**6. Non-oncological and off-label pdt applications in dermatology**

PDT is already approved for the treatment of actinic keratosis and basal cell carcinoma. Offlabel uses for PDT have been indicated for several dermatological conditions such as photodamaged skin, scleroderma, warts, cutaneous leishmaniosis, psoriasis, cutaneous T-cell lymphoma and acne [20, 47]. Infectious disease has the potential to become one of the main indications of PDT in Dermatology. The microbiological control of bacteria, fungi and protozoa ininfectedlesionshasbeenpresented[48-51].Onychomicosisisoneofthenewindications,where PDT presents good results even in cases where the antifungal systemic therapy failured (Paulada-Silva, A. et al., Fast elimination of onychomycosis by hematoporphyrin derivative-photody‐ namic therapy. Accepted by Photodiagnosis and Photodynamic Therapy on December2012).


Alloimmunization and Role of HLA in Pregnancy

*Meenakshi Singh, Jyoti Rajak, Shalaka Kadam and Sunil B. Rajadhyaksha*

### **Abstract**

 Alloimmunization also known as isoimmunization, during pregnancy is the production of IgG antibodies by the mother against the paternally inherited antigens (IPA) in the foetus/newborn. The alloimmunization during pregnancy leads to various alloimmune disorders, such as, haemolytic disease of the foetus and newborn (HDFN), neonatal alloimmune neutropenia (NAN) and foetal and neonatal alloimmune thrombocytopenia (FNAIT) due to the production of maternal alloantibodies against the red blood cell antigen, neutrophils and platelets cell antigens, respectively. Recent studies suggest that maternal anti-HLA class I alloantibodies may also be the cause of FNAIT in addition to antibodies against platelet antigens. On the contrary, studies have also suggested that HLA-C, a classical HLA class I molecule, and HLA-G, a nonclassical HLA molecule, play an important role in placentation and modulation of the maternal immune system during pregnancy, respectively, and thereby leading to acceptance of the semi allogeneic fetus. So far most of the studies have discussed alloimmunization in pregnancy relating to Rh antigen. Thus, in this chapter an attempt has been made to discuss alloimmunization in pregnancy caused because of maternal alloantibody against HLA antigen and its role in immune modulation during pregnancy.

**Keywords:** HLA, MHC, HLA-G, IPA

## **1. Introduction**

Mammalian pregnancy with haemochorial placentation is an immunological contradiction with suppression of an immune response against the semi allogeneic foetus having inherited paternal antigens foreign to mother [1]. The foetus is not rejected as it would have, ideally in case of an unmatched organ transplant, wherein the immune system forbids the incursion of any genetic material or protein foreign to itself [2].

The protective mechanism which leads to acceptance of the semi allogeneic foetus includes: (i) Complete separation of maternal and foetal blood circulation. (ii) Low expression of foetal antigens that may stimulate graft rejection. The trophoblast cells originating from foetus lack expression of classical HLA class Ia and class II antigen except for very low expression of HLA-C antigens. They express HLA class Ib antigens which are known to modulate immune responses at the feto-maternal interface [3]. (iii) Involvement of both mother and foetus in order to

maintain pregnant uterus as an immune privileged site. (iv) Programming of the maternal immune response by factors obtained from placental and extra placental membrane [4].

Maternal and foetal cells interact in co-ordination to maintain an immune privileged environment at feto-maternal interface; some instances do occur which lead to maternal sensitization thereby leading to various disorders in the foetus/neonate. Alloimmunization during pregnancy is the stimulation of maternal immune response by the paternal inherited foetal or placental antigens [5].

 The recognition of antigens as self and non-self is the essential process by which the immune system determines whether or not to develop an immune response. The response of the maternal immune system will depend on genetic and acquired factors related to the foetus and to antigen immunogenicity. Maternal alloimmunization occurs when the foetal and the maternal lymph combine due to rupture of placental barrier which often happens during delivery, although feto-maternal haemorrhage (FMH) may also result early in pregnancy. The instance of FMH has been observed in 7, 16, and 29% of mothers during their first, second and third trimesters, respectively [6]. Other maternal factors responsible for maternal sensitization involve factors such as Rh incompatibility, major surgical procedure, blood transfusion, multiparity, or operative removal of placenta [7].

The major antigens against which the maternal alloimmunization occurs are RBC, granulocytes (neutrophils), human platelet antigens and HLA antigens. The sensitization against these antigens leads to disorders, such as, haemolytic disease of foetus and newborn (HDFN), neonatal alloimmune neutropenia (NAN) and foetal and neonatal alloimmune thrombocytopenia (FNAIT), respectively.

Till now most of the studies have addressed the maternal alloimmunization relating to Rh and platelet antigen, this book chapter aims at exploring the role of human leukocyte antigen (HLA) in pregnancy and alloimmunization along with other factors.

## **2. Role of HLA in pregnancy**

The human leukocyte antigen (HLA) also known as the major histocompatibility complex (MHC) play a very crucial role in enabling the immune system to differentiate between "self" and "non-self-antigen" [8]. It is situated on the short arm of human chromosome 6p21.3, and codes for nearly 130 structural genes known to function in antigen presentation to immune system thereby modulating the immune response. It is categorized into three classes i.e., class I, class II and class III [9]. The HLA class I gene is further classified into classical HLA class Ia genes (HLA-A, -B, -C) and non-classical HLA class Ib genes (HLA-E, -F, -G) [3]. The non-classical HLA class Ib genes show limited polymorphism as compared to those of classical HLA class Ia genes [10]. The HLA class II is differentiated into HLA-DR,-DQ ,-DM and -DP.

The HLA class I molecules are known to interact with CD8+ T cells, natural killer cells (NK cells), and class II molecules with CD4+ T cells, respectively [11, 12] to elicit an immune response in order to eliminate the foreign or non-self-antigens. In contrast the non-classical HLA class Ib molecules interact with natural killer cells (NK cells) and other immune cell to develop an immunological tolerogenic effect. HLA plays a very important role in transplantation, as it is known to evoke an immune response to the transplanted graft, thus are very critical in pregnancy from gamete formation to completion of development, as foetus is the most successful semi-allograft.

*Alloimmunization and Role of HLA in Pregnancy DOI: http://dx.doi.org/10.5772/intechopen.84211*

 The non-classical HLA class Ib molecules, primarily HLA-G plays a very important role in maintaining maternal tolerance i.e., an immunosuppressive state during pregnancy thereby contributing to foetal endurance and growth. Consequently inability in maintaining the maternal tolerance to the foetus, adversely affects the pregnancy leading to complications such as recurrent spontaneous abortion, foetal growth restriction, and preeclampsia [13]. Also HLA-C along with KIR (killer-immunoglobulin receptor) has been implicated to play a role in placentation. Thus, the role of HLA along with other immune cells may be implicated in maintaining pregnancy as well as at time in leading to pregnancy complications.

### **2.1 HLA-G in immune modulation during pregnancy**

Among the non-classical HLA class Ib genes HLA-G is the most studied because of its role in immune modulation during pregnancy and its association with complexities like pre-eclampsia and recurrent spontaneous abortion in pregnancy [14].

