**Maria Martinez \*, David Reigosa, Daniel Fernandez and Fernando Briz**

Electrical, Electronic, Computers and Systems Engineering, University of Oviedo, 33204 Oviedo, Spain<EMAIL_ADDRESS>(D.R<EMAIL_ADDRESS>(D.F<EMAIL_ADDRESS>(F.B.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +34-985-103000 (ext. 6436)

Received: 31 December 2019; Accepted: 20 January 2020; Published: 28 January 2020

**Abstract:** Torque estimation in permanent magnet synchronous machines and synchronous reluctance machines is required in many applications. Torque produced by a permanent magnet synchronous machine depends on the permanent magnets' flux and *dq*-axes inductances, whereas torque in synchronous reluctance machines depends on the *dq*-axes inductances. Consequently, precise knowledge of these parameters is required for proper torque estimation. The use of high frequency signal both for permanent magnets' flux and *dq*-axes inductances estimation has been recently shown to be a viable option. This paper reviews the physical principles, implementation and performance of high-frequency signal injection based torque estimation for permanent magnet synchronous machines and synchronous reluctance machines.

**Keywords:** torque estimation; online parameters estimation; permanent magnet synchronous machines; synchronous reluctance machines; high frequency signal injection


**Abstract**

There is currently a trend in Western countries to increase the intake of plant proteins. In this chapter, the author explains that this is due to the beneficial physiological functions of plant proteins, based on the latest literature review and our own research results. Among plant proteins, soy protein has been reported to have many beneficial effects on the improvement and prevention of metabolic syndrome. This chapter outlines the excellent effects of soy protein on renal function [improvement of early symptoms of diabetic nephropathy], which is closely related to metabolic syndrome, and the effects of combining these effects as complementary medicine. In addition, recent findings about the anti-inflammatory and immune activation effects of soy protein as hydrolyzed peptides are outlined. A brief introduction of the recent results of other legume-derived proteins that have replaced soy proteins are also explained. By further deepening our understanding of the superior physiological functions of plant proteins, it is hoped that their use expands even further.

**Keywords:** soy protein, metabolic syndrome, chronic kidney disease, inflammatory disorder, pea protein, lupin protein, mung bean protein


**8. Significance of surgical anatomy in hip arthroscopy**

Surgical anatomy plays a pivotal role in hip arthroscopy as erroneous placement of portals can lead to injury to various important structures as described previously in the chapter. A surgeon should be well aware of various landmarks as well as vital structures around the hip joint while making portals and also while working through these portals.

One of the disastrous complication of hip arthroscopy is injury to sciatic nerve or femoral nerve and vessels. Various evidences suggest that these injuries can be prevented when proper technique in portal placement was used.

Another structure that is quite often injured during hip arthroscopy id lateral femoral cutaneous nerve of thigh (LFCN). LFCN is mostly injured during anterior portal placement. Injury to this nerve leads to loss of sensation on lateral aspect of thigh. The nerve is essentially vulnerable to laceration by a skin incision placed too deeply through the subcutaneous tissue.

Apart from these, if proper surgical anatomy is known, injury to various soft tissues around hip can also be minimized considerably. It is prudent for the surgeon to be aware of various anatomical structures around hip then only a safe and successful surgery can be performed.

**21**

**Author details**

Sachin Kumar Sharma\* and Hemant Mathur

provided the original work is properly cited.

Government Medical College and SSG Hospital Vadodara, Gujarat, India

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to<EMAIL_ADDRESS>
*Surgical Anatomy of Acetabulum and Biomechanics DOI: http://dx.doi.org/10.5772/intechopen.92330*

injuries can be avoided considerably.

Anatomy has always been a second wife for a surgeon. Its role cannot be neglected whether it is a minor or major surgery. When it comes to advanced surgeries like arthroscopy requiring meticulous techniques, surgical anatomy becomes more important. If a surgeon is well versed with surgical anatomy, many iatrogenic

**9. Conclusion**

**Conflict of interest**

**Source of funding**

None.

Self.

*Surgical Anatomy of Acetabulum and Biomechanics DOI: http://dx.doi.org/10.5772/intechopen.92330*


5. Discussion

in health expenditure in an epidemic year is 0.16% for the countries with the highest number of cases (Table 5). Although this result cannot be perfectly extrapolated to all the countries of the Americas, it could be interpreted as an upper bound for the size of the economic burden of

Figure 6 shows the normalized treatment cost14 and burden for each country. We can observe that while the Dominican Republic is the country with the highest number of DALY adjusted by population, it has the lowest cost per each DALY lost because of the disease. In contrast, Mexico has the highest cost per DALY lost, but the lowest number of DALY adjusted by population.

Finally, Table 6 exhibits the 2015 total economic cost per DALY adjusted for purchasing power parity (PPP). There appears to be high variance in the total treatment cost, with its ranges from \$1,157 in the case of the Dominican Republic to \$17,703 for Mexico. It is noteworthy that Mexico, in relative terms, always presents the highest treatment cost. This result is consistent whether we analyze the total treatment cost per patient, the share of total annual cost over the

Total ambulatory (%) 0.16 0.17 0.08 0.17 Total hospitalized (%) 0.00 0.01 0.04 0.01 Total cost (%) 0.16 0.18 0.12 0.18 Source: PAHO. 2015. Week 52. Martelli, et al. (2015) [19], Castro, et al. (2015) [18], Undurraga, et al. (2014) [17], Shepard,

Brazil Colombia The Dominican Republic Mexico

Figure 6. Normalized results. 2015. Source: PAHO. 2015. Week 52. Martelli, et al. (2015) [19], Castro, et al. (2015) [18],

Variable definition: level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, innovation and technology and stocks of vaccines for emergency or

Undurraga, et al. (2014) [17], Shepard, et al. (2011) [20]. Prices 2017.

total health expenditure or the total treatment per DALY adjusted for PPP.

Table 5. Total annual treatment cost as share of the total health expenditure (2015).

dengue in the region.

32 Dengue Fever - a Resilient Threat in the Face of Innovation

et al. (2011) [20]. Prices 2017.

14

outbreaks.

According to the review made by Shepard [22], who estimate the burden at a global scale for the year 2013, Latin-American and the Caribbean regions exhibit the highest treatment cost per case. From this perspective, our review presents the burden of the disease for the most affected countries, in terms of reported cases, of the region with the most expensive treatment cost. The latter is particularly relevant if we considered the estimates of the share of total treatment cost over the total health expenditure presented because our results could be interpreted as an upper bound for relative economic burden of dengue.

As mentioned before, in this section, we will discuss how the total treatment cost and the burden change in 2017, which we consider to be an inter-epidemic year given the low number of cases relative to previous years (2010–2017) (Table 7).

Source: PAHO. 2017. Week 52. Martelli, et al. (2015) [19], Castro, et al. (2015) [18], Undurraga, et al. (2014) [17], Shepard, et al. (2011) [20]. Prices 2017.

Table 7. 2017 Economic and DALY lost per million inhabitants by country (2017 dollars).

