*Mn* = *FPn* − *FP*min*n FP*max*n* − *FP*min*n*

(6)

$$M\_n = \frac{FN \text{max}\_{\text{n}} - FN\_{\text{n}}}{FN \text{max}\_{\text{n}} - FN \text{min}\_{\text{n}}} \tag{7}$$

*FPn* = *FP*min*n* + (*FP*max*n* − *FP*min*n*) · *Mn* (8)

$$FN\_{\text{ll}} = FN \text{max}\_{\text{ll}} - \left(FN \text{max}\_{\text{ll}} - FN \text{min}\_{\text{ll}}\right) \cdot M\_{\text{ll}} \tag{9}$$

where:

*FP*min to *FP*max is the range of positive factors;

*FN*max to *FN*min is the range of negative factors.

Data for both experiments are summarized in Table 3. The "reference value" of a factor and the corresponding monkey position value show the actual (current) data. All factor values are commented on in Section 5.

**Table 3.** *Cont.*

#### *4.3. Experiment 1, Testing the Changeability of Factors' Values*

In this experiment, the initial values of all monkey positions are midway between the minimum and maximum, which is 0.500. The followers coefficient (*L*) is 1.5, and the range of hierarchy modifiers (*R*min, *R*max) are −0.3 and 0.3. These values were selected based on testing various algorithm stop criteria. This aspect is commented on in Section 5. The effect of the NOAH algorithm after 32 steps is shown in Figure 4.

**Figure 4.** Changes in decision measures during the steps in Experiment 1.

Because the initial monkey position value assumption is 0.5 and the number of monkeys considered is 16, the starting value of the first decision measure (*M*) is 8.0. The value of this measure is changed in the subsequent steps of the algorithm in accordance with the procedures described in Section 3. These changes should be observed in order to formulate the algorithm stopping criteria. One such criterion could be the difference between the *M* values in the following steps. When this difference reaches a predetermined minimum, the algorithm stops. We observe "stabilization in the nest".

The second decision measure is the sum of importance values (*I*). Because each individual's value for importance is random, the measure *I* varies from step to step but fluctuates

around an average of 8.0. Observing the changes in the value of *I* makes sense in order to formulate the second algorithm stopping criterion. If you change this value significantly, you can stop the algorithm or ignore this particular step. Here we see "a remarkable change of subjective meaning in the nest".

The average value of the hierarchy (*H*) is the third decision measure. This value drops to zero. Achieving the assumed minimum value may terminate the algorithm's work. A similar situation takes place in the next decision measure—the average value of the random hierarchy modifier (*R*). It is possible to use only one decision measure and only one stop criterion, but the observation of all of them increases the set of analyzed solutions. In this experiment, stabilization in the socket was achieved after 32 steps, and the algorithm was completed. The values of all NOAH parameters for all monkeys and the decision measures in the final step are presented in Table 4. This table also shows the recalculated values of the analyzed factors as a set characterizing the final step of the algorithm. The interpretation of the coefficient values is presented in Section 5

**Table 4.** The results of the first experiment.

#### *4.4. Experiment 2, Considering the Real Data*

In this experiment, the initial values of the monkeys are calculated according to actual (reference) factor values (see Table 3). The values of the followers coefficient and hierarchy modifier ranges are the same as in Experiment 1. The effect of the NOAH algorithm after 32 steps is shown in Figure 5. The same four decision measures and stopping criteria were also used as in Experiment 1. The values of all NOAH parameters for all monkeys and decision measures in the final step are presented in Table 5. This table also shows the re-converted values of the analyzed factors as a set characterizing the final step of the algorithm. The interpretation of the factor values is presented in Section 5.

**Table 5.** The results of the second experiment.

**Table 5.** *Cont.*

**Figure 5.** Changes in decision measures during the steps in Experiment 2.

#### **5. Discussion**

Table 6 summarizes the important data for all factors. The minimum, maximum, reference (actual), and final values from both experiments are shown. The reference values correspond to the observed (measured) situations. Measurements and data collection were performed in the spring of 2022. The minimum and maximum values are calculated or assumed according to physical conditions or other possibilities. For example, "percentage of spaces occupied" (*F*4) can of course vary between 0 and 100%, and "parking volume on PR" (*F*7) can vary between 0 and 100, the upper limit being the actual number of parking spaces in all of the considered locations. The "cost" (*F*10) could vary from PLN 4.6 to 12.0, which results from the comparison of various fees in the considered journeys between the origin and the destination; "travel times" (*F*12–*F*14) oscillate between values obtained from measurements or calculated taking into account the possible speeds.

The values obtained in the experiments depend on the values of the parameters adopted in the algorithm: the followers coefficient (*L*) and the range of hierarchy modifiers (*R*min, *R*max). These values may vary depending on the specifics of the problem under consideration (e.g., depending on a number of factors) and should be tested in the experimental phase of the research. Finally, the values were selected—*L* = 1.5, *R*min = −0.3 and *R*max = 0.3—as the best according to the decision measure change process. The impact of

the abovementioned parameters on the results of NOAH requires further research and will be explored in the future.

**Table 6.** Set of values for all factors.

The factors values obtained from the experiments should be analyzed as a complex set of parameters. In both experiments, sufficient numbers of trips (*F*1) and stops (*F*5) were identified in relation to the values of the other factors. These values led to an intuitive decrease in the number of passengers during the day (*F*2). Comparing these values with the stable number of residents (*F*6), there is a need to increase the role of the delivery system. The delivery system is represented here by factors including cars in PR (*F*7) and passengers arriving from correlated forms of public transport (*F*8). The travel cost (*F*10) should be lower. The conditions tested above also show a lower number of journeys by means that compete with rail, both with private cars (*F*16) and buses (*F*15).

Some results differ in both experiments. Because the first experiment starts with the "average" values of the factors, the results are close to the middle between the minimum and the maximum. This shows the potential NOAH effect but is not of practical use. The results of the second experiment are more realistic and allow you to judge the real conditions. The maintenance of a clear difference between the travel time of rail travel and those of competitive forms is particularly visible. The results show the possibilities of further use of the constructed methodology and algorithm. For example, it is possible to test other numbers of residents (*F*6) or numbers of departures (*F*1). It is also possible to test different values of the minimum and maximum factors. They should allow the modeling of various conditions in the suburban rail system and the observation of correlations between all factors. Initial values of the abovementioned parameters should be carefully collected. This limitation requires further work to be overcome.

