Mag/CM-β-CD. For β-CD, two bands at 1016 and 1154 cm −1 were assigned to stretching vibrational bands of C–O. The prominent band at 3323 cm −1 was related to the hydroxyl bond (O–H). Furthermore, the band at 2926 cm −1 belonged to the aliphatic C–H bond. For the prepared CM-β-CD, a new peak at 1597 cm −1 related to the C <svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="13.200000pt" height="16.000000pt" viewBox="0 0 13.200000 16.000000" preserveAspectRatio="xMidYMid meet"><metadata> Created by potrace 1.16, written by Peter Selinger 2001-2019 </metadata><g transform="translate scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg> O bond of the carboxymethyl group, along with all peaks of pristine β-CD, approved the successful formation of CM-β-CD. The band shift to a low wavenumber could be attributed to the carboxylate anion form of the carboxyl functional group due to the use of sodium hydroxide to prepare CM-β-CD. 42 The spectrum of Fe 3 O 4 showed a band at 558 cm −1 assigned for Fe–O bond, the other one at 3400 cm −1 belonged to the O–H bond, which was found at the surface of the synthesized Fe 3 O 4 and indicated the successful synthesis of Fe 3 O 4 . 26 All the mentioned bands for CM-β-CD and Fe 3 O 4 were found in the Mag/CM-β-CD spectrum with little shifts. Besides, the peaks at 1623 cm −1 and 1574 were related to the bounded and unbounded C O of the carboxyl group. The presence of both CM-β-CD and Fe 3 O 4 characteristic peaks in Mag/CM-β-CD and a small shift in the C O bond from 1597 cm −1 to 1623 cm −1 confirmed the successful synthesis of Mag/CM-β-CD. The spectra of amantadine and Mag/CM-β-CD/Amn are exhibited in Fig. 1b . The spectrum of amantadine showed two peaks at 3358 and 1522 cm −1 for the stretching and bending vibrations of the N–H bond, respectively. The peak at 1667 cm −1 , belonged to the stretching vibrational band of C–N. Also, the bending vibrational band of CH 2 groups is found at 1428 cm −1 . The band at 2800 cm −1 was related to the stretching vibrational band of CH. The obtained FT-IR spectrum of Amn was similar to a previous report. 43 The FT-IR spectrum of Mag/CM-β-CD/Amn showed different peaks at 2926 (CH), 1667 (CN), 1522 (NH), and 558 (FeO) cm −1 , and the broad peak at 3300–3500 cm −1 belonged to OH and NH bonds. The presence of both amantadine and Mag/CM-β-CD characteristic peaks in the FT-IR spectrum of Mag/CM-β-CD/Amn with small changes in shape and intensity approved successful loading of amantadine in the prepared Mag/CM-β-CD carrier. The crystallinity of Fe 3 O 4, Mag/CM-β-CD, and Mag/CM-β-CD/Amn was investigated using the X-ray diffraction technique. The results are exhibited in Fig. 1 . The XRD pattern of pure Fe 3 O 4 shows six characteristic signals at 2 θ values of 30.12, 35.60, 43.52, 53.86, 56.95, and 62.80, which belong to the planes of (220), (311), (400), (422), (511), and (440), respectively. These data show that the synthesized Fe 3 O 4 has a cubic structure according to the JCPDS card standard and is in good agreement with magnetic particles (Fe 3 O 4 ) that were reported previously. 44,45 As can be seen in the diffractogram, the synthesized Fe 3 O 4 has a crystalline nature. The X-ray pattern of Mag/CM-β-CD depicts three diffraction signals at the values of 30.47°, 35.49°, and 43.55°, which are similar to those of pure Fe 3 O 4 with some modification. This observation confirms the linkage of Fe 3 O 4 to CM-β-CD; however, the peak intensity has been reduced. From the Mag/CM-β-CD/Amn XRD pattern , the main characteristic peak of Fe 3 O 4 is still visible in the resultant magnetic nanoparticles, with shifting to 36.01° at an angle of 2 θ . Besides, the crystalline nature of yield is completely different compared to the starting materials. The XRD pattern of Fe 3 O 4 was completely changed after modification with CM-β-CD and loading of amantadine. These changes in peaks' presence, shape, and intensity exhibit the successful preparation of Mag/CM-β-CD/Amn in a new solid crystalline phase. 46 Brunauer–Emmett–Teller (BET) surface ( S BET ) area analysis is a sufficient technique to study the size and pores of carriers for drug delivery systems. These characters show a substantial impact on the drug encapsulation process. 47,48 In the present study, BET analysis was run along with the FT-IR technique to acquire more evidence about the successful loading of amantadine molecules in the Mag/CM-β-CD mesh. The porous features of Mag/CM-β-CD/Amn and Mag/CM-β-CD are represented in Fig. 2 . The S BET values and pore volume of Mag/CM-β-CD (59 m 2 g −1 , 0.19 cm 3 g −1 ) and Mag/CM-β-CD/Amn (108 m 2 g −1 , and 0.27 cm 3 g −1 ) were measured. An increase in values was observed after loading amantadine molecules in Mag/CM-β-CD. This phenomenon may be correlated to the formation of new pores after inserting amantadine in the magnetic carriers, resulting in more steric hindrance. 49 The mean pore diameter of Mag/CM-β-CD/Amn is relatively smaller than the pore diameter of pure Mag/CM-β-CD by 9.896 and 13.24 nm, respectively. This observation indicates that the entrapment of drug molecules in the Mag/CM-β-CD pores happened successfully. Besides, N 2 adsorption/desorption plots of Mag/CM-β-CD and Mag/CM-β-CD/Amn show IV isotherm, indicating the mesoporous structure. 7 It seems the formation of Mag/CM-β-CD was applied as a complex magnetic network. So, the entrance of Amn molecules into the network causes the increase of the gap between the network units and leads to an increase in the S BET and total pore volume of the product. The BET analysis of Mag/CM-β-CD has been compared to the other magnetic carriers, which were reported previously (see Table 1 ). Among the carriers, a magnetic metal–organic framework conjugated with β-CD (Fe 3 O 4 -SiO 2 -MIL-100 (Fe)-β-CD) as a drug delivery system. 3 A polymer of β-CDs (β-CD-P) was synthesized using pyromellitic dianhydride to link the surface of Fe 3 O 4 nanoparticles (Fe 3 O 4 @β-CD-P NPs). Additionally, Fe 3 O 4 -β-CD and Fe 3 O 4 -β-CD-CTD-FA were developed as magnetic nanocarriers, where citric acid and triazine dendrimer were employed to attach folic acid to the surface of Fe 3 O 4 -β-CD. In comparison, Mag/CM-β-CD demonstrated a significantly higher BET surface area and pore volume than the other carriers. The increased porosity enhances drug encapsulation capabilities, which is crucial for improving the efficiency of drug delivery systems. Furthermore, this property indicates that Mag/CM-β-CD could serve as an effective adsorbent in other applications, such as water and air purification. The magnetization properties of Fe 3 O 4 , Mag/CM-β-CD, and Mag/CM-β-CD/Amn were analyzed using a vibration sample magnetometer (VSM). The results are shown in Fig. 3 . The amounts of 48, 40, and 38 were found for saturation magnetization ( M S ) values of Fe 3 O 4 , Mag/CM-β-CD, and Mag/CM-β-CD/Amn, respectively. The decrease in ( M S ) can be related to the stepwise introduction of dielectric particles to the Fe 3 O 4 surface. Also, the absence of a hysteresis loop ( B r ) and zero coercivity ( H c ) signify that the prepared Mag/CM-β-CD/Amn has a superparamagnetic nature, which is caused by nano-size particles. 3 By coating Fe 3 O 4 with CM-β-CD shells, the value of B r ( y -intercept where x = 0) was decreased. This phenomenon results from reducing the interaction between Fe 3 O 4 nanoparticles. This is a beneficial feature for Mag/CM-β-CD in biomedical applications by dispersing it in water for hours before precipitation. 3 So far, a few researches have been carried out on the formation and usage of magnetic carriers in drug delivery systems. A comparison between the obtained results of the present study and the reported ones is given in Table 2 , The M S value of Mag/CM-β-CD showed better magnetic properties in comparison with magnetic cyclodextrin/maltodextrin nanosponge (magnetic NSs), mefenamic acid-loaded magnetized seed hydrogel (Fe/POSH/MFA), 26 amine-functionalized Fe 3 O 4 /microporous silica nanoparticles (Fe 3 O 4 /MSN-NH 2 ), 52 poly- N -5-acrylamidoisophthalicacid grafted onto Fe 3 O 4 magnetic nanoparticles (MNPs@PAIP-CD), and β-CD surface grafted to Fe 3 O 4 @silica@MOF nanocomposite (Fe 3 O 4 @SiO 2 @MIL-100 (Fe)/β-CD). Therefore, the prepared Mag/CM-β-CD can be considered an appropriate drug delivery agent as it can be separated easily by an external magnet. 53 Fig. 4a exhibits the TEM images and particle size distribution (PSD) of Mag/CM-β-CD and Mag/CM-β-CD/Amn. The results reveal a spherical shape for the magnetic nanoparticles with a mean diameter of 8.34 and 8.49 nm for Mag/CM-β-CD and Mag/CM-β-CD/Amn, respectively. Moreover, they exhibit a narrow size distribution ranging from 6.8 to 11.1 nm for the carrier and 5.6 to 11.3 nm for the final product. The CM-β-CD layer cannot be seen in TEM images. 4 Also, it is proved that the magnetic nanoparticles with zero coercivity and remanence that have a size smaller than 30 nm show superparamagnetic properties. 35 Therefore, the prepared Mag/CM-β-CD/Amn shows this remarkable feature, which is also approved by VSM results. Nanoparticles with spherical shapes are the best candidates for participation in drug delivery systems, as they provide a larger surface area. 7 The morphology and size of the final product indicate that Mag/CM-β-CD have significant capabilities for utilization as drug carriers. To evaluate the hydrophobic diameter of Mag/CM-β-CD and Mag/CM-β-CD/Amn, dynamic light scattering (DLS) analysis was run . According to previous reports, a high aggregation of products is an undesirable property in drug delivery systems. However, Mag/CM-β-CD shows appropriate dispersion with an average particle size of 40.40 nm and a polydispersity index (PDI) of 0.0931. Moreover, Mag/CM-β-CD/Amn shows approximately similar particle size distribution with an average of 41.70 nm. Because of the aggregation of Fe 3 O 4 in water, the diameter of the Mag/CM-β-CD and Mag/CM-β-CD/Amn is quite different from that proven by TEM analysis. 7 Thermal analysis is an efficient method to investigate thermal stability and further evidence of the formation of the inclusion complex. 54,55 The thermal analysis of amantadine and the Mag/CM-β-CD/Amn are shown in Fig. 5a–c . As shown in Fig. 5a , an initial weight loss occurred at 96 °C due to moisture loss for amantadine. Then, the decomposition is observed at 206–391 °C, where 77.6% of weight is lost. In contrast, the weight of Mag/CM-β-CD/Amn stood quite constant by increasing in temperature, which reveals the high thermal stability of the prepared complex. Meanwhile, the TGA thermogram of Mag/CM-β-CD/Amn reveals the first weight loss (4.41%) at 100–150 °C due to the loss of physically adsorbed water. 28 The second weight loss (8.57%), which occurs at 150–420 °C, is related to the decomposition of organic residue on the nanoparticles. Mag/CM-β-CD/Amn indicated good stability in the range of 420–600 °C. DTG diagram is represented in Fig. 5b . Amantadine shows two minor reactions against temperatures at 102.7 and 231 °C and a maximum reaction at 312.42 °C. DTG profile of Mag/CM-β-CD/Amn displays no detectable decomposition. Also, the DTA graph is exhibited in Fig. 5c , where endothermic peaks of amantadine are placed at 143.5, 192.4, 505, and 556.46 °C, and exothermic peaks are located at 238, 288, and 366 °C. However, Mag/CM-β-CD/Amn shows two minor endothermic peaks at 242 and 391 °C, utterly different from the observed peaks for amantadine. DTG and DTA profiles reveal that the prepared inclusion complex is formed successfully, and there is no evidence of uncomplexed drug molecules in the final product. 56 To identify the capacity of Mag/CM-β-CD as a magnetic drug carrier in delivery systems, the co-loading behavior of amantadine on the surface of Mag/CM-β-CD was investigated. 7 For this case, a pre-determined amount of amantadine was loaded on the Mag/CM-β-CD. The concentration of the remaining drug was obtained by spectroscopic method at 286 nm and compared with the calibration curve of amantadine (10–100 mM). The loading percentage and EE% were found to be 26.9% and 81.51%, respectively. The Mag/CM-β-CD carrier shows a remarkable loading capacity. As the interaction of the host and guest molecules is a non-covalent such as van der Waals forces, hydrophobic interaction, hydrogen binding, and London dispersion forces, 41,57 different factors affect the high drug loading: (I) strong hydrogen bonding between the amino (–NH 2 ) group of amantadine and hydroxyl (–OH) or carboxyl (–COOH) groups of Mag/CM-β-CD. In addition, the p K a of amantadine is 10.48, so it has a positive charge in the PBS media (pH = 7.4). 58 During encapsulation, the –NH 2 group of amantadine protonated with a positive charge. On the other hand, the –OH and –COOH groups of Mag/CM-β-CD become deprotonated with many negative charges on the surface, which leads to the formation of an electrostatic interaction between amantadine and Mag/CM-β-CD. 7 (II) The high surface area and total pore volume of Mag/CM-β-CD cause the trapping of amantadine molecules in the pores and spaces of the carrier. (III) The geometrical dimension of the host molecule affects the efficacy of the co-loading process. 59 As can be seen in Scheme 1 , the specific three-dimensional structure of amantadine fit very well with the cavity of CM-β-CD. Accordingly, Mag/CM-β-CD shows high drug encapsulation efficiency. The in vitro amantadine release from Mag/CM-β-CD was conducted in different pH values simulated for tumor region pH (5), physiological pH (7.4), and pancreas pH (8) at 37 °C to find the cumulative release of the drug in different parts of the human body and examine the pH responsiveness of prepared magnetic nanocarrier. 60 Fig. 6a illustrates the obtained results for the drug release. The profiles show that nearly 20% of amantadine was released in the first hour in all pHs. This could be related to the desorption of amantadine molecules that are entrapped on the surface of the carrier. 61 While 96.6%, 90%, and 52% of the drug are released from Mag/CM-β-CD after 72 hours at pH = 5, followed by pH = 8 and pH = 7.4, which are related to amantadine molecules bound to cyclodextrins. 62 The best result was found for the acidic media because the acidic pH could weaken the π–π interaction between the drug and carrier. 7 The second result for drug release was obtained for pH = 8 with a value of 90%. This phenomenon may be explained by the specific physical property of β-cyclodextrins containing hydrophobic inner cavities. As the pH moves to 8, the concentration of hydroxide ions has risen in the solution. Consequently, –OH groups were replaced with amantadine molecules in the inner cavity of cyclodextrin due to its higher hydrophobic properties and preferred interaction between two hydrophobic molecules. 40 Besides, basic hydrolysis can affect the release of drug molecules from CDs in the basic pHs. On the other hand, the degree of protonation depends on the pH value. As the pH moves to acidic or basic values, the degree of protonation increases, which could weaken the hydrogen bondings between the drug and carrier. In this way, the release profile of amantadine is boosted in non-neutral pHs. At acidic pH, the –NH 2 group of amantadine converts to (NH 3 + ), so it cannot form hydrogen bonding. Besides, the –COOH and –OH groups of carriers deprotonate to (COO − ) and (O − ) cannot participate in the formation of hydrogen bonds. 63 From this point, the charge of both drug and carrier molecules stood neutral at pH = 7.4. It is assumed that the sustainable release of amantadine from Mag/CM-β-CD (52% after 72 hours) at this pH could result from this phenomenon. Because of the acidic environment of tumors, lysosomes, and endosomes, Mag/CM-β-CD, a pH-responsive drug carrier, is a promising candidate to transport cancer drugs to specific regions and release them with high efficiency. 62 To evaluate the kinetic amantadine release, the results of the last section are fitted to standard mathematical equations, which were introduced previously. 64 Table 3 and Fig. 6b represent the calculated kinetic parameters and curves. The comparison of obtained mathematical data of all curves indicates that the Korsmeyer–Peppas model shows proper fitting to experimental results by higher R 2 values in all pHs. In addition, experimental data for release up to 60% was fitted to the Korsmeyer–Peppas equation. The Ln Q is plotted as a function of Ln t to determine the n (exponent of release) values. The n values signify the mechanism of drug release, where n ≤ 0.43 the release follows the Fickian model; for 0.43 < n < 0.85 follows non-Fickian and n > 0.85 indicates case II models. 65 As can be seen in Table 3 , all the obtained n values are less than 0.43 at all examined pHs, which corresponds to the Fickian mechanism. The Fickian mechanism is a fundamental concept in drug release that describes the transport of drug molecules through a polymeric medium based on concentration gradients. 66 The Fickian model describes drug release from carrier systems based on diffusion. In other words, diffusion is the primary mechanism governing drug release in this model. In Fickian diffusion, the rate of drug release is independent of the drug concentration within the carrier. This means that even as the drug concentration decreases in the polymer, the release rate remains constant. 67 To evaluate the biocompatibility of the prepared drug carrier and its inclusion complex, the cytotoxicity of Mag/CM-β-CD and Mag/CM-β-CD/Amn were assessed against the normal cells of HUVEC at different concentrations . HUVECs are utilized to investigate the toxicity of various metallic nanoparticles. 68 As shown in Fig. 7a , Mag/CM-β-CD shows no obvious toxicity, even at high concentrations. This observation can be related to the presence of β-CD in the structure of the prepared drug carrier, which is a non-toxic ingredient. 69 Therefore, Mag/CM-β-CD can be considered a safe and biocompatible carrier in drug delivery systems. Besides, after loading the Amn drug on the Mag/CM-β-CD, the cell viability of the inclusion complex increases to 57.13%. The images of cells treated by Mag/CM-β-CD and Mag/CM-β-CD/Amn are shown in Fig. 7b and c . This observation may be related to the fact that Fe 3 O 4 has unoccupied orbitals that cause damage to the normal cells. After loading amantadine, these orbitals become occupied by amantadine's free electrons, and the cytotoxicity of the product decreases. The same result was reported in a previous paper. 70 Moreover, the prepared Mag/CM-β-CD/Amn shows higher cell viability compared to the other drug-loaded magnetic carriers, such as the magnetic composite hydrogel that was fabricated by the graft copolymerization of itaconic acid (IA) onto starch and alginic acid in the presence of graphene sheets and Fe 3 O 4 nanoparticles for guaifenesin delivery (85% cell viability at 3 μg mL −1 ), 71 and doxorubicin-loaded core–shell mesoporous silica folic acid-grafted nanocomposite for intracellular enzyme-triggered drug delivery (72% cell viability at 125 μg mL −1 ). 72 Magnetic Fe 3 O 4 coated by layered double hydroxide as a methotrexate delivery system for targeted cancer therapy (≈80% cell viability at 120 μg mL −1 ). 73 The low toxicity of Mag/CM-β-CD/Amn, even at the concentration of 1000 μg mL −1 , indicates that the prepared inclusion complex is a safe drug to replace instead of amantadine. To gain more insights into the advantages of the prepared drug delivery systems for amantadine, which were explored comprehensively in this study, we have compared some of the characteristics of the prepared Mag/CM-β-CD with others. In this way, poly( dl -lactic acid)–methacrylic acid nanospheres bound to the chelating ligand diethylenetriaminepentaacetic acid (DTPA) that was synthesized for targeted delivery of amantadine show substantially lower drug encapsulation by 35.84% compared to Mag/CM-β-CD by 81.51%. The low amount of drug encapsulation efficiency leads to the utilization of a higher amount of prepared drug delivery systems to obtain the recommended dosage of amantadine for appreciable affection. Moreover, the particle size of drug carriers is so critical in these types of materials, and these nanospheres were synthesized with a particle size of 81.94 nm, which is dramatically bigger than that of Mag/CM-β-CD. In addition, the cumulative release of amantadine from these nanospheres records just below 10% after 72 h. Even though this carrier shows a more prolonged drug release compared to the Mag/CM-β-CD, it should be considered that this carrier system can't provide a sufficient dosage of the amantadine in a certain region of the body even after 72 hours. 74 Moreover, the amantadine-based ion-pair amphiphiles were synthesized using oleic acid surfactant through a proton transfer reaction between amantadine and oleic acid molecules. These vesicles exhibited a broad size distribution, aggregating at 200–300 nm in aqueous solution, which is significantly larger than the 43 nm size of Mag/CM-β-CD/Amn. This aggregation is an undesirable property in drug delivery systems. Furthermore, the drug loading in the vesicles was found to be 34.87%, which is lower than the encapsulation efficiency (81.51%) achieved by Mag/CM-β-CD. Finally, the release rate of ion-pair amphiphile vesicles was much faster compared to Mag/CM-β-CD, with approximately 65% of amantadine being released in PBS within just 2 hours. After this initial burst, there was no significant further release, and the maximum drug release was around 70% after 6 hours. That has no remarkable difference from the free drug. 75 In this study, we successfully synthesized magnetic nanoparticles (Fe 3 O 4 ) conjugated with CM-β-CD for targeted drug delivery applications, specifically focusing on the encapsulation and release of amantadine. The findings demonstrate that the MNPs exhibit a high encapsulation efficiency of 81.51%, attributed to the high surface area of the carrier and the strong interactions between amantadine and the functional groups on the CM-β-CD surface. The pH-sensitive release profile observed in vitro indicates that the MNPs can effectively release the drug in acidic environments, which is particularly relevant for targeting tumor tissues where the pH is often lower than in healthy tissues. The characterization techniques employed, including FT-IR, XRD, TGA, BET, and DLS, confirm the successful formation of the inclusion complex and highlight the favorable physicochemical properties of the synthesized MNPs. The thermal stability of amantadine increased dramatically and showed high particle magnetization. Furthermore, Mag/CM-β-CD/Amn has spherical geometry with a narrow particle size distribution in the nano-sized territory. The superparamagnetic nature of the nanoparticles allows for the potential application of an external magnetic field to enhance targeted delivery, minimizing systemic side effects and improving therapeutic outcomes. Looking forward, the approach of synthesizing magnetic CM-β-CD offers significant opportunities for further development in the field of drug delivery systems. Additionally, the biocompatibility and long-term stability of these carriers in biological systems warrant further investigation to assess their practical implementation in clinical settings. The potential for these designed carriers in medicine is promising, particularly in the treatment of diseases requiring targeted therapy, such as cancer and neurodegenerative disorders. By leveraging the unique properties of magnetic nanoparticles and cyclodextrins, we envision a new generation of drug delivery systems that can provide precise and controlled release of therapeutics, ultimately improving patient outcomes and advancing the field of personalized medicine. | Review | biomedical | en | 0.999996 |