To estimate the total number of DALY for the year 2017, we calculated the ratio between dengue cases and DALY for 2015 with the WHO and PAHO figures and extrapolate the results as discussed in section "Materials and methods." Population has been updated too, to correspond to the year 2017. DALY adjusted for population present a dramatic decline of 76.53% in average due to the decrease in total number of dengue cases. Additionally, the total treatment cost of the disease decreased 77.8% in average (from 2015 to 2017), being Dominica Republic the country with the highest reduction rate (92.8%). It is worth saying that the change in the figures is closely tied to the change in the number of dengue cases since the cost per patient remained the same (Table 8).15

Normalized results for 2017 show that, in contrast to 2015, the Dominican Republic exhibits both the lowest total treatment cost per DALY and DALY per million of inhabitants, which suggest an improvement for the Caribbean country, especially considering the noneconomic burden. On the other hand, Mexico went from having the lowest number of DALY adjusted

The Burden of Dengue Illness and Its Economics Costs in the Americas: A Review on the Most Affected Countries

http://dx.doi.org/10.5772/intechopen.79887

35

One important limitation about this review and potentially other burden analyses is the accuracy of the epidemiological information. As the number of total dengue cases, severe and non-severe, corresponds to reported cases, one cannot assume that they correspond to the effective number of cases. Reported (or febrile) cases can overestimate the total actual burden of the disease. On the other hand, one should consider that by using laboratory confirmed cases, bias in the other direction is introduced, since not only that figure would be affected by laboratory confirmation policies and practices but also by underreport. After reviewing the latter, we decided to use reported cases and allow for potential overestimation in the economic burden. Thus, our results should be considered as upper bound estimates of the economic

The results found have three interesting implications for further studies and reviews. Economic burden of dengue should follow a structured costing framing, which allows for proper comparison between results and better estimation of treatment cost per case. The data and sources used in this chapter could serve as inputs in future cost-effectiveness analysis (CEA); once economic cost per cases has been covered, the remaining element to conduct a CEA would be the approximate reduction in dengue cases due to the use of a prevention technology. Finally, the similar results in terms of relative economic burden suggest that 0.16%17 could be interpreted as an upper bound of the total treatment cost of dengue as share of total health expenditure.

Raúl Castro Rodríguez\*, Jorge Armando Rueda-Gallardo and Manuel Felipe Avella-Niño

[1] Martín J, Brathwaite O, Zambrano B, Solórzano J, Bouckenooghe A, Dayan G, Guzman M. The Epidemiology of Dengue in the Americas over the last Three Decades: a Worrisome Reality. The American Journal of Tropical Medicine and Hygiene. 2010;82(1):128-135

170.16% is the mean share for the four countries reviewed during a period of high incidence in the region with the most

Department of Economics, Universidad de los Andes, Bogotá D.C., Colombia

16Burden measured as DALY is not affected by overestimation since WHO figures are used.

for population to having one of the highest.

burden16 and not completely accurate figures.

\*Address all correspondence to<EMAIL_ADDRESS>
Author details

References

expensive treatment cost per case.

As result of the decline in the number dengue cases in 2017, the share of the total costs decreased to almost a fifth of the share in 2015 (Figure 7).

Source: PAHO. 2017. Week 52. Martelli, et al. (2015) [19], Castro, et al. (2015) [18], Undurraga, et al. (2014) [17], Shepard, et al. (2011) [20]. Prices 2017.

Table 8. Total health expenditure and 2017 total economic cost as share of the total health expenditure (2017 dollars).

Figure 7. Normalized results. 2017. Source: PAHO. 2017. Week 52. Martelli, et al. (2015) [19], Castro, et al. (2015) [18], Undurraga, et al. (2014) [17], Shepard, et al. (2011) [20]. Prices 2017.

15Cost per patient do not vary since they are expressed in 2017 dollar are correspond to the figures obtained in the respective studies reviewed.

Normalized results for 2017 show that, in contrast to 2015, the Dominican Republic exhibits both the lowest total treatment cost per DALY and DALY per million of inhabitants, which suggest an improvement for the Caribbean country, especially considering the noneconomic burden. On the other hand, Mexico went from having the lowest number of DALY adjusted for population to having one of the highest.

One important limitation about this review and potentially other burden analyses is the accuracy of the epidemiological information. As the number of total dengue cases, severe and non-severe, corresponds to reported cases, one cannot assume that they correspond to the effective number of cases. Reported (or febrile) cases can overestimate the total actual burden of the disease. On the other hand, one should consider that by using laboratory confirmed cases, bias in the other direction is introduced, since not only that figure would be affected by laboratory confirmation policies and practices but also by underreport. After reviewing the latter, we decided to use reported cases and allow for potential overestimation in the economic burden. Thus, our results should be considered as upper bound estimates of the economic burden16 and not completely accurate figures.

The results found have three interesting implications for further studies and reviews. Economic burden of dengue should follow a structured costing framing, which allows for proper comparison between results and better estimation of treatment cost per case. The data and sources used in this chapter could serve as inputs in future cost-effectiveness analysis (CEA); once economic cost per cases has been covered, the remaining element to conduct a CEA would be the approximate reduction in dengue cases due to the use of a prevention technology. Finally, the similar results in terms of relative economic burden suggest that 0.16%17 could be interpreted as an upper bound of the total treatment cost of dengue as share of total health expenditure.


**3. Results and Discussion**

The proposed methodology makes use of a high-resolution gel (Sephacryl S500) designed to resolve large macromolecules between 20 million and 400,000 daltons (g/mol). The upper protein size range is in the order of circa 750 kDa containing many thousands of amino acids, thus the gel matrix should be able to resolve plastic NPs larger from the protein/lipid pool in cells. Since SEC separates particles based on the Stokes radius rather than weight, the following discussion will be provided in size rather than mass. It is estimated that the protein size range is between 3 and 700 kDa where large 500 kDa globular proteins theoretically occupy a radius of 5.2 nm, which is well within the separation range of the gel matrix [14] and sets the theoretical lower size range of the column. Hence, the gel matrix offers the possibility to separate large plastic nanoparticles (10–1000 nm) from the protein pool in the sample without the need to extract/purify nanoparticles before running the column (Table 1). Large filamentous proteins (e.g., actinomyosin) should precipitate during centrifugation with appropriate speeds depending of the tissues. In the present study with molluscs, homogenisation with blender or Teflon pestle followed by 3000× *g* centrifugation appeared satisfactory. To validate these assumptions, the reported diameter of albumin is about 4 nm (mass of 66 kDa) [15] and should separate well from 50 or 100 nm NPs. A 40 cm × 1 cm column was filled with Sephacryl S-500 gel for separation of pure fluorescently labeled polystyrene nanoplastics (50 and 100 nm mean diameter), albumin, and NaCl (Figure 1). The total volume (Vt = 25 mL) was calculated with a conductivity (NaCl) peak, which permeates completely the gel beads, and the theoretical void volume (Vo) of the column was at Vo/Vt = 0.3 according to the supplier's information for Sephacryl S500 (Table 1). The 100 and 50 nm NPs were resolved at Ve/Vt = 0.4 and 0.45 respectively, albumin eluted at Ve/Vt of 0.75, and NaCl at Ve/Vt = 1 using the 0.2% Tween-20–14 mM NaCl elution buffer. The addition of the non-denaturing detergent (Tween-20) was necessary to maintain separation of albumin from nonspecific hydrophobic interactions of polystyrene NPs. Indeed, NPs adsorb proteins forming a corona [16] and could change the elution volume (Ve) for polystyrene NPs. This was observed by lowering the detergent concentration to 0.01%, which increased the Ve/Vo to 0.60 for the fluorescent NPs in the presence of 1 mg/mL albumin (results not shown). The addition of denaturing detergent such as SDS (0.1%) denatured albumin and eluted at the void volume Vo/Vt = 0.30 and overlapped upper size range of plastic nanoparticles (>100 nm). A significant and linear relationship between the size (log size in nm) and the Ve/Vt of the samples was obtained with r = 0.99 (Figure 1B). This relationship sets the upper and lower size range of NPs between 161–11 nm respectively.