The obtained results correspond with actual topics involved in the interdisciplinary field of sustainable urban planning and transport development. Significantly, connection with heuristics used in analyses of transportation and railway systems [64,65] occurred. This aspect shows the potential of the proposed algorithm and methodology to be developed in other studies not only connected with railway systems and not only in transportation engineering.

#### **6. Conclusions**

The parameters describing a suburban rail system have different characteristics. The set of possible factors is not precisely defined, and the data collection process has specific problems. Some data are incomplete, while others contain errors. The influence of each selected factor and its importance are not fully understood. Therefore, modeling and analysis of suburban rail systems requires specific methods that also take into account human behavior. Here, heuristics as a holistic tool can help in the analysis of possible correlations between factors, as well as be helpful in the decision-making process. The novel method presented here allows using an open set of data. The factors could be different and somewhat "chaotic" at first sight (as in the presented experiments). This is the key innovation in analyses of "technical systems" like the suburban railways considered here.

The main advantage of the proposed method is the creation of the possibility to observe various sets of data and their interactions without precise knowledge about the influence of a specific factor on the result. The data (describing the transportation systems) depend partially on human decisions. For example, the choice of the mode of transport could be a reaction to the behaviors of other people (neighbors, social media groups). So, an individual can observe and copy leaders as a follower. The use of the specific transport means influencing the number of passengers or parking volume in PR has some uncertainties and could be modeled using heuristics.

NOAH, which is inspired by the behavior of groups of monkeys, especially taking into account dynamic changes in leadership, is likely to be useful in specific technical problems where physical parameters (such as the number of departures or stops) are compared with human decisions moderated by travel time, prices, etc. The basic definitions, the procedures, the algorithm, and the potential application of NOAH are illustrated in simple examples with a relatively small set of factors. The NOAH algorithm modifies the values of factors in a heuristic way and shows possible solutions that could be introduced in reality. This method created originally by the author is quite similar to swarm intelligence algorithms (like PSO and ACO) but contains new elements based on specific behaviors of monkeys which were described in Section 3.

Strict comparison of this new method with others is not possible because of the other goals of those methods. The heuristics search mainly for optimal solutions. NOAH can compare factors, not showing the best result. This is an intentional assumption representing the difficulty of evaluation of data by an individual person. Thus, the evaluation of the effectiveness of the proposed method is difficult, especially in the present stage of research. The study introduced in one of the Polish agglomerations will be continued and should formulate remarks to modify selected elements of the transport system (e.g., number of departures or cost) with the observation of changes in other factors. When the obtained results are similar to the model, NOAH will be effective.

The presented material introduces the new algorithm by showing its procedures that can allow for its use in similar problems. This could test obtained assumptions and improve procedures in the future. Future works should contain influence analyses of the parameters adopted in the algorithm (the followers coefficient and the range of hierarchy modifiers), other (broader) sets of factors, and comprised studies in other areas. The NOAH method could be also used for other problems where incomplete and different data make observations of technical systems difficult.

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

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

#### **References**

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.


**Dongxiao Yin 1, Z. George Xue 1,2,3,\*, David J. Gochis 4, Wei Yu 5, Mirce Morales 6 and Arezoo Rafieeinasab 4**

Received: 18 April 2020; Accepted: 24 June 2020; Published: 26 June 2020

**Abstract:** A soil erosion and sediment transport model (WRF-Hydro-Sed) is introduced to WRF-Hydro. As a process-based, fully distributed soil erosion model, WRF-Hydro-Sed accounts for both overland and channel processes. Model performance is evaluated using observed rain gauge, streamflow, and sediment concentration data during rainfall events in the Goodwin Creek Experimental Watershed in Mississippi, USA. Both streamflow and sediment yield can be calibrated and validated successfully at a watershed scale during rainfall events. Further discussion reveals the model's uncertainty and the applicability of calibrated hydro- and sediment parameters to different events. While an intensive calibration over multiple events can improve the model's performance to a certain degree compared with single event-based calibration, it might not be an optimal strategy to carry out considering the tremendous computational resources needed.

**Keywords:** WRF-Hydro; CASC2D-SED; Goodwin Creek Experimental Watershed; NLDAS-2; calibration


2.1.1. Oxidative Enzymes Generating Reactive Oxygen Species

Reactive oxygen species (ROS) include several free radicals, such as superoxide (O2 −•) and hydroxyl radical (OH•) and nonradical molecules such as hydrogen peroxide (H2O2) and organic hydroperoxide (ROOH) (Table 2). ROS are produced in endogenously in the cytosol, the plasma membrane, the membranes of mitochondria and endoplasmic reticulum, peroxisomes, and phagocytic cells [42,43] (Figure 2). ROS is difficult for direct measurement in biological tissues due to its highly reactivity and short life.

**Figure 2.** Reactive chemical species. Reactive chemical species comprise of not only reactive oxygen species and reactive nitrogen species, but also reactive sulfur, carbonyl, halogen, and selenium species. Sulfur reacts with oxygen or nitrogen to form reactive thiol or nitrosothiol species, respectively. All reactive chemical species react in concert during regular cellular activity but may cause oxidative stress to damage cellular components such as proteins, lipids, and nucleic acids.

**Table 2.** Reactive chemical species. During regular cellular activity living cells generate numerous reactive chemical species containing oxygen, nitrogen, sulfur, carbonyl, halogen, or selenium. Reactive sulfur species can contain thiols or nitrothiols. Free radicals possess at least one unpaired electron that makes highly reactive and short-lived. Nonradicals are oxidizing chemicals or easily converted to free radicals. Superoxide, reactive sulfur species, or reactive selenium species can be reducing agents as reactive nucleophilic species.

