**1. Introduction**

Postpartum hemorrhage (PPH) is one of the leading causes of maternal death. Multidisciplinary treatment, including blood transfusion, should be set up beforehand, especially for pregnant women at a high risk of PPH due to such conditions as placenta previa or placenta accreta [1]. Hemorrhage during cesarean section that is associated with placenta previa is highly correlated with the need for allogenic blood transfusion [2]. Despite the markedly improved safety of allogenic blood transfusion, the risk of transmission of viral or bacterial infection cannot be completely eliminated [3]. Placenta previa is one of the most serious complications during pregnancy and is associated with increased blood loss at delivery; it is also an important cause of serious fetal and maternal morbidity and mortality [4]. Prenatal diagnosis, followed by the careful planning of cesarean delivery and preparation for possible blood loss by a multidisciplinary team, reduces the risk of fetal and maternal morbidity and mortality [5]. Allogeneic blood transfusion has been used for postpartum hemorrhage, although there are substantial risks, such as viral infection, allergy,

**Citation:** Fukuda, E.; Misugi, T.; Kitada, K.; Fudaba, M.; Kurihara, Y.; Tahara, M.; Hamuro, A.; Nakano, A.; Koyama, M.; Tachibana, D. The Hematopoietic Effect of Ninjinyoeito (TJ-108), a Traditional Japanese Herbal Medicine, in Pregnant Women Preparing for Autologous Blood Storage. *Medicina* **2022**, *58*, 1083. https://doi.org/10.3390/ medicina58081083

Academic Editors: Marius L. Craina, Elena Bernad and Edgaras Stankeviˇcius

Received: 25 June 2022 Accepted: 10 August 2022 Published: 11 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

posttransfusion immune suppression and graft versus host disease [6]. The usefulness of autologous blood transfusion has been previously reported [7–9]; however, this method of blood storage can lead to preoperative anemia.

As an alternative to allogenic blood transfusion, preoperative autologous blood storage is useful [10], although the process can induce anemia.

Ninjinyoeito (TJ-108) (Tsumura Co., Tokyo, Japan) is a Japanese herbal medicine that has been used to improve anemia in patients for several years [11]. TJ-108 is composed of 12 unrefined ingredients in fixed proportion: 3.0 g of ginseng; 4.0 g of Japanese angelica root; 2.0 g of peony root; 4.0 g of rehmannia root; 4.0 g of atractylodes rhizome; 4.0 g of poria sclerotium; 2.5 g of cinnamon bark; 1.5 g of astragalus root; 1.5 g of unshiu peel; 2.0 g of polygala root; 1.0 g of schisandra fruit; and 1.0 g of glycyrrhiza. However, to our knowledge, there have been no reports showing the hematopoietic effect of TJ-108 on pregnant women until now.

The aim of this study was to investigate the effect of TJ-108 on the hemoglobin and hematocrit levels, and white blood cell and platelet counts of pregnant women complicated with placenta previa who were managed with autologous blood storage for cesarean section.

### **2. Materials and Methods**

This retrospective observational study was approved by the institutional review board (Approved Number: 2022-030, 13 June 2022). Between January 2016 and December 2020, pregnant women who underwent cesarean delivery with the indication of placenta previa were selected for this study. Patients whose hemoglobin (Hb) level was less than 10.0 g/dL were excluded from this study. Cesarean delivery was performed at 36 or 37 gestational weeks. Data of each patient were retrospectively obtained from medical records.

Autologous blood storage was performed when the patient's Hb level was above 10.0 g/dL and canceled at less than 10.0 g/dL. All patients had 300 mL of blood stored each time for a maximum of 1200 mL (4 times). The initial storage was scheduled according to the gestational weeks. The storage was scheduled every week until 7 days before the scheduled day of cesarean delivery. All patients took a daily oral iron medication (100 mg per day) from the day of the initial storage until the day before the cesarean delivery. From 2019, TJ-108 prescriptions in addition to iron administration were initiated for all women who performed autologous blood storage.

We studied two groups of patients. Group A consisted of women who underwent cesarean delivery between January 2016 and December 2018 who did not take TJ-108. Group B consisted of women who underwent cesarean delivery between January 2019 and December 2020 who were treated with TJ-108 at a dose of 9.0 g per day, in addition to the oral iron medication, from the first day of blood storage until the day before the cesarean delivery. Informed consent for the research was obtained from all patients. We informed the patients of their choice to opt-out.

To evaluate the effect of TJ-108, red blood cell (RBC); Hb; hematocrit (Ht); white blood cell (WBC); and platelet count (PLT) levels were measured after 7 days of storage and at postoperative days (POD) 1 and 5. For laboratory tests, serum aspartate aminotransferase (AST); alanine aminotransferase (ALT); creatinine; blood urea nitrogen; sodium; potassium; and chlorine were measured on the day of storage and at POD 1.

Cesarean delivery was performed on the scheduled day for non-eventful patients, and emergency cesarean delivery was performed in cases with bleeding or uterine contraction. All patients were allowed to take clear liquid until 3 h before the scheduled operation and were administered a continuous infusion of lactate Ringers' solution (200 mL/h). The intraoperative loss of blood (including amniotic fluid) was estimated by measuring the amount of blood in the suction collection unit and by weighting the used surgical gauzes.

Continuous variables were expressed as median (range), and categorical variables were expressed as a number (%). Statistical analyses were examined using the Mann– Whitney U-test and Fisher's exact test performed with BellCurve for Excel (Social Survey

Research Information Co., Ltd., Tokyo, Japan). A *p*-value of < 0.05 was considered to indicate significance.

### **3. Results**

During the survey period, 38 patients were treated without TJ-108 (Group A), and 27 patients were treated with TJ-108 (Group B). Table 1 shows the clinical characteristics of the participants in both groups, and the characteristics were not significantly different between the two.

**Table 1.** Clinical characteristics of participants.

BMI: body mass index; ART: assisted reproductive technology.

The characteristics of maternal and neonatal outcomes data are shown in Table 2. Allogeneic transfusion during cesarean delivery was not significantly different between the two groups, and there was no significant difference between the two in the total amount of blood storage. The median autologous transfusion was 300 mL in both groups, and the frequency of transfusion during cesarean section or blood loss wase not significantly different between the two groups.

Table 3 shows the results of the patients' complete blood count before and 7 days after the initial blood storage of both groups. Hb levels were significantly higher in Group B at 7 days after the initial storage (Group A: 10.0 g/dL; Group B: 10.6 g/dL. *p* = 0.001, 95% confidence interval, 0.13–1.01) (Figure 1a,b). Moreover, the Hb level in Group B was reduced by 0.2 g/dL, as compared to the initial Hb level before storage, whereas the Hb level was reduced by 0.6 g/dL in Group A (*p* = 0.012). The cancellation rate of blood storage in the next instance was significantly lower in group B due to the reduced decrease in hemoglobin levels.

**Table 2.** Operative outcomes of both groups.

RBC: red blood cell; FFP: fresh frozen plasma; PC: platelet concentrates.

**Table 3.** Blood cell counts and differences in the initial autologous blood storage.

Hb: hemoglobin; Ht: hematocrit; RBC: red blood cell. \* *p* < 0.05, \*\* *p* < 0.0.

Table 4 shows the results of the parameters after the second and subsequent storages in both groups. There were no significant differences in Hb and Ht levels (Hb: 10.7 vs. 10.5; *p* = 1.000, Ht: 32.2 vs. 31.8; *p* = 0.259, respectively) (Figure 1c,d).

No significant differences were found between the two groups regarding white blood cell and platelet levels after 7 days of blood storage, and regarding Hb, Ht and RBC on POD 1 and 5. Blood tests on POD 1 showed elevated AST and ALT in two cases in group A and two cases in group B. No other abnormalities were found in other laboratory values.

**Figure 1.** The dot-plot diagram shows the distribution of hemoglobin values (**a**) before the initial storage; (**b**) seven days after the initial storage; (**c**) before the second or subsequent storage; (**d**) seven days after the second or subsequent storage. \*\* *p* < 0.01, N.S: not significant.

**Table 4.** Blood cell counts and differences in the second and subsequent storages.

Hb: hemoglobin; Ht: hematocrit; RBC: red blood cell.

### **4. Discussion**

The present study first revealed that the reduction in Hb levels in the TJ-108 treated patients was significantly less than that observed in the non-TJ-108 treated patients 7 days after storage. At the initial storage, a 0.2 g/dL reduction in Hb levels was observed, as compared to the initial Hb levels in the TJ-108 treated patients, whereas a 0.6 g/dL reduction in Hb levels was observed in the non-TJ-108 treated group. On the other hand, regarding the second and subsequent storages, no significant difference was found in the decrease in Hb levels of both groups.

Takano et al. reported that an oral administration of TJ-108 protected against hematotoxicity in mice treated with 5-fluorouracil (5-FU), which causes severe anemia [12]. They showed that TJ-108 inhibited 5-FU-induced decreases in peripheral reticulocyte and bone marrow cell counts on day 10, and markedly hastened their recovery on day 20 in a dosedependent manner. Erythroid progenitor colonies, such as colony forming units-erythroid (CFU-E) and burst forming units-erythroid (BFU-E) formed by marrow cells from mice treated with 5-FU were significantly increased by an oral administration of TJ-108 [13].

In the differentiation of erythroblastic cells, BFU-E becomes CFU-E cells, and they then differentiate into erythroblasts. During the late stage of erythroid differentiation, proerythroblasts undergo mitosis to produce basophilic, polychromatic and orthochromatic erythroblasts, and these orthochromatic erythroblasts expel their nuclei to generate reticulocytes. Finally, the reticulocytes mature into RBC, initially in bone marrow, and then in the circulation [14]. In these processes of RBC differentiation, EFU-E or CFU-E are in an earlier stage of differentiation dependent on erythropoietin, and erythroblast or normoblasts are in a later stage of differentiation dependent on iron. [15]. In our study, TJ-108 showed a significant potentiate effect in hematogenesis at the initial storage, yet it had no effect at the second and subsequent blood storages. This may be because TJ-108 stimulated BFU-E, CFU-E or the upper stream of differentiation of erythroblasts and showed a synergistically boosting effect on myelohematopoiesis with iron preparation. Promoting hematopoiesis after blood storage takes some time, and the TJ-108-treated group showed an early effect due to boosting. Therefore, it showed a difference from the non-treated group. However, after the second and subsequent storage, the hematopoiesis of the non-treated group caught up, and no difference was observed.

Hatano et al. reported that angelica roots, one of the components of TJ-108, increase the recovery of erythrocytopenia and stimulates the differentiation of erythroid progenitors without promoting erythropoietin synthesis. They also reported that TJ-108 lowered plasma interferon-γ levels, which may suppress the activity of erythroid progenitor cells. They considered the possibility that the polysaccharides in angelica roots promote hematopoiesis by activating immature erythroid cells, in part, by suppressing cytokine secretion [16].