We tested this chromatographic method with transparent polystyrene NPs (50 nm diameter) alone and in *Mya arenaria* soft tissues homogenates (3000× *g* supernatant) using NR staining for plastic particles (Figure 2). The solvatochromic NR dye is well-recognized as a fluorescent dye for plastics but also stains lipids [12,17]. Transparent polystyrene NPs eluted at Ve/Vt = 0.45 based on NR fluorescence (Figure 2A). The addition of soft tissues fractions revealed two peaks at 280 nm (Figure 2B): one at Ve/Vo corresponding to polystyrene NPs and the other at Ve/Vo = 0.9–0.95 corresponding to the protein/lipids pool. NR staining revealed also two major peaks overlapping with 280 nm absorbance corresponding to polystyrene NPs at Ve/Vo = 0.44 and the protein/lipid pool at Ve/Vo = 0.9. The elution profile of tissue fractions alone did not show any peaks at Ve/Vo < 0.7 in keeping with the resolution range of the gel matrix. The NR dye is a well-recognized fluorescent stain for many types of plastics such as polyethylene, polystyrene, nylon, polyethylene terephthalate, and polypropylene at the micro- and nanoscales, thus could be used with these plastic polymers [12,18]. The average length of phospholipids is about 2 nm [19] giving a Ve/Vo = 0.8–0.9 overlapping to the second NR peak, hence not suitable for measuring small plastic NPs (<10 nm). The presence of the detergent Tween-20 in the elution buffer also serves to solubilize membranes vesicles in the extracts removing larger lipid vesicles/structures in the sample.

1 Chromatography column: 40 cm × 1 cm (31.4 mL total volume), sample buffer 0.70 M NaCl-0.05% Tween-20 and elution buffer 14 mM NaCl-0.2% Tween-20. Ve: elution volume, Vt: total volume (NaCl). The void volume Vo was Vo/Vt = 0.3 according to the suppliers' information for Sephacryl S500.

**Figure 1.** *Cont*.

**Figure 1.** Gel chromatographic analysis of fluorescently labeled plastic NPs. Elution profile of polystyrene NPs (**A**) using 100 nm, 50 nm polystyrene NPs (fluorescence), albumin (absorbance 280 nm, MW = 60 kDa), and NaCl (conductivity: mS); calibration of the size exclusion chromatography column (**B**).

This chromatographic procedure was used to screen for plastic NPs in two real-life case studies, one involving wild *Mya arenaria* clams collected at a harbor/marina and the second with caged *Elliptio complanata* mussels at downstream sites of a major urban area, both generally associated to plastics pollution [20]. The first case consisted of feral *Mya arenaria* clams collected at a reference site under no direct source of pollution and a harbour site supporting intense commercial and touristic boating activities (Figure 3A,B). Clam tissues collected at the reference site showed one major UV absorbance (280 nm) peak at Ve/Vt = 0.8 corresponding to the protein pool, one NR peak at Ve/Vt = 0.8–0.9 corresponding to hydrophobic proteins and lipids, and a conductivity peak at Ve/Vt = 1 corresponding to salts/NaCl (Figure 3A). Clams collected at the contaminated harbor site showed two major UV and NR peaks at Ve/Vo = 0.4 and 0.8 (Figure 3B) corresponding to large-size compounds consistent with UV-absorbing plastics (polystyrene and polyethylene terephthalate) followed by the protein/lipid pool peak. Large hydrophilic carbohydratebased polymers (sugars, glycogen), if present, do not absorb at 280 nm and should not interact with the NR dye. The second case concerned exposure of caged mussels exposed to pristine lake (reference) and downstream of a highly populated city of circa 3 million inhabitants in the Saint-Lawrence river (Figure 3C,D). Sediments in the Saint-Lawrence river were recently reported contaminated by microplastics [20] and prompted investigation on plastic NPs in this study. The major form of plastic was polyethylene (UV absorbance negative and NR positive) and reached densities of 1.4 × 105 microbeads·m−2. *Elliptio complanata* digestive gland homogenates caged at the reference lake revealed one major band at Ve/Vt = 0.75 and one small band at the void volume (Figure 3C). One conductivity peak was observed at the Ve/Vt = 1 corresponding to dissolved salts with no evidence of conducting large-size molecules (i.e., absence of peaks at Ve/Vt < 0.8). The NR fluorescence

peak generally followed the UV peak with a major band at Ve/Vt = 0.8 of the protein/lipid pool. In mussels caged downstream of a large city, the UV signal was distributed over the elution profile (Figure 3D) with a maxima at Ve/Vt = 0.75. The same conductivity peak at Ve/Vo = 1 was obtained, suggesting no presence of conductive large-molecular-size compounds (e.g., elemental nanoparticles). In the case of NR staining, we observed two major peaks at Ve/Vt = 0.6–0.65 and Ve/Vt = 0.35–0.5, corresponding to compounds of sizes ranging from 11 to 120 nm. This suggests that exposure of mussels to urban activities leads to a complex distribution pattern of NR-stained materials outside the protein and lipids pools in tissues. This is in keeping with the reported levels of microplastics in sediments in the Saint-Lawrence river [20].

**Figure 2.** NP addition in mussel tissue homogenates. Pure transparent 50 nm polystyrene NPs at 10 μg/mL (**A**) and spiked mussel tissue homogenates (**B**), centrifuged at 3000× *g* for 10 min, the supernatant mixed with one volume of 1.5 M NaCl containing 0.1% Tween-20 and injected into the column. The eluted samples (1 mL) were collected and were analyzed for absorbance at 280 nm, conductivity and neutral red (NR) staining.

**Figure 3.** Chromatographic profile marine and freshwater bivalves exposed to anthropogenic pollution. Representative chromatographic profiles of *Mya arenaria* clam tissue homogenates from a reference site (**A**), a harbor supporting intense boat traffic (**B**), *Elliptio complanata* mussel digestive glands caged at reference lake (**C**), and downstream of a large-population city (**D**) for 3 months during the summer of 2017. The homogenates were centrifuged at 3000× *g* for 10 min, the supernatant mixed with one volume of 1.5 M NaCl-0.1% Tween-20 and directly injected to the size exclusion chromatography column. The data represented the measurements at each 1 mL fraction and normalized to 1 for viewing.

Direct sample analysis of homogenate fractions offers the advantage of measuring other endpoints (enzyme activities, proteins) or specific metabolites in addition to the presence of nanoplastic materials. In the attempt to increase the sensitivity of this methodology and as a means to remove potential interfering large protein filaments, a salting-out step with saturated NaCl and acetonitrile (NaCl/ACN) was used to extract and concentrate NPs. Mussel homogenates were spiked with increasing amounts of fluorescent polystyrene NPs, fractionated with NaCl/ACN methodology, and centrifuged at 3000× *g* for 5 min. The ACN upper phase was mixed with 0.1 volume of 1.4 M NaCl (for total volume calibration of the column) and resolved by SEC with the same elution buffer. Because polystyrene absorbs at 280 nm, the absorbance readings of the elution profile were included (Figure 4A). The data revealed that UV absorbance increased linearly but at Ve/Vt = 0.75–0.8 with the added NPs. In an unspiked sample, low absorbance at 280 nm was measured at Ve/Vt = 0.95–1, suggesting the presence of nonprotein UV-absorbing compounds—perhaps nonpolar metabolites such as tyrosine or phenylalanine amino acids/peptides. Lipids would elute near this volume but would not absorb at 280 nm. The fluorescence peak of the fluorescent NPs eluted at Ve/Vt = 0.75 and peak height/area increased linearly with added NP in the homogenates (Figure 4B). The elution of polystyrene at these fractions (Ve/Vt = 0.75–0.8) suggests that polystyrene NPs shrunk in size with ACN. Polystyrene is completely soluble in ACN or acetone and was shown to shrink in the presence of organic solvents. Polystyrene shrinks in apolar environments from increased electrostatic interactions between the polystyrene polymer layers [21,22]. Hence, the salting-out step displaces polystyrene nanoparticles from water to the ACN phase and leads to osmotic shrinkage. This phenomenon is readily observed with soft polystyrene polymers but seemingly less in hard plastic polymers.