In the cytosol, ROS can be generated by soluble intercellular components, such as catecholamines, hydroquinones, flavins, and thiols (RSHs) that undergo reduction reactions [44]. The cytosolic enzyme XDH normally catalyzes xanthine, NAD+, and water (H2O) to urate, reduced form of NAD+, NADH, and hydrogen ion (H+). Reversible oxidation of cysteine residues or irreversible Ca2+-stimulated proteolysis converts XDH to xanthine oxidase (XO) that transfers electrons to molecular oxygen (O2), producing superoxide (O2 −•) during xanthine or hypoxanthine oxidation [45]. The serum levels of uric acid, a major endogenous antioxidant was measured in patients with PPMM, RRMM, and SPMM. The uric acid levels were significantly lower in active MS than inactive MS, and the uric acid levels were independently correlated with gender, disease activity and duration of the disease [46] (Table 3). **Table 3.** Oxidative stress biomarkers of multiple sclerosis. The redox status can be monitored by the activities of oxidative and antioxidative enzymes and the presence of degradation products derived from cellular components. ↑: increase, ↓: decrease, **-**: unknown.

The plasma membrane is a network of phospholipid bilayer and integral proteins which protects the cellular organelles from the outer environment and is responsible for several cellular functions, such as cell adhesion, ion transport, cell signaling, and phagocytosis. The main ROS of the plasma membrane is a superoxide (O2 −•) produced by the membrane-bound enzyme nicotinamide adenine dinucleotide phosphate oxidase (NOX), which is composed of two membrane proteins, three cytosolic proteins, and a small GTP-binding protein [89,90]. The expression of NOX isoform NOX5 was significantly increased, but the expression NOX4 was significantly decreased in serum of RRMS patients, suggesting that differential NOX isoform expression contributes to OS-associated vascular changes in MS [48]. ROS is also produced by COX and LOX, which convert arachidonic acid to prostaglandins, thromboxanes, and leukotrienes. Phospholipase A2 generates ROS during arachidonic acid oxidation [91]. In the presence of transition metal ions, such as Fe2+ and Cu+, hydrogen peroxide (H2O2), organic hydroperoxide (ROOH), and organic peroxide (ROOR) produce hydroxyl (OH•), alkoxyl (RO•), and peroxyl radical (ROO•), respectively [92] (Table 3).

Superoxide dismutase (SOD) catalyzes the disproportionation of two superoxide (O2 −•) into molecular oxygen (O2) and hydrogen peroxide (H2O2). These enzymes are present in almost all aerobic cells and in extracellular fluids. SODs contain metal ion cofactors that, depending on the isozyme, can be copper, zinc, manganese, or iron [93,94]. There are three isozymes in humans. Dimeric copper- and zinc-coordinated SOD1 is in the cytoplasm; tetrameric manganese-coordinated SOD2 is confined to the mitochondria; tetrameric copper- and zinc-coordinated SOD3 is extracellular [95]. The mitochondrial ROS production takes place at four protein complexes, ubiquinone, and cytochrome c of the ETC, embedded in the inner membrane of the mitochondria [96]. The Complexes I/III/IV utilize NADH as the substrate, while Complexes II/III/IV use succinic acid. The complex II is also glycerol 3-phosphate dependent. The primary mitochondrial ROS is superoxide (O2 −•) that is converted by

mitochondrial SOD into hydrogen peroxide (H2O2), which can be turned into hydroxyl (OH•) via the Fenton reaction [97]. The Complex I and III release superoxide (O2 −•) into the mitochondrial matrix where it can damage the mitochondrial DNA, while the Complex III also releases superoxide (O2 −•) into the intermembrane space where it is accessible to the cytosol [98]. Reduced levels of antioxidant α-tocopherol was observed in the blood of patients with Leber's hereditary optic neuropathy, which is caused by mitochondrial mutation of the Complex I, suggesting that oxidative load was elevated and antioxidant capacity was compromised [99]. Other mitochondrial enzymes that contribute to hydrogen peroxide (H2O2) production are monoamine oxidases, dihydroorotate dehydrogenase, α-glycerophosphate dehydrogenase, and α-ketoglutarate dehydrogenase (α-KGDH) complex. Succinate dehydrogenase also generates ROS [100].

Significantly higher mean activity of SOD in erythrocyte lysates was reported in RRMS than controls. Interestingly, the SOD activity of CIS was higher than that of RRMS [101]. The erythrocyte SOD activity can be a diagnostic biomarker of CIS and RRMS. The SOD activity was significantly lower in the erythrocyte lysates of RRSM patients upon relapse than controls but increased following the intravenous administration of corticosteroid methylprednisolone and remained higher during remission period than controls. The SOD is a potent predictive and therapeutic biomarker. The serum/plasma samples also showed significantly higher mean SOD activity in RRMS compared to control groups [82]. However, platelet SOD1 and SOD2 activity was unchanged in MS patients [49]. Intriguingly, SOD activity was significantly low in CSF of CIS and RRMS patients, despite the significantly high activity of plasma SOD. Furthermore, there were negative correlations between the erythrocyte SOD activity and disease duration and expanded disability status scale (EDSS) in CIS and RRMS, between the erythrocyte SOD activity and Gd+ enhancement lesion volume in CIS patients [50]. These findings suggest the SOD activity as a possible diagnostic and prognostic marker (Tables 3 and 4).

The peripheral blood mononuclear cells (PBMCs) SOD1 proteins and mRNA expression were significantly lower in RRMS patients than controls and became significantly more elevated following IFN-β1b treatment than the baseline [51]. These studies suggest SOD as a potential therapeutic biomarker (Table 4). However, the erythrocyte SOD activity remained unchanged following the treatment of natalizumab, a humanized monoclonal antibody against the cell adhesion molecule α4-integrin. But levels of carbonylated protein and oxidized guanosine were reduced [52].

In the inner membrane of mitochondria and the endoplasmic reticulum, a heme-containing monooxygenase cytochrome P450 (CYP) enzymes are responsible for oxidizing steroids, cholesterols, and fatty acids. The CYPs forms ROS superoxide (O2 −•) and hydrogen peroxide (ROOH) by substrate cycling [102]. Protonation of hydrogen peroxide (ROOH) forms hydrogen peroxide (H2O2) which, furthermore, cleaves into hydroxy radicals (OH•). The redox cycling produces free radical semiquinone from quinoid substrates [103]. In the mitochondrial transport chain, flavoprotein reductase forms ROS by direct reduction of O2 and via the mediation of quinones. [104]. Superoxide (O2 −•) is produced by XO in the reperfusion phase of ischemia, LOX, COX, and NADPH-dependent oxidase [105]. In the endoplasmic reticulum NADH cytochrome b5 reductase can leak electrons to molecular oxygen (O2) to generate superoxide (O2 −•) during the NADPH-dependent oxidation of xenobiotics [106].