### *2.1.1 Structure and expression of HLA-G*

The non-classical class Ib HLA-G gene is situated near to the classical class Ia HLA-A gene on chromosome 6 and is also greatly homologous to it. The HLA-G gene comprises of seven intronic regions and eight exonic regions. The exon 1 codes for the signal peptide. The exons 2–4 code for the external part of HLA-G molecule which consist of three parts viz. α1, α2 and α3 domains, exon 5 encodes for the transmembrane region, where as exon 6 and a very short part of exon 8 encode for the cytoplasmic tail of the HLA-G molecule. The HLA-G gene is translated into a 38 kDa protein having similar structure as that of HLA class I antigens. It consists of a heavy chain that binds a light chain β2-microglobulin a protein coded by a gene on chromosome 15. The α1 and α2 extracellular domain of heavy chain form the peptide binding groove [15].

 The HLA-G antigen has seven splice variants (HLA-G1-HLA-G7) as a result of alternative splicing event of the mRNA from the single HLA-G gene, of which HLA-G1 is the full length variant and the rest are formed by out-splicing of exons. Out of the seven isoform HLA-G1, HLA-G2, HLA- G3 and HLA-G4 are membrane-bound and the remaining three i.e., sHLA-G5, sHLA-G6 and sHLA-G7 are soluble isoforms [16]. The coding region of HLA-G gene shows meagre polymorphism but the polymorphisms that are present are equally shared by introns and exon 2, 3 and 4. Most of these polymorphism do not modify the protein sequence and those which do modify the protein sequence permit to be grouped in major allele groups like G\*01::xx, G\*01:02, G\*01:03: xx, G\*01:04: xx, G\*01:05 N (null allele), G\*01:06, and G\*01:07 to G\*01:18. Overall there are 61 alleles and 19 protein groups representing an amino acid substitution have been delineated in the HLA-G gene sequence [WHO Nomenclature Committee for factors of the HLA System and the International Immunogenetics Information System (IMGT)/ HLA Database] [15].

HLA-G expression was first reported on human non-villous cytotrophoblast cells [17]. Its expression is stringently confined to certain cells, being largely expressed in extravillous cytotrophoblast cells. HLA-G5 a soluble variant is present throughout the placenta, within the chorion membrane, maternal blood and the decidua [18]. Apart from placental expression soluble HLA-G proteins are present in peripheral blood of pregnant women, non-pregnant women and men. The presence of sHLA-G in the blood of non-pregnant women indicates that it may play

### *Complications of Pregnancy*

an important role in reproduction even before conception. Soluble HLA-G is also found in follicular fluid, fertilized oocyte and in male reproductive tissue including semen [14]. HLA-G presence can also be detected in tissues like thymic medulla, cornea, pancreas and in human mononuclear phagocytic cells [53]. Its expression can be stimulated in condition following transplant, viral infections, autoimmune diseases and tumors [19].

### *2.1.2 HLA-G polymorphism*

Both coding and non-coding regions of HLA-G gene display polymorphism. Exonic polymorphism may affect biological function such as binding of peptides or production of isoforms; whereas intronic polymorphism may influence the expression of the gene. Codon 31, 35, 57 and 69 of Exon 2 and codon 93, 107 and 110 of exon 3 coding region display majority of the polymorphism. A large number of SNPs (single nucleotide polymorphism) have been found in the non-coding region, including the promoter region at 5′UTR (untranslated region) and the 3′UTR. Some of these polymorphisms located near the regulatory element have an impact on the binding of the corresponding factors.

A 14 bp deletion/insertion have also been reported in the 3′UTR region of exon 8. These 14 bp del/ins are suspected to affect the size and stability of the mRNA transcripts. It was noticed by Rousseau et al. that a 14 bp insertion of sequence (5′-ATTTGTTCATGCCT-3′) lead to deletion of 92 bp sequence in the 3′UTR region, thereby resulting in production of more stable transcript. Few polymorphisms that may also effect the stability of mRNA transcripts, including the SNPs located at the position +3142 (C/G) and at +3172 position (G/A) in the 3′UTR region [19].

### *2.1.3 Immune modulation by HLA-G during pregnancy*

Approximately 40% of the decidual tissue comprise of maternal immune cells at the beginning of pregnancy. Majority of these immune cells are natural killer (CD56 bright 16−) cells which are distinct from the NK cells (CD56 dim16+) present in the peripheral blood as the decidual NK cells have reduced cytotoxic activity. Also the decidual NK cells have a higher expression for genes encoding for integrins, lectinlike receptors, KIR (killer-immunoglobulin like receptors) and cytokines. Along with the presence of NK cell decidual tissue also show the presences of macrophages, T lymphocytes and dendritic cell [19, 20].

The trophoblast cells of the embryo express HLA-G antigen since the beginning of the first trimester and are present till the end of pregnancy. The interaction of HLA-G protein at the feto-maternal interface with the immune cell of the decidua ensures the foetal tolerance by inhibition of cytotoxic activity of NK cells and CD8+ T cells, suppressing the proliferation of alloreactive CD4+ T cells, suppressing the B cell activity, leading to the secretion of Th2 cytokines and stimulation of regulatory T cells (Treg) [19].

 HLA-G intercedes its immunosuppressive activity by interaction of alpha 1 domain with inhibitory receptors expressed on immune cells. Leukocytes express inhibitory receptors like immunoglobulin-like transcripts (ILT)-2, ILT-4 and KIR. CD4+ and CD8+ T cells, B cells, monocytes, macrophages and myeloid dendritic cells (DC) display ILT-2, interact with only heterodimers of HLA-G1 or sHLA-G5 and ß2m. ILT-4 displayed by monocytes, macrophages and myeloid DC's have the capability to also interact with monomers of HLA-G whereas the NK cells interact through the KIR2DL4 expressed by them [19] (**Figure 1**). The interaction of these immune cells like decidual NK cells, CD4+ and CD8+ T cells which are

### **Figure 1.**

*HLA-G molecules expressed by the trophoblast cells interact with the inhibitory receptor KIR2DL4 expressed by the NK cells, ILT-2 (Ig-like transcript 2) expressed by NK cells, CD4+ and CD8+ T-cells and B-cells, and ILT-2 and ILT-4 expressed by macrophages and dendritic cells. The interaction of the HLA-G with its cognate receptors leads to immune tolerogenic effect thereby leading to the acceptance of the foetus by the maternal immune system.*

HLA-G negative cells with trophoblast cell expressing HLA-G molecule leads to the acquisition of HLA-G molecule by immune cells thereby making them HLA-G+ cells through a process called as trogocytosis. Trogocytosis is a mechanism by which surface molecules may be transferred from one cell to another via cell to cell contact. The acquisition of the HLA-G molecule by these immune cells contributes to an immune suppressive milieu without them expressing HLA-G molecule, but only temporarily displaying it [21]. The acquiring of the HLA-G molecule by the decidual NK cells is preceded by a cycle of internalization, degradation and reacquisition of HLA-G. This cycle helps NK cells to maintain both tolerance and immune function [20].

### *2.1.4 HLA-G and pregnancy disorders*

Abnormal placentation and immunological interaction at the feto-maternal interface are believed to play very crucial role in placenta-mediated complication of late pregnancy (viz. pre-eclampsia, foetal growth restriction, still birth and placental abruption) and foetal rejection in some pathological pregnancy, respectively. Deficient level of HLA-G expression and polymorphisms at HLA-G loci are known to be correlated to pregnancy complication especially pre-eclampsia and recurrent miscarriages (RM).

