Artepillin C

**Figure 2.** The primary mass spectrum for each of the seven phenolic acids in both positive and negative ESI modes: Chlorogenic acid (1) with (M + Na)+ at *m*/*z* 377 and (M − H)− at *m*/*z* 353, whereas caffeic acid (2) with (M + Na)+ at *m*/*z* 203 and (M − H)− at *m*/*z* 179, isochlorogenic acid B (3) with (M + Na)+ at *m*/*z* 539 and (M − H)− at *m*/*z* 515, isochlorogenic acid A (4) with (M + Na)+ at *m*/*z* 539 and (M − H)− at *m*/*z* 515, isochlorogenic acid C (5) with (M + Na)+ at *m*/*z* 539 and (M − H)− at *m*/*z* 549, caffeic acid phenethyl ester (6) with (M + Na)+ at *m*/*z* 307 and (M − H)− at *m*/*z* 283, and artepillin C (7) with (M + Na)+ at *m*/*z* 323 and (M − H)− at *m*/*z* 299.

**Table 1.** The identification of seven phenolic acids from Brazilian propolis samples in both positive and negative ESI modes.

Brazilian green propolis has become a popular health supplement due to its many biological properties. Characteristically, it has an herbal odor and a unique, irritating taste. Previous work provided the first evidence that artepillin C was the main, pungent ingredient in the ethanol extract of Brazilian green propolis (EEBP). Moreover, that artepillin C potently activated human transient receptor potential ankyrin 1 (TRPA1) channels [23].

In this study, artepillin C was successfully identified and was the same compound that has previously been shown to have a variety of beneficial, biological activities. Notably, three isomers of chlorogenic acid were identified by the UPLC-ESI-QTOF-MS, which has rarely been reported. Thus, these results indicate that this method is useful for identification of the constituents of Brazilian green propolis. It should be noted that Brazilian green propolis contains predominantly phenolic compounds, including flavonoids and phenolic acid as well as its derivatives [24]. There were many unidentified flavonoids in the total ion chromatogram; moreover, the TIC of the methanol extracts obtained from Brazilian green propolis (Figure 1) did not include analysis of its water extracts. In subsequent studies, we will need to further identify these characteristic compounds obtained from Brazilian green propolis.

#### *2.2. Determination of Phenolic Acids in Brazilian Green Propolis*

After the seven phenolic acids obtained from the Brazilian green propolis were identified from the total ion chromatogram, we further quantified them using HPLC. According to the study carried out by Cuiping Zhang et al., nine phenolic compounds were quantified using HPLC by comparing them with standard substances [22]. The polyphenol fraction in propolis was quantitatively characterized

by HPLC-ESI-MS/MS [25]. Using aqueous ethanol along with the addition of the internal standard veratraldehyde, an RP-HPLC procedure for phenolic compounds was developed and 10 compounds were subsequently quantified [26].

Similarly, and as presented here, the main, characteristic peaks were identified by their chromatographic behavior and UV spectra relative to those reference compounds in the HPLC chromatogram (Figure 3). The seven phenolic compounds were separated using the chromatographic conditions indicated in the experimental section of this paper. The content of each phenolic acid component as obtained from the Brazilian green propolis was based on a linear regression equation of the phenolic acid component standard. Each component's content ratio was calculated based on the relationship between the peak area of the phenolic acid component in the sample and the injection amount. We also conducted a methodological verification to evaluate the scientific nature of our HPLC-mediated, determination method for different phenolic acid compounds found in Brazilian green propolis.

**Figure 3.** Comparison of chromatograms obtained at 310 nm of chlorogenic acid (1), caffeic acid (2), isochlorogenic acid B (3), isochlorogenic acid A (4), isochlorogenic acid C (5), caffeic acid phenethyl ester (6), and artepillin C (7) mix of standards and Brazilian green propolis sample.

## *2.3. Method Validation*

Methodological verification was performed according to International Council for Harmonization (ICH) guidelines [27] and included measurements for: Linearity estimation, system precision and repeatability, and accuracy and stability.