Most enzymes in the peroxisomes produce ROS during the catalysis of fatty acid α- and β-oxidation, amino acid and glyoxylate metabolism, and synthesis of lipidic compounds. A large fraction of hydrogen peroxide (H2O2) generated inside peroxisomes was observed to penetrate the peroxisomal membrane and diffuse to the surrounding media [107]. The peroxide can diffuse through the channel formed by the peroxisomal membrane protein Pxmp2 and hydrogen peroxide (H2O2) generated by the peroxisomal urate oxidase can release through crystalloid core tubules into the cytosol [108]. Meanwhile, peroxisomes also possess protective mechanisms to counteract oxidative stress and maintain redox balance. Reduction in peroxisomal gene and protein expression was observed in MS gray matter [109].

**Table 4.** Possible redox biomarkers in multiple sclerosis. Reactive chemical species, oxidative enzymes, antioxidants, antioxidative enzymes, degradation products, and end products are potential biomarkers for multiple sclerosis (MS). Diagnostic biomarkers allow early detection and secondary prevention; prognostic biomarkers suggest the likely clinical course; predictive biomarkers predict the response of MS patients to a specific therapy; and therapeutic biomarkers indicate a target for therapy. CIS: clinically isolated syndrome, PPMS: primary progressive MS; RRMS: relapsing-remitting MS, SPMS: secondary progressive MS, mixedMM: mixed population of MS.

The lysosomal ETC plays a central role to support the positive proton gradient to maintain an optimal pH of the acid hydrolases [110]. The ETC is made up of a flavin-adenine dinucleotide, a b-type cytochrome and ubiquinone with the donor NADH and ending to acceptor molecular oxygen (O2), transferring three electrons. Superoxide (O2 −•) is possibly produced in the acidic environment which favors dismutation of hydrogen peroxide (H2O2) into hydroxy radical (OH•) by ferrous iron [111]. Furthermore, ozone (O3) and ozone-like oxidants are generated from singlet oxygen (O2 1Δg) catalyzed by antibody or amino acid. Ozone (O3) reacts with superoxide anion (O2 −•) to form hydrogen peroxide (H2O2) in the presence of Fe+2 [112].

2.1.2. Oxidative Enzymes Generating Reactive Nitrogen Species

Reactive nitrogen species (RNS) are a group of nitrogen-congaing molecules including free radicals, nitric oxide (NO), and nitrogen dioxide (NO2). Free radicals are nitric oxide (NO•) and nitrogen dioxide (NO2 •) radicals, while nonradicals are nitrite (NO2 −) and nitrate (NO3 −), among others (Table 2). RNS are derived from nitric oxide (NO) and superoxide anion (O2 −•) produced by nitric oxide

synthetase 2 (NOS2), NADPH oxidase, XO, LOX, and COX, among others [113,114]. At physiological concentrations, a gaseous molecule nitric oxide (NO) is a second messenger involved in blood pressure regulation, smooth muscle relaxation, defense mechanisms, immune regulation, and neurotransmission contributing the function of memory and learning [47].

Cross-sectional studies show that the levels of nitric oxide metabolites nitrite (NO2 −) and nitrite (NO3 −), measured as a total value (tNOx), are significantly higher in plasma or serum of patients with RRMS [83,115]. A longitudinal study revealed that higher serum tNOx is significantly correlated with relapsing rate, suggesting prognostic biomarker of NOS [116]. Many studies of CSF samples reported significantly higher levels of tNOx in RRMS and PPMS, compared to healthy controls [117,118]. A study observed significantly higher levels of CSF tNOx in RRMS than SPMS, suggesting an inflammatory role of RNS [119]. Significantly higher levels of CSF tNOx were reported in patients with acute relapsing phase of RRMS than those with stable remitting phase of RRMS [120,121] (Tables 3 and 4).

In cGMP-dependent pathways, nitric oxide radical (NO•) generated by endothelial NOS in endothelium, brain, and heart relaxes blood vessels and maintains normal blood pressure, while nitric oxide radicals (NO•) produced by neuronal NOS serve as a neurotransmitter to regulate blood pressure in the brain. Inducible NOS (*i*NOS) in macrophages and smooth muscle cells gives rise to nitric oxide radicals (NO•) as in reaction to bacterial lipopolysaccharides and/or cytokines [122].

Nitric oxide radical (NO•) is produced from the metabolism of L-arginine by NOS that converts L-arginine into L-citrulline and nitric oxide radical (NO•) by a 5-electron oxidation of a guanidine nitrogen of L-arginine [123]. In mitochondria nitric oxide radicals (NO•) react with respiratory Complex III to inhibit electron transfer and facilitate superoxide anion (O2 •−) production. The nitric oxide radicals (NO•) also compete with molecular oxygen (O2) for the binding site at the binuclear center of cytochrome *c* oxidoreductase, inducing a reversible inhibition of cytochrome *c* oxidase. Nitric oxide (NO) neutralizes ROS [45]. However, RNS react with oxygen molecules (O2) and ROS, giving rise to a variety of nitrogen oxides (NOs), such as nitrogen dioxide radical (NO2 •), nitrogen dioxide (NO2), dinitrogen trioxide (N2O3), peroxynitrite (ONOO−), nitrite (NO2 −), and nitrate (NO3 −). Higher concentrations of nitric oxide (NO) become toxic by forming nitrosothiols which oxidize tyrosine, cysteine, methionine, and GSH. In mitochondria, nitric oxide radicals (NO•) inhibit Complex I by *S*-nitrosation [124]. Together with other RNS, this contributes the damage of cell membranes, proteins, and lipid membrane leading to the degradation of mitochondria, lysosomes, and DNA. The chain of events culminates in the inhibition of immune response and production of carcinogenic nitrosamines [125]. Nitric oxide (NO) is also involved in metal homeostasis including Fe, Cu, and Zn [126].

Highly toxic peroxynitrite (ONOO−) is formed by the reaction of nitric oxide (NO) and superoxide anions (O2 −•), leading to the production of more reactive compounds that oxidize methionine and tyrosine residues of proteins, lipids, and DNA. Reacting with superoxide anion (O2 −•), nitric oxide radicals (NO•) form peroxynitrite which causes reversible inhibition of cellular respiration in the mitochondria [127]. In peroxisomes nitric oxide radicals (NO•) react with superoxide anions (O2 −•) produced by XO to form peroxynitrite and hydrogen peroxides [111]. In addition, insulin resistance favors peroxintrite formation [128].