 Pre-eclampsia is a pregnancy disorder which is clinically evident in the late second and third trimester of pregnancy [19]. It is characterised by high blood pressure, proteinuria and oedema associated with organ damage and prematurity. It is known to be a major cause of perinatal deaths, premature births and intrauterine growth restriction with an occurrence of 5–10% of all the pregnancy [22]. Though the major cause is unknown, various studies suggest that it may be related to maladapted immune system, with low levels of immune regulatory cell and low expression of HLA-G molecules [23]. The development of pregnancy has been strongly associated with level of soluble HLA-G molecules in the maternal blood. Maternal serum with higher levels of sHLA-G has been identified in females with successful pregnancy when compared with pre-eclampsia patients [24]. Lower sHLA-G levels in the maternal blood, with down regulated HLA-G and decreased proportion of HLA-G+ cells have been identified in patients with preeclampsia and are thought to be are strongly associated with preeclampsia [24–26]. The 14 bp ins/del in the 3′UTR of region exon 8 have been extensively studied, and it has been reported to be related to severe pre-eclampsia [15]. But on the contrary, there are studies which have reported no significant association of the 14 bp polymorphism with preeclampsia [24–26] implicating that differences in the ethnic population should be considered for the association between HLA-G 14 bp polymorphism and serum sHLA-G level. The SNP at position +3172 (G/A) leading to decreased in mRNA stability has been linked to pre-eclampsia [19]. Steinborn et al. reported that women with soluble HLA-G levels lower than 9.95 ng/ml have a risk of 7.1 for developing placental abruption during pregnancy as compared to healthy women [3].

 Recurrent spontaneous abortion (RSA) is defined as the loss of two or more consecutive pregnancy with the same partner [27]. The major causes of RSA are considered to be chromosomal abnormalities, anatomical anomalies and endocrine disorders along with immunologic dysfunction [28]. Many studies have reported increased occurrence of HLA-G allele homozygous for 14 bp ins in women with RSA [19]. Also, a study has reported that decreased expression of HLA-G suppresses the function of decidual NK cells and thereby may lead to RSA [28]. SNPs−1573T>C and −1746C>A in the promoter region of HLA-G gene are shown to be associated with RSA [29]. As the level of sHLA-G is known to be associated with the pregnancy complication, the measurement of sHLA-G protein may be useful in primary diagnosis for the pathogenesis of pregnancy complications.

### **2.2 Role of HLA-C and KIR during pregnancy**

Health of a foetus during pregnancy depends on the supply of nutrients and oxygen to the placenta. During placentation the foetal trophoblast cells infiltrate into the uterine wall, transforming the spiral artery (maternal artery supplying blood to the placenta) into a high-conductance vessel, thereby increasing the blood flow to about 100 folds [30]. This transformation allows adequate time for gas exchange and also provides sufficient nourishment to the foetus. Defective

### *Alloimmunization and Role of HLA in Pregnancy DOI: http://dx.doi.org/10.5772/intechopen.84211*

infiltration of the trophoblast cells into the uterus leads to failure in arterial conversion, thereby leading the arterial blood to squirt into the intervillous space from the non-transformed arteries causing impairment of the villous tree (placentation). The impaired placentation leads to reduced transport of oxygen and starving of the foetus. The clinical manifestation of this failure may result in disorders such foetal growth restriction (FGR), preeclampsia, recurrent miscarriage (RM), unexplained still birth, placental abruption and preterm labour [31, 32].

 The uterus shows abundance of decidual natural killer cells (NK cells) and thus, is thought to be involved in placentation and thereby in foetal development. Placenta is the site at which maternal allorecognition of the foetus takes place, wherein the foetal extra villous trophoblast cells (EVTs) encroach and unify with the maternal immune cells. Interaction of maternal KIR present on the uterine NK cells and its corresponding ligand, HLA-C which is the only classical HLA class I antigen expressed on the trophoblast cells of the foetus are claimed to regulate the process of placentation.

There are approximately 14 different KIR genes existing in a linear array in the leukocyte receptor complex (LRC) on chromosome 19q13.4. KIRs are differentiated on the basis of number of extracellular Ig-like domain (2 or 3) and cytoplasmic tail (long or short). They are known to regulate the activity of NK cells, either conferring them with an inhibitory or activating signal. Interaction of KIR with a long cytoplasmic tail (e.g., KIR2DL1) with its corresponding ligands leads to generation of an inhibitory signal, where as those having a short tail (e.g., KIR2DS1) results in activation of NK cells.

 The activating and inhibitory genes are differentiated into two haplotype A and B. The KIR A haplotype is the most frequently occurring haplotype and consists of six inhibitory genes. The KIR B haplotype consist of gene which are more variable in their genetic content and are mostly activating KIR. Hence an individual's KIR genotype can be designated as "AA", "AB", and "BB".

 The most important ligand for KIR is the HLA-C molecules, and there are approximately 4000 alleles of HLA-C. The HLA-C are differentiated into two distinct group by KIR, i.e., HLA-C1 and HLA-C2 [33]. HLA-C molecules with amino acid asparagine (Asn) at position 80 belong to HLA-C1 group, where as those with amino acid lysine (Lys) at the 80th position belong to group C2 of HLA-C. KIR2DL2/3 (inhibitory receptor) interacts with HLA-C1 allotype and KIR2DL1 and KIR2DS1 act as receptors for HLA-C present in C2 group [34].

 Existence of KIR B haplotype in mother confers protection from pregnancy complications, where as its absence may increase the risk of complications [35]. The KIR B haplotype consist of activating KIR2DS1 receptor, which on interacting with its cognate ligand induces the NK cells to secrete granulocyte-macrophage colony stimulating factors and other chemokines known to promote placental trophoblast invasion. It also consists of KIR2DL1\*004 which is the most common inhibitory KIR2DL1 allele on the B haplotype, and is known to have a weak interaction with HLA-C2 allotype as compared to alleles present on KIR A haplotype [36]. On the contrary, mothers homozygous for KIR A haplotypes (KIR "AA" genotype), with foetus having an additional C2 copy as compared to mother (i.e., mother C1/C2 with foetus C1/C2 or mother C1/C2 with foetus C2/C2), that to when the extra copy is of paternal origin are at an increased risk of having a complicated pregnancy [6]. As mothers with KIR AA haplotype have two copies of inherited inhibitory KIR for HLA-C2 allotype i.e., KIR2DL1, thus when the mothers uterine NK cells possessing KIR AA genotype interact with foetal trophoblast cells expressing HLA-C2 allotype, it induces a strong inhibitory effect on NK cell which is one of the reasons for defective placentation and in turn for various pregnancy related complications [37] (**Figure 2**).