PMC11698299 | The goal for educators in Science, Technology, Engineering and Mathematics (STEM) disciplines is to foster understanding of difficult concepts that are inherent in these disciplines. Some common STEM courses in the undergraduate level are pre-requisites for upper-level courses in the major and for professional schools. As a result, students from diverse backgrounds and majors are enrolled in these courses, particularly physics, general chemistry, general biochemistry, with a high demand for students to comprehend enough material to be proficient at some arbitrary level . Continual student engagement in highly dense informational STEM courses is challenging in the traditional lecture format . To address this, educators have flipped their classrooms in which the lectures are provided online before class and class time is spent on the demonstration of application problems, case studies, question/answer periods, and other activities that engage the students . The addition of added activities in the classroom other than just a lecture promotes student centeredness and increases conceptualization of STEM principles . Flipping the classroom is not a new technique , but has become a more popular pedagogy in the past 15 years. In fact, there are instructional guides for implementing coursework in a flipped classroom format . The outcomes of flipping biochemistry courses at the university and medical school level have resulted in improved student satisfaction and engagement in the course , motivate and promote better learning , and even increase student understanding of the primary research literature . Variants of flipping have also included individual versus cooperative flipped formats , integration with team-based learning , and massive open online courses (MOOCs) . Anecdotal statements about traditional lecture indicate that many students are accustomed to this methodology for teaching. However, in our experience there are a few persistent problems associated with the traditional lecture. First, in courses with a high amount of information given in each class period, it is easy for students to be overwhelmed . Overwhelmed students will either “zone out” or get mentally “lost” and rarely re-engage the class. Poorly engaged students lack the learning required to achieve the course objectives . This may lead to a lost opportunity to learn the material. Second, the classroom time is 75 min long, which is beyond the attention span of many individuals concerning the amount and level of material that is required to learn. For some subjects, the twice per week class that is about 1.5 h long per session/class time is optimal , but may be too long for some STEM subjects . Third, students come from a variety of disciplines and may not be as well prepared as their peers, even though they all take the same prerequisite courses such as general chemistry, algebra, calculus, and physics. Due to this variability, learning instruments have been developed to address this problem. For example, the Organic and Biochemistry Readiness Instrument (ORBI) reinforces critical knowledge gaps in inorganic chemistry to prepare students for organic and biochemistry courses . Unfortunately, ORBI nor any similar instrument was not applied here. This course (Biochemistry I) is a requirement for many health professions (medical, dental, pharmacology, etc.) and biochemistry majors. Therefore, students majoring in humanities, for example, are still held to the same academic performance bar as biochemistry majors. To address our observed classroom problems (poor engagement, long class, variable readiness), we asked the following question, “Does changing format and delivery of the material rather than the content increase student comprehension or performance in the course objectives of Biochemistry? This report on the results of our course should reinforce the notion that flipping a biochemistry course facilitates student learning through higher classroom engagement and repeated exposure to fundamental concepts. During the last week of classes, the students were given an anonymous survey to assess their experience with both the flipped and traditional formats of the same course. Of the 105 students that were enrolled in the course, 57 students responded. The motivation for an anonymous survey was to include an honest write-in answer to the question “What advice would you give the instructor to make this class better?” at the end of the survey. There was no extra credit or grade for this survey and students filled it out on their own motivation to improve the course. Anonymity promoted critically honest answers and their written responses are found in Fig. S1 . A majority of the students enrolled in the course found it difficult to very difficult with 28% responding that it was moderately difficult . Concerning the content, most students (81%) found that the difficulty was on par for an upper-level division biochemistry course with 16% and 4% citing that it was either too difficult or too easy, respectively . After experiencing both flipped and traditional formats of the course, 89% preferred the flipped format, 9% preferred traditional lecture style, and 2% preferred Zoom-delivered course only which is online lecture and no class attendance required . Fig. 6 End-of-semester survey on difficulty, content, and format. ( A ) Students were survey with the question “How difficult is this course?” with 1 = easy, 2 = moderately easy, 3 = slightly challenging, 4 = difficult, 5 = very difficult. ( B ) Students were surveyed with the question “Was the content of this material 1) more difficult than it should be, 2) About right for an upper division biochemistry course, 3) Generally easy”. ( C ) Students were surveyed with the question “What format do you prefer learning this course material 1) Traditional Lecture, 2) Flipped, 3) Zoom only”, n = 57 Is there evidence that flipping the classroom improves student learning and retention? In our survey, this pedagogy was effective for the weaker cohort of students. Others who have performed meta-studies to systematically and quantitatively assess the flipped model argue that student performance improves over the traditional lecture model in several disciplines . The addition of pre-class, in-class, and post-class material to support a recorded lecture reinforces concepts that are either difficult to understand or that are easily missed in the traditional lecture. Now specifically in the biochemistry subject material, most instructors and researchers have found that there is improved learning, teamwork, and perception of the subject material . Not all of these and others report improved final grades for their courses. Anecdotal evidence from this author in discussions with other instructors at national conferences focusing on biochemistry education is that there are many flipped/inverted class failures. Reasons for this can vary from preparation of in-class activity to classroom layout. In supplemental Fig. 2 , we provide the in-class material for other instructors to get started. This material is derived from several common textbooks as well as original ideas from the instructors . Feedback from multiple instructors and students within the discipline suggests that our methodology is more successful than in traditional = based biochemistry classes. | Other | biomedical | en | 0.999995 |
PMC11698300 | Driven by evolving consumer preferences for convenience, health, and satisfaction, the food product market constantly pushes the envelope of innovation. One area of noteworthy progress has been in functional foods and biopharmaceuticals. These advancements cater to consumers’ inclination toward health-promoting foods that offer potential protection against diseases . Functional foods are those that provide health benefits beyond their basic nutritional value. They can be naturally occurring, nutrient- or ingredient-enriched, and recognized for their diverse health-promoting properties. On the other hand, a nutraceutical is identified as a product extracted or purified from food sources and typically marketed in medicinal forms, which are not commonly associated with traditional food items. The term "nutraceutical" is often used synonymously with functional food, highlighting their shared health-benefiting characteristics. Despite their growing popularity, an acceptable, all-encompassing definition for these terms remains elusive, contributing to their continued interchangeability. The varieties and nuances of functional foods are numerous, ranging from foods naturally containing bioactive compounds to those synthesized to have an increased level of such compounds. The diversity of functional food categories and their examples is further elucidated in Table 1 . Table 1 Different categories of functional foods Category Definition Examples Basic foods Food/ food products that naturally contain the bioactive • Carrots with beta-carotene • Oat bran and barley cereals with beta-glucan • Green vegetables rich in lutein • Fruits, tea, and citrus containing neutralize free radical Processed foods with added bioactive The bioactive does not exist naturally in the food and is added during processing • Orange juice with added calcium • Milk with added omega-3 fatty acids • Salmon and other fish oils rich in omega-3 fatty acids • Cheese, meat products (a good source of Conjugated Linoleic Acid (CLA)) • Soy-based foods with Isoflavones: Daidzein Genistein • Yogurt and other dairy products (essential source of Lactobacillus) Food ingredients synthesized to have more bioactive compounds The level of the bioactive compounds is modified or concentrated beyond its natural level by traditional breeding, special livestock feeding, or genetic engineering • Yogurt with increased levels of probiotics • Tomatoes with increased levels of lycopene • Eggs with increased levels of omega-3 fatty acids • Foods fortified with indigestible carbohydrates Source: [ 1 – 6 ] Simultaneously, the field of biopharmaceuticals has seen tremendous strides. Biopharmaceuticals, comprising biomolecules such as proteins, nucleic acids, antibodies, enzymes, hormones, and vaccines, have been recognized and utilized for their immense therapeutic potential for decades . However, the efficient preservation and processing of these beneficial products pose a remarkable challenge due to their heat-sensitive nature. Both functional foods and biopharmaceuticals contain bioactive components that, while contributing to their health benefits, are highly susceptible to heat, and therefore, careful selection of drying methods and conditions is required . Drying, an integral part of the food and biopharmaceutical industry, serves as an effective preservation technique, ensuring a longer and safer shelf-life of the products. The drying process, however, must be conducted judiciously to minimize damage to the heat-sensitive bioactive components . The current review aims to amalgamate the research and advancements in drying methods, shedding light on their suitability for different biomaterials, their associated problems, and strategies to preserve the active ingredients in biopharmaceuticals, nutraceuticals, and functional foods. This review explores the nature of functional foods and biopharmaceuticals, addressing the challenges of drying these heat-sensitive materials. It will examine various drying techniques used to preserve diverse, valuable functional foods and biopharmaceuticals, elaborating on the advantages and disadvantages of each method. The discussion extends to hybrid drying technologies that enhance product performance and quality . By providing a comprehensive overview of these drying techniques and their applications this review is poised to stimulate further research into developing even more effective preservation methods for these invaluable resources by providing a comprehensive overview of these drying techniques and their applications. Fig.1 Overview of the review methodology The world has witnessed the emergence of new technologies in modern medicine and health care, followed by the Second World War. Freeze-dried plasma and antibiotics were the two remarkable medical advances made during wartime. After the discovery of penicillin by Alexander Fleming in 1928, a series of curious investigations were conducted to stabilize pure penicillin. Later, in 1939, Dr. Howard Florey and Ernst B. Chain, working at Oxford, used freeze-drying to stabilize penicillin, earning them the Nobel Prize in 1945 . Later, numerous research studies were done in the field of drying, and many were primarily focused on drying heat-sensitive products. At the end of the 19th century, spray-drying technology emerged, and a patent was issued for spray-drying liquid eggs. The technique proved suitable for drying heat-sensitive biopharmaceutical products as well. With the advent of technology and research, different modifications and designs were studied in various drying methods to improve the quality and safety of dried products, retaining their functional and nutritional properties. In the large-scale production of bioactive ingredients for nutraceuticals and functional foods, drying is a critical operation demanding significant energy. As demonstrated in Fig. 2 , the removal of moisture in the drying process can occur in different ways: simple evaporation as in hot air or vacuum drying, condensation and evaporation as in superheated stem drying, atomization and evaporation as in spray drying, sublimation as in freeze drying, and precipitation as in supercritical fluid (SFC) drying . Several factors, such as temperature, humidity conditions, pressure, and exposure time, can influence the end product's quality and functionality. Although drying's primary objectives include microbial stability, reducing chemical degradation, facilitating storage, and minimizing transportation costs, researchers also aim to develop drying strategies to mitigate the loss and deformation of bioactive compounds in the dried product . Fig. 2 Different mechanisms of removing moisture in the drying process Key indicators of product quality include cellular shrinkage, reduced rehydration capacity, absorbency, solid mobility, surface hardening, and the diminution of volatile aromatic compounds. The evolution of drying methods has led to the categorization of drying technologies into four generations. The first-generation drying technologies, being the most rudimentary, primarily relied on natural elements like sun and wind for drying. With the second generation, artificial heat sources such as ovens and stoves were introduced, greatly improving the reliability and control over the drying process. The third generation brought about mechanized drying techniques, employing hot air ovens, spray drying, and drum drying. This generation saw widespread use in industrial settings, demonstrating enhanced efficiency and control over drying conditions. Currently, we are in the era of fourth-generation drying systems, which use advanced technologies like microwave, infrared, radiofrequency, refractance window, heat pump fluid bed drying, and various hybrid systems. The central aim of these fourth-generation systems is to prioritize preserving food quality, ensuring the retention of essential nutritional components and taste attributes [ 17 – 20 ]. With each progressive generation, the field of drying technology becomes increasingly refined, balancing efficiency, energy consumption, and quality retention. Drying processes are characterized by conductive and/or convective heat-transfer mechanisms. The primary aim of these processes is to diminish the concentration of residual volatile components in process streams rich in nonvolatile compounds. These procedures facilitate the transfer of energy from the outer surface to the interior of the wet material, resulting in the generation of internal heat within the solid substance. The different types of functional foods, including dairy, meat, grain, and plant-based functional food ingredients, are rich in bioactive elements such as vitamins, essential fatty acids, minerals, antioxidants, etc. These components, however, are highly susceptible to damage from high temperatures. The process of dehydrating these biological molecules may result in substantial chemical, physical, and nutritional degradation, including but not limited to browning reactions, lipid oxidation, colour and aroma depletion, and loss of vitamins and minerals . Certain products are solvent-wet forms that are centrifuged before drying to minimize degradation. However, intense evaporation during drying can still cause a carry-over of solid product particles by the vapour flow. This carry-over can cause clogging of the filters and ducts, resulting in damage to the dryer system. Another common issue with biopharmaceutical and functional products is the agglomeration of particles and their hygroscopic nature, often leading to undesired “lumps” in the end product. Moreover, in the case of organic solids, in which the drying process is controlled by the diffusion of the liquid through the solid, larger lumps lead to longer processing times . As most functional foods, nutraceutical foods, and biopharmaceutical ingredients are thermos-liable with a tendency for structural and functional deformation at extreme drying conditions, selecting the appropriate drying system is key. These drying methods and strategies are also chosen based on the nature of end products, such as food powders, flakes, leathers, or sheets from juices, purees, pastes, or suspensions . Thermal degradation models for various biomolecules and nutrients are essential for understanding and predicting the behavior of heat-sensitive biomaterials under various temperature conditions . Several numerical models aim to describe the kinetics of degradation reactions, assess the impact of temperature on biomaterial stability, and optimize processing parameters to minimize thermal degradation . Thermal degradation of the primary macro-nutrients such as carbohydrates, proteins, and lipids is sometimes essential for converting them to more digestible forms for enhancing nutrient intake. On the other hand, changes to micronutrients such as vitamins, minerals and other functional micronutrients may significantly affect on their functionality and bioavailability . Table 2 summarizes the thermal degradation mechanism and models of macro-nutrients and micro-nutrients. The following section delves into an array of drying methods commonly used in the food and biopharmaceutical industries, specifically focusing on their application in the drying of functional foods and biopharmaceuticals. These methods are critical for preserving the products' quality, nutritional value, and bioactive properties while ensuring safety for consumption or use. The methods discussed include heat pump drying (HPD), freeze-drying, spray drying, vacuum drying, fluidized bed drying (FBD), superheated steam drying, infrared (IR) drying, microwave drying, osmotic drying, and supercritical fluid drying. Each method will be discussed in detail, highlighting its working principle, advantages, limitations, and particular applications in drying functional foods and biopharmaceuticals. Table 2 Thermal degradation mechanism and models of macro-nutrients and micro-nutrients Nutrient type Mechanism and effects of thermal