**Figure 4.** *Cont*.

**Figure 4.** Calibration of the NaCl/acetonitrile fractionation method. Mussel homogenates were spiked with increasing quantities of fluorescently-labeled polystyrene NPs, fractionated using the salting-out step, and the acetonitrile upper phase injected in the SEC column. The elution buffer consisted of 0.2% tween-20 and 14 mM NaCl at pH 7.4. Absorbance at 280 nm (**A**) and fluorescence (**B**) were taken at each 1 mL volume of the eluate. The flow rate was 0.75 mL/min.

This approach was also tested on clam and mussel homogenates at sites under anthropogenic pollution/activity, which are likely sources of plastic pollution [23,24]. The eluted fractions of the Sephacryl S500 column were stained with 10 μM NR dye to detect plastic (apolar) material (Figure 5). In mussels caged for 3 months at downstream and rain runoff sites, NR staining was increased at Ve/Vt = 0.75–0.85 compared to reference Lake values (Figure 5A). In control mussels, the NR peak was Ve/Vt approximately 1 co-eluted with salts, suggesting the presence of other nonpolar low molecular weights compounds soluble in ACN. Moreover, the NR positive and staining peak was also detected at Ve/Vt = 0.3–0.4, suggesting the presence of larger plastic nanomaterials in the 100-nm size range. In *Mya arenaria* clam homogenates collected at a polluted site, the same pattern was observed, i.e., the appearance of NR staining at Ve/Vt = 0.75–0.80 at the polluted site and NR staining at Ve/Vt approximately 1 at the reference site (Figure 5B). Interestingly, the presence of large plastic compounds near the void volume of the column (at 160 nm) was not observed in this case.

The present methodology proposed a convenient and cost-effective means to isolate plastic NPs in tissues based on NR staining. Other, more specific, fluorescent stains could be used, such as molecular rotor probes (9-(2,2-dicyanovinyl)julolidine) that are more specific to plastic materials [12,13]. These approaches could be used as a screening tool to separate and detect plastic NPs but this methodology should be confirmed by more specific methods such as single particle plasma–mass spectrometry or pyrolysis gas chromatography–mass spectrometry [25,26]. The isolated fractions could be further analyzed for metals (e.g., arsenic, copper) or organic compounds to detect *Trojan horse* interactions i.e., xenobiotics sorbed to plastic NPs. They could also be measured using FT–infrared spectroscopy

directly or after further ultrafiltration [27]. Nevertheless, the SEC methodology using NR or molecular rotor probes represents a convenient tool for the screening of biological tissues for NPs. However, the proposed methodology is convenient and easy to implement, and more accessible to low-budget laboratories interested in animals exposed to plastic NPs.

**Figure 5.** Chromatographic profile of freshwater mussels *Elliptio complanata* caged for 3 months at downstream a large population city (Montréal, Québec, Canada) (**A**) and in wild *Mya arenaria* clams at a polluted harbor, Saint Catherine (S-C) Bay (**B**). Digestive gland homogenates were prepared were extracted using the salting out step and injected to the SEC column using 0.2% Tween-20 and 14 mM NaCl elution buffer. The data represents NR fluorescence at each 1 mL fraction.

**Funding:** The project was funded under the Saint-Lawrence Action Plan of Environment and Climate Change Canada.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Supplementary or raw data are available at ECCC upon demand.

**Acknowledgments:** This work was funded by the Aquatic Contaminants Research Division of Environment and Climate Change Canada.

**Conflicts of Interest:** The author declares no conflict of interests.

#### **References**


**2. Pattern recognition in the complex spectral database – Example of fluorescence spectroscopy used in forensic medicine**

One of the limitations of conventional methods to determine the Postmortem Interval (PMI) of an individual is the fact that the measurements cannot be performed in real time and *in situ*. Several factors, environmental and body conditions influence the tissue decomposition and the time evolution, resulting in a poor resolution. Considering this limitation to determine the PMI, a possible solution is a new more objective method based on a tissue characterization of the degradation phases through optical information using fluorescence spectroscopy. If proven sensitive enough, this method shows a main advantage over conventional methods: less inter-variance and quantitative tissue information. These characteristics are relevant because they are less influenced by individual skills.[6]

During the decomposition process a wide variety of organic materials are consumed by natural micro-organisms and other unknown compounds produced by them. Using an objective method based on the tissue characterization of the stages of degradation by means of optical information using ultraviolet-visible fluorescence spectroscopy, followed by a statistical method based on PCA (Principal Component Analysis) made it possible to identify well features with time progression. The characteristic pattern of time evolution presented a high correlation coefficient, indicating that the chosen pattern presented a direct linear relationship with the time evolution. The results show the potential of fluorescence spectroscopy to determine the PMI, with at least a similar resolution compared to conventional methods.[6]

484 Advanced Aspects of Spectroscopy

with any parameter of interest. [3-5]

Spectroscopic techniques in this range of the electromagnetic spectrum have shown applications in different areas, from analytical chemistry to the diagnosis of some types of cancer, detection of citrus diseases and of dental caries, with a high sensitivity and good specificity rates. This is possible because the analyzed systems are composed of different types and concentrations of molecules. Thus, the spectrum of samples obtained under different conditions will also be different. It is therefore possible to identify and also quantify different compounds. However, the spectral variation can be characterized and correlated only with difficulty. This is mainly due to the fact that other phenomena, such as scattering and/or absorption, happen with the emitted light. In some cases, there may be other molecules in the sample presenting absorption bands that overlap in the same spectral region of the compound of interest, this mainly happens for absorption spectroscopy. In other cases, as in the case of fluorescence spectroscopy, the excitation and emitted light can be absorbed by other molecules making the signal too weak to be detected. A solution to this problem may be statistical procedures applied where the spectral information is correlated

A new application of a statistical method to process multi-layer spectroscopy information will be presented in this chapter. A brief review of the mathematical methods to analyze these spectroscopy data will be shown here, followed by two distinct examples. The first example is UV-VIS fluorescence spectroscopy, applied to detect the postmortem interval (PMI) in an animal model. The spectroscopy and statistical methods of analysis presented can be extended to other samples, like food and beverage. Here, a MIR absorption spectroscopy of liquid samples will be presented to detect and quantify certain compounds during the production of beer. Another system to measure liquid samples, which consists of a sample holder, will also be presented. This system offers a cheaper technique with a better

signal compared to techniques used to analyze liquid samples in the MIR region.

**fluorescence spectroscopy used in forensic medicine**

**2. Pattern recognition in the complex spectral database – Example of**

characteristics are relevant because they are less influenced by individual skills.[6]

One of the limitations of conventional methods to determine the Postmortem Interval (PMI) of an individual is the fact that the measurements cannot be performed in real time and *in situ*. Several factors, environmental and body conditions influence the tissue decomposition and the time evolution, resulting in a poor resolution. Considering this limitation to determine the PMI, a possible solution is a new more objective method based on a tissue characterization of the degradation phases through optical information using fluorescence spectroscopy. If proven sensitive enough, this method shows a main advantage over conventional methods: less inter-variance and quantitative tissue information. These

During the decomposition process a wide variety of organic materials are consumed by natural micro-organisms and other unknown compounds produced by them. Using an objective method based on the tissue characterization of the stages of degradation by means of optical information using ultraviolet-visible fluorescence spectroscopy, followed by a Another attractive feature of optical technologies is the fact that in situ information is achieved through a noninvasive and nondestructive interrogation with a fast response. Conventional laboratory techniques to determine PMI are time consuming and also require the cadaver removal from the location where it was found to a forensic lab facility. This operation already introduces additional changes to the analysis.

