Results of FNN TSK forecasting.
\r\n\tGlobalization does not represent a pure and generous process for humanity or other species, but rather it implies social exclusion and also provokes situations of vulnerability in groups of people, forced exclusion, and apartheid: poor job opportunities, lack of access to education, worse socio-sanitary conditions. Specifically, it can be said that social segregation entails the apartheid of social groups of different ages, genders, and ethnicities; these groups live a reality manifested through the deepening of poverty, in terms of increased vulnerability of the poor and groups with little economic, social, cultural, labor and health stability.
\r\n\r\n\tThis book aims to talk about some topics that are neglected in the discourses of academic communities and political elites. The inequality process is deeply rooted among humans and is part of many people's lives in the form of modern apartheid, gender segregation, lack of health access, and cultural gap. All those structural inequality processes are the product of the biopower perpetuated and produced in the macrosystem, exosystem, mesosystem, and microsystem. For many people from the academy, the information-consuming public, and the society in general, it is a problem to talk about these processes, since they have either lost interest or have normalized the structural and social inequity. For this reason, we see it as transcendental to explain how this situation occurs from the most internal fibers to the most evident processes, intending to make it more visible and thus expose the situation for possible solutions.
",isbn:"978-1-83768-406-9",printIsbn:"978-1-83768-405-2",pdfIsbn:"978-1-83768-407-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"cefab077e403fd1695fb2946e7914942",bookSignature:"Ph.D. Yaroslava Robles-Bykbaev",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11473.jpg",keywords:"Wage Gap, Gender Segregation, Fundamental Human Rights, Health Access, Social Inequity Processes, Modern Apartheid, Resilience, Cultural Gaps, Globalization, Geopolitics of Social Inequality, Public Policies, Social Vulnerability",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 15th 2022",dateEndSecondStepPublish:"July 13th 2022",dateEndThirdStepPublish:"September 11th 2022",dateEndFourthStepPublish:"November 30th 2022",dateEndFifthStepPublish:"January 29th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"5 days",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Bykbaev is a member of the UNESCO Chair of Politecnica Salesiana University. She has contributed as co-author and author to approximately thirty scientific publications in the field of statistics, inclusive education, and social and cultural anthropology. These publications focus on the visibility of problems in the field of public health and focus on the creation of proposals to improve community health. Dr. Bykbaev is an active member of the NODO Ecuadorian Network of Women Scientists (REMCI).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"313341",title:"Ph.D.",name:"Yaroslava",middleName:null,surname:"Robles-Bykbaev",slug:"yaroslava-robles-bykbaev",fullName:"Yaroslava Robles-Bykbaev",profilePictureURL:"https://mts.intechopen.com/storage/users/313341/images/system/313341.jpg",biography:null,institutionString:"Politecnica Salesiana University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Politecnica Salesiana University",institutionURL:null,country:{name:"Ecuador"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"444316",firstName:"Blanka",lastName:"Gugic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/444316/images/20016_n.jpg",email:"blanka@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6926",title:"Biological Anthropology",subtitle:"Applications and Case Studies",isOpenForSubmission:!1,hash:"5bbb192dffd37a257febf4acfde73bb8",slug:"biological-anthropology-applications-and-case-studies",bookSignature:"Alessio Vovlas",coverURL:"https://cdn.intechopen.com/books/images_new/6926.jpg",editedByType:"Edited by",editors:[{id:"313084",title:"Dr.",name:"Alessio",surname:"Vovlas",slug:"alessio-vovlas",fullName:"Alessio Vovlas"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"51376",title:"Cytokines in Inflamed Mucosa of IBD Patients",doi:"10.5772/64286",slug:"cytokines-in-inflamed-mucosa-of-ibd-patients",body:'\nBoth ulcerative colitis (UC) and Crohn’s disease (CD), usually referred to as inflammatory bowel disease (IBD), are examples of complex disorders, which include inflammatory and autoimmune features with prominent intestinal immune dysregulation. Cells of the innate and the adaptive immune system have been identified as the key players of IBD. Cytokines are central components of the inflammatory pathways that take place during the active and chronic phases of IBD. However, a clear picture of these processes is still missing. Since the inflammation is located in the intestinal mucosa, the latter is the main source of biomarkers in IBD allowing various immunological pathways to be explored in the gut. Thus, the determination of cytokine expression profile could help to elucidate the local immune responses during intestinal inflammation. Expression of IBD-related proteins such as cytokines, chemokines, adhesion molecules, and their corresponding cellular and soluble receptors has revealed their significant role in the pro- and anti-inflammatory processes in the inflamed gut mucosa. Indeed, the implication of some cytokines in the immunopathogenesis of IBD is investigated intensively and proved in experimental models of intestinal inflammation. Lack of enough investigation in humans, however, predetermines the need for further studies since it is proved that the common clinical phenotype of colitis may result from largely diverse genetic or immunological backgrounds.
\nSince the pathogenesis of IBD is related to both dysregulated innate and adaptive immune pathways, which contribute to the aberrant intestinal inflammatory response in genetically susceptible individuals, the main focus of research attempts is directed to the initiation, perpetuation, and cessation of gut inflammation associated with IBD [1].
\nCytokines are abundantly produced by the cells of the gut-associated immune system maintaining lymphocyte homeostasis under both steady-state and inflammatory conditions. These small, cell-signaling protein molecules act in a paracrine, autocrine, or endocrine manner, coordinate the communication between immune and non-immune cells of the intestinal compartment, and modify acute and chronic inflammatory responses at both local and systemic levels [2]. Moreover, elevation of pro-inflammatory cytokines is considered to be associated with the severity of gut inflammation [3]. Therefore, it is no surprise that cytokines have been a major therapeutic management of IBD [4].
\nIt is believed that dysregulated immune mechanisms are related to T cells in the gut in IBD pathogenesis. Unregulated T lymphocytes activities can lead to autoimmunity, especially during inflammation when they can cause excessive tissue damage [5]. The ability of CD4+ T helper (Th) cells to alter the magnitude and outcome of the intestinal tissue-damaging inflammatory responses is mostly dependent on the production of distinct profiles of cytokines. Traditionally, the lesions in CD patients have been associated with a predominant activation of Th1 cells and production of large quantities of IFNγ under the stimulus of IL-12 through STAT4 signaling. By contrast, the lesions in UC patients were believed to be driven by Th2 cytokines, such as IL-4 and IL-13, through STAT6 activation. In the mouse model of IBD, CD3+ (T cell) depletion results in dramatic reduction of the gross pathology, neutrophil influx, and expression of pro-inflammatory cytokines and chemokines [6]. The cytokine expression pattern that strictly follows the polarization model of Th1 versus Th2, however, does not appear to be fully applicable in IBD. Nearly 20 years ago, Mosmann and Coffman concluded their paradigm with the prediction: “… further divisions of helper T cells may have to be recognized before a complete picture of helper T cell function can be obtained” [7, 8]. Indeed, several recent studies had led to the identification of more complex networks of cytokine interaction in IBD tissue, thus shedding light on the role of a distinct subset of T cells in the pathogenesis of IBD—Th17 cells. On the other hand, another T cell subpopulation, namely T regulatory cells (Tregs), is implicated in gut homeostasis and tolerance induction, and it is believed that Th17 and Tregs are in a mutually polarizing relationship [9]. An overview of the main cells and cytokines involved in intestinal inflammation is presented in Figure 1.
\nT-helper cells and cytokines interactions in normal and inflamed mucosa of IBD patients. The fate of naïve T cell depends on the interactions with the antigen-presenting cells (i.e. dendritic cells, macrophages) and the secreted cytokines. In normal mucosa, the abundant TGFβ1 directs naïve Th0 cells to Treg differentiation which secrete IL-10 and TGFβ1. “Danger” signals through TLR activation (on antigen-presenting cells), followed by secretion of IL-6, IL-23, IL-1, etc., with the simultaneous presence of TGFβ1, all favor the development of Th17 cells. The latter secrete many cytokines, for example, IL-21 acts as an autocrine positive regulator but IL-25 and IL-27 inhibit Th17 cells in autocrine manner. Th17 cells could also secrete IL-17 cytokines, TNFα, and in special circumstances—IFNγ; thus, Th17 cells play an intermediate role between innate and adaptive immune response, especially during inflammation in the intestinal mucosa. The balance between Th17 cells and Tregs is desired to maintain the immune homeostasis in the gut. However, Tregs and Th17 cells can convert into each other demonstrating same plasticity, depending on the cytokine milieu. Nevertheless, there are other players in the inflamed mucosa such as Th1, Th2, and Th22 cells. Legend: black arrow—cell differentiation; green arrow—secretion; pink arrow—possible secretion; red arrow—inhibition; TCR—T cell receptor; TLR—Toll-like receptor; MHCII—major histocompatibility complex—Class II; TL1A—TNFα-like 1 A.
Thus, the effector response is largely determined by the combination of cytokines that predominate in the intestinal mucosa, and it defines the mucosal T cell immunophenotype in each case [2].
\nDendritic cells (DCs), macrophages, epithelial cells, and myofibroblasts are able to recognize pathogen-associated molecular patterns (PAMPs) through their pattern-recognition receptors including Toll-like and NOD-like receptors. This recognition results in nuclear factor (NF)-kB activation with gene transcription and production of pro-inflammatory cytokines, such as IL-1 and TNFα, ensuring an effective innate response against microbial antigens. That also triggers antigen presentation, maturation, and up-regulation of co-stimulatory molecules which lead to efficient adaptive immunity involving T cell activation [10]. There is evidence for down-regulated protein level of TLR-3 in IBD, whereas TLR-2 and TLR-4 are up-regulated in intestinal mucosa of active IBD [11]. A specific mutation in NOD2 gene induces loss of NF-kB function during TLR-2 activation with a subsequent increased risk of infection with commensal bacteria and increased susceptibility to the ileal form of CD [12]. Recent studies suggest that increased mucosal permeability in the intestinal mucosa during IBD flare allows infiltration of a large number of granulocytes into the colonic mucosa. These leukocytes are activated, have a prolonged survival time, and release various pro-inflammatory cytokines (e.g. IL-1β, IL-6, TNFα, IL-18), which exacerbate and maintain the inflammation in the gut [13].
\nTNFα links the innate and the adaptive immune responses and has crucial importance in the pathogenesis of IBD by inducing the differentiation of stromal cells into myofibroblasts and promoting their production of matrix metalloproteinases. The latter induce enterocyte apoptosis and digestion of gut basement membrane [10]. TNFα also exerts its pro-inflammatory effect through cytokines such as INFγ, IL-1β, and IL-6 [12].