### **Figure 2.**

*Model for maternal KIR/foetal HLA-C interaction at the placentation site. The figure depicts foetus homozygous for HLA-C2 group with one paternally inherited HLA-C2 molecule. If the mother possesses KIR AA genotype, then the inhibitory KIR2DL1 receptor will interact with HLA-C2 molecule expressed by trophoblast cells leading to strong inhibition of uterine NK cells thereby resulting in defective placentation. On the contrary if the mother possess KIR AB or BB genotype, the activating KIR2DS1 receptor present in the KIR B haplotype interacts with HLA-C2 molecule expressed by trophoblast cells stimulating the of uterine NK to produce cytokines such as GM-CSF thereby resulting in normal placentation.*

### **2.3 Clinical impact of H-Y alloimmunity in secondary recurrent miscarriages (SRM)**

H-Y antigens are a class of minor histocompatibility antigens (mHAs), encoded on Y-chromosome are omnipresent in male cells including foetal and trophoblast cells. H-Y antigens exhibit a large amount of similarity to H-X antigen encoded on the X-chromosome, but they possess few distinct regions which make them highly immunogenic in nature. The H-Y alloimmunity has turned out to be, one of the potential reasons for SRM during pregnancy.

SRM is defined as three or more consecutive miscarriages following a successful pregnancy as compared to primary recurrent miscarriage (PRM), which is characterized by three or more miscarriages without a previous successful birth. PRM is supposed to be caused because of chromosomal defect along with improper implantation of the embryo, whereas SRM are more probably caused due to immunological responses [38].

Generally, during pregnancy the cellular and humoral anti-HY immune response is well tolerated by the foetus, but in minority of cases the H-Y alloimmune response may lead to complications during early or late pregnancy. It has *Alloimmunization and Role of HLA in Pregnancy DOI: http://dx.doi.org/10.5772/intechopen.84211*

been implicated that pregnancy with a male foetus results in the development of alloimmune response towards the H-Y antigen by mother's immune system, thereby predisposing the mother to SRM and also impacting the prospect of subsequent pregnancy negatively in terms of perinatal complications and live birth [38, 39].

The presence of H-Y restricted HLA alleles along with H-Y antibodies, have also been related to the development of SRM and other pregnancy complications [38, 40]. The term "H-Y restricting HLA" is utilized to describe HLA alleles documented till date, which functionally exhibit H-Y peptides. H-Y restricting HLA alleles include the following HLA class I alleles: HLA-A\*01, HLAA\*02, HLA-B\*07, HLA-B\*08, HLA-B\*52, HLA-B\*60 and HLA class II alleles: HLA-DRB1\*15, -DQB1\* 0501/2, -DRB3\*03.

 Possessing a HY restricting HLA class II alleles substantially decreases the prospect of live birth in patients with SRM and firstborn boys as compared to those with firstborn girl. It is implicated that the prospect of live birth decreases in a doseresponse fashion with increasing number of maternal HY-restricting HLA class II alleles in patients with firstborn boys [40]. Maternal possession of HY restricting HLA class II alleles also reduces the long-term chance of live birth in females with SRM [41]. Whereas a mother homozygous for HLA-G 14 bps ins and carrying HY restricting class II alleles are predisposed to SRM with the first born boy and also negatively affect the birth weight of the boy [42].

 In a study, antibodies against both HLA and H-Y antigens during early pregnancy were significantly higher in SRM females as compared to females with normal pregnancy. The prevalence of these antibodies were associated to low subsequent live birth rate whereas the existence of anti H-Y antibodies were related to low male/female ratio in subsequent live births [40]. The male: female ratio for SRM was observed to be 1.49 prior to miscarriages and 0.76 subsequent to miscarriages in a 20 years cohort study [38]. Thus, implicating H-Y antibodies in preventing implantation or successful gestation of male foetus. Considering H-Y antibodies as one of the factors responsible for SRM, IVIG (intravenous immunoglobulin) infusions are commonly used as treatment to neutralize the circulating antibodies, and it has been shown to improve the birth rates in patients with SRM [38].

## **3. Rh alloimmunization**

The rhesus (Rh) blood group system comprises of more than 50 independent antigens and is highly polymorphic of the human blood group [43]. Following ABO, Rh blood grouping system is clinically important in transfusion medicine. The common Rh antigens are D, C or c, and E or e. Of which the D-antigen is greatly immunogenic and stimulates an immune response in 80% of person negative for D-antigen, when transfused with a D-antigen positive blood [44, 45]. Thus, D-antigen typing is routinely performed on every blood donor and transfusion recipient in order to avoid clinical complications due to mismatched transfusions. On the contrary, even with the use of anti-D immunoglobulin prophylaxis, there is still occurrence of D-alloimmunization in pregnancy.

D-alloimmunization (Rh alloimmunization) in pregnancy occurs due to incompatibility of D-antigen between the mother and the foetus. Generally, an individual is categorized as Rh-positive if they show an expression of Rh D-antigen on the erythrocytes, and Rh negative if there is no expression of D-antigen on the erythrocyte surface [46]. The Rh alloimmunization becomes clinically substantial when an Rh negative mother carries a foetus which is Rh-positive. The incompatibility of Rh antigen leads to sensitization of mother to the D-antigen, and also to the production of anti-D antibodies, which can adhere to and possibly lead to destruction of

Rh-positive erythrocytes of foetus. Nevertheless, the Rh incompatibility typically does not have an adverse consequence on the initial pregnancy as the foetus is delivered prior to the development of anti-D alloimmune response [47]. Although, it may also occur during the initial pregnancy due to spontaneous antenatal mixing of the foetal and maternal blood. In some instances such as miscarriage, abortion, trauma, childbirth and invasive prenatal diagnosis viz. chorionic villous sampling, amniocentesis, and pregnancy related uterine curettage may lead to feto-maternal haemorrhage thereby causing maternal exposure to foetal blood and consequently leading to alloimmunization [48]. The risk of Rh D immunization is estimated to be 1.5–2% in sensitized women following spontaneous miscarriage and 4–5% after dilation and curettage [49, 50]. Once the mother has been alloimmunized, subsequent pregnancies are at an increased risk, for the development of haemolytic disease of the new born (HDN) if the foetus is incompatible i.e., Rh-positive [45]. The diagnostic and clinical management of HDN is described in Section 4.1.


**1. Introduction**

Benign prostatic hyperplasia (BPH) refers to the progressive enlargement of the prostate gland and is one of the most common diseases in the aging male population. Approximately, half of men over 50 years of age develop BPH [1]. BPH is usually accompanied by lower urinary tract symptoms and related complications such as urinary frequency, urinary retention, and urinary tract infection, which may lead to a decrease in the quality of life and even cause extreme annoyance and embarrassment. BPH is typically treated with pharmacological intervention or surgery [2].

Notwithstanding, the first treatment option for many BPH patients is still pharmacological therapies, among which the main classes of medicine include 5α-reductase inhibitors and α-adrenergic blockers are currently used for symptom relief [3]. However, the adverse side effects of these traditional medications, such as retrograde ejaculation, orthostatic hypotension, and erectile dysfunction, which affects many patients, remain a major challenge [4]. Therefore, it is necessary to search for and use "safer" and effective alternatives against BPH.