degradation Models Reference Macro-nutrients Carbohydrate Mechanism • Temperature < 200 °C -loss of free water and hydroxyl groups in physical and polymeric changes • Temperature 220 °C -550 °C -formation of dehydrated anhydrides with structural and chemical changes • Temperatures > 550 °C further carbonization to degrade to smaller molecules CO 2 , H 2 , CH 4 , etc. Effects o Caramelization of carbohydrates leading to the formation of brown color, aroma and flavor compounds o Pyrolysis of carbohydrates, resulting in the breakdown of complex carbohydrates The Arrhenius equation and Reaction rate models: k = k 0 · e −Ea/RT Ea is the activation energy (kJ mol −1), k is the rate constant, and k 0 is the frequency/pre-exponential factor. R is the universal gas constant (8.314·10 −3 kJ mol −1 K −1), and T is the absolute temperature (°K) E.g.: Friedman model, Ozawa model, Kissinger model, Flynn-Wall-Ozawa model, Coast-Redfern model [ 24 – 30 ] Proteins Mechanism • Temperature 100 °C -200 °C -spatial structure changes -thermal aggregation • Temperature > 200 °C – thermal degradation Effects o Denatured protein molecules undergo aggregation or covalent cross-linking, leading to the formation of insoluble protein aggregates o Pyrolysis of proteins, resulting in the breakdown of peptide bonds First and second-order Kinetic Models Arrhenius model and reaction model [ 31 – 33 ] Lipids Mechanism • Thermal oxidation 100 ~ 200 °C • Polymerization Effects o Free radicals formation leading to the formation of off-flavors, rancidity, and potentially harmful compounds such as lipid peroxides o Hydrolysis of lipids, resulting in the free fatty acids Second-order polynomial model Arrhenius model [ 24 , 34 – 37 ] Micro-nutrients Vitamins Mechanism • Thermal oxidation and degradation Effects o Lower bioactivity, irreversible binding to other molecules, or degradation to inactive compounds o 300–500 °C, some vitamins (Vitamin A) decomposes to form aromatic substances Vitamin C, D, & β-carotene- first-order reaction kinetic \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ln\left(\frac{{C}_{t}}{{C}_{0}}\right)=-kt$$\end{document} l n C t C 0 = - k t Where, C 0 -initial concentration of vitamin, C t – measured concentration of vitamin at time t and k temperature- dependent rate constant [ 24 , 38 – 42 ] Minerals Mineral stability and availability are reported to have minimal impact by drying processing than other macro- and micronutrients - Phenols, Flavonoids and Glycosides Mechanism • Maillard reaction Effects o Some polyphenols and flavonoids may increase during drying, but long-term exposure of heat reducing their concentration and bioavailability First-order reaction kinetic The Heat Pump Dryer System (HPDS) represents an innovative and energy-saving approach to drying and dehydration processes that harnesses low-grade energy to heat the drying medium. Heat pump drying technology is used in high-value foods and biomaterials where low-temperature drying generally ranges from 45 to 70 °C and well-controlled conditions are essential . Its potential as a waste heat recovery system and high drying efficiency have boosted HPD’s popularity . Heat pumps can be classified into different designs, such as gas-engine-driven heat pumps [ 50 – 53 ], ground source heat pumps (GSHP), solar heat pumps [ 54 – 56 ], photovoltaic/thermal (PV/T) heat pumps , chemical heat pumps , and desiccant heat pumps . This technology is particularly suitable for high-value products, as it allows for controlled transient drying conditions in terms of temperature, humidity, and air velocity, thereby improving product quality attributes and reducing drying costs. HPD has proven to be a reliable method for biomaterials or food materials, including aquatic food products with a high content of phenolics, chlorophyll, ascorbic acid, phycocyanin, and antioxidant activity [ 48 , 61 – 64 ]. Some of the advantages of HPD include : Lower energy consumption (about 60%) for each unit of water removed, and therefore, higher energy efficiency with improved heat recovery Well-controlled temperature profiles, making it highly suitable for heat-sensitive high-value products with better quality outcomes Flexibility in drying conditions as it can generate temperatures typically ranging from -20 to 70 °C (with auxiliary heating) and a relative humidity of 15–80% (with a humidification system). Another notable hindrance to the widespread adoption of HPD is the constraints in achievable drying temperatures and the substantial capital required for setting up the system. However, these challenges do not overshadow the key benefits of this technology. Its ability to precisely control the operating temperature and relative humidity makes it ideal for drying functional foods, yielding minimal discoloration and ascorbic acid degradation. Despite the limits on temperature range, this precision positions HPD as a promising technology for enhancing the preservation of high-value foods and biomaterials. Freeze drying, also known as lyophilization, is primarily utilized to remove water from sensitive biological molecules. This procedure prevents damage, enabling their preservation in a storable state that can be reconstituted simply by adding water. This method is optimal for preserving biopharmaceutical/nutraceutical products (Table 2 ) like antibiotics, bacteria, vaccines, diagnostic medications, protein-containing, biotechnological products, cells and tissues, and chemicals . Furthermore, freeze-drying is apt for preserving and drying various high-value functional foods like fruits, dairy products, meat proteins, eggs, etc. . Owing to the water being removed in its frozen state rather than its liquid state, the material's morphology, solubility, and chemical integrity are largely maintained after freeze-drying . Freeze-drying is a three-phase process: initially, the product is frozen, decreasing the temperature to cause most of the water to crystallize, leaving only a small fraction unfrozen and incorporated within the product. Subsequently, the primary drying phase occurs, during which the chamber pressure is reduced to enable sublimation while heat is concurrently supplied to the product. The sublimation process is initiated from the material surface, which is driven by the vapor pressure gradient above the sublimation surface P vi and the evaporated surface P a and the rate of sublimation is computed by Eq. 1 . 1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S=\frac{\left({P}_{iv}-{P}_{a}\right)}{\left({R}_{d}+{R}_{d}+{R}_{i}\right)}$$\end{document} S = P iv - P a R d + R d + R i where S is the sublimation rate, kg/(m 2 ·s); R d is the resistance inside the dry layer, m 2 /(Pa·s kg); R s is the resistance to mass transfer from the dry surface to the resublimation surface, m 2 /(Pa·s kg); and R i is the ice sublimation resistance, m 2 /(Pa·s kg). In primary stage drying, there is a moving interface of freeze-dried layer and frozen layer, and there is no distinctive boundary between the first and second phases of freeze-drying . Equations 3a and 3b give the initial conduction heat transfer from the material surface to the sublimation interface and the frozen layer to sublimation interface, respectively . 3a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Q}_{d}=\frac{2{\lambda }_{d}}{\left(\nicefrac{1}{\sqrt{\pi {A}_{s}}}\right)-\left(\nicefrac{1}{\sqrt{\pi {A}_{ext}}}\right)}\left({T}_{ext}-{T}_{s}\right)$$\end{document} Q d = 2 λ d 1 π A s - 1 π A ext T ext - T s 3b \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Q}_{i}=\frac{2{\lambda }_{i}}{\left(\nicefrac{1}{\sqrt{\pi {A}_{f}}}\right)-\left(\nicefrac{1}{\sqrt{\pi {A}_{s}}}\right)}\left({T}_{s}-{T}_{f}\right)$$\end{document} Q i = 2 λ i 1 π A f - 1 π A s T s - T f where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Q}_{d}$$\end{document} Q d and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Q}_{i}$$\end{document} Q i are the heat flux through dried layer and frozen layer in (W), respectively; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{d}$$\end{document} λ d and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{i}$$\end{document} λ i are the thermal conductivity of dried layer and frozen layer in (W/mK), respectively; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${A}_{s}$$\end{document} A s , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${A}_{f}$$\end{document} A f , and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${A}_{ext}$$\end{document} A ext are the surface area of the sublimation interface, frozen layer, and external surface in (m 2 ), respectively; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{s}$$\end{document} T s , \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{ext}$$\end{document} T ext , and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{f}$$\end{document} T f are the temperature of the sublimation interface, external surface, and frozen layer in (K), respectively. In the secondary drying phase, the product's temperature is increased to remove residual moisture, including bound and unfrozen water . The heat transfer rate by conduction can be defined as the heat flux conducted through the frozen layer of the material (Eq. 3b ). This technique has captivated researchers due to its capability to dry materials at lower temperatures, thereby maintaining their original colour, texture, and quality . The application of novel freeze-drying technologies such as Thin film freeze-drying (TFFD) enabled the production of uniform-sized aerosol particles for biopharmaceutical products such as Inhalation-based medication delivery. TFFD has several advantages over traditional freeze-drying processes in biopharmaceutical applications. TFFD uses an intermediate freezing rate, typically between 10 2 and 10 3 k/s, which is faster than standard freeze-drying . This intermediate freezing rate improves the structural integrity and bioactivity of sensitive biopharmaceutical compounds enabling the production of engineered dry powders and facilitating precise dosing. Table 3 illustrates the freeze-drying conditions for different foods with bioactive components in FBD. Table 3 Freeze drying conditions for different foods with bioactive components Product Drying condition Drying Pressure (Pa) References Green banana flours (Starch and crude fibre) Temperature: - 47 to - 50 °C 700 Brazilian ginseng root (beta-ecdysone & fructo-oligosaccharides) Temperature: - 40 °C Atmospheric Symbiotic drink with lactobacillus casei Temperature: - 49 °C 1000 Seabuckthorn berries (phenolic, carotenoids, fatty acids, and vitamin contents) - 20 to - 50 °C shelf plate temperature Atmospheric Blueberries (polyphenols, antioxidant activity, and ascorbic acid) Temperature: - 30 °C Atmospheric Submicron lactate dehydrogenase (LDH) protein particles lyophilization (1 K/min) and spray freeze-drying (SFD) (10 6 K/s), temperature –50 to -140 oC Atmospheric Encapsulated Probiotic bacteria chamber freeze-drier at -80 oC 0.02 mbar Encapsulated Spirulina Maxima in whey protein Temperature: −50 °C 0.04 mbar Monoclonal antibodies formulated with lactose/leucine Temperature - - 100 °C Atmospheric Among the key advantages of freeze-drying for food and biomaterial drying are: Preservation of structural, biochemical, and immunological characteristics Enhanced viability or activity rates, along with improved textural attributes, owing to drying at low temperatures Effective recovery of volatile substances, maintaining structural integrity, surface area, and stoichiometric balances, leading to high product yield, prolonged shelf life, and decreased weight for easier storage, transportation, and handling Minimal oxidative reactions due to the absence of oxygen during drying, maintaining the quality of the final product. However, the broad implementation of freeze-drying is constrained by the significant capital investment required. It is a high-energy, high-cost process for both operation and maintenance. Despite these limitations, freeze-drying remains an effective method for protein powder production. Nevertheless, issues such as ice formation, solute and protein concentration affecting protein stability, and potential cold denaturation during the freezing process are concerns. To address these issues, hybrid techniques such as combined spray- and freeze-drying, thin film freeze-drying, etc., have been developed, which involve spraying the product into a cryogenic medium, followed by the standard primary and secondary drying processes of freeze-drying [ 86 – 90 ]. The spray drying process involves rapid heat and mass transfer as the liquid feed is atomized into fine droplets and introduced into a hot airstream. The water evaporates from the droplet during this process, and the resulting dried powder is cooled and collected using cyclone separators. Spray drying modelling is one of the most commonly simulated models using computational fluid dynamics (CFD) . The advanced computational power of CFD was reported to be effective in solving very sophisticated models such as the continuous phase flow model, droplet agglomeration models, particle droplet tracking, and wall depositions models . The mechanism of increased surface area for evaporation of moisture from the atomized particles is attributed to the uniform and faster drying of spray droplets. Consequently, a single heat transfer equation can be utilized to model the heat flux to the droplet in the heating period and the following wet bulb temperature period (Eq. 4 ) . 4 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{d{T}_{p}}{dt}=h\left({T}_{g}-{T}_{p}\right)\frac{4\pi {R}_{p}^{2}}{{C}_{pp}{m}_{p}}- \frac{{h}_{l}{m}_{r}}{{C}_{pp}{m}_{p}}$$\end{document} d T p dt = h T g - T p 4 π R p 2 C pp m p - h l m r C pp m p where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{g}$$\end{document} T g and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{p}$$\end{document} T p are the drying medium and spray particle temperature in (K), respectively; R p is the spray particle diameter in m; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${C}_{pp}$$\end{document} C pp is the specific heat capacity of spray particle in (J/kg⋅K); \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${m}_{p}$$\end{document} m p represents spray particle mass; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${m}_{r}$$\end{document} m r is mass flow rate and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{l}$$\end{document} h l is the latent heat of vaporization in (J/kg). The spray drying technique has widespread application in the biopharmaceutical industry and in drying of encapsulated food ingredients (Table 3 ). It is mainly used for microencapsulating the active ingredients of many biological materials, such as flavours, lipids, essential fatty acids, carotenoids, and more. The spray drying technique with microencapsulation was also reported to be a potential solution for manufacturing food additives for food fortification applications such as minerals . The active ingredient is homogenized in an emulsion, which forms the microcapsules' coating. Subsequently, the active ingredient emulsion is spray-dried (Table 4 ). Table 4 Drying conditions for different encapsulated active ingredients in spray drying Encapsulated ingredient Wall material Air inlet temperature (°C) Air outlet temperature (°C) References Anhydrous milk fat Whey proteins/lactose/ Maltodextrin 160 80 Ethyl butyrate ethyl caprylate Maltodextrin/gum arabic 160 80 Caraway essential oil Maltodextrin/Skim milk powder 175–185 85–95 Cardamom oleoresin Gum arabic/modified starch/maltodextrin 176–180 115–125 Bixin Maltodextrin/gum arabic/modified starch 180 130 d -Limonene Maltodextrin/gum arabic/modified starch 200 100–120 l -Menthol Gum arabic 180 95–105 Black pepper oleoresin Gum arabic/whey protein concentrate 176–180 105–115 Cumin oleoresin Maltodextrin/ gum arabic/modified starch 158–162 115–125 Arachidonyl l -ascorbate Maltodextrin/gum arabic/soybean polysaccharides 200 100–110 Fish Oil konjac glucomannan, Soybean protein isolate, potato starch 200 80 Fish oil Sugar beet pectin/glucose syrup 170 70 Short-chain fatty acid Maltodextrin/gum arabic 180 90 Hawthorn Berry polyphenols β-cyclodextrin, whey protein isolate, gum arabic 165 Lycopene Gelatin/sucrose 190 52 Turmeric oleoresin Maltodextrin/gum arabic 150–200 90 This prevalent method for drying liquid products has numerous advantages, including: Drying time is comparatively less than other drying methods since the heat transfer rate is high Good reconstitution capacity and product quality Minimal chances of thermal denaturation as the droplet's surface temperature is maintained at the wet-bulb temperature, significantly lower than the drying gas temperature Enhanced bioavailability of active ingredients and controlled release in encapsulated products Improved control over particle size as the feed droplet size can be easily regulated. Fluidized bed drying is widely applied in the drying of granular solids in various industries such as food, ceramics, biopharmaceuticals, and for drying phytochemicals like organic acids, carbohydrates, reducing sugars, lipids, and proteins [ 126 – 131 ]. This method is suitable for drying powders in the 50–2000 μm range, thanks to its high heat and mass transfer rates. FBD's effectiveness lies in its fluidization process, allowing for improved drying rates and reduced drying time. The fluidized bed drying process is another multiphase drying model, as the fluid and solid phases are in an interacting continuum. The drying process is governed by the continuum phase heat transfer from the drying medium into the solid phase. Therefore, the general continuum equation for heat, mass and momentum transfer for the fluid medium is set as the boundary conditions for the solid phase drying modelling. The solid phase dying is governed by diffusion equations (Eqs. 5a and 5b ) for energy and mass transfer, respectively . 5a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial {T}_{m}}{\partial t}=\frac{{\lambda }_{m}}{{\rho }_{m{C}_{pm}}}\frac{{\partial }^{2}{T}_{m}}{\partial {{R}_{m}}^{2}}$$\end{document} ∂ T m ∂ t = λ m ρ m C pm ∂ 2 T m ∂ R m 2 5b \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial {M}_{m}}{\partial t}=Deff\frac{{\partial }^{2}{M}_{m}}{\partial {{R}_{m}}^{2}}+\frac{2}{{R}_{m}}\frac{\partial {M}_{m}}{\partial {R}_{m}}$$\end{document} ∂ M m ∂ t = D e f f ∂ 2 M m ∂ R m 2 + 2 R m ∂ M m ∂ R m where, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${M}_{m}$$\end{document} M m is the moisture content of the material in kg/kg; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${T}_{m}$$\end{document} T m is the temperature of the material in K; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${C}_{pm}$$\end{document} C pm is the specific heat capacity of the material; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\lambda }_{m}$$\end{document} λ m is the thermal conductivity of the material; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\rho }_{m}$$\end{document} ρ m is the density of the material kg/m 3 ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}_{m}$$\end{document} R m is the effective radius of the material in m; Deff is the effective diffusivity coefficient m 2 /s. In FBD, the product is subjected to a high flow velocity greater than its specific gravity. This flow lifts it above the periphery of the dryer mesh. It then decelerates and falls onto an annular zone between the central core and the equipment wall. This flow pattern establishes a unique solid–fluid suspension, ensuring uniform and faster drying . This drying system has several advantages, such as: Rapid drying speeds, facilitated by superior contact between gas and particles, results in high rates of heat and mass transfer Enhanced thermal efficiency and a reduced flow area in comparison to traditional pneumatic dryers Ease of control of the drying process by controlling the fluidization velocity and pressure drop. However, the method does come with its limitations. It involves high power consumption, requiring suspending the entire bed in the gas phase, leading to a substantial pressure drop. There's an increased chance of attrition and, in some instances, granulation or agglomeration. FBD also has low flexibility for the type of product that can be dried (e.g., it is unsuitable for wet products). Furthermore, frequent issues during the drying of phytochemicals include instability within the drying bed, accumulation of products, coating on non-reactive substances, clumping of particles, and potential system failure. Moreover, there can be losses in bioactive components due to thermal degradation . Therefore, precise control of the drying conditions is necessary for such products with bioactive components, as detailed in Table 5 . Table 5 Drying conditions for different foods with bioactive components in fluidized bed drying and superheated steam drying Product and bioactive compound Drying condition (air or superheated steam) References Phytochemicals Inlet temperature: 60 to 180 °C Feed flow rate: 3 to 12 g/min Green vegetables (broccoli) Inlet temperature: 60 to 80 °C Particle size: 1-3 cm Air flow rate: 1-3 m/s Pellet coated pharmaceuticals Inlet temperature: 90 °C Gas flow rate: 50 kg/h Soybeans Inlet temperature: 110–140 °C Air velocity: 2.4–4.1 m/s Probiotic bacteria Inlet temperature: 40 °C Bee pollen Inlet temperature: 40 °C Air velocity: 6.0 m/s Muskmelon seed Inlet temperature: 40–60 °C Air velocity: 7–11 m/s Wheat grains (dietary fibre and polyphenols) Steam temperature: 110 to 180 °C Fish (omega-3 fatty acid) Steam temperature: 300 °C Flow rate: 150 kg/h Beef (Bioactive antihypertensive peptides) Steam temperature: 130 to 180 °C Flow rate: 35 -55 kg/h Shrimps (carotenoprotein, calcium) Steam temperature: 120 to 180 °C Soybeans (Lysine content) Steam temperature: < 135 °C Steam velocity: 3.2 m/s Oats (beta-glucan) Steam temperature: 110 to 160 °C Steam velocity: 0.35 to 1.0 m/s Waxy rice (Amylose content, Gamma-aminobutyric acid) Steam temperature: 130–150 °C Steam velocity: 3.5 m/s The drying medium in this method is superheated steam, which operates in a closed cycle, picking up moisture from the wet product in the drying chamber and then condensing the evaporated water in a heat exchanger . Since the drying occurs in a closed environment, the probability of oxidative reaction is minimal, preserving the quality and aroma of the dried material . Moreover, superheated steam drying resembles high-temperature short-time (HTST) treatment in which food gets decontaminated while drying . Low-pressure superheated steam is highly suitable for drying heat-sensitive products like fruits and vegetables, herbs, and other bioactive materials. Low-pressure superheated steam drying takes place in the pressure range of 5–10 kPa , at which the steam becomes saturated [ 152 – 154 ]. Compared to hot air-based drying, superheated steam drying has a faster drying rate, as part of the initial accelerated heat transfer is aided by latent heat contributed by the initial condensation and the subsequent free water evaporation (Eqs. 6a and 6b ) followed by the diffusion model (Eqs. 5a and 5b ) . 