Presently, complementary and alternative medicines, such as traditional Japanese Kampo medicines, are frequently used together with Western medicines for the treatment of various diseases, including anemia. Motoo et al. showed a randomized controlled trial with TJ-108 for patients complicated with hepatitis C and receiving ribavirin, which is known to cause severe anemia. They reported that a maximal decrease in Hb in the TJ-108 group was significantly smaller than that in the control group (TJ-108: 2.59 g/dL vs. non-TJ-108: 3.71 g/dL, respectively). They also concluded that TJ-108 could be used as a supportive remedy to reduce the ribavirin-induced anemia in the treatment of chronic hepatitis C [13].

In Japan, the current health insurance system covers the prescription of Kampo medicines including TJ-108, available as both herbs for decoctions and extract formulations. It has been reported that herbal medicines are widely used worldwide to treat a variety of ailments during pregnancy [17–19]. Though there have been no reports of TJ-108 being administered to pregnant women, the herbal medicines that comprise TJ-108 have been reported to be safe for use in pregnant women [20].

There were some limitations to this study. Firstly, as we did not evaluate the condition of iron or related inspection items (reticulocytes, ferritin, transferrin saturation, vitamin B12 and folates), we were unable to determine the effect of TJ-108 on bone marrow's ability to produce new blood cells. Secondly, we retrospectively reviewed a relatively small number of patients. Further studies are needed to elucidate the effect of TJ-108 on bone marrow activity in autologous blood storage.

### **5. Conclusions**

This study is the first report showing the effect of TJ-108 on improving anemia in pregnant women, presumably with a boosting effect on myelohematopoiesis. It is suggested that the combined administration of iron and TJ-108 is an effective strategy for pregnant women at a high risk of PPH such as placenta previa.

**Author Contributions:** Conceptualization, E.F., T.M. and D.T.; methodology, M.K.; software, T.M.; validation, K.K., M.T. and A.N.; formal analysis, A.H.; investigation, E.F. and T.M.; resources, T.M.; data curation, T.M., M.F., Y.K., M.T., A.H. and A.N.; writing—original draft preparation, E.F.; writing—review and editing, T.M.; visualization, E.F. and T.M.; supervision, D.T.; project administration, D.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was approved by the Institutional Review Board of Osaka Metropolitan University (Approved Number: 2022-030, 13 June 2022).

**Informed Consent Statement:** We informed the patients of their choice to opt-out.

**Data Availability Statement:** The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

**Acknowledgments:** We are sincerely grateful to the clinical staff at the Department of Obstetrics and Gynecology who contributed to the present work. The authors thank Brian C. Nolan for English proofreading.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


**2.2 Aim and method of this study**

The present study was designed to investigate early intervention in atopic dermatitis patients with food allergies, a population considered to be at high risk for the development of asthma. Suplatast tosilate was chosen for early intervention therapy in the present study based on the hypothesis that selective inhibition of Th2 cytokines would improve the Th1- Th2 balance in the high-risk group for asthma (children with Th2 cytokine dominance). Comparison was made with a histamine H1-blocker, ketotifen fumarate, as the control drug.

#### **2.2.1 Subject**

We studied 60 consecutive infants who presented to our department with symptoms of atopic dermatitis caused by food allergies that had persisted for at least 2 month. All of the infants had a family history (at least one parent) of atopic disease, such as bronchial asthma, allergic rhinitis, or atopic dermatitis. The diagnosis of atopic dermatitis was based on the criteria of the Japanese Dermatological Association (22), and all of the infants had predisposing factors for atopic disease (a family history of atopy, a history of allergic disease, or a predisposition to IgE production). They primarily developed pruritic eczema that showed repeated episodes of exacerbation and remission. Only infants with chronic dermatitis that had persisted for 2 months or more were diagnosed as having atopic dermatitis. Food allergies were diagnosed from the history using the following criteria: (i) a history of skin disease influenced by food, and (ii) a raised serum level of specific IgE for cow's milk or egg white (CAP RAST class ‡2). Children were excluded who had a history suggesting the prior existence of asthma, such as wheeze and chronic cough unrelated to respiratory tract infection. Infants who had used oral sodium cromoglycate or other antiallergy drugs within the previous 4 wk (agents that could potentially affect the outcome of this study) were also excluded, but those using topical steroids rated as medium strength or weak to control atopic dermatitis were permitted to enter the study.

#### **2.2.2 Study design**

290 Atopic Dermatitis – Disease Etiology and Clinical Management

eggs or milk, during early infancy (6), while another study has shown that the strongest risk factor for life- threatening asthma is the presence of food allergy (7). Atopic dermatitis was reported to be associated with asthma that has a later onset (8), while inhibition of the occurrence of asthma has been reported in dermatitis patients treated with H1-blocker

We have tested the preventive effect of treatment with a Th2 inhibitor, suplatast tosilate. In previous in vitro and in vivo studies, suplatast tosilate has been shown to inhibit the production of IgE antibodies, reduce tissue eosinophil infiltration, and block the production of IL-4 and IL-5 (11–13). More recently, it was shown to prevent goblet cell hyperplasia by blocking IL-13 production (14). According to a study performed in a mouse model of food allergy, suplatast tosilate decreases IL-4 and IL-6 production in the small intestine, and also ameliorates small bowel necrosis (15). In adults with asthma, suplatast tosilate shows comparable anti-inflammatory activity to inhaled steroids (16), as well as improving lung function, asthma symptoms, and airway hyperresponsiveness (16, 17). In patients receiving high-dose inhaled steroids, addition of suplatast tosilate allows the steroid dose to be reduced (18). Furthermore, evaluation by bronchial biopsy has shown that this drug decreases the number of eosinophils and EG2-positive cells in the airway mucosa, as well as inhibiting goblet cell hyperplasia (17, 19). Infants with a family history of atopy and infants in whom atopic disease occurs soon after birth have been reported to show a shift of the Th1-Th2 cytokine balance towards Th2 dominance (20, 21). Accordingly, the Th1-Th2 balance during the early postnatal period is believed to be a key factor in determining

The present study was designed to investigate early intervention in atopic dermatitis patients with food allergies, a population considered to be at high risk for the development of asthma. Suplatast tosilate was chosen for early intervention therapy in the present study based on the hypothesis that selective inhibition of Th2 cytokines would improve the Th1- Th2 balance in the high-risk group for asthma (children with Th2 cytokine dominance). Comparison was made with a histamine H1-blocker, ketotifen fumarate, as the control drug.

We studied 60 consecutive infants who presented to our department with symptoms of atopic dermatitis caused by food allergies that had persisted for at least 2 month. All of the infants had a family history (at least one parent) of atopic disease, such as bronchial asthma, allergic rhinitis, or atopic dermatitis. The diagnosis of atopic dermatitis was based on the criteria of the Japanese Dermatological Association (22), and all of the infants had predisposing factors for atopic disease (a family history of atopy, a history of allergic disease, or a predisposition to IgE production). They primarily developed pruritic eczema that showed repeated episodes of exacerbation and remission. Only infants with chronic dermatitis that had persisted for 2 months or more were diagnosed as having atopic dermatitis. Food allergies were diagnosed from the history using the following criteria: (i) a history of skin disease influenced by food, and (ii) a raised serum level of specific IgE for cow's milk or egg white (CAP RAST class ‡2). Children were excluded who had a history

therapy (9, 10).

**2.1 Th2 cytokine inhibitor: Suplatast tosilate**

whether or not allergy develops.

**2.2.1 Subject**

**2.2 Aim and method of this study**

Thirty children were randomly assigned to oral treatment with suplatast tosilate dry syrup (IPD dry syrup 5%, Taiho Pharmaceutical Co. Ltd, Tokyo Japan) at a dose of 3 mg/kg twice daily (the total daily dose was 6 mg/kg) and 30 were assigned to receive ketotifen fumarate dry syrup at dose of 0.03 mg/kg twice daily (the total daily dose was 0.06 mg/kg). Both these drugs were administered for at least 2 yr, and could be continued for up to 4 yr at the request of the parents. After the start of the study, subjects were reviewed at the outpatient department once monthly until completion of the study. Blood was collected at the start and after 6 months of treatment for measurement of the total IgE level; the specific IgE levels for egg white, cow's milk, Der P, and house dust mite; the eosinophil count; and the Th1/Th2 ratio. The primary endpoints of this study were the incidence of asthma and the time to the initial episode of wheezing among the patients in whom asthma occurred. Wheezing was confirmed by the authors via auscultation, and the date of the initial of wheezing, as well as subsequent episodes of wheezing and persistent cough excluding those caused by infection, was recorded in each infant. Asthma was defined as the occurrence of three or more episodes of wheezing associated with expiratory dyspnea, and the day of onset of asthma was defined as the day when the initial episode of wheezing occurred in each patient with asthma. The secondary endpoints of this study were the percentage of patients admitted to hospital because of asthma and changes of the peripheral blood eosinophil count, IgE level, and Th1/Th2 ratio. For the treatment of atopic dermatitis and food allergies, the use of concomitant drugs other than medium-strength to weak topical steroids was not allowed during the study period. After the onset of asthma, antiasthma medications, mainly antiinflammatory drugs, could be prescribed.

#### **2.2.3 Measurement of the Th1/Th2 ratio**

The Th1/Th2 ratio was measured by flow cytometry using a FACScan (23). In brief, CD4 positive cells (helper T cells) were isolated. Then Th2 cytokine-positive cells were detected and counted after staining with an anti-IL-4 antibody, while Th1 cytokine – positive cells were identified with an anti-IFN-c antibody, after which the Th1/Th2 ratio was calculated.

#### **2.2.4 Measurement of serum total IgE and specific IgE**

Serum total IgE was measured using a fluoroenzyme immunoassay (FEIA) (CAP RIST FEIA, Pharmacia, Uppsala, Sweden). The levels of specific IgE for house dust mite, Der P, egg white, and cow's milk were measured using FEIA (CAP RAST FEIA, Pharmacia).

#### **2.2.5 Ethics**

This study was approved by the Regional Ethics Committee for Human Research at Dokkyo University School of Medicine Hospital. The parents of all patients participating in this study gave oral and written informed consent.