Fluorescence spectroscopy has been presented as a sensitive technique to biochemical and structural changes of tissues. The investigation of biological tissues is quite complex. Photons interact with biomolecules in several ways, and depending on the type of the interactions, they can be classified into three groups. The absorbers are the biomolecules that absorb photon energy. The fluorophores are biomolecules that absorb and emit fluorescent light. The scatterers are biomolecules that do not absorb the photons but change their direction. Several endogenous cromophores contribute and modify the final tissue spectrum. Distinct fluorophores emit light but the collected spectrum will be modified depending on the presence of absorbers and scatterers in the microenvironment on the path of the emitted photons and the probe interrogator. Taking into account all these light interactions that occur within the biological tissues, it is important to keep in mind that a tissue fluorescence spectrum is a result of the combination of all these processes occurring in the pathway between excitation and collection: excitation absorption and scattering, fluorescence emission, and fluorescence absorption and scattering.[5, 7]

Tissue changes begin to take place in the cadaver as soon as there is cessation of life. Optical characteristics change, and these changes may be detected using fluorescence spectroscopy. With the cessation of the metabolic reactions, tissue modifications are induced by several distinct factors, e.g. lack of oxygen and adenosine triphosphate, and intestinal microorganism proliferation. In this type of analyses, we first aimed to establish a proof of concept that fluorescence spectral variations for distinct PMIs are higher than the variance observed within each PMI. If the results are positive, a spectral time behavior can then be determined, i.e. fluorescence spectral changes identifying each PMI. This proposed method can be used to determine an unknown PMI based on a comparative analysis of a spectral database pattern. There is a potential correlation of the tissue fluorescence changes and the PMI, even though the same limitations concerning the time course variability of the cadaveric phenomena are also present, the optical spectra information can provide a more objective estimation.

Taking into account the resolution limitation to determine PMI in biological tissues, where the degradation process is non-homogenous and influenced by environment and cadaver intrinsic factors, the optical techniques may show a better PMI prediction when compared to current techniques.

The process using the principal component analysis is necessary to change the space analysis. For any type of spectroscopy, where space is determined by an analysis of light intensity in wavelengths of absorption or fluorescence, a change of base can be accomplished, where the variables become the variance of the dataset. We can explore this idea mathematically using a practical example. The set of spectra shown in figure 1 is a typical result of multiple samples.

**Figure 1.** Fluorescence spectra of different samples.

In this case, we are dealing with intensities as a function of the wavelength, presented in a graphic form. These same curves can be represented in a matrix, as shown in table 1. In this form, each row corresponds to a single measurement, i.e., spectrum of a sample, and each column is the value of the wavelength considered, which makes up this spectrum. Thus, each array element is the intensity measured at a wavelength specific to a spectrum.

**Table 1.** Fluorescence spectra in a matrix.

The next procedure to be performed after inserting the data set into this matrix representation is the centralization of the data around their average value. By fixing one column (wavelength) at a time we can calculate the average value for all lines (samples). Table 2 shows the mean values obtained.

**Table 2.** Matrix of mean values for each wavelength.

486 Advanced Aspects of Spectroscopy

typical result of multiple samples.

**0**

**Table 1.** Fluorescence spectra in a matrix.

Table 2 shows the mean values obtained.

**Figure 1.** Fluorescence spectra of different samples.

**500**

**1000**

**1500**

**2000**

**2500**

**Fluorescence Intensity (arb. unit)**

**3000**

**3500**

The process using the principal component analysis is necessary to change the space analysis. For any type of spectroscopy, where space is determined by an analysis of light intensity in wavelengths of absorption or fluorescence, a change of base can be accomplished, where the variables become the variance of the dataset. We can explore this idea mathematically using a practical example. The set of spectra shown in figure 1 is a

**4000 Sample 1**

 **Sample 2 Sample 3 Sample 4 Sample 5**

**500 550 600 650 700 750**

**Wavelength (nm)**

In this case, we are dealing with intensities as a function of the wavelength, presented in a graphic form. These same curves can be represented in a matrix, as shown in table 1. In this form, each row corresponds to a single measurement, i.e., spectrum of a sample, and each column is the value of the wavelength considered, which makes up this spectrum. Thus,

**Wavelength (nm) 540 541 542 543 ... ... 749 750 Intensity Sample1** 27.5 30.6 33.9 37.7 ... ... 199.9 198.4 **Intensity Sample2** 25.5 26.7 29.6 35.9 ... ... 180.5 186.3

**Intensity SampleN** 24.1 25.3 27.9 30.2 ... ... 176.8 180.6

The next procedure to be performed after inserting the data set into this matrix representation is the centralization of the data around their average value. By fixing one column (wavelength) at a time we can calculate the average value for all lines (samples).

**...** ... ... ... ... ... ... ... ...

each array element is the intensity measured at a wavelength specific to a spectrum.

In the next step, each of the intensity values of each sample should be subtracted from this average value in the respective wavelength. The results for our example are shown in table 3.

**Table 3.** Normalized fluorescence spectra.

It is important to note that this procedure resulted in a better match between the variables. The first values had higher intensities (approximately 8 times) than the wavelengths around 750nm in relation to values around 540 nm. If this normalization of the data had not been performed, the outcome would have had a greater influence for the longer wavelengths, as if they possessed some kind of "privilege", which would not be correct from the standpoint that all the measured variables are also important.

Since each element of the initial data array is represented by an element qij, we can consider this procedure performed using the equation 1:

$$X\_{ij} = q\_{ij} - \overline{q}\_j \tag{1}$$

Where: x ij is the element of our new data matrix; qij is the array element data corresponding to the i-th measurement variable j; *j q* is the mean value of the variable j;

As the data were previously normalized, i.e. centered on their mean values, we proceed with the construction of the correlation matrix, where we obtain information about a dataset that indicates how the variables are correlated. This is possible by calculating the product of the transposed data matrix by itself. In mathematical terms, if x is our new array of standardized data xij composed of elements, then the correlation matrix R formed by these correlation coefficients is given by:

$$\mathbf{R} = \mathbf{X}^{\mathsf{T}} \cdot \mathbf{X} \tag{2}$$

A matrix whose elements are given by:

$$r\_{\vec{j}\vec{j}^{\circ}} = \sum\_{i=1}^{n} \mathbf{x}\_{i\vec{j}} \mathbf{x}\_{i\vec{j}^{\circ}} = \sum\_{i=1}^{n} \frac{(q\_{i\vec{j}} - q\_{\vec{j}}) . (q\_{i\vec{j}^{\circ}} - q\_{\vec{j}^{\circ}})}{\sigma\_{\vec{j}^{\circ}} \sigma\_{\vec{j}^{\circ}}} \tag{3}$$

The r*jj* value is a standardized covariance between -1 and 1. It should be noted that the matrix is Hermitian (symmetric in the case of real variables, which is our case). We can also confirm that the elements along the main diagonal of the correlation matrix (elements where j = j') correspond to the variance of the variable *qj*. As noted earlier, the correlation matrix R is Hermitian and therefore its eigenvalues are real and positive and its eigenvectors are orthogonal. For this procedure, it is important to note that the values of the wavelengths themselves are not taken into account in the mathematical calculation. The data selected for the next step are the rows and columns highlighted in table 3.