\nTNFα is a well-established inflammatory mediator in CD whereas contradictory reports exist in UC [12]. There is a lack of studies on the mucosal expression of TNFα and the prediction of the clinical course, and only a few reports announced the predictive value of mucosal TNFα concentrations and the response to therapy in IBD patients. In fact, increased levels of TNFα and IL-15 have been previously reported in intestinal biopsies from IBD patients in remission without biopsy alterations [14]. Interestingly, the presence of TNFα in non-affected areas of IBD mucosa may not be sufficient to trigger mechanisms of mucosal damage. In preliminary reports, normalizing of mucosal TNFα seemed to predict a longstanding remission after stopping of anti-TNFα therapy in UC [12].
\nCertain TNFα polymorphisms (i.e. TNFα-308 A allele) are associated with increased serum levels of TNFα and therefore with higher susceptibility of IBD [15].
\nTNF-like cytokine 1A (TL1A) is a novel member of TNF superfamily of proteins, produced by endothelial cells, macrophages, lamina propria T cells and plasma cells, monocytes, and monocyte-derived DCs [16]. Association with its functional receptor provides co-stimulatory signals for activation of T lymphocytes, leading to cell proliferation, cytokine secretion, and amplification of pro-inflammatory pathways, as well as induction of apoptosis in target cells [2]. Several studies have clearly demonstrated that TL1A and its receptor are up-regulated at mucosal protein and mRNA levels in IBD patients. TL1A is localized in the lamina propria and shows preferential expression on plasma cells and mucosal DCs. Of great importance is the fact that TL1A was shown to increase IL-13 secretion by natural killer T (NKT) cells, which are considered to be central to the mucosal injury that takes place in UC pathogenesis. Furthermore, TL1A induces IFNγ secretion in synergy with stimulation via TCR or IL-12/IL-18 [2]. TL1A expression is induced by TNFα and IL-1α as well and since the latter are abundantly expressed in the inflamed mucosa of UC patients, they may provide a strong stimulus for enhanced TL1A expression. On the other hand, several microorganisms were shown directly to stimulate TL1A secretion by DCs via TLR-signaling (TLR-4), LPS-induced and NFkB-dependent pathway [16]. Moreover, there is an inhibitory component of the TL1A receptor which could augment pro-inflammatory pathways at the intestinal mucosa by rendering activated lymphocytes resistant to apoptosis. Thus, increased expression of this inhibitory TL1A receptor may offer a survival advantage to effector lymphocytes, preventing their elimination and perpetuating tissue injury [2].
\nIL-8, as a member of the CXC chemokines family, is not only a strong chemoattractant for neutrophils, monocytes, etc. but also triggers the secretion of superoxide anions and lysosomal enzymes in neutrophils, thus contributing to the tissue damage during inflammation. IL-8 mRNA expression in the inflamed mucosa is shown to be significantly higher than the level in non-inflamed mucosa of IBD patients or in the normal mucosa of non-IBD patients [13].
\nTh1 cells are an essential part of the adaptive immune response, mainly against intracellular microorganisms and protozoa. The master transcription factors for Th1 definition are STAT4 and T-bet. Th1 cells in gut mucosa which are induced by increased levels of IL-12 and IL-18 are thought to cause intestinal inflammation in CD patients via production of high amounts of IFNγ. The latter induces enterocyte apoptosis and triggers the release of TNFα by activated mucosal macrophages. Th1 cells by themselves appear as an important source of TNFα [10].
\nIFNγ is a mediator of intestinal inflammation in CD, but contradictory reports exist for UC. However, increased levels of IFNγ have been observed in the inflamed mucosa from UC patients too. IFNγ levels also correlated with the clinical activity but not with the endoscopic score in UC, whereas no correlation to the clinical activity was observed in CD patients [12].
\nThe role of IL-12 in intestinal inflammation will be discussed later along with IL-23.
\nIL-1 exists in two forms, IL-1α and IL-1β, encoded by different genes but exhibit almost identical functions [16]. The major sources of IL-1 are activated myeloid cells and its production can be induced by bacterial lipopolysaccharide, TNFα, IFNα, IFN-β, IFN-γ, as well as IL-1. IL-1 was found to promote Th17 development in the presence of IL-6 and TGFβ, and also to potentiate their actions in humans but not in mice. Moreover, it has been reported that IL-1 can increase their effect on Th17 definition. However, the mechanism through which IL-1 influences Th17 differentiation is not fully determined yet [17]. Some suggestions include that IL-1β or IL-1α cooperates with IL-23 to enhance IL-17 production independent of TCR stimulation. Additionally, IL-1 may suppress the inhibitory effect which IL-2 exerts on Th17 cell production through induction of IL-1R, IL-23R, and transcription factor RORγt [16].
\nIL-1β was shown to be increased in CD and UC patients, whereas the IL-1-receptor/IL-1β ratio was negatively associated with the IBD activity. When comparing the IBD patients with controls, a significant variation in genotype frequency of the IL-1β promoter polymorphism was found. Higher levels of the pro-inflammatory cytokine IL-1β would be expected to increase the likelihood of developing IBD since higher levels of such cytokines occur in this disease [15].
\nIL-18 is another member of the IL-1 pro-inflammatory cytokine family. IL-18 is an epithelial-derived cytokine that has been proposed to promote barrier function in the intestine, but its effects on intestinal T cells are poorly understood. Although IL-18 is mainly responsible for inducing IFNγ production and Th1 differentiation, this cytokine might be involved in Th17 cell definition as well. Antigen-presenting cells express IL-18R on their surface and its binding with the cytokine is required for generation of Th17 cells through an IL-23-dependent mechanism. Moreover, IL-18 synergizes with IL-23 in the induction of Th17 cell [16] However, there are more reliable proofs about the involvement of IL-18R in Th17 cell definition, but not for IL-18 itself. Probably this action might be fulfilled through binding of an unknown alternative ligand, distinct from IL-18, to the receptor [17]. In contrast, Maloy et al. [18] demonstrated that during steady state, intestinal epithelial cells constitutively secrete IL-18, which acts directly on IL-18R1-expressing CD4+ T cells to limit colonic Th17 cell differentiation. In addition, they found that IL-18R1 signaling was critical for Tregs-mediated control of intestinal inflammation, though IL-18R1 is not required for Tregs development [18]. Thus, since IL-18 may regulate differentially homeostatic and inflammatory subsets of T cells, this finding has potential for treatment of IBD and other chronic inflammatory disorders.
\nIL-18 was found elevated in the inflamed colonic mucosa of UC and CD patients and polymorphisms in the IL-18R1-IL-18RAP locus are associated with IBD susceptibility [18]. Moreover, the local expression of IL-18 has been shown to be associated with the grade of inflammation [19].
\nTh2 cells, another important part of the adaptive immune system, are mainly involved in the effector responses against extracellular parasites, including helminths, as well as in allergy pathogenesis. They are defined by the transcription factors STAT6 and GATA3 [7]. The importance of Th2 response in IBD is still under debate. In UC, the inflammatory response is less skewed along specific pathways, even though there is enhanced production of IL-4, IL-5, and IL-13, cytokines made by Th2 cells, unlike CD where Th1 activation has been mainly employed in pathogenesis [20].
\nIL-13 exerts the potential to increase intestinal permeability and induce both enterocyte differentiation and apoptosis. IL-13 is released mainly by Th2 cells but another source of that cytokine is NKT cells. NKT cells express surface CD161 but not invariant T cell receptor, which is a well-established characteristic of this population. They produce IL-13 in response to stimulation of antigen-presenting cells expressing surface CD1d. Most probably, these atypical NKT cells are stimulated to produce IL-13 in the colonic mucosa by flora-derived microbial products [2]. This was observed in patients with UC, but not in CD patients. Further studies revealed that CD161-expressing NKT cells showed IL-13-dependent cytotoxic activity against colon epithelial cells [2]. Moreover, IL-13 independently exerts harmful effects on epithelial barrier function, such as derangement of tight junction integrity, decreased restitution velocity, etc. [2]. Therefore, blockade of IL-13 downstream signaling may be an effective anti-inflammatory approach in UC which requires further investigations.
\nIL-11 is a member of the IL-6 cytokine family and exerts pleiotropic effects on various cell types as it acts synergistically with other cytokines such as IL-3 and IL-4, thus it has been implicated in Th2-mediated sensitization and inflammation. IL-11 also prevents cell death and inhibits inflammation at sites of tissue injury. IL-11 mediates anti-inflammatory effects by down-regulation of LPS-induced NFkB activation, thus preventing transcription of inflammatory genes [12]. This may be implemented in IBD therapy, but still needs additional verification.
\nIL-33 is the latest identified member of the IL-1 family of cytokines. mRNA and protein expression of IL-33 was detected in normal colonic cells both at the surface epithelium and in crypts, as well as in inflamed bowel onto lamina propria mononuclear cells (CD11b+ monocytes/macrophages and CD19+ B cells), endothelial cells, and subepithelial myofibroblasts. During active intestinal inflammation, IL-33 actively participates in the epithelial-immune cell crosstalk that takes place in IBD mucosa. IL-33 expression is augmented under stimulation with IL-1β and TNFα, two cytokines that are enriched at the inflamed mucosa and are of pathogenic relevance in UC, as well as after TLR-3 and TGFβ signals [2].
\nRegarding mucosal expression, up-regulation of IL-33 appears to be specific for UC, as it was not observed in CD colonic inflammation [2]. Moreover, IL-33-expressing myofibroblasts were absent in fissuring areas in patients with colonic CD. Therefore, these observations may provide information of distinctive pathway between the two forms of IBD [2].
\nIL-33 was shown also to induce particularly the expression of Th2 effector molecules IL-5 and IL-13. Given the central role of IL-13 in UC, IL-33 may be involved in UC pathogenesis through the induction of IL-13 secretion. It has been proposed that IL-33 may function as “alarmin” for the gut-associated immune system activating toward intestinal inflammation or perpetuating the ongoing one [2].
\nTo emphasize the importance of Th17 in intestinal inflammation, here we start with the description of the prerequisite cytokines for the development of Th17 cells from naïve T cells.
\nTransforming growth factor β (TGFβ) is a potent cytokine with multi-faceted regulatory and inhibitory activities and has two forms—TGFβ1 and TGFβ2. TGFβ1 is a pleiotropic cytokine best known for its potential to induce peripheral tolerance in the absence of IL-6 [12]. One of the mechanisms by which TGFβ1 is able to maintain tolerance is to support the survival of naturally occurring FoxP3+ Tregs (nTregs) in thymus. In addition, along with IL-2 and retinoic acid, TGFβ1 promotes the differentiation of induced Tregs (iTregs). Another mechanism of TGFβ-induced tolerance is to suppress the innate immune cells such as DCs and NK cells [5].