Suplatast Tosilate for Prophylaxis of Pediatric Atopy 293

Fig. 1. Onset of wheezing during treatment with a Th2 cytokine inhibitor (suplatast tosilate)

The eosinophil count decreased significantly from 1.212 ± 174 to 679 ± 111/lL (p < 0.05) after 219 ± 42 days of suplatast treatment (n = 21), while no significant change was observed after 280 ± 44 days of ketotifen treatment (n = 19). The change after treatment was significantly larger (p < 0.01) in the suplatast group (-532.4 ± 120.0) compared with the ketotifen group (-2.1 ± 126.0) (Fig. 2). Although there was no significant change of the Th1/Th2 ratio after treatment in either the suplatast group (from 4.88 ± 1.07 to 6.76 ± 1.58%, n = 16) or the ketotifen group (from 10.00 ± 8.52 to 6.44 ± 1.88% n = 10), there was a significant difference of the ratio between the two groups (p < 0.05) (Fig. 3). However, there was no significant difference between the

Fig. 2. Eosinophil count before and after treatment with suplatast tosilate (-532.4 ± 120.0) or

or a histamine H1-blocker (ketotifen fumarate).

groups with respect to changes of the total IgE level (data not shown).

ketotifen fumarate (-2.1 ± 126.0); \*p < 0.01 by the t-test.


**3. Results**

#### *3.1. Biological and Physico-Chemical Characteristics of Drinking Water Distribution Systems (DWDS)*

The sampled drinking water of the eight hospitals was mainly groundwater based and characterized by a high hardness (on average 230–300 mg/L CaCO3 equivalents) and high conductivity (on average 650–900 μS) (Table 1). The average temperature of the cold water ranged between 21.1 ◦C and 24.3 ◦C. The average temperature of hot water ranged between 38.6 ◦C and 51.9 ◦C. The average pH of the cold and hot water was 7.6 and 8.0. The conductivity of the hot water was higher than that of the cold water in all hospitals. Chlorine varied on average between 0.2 and 0.7 mg/L. Heterotrophic plate counts at 37 ◦C ranged from 1.7 × 104 to 1.6 × 105 CFU/L.

**Table 1.** Average of biological and physico-chemical parameters from the drinking water systems (DWDS) of the eight hospitals in the West Bank.

During the study period, *L. pneumophila* was detected in the DWDS of all hospitals and at Al-Quds University. The sampling comprised six campaigns, twice covering the main seasonal changes. Sampling comprised water and biofilms, with comparable numbers of samples taken from each hospital.

From water samples, five *L. pneumophila* strains were isolated from 72 samples. *L. pneumophila* was isolated only from the drinking water of three hospitals (A, F and G). For the collection period, the *Legionella* counts per hospital ranged from 0 to 150 CFU/L (Table 1). In hospital A, on average, 43 CFU/L of *Legionella* spp from cold water were detected; in hospital F, 150 CFU/L of *Legionella* spp were detected from cold water and 91 CFU/L from hot water; finally, in hospital G, on average, 8 CFU/L *Legionella* spp were detected from cold water. Hot water was not continuously available from all hospitals, but *Legionella* spp counts were comparable to cold water [42]. *Legionella* spp, mainly *L. pneumophila,* was isolated from 191 out of 1136 biofilm swab samples (16.8%) (Table 2). The majority of *Legionella* positive samples were detected in 2012 (23.5%), while the fewest samples tested positive in 2013 (8.7%). The highest frequency of *L. pneumophila* in biofilm swabs was detected in hospital F (26.3%), where *Legionella* spp were also detected in the DWDS during 2012–2014. Meanwhile, the lowest frequency was detected in hospital C (3.3%). Finally, a high frequency of *L. pneumophila* was detected in Al-Quds University (36.4%) during the only collection in 2012.

NA: Not Available; BD: Below detection limit (<5 CFU/L); AQU: Al-Quds University; CDA: Cultivation Dependent Analysis; **2** CIA: Cultivation Independent Analysis; **3** The number of isolates corresponds to the number *L. pneumophila* culture-positive water and biofilm samples.

#### *3.2. Cultivation Dependent Analysis (CDA) versus Cultivation Independent Analysis (CIA)*

Although CDA is the standard and recommended technique for environmental surveillance of *L. pneumophila,* CIA provides higher sensitivity and overcomes the problems of CDA for *Legionella* because of the VBNC state and its overgrowth by competing bacteria. In this study, both methods were used to detect *Legionella* spp in hospital DWDS (Table 2).

A total of 72 water samples and 225 biofilm swabs from the eight hospitals were tested by conventional PCR using three di fferent primers (com, Lgsp, Lpn). Almost all of the samples were positive using com primers (n = 71, 98.6% and n = 225, 100%) for water samples and biofilm swabs respectively. *Legionella* spp were detected in biofilm swabs more than in water samples (n = 167, 74.2% and n = 42, 58.3%), respectively (Table 2). Similar results were obtained using *L. pneumophila*-specific

primers: 60% of the biofilm swabs and 50% of the water samples were positive. As expected, the PCR-based CIA showed higher sensitivity than CDA. CIA analysis increased the detection of *L. pneumophila* from 8.3% (CDA) to 50% (CIA) for water samples, and from 16.8% (CDA) to 59.5% (CIA) for biofilm samples.

#### *3.3. MLVA-8(12) Genotypes of L. pneumophila Isolates*

MLVA genotyping was carried out for the 180 isolates. MLVA-8, as well as MLVA-12 and the joint scheme MLVA-8(12), were analyzed for the study of the population of *L. pneumophila* isolates. The 180 isolates were categorized as 16 MLVA-8 genotypes (Index of Diversity ID = 0.771, 95% Confidence Interval CI 0.721–0.822), 25 MLVA-12 genotypes (ID = 0.790, 95% CI, 0.739–0.841) and 27 MLVA-8(12) genotypes (ID = 0.790, 95% CI 0.739–0.841). This indicates a lower genotypic resolution for MLVA-8 using eight loci compared to MLVA-8(12) using a total of 13 loci. For details on the comparison based on a larger set of *L. pneumophila* isolates, see [37].

The use of the MLVA-8(12)-genotype nomenclature made it possible to directly compare strains genotyped from MLVA-8 and MLVA-12: the first number reflects the MLVA-8-classification, while the number in brackets reflects the 12 loci-classification, e.g., Gt 4(17) is a Gt 4 according to the MLVA-8, and a Gt 17 according to MLVA-12. Fourteen MLVA-8(12) genotypes were represented by 2 to 74 strains, whereas 13 genotypes were represented by just a single strain from the West Bank isolates (Figure 1). The MLVA-8(12) genotypes comprising the most strains were Gt 4(17), Gt 6(18) and Gt 10(93).

**Figure 1.** Comparison of the number of strains per MLVA8(12)-Genotype (Gt) of the isolated 180 *L. pneumophila* strains and their respective sequence types (ST). Each group of bars, outline color and colored frame indicate VACCs as follows: blue outline represents VACC1, yellow outline represents VACC2, green outline represents VACC5 and purple outline represents VACC11. The black round-edged frames indicate the group of genotypes from the same ST. The wide upward diagonal hatches inside the bars indicate Sg as follows: Sg1—red, Sg 6—sky blue, Sg8—green, Sg10—yellow and Sg2 to Sg14—black. NA—not available ST; \*, ST was assessed for strains of the same MLVA-8(12) genotype, and not directly for the West Bank strains; ˆ, ST was estimated from the MLVA-8 pattern.

#### *3.4. VNTR Clonal Complexes (VACC) and Relationship among the Genotypes*

An analysis of the relationship among the genotypes was achieved by UPGMA-based cluster analysis of the MLVA-8(12) profiles of the 180 *L. pneumophila* strains (Figure S2). The MLVA clonal complexes (VACC) were defined by a cutoff level of 60% similarity. In addition, the genetic relationship among genotypes was estimated by a minimum-spanning tree based on the MLVA-8(12) profiles (Figure 2).

**Figure 2.** Minimum-spanning tree based on MLVA-8(12) profiles of 180 *L. pneumophila* strains isolated from the West Bank. Each circle in the tree represents a different MLVA-8(12) genotype. The genotype number is indicated within or near the circle, whose size is proportional to genotype frequency. Different colors in the pie charts refer to the eight sampling locations (see legend). The thickness of the branches represents the number of different loci. MLVA clonal complexes (VACC) are shaded in grey. The circles representing the Sg8-singletons from Al-Quds University, i.e., Gt 11(87) and Gt 12(84), overlap visually within the circle that represents Gt4(17) due to the high abundance of this genotype. Blue ellipses indicate genotypes that could be candidates for LD. These genotypes belong to ST1 and ST461 (indicated in blue letters) and were assessed by in situ-SBT and present in half of the LD cases in a West Bank study [43].

All MLVA8(12) genotypes were clustered into four MLVA clonal complexes or VACCs (VACC1, VACC2, VACC5 and VACC11) (Figure 2). VACC1, VACC2 and VACC5 were clonal complexes previously defined in the MLVA *Legionella* database. VACC11 is described for the first time in this study (Figure 2 and Figure S2). VACC1 was the largest cluster, including 110 isolates (61.6%). VACC11, VACC2 and VACC5 were, in comparison, smaller clusters, comprising 31, 19 and 14 isolates, respectively (Figure 1).

Not all strains could be included in VACCs. A small group of six isolates that belonged to two different genotypes, i.e., Gt 11(87) and Gt 12(84), separated from the large VACC1 and remained as singletons, i.e., they could not be directly included in a VACC. They differed from the rest of the isolates contained in VACC1 in the number of repeats observed for VNTR markers Lpms31, i.e., 17, in comparison to 4 or 0 in the rest of the profiles of VACC1, and VNTR Lpms33 and Lpms34, which both presented only one repeat in contrast to the 4 and 2 repeats found, respectively, in VACC1. In total, 96.6% of isolates (n = 174) were clustered into the four VACCs, and only 3.6% (n = 6) were found as singletons.

In addition, single linkage clustering using the Bray-Curtis-Index of association was performed with a cutoff value of 60% corresponding to 13 VNTRs. The MLVA8(12) profiles of four additional reference strains were added to the dataset to show their relationship with the genotypes in this study (Figure 3). This clustering showed the same four groups corresponding to the VACCs observed with the UPGMA-clustering and the minimum-spanning tree (Figure 2 and Figure S2). In addition, it revealed a very close relationship of the *L. pneumophila* strain Paris with Gt 4(17) in VACC1, and of Gt 64(74) with reference strain *L. pneumophila* strain Philadelphia1 in VACC2. The dendrogram showed a high discriminatory power and subclustering between *L. pneumophila* isolates with similarity of (>95%). Gt63(83) was shown as an outlier due to the three "Null" alleles in Lpms31, Lpms34 and Lpms35, i.e., no PCR product was obtained for these VNTR-markers (Figure 3). Overall, the Bray-Curtis-grouping of the genotypes confirmed the four VACC cluster. Also, the aforementioned singleton genotypes Gt11(87) and Gt12(84) were grouped separately from VACC1.