## 2.3.1. Linearity

The linearity of the method used to identify the aforementioned compounds was determined by analyzing the standards used. Equations for the calibration curves were determined by plotting the relationship between the corrected peak areas (peak area/migration time), ratio of the analysis and the internal standard and the concentration (μg/mL). The concentrations of all compounds obtained from the Brazilian green propolis samples were calculated based on the peak area ratio and data are shown in Table 2. As indicated, all R2 values obtained using linear regression analysis were > 0.99. Linear regression equations for the identified compounds are also presented in Table 2, where y is the peak area and x is the concentration.

**Table 2.** Linearity, sensitivity, system precision and accuracy (*n* = 6).

a Response y, is the peak area ratio (area/migration time) of the analytes versus that of standard compound. b Detection was carried out at 310 nm (Chlorogenic acid, Caffeic acid, Isochlorogenic acid B, Isochlorogenic acid A, Isochlorogenic acid C, caffeic acid phenethyl ester, Artepillin C).

The UPLC method enables higher efficiency and higher precision detection. In a previous study, the propolis analysis was conducted using HPLC. In general, and in the case of simple, sensitive, and specific reversed-phase high performance liquid chromatography (RP-HPLC), a rapid determination method of salicin was developed and verified to distinguish poplar gum and propolis [28].

#### 2.3.2. System Precision and Repeatability

System accuracy was assessed by repeated injections (*n* = 6) of the standard mixture. The relative standard deviation (RSD)% values of each compound as well as the chromatogram similarity are calculated; all results indicated that instrument variability was sufficiently low at low concentrations. These results showed that the RSD% of each phenolic acid in peak area is less than 2% and similarity of the chromatogram of each standard mixture sample was more than 98%. Collectively, these results indicated that the precision of the instrument met testing requirements.

The repeatability was obtained as the RSD% by analysis of six same green propolis samples of the standard components and instrumental variability, by taking into account the chromatographic peak similarity: such as peak areas and compound retention. The results were considered satisfactory, since the RSD% (*n* = 6) of seven phenolic acids content is less than 2%. Moreover, the similarity of chromatograms from six propolis samples were more than 98%, indicating sound repeatability of our HPLC analysis.

## 2.3.3. Accuracy

Accuracy was estimated using recovery experiments performed on random Brazilian green propolis. Regarding accuracy and recovery studies, each of the phenolic acids standards was added to the same Brazilian green propolis. Approximately 20% of the analysis used native content obtained before the extraction process. These results are shown in Table 2. The average recovery rate was more than 95%; the acceptable RSD% for all obtained results was less than 5%. It is worth emphasizing that accuracy studies were undertaken for seven phenolic acid compounds. Moreover, the results were considered to be satisfactory for the purposes of our method.

## 2.3.4. Stability

Regarding the stability of this approach, sample size, pH of the mobile phase, and different solvent brands were mandatory in order to standardize our results. The stability study was implemented by following a "time-by-time" approach, in which the injection time of a sample of the same Brazilian green propolis sample was altered (0, 4, 8, 12 and 24 h) when conducting HPLC. The chromatographic peak similarity at different time points and the RSD% of the seven standards compounds were greater than 98% and less than 2.0%, respectively. Therefore, the developed method was considered to be stable, recognizing its use in different laboratories.

In summary, the validation parameters evaluated in the present study indicated that the HPLC method met the requirements for quality control of our Brazilian green propolis analysis [26].

## *2.4. Data Analysis*

The seven identified phenolic acids obtained from Brazilian green propolis were quantified using a validated HPLC method. The contents of each of these phenolic acid compounds are reported in Table 3. Although Brazilian green propolis comes from different parts of Brazil, there was no significant difference in the content of a given phenolic acid across different kinds of Brazilian green propolis samples. This is likely due to these samples coming from the same plant resin sources. Specifically, isochlorogenic acid B had the lowest content (0.08 ± 0.04), while the artepillin C had the highest (2.48 ± 0.94). Artepillin C has been shown to be the main pungent ingredient in Brazilian green propolis [29]; its content as determined here varied from 0–11%, depending on the geographical origin.