\nTGFβ1 also regulates the development of resident macrophages in the normal intestine, which possess some unusual features such as constitutive production of IL-10 and TNFα, refractory to TLR stimulation, high expression of MHCII and CXCR1, and avid phagocytic activity. Thus, this is another mechanism through which TGFβ1 favours local homeostasis [21].
\nTGFβ1 plays an important role under inflammatory conditions. In the presence of IL-6, TGFβ1 drives the differentiation of Th17 cells which promotes further inflammation and augmentation of ongoing autoimmune conditions. In addition, TGFβ1 in combination with IL-4 promotes the differentiation of IL-9-producing and IL-10-producing T cells, which surprisingly lack suppressive function and also promote tissue inflammation [12]. Increased protein levels of TGFβ1 are found in the mucosa of both CD and UC patients, whose levels correlated with the severity of disease in CD but not in UC patients [5, 12]. We also found significantly higher gene and protein levels of TGFß1 in the inflamed mucosa of CD patients alone [22]. This is not surprising since the tissue remodeling properties of TGFβ1 are well-established. Interestingly, TGFβ1 orchestrates the differentiation of both Tregs and Th17 cells in a concentration-dependent manner—low doses induce Th17 cell differentiation while higher doses inhibit Th17 cell development and promote Tregs [5, 11].
\nIL-6 is a pleiotropic cytokine with regulatory effects on inflammation development. In addition to its stimulatory effects (i.e. induction of acute phase proteins), IL-6 also has inhibitory functions (i.e. cessation of the antiviral antibody response after certain immunizations). Recent studies have demonstrated that IL-6 has a crucial role in the regulation of the balance between Th17 cells and Tregs [23]. IL-6 activates a receptor complex consisting of IL-6R and the signal transducing subunit gp130 which activates downstream STAT1 and STAT3. STAT3 regulates IL-6-induced expression of RORγt and RORα, the crucial transcription factors for Th17 cells. In contrast to STAT3 activation, STAT1 inhibits the development of Th17 cells. Although IL-6 activates both STAT1 and STAT3, it has been demonstrated that in Th17 cell activation, they play two different roles—STAT3 maintains while STAT1 suppresses it [23]. Furthermore, STAT family members activated by various cytokines provide both positive and negative regulation for Th17 development (i.e. IL-27 inhibits Th17 differentiation through STAT1) [23]. TGFβ1 can induce gene activation of both FoxP3 and RORγt, but FoxP3 is able to associate with RORγt, thus inhibiting its transcriptional activation. Nevertheless, in the presence of IL-6 this inhibition is abrogated, so IL-6 could act as a potent promoter of Th17 instead of Tregs differentiation. All facts taken together, IL-6 appears as the main partner of TGFβ in priming naïve T cells to IL-17 production, playing a pivotal role in Th17 polarization and initiation of inflammatory immune response. Currently, it is also accepted that IL-6 is able to induce expression of IL-23R in T cells, making them responsive to IL-23 which sustains the Th17 phenotype [17].
\nIncreased levels of IL-6 and its soluble receptor are up-regulated in active CD patients, and mucosal IL-6 levels were correlated with the degree of clinical activity in CD and UC [12]. In consent with these findings, in a group of 37 IBD patients, we also found both mRNA transcripts of TGFβ1 and IL-6 up-regulated in patients’ mucosa compared to the mucosa of non-IBD persons, along with increased IL-17 mRNA in inflamed tissue [22, 24].
\nSeveral polymorphisms regarding the IL-6 gene are described to be also associated with susceptibility to IBD development, such as IL-6 174 [15].
\nAlthough anti-IL-6 antibodies therapy has become a novel therapeutic strategy for some inflammatory and autoimmune disease, including CD, IL-6 inhibitory treatment acts primarily on initial CD4+ T cells response including Th17 differentiation, rather than on the effector phase [23]. However, it still remains controversial whether this antibody can inhibit Th17 differentiation in a manner that is clinically meaningful.
\nIL-12 and IL-23 share the common p40 subunit, but whereas IL-12 drives the classical Th1 response characterized by IFNγ production, IL-23 maintains an IL-17-secreting T cell population. Th1 responses may develop normally in the absence of IL-23, but in IBD patients, their manifestations require the presence of IL-23. The systemic inflammatory response and the elevated concentrations of pro-inflammatory cytokines in the serum are driven by IL-12 while the local intestinal inflammation and production of IL-17 in the intestinal mucosa are controlled by IL-23 [11, 12, 25].
\nIL-23 is crucial in orchestrating the crosstalk between innate and adaptive immunity with a key role in driving early responses to microbes. In a recent study, Kamada et al. showed that IL-23 is secreted preferentially by a subset of sentinel mucosal cells expressing both macrophage (i.e. CD14, CD33, CD68) and DC markers (i.e. CD205, CD209) [26]. These cells are present in a large number in CD-involved tissue and produce IL-12 and IL-23 in response to environmental danger signals [8, 26]. The presence of pathogens or pathogen-related products (such as lipopolysaccharide) can strongly influence the production of IL-12 and/or IL-23 depending on the microbial agent. This happens within a few hours after exposure and these early events in pathogen encounter are likely to shape subsequent responses toward IL-12 or IL-23 expression [8]. It was shown that some of the pathogenic functions of IL-23 in the gut are mediated by atypical T cell populations, such as γδT cells, invariant NK cells, and innate lymphoid cells, inducing them to secrete Th17-related cytokines and contributing to intestinal inflammation [10]. IL-23 might be also closely associated with the neutrophil influx [12].
\nThe precise function of IL-23 in Th17 regulation is still not entirely clear, although there are a lot of speculations. IL-23 failed to induce the differentiation of naïve T cells into Th17 cells due to lack of IL-23R on naïve T cells [16]. It was subsequently demonstrated that IL-23R is not expressed on naïve T cells. Instead, IL-23 acts as a survival signal for Th17 cells by the mechanism probably similar to TNFα [23, 27].
\nThe synthesis of the common p40 subunit for both IL-12 and IL-23, and the functional heterodimeric IL-23 is enhanced in the gut of CD patients [11]. Along with other authors’ findings, we detected up-regulated mRNA levels of IL-23 in inflamed mucosa, as well as significantly increased serum level of IL-23 among IBD patients in comparison with non-IBD persons [24], and we suggest that anti-IL-23 therapy could be beneficial for IBD patients.
\nIdentification of multiple single nucleotide polymorphisms (SNPs) in the IL-23 receptor gene that has been associated with both UC and CD suggested that the IL-23 axis might play a central role in chronic inflammation. IL-23R SNPs that influence IBD susceptibility have provided a new picture of the way the local immune response can promote intestinal tissue damage [11]. Small differences in cytokine levels as a result of gene polymorphisms may have an important effect on the inflammatory response and thus, influence the pathophysiology of IBD [15]. Interestingly, one of these polymorphisms, Arg381Gln, confers protection against developing CD [20]. Nonetheless, the mechanism through which these SNPs confer either risk or protection from IBD remains unknown [15].
\nThe discovery of an IL-23-dependent T cell population that produces IL-17 but not IFNγ or IL-4 suggested there is an additional Th cell subset. Th17 cells have derived their name from their ability to produce IL-17, also termed IL-17A. Th17 cells also produce other cytokines including IL-17F, IL-21, IL-22, TNFα, and IL-6 [17, 23]. However, analysis at the single cell level has revealed that not all Th17 cells secrete the whole spectrum of cytokines, probably reflecting the heterogeneity of this cell’s subset [25]. The IL-17 cytokine family also includes IL-17B, IL-17C, IL-17D (IL-27), IL-17E (IL-25), and IL-17A/F (Figure 1). The cytokines IL-27 and IL-25 have lowest protein homology to IL-17A. They are not produced by Th17 cells but act as negative regulators on the Th17 subset development. IL-27 is structurally related to IL-6 and is able to attenuate chronic inflammation by promoting IL-10 production [17]. In line with this, IL-27 and IFNγ are responsible for the inhibition of Th17 development in a STAT1-dependent manner [23], as described above. Another negative regulator of Th17 cells is IL-25, identified as a genetic homologue of IL-17, produced by Th2 and mast cells. IL-25 is involved in the expression of the Th2 cytokines IL-5 and IL-13, thus, favors Th2 responses. IL-25 deficiency is involved in pathologic inflammation, associated with increased expression of IL-17 and IL-23 [17].
\nCCR6, presented not only on Th17 cells, but also on Tregs, B cells, neutrophils and immature DC, plays a critical role in the migration of these cells to the sites of inflammation. TGFβ1 was shown to be the main factor for induction of CCR6 mRNA expression in Th17 cells and DCs [19]. IL-17-producing Th memory cells selectively express both CCR6 and CCR4, unlike Th cells producing IFNγ or both IFNγ and IL-17 which express CCR6 and CXCR3 [16]. Indeed, CCR4 is important for homing to the gut, where most RORγt+IL-17+ T cells are found [16].
\nThe relationship among Th1, Th2, and Th17 cells is complex and still not clear. Th1- and Th2-related cytokines inhibit Th17 cell differentiation while IL-17 is not able to suppress Th1 or Th2 cells, or does it weakly. The suppression of IFNγ and IL-4 or their absence represents a way by which TGFβ1 could promote Th17 cell development. TGFβ1-driven Th17 cell differentiation can also occur in the absence of IFNγ and IL-4 [11]. In parallel with these findings, it was reported that IL-17-producing cells could be generated independent of the specific cytokines and transcription factors required for Th1 and Th2 differentiation [17]. Moreover, Th17 cells could develop from naïve T cells only in the combined presence of IL-6 and TGFβ1 [12, 20]. Thus, TGFβ induction of Th17 cells and also of Tregs, which are usually contradictory acting, is dependent on the presence of IL-6. This explains the apparent discrepancy of TGFβ1 involvement in both anti- and pro-inflammatory events in the intestine mucosa [17].