Carotenoid astaxanthin (AST) is a red-orange pigment primarily found in organisms, such as shrimp, algae, fish, and crustaceans, containing two oxygenated ends on each tail of the structure (Figure 1) [5,6]. This natural dietary carotenoid has a variety of functions,

**Citation:** Wang, L.; Hou, Y.; Wang, R.; Pan, Q.; Li, D.; Yan, H.; Sun, Z. Inhibitory Effect of Astaxanthin on Testosterone-Induced Benign Prostatic Hyperplasia in Rats. *Mar. Drugs* **2021**, *19*, 652. https:// doi.org/10.3390/md19120652

Academic Editors: Hayato Maeda and Masashi Hosokawa

Received: 15 October 2021 Accepted: 17 November 2021 Published: 23 November 2021

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

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

including antioxidant, anti-inflammatory, and antitumor properties [7–10]. Moreover, a previous study demonstrated that AST might be safe and well tolerated in rats [11].

Rat prostate mainly consists of paired ventral lobes and paired dorsal lobes [12]. Each lobe is composed of acini, lined by a single epithelium layer and separated by connective tissues containing stromal cells [12,13]. Prostate growth may be stimulated by the continued presence of certain hormones such as testosterone (T) [14]. Dihydrotestosterone (DHT), which is the most active and principal androgen in the prostate, is synthesized from T by the enzyme 5 α-reductase. Therefore, the activity of enzyme 5 α-reductase, whose isoform 2 predominates in the prostate, may play an important role in the pathogenesis and progression of BPH. Research has suggested that low levels of AST can inhibit 5 α-reductase significantly in vitro [15].

Moreover, AST may have an inhibitory effect on prostate cancer. Studies have indicated that AST can modulate the proliferation and apoptosis of cells and inhibit PC-3 xenograft prostate tumors in nude mice [10]. Research has also shown that AST can inhibit the proliferation of aggressive PCa DU145 cells [16].

However, to the best of our knowledge, there is no evidence of the efficacy of AST on BPH in vivo. In the present study, we investigated whether AST could inhibit the development of T-induced BPH in rats.

**Figure 1.** Chemical structure of astaxanthin [7].


**5. Association with gynecological surgery**

*Ureteric Injury in Gynecology Surgery DOI: http://dx.doi.org/10.5772/intechopen.99649*

*Structures in the cardinal ligament—descending cervical branch of the uterine artery, ureter in the ureteric tunnel, lymph node and Frankenhauser plexus. Taken from [1].*

#### **Figure 2.**

*Course of pelvic ureters. Taken from [1].*

### *Urinary Tract Infection and Nephropathy - Insights into Potential Relationship*

**Figure 3.**

*Relations of the pelvic ureter. It crosses the bifurcation of vessels and then crosses under the uterine artery to enter the ureteric tunnel. Taken from [1].*

#### **Figure 4.**

*Pelvic lymphadenectomy. The external iliac artery (black arrow), vein (black double arrow), bifurcation of common iliac artery (white arrow) and internal iliac artery (white double arrow). The ureter is seen crossing the common iliac at its bifurcation (blue arrow). Taken from [1].*

#### **Figure 5.**

*Various points at which ureter is prone to injury in gynaecological surgery. (1) At the pelvic brim, while clamping the ovarian vessels. (2) In the broad ligament while clamping the uterine vessels. (3) In the ureteric tunnel in cardinal ligament. (4) At the vault of the vagina before it enters the bladder. Taken from [1].*


*4.2.2. Antisense oligonucleotides*

The single-stranded antisense oligodeoxynucleotides (AS ODNs) are short stretches of synthetic DNA, approximately 12–30 nucleotides long, and are complementary to a specific mRNA strand. Hybridization of the AS ODNs to the mRNA by Watson–Crick base pairing prevents the target gene from being translated into protein, thereby blocking the action of the gene, and resulting in the degradation of the target mRNA. The specificity in the AS ODN approach is based on the fact that any sequence of approximately 13 bases in RNA and 17 bases in DNA is estimated to be represented only once in the human genome. AS ODNs targeting survivin expression in human lung adenocarcinoma cell lines decreased survivin protein levels in a dose-dependent manner, induced apoptosis, stimulated higher levels of caspase-3 activation, and increased the sensitivity of cells to chemotherapeutics. XIAP AS ODNs effectively down-regulated both specific mRNA and protein levels in human non-small cell lung cancer growth both in vitro and in vivo. XIAP AS ODNs effectively induced apoptosis on their own and sensitized the tumor cells to the cytotoxic effects of several chemotherapeutics, including Taxol, etoposide and doxorubicin [26]. Furthermore, the administration of XIAP AS ODNs in a xenograft model of human nonsmall cell lung cancer results in a significant down-regulation of XIAP protein [124].

Apoptosis as a Therapeutic Target in Cancer and Cancer Stem Cells: Novel Strategies and Futures Perspectives 129

Smac interacts with IAPs via its N-terminal tetrapeptide, Ala1- Val2-Pro3-Ile4 (AVPI). Using this structural information, several groups designed small molecules mimicking AVPI to derive proteolytically stable compounds. Monovalent Smac mimetics were designed to mimic the Smac AVPI-binding motif and so target the XIAP-BIR3 domain. They exhibit high affinities, not only to XIAP-BIR3 but also to cIAP1, cIAP2 and MLIAP proteins. Bivalent Smac mimetics, containing two AVPI-binding motifs, bind to XIAP-BIR2–BIR3 with an extremely high affinity, exceeding that of Smac protein. Preclinical profiling studies have shown that Smac mimetics effectively sensitize cancer cells to other therapeutic agents, but they are also capable as single agents of inducing apoptosis in some but not all human cancer cell lines. To date, two Smac mimetics have reached phase I clinical development, and approximately ten are in an advanced preclinical development stage and are expected

Smac mimetic binds to XIAP and induces cIAP degradation. There are two cellular events that result from cIAP degradation: (1) activation of the non-canonical NF-κB pathway and subsequently increased production of autocrine TNF-α and (2) release of RIP1 from the TNF-α receptor complex, leading to caspase-8 activation, which requires TNF-α receptor to be activated in the first place. Accumulating evidence suggests that whether or not Smac mimetic induces autocrine TNF-α production is the key factor in deciding the cell's fate upon Smac mimetic treatment. Depending on their response to Smac mimetics cells can be grouped into three general classes: in class I cells, Smac mimetic induces autocrine TNF-α production and massive cell loss; in class II cells, Smac mimetic still induces autocrine TNFα production but this does not have a major effect on the cell population; and in class III cells, Smac mimetic has no effect on autocrine TNF-α production or on cell death. The ability of cells to produce TNF-α is necessary but not sufficient for Smac mimetic to induce cell

The miRNAs, is a class of endogenous, small, non-coding RNAs of 18–25 nucleotides in length, that negatively regulates gene expression by degradation of mRNA or suppression of mRNA translation. Mature miRNA products are formed in from a longer primary miRNA (pri-miRNA) transcript through sequential processing by the ribonucleases Drosha and Dicer1 [143,144]. miRNAs are known to repress thousands of target genes because only partial complementarity to the target mRNA is required. Thus, one miRNA may be simultaneously targeting a complexity of mRNAs as well as the expression of a single

The miRNAs are involved in normal processes, including cellular development, differentiation, proliferation, apoptosis, and stem cell self-renewal [28].The aberrant expression or alteration of miRNAs contributes to a range of human pathologies, including cancer. Furthermore, the deregulation of miRNA causes evasion of apoptosis which involved tumourigenesis and drug resistance [122]. During tumour initiation and progression, the functionality of aberrant miRNAs may act as oncogenes (OncomiRs) or tumour suppressors (TSmiRs), a numbers of

to enter human clinical testing soon [141].

death as a single agent [142].