**Table 3.** Determination of seven phenolic acids compounds obtained from fourteen Brazilian green propolis samples.

The data obtained here were analyzed using a one-way analysis of variance (ANOVA), followed by Tukey's honestly significant difference (HSD) test. Tukey's multiple comparisons test was performed for different phenolic acids groups, with results showing that artepillin C had an extremely significant difference in terms of its content in Brazilian green propolis when compared with other compounds (*p* < 0.01; Figure 4). Regarding the isochlorogenic acid C group, a one-way ANOVA also detected an extremely significant difference between its content in Brazilian green propolis and other tested compounds (*p* < 0.01). The data for the different phenolic acids for each group were normally distributed (Figure 4), a requirement necessary to conduct a one-way ANOVA. Using our validated HPLC method, these results suggest that artepillin C quantification can be used as an important factor for determining Brazilian green propolis quality. Moreover, it has potential for use as a chemical marker for the future development of better quality control measures for Brazilian green propolis.

**Figure 4.** Statistical analysis using one-way (ANOVA) followed by Tukey's honestly significant difference (HSD) test. C1–C7 are represented with Chlorogenic acid, caffeic acid, isochlorogenic acid B, isochlorogenic acid A, isochlorogenic acid C, caffeic acid phenethyl ester, Artepillin C, respectively. *p* < 0.01 indicates a significant difference between the test groups.

## **3. Experimental Section**

## *3.1. Reagents and Chemicals*

Chlorogenic acid, caffeic acid, isochlorogenic acid A, isochlorogenic acid B, isochlorogenic acid C, caffeic acid phenethyl ester (all used as standard) were from Chengdu Alfa Biotechnology Co., Ltd. (Chengdu, Sichuan, China). Artepillin C (used as standard) was purchased from Richmond, VA (USA). Methanol (HPLC grade) and ethanol (95%) were from Merck Chemicals. Formic acid (95%) was from Sigma-Aldrich (Milan, Italy).

## *3.2. Sample Preparation*

All Brazilian green propolis samples were collected in summer 2017 from different manufacturers (Figure 5) and stored at −25 ◦C until analysis [30]. Three samples were randomly selected from 14 samples of green propolis tested, and only 0.1 g of each was weighed into a 10 mL volumetric flask and well mixed. After adding methanol to the propolis sample and diluting to volume (10 mg/mL), it was ultrasonically extracted (360 W, 25 KHz) for 30 min, and then the supernatant was prepared. Resulting supernatants were then filtered using a 0.45 μm filter from VWR (Radnor, PA, USA) prior to detection by UPLC-ESI-QTOF-MS (Agilent Technologies, Waldbronn, Germany).

**Figure 5.** Fourteen Brazilian green propolis obtained from commercial suppliers.

The standard Chlorogenic acid, ca ffeic acid, isochlorogenic acid A, isochlorogenic acid B, isochlorogenic acid C, ca ffeic acid phenethyl ester, Artepillin C (5 mg of each) were placed in a 5 mL volumetric flask and dissolved in methanol. The standard was identified after the analysis of UPLC-ESI-QTOF-MS. The standard solution was stored at 4 ◦C until later use. The three mL of 95% ethanol was added to 100 mg of each Brazilian green propolis sample. The sample was then extracted using ultrasound (360 W, 25 KHz) for 1 h, after which it was centrifuged at 3000 r/min for 10 min. Finally, the 0.1 mL supernatant was obtained and adjusted to 1 mL with ethanol, after which it was filtered using a 0.45 μm filter. The resulting solution was then used for HPLC.

## *3.3. UPLC-ESI-QTOF-MS*

Brazilian green propolis was identified using an Agilent 1290 ESI-QTOF-MS spectrometer and an Agilent 6545 Series UPLC system (Agilent Technologies, Waldbronn, Germany). The instrument was equipped with an electrospray ionization (ESI) source (Dual AJS ESI) and a proprietary Agilent jet stream dual nebulizer. In the UPLC analysis, chromatographic separations of Brazilian green propolis were conducted using a C18 Agilent SB column (Waldbronn, Germany) (RP-18,100 mm × 2.1 mm, 1.8 μm particle size) at a flow rate of 0.3 mL/min with 0.1% formic acid (A) and methanol (B) as solvents (99.9%, HPLC grade; Merck, Darmstadt, Germany). Starting with 25% B, to reach 45% B at 11 min, 55% B at 22 min, 70% B at 29 min, 95% B at 40 min, and then it became isocratic for 4 min. The injection volume was 2 μL for each sample.