6a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{If\,T_{m}}<\mathrm{T_{sat}},\frac{d{M}_{m}}{dt}=\frac{{h}_{f}\left({T}_{sat}-{T}_{m}\right)}{{h}_{l}}$$\end{document} If T m < T sat , d M m dt = h f T sat - T m h l 6b \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{If\,T_{m}}\,=\,\mathrm{T_{sat}},\frac{d{M}_{m}}{dt}=\frac{h\left({T}_{ss}-{T}_{sat}\right)}{{h}_{l}}$$\end{document} If T m = T sat , d M m dt = h T ss - T sat h l where, T m , T sat , T ss are the material temperature, saturation temperature and superheated steam temperature in K, respectively; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{f}$$\end{document} h f is the film condensation heat transfer coefficient; h is the convective heat transfer coefficient; and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{l}$$\end{document} h l is the latent heat of condensation/evaporation. Various scholars have utilized superheated steam drying to investigate its ability to preserve bioactive components, primarily antioxidant components, in multiple products. These include tea leaves, where studies have shown significant preservation of antioxidant properties compared to conventional oven drying methods , and in other products like onions, where low-pressure superheated steam drying has demonstrated better retention of bioactive components . Another relevant work Suvarnakuta et al. examined the effects of drying methods on the assay and antioxidant activity of xanthones in mangosteen rind. They concluded that hot air drying or low-pressure superheated steam drying at 75 °C is the most suitable drying method to maximize the quantity and quality of mangosteen. Superheated steam drying has several advantages over hot air drying, including [ 159 – 162 ]: Improved drying efficiency when compared to other drying techniques, especially with the closed-loop system. Clean process without any emission of flue gases and odor emissions to the environment. Absence of direct contact between the product and hot, oxygen-rich gas, reducing the likelihood of oxidation Beyond drying, hot steam serves as a sterilizing agent Improved control over the drying process by regulating the amount of steam introduced into the compressor, aiding in achieving precise product dryness. The primary concern with superheated steam drying is the phenomenon known as initial condensation. This occurs when superheated steam comes into contact with a cold solid feed at ambient temperature, leading to vapor condensation on the material surface. This condensed moisture could increase the drying time unless the feed material is preheated by other means. A low-pressure superheated steam system is required to minimize prolonged drying of heat-sensitive bioactive compounds . The full energy efficiency advantage of superheated steam drying can only be fully utilized in a closed-loop system, where the output steam is diverted elsewhere in the processing plant. Such design modifications could add to the system’s complexity and cost . Infrared drying technology uses IR energy directly transferred from a heating element to the food, bypassing the need to heat the surrounding air. Thus, it helps to save energy and drying time. In IR drying, the radiant energy penetrates the product and converts it into heat, heating its surface and inner layers. This intense heating produces a higher heat and mass transfer rate than conventional drying methods. Recent research has highlighted this technique's capability to preserve bioactive components in foods post-drying, showing its effectiveness in maintaining the quality of various food products by preserving their phytochemical content and minimizing the loss of antioxidant activity [ 163 – 167 ]. The drying mechanism is also governed by the diffusion equation as explained in " Fluidized Bed Drying " section, and the energy balance is governed by the conduction, convection and radiation energy as given by Eq. 7 . Lee et al. studied the effects of far-IR drying on the antioxidant and anticoagulant activities of Ecklonia cava (brown seaweed) extracts. Their findings indicated that far-infrared radiation releases and activates low molecular weight bioactive compounds, such as polyphenols, due to its ability to heat materials without degrading their surface molecules . Senevirathne et al. reported that far-infrared radiation drying at 80 °C is an effective and economical method for drying citrus press cakes with minimal loss of antioxidant activity. 7 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\rho }_{m{C}_{pm}\left(\frac{\partial {T}_{m}}{\partial t}\right)={Q}_{m}-h\left({T}_{m}-{T}_{g}\right)+\zeta \sigma \varepsilon \left({{T}_{r}}^{4}-{{T}_{m}}^{4}\right)-{\dot{m}}_{v}\left({h}_{v}-{h}_{d}\right)}$$\end{document} ρ m C pm ∂ T m ∂ t = Q m - h T m - T g + ζ σ ε T r 4 - T m 4 - m ˙ v h v - h d where T m , T g , T r are the material temperature, drying medium (hot air) temperature, and radiation temperature in K, respectively; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Q}_{m}$$\end{document} Q m is the material energy, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\zeta$$\end{document} ζ is the material shape factor; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma$$\end{document} σ is the Stefan-Boltzmann constant, W/cm 2 ·K 4 ; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document} ε is the emissivity of the material; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{v}$$\end{document} h v is Latent heat of vaporization, J/g; \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}_{d}$$\end{document} h d is the heat of desorption. Moreover, IR drying is an effective intermittent irradiation method when combined with convective air drying for heat-sensitive materials. An infrared-augmented convective dryer can rapidly remove surface moisture during the initial drying stages, followed by intermittent drying for the remainder of the process. This approach ensures a faster initial drying rate and offers better process control, as the IR power source can be easily cut off in the event of excessively high temperatures in the chamber, preventing overheating of the product. Ratseewo et al. reported that far-infrared radiation drying of pigmented rice enhanced the content of total phenolic, flavonoid, tocopherols, anthocyanins, gallic and ferulic acids, and quercetin compared to traditional hot air drying. Overall, as demonstrated in Table 6 , the IR drying method is reported to be appropriate for drying high-valued heat-sensitive food products. Table 6 Drying of different functional foods with bioactive components in Infrared Radiation drying Product and bioactive compound Drying condition References Ecklonia cava (Brown seaweed) (antioxidants) Temperature: 40 to 80 °C Optimum temperature: 80 °C Citrus press-cakes (antioxidants) Temperature: 40 to 80 °C Optimum temperature: 80 °C Saffron (antioxidants and aroma compounds) Temperature: 50 to 80 °C Optimum condition: 80 °C for 30 s Gamguk flower (herb) ( Chrysanthemum indicum L. ) (phenolic and flavonoid) Temperature: 50 °C Rice hulls (phenolic compounds) Temperature: 100 °C Peanut hulls (antioxidants and radical scavenging activity) Temperature: 150 °C for 60 min Ginkgo biloba seeds (Flavanoids and anti-oxidants) Temperature: 80 °C Garlic (thiosulfinates, phenolic compounds and antioxidants) Temperature: 50 to 80 °C optimum temperature - < 70 °C Microwave drying, or microwave-assisted drying, is a rapid drying technique extensively used in the food industry. This method involves transmitting microwave energy through the product, generating heat due to dipolar polarization and ionic conduction phenomena. This method is distinguished by its volumetric heating, propelled by electromagnetic radiation at 915 or 2,450 MHz frequencies. The heat is generated by the interaction between microwaves and the material, converting a portion of the electromagnetic energy into heat throughout the volume, primarily heating polar molecules like water in the product . The heat transfer mechanism by internal heat generation results in a volumetric heating mechanism of electromagnetic energy supplied by microwaves. The volumetric heat flux is represented by the Eq. 8 . 8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$V\rho {C}_{p}\frac{\partial T}{\partial t}=V\left[\frac{\partial }{\partial x} \left(\frac{\partial T}{\partial t}\right)\right]+P$$\end{document} V ρ C p ∂ T ∂ t = V ∂ ∂ x ∂ T ∂ t + P where, V is the product volume in m 3 , and P is the Power in W , generated by the absorption of Microwave. This microwave power absorbed by water molecules (polar) is converted to heat. Various studies have explored the potential of microwave drying in producing high-quality end products. The utilization of a two-stage microwave power system, which adjusts the power levels during the drying of functional food products, was proposed by . They proposed that adjusting the power levels of microwave energy (1- 2 kW/kg depending on the initial moisture content) could facilitate higher retention of β-carotene in dried carrots. Microwave-assisted vacuum drying has also been recognized as an appropriate drying method for thermolabile products, including certain foods (e.g., cranberries, carrots, garlic, mushrooms) and biopharmaceutical powders and granules . Condurso et al. found that microwave drying considerably increased the concentration of trisulfides and cyclic sulfur compounds, which contribute to the specific aroma of garlic and possess potent anticancer and chemoprotective properties, in Sicilian garlic compared to hot air drying. Moreover, Berteli et al. studied the microwave vacuum drying process for biopharmaceutical granules and found that it is faster than other drying techniques. They highlighted several benefits: Enhanced heat and mass diffusion through biomaterial due to its volumetric heating nature Quicker formation of internal moisture gradients, leading to enhanced drying speeds Accelerated drying rates achieved without raising the surface temperatures Enhanced product quality, making it suitable for heat-sensitive products (such as carrots, garlic, mushrooms). Osmotic dehydration is a critical process in drying functional foods such as grapes, berries, tomatoes, carrots, and mushrooms, as it minimizes the loss of functional components [ 192 – 196 ]. The technique operates on the principle of osmotic pressure difference caused by the salt and sugar concentration gradient between the cells of the food product and the surrounding medium. This method minimizes organoleptic and nutritional elements in the product, preserving its flavour and nutritional value . Singh et al. conducted studies on drying carrots by osmotic dehydration using sucrose (50° to 80°Brix) and salt solutions (5 to 15%). They reported that the drying occurs through a simultaneous process of water loss and solute diffusion, effectively drying the food product without excessive loss of nutrients following the Ficks diffusion equation. The osmotic pressure of the drying surface rises until it reach a critical level as the diffusion proceeds, resulting in cell membrane rupture. This facilitates increased cell permeabilization index which is measured by electro-physical measurements . The chemical potential gradient, closely associated with the concentration gradient, represents the force exerted on each penetrant molecule during osmosis and diffusion. Under constant temperature and pressure conditions, the chemical potential (μ) can be described by the following equation : 9 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu ={\left(\frac{\partial {E}_{G}}{\partial n}\right)}_{{T}_{i}, {P}_{i}}$$\end{document} μ = ∂ E G ∂ n T i , P i where, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(\frac{\partial {E}_{G}}{\partial n}\right)$$\end{document} ∂ E G ∂ n is the partial derivative of the ratio of Gibbs free energy and number of moles of the penetrant. The chemical potential in a liquid phase as a function of temperature and water activity is determined by Eq. 10 . 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =\mu^\circ +RT \,\text{ln}\,{a}_{w}$$\end{document} μ = μ ∘ + R T ln a w where, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu^\circ$$\end{document} μ ∘ is the standard chemical potential, R is the universal gas constant (J/Kmol), and T and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${a}_{w}$$\end{document} a w are the absolute temperature (K) and water activity of the substance in the liquid phase, respectively. García-Segovia et al. investigated the effect of osmotic dehydration on Aloe Vera, focusing on retaining its immunomodulatory, anti-inflammatory, and antibacterial properties. Their research found that optimal results were achieved when osmotic drying was conducted at lower temperatures, specifically at 40 °C, demonstrating the potential for preserving bioactive compounds during the osmotic dehydration process. Overall, the osmotic drying process is particularly effective for fruits and vegetables. The technique can dewater these items without compromising their nutritional and functional elements, preserving their inherent health benefits. Moreover, the capability to fine-tune the osmotic solution allows for optimization based on the specific properties of the food product, making it a versatile and efficient drying method. However, the technique has limitations, such as potential changes in texture and the need to remove the osmotic agents from the product after drying, which warrant further research and technological improvements. Also, the diffusion rate of water differs for different materials depending on their composition, geometry, and size, and this limits the drying rates, affecting their nutritional quality and organoleptic properties . Leveraging the universal gas laws, where temperature and pressure are directly proportional, pressure-regulating drying, commonly known as vacuum drying, has gained widespread attention among researchers. By reducing atmospheric pressure, vacuum drying enables water to evaporate at lower temperatures, making it an ideal choice for drying heat-sensitive food products like herbs, curry leaves, and carrots. This approach allows for achieving the desired dryness level without compromising the product's quality, as it operates in a pressure-regulated environment [ 204 – 206 ]. Typically, the operating pressure range varies from a vacuum to close to one atmosphere . Orikasa et al. investigated the effect of vacuum drying on the quality attributes of kiwi fruit. They reported that vacuum drying helps to improve the quality and nutritional value of the dried kiwifruit when compared to hot air drying by retaining l -ascorbic acid, a crucial vitamin. In another relevant study, Šumić et al. noted a remarkable retention of functional elements such as phenols, anthocyanins, and total solids after vacuum-drying frozen sour cherries. Supercritical fluid drying is a relatively new drying method utilized in the food and biopharmaceutical field, especially in the drying of proteins [ 211 – 215 ]. This technique leverages the anti-solvent properties of SCFs to induce protein precipitation and remove water from formulations. SCFs, existing at temperatures and pressures beyond their critical points, exhibit distinctive characteristics of both liquid and gas states. Their density can exceed that of a liquid under increased pressure, yet they maintain the diffusivity and viscosity similar to a gas, facilitating effective mass transfer. When subjected to a supercritical jet of cosolvent, it dissolves the free water in the material and as penetrates deeper to dissolve entrapped water and bound water. The convective mass transfer is driven by the concentration gradient of cosolvent between the material surface and the fluid medium. This mechanism has it’s disadvantages as there is a high risk of removing water-soluble nutrients and bioactive compounds along with the water. Hence, solvent pressure, flow rate, temperatures, etc., impact the techno-functional properties of the dried material. Supercritical carbon dioxide (CO2) is commonly employed in supercritical fluid drying due to its relatively low critical temperature of 31.5 °C, significantly lower than water's 374.4 °C. Additionally, the Food and Drug Administration recognizes it as a safe substance for food treatment applications. However, research in this drying area is somewhat limited, and potential issues such as residual CO2 in the product, which may alter the pH of the end product, need further investigation . SCF drying is primarily utilized for drying foods and biopharmaceuticals where preserving the structures of the material pores is not critical . Some notable patented applications of SFD in drying foods and biopharmaceuticals are detailed in Table 7 . Table 7 Applications of SCD in Foods and biopharmaceuticals Compounds SC-Solvents Reference Protein, peptides, nucleic acids, bacterial cells, antibodies, serums, liposomes, and viruses Near supercritical CO 2 [ 217 – 222 ] β-Carotene, α-tocoferol and rosmarinic acid Supercritical CO 2 Strawberries (ascorbic acid, anthocyanins) Supercritical CO 2 drug substance, liposome CO 2 or other gases /co-solvent (ethanol) Theophylline ethanol/CO 2 Phenolic compounds (gallic acid resveratrol) Supercritical CO 2 Salmon calcitonin Supercritical CO 2 Insulin Supercritical CO 2 Fenofibrate particles Supercritical CO 2 + ethanol Green tea extract Supercritical CO 2 In conclusion, " Drying System for Heat-Sensitive Biomaterials " section provided a comprehensive review of the several drying methods used in drying food and biopharmaceuticals. It is evident that the preservation and retention of the nutritional value and bioactive properties of functional foods and biopharmaceuticals during drying is an area that needs further research. Our thorough research of the published work also showcased that each reviewed method offers unique advantages and presents certain limitations, influencing its suitability for different applications. Despite the challenges associated with each method, ongoing research and development efforts are continually seeking to optimize these techniques and address their limitations. The following section will build on this foundation to explore hybrid drying methods that combine the strengths of multiple techniques, pointing toward the future of drying technology. The effectiveness and appropriateness of the aforementioned drying methods depend on the of biomaterial or bioactive compound type, the initial state of the material to be dried, and the desired final product form and functionality. Table 8 provides a comprehensive summary of the various drying methods, highlighting their strengths and limitations and the biomaterials for which each method holds the greatest application potential. As reported in Table 8 , many of these drying techniques have limitations that could be minimized by combining the different techniques to improve the overall drying process, preserving the product integrity, efficacy, and quality of biopharmaceuticals and nutraceuticals while enhancing efficiency and cost-effectiveness. Table 8 A comprehensive summary of the various drying methods, highlighting their strengths and limitations and the suitability for biomolecules or bioproducts Drying Method Suitable Biomolecules/Products Benefits Overall Limitation References Heat Pump Drying High-value foods, aquatic products preserve essential amino acids Lower energy consumption, well-controlled temperature profiles Regular maintenance, environmental concerns, high capital cost Freeze Drying Biopharmaceuticals, high-value functional foods and ingredients such as Gelatin, isolated proteins, probiotics Preserves structure, biochemical and immunological characteristics High energy and cost, ice formation, protein stability issues Spray Drying Microencapsulation of biopharmaceuticals and biomaterials, food additives, gelatins, active biomolecules Rapid drying, good reconstitution capacity, suitable