**5. miRNA-based therapy**

mRNA may be regulated by many miRNAs [143].

AEG35156 is a 19-mer oligonucleotide targeting XIAP. Its sequence was designed to achieve maximal stability and potency and to minimize immunostimulation through avoiding CpG motifs. Phase I studies in patients with refractory malignancies established safety [136]. A phase I/II study of AEG35156 in combination with idarubicin and high-dose cytarabine in patients with relapsed or refractory AML demonstrated a dose-dependent knock-down of XIAP mRNA and protein and a promising response rate [137]. The molecule may rapidly enter randomized studies in AML while being also tested in lymphomas. The dose-limiting toxicity of an antisense oligonucleotide designed to inhibit surviving mRNA expression (LY2181308) was headache and the compound demonstrated some biological efficacy in decreasing survivin expression [138]. A phase II study has opened in solid tumours. The locked nucleic acid strategy was used to design other survivin-targeting antisense oligonucleotides, including SPC3042 [139] and EZN-3042 [140], which are currently being evaluated in Phase I clinical trials as single agents and in combination with cytotoxic drugs.

## *4.2.3. Smac-mimicking IAP antagonist*

Smac mimetics may be a useful therapeutic target as over-expression of Smac may potentiate apoptosis by neutralizing the caspase-inhibitory function of IAPs. Following the discovery that an IAP-binding motif consisting of four NH2-terminal amino acid residues was sufficient to bind to the BIR3 domain of XIAP, Smac-peptide mimetics were constructed which were capable of competing with caspase-9, displacing it from the BIR3 domain of XIAP. The three members of the IAP family, XIAP, cIAP1, and cIAP2, are structurally homologous (XIAP amino acid sequence identity to cIAP1 and cIAP2 of 36% and 39%, respectively, the amino acid sequence identity between cIAP1 and cIAP2 is 70%). In particular, the BIR3 IBM region is well conserved among the three IAPs. The XIAP BIR3 residues involved in van der Waals contacts (Val298, Lys299, and Trp310) and hydrogen bonds (Gly306, Leu307, and Trp323) with the inhibitory compounds are conserved. Minor exceptions are Leu292, replaced by Val in the cIAPs, Glu314 substituted by Asp in both cIAPs, and Gln319, which is Glu325 in cIAP1, and Gln311 in cIAP2. Finally, residues Thr308 and Asp309 that were found relevant for Smac-mimetics interaction with XIAP BIR3, are replaced by Arg314/Arg300 and Cys315/ Cys307 in cIAP1/cIAP2, respectively [127].

Smac interacts with IAPs via its N-terminal tetrapeptide, Ala1- Val2-Pro3-Ile4 (AVPI). Using this structural information, several groups designed small molecules mimicking AVPI to derive proteolytically stable compounds. Monovalent Smac mimetics were designed to mimic the Smac AVPI-binding motif and so target the XIAP-BIR3 domain. They exhibit high affinities, not only to XIAP-BIR3 but also to cIAP1, cIAP2 and MLIAP proteins. Bivalent Smac mimetics, containing two AVPI-binding motifs, bind to XIAP-BIR2–BIR3 with an extremely high affinity, exceeding that of Smac protein. Preclinical profiling studies have shown that Smac mimetics effectively sensitize cancer cells to other therapeutic agents, but they are also capable as single agents of inducing apoptosis in some but not all human cancer cell lines. To date, two Smac mimetics have reached phase I clinical development, and approximately ten are in an advanced preclinical development stage and are expected to enter human clinical testing soon [141].

Smac mimetic binds to XIAP and induces cIAP degradation. There are two cellular events that result from cIAP degradation: (1) activation of the non-canonical NF-κB pathway and subsequently increased production of autocrine TNF-α and (2) release of RIP1 from the TNF-α receptor complex, leading to caspase-8 activation, which requires TNF-α receptor to be activated in the first place. Accumulating evidence suggests that whether or not Smac mimetic induces autocrine TNF-α production is the key factor in deciding the cell's fate upon Smac mimetic treatment. Depending on their response to Smac mimetics cells can be grouped into three general classes: in class I cells, Smac mimetic induces autocrine TNF-α production and massive cell loss; in class II cells, Smac mimetic still induces autocrine TNFα production but this does not have a major effect on the cell population; and in class III cells, Smac mimetic has no effect on autocrine TNF-α production or on cell death. The ability of cells to produce TNF-α is necessary but not sufficient for Smac mimetic to induce cell death as a single agent [142].


**4. Multiple objects recognition**

Multiple objects of the same class can be accommodated by the G-HONN type filters to be recognised within an input cluttered image due to the shift invariance properties inherited by its correlator unit. Thus, in the M-HONN system all the training set images pass through the NNET unit. This time, instead of multiplying each training image with the corresponding weight connections (mask) as for the C-HONN filter, we keep constant the weight connection values, setting them to be equal with a randomly chosen image included in the training set. All the test set images are multiplied with the same randomly chosen image's weight connection values. Then, the training set images, after being transformed (masked) through the NNET unit by being multiplied with the mask, pass through the correlator unit where they are correlated with the masked test set images. In effect, the cross-correlation of each masked test set image with the transformed training set images (reference kernel) returns an output correlation plane peak value for each cross-correlation step. Hence, the maximum peak height values of the output correlation plane correspond to the recognised true-class objects.

#### **4.1 Modified NNET block architecture for multiple objects of different classes recognition**

As for all the HONN-type systems (Kypraios et al., 2004; Kypraios et al., 2003; Kypraios et al., 2009), in the M-HONN system's NNET block (unit) there is a single input source used for all the input data. Assuming we have N = 3 input still images or video frames of size 256×256 in pixels, then the input source consists of 65.536 i.e. [1(256×256)] input neurons equal to the size of each training image or frame (in vector form). Each layer needs, by definition (Hagan et al., 1996), to have the same input connections to each of its hidden neurons. Therefore, the shown NNET architecture is referred to as N+1 = 3+1 = 4, fourlayered since there are, N = 3, three hidden neurons (though shown here aligned under each other, they do not belong in the same hidden layer but rather create three separate hidden layers each of a single hidden neuron) and one output layer. Each of the hidden layers consist of only one hidden neuron. The input layer does not contain neurons with activation functions and so is omitted in the numbering of the layers. Though the network initially is fully connected to the input layer during the training stage, only one hidden layer is connected for each training image presented through the NNET. NNET is thus not a contiguous three layer network during training, which is why the distinction is made. In effect, neuron 1 is trained with the training still image or video frame x 1 , neuron 2 is trained with the training still image or video frame x 2 and so on, ending with neuron N being trained with the training still image or video frame x N . Thus, the number of the input weights increases proportionally to the size of the training set:

$$\mathbf{N}\_{\rm tw} = \mathbf{N} \times \left[ \mathbf{m} \times \mathbf{n} \right] \tag{24}$$

where N is the number of the input weights, N, is i w the size of the training set equal to the number of the training images and [m×n] is the size of the image of the training set.