The mass spectrometer was operated in the positive and negative electrospray ionization modes using Agilent technology. The optimized ESI operating conditions were as follows: Capillary voltage of 3500 V and drying and sheath gas temperature of 320 ◦C and 350 ◦C, respectively. The drying and sheath gas flows were set at 8 L/min and 11 L/min, respectively. The range for mass spectrometry detection was: MS: 100–1700 ( *m*/*z*) and MS/MS: 50–1000 ( *m*/*z*). The ion source temperature was 150 ◦C. Finally, the nebulizer pressure was 35 psi [31].

## *3.4. HPLC*

Quantification of Brazilian green propolis was achieved using di fferent instruments; specifically, a Shimadzu HPLC-20AT (Shimadzu, Japanese) using a Kromasil C18 (AKZONOBEL, Sweden) column (RP-18, 250 mm × 4.6 mm, 5 μm particle size) at a flow rate of 1.0 mL/min. The HPLC system consisted of a binary pump, an auto sampler, and photodiode-array detector, which was software-controlled. The mobile phase used was water/formic acid (999:1, *v*/*v*) (solvent A) and HPLC grade methanol (solvent B) (99.9%, HPLC grade). Elution was performed with a gradient starting with 75% B to reach 30% B at 22 min, 45% B at 45 min, 30% B at 58 min, 20% B at 75 min, 5% B at 80 min, 75% B at 95 min, and then it became isocratic for 10 min. The injection volume for both the sample and the standard was 2 μL. Finally, all chromatograms were measured at 310 nm [32].

## *3.5. Method Validation*

In total, 14 samples of Brazilian green propolis were analyzed using HPLC. To verify the rationality of the HPLC method, we conducted a method validation test, which assessed the system's suitability, linearity, system precision, accuracy, and stability.

## 3.5.1. Linearity (Calibration Curve)

The calibration curves were constructed using five concentrations—including the Lower Limit of Quantitation (LLOQ)—that ranged from 0.5 to 100 μg/mL. The linear regression equation was determined by taking the sample injection amount as the abscissa and the peak area as the ordinate. The peak area of each standard was considered for the purpose of plotting the linearity graph. Linearity was evaluated using linear regression analysis, which was calculated by the least square regression method.

## 3.5.2. System Precision

System precision was assessed by repeated injections (*n* = 6) of a standard mixture of the analytes (concentration ≥ Limit of Quantity (LOQ) values). The RSD% values of peak area as well as the similarity of chromatograms were then determined. The acceptance criterion was ±2% for the RSD.

## 3.5.3. Repeatability

Repeatability was assessed by repeated injection (*n* = 6) of the same Brazilian green propolis sample, weighing approximately 0.25 g. The RSD% values of the seven phenolic acids as well as the similarity of the chromatograms of the six green propolis samples were then determined. The acceptance criterion was ±2% for the RSD.

## 3.5.4. Accuracy

Accuracy was conducted by a recovery experiment performed on a representative sample (*n* = 6) of Brazilian green propolis. More specifically, equal amounts of standard solution were added to each of the Brazilian green propolis samples and the average recovery rate of the Brazilian green propolis was then calculated. The accuracy method was also evaluated on the basis of RSD.

## 3.5.5. Stability

The stability of the solution was determined using the green propolis samples for short-term stability by keeping at room temperature for 12 h prior to analysis. Representative propolis samples were then injected at 0, 4, 8, 12 and 24 h. The stability of the instrument detection was judged by the chromatogram similarity at different time points as well as the RSD of the peak area.

## *3.6. Statistical Analysis*

The validation experiment as well as the analysis of the quantified sample solution of the Brazilian green propolis extract were performed in six replicates. Experimental data are represented as means ± SD and *p* < 0.05 was considered to be statistically significant. Multiple comparisons were implemented using Tukey's honestly significant difference (HSD) test. To control for relevant confounding factors, one-way analysis of variance (ANOVA) was used. A *p*-value of < 0.01 indicated extreme statistical significance.