In response to bacterial lipopolysaccharides and inflammatory stimuli, *i*NOS generates nitric oxide (NO) that protects tissue hypoxia and serves as a neurotransmitter. However, overexpression *i*NOS, increase of nitric oxide (NO), and subsequent inflammation have been implicated in pathophysiology of neurodegenerative diseases including MS [47]. Calmodulin mediates the oxidative stress and inflammasome activation involving calmodulin-binding proteins, calcineurin, and calcium/calmodulin-dependent kinase II. Calmodulin inhibitors improved cognitive functions in an animal model of vascular dementia [129]. The *i*NOS activity is upregulated in acute MS plaques [19,53]. Increased activity and expression of *i*NOS in lymphocytes were found in active relapsing phase of RRMS [54]. CSF *i*NOS expression was shown in MS patients and mean CSF NOS activity was significantly higher, compared to controls [55] (Table 3).


**1. Introduction**

Amplified in breast cancer 1 (AIB1) is a transcriptional co-activator and a member of the nuclear co-activator (NCOA) family; the protein was discovered concurrently by many groups and given a variety of names, including AIB1 [1], ACTR [2], TRAM-1 [3], RAC-3 [4], CIP1 [5], and SRC-3 [6]. AIB1 is an oncogene that is amplified and overexpressed in cancer, and acts by recruiting and stabilizing chromatin remodeling complexes [1, 2, 7]. In its well-known capacity, AIB1 interacts with nuclear receptors such as the estrogen, progesterone, and androgen receptor, to promote hormone dependent transcription and repression. Less wellstudied, AIB1 promotes disease progression and de-differentiation by potentiating oncogenic signaling through interaction with a diversity of transcription factor interactions in hormone-independent disease contexts [8–11]. Thus, AIB1 acts as an oncogene by stabilizing transcription complexes, recruiting chromatin modifying enzymes, and thereby amplifying oncogenic signals. Unsurprisingly, high AIB1 levels are a poor prognostic marker across many cancer types, and also the protein can facilitate resistance to therapeutics in patients. Herein, we describe the form and function of AIB1, and its role in cancer and the clinical setting.


**5. Conclusion**

This study evaluated the application of PZT sensors for monitoring the strength development of high strength concrete. The applicability of the conventional impedance measuring technique, which is normally used to detect damage, was extended to monitor the curing process of concrete. The impedance signals were obtained at six different curing ages. The compressive strengths of the test concrete cylinders were also evaluated by considering the resonant frequency variations and cross-correlation coefficient. Based on the experimental results, the resonant frequencies in the impedance signals shifted gradually to the right side with increasing curing time, which confirms the applicability of impedance measurements to monitor the strength development of concrete. The largest deviation of the resonant frequency shift was observed between days 3 and 5, and the change decreased with time. In addition, the 1-CC values increased due to strength development during the curing process. A wireless impedance system showed similar results to that of the wired impedance system. Therefore, a wireless system that can improve the applicability to a construction site can be used to monitor the strength development of concrete. Consequently, the wireless strength development monitoring system for concrete can be employed comfortably in construction sites. Furthermore, piezoelectric sensors that monitor the strength development can be used for structural health monitoring (SHM) after construction. In addition, embedded curing monitoring and a SHM system for high strength concrete can be developed to improve the applicability and efficiency of this system.


2.1.1. Geometries

As this was a theoretical study, the IVUS data were simulated by using FE models with idealized and real patient geometries. The idealized geometries consisted of a 3D geometry with a 13 mm long atheroma plaque and with a lipid core length of 6.5 mm and different FCTs [33]. We analyzed three different FCTs, trying to cover the different geometric possibilities. A FCT of 65 μm was considered to represent a vulnerable case [5],

300 μm represented a stable plaque, and finally, 150 μm was an intermediate value between the two extremes. The geometry was reconstructed following the Glagov results [34] and the Finet law [6]. The model was constructed with symmetric conditions, so only a quarter of the geometry is shown in Figure 2a. The model had different tissues: adventitia, healthy media and intima, fibrotic tissue, and lipid core. As one of the aims of this study was to check the influence of the FCT on the segmentation process, three thicknesses, 65, 150, and 300 μm, were considered, and they are represented in Figure 2b–d. Despite the use of a 3D model, we only analyzed the section of maximum stenosis with the plane strain assumption in order to reproduce the IVUS technique. On the other hand, the real patient geometries were obtained from three IVUS images of human coronary plaques that were manually segmented by an expert cardiologist in a previous study [35]. Both IVUS images and the cardiologist segmentation of the three plaques are shown in Figure 3. When there was lack of information in the axial direction, in these cases the FE models were 2D. In two IVUS geometries, only the fibrotic tissue and the lipid core were considered, and on the third plaque a calcification was also included.

**Figure 2.** (**a**) 3D Idealized geometry; (**b**) fibrous cap thickness of 65 microns; (**c**) fibrous cap thickness of 150 microns; (**d**) fibrous cap thickness of 300 microns.

**Figure 3.** The first column presents IVUS images [35] of the three different plaques; the second column is the manual segmentation performed by a cardiologist [35]; the third column is the IVUS reconstruction in Abaqus of the pressurized geometry; the fourth column shows the plaque models with zero-pressure geometry in Abaqus. These geometries were used to initiate the FE simulations.The fifth column is the final FE model after applying an internal pressure of 115 mmHg to the previous geometry.