liquid suspensions, higher foam expansion Sensitive to moisture, potential for product sticking, non-uniform particle size, high temperatures driven denaturation Fluidized Bed Drying Granular solids, phytochemicals, coating/tableting for pharmaceutical and probiotics Rapid drying speeds, enhanced thermal efficiency High power consumption, attrition, unsuitable for highly wet products Superheated Steam Drying Fruits, vegetables, herbs, aquatic products Non-polluting, preserves quality and aroma, faster drying rates Complex setup, risk of overcooking, expensive equipment, Infrared Drying Antioxidant-rich foods, herbs, nuts and seeds, green tea, fruit peels Reduced drying time, high heat transfer rates, Uneven heating, high initial setup cost, limited to certain products due to browning reactions and changes to functional properties Microwave Drying Heat-sensitive foods, biopharmaceutical powders. Volumetric heating, enhanced heat and mass diffusion, improved energy efficiency. Suitable for hybrid drying Non-uniform heating, high equipment cost, shielding needed Osmotic Drying Fruits, vegetables for bioactive compounds Minimizes loss of functional components, preserves flavor, suitable as a pre-treatment Texture changes, nutrient leaching, time-consuming [ 243 – 246 ] Vacuum Drying Herbs, heat-sensitive foods, fruits, formulation of proteins High-quality dried products, retains nutritional value, Suitable for hybrid drying for bioactive compounds Expensive equipment, slower drying times, oxidation issues Supercritical Fluid Drying Proteins, peptides, sensitive biomolecules Effective for heat-sensitive materials, minimal nutrient loss, suitable for bioactive compound extractions and hybrid drying High pressure, residual solvents, complex setup, scaling challenges The industry and researchers increasingly recognize emerging hybrid techniques such as microwave-assisted vacuum drying, microwave sprouted bed drying, superheated steam fluidized bed drying, vacuum double-drum drying, spray freeze drying, and infrared-assisted drying in functional foods and biopharmaceuticals due to their superior efficiency and performance . The advent of particle engineering, encapsulation, and the development of novel functional food ingredients in biopharmaceuticals have underscored the need for comprehensive research on tailored drying strategies and hybrid methods. Table 9 presents examples of hybrid drying methods and their applications in various functional foods. Table 9 Hybrid drying methods for various functional foods Functional Food Product Drying method and condition Reference Viable Probiotics Fluidized bed drying with encapsulation Egg White Powder Foam mat freeze-drying Lactobacillus plantarum in aloe vera and agave fructans, whey protein Spray Freeze-Drying Apple pomace powder, blueberries Microwave-assisted vacuum drying Biologics and Vaccines Microwave Vacuum Drying Passionflower ( Passiflora alata ) Spray and spouted bed Mexican plum fruit extract Spray Drying and Spout-Fluid Bed Drying Microencapsulation Wolfberry ( Lycium barbarum L. ) Far-infrared radiation heating assisted pulsed vacuum drying (temperature of 65 °C, vacuum pressure for 15 min, and normal pressure for 2 min) Pre-osmodehydrated watermelon CO 2 convective drying with Far-Infrared radiation heating assisted pulsed vacuum drying Potato slices (Phenolic and Flavonoids) Ultrasound-assisted far-infrared radiation drying (ultrasonic resonant frequency of 28 ± 0.5 kHz and temperature of 50 °C) Pear slices (Phenolic and Flavonoids) Contact ultrasound-assisted far-infrared radiation drying (ultrasonic resonant frequency of 28 ± 0.5 kHz and temperature of 30 °C) Garlic Slices (allicin content) Ultrasonic-assisted vacuum drying (ultrasonic resonant frequency of 40 kHz and temperature of 60 °C Acai puree (anthocyanin, phenolic compounds, antioxidants) Infrared-assisted freeze-drying Polyphenol-enriched maple sugar Vacuum double-drum drying (80 °C and 87.99 kPa) Mulberry leaves extract Supercritical fluid extraction and spray drying Chrysanthemum cake (Phenols) Infrared and Hot Air Drying The novel, fourth-generation dryers primarily focus on product quality, drying efficiency, time and temperature changes. This category's major types of dryers are high-vacuum, microwave, radio-frequency, and refractive window drying . Among them, microwave and radio-frequency drying have gained comparatively faster commercial applications and attention from food processors and researchers over the others. Even though the technologies using electromagnetic heating, such as radio-frequency and microwave drying, have been researched for decades, the commercial application is still lagging behind the other types. The commercial-level scale-up of radio-frequency drying is limited by the large number of parameters that control the drying efficiency, such as dielectric, physical, and thermal characteristics of the biomaterial to be dried, voltage of electrode, electrode distance, etc. All these factors result in non-uniform heating and uneven distribution of temperature . Hence, there is ongoing research on novel drying technologies such as halogen drying and refractive window drying [ 269 – 271 ]. Refractive window drying has recently been researched for its specific indirect heating of the material and its potential application for low-temperature and short-time processes to dry delicate, heat-sensitive products . This novel drying technique is based on all three modes of heat transfer through conduction, convection, and radiation. It is ideally suitable for liquid materials where high moisture material is spread over a thin infra-red film; the refractive indices of the water and the material become similar, reducing reflection at the interface and enhancing the transmissivity of radiant energy to the product. The method is reported to maintain product temperature between 60–70 °C due to evaporative cooling and convective heat transfer to the ambient air above the material . Despite the greater research and development in novel drying techniques, the commercial application of these techniques in the biopharmaceutical and nutraceutical food industries is limited by various factors such as cost, scalability, infrastructure requirement, technical expertise, etc. Implementing AI and machine learning has demonstrated its efficacy across various sectors, including biopharmaceuticals [ 275 – 277 ], drying technologies [ 278 – 280 ], and agri-food quality monitoring [ 281 – 283 ]. Alongside automation, incorporating AI and machine learning within drying technologies has unveiled new avenues for enhancing process efficiency. Machine learning algorithms can analyze historical and real-time data, enabling the prediction of optimal drying conditions and swift responses to changes in process variables. Likewise, predictive models serve to optimize drying schedules, reduce energy consumption, and improve product quality. Moreover, advanced control systems developed with the help of AI and profound learning neural networks can learn, adapt, and make autonomous decisions based on complex data inputs, thus managing the nonlinear and dynamic nature of drying processes [ 278 , 280 , 284 – 286 ]. Drying technologies for functional foods and biopharmaceuticals have made remarkable strides, yet the future has potential for further innovation and refinement. A fundamental challenge lies in developing drying techniques that balance energy efficiency, cost-effectiveness, scalability, and preservation of nutritional and bioactive properties. To discern patterns and trends of advancement and innovations in the drying of biopharmaceuticals, nutraceuticals, and functional foods, a network visualization map was generated using VOSviewer as shown in Fig. 3 . The figure visualizes the trends in drying of biopharmaceuticals, nutraceuticals, and functional foods over the years classified with keywords/terms with a technique of full counting generating 5 clusters of keywords. Based on the network visualization map, the advancement and innovations landscape in the drying of biopharmaceuticals, nutraceuticals, and functional foods showed that the earlier years of studies and focus were more skewed to the application of spray drying and micro-encapsulated spray-drying for biopharmaceuticals, nutraceuticals, and functional foods. This trend in technology and research has turned more towards hybrid drying systems and the application of machine learning, AI and IoT technology for improved hybrid drying systems for better drying efficiency and techno-functional properties of the final products in recent years. Fig. 3 Network visualization map on advancement and innovations landscape in drying of biopharmaceuticals, nutraceuticals, and functional foods In the meantime, as discussed in " Advancements and Future Directions in Drying Technoligies " section, the digital age allows further integration of advanced technologies into these drying methods. Artificial intelligence, machine learning, and smart sensor technologies can profoundly transform drying processes. These technologies enable superior control, permit real-time modifications, and facilitate comprehensive optimization of drying processes, effectively ushering in a new era of intelligent and responsive drying techniques. These technologies use advanced sensors, data analytics, automation, and connection to improve drying efficiency, reduce energy consumption, and maintain product quality and safety. Preliminary studies on the IoT-based control system for smart drying technologies demonstrated the potential to preserve food's functional qualities and nutraceutical values, such as rehydration capacity, crude fibre, protein, and vitamin C levels, etc., compared to conventional drying method counterparts . In parallel with technological innovations, the shift towards sustainable production systems necessitates a thorough understanding of the environmental impact and sustainability of drying techniques. This includes an evaluation of energy consumption, water usage, waste production, and how these techniques align with evolving regulatory requirements worldwide. The specific energy consumption (SEC) of drying technologies refers to the energy required to remove a unit of moisture from a product. Even though SEC is considered a good indicator of the energy performance of drying methods, it is often not proportional to the techno-functionality of this drying technology application in biopharmaceuticals and nutraceuticals. Figure 4 compares available data on SEC of different drying methods . For instance, freeze-drying (lyophilization) is often preferred for biopharmaceuticals and certain nutraceuticals despite its relatively high energy consumption compared to other drying methods . It has been reported to have a lower specific energy consumption (SEC) than freeze-drying, although it reduces total phenolic compounds . Consequently, a hybrid infrared-freeze drying method has been reported effective for bioactive compounds to combine the benefits of both techniques, ensuring quality and energy efficiency . Therefore, the choice of drying technology involves a trade-off between energy efficiency (as measured by SEC) and other factors such as product quality, safety, and regulatory compliance. Fig. 4 Comparison of specific energy consumption (SEC) for different drying techniques [ 161 , 174 , 245 , 296 – 299 ] Lastly, it is crucial to continue exploring the challenges and limitations inherent in different drying techniques, the potential integration of drying techniques for enhanced quality and sustainability, and the dedication of research efforts to finding potential pathways for digital transformation in the automation of these drying systems. This exploration will shape the evolution of drying technology, ensuring its practical viability and suitability for both functional foods and biopharmaceuticals. The process of drying functional foods and biopharmaceuticals poses unique challenges to industries due to the heat-sensitive nature of these products. Consequently, selecting the appropriate drying strategy and methods requires careful consideration, as different products require varying initial conditions to maintain their bioactive and functional components. Ongoing research focuses on enhancing existing systems and designing innovative hybrid solutions to improve drying outcomes. Functional foods and biopharmaceuticals are commonly dried under controlled conditions - either at lower temperatures or higher temperatures for brief periods - to safeguard the intrinsic functional properties. Critical to this process is a deep understanding of particle engineering for optimal rheology and microstructure and devising product-specific drying strategies. This understanding is fundamental given that several chemical instabilities, such as oxidation, aggregation, chemical bonding, and glycation, are commonplace in biomolecules. As such, optimizing various drying methods, including freeze-drying, vacuum drying, and superheated steam drying techniques, is essential for each category of these products, necessitating a comprehensive study of their nutritional and functional properties. The ongoing evolution of drying techniques is pivotal for the future of functional foods and biopharmaceuticals, seeking to balance quality retention, efficiency, and industrial feasibility in an ever-changing landscape. This comprehensive account of the advantages and limitations of each commonly used drying method provides researchers with a critical first building block to devise future innovative modifications to push the state-of-the-art into its future for drying products rich in bioactive volatiles. | Review | biomedical | en | 0.999999 |
PMC11698305 | The environment is getting increasingly contaminated with xenobiotic pollutants, including polycyclic aromatic hydrocarbons (PAHs) mainly due to the expansion and rapid rise in industrialization. These hazardous PAHs are ubiquitous and pose a potential threat to ecosystems including human beings as they are mutagenic, carcinogenic, and endocrine disruptors . Sixteen PAHs are recorded as priority contaminants by the United States Environmental Protection Agency (USEPA) because of their deleterious effects on ecosystems . PAHs are a group of organic compounds composed of two/more merged benzenoid rings. They are ubiquitous in the environment that originate both from natural phenomena such as fires and volcanic activities, and from anthropogenic sources like fossil fuel combustion, carbon black processing, coal tar, and petroleum seepage [ , , , , ]. More aromatic rings, more structural angularity, and higher hydrophobicity make PAHs more electrochemically stable, persistent, and biodegradation resistant making them difficult to degrade . Zhang and Tao stated that the global PAHs emission was 530,000 tons into the atmosphere resulting from various man-made actions like biofuel combustion (56.7 %) and wildfire (17 %) in 2004. While traffic fuel, and domestic coal burning accounted for 4.8 %, and 3.7 % respectively. China is the major contributor of PAHs emission with 114,000 tons, followed by India with 90,000 tons, the USA contributing 32,000 tons . Through dry or wet deposition processes, atmospheric PAHs accumulate in water, soil, sediment, and vegetation . In fact, soil and sediment are considered as long-term repository of PAHs and, as such, soil can be considered as a representative of the pollution status with respect to the PAHs . The low vapor pressure and high hydrophobicity of PAHs higher benzenoid rings result in their intense adsorption to soil particles . In research by Hoffman et al. 79 %–93 % of PAHs were linked with suspended solids, indicating that their physical state is mostly solid. The absorption and translocation of PAHs by plants can result in groundwater and food contamination as a result of their accumulation in soil. Exposure to PAHs is inevitable and primarily occurs through ingestion, inhalation, and dermal contact . One cigarette can introduce 20–40 ng of benzo [a]pyrene to smokers, while non-smokers can expose themselves to up to 70 % of their PAHs through their diet . Conventionally, PAHs are categorized into two groups based on their molecular weight. The low molecular weight PAHs (LMW-PAHs) include naphthalene (NAP), acenaphthene (ACE), fluorene (FLR), phenanthrene (PHE), and anthracene (ANT), all of which contain fewer than four rings. The high molecular weight PAHs (HMW-PAHs), which contain four or more rings include compounds such as pyrene (PYR), chrysene (CHRY), fluoranthene (FLU), benzo [a]anthracene (BAA), benzo [a]pyrene (BAP), benzo [b]fluoranthene (BBF), benzo [k]fluoranthene (BKF), dibenzo [a,h]anthracene (DBA), benzo [g,h,i]perylene (BghiP), and indeno [1,2,3-c,d]pyrene (ICP) . HMW-PAHs, including PYR, BAP, and BBF, are generally resistant to microbial degradation due to their low solubility and bioavailability, making them persistent in the environment, resistant to degradation and prone to bioaccumulation . BAP is linked to cardiopulmonary disorders, psychiatric conditions, and various cancers, including skin, lung, bladder, and gastrointestinal cancers . NAP, PYR, FLR, and ANT have been associated with pulmonary disorders, while CHRY induces oxidative stress and cytotoxicity in human hepatocytes . BAA, BAP, and NAP are known to be embryotoxic as reported in animal studies . Skin contact with PAHs like NAP, ANT, and BAP can cause irritation, allergic reactions, and inflammation . PAHs such as PHE and FLR induce oxidative stress and inflammation in human lung epithelial cells . PAHs are associated with skin, bladder, lung, and gastrointestinal cancers. Short term exposure to PAHs leads to headaches, nausea, and eye irritation, whereas long-term contact causes respiratory difficulties, lung infection, asthma, and various cancers . PAHs are carcinogenic due to the presence of bay or K region in their molecular structure, directly associated with the generation of bay or K region epoxides, which are extremely reactive and have high affinity towards mammalian DNA . Furthermore, this might lead to the creation of DNA adducts, which can alter normal cells into tumorigenic cells, exacerbating the health hazards linked to PAHs . Fig. 1 displays the interactions between PAHs and DNA and other cellular organelles, specifically demonstrating cytotoxicity and genotoxicity. Remediation measures are implemented to address these problems, combat PAH contamination, and protect both humans and the environment . Several techniques for reducing the toxicity of PAHs by degradation or transformation, including physical, chemical, and biological mechanisms that can be used to protect the ecosystem and eradicate the toxicity of PAH contamination, which is crucial for remediation . Fig. 1 Toxicity of PAHs in soil and marine ecosystem. Fig. 1 Nanomaterials have drawn significant attention due to their unique properties which motivates for potential applications, particularly in bioremediation . Their small size and large surface area-to-volume ratio offer distinct advantages for increasing the adsorption and biodegradation of hazardous contaminants like PAHs in contaminated soil . However, the nanomaterials have certain limitation such as nanotoxicity, antimicrobial properties, high production cost, and poor eco-sustainability. Therefore, nanomaterials such as carbon nanotubes (CNTs), biopolymers, metal-oxide, and nanoscale zeolites, combined with microorganisms, provide a sustainable, eco-friendly, and resourceful approach to remediation . Nanoparticles (NPs) are particularly suitable for PAH bioremediation due to their larger surface area, offering abundant active sites for PAH adsorption . They immobilize and concentrate PAH molecules through interactions like Van der Waals forces, hydrophobic interactions, and π-π stacking facilitating degradation . Some nanomaterials also possess catalytic properties, accelerating PAH breakdown. Yang et al. found that the photocatalytic degradation of FLT in soil was facilitated by a graphitic carbon nitride/iron (III) oxide (g-C3N4/α-Fe2O3) photocatalyst when exposed to sunlight. A dual-doped FeCo/NC catalyst exhibited 24 % better catalytic performance compared to catalysts doped with only Fe or Co, this catalyst was capable of effectively removing 98.87 % of ANT from the soil within 6 h . The interaction between microbes and nanomaterials is complex and varies based on bacterial strain and NPs properties, with mechanisms suggesting bacteria may secrete enzymes immobilized on nanomaterials to aid PAH adsorption and degradation . S-layers and lipopolysaccharides in bacterial cells play a crucial role in NPs attachment and contaminant degradation, affecting biofilm development and microbial colonization. Combining nanomaterials with biochar, enhances bioremediation by increasing porosity and surface area, promoting microbial colonization and PAH adsorption . Nanomaterials can also be designed for the controlled release of agents or enzymes, allowing sustained PAH degradation over time. The synergistic effect between nanomaterials and microorganisms improves PAH uptake and degradation, while the easy dispersion of NPs in the environment ensures better distribution and accessibility to contaminated areas. Most reviews in the scholarly databases focused on either nanomaterial-based remediation or microbe-mediated PAHs remediation, rather than the combined approach of bioremediation and nanomaterial application. Very few articles addressed the integration of these two remediation approaches . Basak et al. provides