## **4. Conclusions**

We present here a simple identification and determination method that was optimized and validated for the analysis of seven phenolic acids. This approach was then applied to the quantitative analysis of 14 Brazilian green propolis samples. When compared with other methods cited in the literature, this approach offers a number of additional advantages, including the use of fewer reagents and reducing the cost of analysis. Given these improvements, one quality control laboratory could analyze more samples per day in addition to saving time and money. And compared with previous publications, the combination of UPLC-ESI-QTOF-MS and HPLC system was the first application in Brazilian green propolis study. The advancement and practicality of the method greatly improved the research of analytical performances. Our results showed that there were some significant differences in phenolic acid content across different Brazilian green propolis samples. Notably, artepillin C has been found only in Brazilian green propolis, indicating promising development for this particular propolis. We report for the first time three different isomers of isochlorogenic acid, indicating the precision and accuracy of our UPLC-ESI-QTOF-MS method. In the present study, we focused on the identification and quantification of different phenolic acids in Brazilian green propolis. Our results suggest that this method could be considered new and effective method that could provide a valuable reference for the future quality control of Brazilian green propolis.

**Author Contributions:** S.S., M.L. and J.H. conceived and designed the experiments; K.L. performed the sampling and carried out the sample preparation; G.Y. and K.L. analysed the data; S.S. wrote the paper; X.Z. revised the manuscript; all authors read and approved the final manuscript to be submitted to Molecules.

**Funding:** This work was supported Guangdong Pharmaceutical University.

**Acknowledgments:** We really appreciate that the supplier provides all Brazilian green propolis samples for us.

**Conflicts of Interest:** All authors had full access to study data and had final responsibility to submit the manuscript for publication. The authors had declared that there is no conflict of interest.


**2.10. SGLT6 /SMIT2 (***SLC5A11***)**

it can be speculated that SGLT4 and SGLT5 may act as complementary mannose transporters

Mice deficient for SGLT5 [20] were shown not to display any renal sodium-dependent fructose uptake but increased urinary fructose indicating that SGLT5 is the major transporter respon‐ sible for fructose reabsorption in the kidney. Fructose consumption especially by corn syrup containing beverages has been epidemiologically linked to dyslipidemia, obesity, and diabetes. Despite its role in renal fructose reuptake, knockout of SGLT5 in mice paradoxically

The massive urinary fructose excretion was accompanied by reduced levels of plasma triglycerides and epididymal fat but fasting hyperinsulinemia. No difference in food con‐ sumption, water intake, or plasma fructose was described for SGLT5 deficient mice. The phenotype of SGLT5 knockout mice indicated an apparent link between renal fructose

A Na(+)/myoinositol cotransporter cDNA (SLC5A3) was cloned from canine renal cells and sequenced in 1992 followed by the human SLC5A3 in 1995. The human transporter is mainly

Myoinositol is a precursor of the main inositol-containing phospholipids phosphatidylinositol and phosphatidylinositol-4,5-bisphosphate, a key molecule in cellular signal transduction. In addition, myoinositol has an important role in osmoregulation. The highest myoinositol levels are found in certain regions of the brain with cerebrospinal fluid levels ranging from 2-25 mM,

One hypothesis as to why lithium is effective in the treatment of bipolar disorders is based on its effect on reducing cellular concentrations of myoinositol (the inositol depletion model). Ablation of the murine *SLC5A3* gene demonstrated the significant role of this transporter in maintaining central myoinositol concentrations. SMIT1 knockout mice have significantly reduced central inositol levels with no changes in phosphatidylinositol concentrations. Besides the severe myoinositol deficiency in the brain, those animals display congenital central apnea due to abnormal respiratory rhythmogenesis leading to death shortly after birth. The neonatal lethality of SMIT1 knockout animals appears to be caused by failures in development of peripheral nerves, specifically in nerves controlling breathing. The peripheral nerve abnor‐ malities can be corrected by prenatal myoinositol supplementation suggesting that myoino‐ sitol is required for peripheral nerve development. Phenotypic analysis of homozygous SMIT1 knockout mice indicated, that a reduction of central inositol levels is associated with lithium-

expressed in kidney, brain, placenta, pancreas, heart, skeletal muscle, and the lung.

that regulate intestinal absorption and renal reabsorption of mannose, respectively.

*Mouse models of SGLT5 deficiency*

16 Glucose Homeostasis

exacerbated fructose-induced hepatic steatosis.

reabsorption and hepatic lipid metabolism.