After calculating all the elements of the correlation matrix, the diagonalization is necessary. The diagonalization process provides two sets of data. The first are the eigenvectors: vectors which constitute a new base having the direction and sense in which the initial data set has more tendencies to vary, i.e. the maximization of the variance. The second sets of data are the eigenvalues, which provide the weight information, i.e. the relevance of each of the directions of the eigenvectors.

The eigenvalues are represented by the matrix K and the eigenvectors by the matrix V:

$$K = \begin{bmatrix} \lambda\_1 & 0 & \dots & 0 \\ 0 & \lambda\_2 & \dots & 0 \\ \dots & \dots & \dots & 0 \\ 0 & 0 & \dots & \lambda\_n \end{bmatrix} \tag{4}$$

As the diagonalized matrix was a correlation matrix, we assume that this new base formed by the set of eigenvectors of each R represents a percentage of the total variance; and the information contained in each eigenvector are unique and exclusive, since they are mutually orthogonal. Each *λi* in the K matrix represent the weight of the specific eigenvector. Through these eigenvalues we can determine which of the principal components explains the greatest amount of data. For the simple relationship between the value of each eigenvalue divided by the sum of all eigenvalues, i.e. / *i i* , we can determine the weight (or representation) of each eigenvector.

Once we determined the basis that maximizes the variance of the data set, which should be done by "projecting" the initial data matrix in this new basis through the product between the eigenvectors and the matrix of normalized data, we obtain:

$$\mathbf{S} \mathbf{\cdot} \mathbf{V} \cdot \mathbf{Q} \tag{5}$$

This data set designed in the new base (matrix S) is known as *Score*. The transformation of the basic matrix data S into the matrix Q by the base which maximizes the variances is known as Karhunen-Loève transformation.

The matrix of scores representing the data in our new base is expressed in such a way that each column represents the projection of the initial data into one of the eigenvectors, or in other terms, in either direction variance. Each line of the S matrix still represents a measure, or spectrum, as shown in table 4.

**Table 4.** Presentation of the available data with the new basis

directions of the eigenvectors.

by the sum of all eigenvalues, i.e. / *i i*

known as Karhunen-Loève transformation.

or spectrum, as shown in table 4.

of each eigenvector.

The r*jj* value is a standardized covariance between -1 and 1. It should be noted that the matrix is Hermitian (symmetric in the case of real variables, which is our case). We can also confirm that the elements along the main diagonal of the correlation matrix (elements where j = j') correspond to the variance of the variable *qj*. As noted earlier, the correlation matrix R is Hermitian and therefore its eigenvalues are real and positive and its eigenvectors are orthogonal. For this procedure, it is important to note that the values of the wavelengths themselves are not taken into account in the mathematical calculation. The data selected for

After calculating all the elements of the correlation matrix, the diagonalization is necessary. The diagonalization process provides two sets of data. The first are the eigenvectors: vectors which constitute a new base having the direction and sense in which the initial data set has more tendencies to vary, i.e. the maximization of the variance. The second sets of data are the eigenvalues, which provide the weight information, i.e. the relevance of each of the

The eigenvalues are represented by the matrix K and the eigenvectors by the matrix V:

1

*K*

the eigenvectors and the matrix of normalized data, we obtain:

2

As the diagonalized matrix was a correlation matrix, we assume that this new base formed by the set of eigenvectors of each R represents a percentage of the total variance; and the information contained in each eigenvector are unique and exclusive, since they are mutually orthogonal. Each *λi* in the K matrix represent the weight of the specific eigenvector. Through these eigenvalues we can determine which of the principal components explains the greatest amount of data. For the simple relationship between the value of each eigenvalue divided

Once we determined the basis that maximizes the variance of the data set, which should be done by "projecting" the initial data matrix in this new basis through the product between

This data set designed in the new base (matrix S) is known as *Score*. The transformation of the basic matrix data S into the matrix Q by the base which maximizes the variances is

The matrix of scores representing the data in our new base is expressed in such a way that each column represents the projection of the initial data into one of the eigenvectors, or in other terms, in either direction variance. Each line of the S matrix still represents a measure,

0 ... 0 0 ... 0 ... ... ... 0 0 0 ... *n*

(4)

, we can determine the weight (or representation)

**SVQ** = (5)

the next step are the rows and columns highlighted in table 3.

Now, instead of analyzing the data obtained on the basis of the variables that were defined as the value of the intensity at each wavelength, these are considered in the space of the variances of these values. This change of base allows a significant reduction of the information in which the data are analyzed.

Spectroscopy experiments measure intensity values in hundreds or thousands of wavelengths, providing up to hundreds or thousands of variables to be analyzed. Depending on the experiment, when calculating the principal components of this system we represent around 90% of the information system, i.e. the spectra obtained in only two components of this new base. The graph of two major principal components (PC1 versus PC2) provides a much better view to then analyze these data rather than the hundreds of dimensions we had obtained before. In other words, we now work with a significantly reduced number of variables, wavelengths, with no loss of information. On this new basis, each sample, which was previously represented graphically by a curve with hundreds of points, shall be represented by a single point only. This significant reduction and simplification makes it much easier to detect spectral patterns.

We will now go back to the example of determining the postmortem interval - the set of measures shown in figure 1. Each sample is a fluorescence spectrum from different postmortem intervals. We apply the above procedure and obtain results on this new basis. For this case, the first two principal components show a representation greater than 91%. These results are shown in figure 2, where each point represents a spectrum.

Comparing data of figure 2 to those of figure 1 a temporal evolution of standard measurements becomes obvious. Based on this analysis, each region of space PC1 x PC2 is characterized by a postmortem interval. So in terms of a practical application, if we have a spectrum obtained from an unknown postmortem interval, we just design it based on this new basis and thus allow matching of the region of space to this spectrum, represented by a new point. Thus, this procedure can be used to determine a postmortem interval using fluorescence spectroscopy for situations where this value is unknown.

The present methodology and results shown by Estracanholli et al.[6, 8] demonstrated the use of fluorescence tissue spectroscopy to determine PMI as a valuable tool in forensic medicine. Two approaches were employed to associate the spectral changes with the time evolution of tissue modification. First, direct spectral changes were computed using interspectra analysis, allowing establishing a pattern of the sample distribution with a time evolution. Second, the use of a statistical method based on PCA helped to identify features over time. In both cases, the characteristic time evolution pattern presented a high correlation coefficient, indicating that the chosen pattern presented a direct linear relationship with time.

**Figure 2.** PC1 versus PC2 showing a more evident distinction between the samples.

However, other cases of application of spectroscopic techniques require a more robust processing. One such case is the other example cited above, where the goal is to quantify various compounds in a complex sample containing various interferences, and this most often occurs in the regions of overlapping absorption (or transmission and emission) of compounds of interest. For these cases, the application of artificial neural networks is a powerful solution.


*2.2. Bioassays*

All of the isolated compounds were tested for their antibacterial, antifungal, cytotoxic, and 1,10-diphenyl-2-picryl-hydazyl (DPPH) scavenging activities. Their protein tyrosine phosphatase 1 B (PTP1B) inhibitory activities were also measured—PTP1B is an important hypoglycemic target in diabetes. We found that compounds **2** and **3** displayed antibacterial activities against *Pseudomonas aeruginosa* ATCC 27853 with minimum inhibitory concentration (MIC) values of 64 μg/mL and 32 μg/mL, respectively. Intriguingly, compound **3** also exhibited antifungal activity against *Candida albicans* ATCC10231 with a MIC value of 32 μg/mL. Compounds **3**, **5**, and **7** showed PTP1B inhibitory activities with inhibitory concentration (IC)50 values of 12.25, 25.70 and 24.32 μM, respectively. In addition, compound **9** exhibited a weak DPPH scavenging activity, with an IC50 value of 147 μM. All of the isolated compounds showed no cytotoxicity.