the first review offering information on the global use of functionalized NPs for aromatic hydrocarbon remediation. However, none of the reviews provide a clear view on how much the efficiency of PAHs degradation increases while incorporating nanomaterial in bioremediation. There is a lack of systematic reviews, current research compilation, and secondary data analysis on how nanomaterials enhance bioremediation for eliminating PAHs. As such, no strategies for nano-bioremediation of PAHs have been mentioned so far. To overcome this literature gap, this review offers an overview of how the application of nanomaterials influences bioremediation. It also discusses the current research scenario focusing on emerging trends in the use of specific types of PAHs, nanomaterials, and microbes. The primary objective of the article is to systematically review studies involving both bioremediation and nano-bioremediation of PAHs, discussing the different nanomaterials used and their attributes. It investigates how these nanomaterials influence microbial degradation, thus improving the remediation of PAHs in both liquid and soil environments. Another objective of the study to identify current research trends and international collaborations on this topic. The paper also focuses on different strategies of microbes-mediated nanomaterial for PAHs degradation, highlighting the synergistic effects and mechanisms through which nanomaterials enhance microbial degradation of PAHs. Additionally, it addresses the challenges and future direction involved in using nanomaterials and their implications for remediation. A comprehensive and impartial systematic literature search was conducted using Web of Science, Scopus, and Google Scholar for the keywords “Nanomaterials”, “nanoparticles”, “nanotechnology”, “bioremediation”, “microbial degradation”, “biodegradation”, “Polycyclic Aromatic Hydrocarbons”, and “PAHs” from 2013 to 2023. Boolean search terms combined “AND” logic to widen the scope and “OR” logic within each category to limit irrelevant papers. A total of 4731 articles were exported from the mentioned databases. The review focused on current experimental research, so review papers, book chapters, and duplicates were excluded during the screening process. This filtering resulted in 360 research articles being retained. After reviewing the abstracts and full texts, 26 research publications were selected based on criteria such as the use of standard analytical methodologies and efficient PAH measurement instruments. Additionally, each article included information on the bioremediation and nano-bioremediation PAH degradation percentages for better understanding and analysis. The articles were precisely analyzed for specific data such as the percentage of removal, and the initial and final concentrations of PAHs. If the articles did not directly mention this information but depicted it, the data was extracted directly from the articles by using software like GetData Graphical Digitizer when necessary. The inclusion criteria were directed toward only those datasets that provide a direct comparison between nano-bioremediation and traditional bioremediation under the same experimental conditions. The methodologies and filters used during the literature search are followed by a Prisma flowchart . Fig. 2 PRISMA flowchart for literature search. Fig. 2 The exported dataset was utilized to construct a co-occurrence network using VOSviewer software , trend topics and country wise contributions were analyzed using the Biblioshiny Package in R Studio version 2023.06.1. Fig. 3 illustrates a fractionalization network generated using VOSviewer software , where five co-occurrence keywords have been precisely screened and aligned. These keywords represent the most commonly occurring search terms in the research articles analyzed. In this analysis of keyword co-occurrence, a total of four distinct clusters were identified. Varying sizes and colours, highlighting the anecdotal relations, represent the connecting lines between co-occurring keywords and each cluster. Cluster 1 (Red): Bioremediation, sustainable approach employs specific bacterial strains to degrade petroleum hydrocarbons like PAHs found in crude oil. To enhance this process, biosurfactants are produced by these bacteria, which aid in emulsifying the oil, making it more accessible for degradation. Moreover, NPs, such as iron or titanium dioxide (TiO 2 ), can be employed in combination with immobilized microbes, enhancing the efficiency of PAH and crude oil degradation. Cluster 2 (Green) Nano-enhanced biodegradation synergistically employs TiO 2 NPs to catalyze the photocatalytic degradation of PAHs while harnessing bacteria to enhance the degradation of pollutants such as PAHs, crude oil, waste water, and other organic pollutants. Cluster 3 (Blue): Nanomaterials have the potential to substantially alter the bioavailability of organic pollutants like PAHs through enhanced sorption properties, affecting their environmental mobility. Microbes, in turn, play an important role in the biotransformation of these sorbed pollutants, influencing their ultimate fate and environmental impact. Cluster 4 (Yellow): In contaminated or polluted soil, the collaborative act of microbes and NPs such as zero-valent iron initiates the efficient biodegradation of PAHs, offering a sustainable approach to soil remediation. Fig. 3 Keywords co-occurrence network analysis. Fig. 3 Nanomaterial-assisted bioremediation of PAHs and other organic pollutants has emerged as a significant trend within the broader scientific landscape. The data reveals that while nanomaterials and bioremediation were trending topics independently and their convergence gained prominent attention from 2019 to 2022 (high frequency) depicted in Fig. 4 a. This synergy offers a promising avenue for addressing the persistent challenge of PAH contamination in the environment. Nanomaterials, including NPs and nanofibers, have shown significant potential for enhancing the performance of bioremediation processes. The porous nanofibers generated through electrospinning cause a substantial surface area that enables the electrostatic attraction of various chemical groups, resulting in the formation of ion exchange membranes. The surface groups of these membranes can be either derived from the polymer or introduced through surface modification. They can be positively, negatively, or both charged . Recent research has been facilitated by advancements in nanotechnology, resulting in the immobilization of microbes or enzymes on electrospun polymeric fibres. PHE, FLT, BAA, and BAP were all removed with efficiencies exceeding 95.1 %, 93.2 %, 79.1 %, and 72.5 % in 6 h, respectively, using laccase carrying electrospun nanofibers. Here, laccase was utilized as the biocatalyst for the degradation of PAHs . By emulsion electrospinning, laccase carrying electrospun fibrous membranes with high laccase catalytic activity and sorption capacity were fabricated, eliminating BAP at 70 % and PHE at 93.8 % from contaminated water. The degradation efficiencies of PAHs by laccase could be noticeably improved by the sorption of PAHs on the laccase-carrying electrospun fibrous membranes, which is higher than free enzyme . These materials can serve as carriers, catalysts, or immobilization matrices for microorganisms, such as Mycobacterium or Arthrobacter , facilitating their action in degrading PAHs. Additionally, discussions on the bioavailability of pollutants, such as PAHs, have been prominent from 2016 to 2022, highlighting the importance of understanding how nanomaterials can improve the accessibility of these compounds to biodegrading microbes. This trend reflects the global growing interest in developing sustainable and effective remediation techniques for PAH contamination. Fig. 4 Bibliometric analysis 4a) Global landscape of nano-bioremediation research on PAHs: Country contributions; 4b) Convergence of nanomaterials and bioremediation trends. Fig. 4 Metal NPs, including iron (Fe), silver (Ag), and gold (Au), have received extensive attention in the field of PAH bioremediation due to their considerable surface area and reactivity. These NPs act as carriers for PAH-degrading enzymes or electron mediators, thereby aiding in PAH degradation. Apart from metallic NPs, various other nanomaterials, such as photocatalytic nanomaterials, biopolymers, and CNTs, have also been explored for their potential to address PAH contamination . NPs have proven effective in breaking down PAHs in both water and soil environments. In the study by Al-Zaban et al. study, indigenous fungi, specifically Aspergillus flavus and Trichoderma harzianum , were employed for in situ crude oil bioremediation by employing silver NPs (AgNPs). As much as 57.8 % crude oil degradation was achieved within one week of incubation under optimum conditions at 30 °C, pH 7, 4 g/L of crude oil concentration, 0.05g of AgNPs, and two fungal strains with equal proportion. This demonstrates the potential of low concentrations of NPs in enhancing the biodegradation process, emphasizing the role of nanomaterials in environmental remediation. Metal oxide NPs (e.g. Fe 3 O 4 , ZnO, and TiO 2 ) exhibit potent photocatalytic properties, enabling the degradation of PAHs when exposed to light. These nanomaterials effectively break down PAHs within the aquatic and soil environments . Innovative 3D-printed photocatalyst-polymer composites are also promising for PAH degradation in complex mixtures . The microbial strain B . licheniformis showed its bioremediation efficiency to crude oil especially when combined with biosurfactant and NPs (Zn 5 (OH) 8 Cl 2 at 0.1 g/100 ml concentration or Fe 2 O 3 at 0.2g/100 ml concentration), was 60 % of crude oil . Oyewole et al. observed that Alcaligenes faecalis , in combination with iron oxide NPs and biosurfactants, exhibited increased crude oil biodegradation of up to 84 % of petroleum from contaminated soil. Biosurfactants prevented NPs oxidation and aggregation, enhancing their reactivity for petroleum remediation. According to Parthipan et al. , the iron NPs (Fe 3 O 4 ) were employed to enhance the mineralization process of blend PAHs (PYR, ANT and BAP) by a bacterial consortium composed of Pseudomonas stutzeri and Acinetobacter baumannii . An integrated approach, involving B . subtilis producing biosurfactants, iron NPs, and the bacterial consortium led to an impressive 85 % degradation of mixed PAHs. The inclusion of iron NPs not only enhanced microbial biomass but also facilitated the adsorption of PAHs, ultimately contributing to the proficient removal of these complicated contaminants found in soil and aquatic surroundings. CBMs are composed of sp2-hybridized carbon atoms, exhibiting multi-dimensional hybridization. CBMs, such as CNTs, carbon quantum dots (CQDs), carbon black, graphene oxide, and graphene, constitute a versatile class of nanomaterials well-known for their proficiency in PAH adsorption . These nanomaterials could be further improved by incorporating functional groups like carboxyl groups, hydroxyl, and carboxylic acid, thereby imparting hydrophilic properties. This modification enables them to effectively sorb polar compounds with relatively lower molecular weights . CQDs are an advanced carbon nanomaterial that are universally employed in a variety of disciplines due to their exceptional properties. CQDs have been employed to enhance the electronic transmission capabilities of binary semiconductor nanomaterials . Fluorescence, water solubility, biocompatibility, low toxicity, small size and ease of modification, inexpensive scale-up production, and versatile conjugation with other NPs are among the numerous advantageous properties of CQDs . CQDs utilizes selective absorption wavelength for PAHs elimination . Labedella gwakjiensis demonstrated potent PAHs biodegradation capabilities in saline oil-contaminated soils. With the addition of carbon quantum dots iron oxide (CQD.Fe 3 O 4 ) NPs at a concentration of 0.5 g/L significantly enhanced PAHs degradation, particularly PHE, with a degradation rate of 63.63 % and 81.77 % after 48 and 72 h, respectively . The use of graphene-based NPs for PHE extraction has proven to be successful reported by Zhao et al. . Mahpishanian et al. effectively utilized a composite of graphene oxide nanosheets and silica-coated Fe 3 O 4 microparticles, modified with 2-phenylethylamine, for the isolation of a variety of PAHs from aqueous solutions. The hybrid nanostructure formed by gold NPs immobilized in laccase enzyme (laccase-AuNPs@vesicles) achieves a 98.5 % reduction in 4-nitrophenol, surpassing free laccase efficiency by 2.3 times, indicating its superior catalytic performance in environmental remediation . Graphene oxide has proven effective as the substrate for immobilizing bacteria, offering protection in intricate soil environments and enhancing their capabilities in addressing hydrocarbons polluted sites . CNTs, such as single-walled CNTs (SWCNTs) and multiwalled CNTs (MWCNTs) are valued for their chemical stability, thermal resistance, strong adsorption, pH tolerance, and π-π and Van der Waals interactions with PAHs making them excellent adsorbents for contaminant removal . A magnetic adsorbent was created by blending Fe 3 O 4 NPs, MWCNTs, and polypyrrole. Interactions between π–π were crucial, facilitated by CNTs and polypyrrole's π bonds. Polypyrrole's –NH 2 groups aided material dispersion in water. Versatile iron and carbon composites are also promising. NPs like ZnFe 2 O 4 were enclosed in a carbon matrix for superparamagnetic C/ZnFe 2 O 4 . The nanocomposite effectively removed NAP and 2-naphthol from water, driven by electrostatic interactions between PAHs and C/ZnFe 2 O 4 for efficient pollutant remediation . Magnetic NPs are a potential agent for environmental remediation facilitated by huge surface area, magnetic receptiveness, and affluence of functionalization . Utilization of magnetic chromium ferrite (CrFe 2 O 4 ) NPs aid in the degradation of ANT, PHE and NAP up to 99 %, 90 %, and 86 % respectively . Composites are solid materials with at least one phase <100 nm. Combining metallic NPs and nano supports creates nanocomposites, boosting surface area and adsorption. They protect microorganisms, preventing degradation by toxins, making them ideal for removing pollutants. Magnetic activated carbon nanocomposite from green tea leaf waste effectively removed PAHs, adsorbing them at rates of 28.08, 22.75, 19.14, and 15.86 mg/g for BBF, BAP, CHRY, and BAA respectively . In another study, Mukwevho et al. synthesized a ZnO/Ag/GO nanocomposite, preserving ZnO hexagonal structure. That nanomaterial demonstrated a notable adsorption capacity of 500 mg/g for NAP removal. Through photodegradation, it achieved an 80 % reduction in NAP within 20 min, with further enhancement to 92 % reduction in 50 min. Rani et al. reported that iron oxide based chitosan nanocomposites, specifically ZnFe₂O₄-CS, achieved degradation efficiencies of 95 % for ANT and 92 % for PHE. The NiO–ZnO bimetallic oxide nanocomposites able to degrade 2 ppm of PAHs, achieving 98 % degradation of ANT and 93 % of PHE within 12 h under sunlight exposure . TiO 2 based zinc hexacyanoferrate framework (TiO 2 @ZnHCF) nanocomposite demonstrated superior photocatalytic degradation of PAHs, achieving removal rates of 93%–96 % in water, 82%–86 % in soil, and 81.63%–85.43 % in river sediment . The bacterial consortium of Flavobacterium johnsoniae and Shewanella baltica , immobilized on a goethite-chitosan nanocomposite, achieved a maximum PAHs degradation efficiency of 93.32 % within 3 days . Bioremediation of crude oil in polluted surface water can be effectively achieved using specialized alginate-based nanocomposite beads containing iron oxide NPs immobilized with Bacillus, Pseudomonas , and Klebsiella pneumoniae on biochar, able to degrade 93.7 % of PAHs . This nanocomposite shows promise for efficient PAHs degradation. MOFs are highly specialized nanomaterials characterized by their unique porous and crystalline structure, created through the coordination of metal ions or clusters with organic ligands. MOFs are renowned for their exceptional surface area and customizable porosity, reusability, and versatile design options, making them invaluable for gas storage, separation, catalysis, and drug delivery applications. These materials offer a precisely engineered structure, allowing them to be tailored for specific uses. MOFs, along with other advanced nanosorbents like nano-polymers, represent the forefront of petroleum wastewater treatment, showcasing their potential in the efficient removal of pollutants like PAHs . Using bimetallic metal-organic frameworks with 1,4-benzenedicarboxylic acid activated through peroxymonosulfate, 99 % of PHE removal efficiency was reported at pH 3.15, 1.0 mg/L PHE, and a reaction time of 30 min . While MOFs offer benefits like low energy consumption and high efficiency, it often suffers from instability in aqueous environments, leading to phase changes, loss of crystallinity, and structural decomposition. Research suggests that incorporating secondary metal nodes can enhance both catalytic activity and stability, resulting in bimetallic MOFs. Peroxymonosulfate activated molecularly imprinted bimetallic MOFs (Al/Co-MOFs@MIP) effectively target the removal of PAHs from soil washing effluents, resulting in 94 % PHE degradation in 1 h 30 min . The study found that using nanomaterials like zeolite imidazolate framework-8 (ZIF-8) and combining it with citric acid (CA) greatly improved the removal of PHE by B . subtilis . The integration of ZIF-8 and CA significantly improved the growth and cell viability of Bacillus subtilis ZL09–26, while also reducing the toxic effects of PHE stress. Acting as an anionic surfactant, CA modified the surface charge of ZIF-8, facilitating the formation of a biomimetic mineralized protective shell around the bacteria. This ZIF-8-CA coating, characterized by surface roughening and particle aggregation, effectively encapsulated the bacteria, enhancing their ability to degrade PAHs. Compared to the control condition ( Bacillus subtilis ZL09–26 alone), these nano-modified conditions showed significant increases in PHE removal rates. Particularly, Bacillus subtilis ZL09–26@ZIF-8-CA was highly influential, removing 94.14 % of PHE in just 6 days. These highlights are the vital character of the nanomaterials in enhancing PAHs cleanup when combined with bacteria . Recently, as an emerging technology, interest growing in utilizing nanomaterial-based tools for practical solutions for the remediation of various pollutants in contaminated sites. In this regard, a wide array of nanomaterials has been introduced into the market as nanosorbents, offering enhanced capabilities for treating water contaminated with PAHs, ultimately making it suitable for reclaim . Nanomaterials possess structural elements with dimensions ranging from 1 to 100 nm (nm) in at least one dimension. These materials stand out due to their unique characteristics, notably a significantly increased surface-to-volume ratio as well as enhanced magnetic and catalytic traits when compared to their bulk materials . Nanomaterials are characterized by their diminutive dimension, which enables them to have a greater surface-to-volume ratio. This attribute allows them to engage in more significant interactions with the molecules in their environment. As the dimension decreases, the concentration of ions on the surface increases, thereby increasing their reactivity. Nanomaterials possess distinctive characteristics that render them highly desirable in various applications, such as catalysis and sensing . In many cases, nanomaterials exhibit distinctive magnetic properties. By treating hydrocarbon-degrading microbial cells like Alcanivorax borkumensis having a positively charged polymer i.e., polyallylamine hydrochloride and layered magnetic nanomaterials, a protective shell of about 70–100 nm was formed on the cell wall. Polycation coated magnetic NPs utilize the direct deposition of positively charged iron oxide NPs onto microbial cells during brief incubation in high NP concentrations. Gram-negative bacteria have cell walls with a thin peptidoglycan layer between the outer membrane and inner plasma membrane. The presence of lipopolysaccharides gives the cell walls a negative charge, attracting cationic particles through electrostatic interactions. These intact cells exhibit a negative potential of −16 mV, facilitating the rapid deposition of cationic magnetic complexes on the bacterial cell walls. A cationic charge of polyallylamine and 20 nm iron oxide NPs enables a swift, single-step encapsulation process by exploiting electrostatic interactions with bacterial surfaces. The cationic polycation-coated magnetic NPs act as electron donors, while the negatively charged bacterial cell walls serve as electron acceptors. This electrostatic interaction facilitates the rapid deposition of cationic magnetic complexes on the bacterial surfaces, forming a charge transfer complex . This innovative approach enhances the efficacy and endurance of cells in the degradation of hydrocarbons. The extensive surface area of nanomaterials leads to a highly active reaction environment, which in turn enhances their catalytic efficacy. Quantum size effects at the nanoscale enhance the efficacy of catalytic processes by modifying electronic structures. Nano-catalysts enhance reaction selectivity at lower temperatures, reduce side reactions, promote recycling, and reduce environmental and health