### 2.1.2. Modeling of Tissue Behavior

All tissues were modeled as hyperelastic, non-lineal, and incompressible with the constitutive model proposed by Gasser et al. [36]. The healthy tissues (adventitia, media, and intima) were modeled as anisotropic with two families of fibers. Conversely, the unhealthy tissues (fibrotic tissue and lipid core) were considered as an isotropic behavior model by the use of parameter *κ* = 1/3 in the *Gasser* model. All tissue was assumed to be fully incompressible (D = 0) in the idealized geometries and quasi-incompressible (D = 0.49) in the real IVUS geometries. The material parameters of the equation in (1) were fitted from experimental curves obtained from the bibliography [37,38] using the software Hyperfit [39]. All the parameters of (1) are reflected in Table 1, where *α* is the angle of the fibers with respect to the circumferential direction. The calcification of the third IVUS plaque was modeled with an isotropic neo-Hookean material model [35].

$$\Psi = \frac{1}{D} \cdot \left[ I - 1 \right]^2 + \mu \left[ I\_1 - 3 \right] + \frac{k\_1}{2k\_2} \sum\_{i=4,6} \left( \exp \left( k\_2 \left[ \kappa \left[ I\_1 - 3 \right] + \left[ 1 - 3 \kappa \right] \left[ I\_i - 1 \right] \right]^2 \right) - 1 \right), \tag{1}$$

**Table 1.** Fitted parameters for the hyperelastic model.

### 2.1.3. FE Models

The FE models were created in the commercial software Abaqus [40], where the boundary conditions and loads were imposed. In the 3D idealized FE models, not only were the symmetrical conditions imposed, but also the contact with the heart was mimicked by avoiding displacement in a contour line on the outside of the adventitia [33]. The blood pressure imposed inside the artery was 115 mmHg, which is the average pressure in patients with high normal pressure and grade 1 hypertension [41]. On the other hand, the FE models of real IVUS geometries were 2D, so they were solved following the plain strain assumption and the rigid body motion was constrained by two contour points with zero displacements. Furthermore, the IVUS geometry was previously reconstructed from pressurized images (third column in Figure 3). Therefore, it was necessary to obtain the zero-pressure geometry to be used as the initial geometry. For this purpose, we assumed that IVUS images were taken with an internal blood pressure of 110 mmHg, and the zeropressure geometry was recovered using the pull back algorithm defined by Raghavan et al. [42]. After applying the pull back method, the resulted geometry was extracted as the initial geometry (fourth column in Figure 3). Finally, it was possible to impose the pressure of 115 mmHg in the lumen and achieve the final pressurized geometry (fifth column in Figure 3). In all of the FE models (idealized and real IVUS geometries), the origin of the coordinate system was located in the center of the lumen in order to simulate the position of the IVUS catheter.

A sensitivity mesh analysis was performed to assure good precision and low computational cost. In the 3D idealized geometries, the maximum stenosis section, which was the most important part of the study, was meshed with small-sized elements. The fibrous cap between the lipid and the lumen had a smaller size depending on the thickness (e.g., 0.02 mm in the thickness of 65 microns that is on the order of IVUS technique precision). The rest of the 3D model had larger-sized elements, because of the lack of importance in the segmentation process. The element type selected for 3D was the hybrid quadratic tetrahedral elements with hybrid formulation to avoid numerical problems due to incompressibility (C3D10H), with at least three elements in the FCT in each case. In the 2D IVUS models, the element type was the plain strain hybrid three-node linear element (CPE3H) with additionaly, at least, three elements between the lumen and the lipid core.

### 2.1.4. Strain Variables

After simulating all of the FE models, the post-processing of the data was performed in Matlab 2021b [43]. The nodal coordinates ( *X* and *Y*) and displacements (*ux* and *uy*) of the steps at 110 mmHg and 115 mmHg were collected. Then, the relative node displacements between both pressure steps were computed, as we can see in the example of Equation (2). This data processing attempted to simulate the data obtained by speckle estimators on two consecutive pictures taken by an IVUS in a 5 mmHg pressure increment [24,25,28,31]. Afterwards, the strains were calculated under the infinitesimal strain theory. Despite having the displacement field through the entire idealized 3D FE models, we analyzed the maximum stenosis section with the hypothesis of plane strain to obtain results that are closer to what happens in the IVUS. Different strain variables were computed: strains referring to Cartesian coordinates (*εxx*,*εyy* and *εxy*); strains in cylindrical coordinates (*εrr*,*εθθ* and *εrθ*); principal strains (*εmax* and *εmin*), and equivalent strains of the von Mises, Tresca, and Anisotropic index (FA) [44], defined in Equation (3). Some variables, such as principal strains [45] or equivalent strains, do not depend on the coordinate system. Thus, their value will be the same regardless of the positions of the IVUS catheter.

$$
u\_{\mathbf{x}} = \boldsymbol{u}\_{\mathbf{x}}^{115mmH\mathbf{g}} - \boldsymbol{u}\_{\mathbf{x}}^{110mmH\mathbf{g}},\tag{2}$$

$$FA = \frac{\sqrt{3}}{\sqrt{2}} \cdot \frac{\sqrt{(\lambda\_1 - \lambda)^2 + (\lambda\_2 - \lambda)^2 + (\lambda\_3 - \lambda)^2}}{\sqrt{\lambda\_1^2 + \lambda\_2^2 + \lambda\_3^2}},\tag{3}$$

$$= \frac{\lambda\_1 + \lambda\_2 + \lambda\_3}{3}, \qquad \lambda\_i = \begin{pmatrix} \varepsilon\_{\text{max}} \text{ or } \ \varepsilon\_{\text{med}} \text{ or } \ \varepsilon\_{\text{min}} \end{pmatrix} + 1,$$

Sumi et al. [46] developed a method to obtain a relationship between the vector gradient of the Young modulus and the strain tensor components for the plane stress approach. This criterion was adapted by Le Floc'h et al. [28] under the plane strain assumption, and they developed the elastic gradient of the material (*dWsimpli fied*) by neglecting the shear strains and computing only with the radial strains; Equation (4). This variable was selected due to the good results when marking the lipid core contour shown in different studies [28,29].

$$dW\_{simplified} = -\frac{1}{\varepsilon\_{rr}} \left( \frac{\partial \varepsilon\_{rr}}{\partial r} + \frac{2\varepsilon\_{rr}}{r} \right) dr - \frac{1}{\varepsilon\_{rr}} \frac{\partial \varepsilon\_{rr}}{\partial \theta} d\theta,\tag{4}$$

In this work, the parameter *dW* was also calculated without any type of simplification, and it is developed in Equations (5)–(7). Furthermore, the absolute value of this parameter was computed for segmentation purposes, and it was represented as |*dW*|.