#### **3. Materials and Methods**

## *3.1. Instrumentation*

Optical rotations were measured with a JASCO P-1020 digital polarimeter (Jasco Corp., Tokyo, Japan). UV spectra were recorded with a HITACHI UH 5300 UV spectrophotometer (Hitachi, Tokyo, Japan). ECD data were acquired on a J-815-150S Circular Dichroism spectrometer (JASCO Electric Co., Ltd., Tokyo, Japan). IR spectra were recorded with a Nicolet-Nexus-470 spectrometer (Thermo Electron Co., Madison, WI, USA) using KBr pellets. NMR spectra were acquired by a JEOL JEM-ECP NMR spectrometer (500 MHz for 1H and 125 MHz for 13C, JEOL, Tokyo, Japan), using tetramethylsilane (TMS) as an internal standard. High-resolution electrospray ionization mass spectrometry (HRESIMS) was measured with a Thermo MAT95XP high resolution mass spectrometer (Thermo Fisher Scientific, Bremen, Germany), and electron ionization mass spectrometry (EIMS) spectra with a Thermo DSQ EImass spectrometer (Thermo Fisher Scientific, Bremen, Germany). Single-crystal X-ray crystallographic analysis was performed on an Agilent Xcalibur Eos Gemini diffractometer (Agilent Technologies, Yarnton, England). Samples were analyzed and prepared using a Hitachi L-2000 HPLC system coupled with a Hitachi L-2455 photodiode array detector, and using a semi-preparative C18 column (Kromasil 250 mm × 10 mm, 5 μm). Silica gel (Qing Dao Hai Yang Chemical Group Co., Qing Dao China; 300−400 mesh) and Sephadex LH-20 (Amersham Biosciences, Inc., USA) were used for column chromatography (CC). Precoated silica gel plates (Yan Tai Zi Fu Chemical Group Co., Qing Dao China; G60, F-254) were used for thin-layer chromatography. PTP1B (human recombinant) was purchased from Abcam (ab51277).


**Role of Phytochelatins in Redox Caused Stress in Plants and Animals**

Miguel Angel Merlos Rodrigo, Naser A. Anjum, Zbynek Heger, Ondrej Zitka, Adam Vojtech, Eduarda Pereira and Rene Kizek

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/62160

#### **Abstract**

dase activity in excised red cabbage (*Brassica oleraceae* L.) cotyledons. Biotechnol

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tem architecture. Plant Cell Environ 2005;28:67–77.

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2012;24:842–57. DOI: 10.1105/tpc.111.094912

S1677-04202007000300004

394 Abiotic and Biotic Stress in Plants - Recent Advances and Future Perspectives

journal.pone.0033210

10.1007/s11738-015-1823-x

Varied environmental compartments (such as soil and water) potentially contaminated with different metals/metalloids can impact the health of both plants and animals/ humans. Trace amounts of Cu, Mn, Mo, Ni and Zn are beneficial for higher plants, whereas, Cr, Cu, Co, Mn, Mo, Se, V and Zn are known as the micronutrient metal/metal‐ loids for animals/humans. However, elevated levels of the metals/metalloids can cause severe toxic consequences in both plants and animals/humans. Common in plants and animals/humans, phytochelatins (PCs), the principal non-protein, S-rich, thiolate pepti‐ des, protect (through different mechanisms) cellular functions and metal/metalloid ho‐ meostasis by performing their chelation and/or detoxification. With the major aim of broadening the current knowledge on the subject, this chapter (a) overviews PCs' role and modulation separately in metal/metalloid-exposed plants and animals/humans; (b) discusses major methods for determination of PCs and bioassays for enzymes involved in PC synthesis; (c) evaluates the connection of PCs with bionanoparticles; and finally (d) highlights so far unexplored aspects in the present context.

**Keywords:** Phytochelatin, metal, glutathione, stress

### **1. Introduction**

Anthropogenic activities have caused the release of a wide range of hazardous metals/ metalloids (hereafter termed as 'metal/s') into the environment. In particular, increasing emissions of metals such as Cd, Hg and As into the environment pose an acute problem for all organisms. Metals, unlike organic contaminants, are not degradable and remain persistent in soils [1–3]. Once taken up, these metals can bring severe toxic consequences in cells due to their chemical similarity to replace the metals necessary for cellular functions. Nevertheless,

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

metals at toxic levels have the capability to interact with several vital cellular biomolecules such as nuclear proteins and DNA, leading to excessive augmentation of reactive oxygen species (ROS) [4–6]. In addition, these metals generate ROS which in turn can cause neuro‐ toxicity, hepatotoxicity and nephrotoxicity in humans and animals [7, 8]. Notably, higher plants, algae, certain yeasts and animals are equipped with a repertoire of mechanisms to counteract metal toxicity. The key elements of these are chelation of metals by forming phytochelatins (PCs) and related cysteine-rich polypeptides [9–11]. PCs are produced from glutamine, cysteine and glycine and the process is catalysed by PC synthases known as γglutamylcysteine (γ-Glu-Cys) dipeptidyl transpeptidases [12, 11]. PCs have been identified in a wide variety of plant species, microorganisms and invertebrates. They are structurally related to glutathione (GSH) and were presumed to be the products of a biosynthetic pathway. Numerous physiological, biochemical and genetic studies have confirmed that GSH is the substrate for PC biosynthesis [13, 14]. The general structure of PCs is (γ-Glu-Cys)n-Gly, with increasing repetitions of the dipeptide Glu-Cys, where *n* can range from 2 to 11 but is typically no more than 5 [15]. Except glycine, other amino acid residues can be found on the C-terminal end of (γ-Glu-Cys)*n* peptides. In Figure 1, we show the general structure of PC and the major steps involved in its synthesis from GSH through PC synthase in response to high concentra‐ tions of toxic metals. Originally thought to be plant-specific, PC and PC synthases have now been reported in a few fungal taxa, such as the yeast *Schizosaccharomyces* sp. and the mycor‐ rhizal ascomycete *Tuber melanosporum* [16, 17] and invertebrates belonging to the nematodes, annelids or plathyhelminths [18, 19, 4, 1, 20, 17, 21–24].

In the light of recent literature, the PCs' role and modulation are overviewed separately in metal-exposed plants and animals/humans and major methods for the determination of PCs and the bioassays for enzymes involved in PC synthesis are discussed hereunder. Additionally, connection of PCs with bionanoparticles is evaluated, and finally, major aspects so far unexplored in the present context are briefly highlighted.

**Figure 1.** General structure of phytochelatins (PCs) and the major steps involved in its synthesis from glutathione (GSH) through a PC synthase in response to high concentrations of toxic metals.


**Author details**

Ayse Erturk1 \*, Ugur Kostakoglu1 , Ilknur Esen Yildiz1 , Nur Hursoy2 , Sevda Ozdemir Al1 , Songul Ozyurt3 , Neslihan Ozcelik3 , Hatice Beyazal Polat4 and Aysegul Copur Cicek5

1 Department of Infectious Diseases and Clinical Microbiology, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey

2 Department of Radiology, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey

3 Department of Chest Diseases, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey

4 Department of Internal Medicine, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey

5 Department of Medical Microbiology, Faculty of Medicine, Recep Tayyip Erdogan University, Rize, Turkey

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Demographic, Clinical and Radiological Features of Healthcare Workers and Two Index Cases… DOI: http://dx.doi.org/10.5772/intechopen.99148*


**2.3. Prokineticin in insulin resistance**

The endothelium is essential for insulin transcapillary delivery to the skeletal muscle interstiti‐ um. This process is the rate‐limiting step in insulin‐stimulated glucose uptake. [24] The impair‐ ment ofinsulindeliveryprocess contributes to insulinresistance [25].Onthe otherhand,insulin resistance leads to endothelial dysfunction [26]. Thus, the vascular endothelium is a potential therapeutic target for the prevention of insulin resistance and related complications [27].