**Figure 3.** Single-linkage cluster dendrogram representing the percentage of similarity between MLVA-8(12) profiles of the genotypes retrieved from the West Bank. For a comparison, MLVA-8(12) profiles of *L. pneumophila* reference strains (Bloomington2, Philadelphia-1, Corby, Paris) were added. The resemblance matrix was calculated using the Bray-Curtis index of association. Strains of different VNTR clonal complexes (VACC) are indicated in different colors (see insert).

Overall, a more detailed analysis of the population structure at the level of the 13 VNTR markers showed a balanced variability in the number of repeats for most VNTR markers among the isolates (Table 3). This could be due to the homogenized habitat and location where the isolates were obtained. Some VNTR markers appeared to be less variable and showed a reduced number of repeats, e.g., Lpms3, Lpms17 and Lpms19, while others showed a greater variability, e.g., Lpms31 and Lpms35. In general, the same repeats prevailed independently of the area from which the isolates had been isolated. Null alleles were present at different frequencies in distinct VNTR markers. Especially high were the frequencies of null alleles in Lpms38. Remarkably, a new allele of Lpms34 was described during this study. The new allele had a size of 634 base pairs and was formed by four repeats. A total of 31 isolates (17.2%) contained this allele. This allele has not previously been described in MLVA studies for *L. pneumophila*.

**Table 3.** VNTR characteristics of the *L. pneumophila* strains isolated from the West Bank.

**1** HGDI: Hunter-Gaston Discrimination Index.

#### *3.5. Diversity within the Clonal Complexes (VACC)*

The minimum-spanning tree (Figure 2) make it possible to provide an overview of the diversity and the genetic relationship among the MLVA genotypes. While the grouping by VACCs gives a first estimate of the relationship among the total of the genotypes, the relationship of the genotypes within the VACCs is also indicative.

In VACC1, all genotypes pertaining to ST1 were closely related to Gt 4(17). The replicate number of only one locus had changed compared to Gt 4(17). This is comparable to VACC11, where Gt 10(93) has this central position, with only one locus being di fferent compared to Gt 10(141), Gt 9(92) and Gt 55(94). By contrast, in VACC2 and VACC5, a set of more distantly related strains with a broader set of di fferent changes in the VNTR-loci was observed.

The relationship of the genotypes within the cluster as reflected by the minimum spanning tree were consistent with the results of the UPGMA-based cluster analysis (Figure S2) and Bray-Curtis-based analysis (Figure 3).

#### *3.6. Comparison of MLVA-8(12) Genotypes and Clonal Complexes with Sequence Types (ST)*

In our comparison, 22 of 27 MLVA8(12) genotypes could be assigned to nine sequence types (STs). Most STs with a larger set of strains could be divided in two to five genotypes (Figure 1 and Figure S2). ST1 was split into five MLVA-8(12) genotypes all adhering to VACC1. ST1 comprised the largest fraction of strains, i.e., 111 strains of the total of 180 strains. ST1 comprised the MLVA-8(12) genotypes with the most isolates, i.e., Gt 4(17) and Gt 6(18) (Figures 1 and 2). ST461 comprised 30 strains and three genotypes of VACC11. It was the second largest ST. VACC11 strains were not present in any other ST. Three genotypes were also present in each of ST1326, ST1438, and ST1482. Two MLVA genotypes were present in ST1358. The remaining three STs (9, 93, 187) comprised one to three strains and constituted a single MLVA-8(12) genotype.

In summary, all STs with a larger set of strains were split up in several MLVA-genotypes, i.e., MLVA-8(12)-genotyping showed a substantially higher resolution than SBT. All STs comprised only strains of the same VNTR clonal complex (VACC). Our dataset indicates a high level of consistency between SBT and MLVA8(12)-genotyping. This observation was confirmed by the analysis of a larger and more diverse set of *L. pneumophila* isolates [37].

#### *3.7. Serotype Distribution of L. pneumophila Isolates and Relationship with MLVA Genotypes and Clonal Complexes*

The 180 PCR-confirmed *L. pneumophila* environmental isolates were tested for serogroups (Table 4 and Figure 1). Most of the isolates were characterized as serogroup 1 (Sg1) (n = 111); the remaining 69 isolates were non Sg1. A subset of ten Sg1 isolates was subgrouped according to the monoclonal antibody; all belonged to the MAb 3/1 negative OLDA subtype, which is considered to lack the virulence- associated epitope. The 69 non-Sg1 isolates were analyzed by monoclonal subgrouping; 54 of them were serotyped as Sg6, followed by Sg8 (n = 6) and Sg10 (n = 2). The rest of the non-Sg.1 were characterized as serogroups 2–14, as determined using an agglutination kit.

**Table 4.** Serogroup and monoclonal antibody subtyping of 180 environmental *L. pneumophila* isolates from the West Bank.

In terms of the number of isolates, the *L. pneumophila* population showed a dominance of Sg1, followed by Sg6 (Figure 1). In terms of MLVA-genotypes, 11 could be attributed to Sg6 and seven to Sg1, while Sg8 was represented by only two and Sg10 by only one genotype. In terms of clonal complexes, VACC11 comprised only strains of Sg6. VACC1 comprised mainly Sg1 strains, except for Sg8 for genotypes Gt11(87) and Gt12(84). VACC5 comprised strains of either Sg6 or Sg2–14. By contrast, VACC2 comprised a serogroup-divers set of genotypes, i.e., Sg1, Sg6, Sg10 and Sg2–14. The six singleton isolates were all Sg8-strains isolated from Al-Quds University.

#### *3.8. Prevalence and Abundance of L. pneumophila MLVA-Genotypes and Clonal Complexes (VACCs)*

An overview of the association of the MLVA-8(12) genotypes and VACCs with the different sampling sites is reflected in the minimum-spanning tree (Figure 2). Details on the strains retrieved and their characteristics for each sampling site are listed in Table 5.

1 mAb: monoclonal Antibody; 2 NA: Not analyzed.

**Table 5.** MLVA-8(12) genotype abundances at the sampling sites in the West Bank.

\*: Al-Quds University.

In terms of VACC prevalence, Figure 2 shows that at least two distinct clonal complexes were present at each hospital as well as at the Al-Quds University. VACC1, the largest clonal complex, was present across the West Bank. Genotypes belonging to it were isolated from all eight hospitals and from AQU. VACC2 isolates were isolated from four hospitals distributed throughout the West Bank and AQU. Although VACC11 was present at five hospitals, it was the major clonal complex at hospital F (n = 22, 66.7%). Isolates grouped into VACC5, the smallest clonal complex, were, however, found at four hospitals located throughout the West Bank (Table 5).

At the genotype level, only nine out of the 27 MLVA-8(12) genotypes were isolated in more than one location. The remaining 18 MLVA-8(12) genotypes were isolated exclusively in one specific site. Gt 4(17), the main VACC1-genotype that also comprises *L. pneumophila* strain Paris, was the only genotype present in all hospitals except for hospital G. Furthermore, it represented a high fraction of the isolates in several hospitals. Gt 4(17) was the most abundant genotype in hospital B (68.7%), A (71.4%), D (83.4%), and C (80%). At Al-Quds University, Gt 4(17) accounted for 40% of the isolates. Gt 6(18) was the second most abundant VACC1-genotype; it is closely related to GT 4(17), di ffering by just one additional repeat in the VNTR Lpms35. Gt 6(18) was endemic in the West Bank and found exclusively in hospital G, where it was the most abundant genotype (90.9%). Genotype Gt 10(93), a member of the newly-described VACC11, was found in hospitals C and F; it was isolated only once in hospital C, but was the most abundant genotype in hospital F (n = 15, 45.5%). The remaining genotypes that were found in more than one location had a rather restricted distribution, i.e., they were observed only in one or two additional sampling sites (Figure 2 and Table 5).

In general, the most frequent genotypes in each hospital were isolated repeatedly during samplings performed in following years. Gt 4(17) was recurrently isolated in hospitals A, B, C, D and F between 2012 and 2014. The endemic Gt 6(18) was isolated in hospital G in 2013 and 2014. Genotypes Gt 10(141), Gt 10(93), and Gt 9(92) were isolated in their respective sites (hospital F, B) from 2012 to 2014. The MLVA-8(12) genotypes were shared among the north, central and southern West Bank. According to the geographical distribution of the West Bank, 64 (35.6%), 43 (23.9%) and 73 (40.6%) isolates were isolated from northern, central and southern areas, respectively. Nevertheless, the most abundant and broadly distributed genotype was common to the whole West Bank, i.e., Gt 4(17), which comprised 68.8%, 53.5% and 9.6% of total isolates in northern, central and southern West Bank, respectively. Surprisingly, four genotypes, i.e., Gt 9(92), Gt 10(93), Gt 16(1) and Gt 63(83), were shared between more distant sites, i.e., northern and southern West Bank. As a tendency, the diversity of the genotypes observed decreased from the Southern to the Northern West Bank.

Interestingly, Gt 4(17) was never obtained from water samples, but only from biofilm. Genotypes of rather limited distribution were the only genotypes retrieved from water, i.e., Gt 10(93) and the endemic Gt 10(141), were obtained from hospital F, the endemic Gt 6(18) from G, and Gt 16(1) from A, respectively.

In summary, two thirds of the MLVA-8(12) genotypes were endemic, i.e., they were found exclusively in one hospital or the Al-Quds University. Only one third of the MLVA-8(12) genotypes were isolated in more than a single location, and these common genotypes were usually much more frequent in one of the locations. An exception was Gt 4(17), that was present in most locations and occurred often in high abundance.


*Communication* **A Comparison Study of the Detection Limit of Omicron SARS-CoV-2 Nucleocapsid by Various Rapid Antigen Tests**

**Daniela Dobrynin 1, Iryna Polischuk 1 and Boaz Pokroy 1,2,\***

**Abstract:** Rapid antigen tests (RATs) are widely used worldwide to detect SARS-CoV-2 since they are an easy-to-use kit and offer rapid results. The RAT detects the presence of the nucleocapsid protein, which is located inside the virus. However, the sensitivity of the different RATs varies between commercially available kits. The test result might change due to various factors, such as the variant type, infection date, swab's surface, the manner in which one performs the testing and the mucus components. Here, we compare the detection limit of seven commercially available RATs by introducing them to known SARS-CoV-2 nucleocapsid protein amounts from the Omicron variant. It allows us to determine the detection limit, disregarding the influences of other factors. A lower detection limit of the RAT is necessary since earlier detection will help reduce the spread of the virus and allow faster treatment, which might be crucial for the population at risk.