\nIL-17 is an effector cytokine in gut immunity, which may have either pro-inflammatory or tissue-protective effects in the mucosa depending on the experimental or clinical model used. On one hand, IL-17 contributes to the mucosal barrier function by several mechanisms which, upon activation, result in a mucosal immune response toward pathogens [6]. IL-17 also promotes tight junction formation and increases trans-epithelial resistance in polarized intestinal epithelial cells by stimulating the production of antimicrobial peptides such as lipocalin-2, β-defensins, and calprotectin. This suggests that the latter are involved in the maintenance of immunological homeostasis and/or in the control of specific inflammatory pathways [19]. Thus, the Th-17-related cytokines mediate protective effects in host gut against various bacteria and fungi, particularly at mucosal surfaces [10, 11]. Interestingly, pathogens that have evolved to take advantage of various aspects of the mucosal response gain an edge over the resident commensal bacteria and colonize the gut with priority. Despite that Th17 responses appear to be detrimental by promoting pathogen colonization of the mucosa, in the end, they result in decrease in bacterial dissemination from the mucosa that protects the host by inducing slight inflammation [6]. In line with this, it was shown that Th17 cells are constitutively present in the human and mouse intestinal mucosa and that Th phenotype is driven by the commensal bacteria in the gut. Additionally, stimulation of DCs with TLR ligands (e.g. fungal Dectin-1) induces synthesis of IL-6, TNFα, and IL-23 that promotes the differentiation of Th17 cells [11]. From this point of view, blocking Th-17 cytokines could have more deleterious than beneficial effects for the host [25].
\nOn the other hand, IL-17 might mediate tissue inflammation by triggering several inflammatory pathways and by inducing various pro-inflammatory cytokines (e.g. IL-1, IL-6, TNFα, G-CSF, GM-CSF), chemokines (e.g. IL-18, CXCL-1, CXCL8, MIP-1), and enzymes (COX-2, matrix metalloproteinases). Both IL-17 and IL-22 stimulate granulopoiesis by inducing expression of the granulocyte colony stimulating factor (G-CSF) and IL-17A which rapidly recruits neutrophils to the inflammatory site. This mechanism has important evolutionary significance [25]. The neutrophil response gains time for the induction of the following antimicrobial Th1- IFNγ response which takes several days to develop. Once the appropriate immune effector functions occur, the IL-12/IFNγ axis becomes the dominant pathway in host defence. This is important for initial control of the infection, but if the IL-23/IL-17 immune pathway becomes dysregulated, there is a danger of autoimmune pathology development, such as IBD. These observations, including the fact that T-bet is expressed at lower level in Th17 cells, led McKenzie et al. to favour the hypothesis of a common lineage precursor of Th1 and Th17 cells [8]. Furthermore, the tissue localization and timing of IL-12 versus IL-23 responses explain the idea that IL-12/IFNγ axis is involved in systemic inflammatory conditions (such as lupus), whereas the IL-23/IL-17 axis appears to regulate tissue-specific disorders (such as IBD) [8].
\nAnother layer of complexity to the mucosal existence of Th17 cells is other cell types, which can secrete IL-17-related cytokines: γδT cells (secreting IL-17 in response to IL-23), NK, NKT cells (able to produce IL-17 and IL-22), and DCs (can secrete IL-22 in response to bacterial infection). Paneth cells, which are common in the ileum, also secrete IL-17A [6, 19]. As all these cells express the IL-23 receptor, the secretion of IL-23 by DCs comprises a trigger which potentiates early T cell activation and adaptive immunity development [6]. Thus, it appears that early activation of both adaptive and atypical innate-like T cells can lead to the expression of IL-17 and IL-22. However, dysregulated production of IL-17, IL-22, and TNFα in local tissue can result in chronic immune-mediated tissue destruction [8].
\nStudies in murine models of IBD strongly suggest that Th17 cells and their related cytokines contribute to tissue-damaging immune responses in the gut [25]. Up-regulation of Th17-related cytokines, however, does not represent a specific hallmark of IBD in humans, as increased levels of IL-17A and other Th17-related-markers have been seen in patients with rheumatoid arthritis, multiple sclerosis, psoriasis, etc. [11]. Immunohistochemistry studies have shown that in active UC, the IL-17-expressing cells were located mainly within the lamina propria, while in active CD, these cells were scattered throughout the submucosa and muscularis propria of the gut. Corresponding with this, it was shown that RNA transcripts for IL-17A and IL-17F were up-regulated in the inflamed mucosa of UC and CD patients [3, 11, 22, 28]. Both IL-17 and IL-23 are correlated to the severity of UC [12]. More recently, Annunziato et al. demonstrated that the number of IL-17-producing T cells is higher in CD than in normal gut mucosa, and some of these cells also produce IFNγ [29].
\nTh17 cells have shown possession of functional plasticity. Some of the IL-17A-producing cells simultaneously express IFNγ (Figure 1). Majority of IL-17/ IFNγ-producing cells express CD161, a well-known marker of NKT cells, also identified recently on IL-17-producing memory T cells [11]. Th17 cells can be converted into Th1 cells if they receive appropriate stimuli, such as IL-12 which enhances the expression of Th1-related markers (i.e. T-bet and IFNγ) and down-regulates RORγt and IL-17. Additionally, recent studies have shown that treatment of intestinal lymphocytes with IL-23 can facilitate the production of either IL-17A or IFNγ in UC or CD, respectively [11].
\nThis is in consent with the demonstration that some of the pathogenic effects of IL-23 in the gut are linked to the ability of this cytokine to turn on IFNγ production. Switching from IL-17A to IFNγ production occurs if Th17 cells are activated by a lack of TGFβ1 [25]. Th17 cells and their possible conversion to Treg direction is going to be described later.
\nThis very complex and non-equivocal relationship of both pro-inflammatory and tissue-protective effects of IL-17 in the gut may explain the unsuccessful anti-IL-17 therapy in CD patients [10].
\nIL-21, an IL-2-related cytokine produced by Th17 cells in response to IL-6, increases the expansion of this cell subtype by a positive autoregulatory feedback loop. IL-21, which is up-regulated in inflamed IBD mucosa, induces Th1 and Th17 immune responses in the mucosa [10], but a mixture of both Th1 and Th17 cytokines is needed to promote full pathology in the gut. In this context, a promising inducer could be IL-21, whose activity seems to be necessary for expanding both Th1 and Th17 cell responses in the intestine. [25]. As we have already noticed, IL-21 is overproduced in the gut mucosa of IBD patients, but the vast majority of IL-21-producing CD4+ T cells co-express IFNγ but not IL-17A. This fact suggests that Th1 but not Th17 cells are the major sources of IL-21 in the human gut [11]. There is evidence that IL-21 also enhances the expression of Th1-related transcriptional factors and IFNγ production in NK cells [11].
\nIL-21, like IL-17, stimulates gut fibroblasts to produce tissue-degrading matrix-metalloproteinases and enhances the secretion of chemoattractants (i.e. MIP-3α) by epithelial cells [10, 11]. IL-21, like IL-6, could also initiate Th17 differentiation together with TGFβ1 [23], even in the absence of IL-6 [16, 17]. IL-21 enhances the expression of IL-23R in Th17 cells, through a process that is dependent on STAT3 and RORγt, making these cells responsive to IL-23. IL-21 as well exerts additional biological functions that could contribute to its pro-inflammatory effect in the gut like inhibition of the peripheral differentiation of Tregs and making CD4+ T cells resistant to Treg-mediated immune suppression [11].
\nIL-22 is a member of the IL-10 cytokine family and a Th17-related cytokine but it appears to be differentially regulated. IL-22 provides signals through a heterodimer comprising IL-22R and IL-10Rβ. The IL-22 receptor is highly expressed in tissues such as epithelial cells of the gastrointestinal tract. Via STAT3 signaling pathway, the activation of proliferative and/or anti-apoptotic programs starts, and this allows maintenance of epithelial barriers of the gut [5]. Most of the Th17 cytokines are highly dependent on the transcription factor RORγt for their expression, unlike IL-22 whose expression is dependent on the transcription factor aryl hydrocarbon receptor [5]. Th22 cells are another Th subpopulation characterized by the expression of this transcription factor and secretion of mainly IL-22 [5].
\nIL-22 has a dual functional nature in modulating the responses during tissue remodeling. IL-22 promotes induction of acute inflammatory proteins, mucins, and antimicrobial peptides (i.e. β-defensins), which are important for tissue integrity during inflammation. This mechanism ensures proper organ function and escape of potentially harmful effects by restricting the passage of luminal commensal flora and food antigens to the lamina propria [5, 25, 30]. It is important to point out that this process depends on the inflammatory context (the overall cytokine milieu and the tissues involved). Thus, IL-22 is important for control of pathogenic bacteria that need to translocate through host epithelial barriers to disseminate, especially in the gastrointestinal tract [5]. IL-22 also enhances intestinal barrier integrity by stimulating epithelial cell growth, goblet cell restitution, and mucus production, thus contributing to the healing of damaged tissue.
\nOn the other hand, IL-22 can cause further inflammation by stimulating colonic fibroblasts to secrete inflammatory cytokines (e.g. TNFα, IL-8, IL-11, and leukaemia inhibitory factor), IL-6, chemokines, and matrix metalloproteinases [11]. It is not surprising that IL-22 is highly expressed during chronic inflammation [5] in mucosal samples of patients with active CD, because of the known dysbacteriosis and expected pathological microbial agents, and to a lesser degree in patients with UC, where autoimmune phenomena are more common.
\nIL-22 is also expressed by innate immune cells such as CD11c+ and NK cells located in the colon. The latter cells do not secrete IFNγ and are not highly cytotoxic [30]. IL-23, a traditional activator of NK cells, induces IL-22 expression in NK cells. Unlike TGFβ and IL-10 that directly modulate the immune response, IL-22 does not have direct effects on immune cells since these cells lack the expression of IL-22R [30]. This way, TGFβ1 and IL-10 are involved in maintaining immune homeostasis under steady-state conditions instead.
\nIL-22 is an ideal therapeutic candidate since it specifically modulates tissue remodeling and does not have direct effects on the immune response. Treatment with recombinant cytokine or gene therapy delivery of IL-22 may alleviate tissue destruction during inflammation owing to its selective modulation of tissue responses [5].
\nThe main function of Tregs is to modulate the adaptive immune responses, and forkhead/winged helix transcription factor forkhead box P3 (FoxP3) is the master transcription factor for Tregs [23]. Two main subpopulations of Tregs have been best described: naïve (nTregs) and inducible Tregs (iTregs). The latter is believed to be derived by peripheral transformation of naïve T cells stimulated by IL-19, vitamin D3, antigens, and TGFβ1. So far, Treg function in IBD is not completely characterized [12].