Endothelium‐specific PKR1‐knockout mice (PKR1ec*‐*/*‐* ) display impaired capillary formation and low transcapillary insulin uptake [23]. Impaired insulin delivery and signaling in endo‐ thelial cells (ECs) has been observed in cases of insulin resistance with type 2 diabetes and obesity. Endothelial cells overexpressing PKR1 promotes insulin transendothelial uptake [9] and angiogenesis [28]. PKR1*ec‐/‐* mice display lipodystrophy due to poor capillary formation in the AT. Lipodystrophies, involving a loss of WAT, cause hyperphagia and peripheral insulin resistance [29].

As a summary, prokineticin regulates appetite (effects in central nervous system (CNS)) and suppresses adipocyte expansion (direct effect on adipocyte tissue), promotes normal fat stor‐ age (endothelial‐dependent effect) and increases insulin sensitivity. Therapeutic strategies targeting PKR1 could be important to treat obesity and obesity‐associated insulin resistance.

#### **2.4. Prokineticin in cardiovascular regulation**

pocytes), PKR1 suppress the ability of these precursor cells to become adipocytes (**Figure 1**) [7]. The expansion and metabolism of the adipose tissue are the major problem in obesity.

**Figure 1.** Prokineticin‐2/PKR1 signaling may act as a new connector between development of obesity, diabetes and car‐ diovascular diseases. Prokineticin‐2/PKR1 signaling in central nervous system (CNS) regulates food intake. Prokineti‐ cin‐2 released from adipocytes controls preadipocyte conversion to adipocyte via PKR1 signaling. Prokineticin‐2/PKR1 signaling promotes survival of cardiomyocytes and angiogenesis and involved in neovascularization by activating car‐ diac progenitor cells. Prokineticin‐2/PKR1 signaling contributes to heart and kidney development as well as kidney

The endothelium is essential for insulin transcapillary delivery to the skeletal muscle interstiti‐ um. This process is the rate‐limiting step in insulin‐stimulated glucose uptake. [24] The impair‐ ment ofinsulindeliveryprocess contributes to insulinresistance [25].Onthe otherhand,insulin resistance leads to endothelial dysfunction [26]. Thus, the vascular endothelium is a potential therapeutic target for the prevention of insulin resistance and related complications [27].

and low transcapillary insulin uptake [23]. Impaired insulin delivery and signaling in endo‐ thelial cells (ECs) has been observed in cases of insulin resistance with type 2 diabetes and obesity. Endothelial cells overexpressing PKR1 promotes insulin transendothelial uptake [9] and angiogenesis [28]. PKR1*ec‐/‐* mice display lipodystrophy due to poor capillary formation in the AT. Lipodystrophies, involving a loss of WAT, cause hyperphagia and peripheral insulin

As a summary, prokineticin regulates appetite (effects in central nervous system (CNS)) and suppresses adipocyte expansion (direct effect on adipocyte tissue), promotes normal fat stor‐

) display impaired capillary formation

function. Whether this signaling involves heart and kidney regulation through CNS remains to be studied.

**2.3. Prokineticin in insulin resistance**

118 Adiposity - Omics and Molecular Understanding

resistance [29].

Endothelium‐specific PKR1‐knockout mice (PKR1ec*‐*/*‐*

Obesity is both an independent risk factor and a risk marker for the development of asymp‐ tomatic and symptomatic coronary artery disease, heart failure and atrial fibrillation [2]. The relationship between obesity and cardiovascular diseases may be associated with hemody‐ namic and anatomic cardiovascular changes related to excess body mass [30]. However, the relationship can also be mediated by obesity‐related metabolic, inflammatory and neurohor‐ monal changes.

Altered expression of prokineticins and their receptors has been implicated in the development of a number of pathological cardiac conditions, including heart failure [31]. Prokineticins and their receptors have been identified as an important cardiovascular‐signaling system especially cardiac cell commitment and cell‐to‐cell communications [32].

PKR1‐mediated signaling contributes to cardiomyocyte survival and adult heart repair. PKR1 activates Akt in cardiomyocyte to protect these cells against hypoxia‐mediated apoptosis [33]. Transgenic (TG) mice‐overexpressing PKR1 in the cardiomyocytes (TG‐PKR1) had an in‐ creased number of epicardial‐derived progenitor cells (EPDCs), with an increase of capillary density and coronary arterioles. [32] The cardiac‐PKR1 signaling up‐regulates its own ligand prokineticin‐2 to stimulate the EPDC differentiation into endothelial and smooth muscle cells to promote neovasculogenesis [32]. However, cardiomyocyte‐PKR1 is essential for cardio‐ myocyte survival and contractility. PKR1null mice displayed cardiomyocyte‐contractile defects and apoptosis partially due to lack of PKR1 signaling in cardiomyocytes. [34]

In endothelial cells (ECs), PKR1 activates Akt and MAPK to promote proliferation, migration and angiogenesis. In agreement with the *in vitro* findings, the specific loss of PKR1 from mouse ECs resulted in defective angiogenesis, leading to necrosis/apoptosis in the surrounding tissues in several organs, including the heart and kidneys [23].

There was significantly less capillary formation in adult PKR1ec‐/‐ hearts. The posterior walls of PKR1ec‐/‐ hearts were thinner, which was due to the loss of capillary formation and a high level of apoptosis [23]. The remaining viable heart muscle is subject to greater biomechanical stress, triggering hypertrophy [23]. Shortening fractions (indicators of left ventricular contrac‐ tility) were progressively reduced in mutant mice. PKR1ec‐/‐ hearts displayed EC deregulation, capillary refraction, apoptosis, fibrosis and ectopic lipid deposition, abnormal insulin signaling in hearts resulting in impaired diastolic function.

The ECs of hearts exhibited severely decreased FICT‐insulin uptake, indicating defective *transcapillary* transport of insulin in the vascular wall of these mice. Isolated ECs from the mutant cardiac and renal tissues exhibited very little insulin uptake, confirming that the loss of PKR1 from EC decreased insulin transport [23]. Overexpressing PKR1 in these ECs pro‐ moted fluorescein isothiocyanate (FITC)‐insulin passage. Indeed, the primary defect linking insulin resistance and endothelial dysfunction is believed to be nitric oxide deficiency of endothelial origin [35]. In agreement, insulin uptake and insulin‐mediated eNOS activation were impaired in all mutant ECs. Similarly, altered eNOS activation and low insulin action have recently been demonstrated in the endothelium of patients with diabetes mellitus [36]. Thus, impaired insulin delivery to ECs may lead to defective NOS and eNOS activation in PKR1ec‐/‐ aortas, consequently impairing endothelium‐dependent relaxation. These data highlight the role of PKR1 as a positive regulator of insulin uptake [37].

PKR1 signaling also contributes to heart development. In developing heart, PKR1 regulates epicardial‐mesenchymal transition (EMT) to form epicardial‐derived progenitor cell (EPDC) [38]. Genetic ablation of PKR1 in epicardium leads to ventricular hypoplasia and septal defects during embryogenesis. Impaired vasculogenesis in these mice is due to impaired EPDC proliferation as well as a defective EPDC differentiation into endothelial and smooth muscle cell type. PKR1 in EPDCS activates Akt signaling, changes cell morphology, actin cytoskeleton remodeling and EMT gene expression profile. Epicardial‐PKR1 contributes to cardiomyocyte, proliferation and rhythmicity in a paracrine pathway.