risks. These advances in catalytic nanotechnology promote greener and more sustainable processes by replacing low-quality materials with NPs . Immobilized microbial cells not only improve process stability and catalytic efficiency but also simplify the cell recovery for subsequent reuse. Enzymes can be fixed onto NPs via methods such as adsorption, entrapment, covalent bonding, or membrane confinement. Immobilized enzymes exhibited approximately a twofold increase in catalytic activity compared to their native counterparts, primarily due to heightened surface hydrophobic nature. Their stability depends on the number of bonds formed between the NPs and enzymes . Acevedo et al. harnessed manganese peroxidase (MnP) from the chilean white-rot fungus Anthracophyllum discolor , immobilizing it onto nano clay (100 nm) derived from volcanic soil. Interestingly, physical adsorption process successfully immobilized 75 % of the enzyme. The immobilized MnP demonstrated superior PAH degradation, particularly with PYR (>86 %) and ANT (>65 %) individually. It also exhibited some capacity to degrade FLT (<15.2 %) and PHE (<8.6 %). Nanomaterials possess remarkable mechanical properties, including high strength and flexibility. This is attributed to an increased density of defects and interfaces in nanoscale structures. CNTs and graphene are especially well-known for their exceptional mechanical strength, with applications in nanocomposites [ , , ]. Combining crude enzymes from Trametes maxima and Paecelomices carneous within alginate beads and trimetallic TiO 2 –C–Ag NPs enhances their mechanical stability and resistance to protease attack. This hybrid nanomaterial exhibits increased PHE removal capacity, achieving 94.3 % removal in continuous mode . Nanomaterials possess distinct electrical conductivity properties due to their size and unique characteristics, differing from macroscopic materials. However, they generally exhibit lower thermal and electrical conductivity than bulk materials. Table 1 , Table 2 provide a comparative analysis of nano-bioremediation and bioremediation for PAHs remediation under the same experimental design, specific to liquid and soil samples, respectively. The compilation of data extracted from 26 current research papers focusing on the assessment of bioremediation rates, initial and final concentrations of PAHs, as well as nano-bioremediation rates, type of nanomaterial used initial and final concentrations, and various other parameters ( Tables S1 and S2 ). Zhoa et al. reported that reduced graphene oxide demonstrated a 99 % degradation of NAP in nano-bioremediation with Burkholderia cepacia, compared to 67.3 % with bioremediation alone. Therefore, addition of nanomaterial in bioremediation increased the PAHs remediation. The degradation condition for ANT was achieved using titanium dioxide NPs with Alcaligenes faecalis in a nano-bioremediation approach, resulting in a 37.9 % degradation, compared to 24.2 % with Alcaligenes faecalis . In bioremediation (phytoremediation) without NPs, Proteobacteria, Actinobacteria, Bacteroidota, and Firmicutes achieved a 55.5 % degradation of BBF. Using graphene oxide in a nano-bioremediation approach with the same microbial consortia, the degradation of BBF increased to 74.22 % . Chai et al. reported that bioremediation using lignin peroxidase (LiP)-extracted from Pichia methanolica targeting PHE achieved 23.7 % efficiency, whereas nano-bioremediation with chitosan-modified halloysite nanotubes and Pichia methanolica (LiP) targeting PHE achieved approximately double the removal efficiency at 51.3 %. Table 1 Bioremediation and Nano-Bioremediation for PAHs Degradation in Liquid samples. Table 1 Remediation method Type of NP Bioagent PAHs type Size of NP (nm) Shape of NP PAHs degradation (%) References Bioremediation Without NP Labedella gwakjiensis PHE NA NA 37.12 Nano-Bioremediation Carbon Quantum Dots conjugated with Iron (III) Oxide Labedella gwakjiensis PHE 10 Spherical 81.77 Bioremediation Without NP Alcaligenes faecalis ANT NA NA 10.4 Nano-Bioremediation Titanium Dioxide NPs Alcaligenes faecalis ANT 17.11 Spherical and granular 21.3 Bioremediation Without NP Achromobacter sp. PHE NA NA 56.24 Nano-Bioremediation Titanium Dioxide with biochar Achromobacter sp. PHE 276.1 Stacked graphite sheet structure and rectangular shaped 72.58 Bioremediation Without NP Burkholderia cepacia NAP NA NA 67.3 Nano-Bioremediation Reduced graphene oxide Burkholderia cepacia NAP NM NM 99.0 Bioremediation Without NP Pseudomonas sp. Rhodocucus sp. ANT NA NA 58.3 Nano-Bioremediation Polyimide aerogels Pseudomonas sp. Rhodocucus sp. ANT NM NM 78.6 Bioremediation Without NP Pseudomonas stutzeri and Acinetobacter baumannii ANT, PYR, BAP NA NA 52.6 Nano-Bioremediation Iron nanoparticles Pseudomonas stutzeri and Acinetobacter baumannii ANT, PYR, BAP 134 Spherical 65.7 Nano-Bioremediation Iron nanoparticles Biosurfactant ( Bacillus subtilis ) Pseudomonas stutzeri and Acinetobacter baumannii ANT, PYR, BAP 134 Spherical 85 Bioremediation Without NP Penicillium oxalicum PYR NA NA 72 Nano-Bioremediation Carbon nanotube composites Penicillium oxalicum PYR NM NM 90 Bioremediation Without NP Biofilm from consortia Planococcaceae, Oxalobacteraceae etc PHE NA NA 12.97 ± 0.44 Nano-Bioremediation Copper and Nitrogen co-doped Titanium Dioxide Biofilm from consortia Planococcaceae, Oxalobacteraceae etc PHE 10–30 NM 88.63 Bioremediation Without NP Biofilm from consortia Planococcaceae, Oxalobacteraceae etc PYR NA NA 6.65 Nano-Bioremediation Copper and Nitrogen co-doped Titanium Dioxide Biofilm from consortia Planococcaceae, Oxalobacteraceae etc PYR 10–30 NM 63.89 Bioremediation Without NP Candida tropicalis ICP NA NA 61 Nano-Bioremediation Iron nanoparticles Candida tropicalis ICP 50 Spherical 75 Bioremediation Without NP Rhodotorula sp. Debaryomyces hansenii and Hanseniaspora valbyensis BghiP NA NA 60.0 Nano-Bioremediation Zinc Oxide Rhodotorula sp. Debaryomyces hansenii and Hanseniaspora valbyensis BghiP 10 Rod 60.7 Bioremediation Without NP Rhodotorula sp. Hanseniaspora opuntiae and Debaryomyces hansenii BAP NA NA 76.0 Nano-Bioremediation Zinc Oxide Rhodotorula sp. Hanseniaspora opuntiae and Debaryomyces hansenii BAP 50 Spherical 77.2 Bioremediation Without NP Bacillus thuringiensis PHE NA NA 65.71 Nano-Bioremediation Multi-Walled Carbon Nanotube Buckypaper Bacillus thuringiensis PHE 200 Spherical 93.81 Bioremediation Without NP Sphingomonas sp. PHE NA NA 74.6 Nano-Bioremediation Nano bamboo charcoal Sphingomonas sp. PHE NM NM 93.01 Bioremediation Without NP Paracoccus sp. BAP NA NA 60 Nano-Bioremediation Hematite NPs Paracoccus sp. BAP NM NM 45.8 Bioremediation Without NP Sphingomonas sp. PHE NA NA 69.83 Nano-Bioremediation Nano bamboo charcoal Sphingomonas sp. PHE 401.9 Irregular 94 Bioremediation Without NP Methanosarcina and Methanosaeta, Pseudomonas, Cloastridia, and Synergistetes PYR NA NA 40.8 Nano-Bioremediation Iron (II) Sulfide Methanosarcina and Methanosaeta, Pseudomonas, Cloastridia, and Synergistetes PYR 20–50 NM 77.5 Nano-Bioremediation Magnetic carbon Methanosarcina and Methanosaeta, Pseudomonas, Cloastridia, and Synergistetes PYR 20–50 NM 72.1 Bioremediation Without NP Archaea and methanogen PHE NA NA 60.52 Nano-Bioremediation Magnetite powder Archaea and methanogen PHE 50–100 Spherical 70.94 Nano-Bioremediation Nanoscale Iron (III) Oxide Archaea and methanogen PHE 50–100 Spherical 70.89 NA=Not Applicable, NM=Not Mentioned. Table 2 Bioremediation and Nano-Bioremediation for PAHs Degradation in soil sample. Table 2 Remediation method Type of NPs Bioagent PAHs type Size of NPs (nm) Shape of NPs PAHs degradation (%) References Bioremediation Without NP Alcaligenes faecalis ANT NA NA 24.2 Nano-Bioremediation Titanium Dioxide NPs Alcaligenes faecalis ANT 17.11 Spherical and granular 37.9 Bioremediation (phytoremediation) Without NP Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) FLT NA NA 92.60 Nano-Bioremediation Graphene oxide Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) FLT NM NM 95.57 Bioremediation (phytoremediation) Without NP Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) PYR NA NA 90.11 Nano-Bioremediation Graphene oxide Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) PYR NM NM 95.88 Bioremediation (phytoremediation) Without NP Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) BAA NA NA 87.70 Nano-Bioremediation Graphene oxide Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) BAA NM NM 93.30 Bioremediation (phytoremediation) Without NP Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) CHRY NA NA 88.85 Nano-Bioremediation Graphene oxide Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) CHRY NM NM 91.82 Bioremediation (phytoremediation) Without NP Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) BBF NA NA 55.50 Nano-Bioremediation Graphene oxide Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) BBF NM NM 74.22 Bioremediation (phytoremediation) Without NP Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) BKF NA NA 55.97 Nano-Bioremediation Graphene oxide Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) BKF NM NM 62.00 Bioremediation (phytoremediation) Without NP Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) BAP NA NA 56.15 Nano-Bioremediation Graphene oxide Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) BAP NM NM 67.37 Bioremediation (phytoremediation) Without NP Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) DBA NA NA 58.03 Nano-Bioremediation Graphene oxide Proteobacteria, Actinobacteria, Bacteroidota,Firmicutes etc and Fire Phoenix (Plant Species) DBA NM NM 67.75 Bioremediation Without NP Proteobacteria, Acidobacteria, Gemmatimonadota, Bacteroidota (Soil microbes) PAHs NA NA 19.57 Nano-Bioremediation Graphene oxide Proteobacteria, Acidobacteria, Gemmatimonadota, Bacteroidota (Soil microbes) PAHs NM Irregular 41.07 Bioremediation Without NP Geobacter and Geothrix PAHs NA NA 8.7 Nano-Bioremediation Magnetite NPs Geobacter and Geothrix PAHs 100 Spherical 86 Bioremediation Without NP Pichia methanolica (LiP) PHE NA NA 23.7 Nano-Bioremediation Chitosan-modified halloysite nanotubes Pichia methanolica (LiP) PHE NM Rod 51.3 Bioremediation Without NP Pichia methanolica (LiP) FLU NA NA 25 Nano-Bioremediation Chitosan-modified halloysite nanotubes Pichia methanolica (LiP) FLU NM Rod 38.1 Bioremediation Without NP Soil Microbes NAP NA NA 92.7 Nano-Bioremediation Silver Phosphate on Iron (III) Oxide NP Soil Microbes NAP 20–40 Irregular 93.7 Bioremediation Without NP Soil Microbes ANT NA NA 84.8 Nano-Bioremediation Silver Phosphate on Iron (III) Oxide NP Soil Microbes ANT 20–40 Irregular 91.9 Bioremediation Without NP Soil Microbes PHE NA NA 86 Nano-Bioremediation Silver Phosphate on Iron (III) Oxide NP Soil Microbes PHE 20–40 Irregular 94.6 Bioremediation Without NP Soil Microbes FLU NA NA 68.6 Nano-Bioremediation Silver Phosphate on Iron (III) Oxide NP Soil Microbes FLU 20–40 Irregular 82.3 Bioremediation Without NP Soil Microbes PYR NA NA 63.4 Nano-Bioremediation Silver Phosphate on Iron (III) Oxide NP Soil Microbes PYR 20–40 Irregular 78.8 Bioremediation Without NP Paracoccus aminovorans + Soil microbes PAHs NA NA 46.9 Nano-Bioremediation Graphene oxide Paracoccus aminovorans + Soil microbes PAHs 17.92 Irregular 62.86 Bioremediation Without NP Bacillus cereus, Acidovorax wohlfahrtii, and Bacillus thuringiensis PYR NA NA 88 Nano-Bioremediation Hematite NPs Bacillus cereus, Acidovorax wohlfahrtii, and Bacillus thuringiensis PYR 28–55 Spherical 96 NA=Not Applicable, NM=Not Mentioned. Fig. 5 a and b shows that the degradation of PAHs effectively lowers their final concentration, since there is a negative link between degradation and concentration in both soil and liquid samples as per different studies cited in Supplementary Tables S1 and S2 . A moderate positive association was seen between the presence of bioagents and the degradation of PAHs in both the soil and liquid samples, suggesting that specific bioagents can accelerate degradation in these settings. In contrast, soil samples show a moderate positive correlation with duration and a negative correlation with pH, indicating that higher duration may extend the degradation process and lower pH levels, respectively. Nevertheless, there is a strong negative correlation of temperature, showing that higher temperatures considerably shorten the degradation duration. Liquid samples reveal that the type of NPs affirms a stronger positive correlation with PAHs degradation and bioagent, indicating that NPs play a substantial role in both liquid and soil medium for improving degradation and interacting with bioagents. Temperature significantly influences PAH breakdown in soil samples, time and bioagents exert a minor influence whereas the effects of pH and NP type are minimal. Temperature impacts duration and pH in liquid samples, while bioagents are more important in solid samples. When compared to soil samples, NP type has a more significant impact on bioagent interaction and degradation. Fig. 5 Correlation matrix heatmap for A) Liquid samples B) Soil samples. Fig. 5 The small size of nanomaterials provides with high surface area and are strongly adsorbed on the surfaces of microbial cells. Additionally, electrostatic interactions are a crucial factor of the interacting force between cells and NPs . The desirable modifications in size and shape of nanomaterials improve its functioning, which may offer important advantage for elimination of contaminants . On reviewing, it was generally found that the most frequent sizes of NPs were 40 nm and 50 nm. No studies utilized particles smaller than 10 nm due to their ability to penetrate inside cells, although some involving complex structures exhibited a wider range of nano-sizes. As per the literature, most common shape for nanomaterial was spherical. Spherical shapes often enhance mobility and stability, while irregular shapes may offer more active sites for adsorption. Spherical nanomaterials tend to aggregate less, enhancing their effectiveness in nano-bioremediation. Different shapes of nanomaterials affect their surface area, reactivity, and interaction with contaminants and microorganisms in bioremediation . Biosynthesized palladium NPs, due to their smaller size and higher surface-to-volume ratio, validated superior catalytic performance compared to chemically reduced palladium . Its small size allows it to penetrate deep into polluted regions that microbes cannot reach, thereby extending the applications of nano-bioremediation. Therefore, it is crucial to understand the interactions between NPs and microbes. However, the small size of NPs presents both advantages and limitations. Less soluble NPs, such as gold, platinum, and silver, are generally less toxic compared to others. NPs sized 1–12 nm can enter microbial cells, stimulating the production of reactive oxygen species and thereby reducing microbial growth . El Bestawy et al. found that NPs (10–20 nm) penetrated cells causing cell destruction when incubated for 6 h. To prevent the toxicity, the experiment runs for 4 h creating effective Fe₃O₄-immobilized bacterial cultures capable of degrading total petroleum hydrocarbons by 85 %. Therefore, research should be focused on optimizing methods to avoid NPs toxicity in soil and water, understanding their interactions with biotic and abiotic agents. The compilation of collected secondary data, irrespective of types of PAHs, microbial strains, nanomaterial types, concentrations, and other experimental setups, yielded a generalized outcome, and its graphical representation is illustrated in Fig. 6 . It was observed that, when compared, traditional bioremediation achieved an average removal efficiency of 52.2 %, while nano-bioremediation exhibited a higher average removal efficiency of 71.1 %. The application of NPs in bioremediation greatly enhanced the efficiency of remediation, resulting in an 18.9 % increase in the elimination of PAHs in liquid samples. Soil sample analysis consistently revealed that traditional bioremediation attained an average removal efficiency of 60.8 %. However, when nano-bioremediation is included, there is a significant increase of 14.3 %, resulting in an average removal efficiency of 75.1 %. The pattern emphasised the exceptional hybrid capabilities of nano-bioremediation in effectively eliminating PAHs from the environment. Fig. 6 Different attributes of nano-bioremediation facilitating PAHs breakdown. Fig. 6 Nano-bioremediation technology offers a potential remedial approach for PAH in contaminated soil, contributing to environmental welfare. Its application enhances water and soil quality, nurturing agricultural productivity, vegetation growth, and microbial survival . An additional advantage is its ability to prevent hazardous compounds from leaching into groundwater . The interaction between NPs functional surfaces and biological interfaces enhances environmental restoration. Emerging NPs types and concentrations provide a solution for recalcitrant PAH biodegradation . Previous research highlights specific roles of NPs in enhancing biodegradation rates. For a decade, environmental specialists have focused on remediating PAHs at contaminated sites, facing multifaceted challenges such as soil properties, secondary pollution, treatment duration, and budget constraints. Physicochemical and biological remediation methods, like incineration, electrokinetic, soil washing, and phytoremediation, have been explored . Physicochemical approaches involve limitations such as high costs and monitoring complications . In contrast, integrating nanotechnology and biological methods offers an environmentally friendly approach to addressing the environmental impact of PAHs contamination. The interaction between microbes and NPs in the adsorption of PAHs is complicated, mainly attributed to the complex molecular structures of PAHs. Mechanistic insights into this interaction remain limited, and studies assuming these mechanisms are not yet fully elucidated. Numerous research endeavors have attempted to elucidate the potential degradation strategies of PAHs, which exhibit variation across microbial strains and NP characteristics . The review explores various strategies for nano-bioremediation aimed at the breakdown of PAHs. Fig. 7 illustrates the key mechanisms of nano-bioremediation strategies for PAH degradation. Nanoparticle-mediated electron transfer demonstrates the transfer of electrons from NP to microbes, initiating reactions that lead to the degradation of PAHs. Enzyme-immobilized NP, where microbes secrete enzymes that are immobilized onto NPs through linkers, enabling effective interactions for PAH breakdown. NP enhances microbial activity by promoting enhanced cell metabolism and new metabolic pathways that contribute to the degradation of PAHs. The microbial cell immobilized NPs, employ techniques such as cross-linking, entrapment, and encapsulation for efficient immobilization and targeted PAH biodegradation. Lastly, integrated nano-bioremediation is mentioned, emphasizing the combined action of nanomaterials and biodegradation pathways for comprehensive PAH remediation. Fig. 7 Nano-bioremediation strategies for enhanced PAH degradation. Fig. 7 NPs offer an advanced means of immobilizing microbial cells or consortia for targeted chemical degradation or reclamation . Immobilization confines microbial cells or enzymes within a solid space, preserving their catalytic activity for reuse. Techniques include cross-linkage, encapsulation, adsorption, and entrapment. Surface-confined microbes exhibit resistance to harsh conditions, enhanced stability, greater biomass, and improved contaminant degradation potential . In contrast to conventional methods involving micron-sized media or fixed surfaces, magnetic NPs like Fe 3 O 4 , functionalized with ammonium oleate, have been applied to coat P . delafieldi . Employing an external magnetic field, these coated microbial cells gather at specific reactor wall sites, separate from the surrounding solution, and can be reused for treating the same substrate. This approach not only enhances microbial activity and strength but also facilitates their recyclability and recovery, maintenance them against adverse environmental conditions . This process presents a comprehensive strategy, ensuring prolonged and effective biodegradation while enhancing cellular resilience to harsh environmental challenges . This strategy influences the immobilization of microbial cells through NPs, presenting the remarkable synergy between nanotechnology and bioremediation for sustainable developmental solutions. Biofunctionalized NPs are created by incorporating microbes, enzymes, or biostabilizers onto their surfaces, enhancing their affinity for PAHs. The process entails the initial adsorption of PAHs onto the NPs surface, followed by π–π interactions that drive degradation. After successful degradation, these NPs can be efficiently recycled for additional rounds of PAHs remediation, illustrated in Fig. 8 . An exogenous bacterial consortium ( Enterobacter cloacae and P . otitidis ) immobilized with Fe 3 O 4 NPs achieved prominent oil degradation efficiencies, with the highest removal rates of total petroleum hydrocarbons, and grease recorded at 85 %, and 83.9 %, respectively, within just 4 h . Fig. 8 Mechanisms of biofunctionalized nanoparticles in enhancing PAHs degradation. Fig. 8 One potential mechanism involves the secretion of specialized enzymes by bacteria, which become immobilized on NPs, facilitating the adsorption of PAHs and subsequently mediating their degradation. Alternatively, bacteria