\nTregs are crucially involved in the maintenance of gut mucosal homeostasis by suppressing abnormal immune responses against the commensal flora or dietary antigens. They exert their function by producing the anti-inflammatory cytokines IL-10 and TGFβ, thus preventing both the activation and the effector function of T cells. Additionally, the regulatory activities of the immune response through mediators such as IL-10 and TGFβ still need to be profiled, especially those that might take place in the unaffected areas of IBD patients [14]. A certain number of Th17 and CD4+CD25+FoxP3+ Tregs cell is presented in the intestine even in the healthy state, partly due to the presence of enteric bacteria which favor the production of both Th17 and Tregs. DCs in the intestine or mesenteric lymph nodes also actively promote the production of both cell types. However, there are points of divergence, for example, the retinoic acid produced by DCs in the intestine induces only Tregs. In spite of the essential function of IL-2 as a growth factor of effector T cells, including Tregs, IL-2 has an inhibitory effect on Th17 cell production. Furthermore, IL-2 deficiency leads to systemic autoimmune disease, partly because of its involvement in the differentiation and survival of Tregs [16]. Recent studies have revealed that IL-2 deficiency promotes differentiation of Th17 cell subset in a STAT5-dependent mechanism. At present, the recognized precise mechanism is exerted by suppression of IL-17 expression by directly binding to the IL-17 gene promoter of STAT5 [16].
\nThe importance of Tregs in maintaining immune homeostasis was once again emphasized with the X-linked IPEX syndrome (immune dysregulation, polyendocrinopathy, enteropathy), caused by mutation of FoxP3. IPEX patients quite often complain of gastrointestinal symptoms, suggesting that Tregs dysfunction may be involved in human IBD too [31].
\nA significant increase in production of Tregs in active-phase IBD mucosal lesions, as well as decreased numbers of Tregs in peripheral blood of IBD patients was described [9]. However, in active IBD a reduced number of peripheral Tregs have been reported to be reverted by anti-TNF treatment [12]. Indeed, Tregs are increased in the intestinal mucosa of IBD patients in comparison with the mucosa of healthy volunteers [22, 24, 27, 32]. Tregs isolated from inflamed tissue display no obvious defect in their suppressive function, at least in vitro [9]. However, Monteleone et al. found that Tregs obtained from the active-phase IBD mucosal lesions possess an ability to suppress T cell activation [11, 25]. Since Th17 cells appear to be resistant to the Tregs-mediated immunosuppression, it is likely that during chronic inflammatory process, such as in IBD, Tregs may be dysfunctional and might augment rather than suppress Th17-mediated immune responses [11]. At first, this phenomenon was explained as a feedback loop associated with an increase in the Treg cell attracted by IL-2 which is produced locally at sites of inflammation. On the other hand, however, up-regulated Th17 cells in response to increased production of pro-inflammatory cytokines were postulated [27]. Th17 cells, but not Tregs, are induced in the presence of pro-inflammatory cytokines, in addition to TGFβ1. Thus, Treg dysfunction may not be intrinsic but rather due to extrinsic milieu of activated cells that are resistant to suppression, and pro-inflammatory settings in the affected IBD mucosa [9, 33].
\nPlasticity of Tregs and Th17 is further demonstrated by the possibility of conversion between both subsets [27, 33]. Hu et al. have reported that Tregs express membrane-bound TGFβ and in the presence of IL-6, they convert to Th17 cells [34]. This could be an important warning regarding cell therapy with Tregs to treat chronic immune disease, including IBD, because the “homeostatic” Tregs may convert to pathogenic Th17 cells during inflammation where IL-6 is abundant [27]. Numerous studies have shown that in inflammatory cytokine environment, Tregs can lose FoxP3 expression and acquire expression of other transcription factors that define another lineage of CD4+ T cells as well as effector function. As we have already mentioned, exposure of Tregs to IL-6 results in a partial conversion to Th17 cells. Interestingly, although most IL-17-producing cells lost FoxP3 expression, some cells express both FoxP3 and IL-17. It is unclear, however, whether the resultant cells are suppressive [9]. So, once again it must be mentioned that the Th17/Tregs balance appears to play a very crucial role in IBD development [27].
\nIL-10 is secreted by many types of immune cells including Th2, Tregs, Tr1 (IL-10-producing FoxP3-CD4+ T cells), Th3 (TGFβ and IL-10-producing CD4+ T cells induced in oral tolerance), NKT cells, B cells, macrophages, and DCs [5]. IL-10 binds to its heterodimeric receptor, composed of unique for IL-10 subunit (IL-10Rα) and shared with IL-22 subunit (IL-10Rβ). Although not completely sufficient, STAT3 is required for the inhibitory functions of IL-10. Importantly, STAT3 induces the expression of transcription factors that regulate various cytokine signaling pathways including IL-6. IL-10 down-regulates IL-12 production and expression of co-stimulatory molecules in macrophages and DCs, thereby reducing the Th1 response generation [5].
\nIL-10 is a key regulator of the immune system by limiting the inflammatory responses that could otherwise cause tissue damage. IL-10 is essential for homeostasis of the immune system, especially in the gastrointestinal tract where the tolerance is most needed. Evidence for that is the highly-susceptible-to-colitis IL-10-deficient mice which develop aberrant immune responses to commensal bacteria. This colitis is more severe when combined with a deficiency in TGFβ signaling [5].
\nSmall intestine and colonic lamina propria showed the highest frequency of IL-10-expressing cells. Recent findings show that macrophages in the lamina propria preferentially induce IL-10-producing cells while DCs promote the generation of Th17 cells. On one hand, blocking IL-10 during infection can result in more severe pathology or even fatality of the host, but on the other hand, high production of IL-10 is associated with sustained chronic infections and its blockade promotes pathogen clearance. Thus, once again, the milieu of the intestines favors the generation of IL-10-producing T cells leading to tolerance against commensal bacteria, whereas the expression of IL-10 in peripheral tissues under infectious conditions leads to suppression of the immune response [5]. In line with this, when IL-10 was previously found to be abundantly expressed by macrophages in areas of dense inflammatory infiltrate, it had been directly related to the attenuation of the mucosal inflammation [14]. Knowing nowadays about the dual role of IL-10, it is not unexpected that IL-10 is presented at a higher level in the inflamed mucosa of IBD patients [13]. These findings were confirmed by us as well [24].
\nSome IL-10 gene polymorphisms have been associated with susceptibility to IBD (i.e. IL-10—1082) and more significantly with UC alone. Whether the polymorphisms are directly involved in regulating cytokine production, and consequently disease pathophysiology of IBD, or serve merely as markers that are in linkage disequilibrium with susceptibility genes, is still unclear [15].
\nThe involvement of IL-10 in the regulation of the pathogenic function of Th17 cells has been definitively demonstrated in experiments where non-pathogenic Th17 subtype expressing IL-10 is generated by IL-6 and TGFβ1, even though in the absence of IL-23. These cells also prevent the induction of the disease in an IL-10-dependent manner [35]. Even though IL-10 effectively treats colitis in mouse models and suppresses inflammatory cytokine production in vitro in intestinal cells of patients with IBD, clinical trials using recombinant IL-10 to treat IBD in humans have been largely disappointing, irrespective of the acceptable side-effect profile of the therapy [36].
\nInnate lymphoid cells (ILCs) are recently described cells that have been involved in both maintenance and loss of gut homeostasis. ILCs are phenotypically and functionally distinct subsets of cells that inhabit the intestinal mucosa. However, they produce cytokines associated with effector T-cell responses early in inflammatory lesions of patients with IBD [37]. The novel family of cells comprises three subsets: ILC1, ILC2, and ILC3 [38]. ILC1 express the transcription factor T-bet resembling Th1 cells with production of IFN-γ and TNF; thus, they contribute to host resistance to intestinal pathogens. ILC2 produce Th2 cytokines, such as IL-5 and IL-13, and they are dependent on the transcription factor GATA-3. ILC3 which express the transcription factor RORγt produce IL-17A and IL-22 mirroring Th17 cells [37]. ILC3 is involved in gut homeostasis by secreting IL-22 and promoting IL-10 and antimicrobial peptide production. Epithelial stress-induced ligands and inflammatory conditions may switch ILC3 to ILC1 secreting TNF and IFN-γ under the influence of IL-12. The pro-inflammatory cytokines of ILC1 and ILC3 lead mainly to epithelial apoptosis and neutrophil recruitment. ILC2 are able to contribute to IBD complications by producing the fibrogenic cytokine IL-13 [37].
\nSince ILCs might be substantial drivers of mucosal inflammation, targeting ILC subsets may be a new exciting treatment option for IBD patients.
\nFrom a clinical perspective, IBD is a chronic persistent disease characterized by repeated relapses and remissions. One explanation could be that memory Th cells created during the disease development persist in the body, including during remission, in a manner that is dependent on the various cytokine presentations. Effector cytokines in the mucosa may induce inflammation at the time of the initial episode and during relapses. However, the ambiguity and contradictory actions of given cytokines confound the understanding of their interactions in dynamics of the immune response, and that leads to lack of synonymous conclusions about them. There is still strong need for further investigation, particularly in the gut mucosa, to fully comprehend their roles in the complex dynamic network of the immune mediators.
\nTh17 cells have been shown to play a central role in murine and human IBD. Inhibition of the Th17 pathway may be a promising treatment for IBD, with respect to the role of other subsets of Th1 and Th2 cells. The data in the literature and our own experience make us believe that in order to achieve immune homeostasis in the gut, pro-inflammatory and anti-inflammatory responses that define the mucosal cell immunophenotype, should achieve balance. Thus, following the clinical periods of remissions and relapses, it is important to observe their immunological equivalents in the gut and possibly in whole blood, namely regulatory and pro-inflammatory cytokines secreted by different types of immunocompetent cells.
\nWe would like to thank the Medical University of Sofia, Bulgaria [Grant No.22/2012] and the Medical Faculty of Trakia University, Stara Zagora, Bulgaria [Grant No.4/2012] for the financial support of our studies regarding cytokines expression in inflamed mucosa. We are also immensely grateful to Stoyanka Petrova and Radislav Nakov for coordination of the financial support for this publication. We would also like to show our gratitude to Iliya Karakolev and Kalina Toumangelova-Yuzeir for assistance with some aspects of the methodology.
\nThe problems of banks financial state analysis and bankruptcy risk forecasting are of great importance. The opportune discovery of coming bankruptcy allows top bank managers to make urgent decisions for preventing the bankruptcy. Nowadays, there are a lot of methods and techniques of banks state analysis and determination of bank rating—WEB Money, CAMEL [1], Moody’s S&P, etc. But their common drawback is that all of them work with complete and reliable data and cannot give correct results in case of incomplete and unreliable input data. This is especially actual for the Ukrainian banking system where bank managers often provide the incorrect reports about bank financial state to obtain new credits and loans.