$$H\_{r} = \frac{-1}{\varepsilon\_{rr}^{2} + \varepsilon\_{r\theta}^{2}} \cdot \left[ \varepsilon\_{rr} \cdot \left( \frac{\partial \varepsilon\_{rr}}{\partial r} + \frac{1}{r} \cdot \frac{\partial \varepsilon\_{r\theta}}{\partial \theta} + \frac{2 \cdot \varepsilon\_{rr}}{r} \right) + \varepsilon\_{r\theta} \cdot \left( \frac{\partial \varepsilon\_{r\theta}}{\partial r} - \frac{1}{r} \cdot \frac{\partial \varepsilon\_{r}}{\partial \theta} + \frac{2 \cdot \varepsilon\_{r\theta}}{r} \right) \right], \tag{5}$$

$$H\_{\theta} = \frac{-1}{\varepsilon\_{rr}^{2} + \varepsilon\_{r\theta}^{2}} \cdot \left[ \varepsilon\_{r\theta} \cdot \left( \frac{\partial \varepsilon\_{rr}}{\partial r} + \frac{1}{r} \cdot \frac{\partial \varepsilon\_{r\theta}}{\partial \theta} + \frac{2 \cdot \varepsilon\_{rr}}{r} \right) - \varepsilon\_{rr} \cdot \left( \frac{\partial \varepsilon\_{r\theta}}{\partial r} - \frac{1}{r} \cdot \frac{\partial \varepsilon\_{rr}}{\partial \theta} + \frac{2 \cdot \varepsilon\_{r\theta}}{r} \right) \right], \tag{6}$$

$$d\mathcal{W} = \vec{H} \cdot d\vec{\mathbf{x}} = \left[ H\_{r} H\_{\theta} \right] \cdot \left[ \frac{dr}{r \cdot d\theta} \right], \tag{7}$$

 ·

> *r* ·

·

### *2.2. Adding Noise*

*λ*

The strain information was obtained from the FE models (clean strains). Nevertheless, the in vivo IVUS images have some noise and the speckle estimated strains will also contain that noise. Therefore, to reproduce more realistic strains we added white Gaussian noise to the different FE strain fields. We used an SNR of 20 dB in the FE strains [24] . However, the segmentation procedure was studied in all geometries with and without noise so as to analyze the robustness of the process.

### *2.3. Computing SGVs*

Once all of the strain variables were obtained (with and without noise), the next step was to obtain the modulus of their gradient. For instance, |*εrr*| represents the modulus of the gradient of the radial strains. These SGVs allowed to highlight the contours of the different tissues of the plaque. Each SGV marked different parts of the tissue contours; this is why the segmentation procedure could use one or two combined SGVs to extract the entire lipid contour. We computed the modulus of the gradient of all strain variables, except for *dW*. By definition, *dW* showed the contours of the areas with different stiffness. The use of this single variable marked the tissue contours. At the end, there were 14 single SGVs and 91 possible combinations of two SGVs.

### *2.4. Segmentation Process*

The methodology was based on the representation of two combined SGVs or a single SGV and image segmentation algorithms. The W-GVF processes were imposed on the SGVs representation to extract the lipid core. The watershed process used the contour and the grayscale representation to treat a set of pixels as a topography separating the lipid core. The W-GVF algorithm allowed to segment different tissues as the lipid core

or the calcifications. In this work, only the results of the lipid core are presented due to their relevance to FCT measurements and plaque vulnerability. After the segmentation, the lipid was smoothed in order to reduce the sharp areas of the segmentation. The method was tested in all geometries (three idealized and three real geometries) with all of the 105 SGVs. A sensitivity analysis of different relevant variables in the segmentation process was performed. For this analysis only the idealized geometry with 150 μm of FCT was considered. These variables were related to the plaque morphology or related to the IVUS technology:

Although the methodology was mainly focused on the lipid core segmentation, different areas were segmented as well. Lumen was segmented using the W-GVF technique in each geometry in order to measure the FCT. Large calcifications, such as the one in the third real IVUS geometry, were segmented by using the same segmentation process. On the other hand, fibrotic tissue could be easily segmented as the difference of the whole plaque minus the segmented lipid and lumen. Finally, adventitia and media could be segmented with the W-GVF technique; however, this segmentation has no clinical application due to the fact that IVUS images provide little information on the outermost tissues.

### *2.5. Geometrical Measures*

After the lipid and the lumen were segmented, it was possible to assess the FCT as the minimum distance between them. The area of the lipid core was also computed. Both measurements are closely related to the risk of plaque rupture [5]. The indices *I*1, *I*2, and *I*3 were defined in order to quantify the accuracy of the segmentation for each SGV or combination of two SGVs. The first index (*I*1) in Equation (8) is the relative error between the real and the measured FCT (*treal* and *tmeasure*). The second index (*I*2) in Equation (9) defines the percentage of the lipid area that was correctly segmented (true positive area). This index could be represented as the white area in Figure 4. The third index (*I*3) in Equation (10) corresponds to the extra lipid area that was segmented (false positive area). It could be represented as the green area in Figure 4. The second and third indices were defined to quantify not only the lipid area value, but also the correct segmentation of its shape. In order to quantify the segmentation using only one index, we defined the Segmentation Index (SI) as a linear combination of the previous indices. The final SI parameter was defined in Equation (11) and its value was directly related to the performance of the segmentation. SGV combinations with an *SI* ≥ 90% provided measurements of the lipid area and the fibrous cap thickness with high precision. An SI between 90–85% meant that the segmentation had trouble with one measure, normally the fibrous cap thickness. SI values in the range of 85–75% indicated a poor lipid segmentation. Finally, values of *SI* ≤ 75% were for those SGVs with a high measurement error or those that could not segment the lipid.

$$I\_1 = \left| \frac{t\_{\text{measurre}} - t\_{\text{real}}}{t\_{\text{real}}} \right| \cdot 100 \,, \tag{8}$$

$$I\_2 = \frac{Area\_{RealSegmented}}{Area\_{Real}} \cdot 100,\tag{9}$$

$$I\_3 = \frac{Area\_{FalesSegmented}}{Area\_{Real}} \cdot 100,\tag{10}$$

$$SI = \frac{(100 - I\_1) + I\_2 + (100 - I\_3)}{3},\tag{11}$$

**Figure 4.** Comparison between the real and the segmented lipid core. The lumen is represented in gray color, the true positive area in white, the false negative area in green, the actual area that was not segmented in purple, and the measure of the FCT is the red line.