Fig. 4 shows the modified NNET block architecture for accommodating multiple objects for more than one class recognition. As for all the family of G-HONN filters, NNET is implemented as a feedforward multi-layer architecture trained with a backpropagation algorithm. It has a single input source of input neurons equal to the size of the training image or video frame in vector form. In effect, for the training still image or video frame i 1N x of size [m×n], there are [m×n] input neurons in the single input source. The input weight are fully connected from the input layer to the hidden layers. There are N input i w weights proportional to the size of the training set. The number of the hidden layers, Nl is equal to the number of the images or video frames of the training set N:

$$\mathbf{N} = \mathbf{1}, \mathbf{2}, \mathbf{3}, \dots, \mathbf{i} \quad \text{and} \quad \mathbf{N}\_{\parallel} = \mathbf{N} \tag{25}$$

We have set to each hidden layer to contain a single neuron. The layer weights are fully connected to the output layer. Now, the number of the layer weights Nlw is equal to:

$$\mathbf{N}\_{\rm lw} = \mathbf{N} \times \mathbf{N}\_{\rm opn} \quad \text{and} \quad \mathbf{N}\_{\rm opn} = \mathbf{N}\_{\rm clasos} \tag{26}$$

where N is the number of the output neurons and opn Nclasses is the number of the different classes. In effect, we have augmented the output layer by adding more output neurons, one for each different class. On Fig. 4 we assume N 2 classes . Thus:

$$\mathbf{N}\_{\rm opn} = \mathbf{N}\_{\rm classos} = \mathbf{2} \quad \text{so, there are} \quad \mathbf{N}\_{\rm low} = \mathbf{N} \times \mathbf{2} \tag{27}$$

and

48 Advances in Object Recognition Systems

*,n* (22)

*M HONN i a S m,n i* (23)

*S Xm i N ci N* 1 1

1 *N i N*

In Eq. (23) we have chosen to constrain the correlation peak height values as we did with the constrained-HONN (C-HONN) system's implementation, but we can also easily re-write the system's transfer equation for the case of the unconstrained peak height values as with the unconstrained-HONN (U-HONN) system's implementation (Mahalanobis, 1994; Kypraios

Multiple objects of the same class can be accommodated by the G-HONN type filters to be recognised within an input cluttered image due to the shift invariance properties inherited by its correlator unit. Thus, in the M-HONN system all the training set images pass through the NNET unit. This time, instead of multiplying each training image with the corresponding weight connections (mask) as for the C-HONN filter, we keep constant the weight connection values, setting them to be equal with a randomly chosen image included in the training set. All the test set images are multiplied with the same randomly chosen image's weight connection values. Then, the training set images, after being transformed (masked) through the NNET unit by being multiplied with the mask, pass through the correlator unit where they are correlated with the masked test set images. In effect, the cross-correlation of each masked test set image with the transformed training set images (reference kernel) returns an output correlation plane peak value for each cross-correlation step. Hence, the maximum peak height values of the output correlation plane correspond to

**4.1 Modified NNET block architecture for multiple objects of different classes**

As for all the HONN-type systems (Kypraios et al., 2004; Kypraios et al., 2003; Kypraios et al., 2009), in the M-HONN system's NNET block (unit) there is a single input source used for all the input data. Assuming we have N = 3 input still images or video frames of size 256×256 in pixels, then the input source consists of 65.536 i.e. [1(256×256)] input neurons equal to the size of each training image or frame (in vector form). Each layer needs, by definition (Hagan et al., 1996), to have the same input connections to each of its hidden neurons. Therefore, the shown NNET architecture is referred to as N+1 = 3+1 = 4, fourlayered since there are, N = 3, three hidden neurons (though shown here aligned under each other, they do not belong in the same hidden layer but rather create three separate hidden layers each of a single hidden neuron) and one output layer. Each of the hidden layers consist of only one hidden neuron. The input layer does not contain neurons with activation functions and so is omitted in the numbering of the layers. Though the network initially is fully connected to the input layer during the training stage, only one hidden layer is connected for each training image presented through the NNET. NNET is thus not a

Thus, the M-HONN system's transfer function is formulated as follows:

et al., 2004b).

**recognition**

**4. Multiple objects recognition**

the recognised true-class objects.

$$\mathbf{N}\_{\text{class1 law}} = \mathbf{N} \quad \text{ } \quad \mathbf{N}\_{\text{class2 law}} = \mathbf{N} \tag{28}$$

where N and class1 lw N are the layer weights corresponding to object class 1 and object class2 lw class 2, respectively. There are bias connections to each one of the hidden layers:

$$\mathbf{N}\_b = \mathbf{N} \tag{29}$$

where Nb is the number of the bias connections. There are N target connections from t arg et w the N output neurons of the output layer: opn

$$\mathbf{N}\_{\text{target}\,\text{w}} = \begin{array}{c} \mathbf{N}\_{\text{open}} \\ \end{array} \tag{30}$$

Performance Analysis of

optical correlator.

impossibly large for

input and layer weights:

**5. Performance analysis**

the Modified-Hybrid Optical Neural Network Object Recognition System Within Cluttered Scenes 51

The above Eq. (31) in spatial domain, and Eq. (32) in frequency domain describe the M-HONN system's transfer function for multiple objects recognition (where the upper script *class* is used for the class index, i.e. for Fig. 4 we have *class = class1, class2*). Thus, the M-HONN filter (robust object recognition system) is composed of a non-linear space domain superposition of the training set images or from the video frames of the training set video sequences. As for all the HONN-type systems, the multiplying coefficient now becomes a non-linear function of the input weights and the layer weights, rather than a simple linear multiplying constant as used in a constrained linear combinatorial-type filter synthesis procedure. The non-linear M-HONN system is inherently shift invariant and it may be employed in an optical correlator as would a linear superposition constrained-type filter, such as the synthetic discriminant function (SDF) -type (Bahri & Kumar, 1988) filters. It may be used as a space domain function in a joint transform correlator architecture or be Fourier transformed and used as Fourier domain filter in a 4-f Vander Lugt (Vander Lugt, 1964) type

We have constructed a data set of input images of an S-type Jaguar car model at 10 increments of out-of-plane rotation at an elevation angle of approximately 45 to be used for the M-HONN system. A second set of images was constructed for the Police car model Mazda Efini RX-7 at the same elevation angle to serve as the out-of-class data for discrimination tests (see Fig. 5). A third data set was created of the background images of typical car parks (see Fig. 6) and the images of the S-type car model and the Mazda RX-7 car model added in the background scene. The size of all the images was 256 256 and all the images are in grey-scale bitmap format. All the input training images (and all the input test set images) for M-HONN system are concatenated row-by-row into a vector of size 1 256 256 prior to input to the NNET block. Normally this size of image is

processing by any artificial neural network architecture, since to be implemented by enough

N 10 256 256 i w 10 65,536 655,360

Thus, for a training set of N 10 individual vectors of size 256 256 , there would, in total, be more than half-a-million input weight connections needed. Thus the selective weight connection architecture is employed to overcome this problem. To overcome this problem we developed a novel selective weight connection architecture (see Section 2). Also, applying the heuristic training algorithm with momentum and an adaptive learning rate into the NNET training session (Nguyen & Widrow, 1989; Nguyen & Widrow, 1990), has speeded up the learning phase and reduced the memory size needed to complete fully the training session. Here, it worth mentioning that the NNET block and, in overall, M-HONN system is able to process input still images and video frames for all the test series in few a msec with a Dual Core CPU at 2.4 GHz with 4.0GB RAM. Additionally, due to the