may efficiently degrade PAHs extracellularly, but its efficiency can be upregulated by applying NPs. The nature and strength of interactions between microorganisms and NPs are likely crucial factors influencing the enhanced biodegradation of PAHs . Enzymes functioning as biocatalysts in bioremediation are highly specific and effective, but their practical utility is hindered by instability and short lifetimes . Oxidation reduces enzyme stability and efficiency, limiting their application as cost-effective alternatives to synthetic catalysts. NPs offer a solution to address this by magnifying enzyme stability, longevity, and reusability. Binding enzymes to these magnetic NPs enable easy separation using a magnetic field, significantly prolonging their activity from hours to weeks . The study employs trypsin, peroxides, and catabolic enzymes uniformly coated onto core-shell magnetic NPs, demonstrating a remarkable increase in enzyme lifetime and activity. These nanoparticle-enzyme conjugates prove to be more stable, efficient, and cost-effective, shielding enzymes from oxidation. The magnetization of these NPs allows efficient magnetic separation, further enhancing enzyme productivity . In a recent study, Deng et al. investigated the potential of immobilized laccase from T . versicolor for efficient degradation of PAHs. When immobilized on Fe 3 O 4 –SiO 2 -chitosan, the laccase demonstrates distinguished operational stability and reusability, with a capacity of 158 mg/g. Within 48 h, remarkable degradation efficiencies of 81.9 % for ANT and 69.2 % for BAP are achieved. Quantum calculations and mass spectrum analyses reveal anthraquinone and BAP-dione as degradation products. This recoverable magnetic immobilized laccase indicates significant promise for PAH remediation, emphasizing its potential application in environmental restoration. Bioremediation has limitations due to its time-dependence, high cost, and low bioavailability, especially in highly contaminated environments with HMW-PAHs. To overcome these challenges, integrated approaches like physical-chemical or physical-biological methods may be employed, combining techniques such as solvent extraction, chemical oxidation, and bioremediation for more effective remediation . Recent advancements in nanotechnology have significantly enhanced integrated remediation approaches by leveraging nano-sized materials to modify the physicochemical properties of contaminants. This technology synergizes with chemical methods like surfactant addition, to increase bioavailability and biological methods, such as biodegradation. However, cautious attention is essential, including choosing non-toxic nanomaterial biomolecules for functionalization and avoiding toxic reducing agents in nanomaterial synthesis. To address this, adopting green and biologically synthesized NPs emerges as a promising solution to mitigate environmental impact . However, biosurfactant-producing bacteria such as B. subtilis and iron NPs address the proficient degradation of hazardous PAHs. Biosurfactant enhances PAH bioavailability, aiding bacterial settlement, while iron NPs promote biomass growth and PAH adsorption. This integrated approach achieved an 85 % degradation efficiency for mixed PAHs (ANT, PYR, and BAP) in versatile ecosystems. This strategy highlights the efficient and comprehensive PAH pollutant removal . NPs have demonstrated the ability to boost microbial activity during the biodegradation of PAHs by creating a conducive microenvironment for microorganisms. Notably, magnetic iron NPs have been observed to enhance cell adhesion, increase nutrient availability, and facilitate metabolic processes, thereby significantly expediting PAH degradation . Bioaugmentation with graphene oxide-immobilized bacterial pellets (GOBP) enhances PAHs degradation in contaminated soil. High-efficiency degrading bacteria Paracoccus aminovorans embedded in GO-alginate-Luria-Bertani composites show 18.51 % higher removal of PAHs (62.86 % over 35 days) than traditional pellets. GOBP focuses on high-molecular-weight PAHs while increasing the abundance of embedded bacteria and enriching potential indigenous degraders like Pseudarthrobacter and Arthrobacter . This innovative approach offers an advanced technique for remediating organic pollutants in challenging soil environments using bioaugmentation . GO promotes microbial degradation of PAHs, stimulating bacterial growth and gene expression participating in microbial mobility (flagellar assembly), microbial chemotaxis, the two-component system, and phosphotransferase system in soil. In short-term exposure, GO enhances the abundance of degrading microbes, accelerating PAH breakdown. However, extended exposure may lead to degradation saturation. This study indicates GO's impact on microbial PAHs degradation, providing insights into effective environmental remediation and emphasizing the importance of microbial movement and related genetic processes . Nano-bioremediation of PAHs employs NPs and zero-valent iron (nZVI) to enhance electron transfer, a crucial step in the degradation process . Electron transfer intensifies the transformation of recalcitrant PAHs into simple and less hazardous intermediates through breaking down complex molecules into simpler. Microbial fuel cells (MFCs) offer a sustainable approach to electricity generation and organic contaminant removal. Carbon nanomaterials, including graphene, reduced graphene oxide, and CNTs, enhance MFC performance due to their increased surface area, conductivity, and electrochemical capacity . In a sediment MFC study, reduced graphene oxide -modified anodes exhibited the highest voltage output (30.60–48.61 mV) and PHE removal rates up to 71.2 %. PHE degradation correlated positively with Pseudomonas, Thauera, Diaphorobacter, Tumebacillus, and Lysobacter abundances, while PYR degradation correlated with loss on ignition (LOI) degradation. Carbon nanomaterial-modified MFCs show the ability for efficient electricity generation and organic pollutant removal . Lv et al. found enhanced anaerobic-aerobic treatment with nZVI supplemented with rhamnolipid (biosurfactant) and anthraquinone-2,6-disulfonic acid (AQDS) achieved significant degradation rates of 72.81 % for total PAHs and 79.47 % for HMW-PAHs. Key PAHs-degrading bacteria, including Clostridium , Geobacter , and Rhodococcus were dominant contributors. The breakdown route of PAHs, which involves both aerobic and anaerobic processes, was identified by the study of metabolic enzyme function, where nZVI oxidation under anaerobic conditions released effective electron donors for microbial degradation and nZVI-microorganism interactions aided soil pollutant removal. Utilized as an electron shuttle, AQDS facilitates extracellular electron transfer, expediting the exchange between anaerobic bacteria and nZVI. This process enhances system reduction and boosts the microbial transformation rate of soil PAHs . While nZVI is a player, other NPs like Nano-MoO 2 also activate peroxymonosulfate for PAH derivative degradation . Continuous research aims to refine nano-bioremediation strategies, exploring novel techniques and NPs to achieve more effective PAH degradation. Biogenic nanomaterials, such as those synthesized using microorganisms and plant extracts, have gained significant attention in the field of green nanotechnology for the remediation of PAHs. These environmentally friendly nanomaterials offer a promising alternative to outdated chemical techniques. In contrast to the time-consuming and challenging top-down approaches, biogenic NPs are bottom-up products formed through biological reduction processes. Microbial enzymes and plant phytochemicals act as efficient reducing agents, facilitating the degradation of PAH contaminants in various environmental matrices. This approach aligns with the principles of green chemistry, emphasizing safety and sustainability [ , , ]. Bacterial biogenic nanomaterials have emerged as a potent and eco-green solution for addressing the degradation of PAHs. These nanomaterials are synthesized using various bacterial strains and metabolites, making them environmentally sustainable reducing agents. Biogenic NPs are formed through the addition of a metal precursor to bacterial or plant metabolites, followed by the introduction of ligands or biostabilizers to functionalize the NPs. These functionalized NPs adsorb photons, initiating the degradation of PAHs into harmless metabolites, constituting an environmentally friendly degradation process . For instance, A . extremophiles have been employed for the synthesis of crystalline zirconium dioxide (ZrO 2 ), where metabolites discharged into the growth medium efficiently reduced ZrOCl 2 to ZrO 2 . Lactobacillus bacterial strains have been utilized to synthesize titanium dioxide (TiO 2 ) NPs , while a thermophilic bacterium, Geobacillus stearothermophilus , played a pivotal role in the biogenic synthesis of silver (Ag) and gold (Au) NPs . These bacterial strains and their metabolites have been suitable for reducing metal compounds into NPs, facilitating the green remediation of PAH contaminants . B . subtilis , found in rhizosphere soil, exhibited the ability to biosynthesize iron oxide NPs . Moreover, various other bacterial species have demonstrated their capacity to serve as biofactories for synthesizing diverse NPs, including gold, silver, copper, iron, and more. Fig. 9 Eco-sustainable degradation of PAHs using biogenic nanomaterials: Synthesis and mechanism. Fig. 9 Fungi have emerged as eco-friendly and economically viable candidates for NPs synthesis due to their unique attributes . Ganesan et al. utilized the endophytic fungus Periconium sp. for ZnO NPs synthesis, while Clarance et al. employed Fusarium solani for gold NPs production, with key roles played by secreted polypeptides and proteins. Kobashigawa et al. demonstrated the bio-reduction of AgNPs using the ligninolytic fungus Trametes trogii , while Gudikandula et al. employed white-rot fungi for AgNP synthesis. A . oryzae facilitated SeNP reduction from fermented lupin extract . Vago et al. harnessed Aspergillus, Penicillium, and Trichoderma fungi for AuNP reduction. Fungi offer high bioaccumulation capacity, metal resistance, ease of handling, and enzymatic capabilities, making them promising for NPs production. Chakravarty et al. investigated the biodegradation potential of ANT was investigated using a novel approach involving green-synthesized TiO 2 NPs derived from Paenibacillus sp. and Cyperus brevifolius , known for their PAHs remediation abilities, in conjunction with the bacterium A . faecalis , isolated from crude oil-contaminated soil. The combined application of TiO 2 NPs and A . faecalis led to a considerable reduction in ANT concentration, achieving a 21.3 % decrease in liquid culture after 7 days and a remarkable 37.9 % reduction in soil microcosms over 30 days. GC-MS analysis identified five metabolites, including 1,2-anthracenedihydrodiol, 6,7-benzocoumarin, 3-hydroxy-2-naphthoic acid, salicylic acid, and 9,10-anthraquinone, elucidating a novel ANT biodegradation pathway. These examples illustrate the potential of bacterial biogenic nanomaterials in the sustainable remediation of PAH pollutants, offering a promising avenue for eco-sustainable approaches to tackle this environmental challenge. This review highlights the role of nanomaterials in bioremediation to remediate PAHs in both liquid and soil samples. It compiles recent research leading to the interpretation of significant advancements in applying both nanomaterials and bioagents for PAH remediation. Bibliographical analysis indicates a growing trend in this research area, though limited studies exist. This review also spotlights the potential and need for further exploration in this field. Elumalai et al. reported the degradation of crude oil by 97 % by the combined effect of iron oxide NPs synthesized using Aerva lanata floral part and biosurfactant produced by Bacillus subtilis and Paenibacillus dendritiformis . Similarly, Muthukumar et al. documented the bacterial species Pseudomonas aeruginosa and iron NPs having 5–50 nm size able to synergistically remediate 67 % of ANT. Nickel oxide NPs achieved 79 % degradation of PYR at 2 μg/mL ANT within 60 min under UV and sunlight, confirmed by XRD and SEM analysis showing cubic crystalline structures sized 37–126 nm . Nanomaterials are crucial for addressing hazardous contaminants . Bioelectrochemical systems are versatile, and used in bioremediation, biosensors, microbial fuel cells, and microbial electrolysis cells. When combined with nanotechnology, they significantly enhance the degradation of PAHs. This integration boosts efficiency and effectiveness in environmental cleanup . Besides this advanced research, emphasis is placed on understanding microbial influences on nanomaterials and their effects on toxicity, transport, fate, and bioaccumulation. Developing systems to monitor, detect, and treat trace contaminants in air, water, and soil is crucial for sustainable pollution management . Nanomaterials offer significant advantages in the bioremediation of PAHs due to their high surface area-to-mass ratio, enhanced reactivity, and improved mobility, which enable efficient pollutant degradation. These materials stand out due to their unique characteristics, particularly a significantly increased surface-to-volume ratio as well as enhanced magnetic and catalytic traits compared to their bulk counterparts. Quantum size effects at the nanoscale enhance the efficacy of catalytic processes by modifying electronic structures. They can act as catalysts and be tailored for specific contaminants, making them versatile and cost-effective for large-scale applications. However, potential toxicity to microorganisms, environmental persistence, and high production costs poses challenges. Additionally, the interactions between nanomaterials and microbial cells are not fully understood, and there are regulatory hurdles and risks of unintentional ecological impacts . A significant limitation of nanomaterials lies in their potential environmental impact, as their uncontrolled release can lead to widespread harm to abiotic and biotic components of ecosystems, including microorganisms, algae, plants, and animals . Compared to their bulk counterparts, the raised surface area to volume ratio of NPs results in increased reactivity and efficacy; however, their attributes are parallel to their parent chemical species. Consequently, their active participation in diverse physicochemical and biochemical processes within the environment can have detrimental effects on ecological systems. Certain metal NPs such as ZnO, AgO, CuO, and Fe 2 O 3 are recognized for their toxicity and antimicrobial properties, particularly when present in excessive quantities . Challenges include the long-term presence of NPs in the environment their accumulation in organisms, potential for toxicity. The nano-size attributes provide a high surface area and enhanced adsorption capabilities. However, NPs smaller than 20 nm may pose nanotoxicity risks. Small size NPs able to penetrate beneath the microbial cell causing cell malfunction . The challenges also lie in the intricacies of soil complexity. Understanding the behavior of NPs within the soil is a complex task, primarily because of the solid phase nature of the soil and the interactions NPs engage in with soil constituents, including charged humic acids and clay particles. Many studies about NPs have focused on soil suspensions rather than intact soil, as analyzing and characterizing NPs within the soil matrix presents significant difficulties. The utilization of physico-chemical methods to eliminate these NPs from soil and water may prove impractical due to cost inefficiency and environmental concerns. Moreover, the high production costs associated with NPs and their integration with microbial processes pose significant obstacles to scaling up and implementing these techniques on a larger, more practical scale. Hence, a pressing requirement exists to pioneer an eco-friendly, sustainable, and cost-effective bioremediation technology that can specifically target and address these issues. As nano-bioremediation is still in its evolving stage, there is a critical need for standard guidelines governing the application of nanomaterials in bioremediation practices. Health concerns arise from the potential adverse effects of NPs when inhaled, ingested, or absorbed through the skin, raising questions about occupational safety and consumer exposure. The long-term effects of NPs exposure on human health and the environment remain uncertain, requiring ongoing research . It poses significant threats to biological models, and biomarkers including cell death, oxidative stress, DNA damage, apoptosis, and inflammatory responses . Nanomaterial toxicity, which can vary based on size, shape, and surface chemistry, complicates safety assessments. The risk of unintended NPs release during production, use, and disposal poses potential threats to ecosystems and human health. Regulatory and ethical challenges include establishing safety standards, monitoring environmental release, and addressing ethical concerns related to misuse, such as in surveillance and minimizing direct contact with humans. Balancing innovation with ethical considerations and developing strong governance frameworks are essential to ensure the responsible use of nanotechnology in bioremediation . The future of nanomaterials in bioremediation appears promising, as ongoing research is focused on improving their sustainability and efficacy. Improvements in the synthesis and modification of nanomaterials are anticipated to enhance their stability, reactivity, and adsorption capacities in a variety of environmental conditions. The objective of upcoming research is to create nanomaterials that are cost-effective, biodegradable, and non-toxic by optimizing the formulation process at the nanoscale. Furthermore, the integration of nanomaterials with biochar, immobilized enzymes, and electrokinetic methods could provide a variety of approaches to expedite the degradation of PAHs and other contaminants. The potential for eco-friendly and sustainable remediation solutions is further enhanced by the use of biogenic nanomaterials and genetically modified organisms. Understanding the behavior of NPs within soil is a complex task, primarily due to the solid-phase nature of the soil. However, large-scale production of NPs may reduce expenses, and their reusability can make them a realistically cost-effective solution for environmental remediation. Bioremediation efficacy and microbial metabolic activity can be improved by incorporating “omic” approaches, including transcriptomics, proteomics, and metabolomics. By facilitating the precise monitoring and optimization of remediation processes, the integration of artificial intelligence and machine learning techniques in phytoremediation has the potential to transform the field. The successful implementation of nanomaterials in large-scale bioremediation initiatives will be contingent upon the resolution of challenges related to nanotoxicity, environmental persistence, and implications. In general, the ongoing investigation and innovation in this field hold significant potential for the effective and sustainable management of environmental contamination. PAHs, known for their mutagenic and carcinogenic properties, pose substantial threats to human health and ecosystems. Their hazardous nature emphasizes the need for effective remediation methods. Nanomaterials, including metal oxides, CNTs, biopolymers, and nanoscale zeolites, have gained attention due to their potential for remediation. Their small size and significant surface area-to-volume ratio make them valuable tools for improving the adsorption and biodegradation of PAHs in contaminated soil. It is understood from the review that nano-bioremediation exhibits a higher removal efficiency of PAHs compared to traditional bioremediation. This would mean that the application of NPs could enhance the bioremediation process, leading to a more significant reduction in PAH contamination. The strategies such as nanomaterial-assisted microbial degradation, microbial cell immobilization on nanomaterials, and promoting microbial activity through enzymes and electron transfer mediated by nanomaterials provides for enhanced removal of PAHs. These approaches exhibit the versatile role of nanomaterials in upregulating PAH elimination processes, thereby ameliorating the effectiveness of bioremediation techniques. The use of biogenic nanomaterials, synthesized through microorganisms and plant extracts, offers a sustainable approach to PAH degradation by utilizing microbial enzymes and plant compounds as reducing agents, aligning with green chemistry principles. Future research in nano-bioremediation will likely focus on developing optimization systems, standard protocols, and health safeguard standards for remediating PAHs from the environment. Additionally, standardizing the recovery process of NPs will help reduce production costs. Extending the application of nanomaterials to large-scale bioremediation by integrating other remediation approaches will aid in overcoming challenges and enhancing degradation capability. A standard protocols and regulations might help for a responsible application of nanomaterials in bioremediation endeavors to eliminate PAHs from the environment with a sustainable approach. | Study | biomedical | en | 0.999998 |
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