**2.9. SMIT1 (***SLC5A3***)**

*2.9.1. SMIT1 physiology*

*Phenotype of mice deficient for SMIT1*

which are higher than levels in the blood.

SMIT2 was initially cloned by PCR from rabbit kidney cDNA. Sequence analysis indicated 49% and 43% protein sequence identity to SGLT1 and SMIT1, respectively. SMIT2 mRNA is detected in brain, kidney, heart, skeletal muscle, spleen, liver, placenta, lung, leukocytes, and neurons. Three transcript variants named SMIT1a, SMIT1b, and SMIT1c have been identified for the *SLC5A2* gene. It wasn't until 2002, that the cloned product was functionally character‐ ized and identified as a sodium-coupled myoinositol transporter with a Km of 120 µM and 13 mM for myoinositol and sodium, respectively [22]. Transport mediated by SMIT2 is phlorizin sensitive (Ki of 76 µm). The substrate specificities of SMIT1 and SMIT2 are remarkably different: SMIT2 shows stereospecific transport of D-glucose and D-xylose without affinity for fucose, while SMIT1 transports L-fucose and L-xylose (but not their D-isomers) and does not distinguish between D-and L-glucose. In contrast to SMIT1, SMIT2 transports d-*chiro*-inositol.

SMIT2 mediated myo-inositol transport has been demonstrated to occur at apical membranes of the rat intestine [23] and at the luminal side of of proximal convoluted tubules in the kidney of rabbits [24], a tissue specificity and principle role that appears similar to the sodiumdependent uptake and reabsorption of glucose mediated by SGLT-1.


*3.6. Neural Network*

Multi-layer perceptron (MLP) architecture was implemented in the present study. The model was a deep neural network with three hidden layers composed of 512, 256, and 128 neurons and a one-dimensional output. The output was fed to a sigmoid function and a 0.5 threshold was used to achieve a binary output: when the output of the sigmoid was > 0.5 the label 1 was assigned, otherwise the label 0 was assigned. Rectified linear units (ReLU) were implemented to provide non-linearity between two consecutive hidden layers. In the experiments, stochastic gradient descent was employed as the optimization algorithm and binary cross entropy as the loss function. Eventually, MLP model was trained adopting an early stop technique: the network was trained for a maximum of 100 epochs, stopping when the accuracy on the validation set did not increase for 10 consecutive epochs. The best-performing learned parameters were adopted to evaluate the model performances.


**Abstract**

In this chapter, we propose that pathologies can be understood as chronic excess or lack of emotions, which in essence, in our view, refer to the presence of "vicious" or frequent lack of evaluations about reality; which would generally occur due to the absence of wisdom or rationality. We also suggest that true happiness, to be experienced consistently, depends on putting into practice knowledge that reflects reality reasonably. In this sense, we hypothesize that the essence of pathologies is associated with the absence of a level of knowledge that reflects reality reasonably well or recurrent failures to act based on this knowledge, that is, lack of the habit of acting rationally; and that the understanding and pursuit of true happiness, in turn, can serve as a shortcut to exit the psychopathological condition, as (1) it provides greater engagement in the therapeutic process, as it would be the kind of pleasure that every human being ultimately seeks; and (2) because the pursuit of true happiness leads us to be more and more successful in our interactions with reality, feeling appropriate emotions for each context, instead of feeling, or not feeling, certain emotions chronically.

**Keywords:** happiness, virtues, aristotle, evolutionary science, psychopathology


**4. Conclusions**

In this work, a LAE/microfluidic foil/LAPS sandwich structure was utilized for the detection and manipulation of pH gradients inside a microfluidic system. The LAPS offers the ability to detect spatially resolved pH changes inside the microfluidic channel. In contrast, locally induced pH changes can be triggered using the LAE, whereby the location may be controlled by the illuminated area. To study this sensing-actuating interplay, as a model bioreceptor, the enzyme penicillinase was immobilized inside the microchannel using plant viral (TMV) particles as enzyme nanocarriers. The enzymatic cleavage of penicillin to penicilloic acid, yielding H+ ions, leads to local pH changes, which can be detected by the LAPS. By inducing a further pH shift via the LAE, the enzymatic activity can be inhibited.