(33)

Fig. 4. Modified NNET block of the M-HONN system for multiple objects recognition of different classes

Thus, now for N 2 classes there will be N transformed images being created for class 1 and N transformed images being created for class 2. Then, both sets of transformed images are used for the synthesis of the system's composite image. M-HONN system for multiple objects recognition of different class objects is written as follows:

 *classes classes N ×N i = 1LN ×N M - HONN =* i a *class S m, n i* = 1 a *class c 1 Γ × X m,n +* 2 a *class c 2 Γ × X m,n + L +* (31) N a *class c N Γ × X m,n*

or in the frequency domain the above equation is re-written as:

$$\mathbf{M} \cdot \mathbf{HONN} = \sum\_{i=1:N\_{class}\ge N}^{N\_{class}\ge N} \mathbf{a}\_i \cdot \mathbf{S}\_i^{class} \left(m\_\nu n\right) \tag{32}$$

The above Eq. (31) in spatial domain, and Eq. (32) in frequency domain describe the M-HONN system's transfer function for multiple objects recognition (where the upper script *class* is used for the class index, i.e. for Fig. 4 we have *class = class1, class2*). Thus, the M-HONN filter (robust object recognition system) is composed of a non-linear space domain superposition of the training set images or from the video frames of the training set video sequences. As for all the HONN-type systems, the multiplying coefficient now becomes a non-linear function of the input weights and the layer weights, rather than a simple linear multiplying constant as used in a constrained linear combinatorial-type filter synthesis procedure. The non-linear M-HONN system is inherently shift invariant and it may be employed in an optical correlator as would a linear superposition constrained-type filter, such as the synthetic discriminant function (SDF) -type (Bahri & Kumar, 1988) filters. It may be used as a space domain function in a joint transform correlator architecture or be Fourier transformed and used as Fourier domain filter in a 4-f Vander Lugt (Vander Lugt, 1964) type optical correlator.


**1. Introduction**

It is now acknowledged that non-Newtonian fluids in industrial, physiological and technological processes are more significant than viscous fluids. Few examples of such fluids may include silicon oils, printer ink, mud, ice cream, egg yolk, blood at low shear rate, shampoo, gypsum paste, polymer solutions, nail polish, sand in water, ketchup etc. Rheological properties of such fluids are different and thus all these cannot be explained employing one constitutive relationship between shear rate and rate of strain. The modelled expressions for the non-Newtonian liquids are more tedious and of higher order than Navier–Stokes expressions for viscous fluids. Researchers in the field face challenges in modelling, analysis and computations from different quarters. Through different non-Newtonian fluids, the objective here is to explore second grade and elastico-viscous fluids [1–8].

Nanofluids are described by carbon nanotubes (CNTs) [9–11], Buongiorno [12] and Tiwari and Das [13] models. Therefore, the information is very significant about flows involving thermophoresis aspects. Impact of slip in flow of copper-water nanoliquid over an extendable surface is examined by Pandey and Kumar [14]. Flow of couple stress nanomaterial bound by an oscillatory stretchable surface is analyzed by Khan et al. [15]. Turkyilmazoglu [16] discussed free and circular jets in view of single phase nanomaterial. Few relevant investigatons for nanoliquids can be seen in studies [17–45].

According to previous literature, it is found that magnetohydrodynamic stretched flow of viscoelastic nanofluids with heterogeneous–homogeneous reactions has not been reported yet. Attention in modeling has been specially focused on constitutive relations of viscoelastic fluids. Heat and mass transport process is explored by thermophoresis and Brownian dispersion. Adequate transformations are considered to dimensionless the governing system. Numerical solutions of the resulting system are obtained by employing the shooting method. Contributions of numerous sundary variables on flow fields are interpreted through plots and numerical data.


4. Dynamics of small river plumes

In this section, we address dynamical features of small river plumes using the case study of the Mzymta plume and other small river plumes formed along the Russian coast of the Black Sea (RCBS) between the city of Novorossiysk and the city of Sochi (Figure 2). The drainage basin of RCBS is a narrow area (10–40 km wide) limited by the Greater Caucasus range at the east and the sea coast at the west. Steep gorges of this range form the drainage basins of several dozens of rivers that discharge to the sea at RCBS. Watershed basin areas of these rivers are relatively small, and the total freshwater runoff from the study region to the sea is estimated as 7 km3 in a year [52].

Multiple buoyant plumes are formed along the coast of the study area. The largest plume is generally formed by the Mzymta River, which is the largest river of RCBS with mean monthly discharge equal to 20–120 m3 /s. The area of the Mzymta does not exceed 10 km2 under average climatic discharge conditions. However, it can increase up to 50 km2 during spring and summer freshet periods. Areas of the other river plumes of RCBS are even smaller except for rain-induced flash flooding

#### Figure 2.

Location of the largest rivers and cities of the Russian coast of the Black Sea.

periods. The rivers of the study region are significantly more turbid, as compared to sea water, due to elevated concentrations of terrigenous suspended sediments. As a result, surface salinity and turbidity show good correlation within the river plumes. Thus, optical satellite imagery can be effectively used to detect river plumes of RCBS [45, 46]. Both in situ and satellite measurements revealed high spatial and temporal variability of the river plumes of RCBS. Their areas, positions, and shapes can significantly change during several hours in response to variations of river discharge and local wind forcing [42, 43, 45].

### 4.1 Wind forcing and small river plumes

We used near-simultaneous ocean-color satellite imagery from NASA's Landsat 8 and ESA's Sentinel-2 missions to reconstruct surface currents along RCBS and study spreading dynamics of small river plumes formed in this area [53]. Several times a year, Landsat 8 and Sentinel-2 satellites both pass over the study area within a time interval of less than 10 minutes. The obtained near-simultaneous ocean-color composites can be used to reconstruct surface sea currents. In particular, they can be applied for detection of motion of frontal zones of river plumes, which are visible in optical satellite images. We used an optical flow algorithm applied to nearsimultaneous Landsat 8 and Sentinel-2 images to reconstruct surface currents within the Mzymta plume. The obtained results reveal significant differences in wind-driven dynamics of the Mzymta plume and large plumes [12, 16, 54–58].

The main features of the dynamics of the Mzymta plume reconstructed from the satellite imagery are the following. First, the near-field part of the Mzymta plume is smaller than it is estimated by relevant parameterizations based on river discharge parameters designed for large plumes. Second, under low-wind-forcing conditions, the mid-field plume, i.e., a recirculating bulge adjacent to the river mouth, is not formed. The near-field freshwater jet directly transitions to the far-field part of the plume near the Mzymta mouth. Finally, during upwelling, onshore, and offshore wind-forcing periods, the wind-induced Ekman transport within the Mzymta plume occurs at a wide range of angles to the wind direction. It changes from values of 60–80° near the Mzymta mouth to 30–40° at the far-field part of the plume.