\nTherefore, it is very important to create new methods for banks bankruptcy risk forecasting under uncertainty. The main goal of present investigation is to consider and estimate novel methods of bank financial state analysis and bankruptcy risk forecasting under uncertainty and compare with classical methods. The implementation and assessment of the efficiency of the suggested methods are performed at the problems of bankruptcy risk forecasting for Ukrainian and European banks.
\nAs it is well known, the year 2008 was the crucial year for the bank system of Ukraine. If the first three quarters were periods of fast growth and expansion, the last quarter became the period of collapse in the financial sphere. A lot of Ukrainian banks faced the danger of coming default.
\nFor this research, the quarterly accountancy bank reports used were obtained from National bank of Ukraine site. For analysis, the financial indices of 170 Ukrainian banks were taken up to the date January 01, 2008 and July 01, 2009, that is, about two years before crises and just before the start of crises [2].
\nThe important problem that occurred before the start of the investigations is which financial indices are to be used for better forecasting of possible bankruptcy. Thus, another goal of this exploration was to detect the most relevant financial indicators for obtaining maximal accuracy of forecasting.
\nFor analysis, the following indicators of banks accountancy were considered:
assets, capital, financial means, and their equivalents; and
physical person’s entities, juridical person’s entities, liabilities, and net incomes (losses).
The collected indicators were used for analysis by fuzzy neural networks as well as classic statistical methods. As output data of models for Ukrainian banks were two values:
1, if the significant worsening of bank financial state is not expected in the nearest future
−1, if the bank bankruptcy is expected in the nearest future.
For forecasting of banks bankruptcy risk, the application of fuzzy neural networks (FNN) ANFIS and TSK was suggested [3]. The application of FNN is determined by following reasons:
the capability to work with incomplete and unreliable information under uncertainty; and
the capability to use expert information in the form of fuzzy inference rules.
Let us consider the mathematical model and training algorithm of a fuzzy neural network TSK (Takagi, Sugeno, Kang’a), which is generalization of the neural network ANFIS. The rule base of FNN TSK with M rules and N variables can be written as follows [3]:
where \n
At the intersection of the TSK network rule conditions, \n
With M inference rules, the general output of FNN TSK is determined by the following formula:
\nwhere \n
The fuzzy neural network TSK, which implements the output in accordance with (3), represents a multilayer network whose structure is shown in Figure 1.
\nThe structure of TSK fuzzy neural network.
This network has five layers with the following functions:
The first layer performs fuzzification separately for each variable \n
The second layer performs the aggregation of individual variables \n
The third layer is a function generator TSK, wherein the output values are calculated \n
The fourth layer consists of two summing neurons, one of which calculates the weighted sum of the signals \n
The fifth layer is composed of a single output neuron. In it, weight normalizing is performed and the output signal determined in accordance with the expression:
This is also nonparametric layer.
\nFrom this description follows that TSK fuzzy network contains only two parametric layers: first and third, the parameters of which are determined in the training process. Parameters of the first layer \n
If we assume that at any given time moment, the nonlinear parameters are fixed, then the function \n
In the presence of N input variables, each rule \n
Considering a hybrid learning algorithm which is used for FNN TSK, all parameters can be divided into two groups. The first group includes linear parameters \n
In the first stage after fixing the individual parameters of the membership function by solving a system of linear equations, linear parameters of polynomial \n
where
\nWith the dimension L of training sample \n
where \n
Matrix \n
where \n
In the second stage, after fixing the values of linear parameters \n
Then, the error vector \n
The error signals are sent through the network backward according to the method of back propagation until the first layer, at which gradient vector components of the objective function with respect to parameters \n
After calculating the gradient vector, a step of gradient descent method is made. The corresponding formulas (for the simplest method of the steepest descent) are the following:
\nwhere n is a number of iterations.
\nAfter verifying the nonlinear parameters, the process of adaptation of linear parameters TSK (first phase) restarts and nonlinear parameters are further adapted (second stage). This cycle continues until all the parameters will be stabilized.
\nFormulas (11)–(13) require the calculation of the gradient of the objective function with respect to the parameters of the MF. The final form of these formulas depends on the type of MF. For example, if using the generalized bell-wise functions:
\nthe corresponding formulas for gradient of the objective function for one pair of data \n
In the practice of the hybrid learning method implementation, the dominant factor in adaptation is considered to be the first stage in which weights \n
It is worth to note that the described hybrid algorithm is one of the most effective ways of training fuzzy neural networks. Its principal feature is the division of the process into two stages separated in time. Since the computational complexity of each nonlinear optimization algorithm depends nonlinearly on the number of parameters subject to optimization, the reduction in the dimensions of optimization significantly reduces the total amount of calculations and increases the speed of convergence of the algorithm. Due to this, hybrid algorithm is one of the most efficient in comparison with conventional gradient-based methods.
\nA special software kit was developed for FNN ANFIS and TSK application in bankruptcy risk forecasting problems. As input data, the financial indicators of Ukrainian banks in financial accountant reports were used in the period of 2008–2009 [2] . As the output values were used +1, for bank nonbankrupt and −1, for bank bankrupt. In the investigations, various financial indicators were analyzed, and different number of rules for FNN and the analysis of data collection period influence on forecasting accuracy were performed.
\nThe results of experimental investigations of FNN application for bankruptcy risk forecasting are presented below.
\nIn the first series of experiments, input data at the period of January 2008 were used (that is for two years before possible bankruptcy) and possible banks bankruptcy was forecasted at the beginning of 2010.
\n\n
Training sample—120 Ukrainian banks, test sample—50 banks.
Number of rules = 5.
Input data—financial indices (taken from bank accountant reports):
assets, capital, cash (liquid assets), households deposits, liabilities.
The results of application of FNN TSK are presented in Table 1.
The similar experiments were carried out with FNN ANFIS.
The goal of the next experiment was to find out the dependence of rule number on predicting accuracy. Input data—the same financial indices as in experiment 1.
\nThe results of application of FNN TSK are presented in Table 2.
\nThe similar experiments were carried out with FNN ANFIS.
\nThe comparative analysis of forecasting results versus the number of rules is presented in Table 3 [4].
\nComparing the results in Table 3, one may conclude FNN TSK has better accuracy than FNN ANFIS.
\nThe goal of the next experiments was to explore the influence of training and test samples size on accuracy of forecasting.
\n\n
Training sample—120 Ukrainian banks, test sample—50 banks, and number of rules = 10.
Input data—financial indicators:
assets, entity, cash (liquid assets), household deposits, and liabilities.
The results for FNN TSK are presented in Table 4.
\nThe similar experiments were carried out with FNN ANFIS.
\nAfter analysis of the experimental results the following conclusions were made:
FNN TSK ensures the higher accuracy of risk forecasting than FNN ANFIS;
the variation of the number of rules in the training and test samples makes slight influence on the accuracy of forecasting; and
the goal of the next series of experiments was to determine the optimal input data (financial indicators) for bankruptcy risk forecasting. The period of input data was January 2008.
\n
Number of banks and rules were the same as in previous experiment 4.
Input data—financial indicators (taken from banks financial accountant reports):
profit of current year, net percentage income, net commission income; and
net expense on reserves and net bank profit/losses.
The results of FNN TSK application are presented in Table 5.
\n\n
Number of banks and rules were the same as in the previous experiment 5.
Input data—the financial indicators (taken from banks financial accountant reports):
general reliability factor (own capital/assets);
instant liquidity factor (liquid assets/liabilities);
cross coefficient (total liabilities/working assets);
general liquidity coefficient (liquid assets + defended capital + capitals in reserve fund/total liabilities); and
coefficient of profit fund capitalization (own capital/charter fund).
The results for FNN TSK are presented in Table 6.
\nIt is worth to note that these financial indicators are also used as input data in Kromonov’s method of banks bankruptcy [5, 6, 7], whose results are presented below.
\n\n
Training sample—120 Ukrainian banks and test sample—70 banks.
Number of rules = 5.
Input data—following financial indicators (other than in experiments 5 and 6):
ROE—return on entity (financial results/entity);
ROA—return on assets (financial results/assets);
CIN—incomes-expenses ratio (income/expense);
NIM—net percentage margin; and
NI—net income.
The results of application of FNN TSK for forecasting with these input indicators are presented in Table 7.
\nIt should be noted that these indicators are used as input in the method of Euro Money [1].
\n\n
Training sample—120 Ukrainian banks and test sample—70 banks.
Number of rules = 5.
Input data—financial indicators (banks financial accountant reports):
general reliability factor (own capital/assets);
instant liquidity factor (liquid assets/liabilities);
cross coefficient (total liabilities/working assets);
general liquidity coefficient (liquid assets + defended capital + capitals in reserve fund/total liabilities);
coefficient of profit fund capitalization (own capital/charter fund); and
coefficient entity security (secured entity/own entity).
The results of FNN TSK application with these financial indicators are presented in Table 8.
\nThe comparative analysis of forecasting results using different sets of financial indicators are presented in Table 9.
\n\nNext experiment was aimed on finding the influence of data collection period on the forecasting results. It was suggested to consider two periods: January of 2008 (about 1.5 year before the crisis) and July of 2009 (just before the start of crisis).
\n\n
Training sample—120 Ukrainian banks and test sample—70 banks.
Number of rules = 10.
Input data—financial indices, the same as in experiment 8.
In Table 10, the comparative results of forecasting versus period of input data are presented.
\nResults | \n|
---|---|
Total amount of errors | \n5 | \n
% of errors | \n10 | \n
First type of errors | \n0 | \n
Second type of errors | \n5 | \n
Results of FNN TSK forecasting.
Results | \n|
---|---|
Total amount of errors | \n6 | \n
% of errors | \n12 | \n
First type of errors | \n1 | \n
Second type of errors | \n5 | \n
Results of FNN TSK forecasting.
Network/number of rules | \nTotal number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
ANFIS 5 | \n6 | \n12 | \n0 | \n6 | \n
ANFIS 10 | \n7 | \n14 | \n1 | \n6 | \n
TSK 5 | \n5 | \n10 | \n0 | \n5 | \n
TSK 10 | \n6 | \n12 | \n1 | \n5 | \n
Comparative analysis of FNN ANFIS and TSK in dependence on rules number.
Results | \n|
---|---|
Total number of errors | \n7 | \n
% of errors | \n10 | \n
First type of errors | \n1 | \n
Second type of errors | \n6 | \n
Results of FNN TSK forecasting.