**Keywords:** rapid antigen test; SARS-CoV-2; COVID-19; omicron variant

#### **1. Introduction**

Since the first case of COVID-19 disease in Wuhan in December 2019, there has been a worldwide struggle to reduce the transmission of acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Many countries worldwide imposed temporary local lockdowns in order to reduce person-to-person interactions [1,2], masks became obligatory, especially in closed spaces [3,4], and there was a general requirement for social distancing. However, the most efficient method to reduce the continuing spread of infection among the population, and in the meantime maintain a regular daily life, is the early detection of contagious infected people.

An extensive body of research aims to develop new technologies for SARS-CoV-2 detection [5–8]. One known and reliable method is the reverse-transcriptase PCR test (RT-PCR), which is constantly improving [9]. It is possible to detect the virus even if there is only one RNA strand in the sample and run hundreds of samples simultaneously [10]. This method has a few disadvantages, such as a high cost, is time consuming, the need for medical laboratories and skilled staff to perform the test, and the major flaw: the lack of an appropriate number of available tests. The latterly prominent Omicron variant (B.1.1.529) and its derivatives have caused a tremendous increase in the number of infected people due to its enhanced transmissibility [11,12]. All the above points emphasize the high demand for easy-to-use, cheap, and available detection tests for SARS-CoV-2 and other pathogens [13].

The solution was found in developing rapid antigen tests (RATs), which are based on lateral flow immunoassay technology (LFIA) and provide a result within 15–30 min. For a more detailed description regarding the principles of LFIA, readers are referred to Koczula et al. [14]. The majority of industrially manufactured RATs are designed based on the detection of the nucleocapsid protein, one of four main structural proteins in the SARS-CoV-2 [15]. The nucleocapsid protein has proved to be a good diagnostic marker since it is

**Citation:** Dobrynin, D.; Polischuk, I.; Pokroy, B. A Comparison Study of the Detection Limit of Omicron SARS-CoV-2 Nucleocapsid by Various Rapid Antigen Tests. *Biosensors* **2022**, *12*, 1083. https://doi.org/10.3390/ bios12121083

Received: 25 October 2022 Accepted: 24 November 2022 Published: 27 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

detectable even after one day from the disease onset [16]. The key role of the nucleocapsid protein is to pack the viral genome into helical complexes named ribonucleocapsid (RNP), which interacts with the membrane protein (M) [17].

Former studies tested the RATs' validity by examining PCR-positive COVID-19 patients samples or the isolated virus with various RATs [18–22]. These studies are very informative; however, many factors might affect the RAT result, such as the diversity in the viral load (VL, the virus amount in the sample) or the nucleocapsid concentration in each sample, the swab's surface, exposure to different variants, date of infection, immune system reaction to the virus, vaccination status, etc. For instance, it was shown that various RATs tend to detect the Delta or Omicron variants at different sensitivities [21,23]. The latter implies that infected patients who show PCR-positive results might have a RAT-negative result simply due to each person's uniqueness and situation. Moreover, the nasopharyngeal samples contain many other proteins which might affect the detection of the nucleocapsid protein. In previous studies, it was shown that there is a significant variation in sensitivity between different RATs when applied to both symptomatic participants (34.3–91.3%) and asymptomatic participants (28.6–77.8%) [24]. The sensitivity of RT-PCR tests is higher than that of the RAT tests [24,25]. Nevertheless, the lower the RAT's detection limit, the earlier one can detect COVID-19-infected patients. This is important in cases when urgent treatment is needed, as well as to facilitate early isolation in order not to infect others.

This study compares seven commercially available RATs for detecting SARS-CoV-2 by a simple direct experiment. In contrast to previous studies, here we examined the various RATs by testing with various known, pure, and pre-defined concentrations of Omicron SARS-CoV-2 nucleocapsid protein solutions. The tests were performed according to each manufacturer's instructions utilizing the RAT solution containing known volumes and concentrations of the nucleocapsid protein of the Omicron variant. This method allowed us to determine the detection limit of each RAT while controlling the amount of the added nucleocapsid protein and disregarding all other influencing factors.

#### **2. Materials and Methods**

The nucleocapsid protein of the Omicron variant was purchased from BPS Bioscience (San Diego, CA, USA) and contained the mutations: P13L, R203K, G204R, and Δ31- 33ERSdel. We used seven commercially available antigen tests: (a) Deepblue COVID-19 (SARS-CoV-2) Antigen Test Kit (colloidal gold) (Anhui Deepblue Medical Technology Co., Ltd., Hefei, Anhui, China); (b) Easy Diagnosis COVID-19 (SARS-CoV-2) Antigen Test Kit (Wuhan EasyDiagnosis Biomedicine Co., Ltd., Wuhan, Hubei, China); (c) EcoTest COVID-19 TO-GO (Assure Tech. (Hangzhou) Co., Ltd., Hangzhou, China); (d) GenSure COVID-19 Antigen Rapid Test Kit (GenSure Biotech Inc., Shijiazhuang, Hebei, China); (e) Orient Gene Rapid COVID-19 (Antigen) Self-Test (Zhejiang Orient Gene Biotech Co., Ltd., Huzhou, Zhejiang, China); (f) Panbio COVID-19 Antigen Self-Test (Abbott Laboratories, Chicago, IL, USA); (g) YHLO GLINE-2019-nCoV Ag for self-testing (Shenzhen YHLO Biotech Co., Ltd., Shenzhen, China).

The protein was diluted in Phosphate buffer saline (PBS), pH 7.4 (Sigma-Aldrich, St. Louis, MO, USA) to various concentrations: 550; 55; 5.5; 2.75; 1; 0.55; and 0.275 μg mL−1. After use, the protein solutions were stored at −20 ◦C (for the experiments which were conducted in March and April 2022). For the experiments held in October 2022, new protein solutions were prepared in the same way as before (the same pipettes and volumes were used). The (f) RAT is not included in these experiments (RH 60%) since, by this time, the RAT was not marketing anymore. The highest concentration is 550 μg mL−1, similar to the concentration delivered by BPS Bioscience (580 μg mL−1). A total of 5 μL from the relevant nucleocapsid concentration solution was added to each RAT test solution with a pipette. There was no use of a swab. The volume of the various test solutions varies between the different companies (250–400 μL, Table S1), but the total protein amount present in each test solution is identical. The protein in the test solution was mixed by pipetting until the protein was homogeneously dissolved. At the next step, as described

in each manufacturer's instructions, a specific number of drops were dripped from the test solution to the designated sample loading location at the RAT. After 15 min, the tested RATs were examined by the naked eye, and images were acquired (Figures S1–S7 in Supplementary Materials).

Performance of all the RATs is based on the same concept; the C-line is the control line and should appear if the RAT was conducted correctly, and the T-line appears if the nucleocapsid protein was detected.

Each type of RAT was firstly checked with the highest nucleocapsid concentration, and if the result was clearly positive (the T-line was bold and clear), we proceeded to test lower concentrations. If the T-line was barely visible, we performed three RAT replicates and continued to lower the protein concentration. Once the T-line was completely not visible, we again tested three replicates to ensure a negative result. The detection limit was determined as the lowest protein concentration, where all three replicates show a positive result.

All experiments were conducted in the fume hood at 21 ◦C. The relative humidity (RH) was stabilized by dehumidifier SAD50 (SmartAir, Kfar Kasem, Israel) to 30% (March and April months) and 60% (October).

#### **3. Results and Discussion**

This work provides an evaluation of seven commercially available RATs widely used internationally. In order to determine and compare the detection limit of different RATs, we directly used Omicron nucleocapsid protein solutions in decreasing concentrations. The protein concentration was lowered continuously until only the control line (C-line) was visible, rather than the two lines (C- and T-lines). The same volume of the protein solution (5 μL) was added to the test solutions each time, implying that the total amount of the nucleocapsid was identical. The performance of the different RATs was further compared, eliminating any human factor, such as the manner in which one may conduct the RAT and the actual viral load of the tested patient. The experiments were conducted under different relative humidity (RH) conditions of 30% and 60%.

Our results suggest that all RATs were able to detect the nucleocapsid protein using the 5.5 μg mL−1 solution, while the lowest detected concentration was 0.55 μg mL−1 by (b) RAT (Figure 1). In general, the reduction in the concentration of nucleocapsid protein caused the T-line to be weaker and thinner, as expected. At RH 60%, (c), (d), and (e) RATs were able to detect the nucleocapsid until 1 μg mL−1 solution. (g) RAT showed positive results until 2.75 μg mL−1 nucleocapsid concentration. These results provide additional evidence to previous studies suggesting that there might be significant variation between different RATs in detecting SARS-CoV-2, especially the Omicron variant. In this study, we observe up to one order of magnitude of nucleocapsid concentration difference between the several RATs. A reduced sensitivity was observed at RH 30% compared to RH 60% for the same protein concentration. For instance, for (b) RAT at RH 30%, only one out of three replicates showed a positive result at a concentration of 0.55 μg mL−1, while at RH 60% all replicates showed a clear T-line. In the case of the (c) and (d) RATs, the results are improved as well; for (c) RAT, all replicates showed positive results in RH 60% (1 μg mL−1), while in RH 30%, only two out of three. The (d) RAT could not detect the protein in 0.55 μg mL−1 concentration in RH 30%, in contrast to RH 60%, where two out of three replicates showed a T-line. A previous study showed that at an RH higher than 60% the water evaporation is reduced, and the diffusion across the nitrocellulose strip is improved, which obtains the desired signal [26]. The different results in the same RATs might be due to inaccuracies in the fabrication process or storage and shipment conditions.

There might be several explanations for the change in the detection limit between the different RATs, which are mainly attributed to variations in the manufacturing process. For instance, the T-line color stems from various sources, in particular either colloidal gold (RATs (a), (b), (g)), color conjugation (RAT(c)), polymer label (RAT (d)), etc. Additionally, the development process of the anti-nucleocapsid antibodies might vary between the

companies, which would cause the recognition of different epitopes with different affinity and specificity. Moreover, the number of conjugated antibodies at the T-line location might also affect the detection limit. The latter might be the cause for achieving different results under the same conditions (same RAT, concentration, and RH).

**Figure 1.** Various RATs were tested to detect a pure nucleocapsid protein in different concentrations, at RH conditions of (**A**) 60% and (**B**) 30%. The RATs are: (a) Deepblue COVID-19 (SARS-CoV-2) Antigen Test Kit (colloidal gold); (b) Easy Diagnosis COVID-19 (SARS-CoV-2) Antigen Test Kit; (c) EcoTest COVID-19 TO-GO; (d) GenSure COVID-19 Antigen Rapid Test Kit; (e) Orient Gene Rapid COVID-19 (Antigen) Self-Test; (f) Panbio COVID-19 Antigen Self-Test; (g) YHLO GLINE-2019 nCoV Ag for self-testing. Within each square are presented a number of positive results out of the replicates' number.