Author details

Intan Muchtadi-Alamsyah\* and Yanuar Bhakti Wira Tama Algebra Research Group, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Indonesia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 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.


**1. Introduction**

Graphene is a new kind of two-dimensional single-atom carbon sheet with a single atom thick [1]. Nowadays, this so-called "thinnest in our universe" material [2] has attracted more and more attention, because of its unique properties such as unique electronic properties [3].

Graphene oxide (GO), one of the nanomaterials from graphene family, contains many reactive oxygen functional groups, such as hydroxyl group, a carboxyl group, an epoxy group [4]. It has been extensively used for biosensor research and application. In order to enhance the electrochemical properties of the GO-based biosensor, GO can be modified with other materials, such as macromolecules, small mass organic molecules, metallic oxide, and metallic/nonmetallic simple substances. In this chapter, we describe GO-based biosensors containing various composites of these materials with references such as GO-chitosan nanocomposites, GO-based

© 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. © 2018 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.

glucose oxidase, chitosan-ferrocene/graphene oxide/glucose oxidase, metal oxides, HRP, multi nanomaterials, quantum dots, multiwall carbon nanotubes, DNA, miRNA, etc. We not only describe the relevant preparation process of the above biosensors but also introduce their electrochemical properties to provide more guidance for designing suitable GO-based biosensors.


Preface

**Section 3 Immunosuppressive Properties of Neutrophils 143**

**Pathological Conditions 145**

Bonomo

**VI** Contents

Chapter 7 **Neutrophils Plasticity: The Regulatory Interface in Various**

Suelen Martins Perobelli, Triciana Gonçalves Silva and Adriana

Neutrophils are the most abundant circulating white blood cells, which are considered the first line of innate immune response against various insults and an important link to activate the adaptive immune response when needed. They play an important role in the pathogene‐ sis of various infectious and noninfectious diseases. Recent discovery regarding the forma‐ tion of weblike structures in the extracellular space called neutrophil extracellular traps (NETs) provided better understanding of their role in various disease conditions and for their role as an important future therapeutic target.

This book *Role of Neutrophils in Disease Pathogenesis* provides the most recent evidence re‐ garding the role of neutrophil in various diseases of infectious and noninfectious origin. The first section of this book [Section 1: Neutrophil Extracellular Traps (NETs)] focuses on the role of NETs in various diseases. Chapter 1 (Neutrophil Extracellular Traps in Infectious Hu‐ man Diseases) provides a general background information regarding the mechanisms and various triggers of NET formation. The role of NETs in selected infectious and noninfectious diseases is also discussed. Chapter 2 (Beneficial and Deleterious Effects of Neutrophil Ex‐ tracellular Traps on Infection) mainly focuses on providing recent evidence for the role of NETs in various infectious diseases. The various cells producing NETs, their role in the im‐ mune response, and the pros and cons of NETs are also discussed in this chapter. Chapter 3 (The Role of Neutrophil Extracellular Traps in Postinjury Inflammation) focuses on the role of NET-derived neutrophils in the pathological mechanisms leading to postinjury inflamma‐ tion and secondary tissue injury. The clinical relevance of NETs in postinjury complications and the therapeutic potential of NET inhibition/clearance are also discussed at the end of this chapter.

In the second section of the book (Section 2: Neutrophil Role in Disease Pathogenesis), the role of neutrophils in the pathogenesis of selected disease conditions is discussed. Chapter 4 (Neutrophil Role in Periodontal Disease) focuses on the role of neutrophils in the pathogen‐ esis of periodontal diseases, which leads to tooth loss or increases the risk of developing various systemic diseases in severe cases. Novel therapeutic approaches for periodontitis are also discussed at the end of this chapter. Chapter 5 (Neutrophils in Rheumatoid Arthritis: A Target for Discovering New Therapies Based on Natural Products) discusses the role of neu‐ trophil in the initiation and progression of rheumatoid arthritis (RA). Current pharmacologi‐ cal treatments with their drawbacks and various substances derived from natural products as putative antirheumatic therapies are also discussed at the end of this chapter. Chapter 6 (Role of Neutrophils in Cystic Fibrosis Lung Disease) highlights the important role of neu‐ trophils in the pathogenesis of cystic fibrosis (CF) and the utility of using these cells as a noninvasive biomarker and a readout to determine the efficacy of etiological therapies in CF.

The last section of this chapter (Section 3: Immunosuppressive Properties of Neutrophils) highlights the immunosuppressive properties of neutrophils. Chapter 7 (Neutrophil Plastici‐ ty: The Regulatory Interface in Various Pathological Conditions) focuses on an important yet less-recognized role for neutrophils in *reducing* the inflammatory responses either by di‐ rect interaction with other cells or secretion of factors to modulate the activity of the inflam‐ matory response. More emphasis is given to their role in graft versus host disease (GVHD), which is the main limitation of allogeneic hematopoietic stem cell transplantation.

We hope that the recent evidence described in this book provides a better understanding of the role of these immune cells in various disease conditions and forms the basis for future research activities aiming to provide better therapeutic approaches to treat various disease conditions.

#### **Dr. Maitham A. Khajah, B Pharm, PhD**

Assistant Professor Department of Pharmacology & Therapeutics Faculty of Pharmacy, Kuwait University, Kuwait **Neutrophil Extracellular Traps (NETs)**

The last section of this chapter (Section 3: Immunosuppressive Properties of Neutrophils) highlights the immunosuppressive properties of neutrophils. Chapter 7 (Neutrophil Plastici‐ ty: The Regulatory Interface in Various Pathological Conditions) focuses on an important yet less-recognized role for neutrophils in *reducing* the inflammatory responses either by di‐ rect interaction with other cells or secretion of factors to modulate the activity of the inflam‐ matory response. More emphasis is given to their role in graft versus host disease (GVHD),

We hope that the recent evidence described in this book provides a better understanding of the role of these immune cells in various disease conditions and forms the basis for future research activities aiming to provide better therapeutic approaches to treat various disease

**Dr. Maitham A. Khajah, B Pharm, PhD**

Department of Pharmacology & Therapeutics Faculty of Pharmacy, Kuwait University, Kuwait

Assistant Professor

which is the main limitation of allogeneic hematopoietic stem cell transplantation.

conditions.

VIII Preface