Results | \n|
---|---|
Total number of errors | \n13 | \n
% of errors | \n19 | \n
First type of errors | \n6 | \n
Second type of errors | \n7 | \n
Results of FNN TSK forecasting.
Results | \n|
---|---|
Total number of errors | \n7 | \n
% of errors | \n10 | \n
First type of errors | \n1 | \n
Second type of errors | \n6 | \n
Results of FNN TSK forecasting.
Results: | \n|
---|---|
Total number of errors | \n12 | \n
% of errors | \n17 | \n
First type of errors | \n5 | \n
Second type of errors | \n7 | \n
Results of FNN TSK forecasting.
Results | \n|
---|---|
Total amount of errors | \n8 | \n
% of errors | \n13 | \n
First type of errors | \n1 | \n
Second type of errors | \n7 | \n
Results of FNN TSK forecasting.
Experiment | \nTotal number of errors | \n% of errors | \nFirst type of errors | \nSecond type of errors | \n
---|---|---|---|---|
Experiment 5 | \n13 | \n19 | \n6 | \n7 | \n
Experiment 6 | \n7 | \n10 | \n1 | \n6 | \n
Experiment 7 | \n12 | \n17 | \n5 | \n7 | \n
Experiment 8 | \n8 | \n13 | \n1 | \n7 | \n
The dependence of forecasting accuracy on sets of input financial indices.
Experiment/number rules | \nTotal number of errors | \nFirst type of errors | \nSecond type of errors | \nTotal % of errors | \n
---|---|---|---|---|
January 1, 2008 5 rules | \n7 | \n0 | \n7 | \n10 | \n
July 1, 2009 5 rules | \n5 | \n0 | \n5 | \n7 | \n
July 1, 2009 10 rules | \n7 | \n3 | \n4 | \n10 | \n
Accuracy of forecasting in dependence on data collection period.
In the process of investigations, fuzzy group method of data handling (FGMDH) was also suggested for financial state of Ukrainian banks forecasting [3]. GMDH is the inductive modeling method that enables to construct a model automatically by experimental data [3]. As input data, the same indices were used as in the experiments with FNN TSK.
\nIn Table 11, the forecasting accuracy of FGMDH is presented in dependence on input data collection period.
\nInput data period | \nTotal error number | \n% of errors | \nFirst type of errors | \nSecond type of errors | \n
---|---|---|---|---|
2004 | \n10 | \n14 | \n3 | \n7 | \n
2005 | \n9 | \n13 | \n3 | \n6 | \n
2006 | \n8 | \n11.4 | \n3 | \n5 | \n
2007 | \n7 | \n10 | \n2 | \n5 | \n
2008 | \n6 | \n8.5 | \n1 | \n5 | \n
2009 | \n6 | \n8.5 | \n2 | \n4 | \n
Comparative results of forecasting using method FGMDH in dependence on period of input data collection.
If we compare the results of FGMDH with the results of FNN TSK, one can see that FNN TSK gives better results for short-term risk forecasting (one year before possible bankruptcy) while FGMDH has better accuracy using older input data and so it has advantages in long-term forecasting (2 or more years).
\nIn the concluding experiments, the comparative analysis of application of all the considered methods was carried out. The following methods were considered [4]:
fuzzy neural network ANFIS;
fuzzy neural network TSK; and
crisp forecasting methods: Kromonov’s method and Byelorussian bank association method.
As input data, the financial indices of Ukrainian banks on July 2007 year were used. The results of application of all methods for bankruptcy risk analysis are presented in Table 12.
\nMethod/period | \nTotal amount of errors | \n% of errors | \nFirst type of errors | \nSecond type of errors | \n
---|---|---|---|---|
ANFIS | \n7 | \n10 | \n1 | \n6 | \n
TSK | \n5 | \n7 | \n0 | \n5 | \n
GMDH | \n6 | \n8.5 | \n1 | \n5 | \n
Kromonov’s method | \n10 | \n15 | \n5 | \n5 | \n
BBA method | \n10 | \n15 | \n2 | \n8 | \n
Comparative results analysis of various forecasting methods.
The most widely used approach of banks financial state analysis and bankruptcy risk forecasting is based on the application of rating systems. The determination of bank rating is one of the methods that enables to obtain complex financial assessment of bank financial state and compare them. There are various private and official banks rating systems. The most known of them are systems developed by world leaders in this sphere-rating companies Fitch, Standard & Poor’s, Moody’s, etc. Officially recognized banks rating system that is widely used in the world is system CAMELS. It’s American rating system was developed and implemented by Federal reserve System (FRS) and Federal Deposit Insurance Corporation (FDIC) in 1978 [1].
\nSupervision over banks activity based on risk estimation by system CAMELS lies in determination of general bank state using the common criteria that defines all aspects and spheres of bank activity. This system is also widely used in Ukraine by National Bank of Ukraine (NBU) according to developed “Statement of order of rating estimates determination by rating system ‘CAMELS’.”
\nRating system CAMELS allows NBU to estimate general financial state and stability of banking system of Ukraine. Such assessment enables to obtain information for priority determination in banking supervision activity and necessary materials and financial resources for performing adequate control over banking system.
\nAt the same time, system CAMELS envisages the detail supervision and analysis of bank state. Such analysis may be performed only while complex inspecting checking of bank activity, which enables to determine how the top managers analyze and control bank risks.
\nThe base of rating system, CAMELS, is risk assessment and determination of rating estimates by each component of the system: capital adequacy, assets quality, management, liquidity, and sensitivity.
\nDue to rating system, each bank obtain digital rating by all six components, and integral (complex) rating estimate is determined on the base of rating estimates of all components. Components of rating system are estimated by 5 balls scale in which estimate 1 is the highest, and estimate 5 is the lowest one. Integral estimate is also determined by 5 balls scale. Banks that obtained integral rating estimate 1 or 2 are considered reliable by all the factors capable to overcome economic depression and its management believed to be qualified.
\nBanks that got integral estimate 3 have substantial drawbacks, which may lead to serious problems with liquidity and solvency if these drawbacks won’t be corrected in proper time. In this case, bank’s supervision system should give recommendations to managers how to overcome existing problems.
\nBanks that got rating estimate 4 or 5 have serious problems, which demand strict supervision and special urgent actions to prevent possible bankruptcy (see Table 13).
\nFor bankruptcy risk forecasting in banking sphere of Ukraine, a special data set was collected consisting of 160 Ukrainian banks in the period 2012–2014 . It was divided into training and test subsamples in ratio 70/30 for FNN TSK, i.e., training samples consisted of 110 banks and test samples of 50 banks. The experiments were carried out, and the following results were obtained for FNN TSK (in average, 20 experiments were performed for each year and rules number), which are presented in Table 2. The data were collected in the year indicated in the first column, and the forecasting was made for next year, e.g., 2012-5—means bankruptcy risk forecasting in 2013 with use of 5 rules in FNN TSK by data of 2012. Two types of experiments were carried out with fixed parameters of membership functions (MF) and with training MF parameters. In Table 14, forecasting results for FNN TSK with adaptation of parameters are presented and in Table 15 with fixed parameters values.
\n\n | Bank integral rating | \n||||
---|---|---|---|---|---|
“1” | \n“2” | \n“3” | \n“4” | \n“5” | \n|
Bank financial state | \nBank is stable, reliable, and has skilled management | \nBank has substantial drawbacks, which may lead to serious problems in future. | \nBank faces very serious problems, which may lead to bankruptcy. | \n||
Control from banks supervision service | \n\n | Bank supervision service should give clear instructions to overcome existing problems. | \nBanks need urgent actions to prevent possible bankruptcy. | \n||
Application of special actions | \n\n | Proper influence actions are performed over bank due to demands of existing regulation laws of NBU. | \n
Comparative results analysis of various forecasting methods.
Year and number of rules | \nGeneral number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2012—5 | \n6 | \n12 | \n0 | \n6 | \n
2013—5 | \n9 | \n18 | \n0 | \n9 | \n
2014—5 | \n8 | \n16 | \n1 | \n7 | \n
2012—10 | \n7 | \n14 | \n2 | \n5 | \n
2013—10 | \n5 | \n10 | \n0 | \n5 | \n
2014—10 | \n10 | \n20 | \n4 | \n6 | \n
Forecasting results for FNN TSK with FM parameters’ adaptation.
Year and number of rules | \nGeneral number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2012 | \n8 | \n16 | \n1 | \n7 | \n
2013 | \n8 | \n16 | \n0 | \n8 | \n
2014 | \n9 | \n18 | \n1 | \n8 | \n
2012 | \n9 | \n18 | \n3 | \n6 | \n
2013 | \n7 | \n14 | \n1 | \n6 | \n
2014 | \n11 | \n22 | \n4 | \n7 | \n
Forecasting results for FNN TSK with fixed parameters.
In Table 16, forecasting results for FNN TSK with triangular MF are presented, while in Table 17 with trapezoidal MF.
\nYear and number of rules | \nGeneral number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2012 | \n9 | \n18 | \n1 | \n8 | \n
2013 | \n7 | \n14 | \n1 | \n6 | \n
2014 | \n9 | \n18 | \n0 | \n9 | \n
2012 | \n11 | \n22 | \n4 | \n7 | \n
2013 | \n10 | \n18 | \n2 | \n8 | \n
2014 | \n13 | \n26 | \n4 | \n9 | \n
Forecasting results for FNN TSK with triangular MF.
Year and number of rules | \nGeneral number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2012 | \n7 | \n14 | \n1 | \n8 | \n
2013 | \n8 | \n16 | \n1 | \n6 | \n
2014 | \n5 | \n10 | \n0 | \n9 | \n
2012 | \n9 | \n18 | \n4 | \n7 | \n
2013 | \n12 | \n24 | \n2 | \n8 | \n
2014 | \n11 | \n22 | \n4 | \n9 | \n
Forecasting results for FNN TSK with trapezoidal MF.
The application of well-known matrix method by Nedosekin [11, 12] with level (threshold) of cut 0.7 gave the following results presented in Table 18.
\nYear and number of rules | \nGeneral number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2012 | \n14 | \n28 | \n8 | \n6 | \n
2013 | \n11 | \n22 | \n3 | \n8 | \n
2014 | \n16 | \n32 | \n9 | \n7 | \n
Forecasting results of matrix method by Nedosekin.
Results obtained by rating system CAMEL are presented in Table 19 (threshold of 4).
\nYear and number of rules | \nGeneral number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2012 | \n12 | \n24 | \n7 | \n5 | \n
2013 | \n14 | \n28 | \n5 | \n9 | \n
2014 | \n9 | \n18 | \n5 | \n4 | \n
Forecasting results of rating system CAMEL.