As seen in Figure S8, we compared three different RATs using the same nucleocapsid concentration of the nucleocapsid protein. The emerged T-lines are not similar; in the case of RAT (f), the T-line is very thick and clear, the T-line observed on RAT (d) is weaker but detectable by the human eye, and in the case of RAT (a), the T-line is the least prominent. On the other hand, RAT (d) shows a better detection capacity than these two RATs.

Previous studies have shown that all twelve nucleocapsid proteins interact with ~800 nucleotides of genomic RNA to produce an RNP complex [27]. Within each virus, there are 35–40 RNPs, meaning that the total amount of nucleocapsid protein varies between 420 and 480 entities according to this model. Assuming that the weight of the protein is 48 kDa, we can translate the amount of protein to the corresponding number of viruses. For instance, 5 μL of 55 μg mL−1 nucleocapsid solution is equivalent to a protein mass of 275 ng, which translates to 7.19–8.21 × 109 virions. The lowest detectable protein concentration of 0.55 μg mL−1 corresponds to 7.19–8.21 × 107 virions. This analysis emphasizes the high amount of virus copies needed to receive a positive result, in contrast to nucleic acid amplification techniques, which can detect an infected person in the presence of only several virions.

The average VL of nasopharyngeal swabs (NS) in infected patients is about ~108 genome copies per mL, and it varies between 105–1012 genome copies per mL [28,29]. Such high variation is possible due to various factors, such as vaccination, variant type, number of days since infection, etc. For instance, the VL of vaccinated patients is lower than that of non-vaccinated patients, and the Delta variant causes a higher VL than that of the Omicron variant [29]. Thus, it is reasonable that a higher VL will lead to a higher amount of the nucleocapsid protein in the test solution and a lower probability of having a false-negative result.

Our study has several limitations. We have consistently added a specific volume of the protein solution to the test solution. In contrast, during real-life testing, the nasal mucus that discharges to the test solution is not identical and might be higher or lower. Moreover, here we use pure nucleocapsid protein dissolved in PBS, while the nasal samples contain the whole virus, and the nucleocapsid interacts with the RNA and M protein of the virus. This might cause the nucleocapsid epitopes to be unavailable for interaction with the RAT's antibodies and reduce the detection efficiency. Furthermore, the nasal samples contain various additional proteins, such as mucin, and even other microorganisms. The factors mentioned above might cause changes in the interaction of the nucleocapsid protein with the antibodies present in the RAT, in contrast to the pure nucleocapsid we used. However, the experimental conditions are identical for all the tested RATs, which allows a reliable comparison. In addition, as we can see in Figures S1–S7, for the lowest detectable protein concentration, in some cases only, one out of three replicates showed a positive result. This means there are also variations in performance between the RATs from the same company. The increased number of tested RATs of each type may provide an even more reliable determination of the detection limits. However, the question arises whether the patient will persist and perform the test in a real-life situation more than a few times.

#### **4. Conclusions**

Our study demonstrates a simple method to evaluate the detection limit of various RATs using known pre-defined amounts of nucleocapsid protein. Here, as previously shown in other studies, yet in a new manner, there is a noticeable difference in the detection limits of various commercially available RATs, which customers and medical workers need to take into account when performing the RAT. We found the nucleocapsid protein mass of the various commercial RATs in the range of 2.75–27.5 ng in a test solution to be the lower detection limit for the RATs. This protein mass correlates to protein concentrations in the test solutions ranging between 9.8–78.6 ng mL−1. Obviously, this demonstrates not only the detection limit of RATs in general but also the variation in detection limits between various commercial RATs (eight-fold). Using a pure recombinant protein for RATs' assessment is a simple and cheap method and does not require performing tests on patients (i.e., avoiding the need for a Helsinki declaration) or preserving their samples. For future use, if a new variant of concern emerges, for a quick assessment, it would not be necessary to wait for infected patients in order to test the RATs on human samples, but rather study the updated recombinant protein and compare it to previous ones. Additionally, it will allow the RATs producers to evaluate whether any change is needed in previously marketed RATs, to obtain more reliable tests.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/bios12121083/s1, Table S1. Volumes of test solutions of various rapid antigen tests; Figure S1. "Deepblue COVID-19 (SARS-CoV-2) Antigen Test Kit (colloidal gold)" RATs after reaction with different concentrations of nucleocapsid protein at RH conditions of (A) 30% and (B) 60%. The lowest concentration that showed a positive result is 2.75 μg mL−1; only one out of three RATs showed a weak T-line (red arrow) at RH 30%; Figure S2. "Easy Diagnosis COVID-19 (SARS-CoV-2) Antigen Test Kit" RATs after reaction with different concentrations of nucleocapsid protein at RH conditions of (A) 30% and (B) 60%. The lowest concentration that showed positive result, in all replicates at RH 60%, is 0.55 μg mL−1; only one out of three RATs showed a weak T-line (red arrow) at RH 30%; Figure S3. "EcoTest COVID-19 TO-GO" RATs after reaction with different concentrations of nucleocapsid protein. The lowest concentration that showed positive result is 1 μg mL−1 at RH conditions of (A) 30% and (B) 60%; Two out of three RATs showed a weak T-line (red arrows) at RH 30% and all replicates at 60%; Figure S4. "GenSure COVID-19 Antigen Rapid Test Kit" RATs after reaction with different concentrations of nucleocapsid protein at RH conditions of (A) 30% and (B) 60%. The lowest concentration that showed positive result is 0.55 μg mL−1 at RH 60%; two out of three tests showed a positive result (red arrows); Figure S5. "Orient Gene Rapid COVID-19 (Antigen) Self-Test" RATs after reaction with different concentrations of nucleocapsid protein at RH conditions of (A) 30% and (B) 60%. The lowest concentration that showed a positive

result is 1 μg mL−1 at RH 60%. Only one out of three RATs showed a weak T-line (red arrow) using a concentration of 0.55 μg mL−1, RH 30%; Figure S6. "Panbio COVID-19 Antigen Self-Test" RATs after reaction with different concentrations of nucleocapsid protein at RH 30%. The lowest concentration that showed positive result is 2.75 μg mL−1; Two out of three RATs showed a T-line (red arrows); Figure S7. "YHLO GLINE-2019-nCoV Ag for self-testing" RATs after reaction with different concentrations of nucleocapsid protein at RH 60%. The lowest concentration that showed positive result is 1 μg mL−1; Two out of three RATs showed a weak T-line (red arrow); Figure S8. Three different RATs after reaction with 5.5 μg mL-1 nucleocapsid concentration on the same day. The T-line is the least prominent in the case of "Deepblue COVID-19 (SARS-CoV-2) Antigen Test Kit (colloidal gold)" RAT, intermediate intensity of the T-line observed in the case of "GenSure COVID-19 Antigen Rapid Test Kit" RAT, and the "Panbio COVID-19 Antigen Self-Test" RAT showed the most prominent T-line.

**Author Contributions:** Conceptualization, B.P.; methodology, D.D. and I.P.; writing—original draft preparation, D.D.; writing—review and editing, I.P. and B.P.; supervision, B.P.; funding acquisition, B.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Israel Science Foundation (ISF Grant 3970/19).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** B.P. thanks the Bank Discount Academic Chair for financial support. B.P. thanks A. Futerman from The Weizmann Institute of Science.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


*2.2. RAF—Resilience Assessment Framework Description*

RAF sought alignment with international frameworks for resilience assessment, particularly with UNDRR Disaster Resilience Scorecard, both preliminary and detailed levels [6,7], and UN-Habitat, and made significant developments with regard to its scope and focus on urban services. The RAF considers the UN-Habitat resilience dimensions [29]: organisational (integrates top-down governance relations and urban population involvement, at the city level), spatial (referring to urban space and environment), functional (resilience of strategic services) and physical (resilience of services infrastructure). Time dimension is implicitly integrated as part of the analysis. The RAF (Table 2) has a hierarchical tree structure (Figure 2) meaning that, for each dimension, resilience objectives are defined, representing the ambitions to be achieved in the medium–long term by the city and services. For those dimensions related to the urban services, they firstly unfold into sub-dimensions, where each sub-dimension represents one service to be assessed. Each objective is described by a set of criteria that translate the different points of view associated with it. Each criterion assembles the respective assessment metrics, through which it is possible to classify the resilience development level by comparison with reference values. Metrics are then defined consisting in questions, parameters or functions used to assess the criteria. Some of the RAF metrics correspond to or were adapted from existing frameworks, mainly from UNDRR framework (former UNISDR)—found to be highly relevant for the scope of the RAF, and others were newly developed. In Appendix A, the complete structure is presented. As an example, Table 3 illustrates the metrics definition to assess, within the spatial dimension, the objective of spatial risk management from the perspective given by the criterion impacts of climate-related events, showing the hierarchical tree structure mentioned above.

#### **Table 2.** Overview of the RAF dimensions.

**Figure 2.** RAF tree structure.

**Table 3.** Metrics definition—example for spatial dimension, objective spatial risk management, criterion impacts of climate-related event.

The framework considers past, existing and future conditions in the assessment. To incorporate the uncertainties associated to expected variations in climate-related variables, some metrics are specific to CC assessment scenarios, namely those that address preparedness for CC, and that anticipate the city and services' exposure or vulnerability to future scenarios. Besides, the consideration of reference values allows to generally address uncertainties in the assessment.

A relevance degree is assigned to each metric, namely: essential, corresponding to all metrics with higher relevance, required to integrate the resilience assessment of any city or service, complementary, additional metrics to be considered whenever integration of city or service specific aspects is sought, corresponding to a more detailed resilience assessment and comprehensive, additional metrics recommended whenever a more in-depth assessment is aimed, for a city or service with higher maturity in its resilience path. Accordingly, depending on the resilience maturity, the city or service aiming to apply the RAF may select a given set of metrics, according to their relevance.

Additionally, every city or urban service needs to operate in its own specific political, economic, geographical, climatic and cultural context. Considering the context information is fundamental in interpreting any assessment. Following this, city and services' characterisation profiles were developed to integrate the RAF framework, regarding its scope and focus. These profiles require information on geographical characteristics, climate, population, economy and governance, built environment and infrastructures, for the city. Regarding each service, it considers information on context characterisation, climate and infrastructure assets.

#### *2.3. Research Sites*

#### 2.3.1. General

In order to test and validate the RAF to assess the cities' resilience to climate change with a focus on the water cycle, it was applied to Bristol (UK), Barcelona (Spain) and Lisbon (Portugal) by the respective cities and strategic services managers. These three cities represent diverse context characteristics as well as different climate change-related concerns. The application was undertaken using the RAF App, a web-based application tool reproducing the RAF structure that allows selection of applicable dimensions and services to assess and allows private submission of answers to the metrics. The results may be visualised graphically (Figures 3–5) and reports are also provided [30].