The novel actuator-sensor platform was characterized performing photocurrentvoltage-, photocurrent-time measurements and chemical imaging with the LAPS and by transient current measurements with the LAE. The surface morphology of the LAE and TMV-modified LAPS was analyzed by means of SEM.

In the concentration range from 0.1 to 5.0 mM penicillin, the TMV-penicillinasemodified LAPS sensor achieved a penicillin sensitivity of 42.3 mV/dec, proofing the

functionality as penicillin sensor inside the microfluidic setup. Additionally, the chemical images visualize, that the TMV-immobilized enzymes were confined to the area, predefined through drop-coating during assembly of the system. This extends the use of beneficial plant viral enzyme nanocarriers to a further type of microsystem. For solutions of varying pH, the inhibition of the enzymatic reaction was demonstrated at pH 4.0, whereas enzyme activity increased in LAPS measurements when pH is shifted towards the enzyme's pH optimum. Furthermore, a strategy for a spatially resolved photoelectrocatalytical pH manipulation induced by the LAE was developed. Such a pH gradient inside the microchannel was utilized to control the enzymatic reaction.

By this exemplary application, the feasibility and potential of combining two lightaddressable technologies, LAPS and LAE, was highlighted to be of great benefit for further integration in lab-on-a-chip systems. The advantage of this system lies in the adaptability of both technologies, as the region of interest inside, e.g., the microfluidic channel, can be regulated in time and geometrical locus by changing the illuminated area.

In future studies, such as e.g., enzyme arrays inside a microfluidic channel, each particular enzyme might be controlled individually. Dependent on the adjusted pH value by the LAE, the enzyme activity can be either shifted to the enzyme's pH optimum (leading to increased reaction rates) or to pH values, where inhibition of enzyme takes place.

A further interesting approach for such actuator-sensing platform lies in the field of enantioselective enzymes which catalyse multiple reactions (e.g., acetoin reductase for acetoin and diacetyl determination [34,35]). Here, local pH variations triggered by the LAE could shift the pH optima corresponding to the respective substrate molecule of interest. A separation by different microchannels will address individual enzymes having a two-dimensional monitoring of each single reaction.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/bios11060171/s1, Figure S1: Reference chemical image of the microfluidic structure recorded at an applied potential of −1.65 V in 0.33 mM PBS buffer, pH 7.0. Figure S2: Photocurrent-time curve for 1.0 mM penicillin in PBS buffer, pH 7.1, for different flow rates.

**Author Contributions:** Conceptualization, R.W., M.J., T.W. and M.J.S.; methodology, R.W., M.J., C.W.; T.W. and M.J.S.; validation, R.W., M.J., T.W. and M.J.S.; formal analysis, R.W., M.J., T.W. and M.J.S.; investigation, R.W., M.J.; writing, R.W., M.J., C.W., M.K., P.H.W., T.W. and M.J.S.; supervision, M.K., P.H.W., T.W. and M.J.S.; All authors have read and agreed to the published version of the manuscript.

**Funding:** Part of this research project was funded by the German Federal Ministry of Education and Research (BMBF)–13N12585. Part of this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)–446507449.

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

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank Arshak Poghossian for valuable discussions, and Heiko Iken, David Rolka, Benno Schneider, Rebecca Hummel and Jürgen Schubert for technical support. Part of this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)–446507449. Part of this research project was funded by the German Federal Ministry of Education and Research (BMBF) within the research frame of "Nano-MatFutur": 13N12585. R. Welden thanks Aachen University of Applied Sciences for financial support.

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


**Abstract**

The improvement of surgical and anesthesia techniques has markedly extended the range of patients undergoing hip replacement both in terms of age and comorbidity. Hence, the risk of perioperative complications has increased ranging from hemorrhages, postoperative delirium, incomplete pain control, hypotension, and others. In this regard, a personalized approach from the preoperative evaluation to the choice of the type of anesthesia and the pain control strategy is preferred in order to minimize the risk of complications and accelerate patient's recovering time. In this chapter, we aim to describe different options and propose different possible approaches for the possible scenarios in the light of the existing evidence in the field.

**Keywords:** general anesthesia, regional anesthesia, hip replacement, complications