**Appendices and nomenclature**

The effect of compressibility is shown in **Figure 10**, which shows the ratio of the

*Influence of Mach number of compressible flow on pressure-drop coefficient on the surface of the an ellipse and*

It follows from **Figure 10** that the effect of compressibility for two-dimensional ellipses at the same Mach number is greater than for 3D-spheroids. **Figure 11** shows the results of calculating the pressure-drop coefficient for compressible and incom-

As seen in **Figure 11**, the compressibility effect has a stronger effect on 2D

This paper presents the results of calculating the critical Mach numbers of the flow around two-dimensional and axisymmetric bodies. A sufficiently high accuracy of calculating the critical Mach numbers for engineering calculations using the

for compressible and incompress-

relative maximum velocities *U*max ¼ *U*max*=U*<sup>∞</sup>

bodies than on axisymmetric bodies.

**4. Conclusions**

**186**

**Figure 10.**

*Aerodynamics*

**Figure 11.**

*spheroid.*

ible flows on the surface of 2D ellipses and 3D spheroids.

*Maximal velocities on surface of the ellipse and spheroid vs. Mach number.*

pressible flows on the surfaces of two- and three-dimensional bodies.

*Aerodynamics*


**1. Introduction**

Among the 19 tribes of the subfamily Apinae, only Apini (honey bees) and Meliponini (stingless bees) show highly social behavior or eusociality [1]. In contrast to the mono-genus tribe of Apini that consists of 11 valid species [2], the Meliponini demonstrates the most diverse group, not only of the number of species but also of the morphology, nesting habitats, structures, and behavior among species [3, 4]. Meliponini has a wide distribution and is found in the tropical and subtropical regions of the world (**Figure 1**). The highest diversity of stingless bee species is found in the Neotropical with about 391 species and 32 genera, indicating that this area might be the center of origin and dispersal of stingless bees [4, 6, 7]. By contrast, 60, 10, and 50 species have been reported in Asia, Australia, and Africa, respectively [4]. However, the advance of molecular methods has increased the studies on species complexity, and new species of stingless bees are being added [8].

Like eusocial honey bees, stingless bees form colonies with a single female queen, a few hundred to several thousand female workers, and a few hundred males (drones) [1]. The nests of stingless bees show a large variation in the size, substrate used, habitats, and landscapes [9]. In nature, different stingless bee species nest in various cavities, such as hollow tree trunks and stems, under the ground, crevices within rocks, and the nest of other insects [3, 10]. All stingless bees use the same basic material, cerumen, to construct the nest. The worker bees make cerumen by mixing the wax they produce in the wax gland located on the tergites of their

abdomen, with resins that are collected from plants [4]. In spite of high variation in size and ornaments found in the different stingless bee nests, the basic components are remarkably homogeneous across species [4], as shown in **Figure 2**. The nest connects to the outside through the entrance tube made of cerumen. Among the different species of stingless bees, the entrance tube is quite varied in shape and size (**Figure 3**), and it can be used as the characteristic to identify some of the Indo-Malayan stingless bee species.

In contrast to the Asian honey bee (*Apis cerana*), stingless bee colonies are typically long-lived [3, 4] and have low absconding behavior. Some species of stingless bee continuously occupy the original nest, and the nest lives more than

**Figure 1.** *Geographic distribution of stingless bees (amended in accordance with [4, 5]).*

#### **Figure 2.**

*Basic interior structure of a stingless bee nest: (A) a vertical brood cells, (B) honey and pollen pots, and (C) involucrum.*

**175**

of flora [11, 12].

**Figure 3.**

**2. Stingless bees**

**2.1 General features**

*Southeast Asian Meliponiculture for Sustainable Livelihood*

20 years. These aspects of their biology make stingless bee species successful in meliponiculture in Southeast Asia, including Thailand. At least six of Thailand's native species have had nests successfully transferred into a wood box to pollinate orchard crops. Two species (*Geniotrigona thoracica* and *Tetragonula pagdeni*) have been used for honey production. Additionally, management costs have been lower than in apiculture. The meliponiculture might be useful to improve household income in the countryside of Southeast Asia, where there is a very high diversity

*Variation of entrant tube in some native stingless bee species found in Southeast Asia: (1)* Geniotrigona thoracica*, (2)* Homotrigona fimbriata*, (3)* Lophotrigona canifrons*, (4)* Tetragonilla collina*, (5)* Pariotrigona klossi*, (6)* Heterotrigona itama*, (7)* Tetragonula fuscobalteata*, (8)* Lepidotriogona terminata*, (9)* Tetrigona melanoleuca*, (10)* Tetrigona apicalis*, and (11)* Tetragonula pagdeni.

Stingless bees are one of the most diverse groups of corbiculate bees. Unlike Apini, Meliponini shows great interspecific variation not only in shape but also size,

*DOI: http://dx.doi.org/10.5772/intechopen.90344*

#### **Figure 3.**

*Modern Beekeeping - Bases for Sustainable Production*

Malayan stingless bee species.

abdomen, with resins that are collected from plants [4]. In spite of high variation in size and ornaments found in the different stingless bee nests, the basic components are remarkably homogeneous across species [4], as shown in **Figure 2**. The nest connects to the outside through the entrance tube made of cerumen. Among the different species of stingless bees, the entrance tube is quite varied in shape and size (**Figure 3**), and it can be used as the characteristic to identify some of the Indo-

In contrast to the Asian honey bee (*Apis cerana*), stingless bee colonies are typically long-lived [3, 4] and have low absconding behavior. Some species of stingless bee continuously occupy the original nest, and the nest lives more than

*Geographic distribution of stingless bees (amended in accordance with [4, 5]).*

*Basic interior structure of a stingless bee nest: (A) a vertical brood cells, (B) honey and pollen pots, and*

**174**

**Figure 2.**

*(C) involucrum.*

**Figure 1.**

*Variation of entrant tube in some native stingless bee species found in Southeast Asia: (1)* Geniotrigona thoracica*, (2)* Homotrigona fimbriata*, (3)* Lophotrigona canifrons*, (4)* Tetragonilla collina*, (5)* Pariotrigona klossi*, (6)* Heterotrigona itama*, (7)* Tetragonula fuscobalteata*, (8)* Lepidotriogona terminata*, (9)* Tetrigona melanoleuca*, (10)* Tetrigona apicalis*, and (11)* Tetragonula pagdeni.

20 years. These aspects of their biology make stingless bee species successful in meliponiculture in Southeast Asia, including Thailand. At least six of Thailand's native species have had nests successfully transferred into a wood box to pollinate orchard crops. Two species (*Geniotrigona thoracica* and *Tetragonula pagdeni*) have been used for honey production. Additionally, management costs have been lower than in apiculture. The meliponiculture might be useful to improve household income in the countryside of Southeast Asia, where there is a very high diversity of flora [11, 12].


**Characterization of Industrial Highly Organic Wastewater to Evaluate Its Potential Use as Fertilizer in Irrigation of Agricultural Land**

[134] NEMA. State of the Environment Report for Uganda (2010). Kampala: National Envi‐

[135] Food and Agriculture Organization of the United Nations (FAO). The Republic of

[136] Ibale, R. Towards an appropriate management regime for the fisheries resources of Uganda. Final project report (1998), Fisheries Department, Ministry of Agriculture,

Animal Industry and Fisheries, Uganda, www.unuftp.is/proj98/robbinprf.pdf [137] UBOS and Macro International Inc. Uganda Demographic and Health Survey. Ugan‐ da Bureau, Kampala, and Macro International Inc. Calverton, Maryland, USA. (2007).

[138] International Centre for tropical Agriculture (CIAT). Uganda's Agro-ecological Zones: A guide for Planners and Policy Makers, CIAT, Kampala, Uganda, (1999). [139] Mwebaze, S. Country pasture/forage resource profile, Uganda, (2006), www.fao.org/

[140] Kabeere, F. and Wulff, E. Seed sector country profile: Uganda. Volume 1: Overview of seed supply systems and seed health issues. Copenhagen: Danish Seed Health

[141] Ronner, E and Giller, K. Background information on agronomy, farming systems and

[142] Mbuza, F. A systems analysis of milk production system in Uganda and prospects

for technology change. PhD Thesis, Australia, University of Melbourne.

Centre for Developing Countries, University of Copenhagen (2008).

ongoing projects on grain legumes in Uganda, www.n2africa.org

ronment Authority (NEMA).

uganda

56 Agroecology

Uganda, Nutrition Country profile, (2010).

Esteban Jácome Sandoval and Martin H. Bremer- Bremer

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/59999


**Preface to "Fluid Interfaces"**

Fluid interfaces are ubiquitous in science and technology, playing a main role in different aspects of industry, nature, or even life. This makes it necessary to understand the most fundamental physicochemical bases underlying the behavior of such interfaces. However, this is tricky, and makes it necessary to explore the impact of molecular and supramolecular species on the flows occurring within the interfacial region and the thickness of the interface. Furthermore, the broken symmetry associated with the formation of a fluid interface can be used as a platform for the assembly of innovative materials with reduced dimensionality, which may have impacts in food science, cosmetics, biology, oil recovery, electronic, drug delivery, detergency, or tissue engineering. This has made of the study of fluid interfaces a multidisciplinary challenge involving researchers from different areas of chemistry, physics, pharmacy, biophysics, medicine, engineering, and materials science.

The papers included in this book present a broad perspective of the current trends on the study of fluid interfaces.

> **Eduardo Guzm´an** *Editor*


Technology, Science, and Culture: A Global Vision, Volume II

*Universidad de las Américas Puebla*

**III**

**Technology, Science, and Culture: A Global Vision, Volume II 2019**

From the success of the first volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them.

To reach these objectives, we organized a 1-day forum where recognized specialists in each field interacted with doctorate students generating very fruitful discussions. Thus, Dr. Rafael Vazquez-Duhalt, from the Nanoscience and Nanotechnology Center of Universidad Nacional Autonoma de Mexico, brought to the discussion one of his research streams and talked about viral structures in nanomedicine. Dr. Stella Maris Alzamora, from the Exact and Natural Sciences Faculty of Universidad de Buenos Aires, exposed the opportunities and challenges on mild intervention technologies tending to increase shelf life and/or safety of fresh fruits. Dr. Damian Helbling, from the School of Civil and Environmental Engineering of Cornell University, discussed about water quality, commenting on the importance of characterizing emerging chemical contaminants and explaining why it is mandatory to remediate the water from these compounds, pointing out some tools to achieve it. For his part, Dr. Hemant Tagare, from Yale University, introduced Cryo-EM single-particle reconstruction algorithms, which is a method for reconstructing 3D structures of biological macromolecules. Dr. Gary Hall, from Coventry University, discussed about anti-bourgeois theory, showing how to

speak differently about technology, science, culture, and everything.

kick to continue deepening on each research topic covered in this forum.

America and the Caribbean region, suffers from both weather extremes.

ing a very positive synergy among all attendants.

Other activities in this forum included the participation of graduate students from different doctorates. Graduate students exposed their research works generat-

This book is the result of all these discussions, where we hope to give the initial

The number and impact of water-related natural disasters have increased since the middle of last century. As result of increased climate variability and the effects of global warming, the hydrometeorological risk has increased and spread, while the resilience of societies, in many cases, is not adequate. Consequently, the risk has increased. Floods and droughts, particularly in a changing climate, require greater understanding to generate better forecasts and proper management of these phenomena. Mexico, like other countries in the world, and of course in Latin

Editors

Sergio Picazo-Vela Luis Ricardo Hernández

Knowledge Area Co-editors Erwin Josuan Pérez Cortés Nelly Ramírez Corona José Luis Sánchez Salas Enrique Ajuria Ibarra Roberto Rosas Romero


**3.1.4 Data Dependent Jitter (DDJ)**

212 Modern Metrology Concerns

Sinusoidal jitter in time domain produces a probability distribution function given by

1

*i*

(33)

s

s

x 10-12

x 10-12

*x a*

*otra*

0

where *2a* is the peak-to-peak width of periodic jitter [14]. In Fig. 4b) is shows the histogram

2 2

*PJ i*

*J x a x*

x 10-12

x 10-12

jitter (PJ+RJ), c) duty cycle distortion with added random jitter (DCD+RJ) y d) data

offsets and differences in the rising and falling edge characteristics [14].

Number of events

c) d)

Fig. 4. Histogram for a) Random jitter (RJ), b) Sinusoidal periodic jitter with added random

Duty Cycle Distortion is often also called pulse width distortion [14], is deviation in the duty cycle value from the ideal value, this equates to a deviation in bit time between a 1 bit (logic 1) and a 0 bit (logic 0). DCD can have several sources. The most ommon are threshold level

a) b)

Number of events

*i*

(defining time zero as the center of the distribution)

for sinusoidal periodic jitter with added random jitter.

s

s

dependent jitter with added random jitter (DDJ+RJ).

**3.1.3 Duty Cycle Distortion (DCD)**

Number of events

Number of events

Data dependent jitter describes timing errors that depend on the preceding sequence of data bits [14]. DDJ is a predominant form of DJ caused by bandwidth limitations of the system or electromagnetic reflections of the signal [16-17]. Since there is always only a limited number of different possible patterns in a data stream of limited length, data dependent timing errors always produce a discrete timing jitter, theoretically DDJ distribution is the sum of two o more delta functions [14]:

$$J\_{DDJ}\left(\mathbf{x}\right) = \sum\_{i=1}^{N} \left\{ p\_i \mathcal{S}\left(\mathbf{x} - t\_i\right) \right\} \tag{35}$$

where 1 1 *N i i p N* is number of distinct patterns, *pi* is the probability of the particular pattern occurring, and *ti* is the timing displacement of the edge following this pattern. In Fig. 4d is shows the histogram for data dependent jitter with added random jitter.

In the simulations, two pulse trains of unitary amplitude are generated. The value of reference frequency was accepted as 7 0*f* 1 10 Hz. The hypothetical value of unknown frequency 5878815.277629991 *Xf* Hz is a result of the accepted value of the period 7 1.701023 10 *TX* s, and value of pulse width in both pulse trains is 9 1.0 10 s. The RJ model used is Gaussian distributed with RMS value 0.7 ps. The PJ model is a single-tone sinusoidal with frequency 5 MHz and peak-to-peak value of 10 ps, same peak-to-peak value is assumed in DDJ and DCD jitter models. A component of random jitter is added to the last tree models to generate the jitter models PJ+RJ, DDJ+RJ and DCD+RJ. Such jitter values are selected because of the most typical values for such pulse trains according to [15]. Then the jitter components modeled are applied to the time reference points of each narrow pulse on the two pulse trains. In

## **3.2 Jitter simulation results**

In the first computational experiment is assumed the same magnitude of the jitter in the oscillator under measurement and references oscillator on all models. The experimental results when the two pulse trains started in phase are presented in Table 2, to a simulation time of 0.172 s. In the first row of each table are presented the results obtained without jitter models.

In third column is shown the total number of coincidences obtained with each model for the simulation time and, in the fourth column is shown the number of coincidence where appears the stop event associate to the best approximation in the frequency measurement process (the desired count is represented like *10r* [7]). Program code for computational experiment in Matlab is presented in [22].

Table 2. Simulation results with same magnitude of the jitter and pulse trains in phase.

Comparing the results shown in both columns with the values obtained in the simulation that does not include any jitter model, we see that in the presence of jitter the number of coincidence and the number of coincidence pulse associated with the best approach varies depending on model type. The latter implies the position at the time of the coincidence pulse is not fixed since it depends on the jitter component is dominant, in all case *n0=1701023* and *nX=1000000.*

Fig. 5 is presented the relative error obtained by simulation around best coincidence, obtained under condition (30) with *ε=1×10-12* s. In this plot we can see that due to jitter effect some theoretically expected marginal coincidences may disappear or non-theoretically expected may appear. However, this phenomenon can not be observed in non-marginal coincidences, for instance the expected coincidence under condition (30) and an appropriated pulse width τ.

In third column is shown the total number of coincidences obtained with each model for the simulation time and, in the fourth column is shown the number of coincidence where appears the stop event associate to the best approximation in the frequency measurement process (the desired count is represented like *10r* [7]). Program code for computational

> Total number of coincidences

model 20223 19999

RJ 20222 19998 PJ+RJ 20240 20016 DCD+RJ 20213 19989 DDJ+RJ 20223 19999

RJ 20217 19993 PJ+RJ 20224 20002 DCD+RJ 20218 19995 DDJ+RJ 20218 19994

RJ 20226 20002 PJ+RJ 20218 19995 DCD+RJ 20218 19994 DDJ+RJ 20217 19993

RJ 20227 20003 PJ+RJ 20216 19991 DCD+RJ 20218 19994 DDJ+RJ 20223 20000

Table 2. Simulation results with same magnitude of the jitter and pulse trains in phase.

Comparing the results shown in both columns with the values obtained in the simulation that does not include any jitter model, we see that in the presence of jitter the number of coincidence and the number of coincidence pulse associated with the best approach varies depending on model type. The latter implies the position at the time of the coincidence pulse is not fixed since it depends on the jitter component is dominant, in all case

Fig. 5 is presented the relative error obtained by simulation around best coincidence, obtained under condition (30) with *ε=1×10-12* s. In this plot we can see that due to jitter effect some theoretically expected marginal coincidences may disappear or non-theoretically expected may appear. However, this phenomenon can not be observed in non-marginal coincidences, for instance the expected coincidence under condition (30) and an

Coincidence number for best approximation

Oscillator under test

Without jitter

experiment in Matlab is presented in [22].

Reference Oscillator

Without jitter model

RJ

PJ+RJ

DCD+RJ

DDJ+RJ

*n0=1701023* and *nX=1000000.*

appropriated pulse width τ.

Fig. 5. Jitter effect on frequency relative error for a series of best coincidences (simulation time 0.2.s)


7.6.6 Minimum valid concentration (MVC)

MVC = λ/endotoxin limit

λ: the labeled lysate sensitivity in the gel-clot technique (IU/ml) or the lowest concentration used in the standard curve for the turbidimetric or chromogenic techniques.

Sample 1 Endotoxin limit: 0.5 EU/ml Concentration of sample: 100 mg/ml λ: 0.06 EU/ml MVD = 0.5 EU/ml 100 mg/ml/0.06 EU/ml MVD = 833

Add 1 ml of sample 1 in to 832 ml of LRW. Prepare sample 2 in using the same method.

What Is Limulus Amebocyte Lysate (LAL) and Its Applicability in Endotoxin Quantification… DOI: http://dx.doi.org/10.5772/intechopen.81331

7.6.7 Preparation of CSE dilutions

Using 10-fold and 2-fold dilution methods prepare the following dilutions of control standard endotoxins (CSE)

1. 0.5 EU/ml

(sodium nitrite reconstituted with dilute HCL). Add 0.05 ml (50 μl) of solution 2 (ammonium sulfamate). Add 0.05 ml (50 μl) of solution 3 (NEDA) use new pipette tip agitate the plate to mix. Full color (magenta) should develop immediately. Read

Positive control must be included to verify that it is appropriate to use the parameters of a previous (archived) standard curve to calculate endotoxin

Prepare sample solutions by dissolving or diluting drugs (pH 6.0–8.0). The pH may be adjusted by the use of acid, base, or suitable buffers as recommended. Do not exceed the MVD or MCV while making dilutions and adjusting the pH.

MVD = (endotoxin limit concentration of sample solution)/(λ)

Endotoxin limit given in USP, concentration of a sample of the label, λ: the labeled lysate sensitivity in the gel-clot technique (IU/ml) or the lowest concentration used in the standard curve for the turbidimetric or chromogenic techniques.

λ: the labeled lysate sensitivity in the gel-clot technique (IU/ml) or the lowest concentration used in the standard curve for the turbidimetric or chromogenic

Add 1 ml of sample 1 in to 832 ml of LRW. Prepare sample 2 in using the same

LRW negative controls should be included with each test

1: Equation of straight line (results)

x = endotoxin concentration,

7.6.5 Maximum valid dilution (MVD)

7.6.6 Minimum valid concentration (MVC)

Concentration of sample: 100 mg/ml

MVD = 0.5 EU/ml 100 mg/ml/0.06 EU/ml

MVC = λ/endotoxin limit

Endotoxin limit: 0.5 EU/ml

techniques. Sample 1

method.

98

λ: 0.06 EU/ml

MVD = 833

c = y-intercept and y = mean absorbance

Example calculation

7.6.4 Sample preparation

the test at 540–550 nm.

Growing and Handling of Bacterial Cultures

<IP_ADDRESS> Make standard curve

7.6.2 Positive control

concentrations.

7.6.3 Negative controls

y = mx + c m = slop

X = y-c/m

Reconstitute the lysate with 3.2 ml of buffer provided with it. Follow the standard procedure for reconstitution.

<IP_ADDRESS> Mixing and incubation

Stop reaction.

For sample 1 and sample 2:

Stop the reaction by adding 50% acetic acid. Add 0.025 ml (25 μl) (Tables 2 and 3).

#### Table 2.

Different dilution of CSE and lysate.

#### Table 3.

Make two replicates of each CSE and sample preparation to reduce any errors.