In Figure 2, the probability of error for different forecasting methods and various MF is presented.
\nBankruptcy risk forecasting results for different methods.
In Figure 3, dependence of error probability versus number of rules in FNN TSK is presented.
\nError probability for different rules number in FNN.
Analyzing the performed experiments, the following conclusions may be made.
The experimental investigations of efficiency of different forecasting methods FNN TSK, matrix method of Nedosekin, and rating system CAMELS were carried out for the problem of bankruptcy risk forecasting of Ukrainian banks.
Results obtained by FNN TSK are the best (min error%). Mean forecasting accuracy by TSK with Gaussian MF is equal to 85%, with trapezoidal MF mean accuracy is 82%, triangular MF gives 79%, matrix method of Nedosekin −70%, while standard rating system CAMELS has 75% accuracy.
With the increase of rules, number error probability first decreases, then attains minimum and then begins to raise.
Various methods for Ukrainian banks financial state forecasting were considered and analyzed. The following methods were considered [3, 4]: fuzzy neural network ANFIS, fuzzy neural network TSK, Kromonov’s method, Byelorussian bank association method, rating system CAMELS, and matrix method (Nedosekin).
\nAs the input data, the financial indices of Ukrainian banks were considered.
\nWhile experiments with the adequate financial indicators were detected using which the best forecasting results for Ukrainian banks were obtained:
general reliability factor (own capital/assets);
instant liquidity factor (liquid assets/liabilities);
cross coefficient (total liabilities/working assets);
general liquidity coefficient (liquid assets + defended capital + capitals in reserve fund/total liabilities); and
coefficient of profit fund capitalization.
It was established that FNN TSK gives much more accurate results than FNN ANFIS. With increase of rules, number error probability first decreases, then attains minimum and then begins to raise.
The fuzzy GMDH gives better results using older data that is, more preferable for long-term forecasting (two or more years).
The comparison of FNN TSK with standard rating system CAMELS has shown that TSK enables to obtain more accurate bankruptcy risk forecasting.
In general, the comparative analysis had shown that fuzzy forecasting methods and techniques give better results than the conventional crisp and rating methods for forecasting bankruptcy risk. But at the same time, the crisp methods are more simple in implementation and demand less time for their adjustment.
The results of successful application of fuzzy methods for bankruptcy risk forecasting of Ukrainian banks under uncertainty stimulated the further investigations of these methods application for financial state analysis of European leading banks.
\nThe main goal of this exploration was to investigate novel methods of European banks bankruptcy risk forecasting, which may work under uncertainty with incomplete and unreliable data.
\nBesides, the other goal of this investigation was to determine which factors (indicators) are to be used in forecasting models to obtain results close to real data. Therefore, we used a set of financial indicators (factors) of European banks according to the International accountant standard IFRS. The annual financial indicators of about 300 European banks were collected in 2004–2008, preceding the start of crisis of bank system in Europe in 2009. The data source is the information system Bloomberg [8]. The resulting sample included the reports only from the largest European banks as system Bloomberg contains the financial reports only from such banks. For correct utilization, input data were normalized in interval [0,1].
\nThe period for which the data were collected was 2004–2008. The possible bankruptcy was analyzed in 2009. The indicators of 165 banks were considered among which more than 20 banks displayed the worsening of the financial state in that year. Fuzzy neural networks and Fuzzy Group Method of Data Handling (FGMDH) were used for bank financial state forecasting.
\nIn accordance with the above stated goal, the investigations were carried out for detecting the most informative indicators (factors) for financial state analysis and bankruptcy forecasting. Taking into account incompleteness and unreliability of input data, FNN ANFIS and TSK were suggested for bankruptcy risk forecasting.
\nAfter performing a number of experiments, the data set of financial indicators was found using which FNN made the best forecast. These indicators are the following:
debt/assets = (short-term debt + long-term debt)/total assets;
loans to deposits ratio;
net interest margin (NIM) = net interest income/earning assets;
return on equity (ROE) = net income/stockholder equity;
return on assets (ROA) = net income/assets equity;
cost/income = operating expenses/operating income; and
equity/assets = total equity/total assets.
A series of experiments was carried out for determining the influence of the number of rules and period of data collection on forecasting results.
\nIn the first series of experiments, FNN TSK was used for forecasting.
\nTraining sample = 115 banks of Europe, testing sample = 50 banks, and number of rules = 5.
Input data period = 2004 (experiment 1), 2005 (experiment 2), 2006 (experiment 3), 2007 (experiment 4), and 2007 (experiment 5).
The total results of application FNN TSK for different rules number and data collection period are presented in Table 20.
\nExperiment/number of rules | \nTotal errors number | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2004 | \n8 | \n16 | \n0 | \n8 | \n
2005 | \n7 | \n14 | \n0 | \n7 | \n
2006 | \n5 | \n10 | \n0 | \n5 | \n
2007 | \n1 | \n2 | \n0 | \n1 | \n
2004 | \n8 | \n16 | \n0 | \n8 | \n
2005 | \n8 | \n16 | \n1 | \n7 | \n
2006 | \n11 | \n22 | \n7 | \n4 | \n
2007 | \n4 | \n8 | \n0 | \n4 | \n
Forecasting results for FNN TSK versus number of rules and data period.
Furthermore, the similar experiments were performed with FNN ANFIS, while the period of data collection varied since 2004–2007. The corresponding results for FNN ANFIS are presented in Table 21 showing the influence of data collection period on forecasting accuracy.
\nExperiment/number of rules | \nTotal errors number | \n%% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2004 | \n8 | \n16% | \n0 | \n8 | \n
2005 | \n8 | \n16% | \n1 | \n7 | \n
2006 | \n8 | \n16% | \n4 | \n4 | \n
2007 | \n4 | \n8% | \n0 | \n4 | \n
Forecasting results for FNN ANFIS versus number of rules and data period.
After analysis of these results, the
FNN TSK has better forecasting accuracy than FNN ANFIS;
the best input variables (indicators) for European banks bankruptcy risk forecasting are the following:
debt/assets = (short-term debt + long-term debt)/total assets;
loans to deposits;
net interest margin (NIM) = net interest income/earning assets;
return on equity (ROE) = net income/stockholder equity;
return on assets (ROA) = net income/assets equity;
cost/income = operating expenses/operating income; and
equity/assets = total equity/total assets.
Input data collection period (forecasting interval) makes influence on forecasting results.
\nIn next experiments, Fuzzy Group Method of Data Handling (FGMDH) was applied for European banks financial state forecasting. Fuzzy GMDH enables to construct forecasting models using experimental data automatically without expert [3]. The additional advantage of FGMDH is possibility to work with the fuzzy information.
\nAs the input data in these experiments, the same indicators as in experiments with FNN TSK were used. In Table 22, forecasting results are presented in dependence on input data period collection for FGMDH
\nInput data period | \nTotal number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
2004 | \n7 | \n14 | \n0 | \n7 | \n
2005 | \n6 | \n12 | \n1 | \n5 | \n
2006 | \n4 | \n8 | \n1 | \n3 | \n
2007 | \n2 | \n4 | \n0 | \n2 | \n
Comparative analysis of forecasting results for FGMDH.
If to compare the results of FGMDH with the results of FNN TSK, one can see that neural network has better accuracy at the short forecasting interval (1 year), while fuzzy GMDH has better accuracy at the greater intervals (2 or more years). This conclusion coincides with similar conclusion for Ukrainian banks.
\nIn Table 23, the comparative results of application of different methods for bankruptcy risk forecasting are presented
\nMethod (period) | \nTotal number of errors | \n% of errors | \nNumber of first type errors | \nNumber of second type errors | \n
---|---|---|---|---|
ANFIS (1 year) | \n4 | \n8 | \n0 | \n4 | \n
TSK (1 year) | \n1 | \n2 | \n0 | \n1 | \n
FGMDH (1 year) | \n2 | \n4 | \n0 | \n2 | \n
ANFIS (2 years) | \n8 | \n16 | \n4 | \n4 | \n
TSK (2 years) | \n5 | \n10 | \n0 | \n5 | \n
FGMDH (2 years) | \n4 | \n8 | \n1 | \n3 | \n
Forecasting results of different fuzzy methods.
For estimation of fuzzy methods’ efficiency at the problem of bankruptcy risk forecasting the comparison with crisp method, the regression analysis of linear models was performed. As input data, the same indicators were used, which were found optimal for FNN. Additionally, the index net financial result was also included in the input set. This index makes great impact on forecasting results. Thus, input data in these experiments were eight financial indicators of 256 European banks according to their reports:
debt/assets—X1;
loans/deposits—X2;
net interest margin—X3;
ROE (return on equity)—X4;
ROA (return on assets)—X5;
cost/income—X6;
equity/assets—X7; and
net financial result—X8.
The input data were normalized before the application. The experiments were carried out with full regression ARMA model, which used eight variables and shortened models with six and four variables.
\nEach obtained model was checked on testing sample consisting of 50 banks. The comparative forecasting results for all ARMA models are presented in Table 24.
\nInput data | \nTesting sample | \nFirst type of errors | \nSecond type of errors | \nTotal number of errors | \n% of errors | \n
---|---|---|---|---|---|
All variables (eight) | \n50 | \n5 | \n4 | \n9 | \n18 | \n
Six variables | \n50 | \n5 | \n4 | \n9 | \n18 | \n
Four variables | \n50 | \n5 | \n4 | \n9 | \n18 | \n
Comparative analysis of ARMA models.
As one may see in Table 24, the application of all types of linear regression models gives the same error of 18%, which is much worse than application of fuzzy neural networks.
\nFurthermore, the experiments were performed using logit models for bankruptcy forecasting [9, 10]. The training sample consisted of 165 banks and the testing sample of 50 banks.
\nThe first one was constructed, linear logit model, using all the input variables. It has the following form (estimating and forecasting equations):
\nThe next constructed model was a linear probabilistic logit model with six independent variables. The final table including the forecasting results of all the logit models is presented below (Table 25)
\nInput data | \nTesting sample | \nFirst type of errors | \nSecond type of errors | \nTotal number of errors | \n% of errors | \n
---|---|---|---|---|---|
All variables (eight) | \n50 | \n6 | \n2 | \n8 | \n16 | \n
Six variables | \n50 | \n6 | \n2 | \n8 | \n16 | \n
Comparative analysis of logit models.
The next experiments were carried out with probit models [9, 10]. The first constructed model was the linear probit model based on 206 banks using all the input variables. It has the following form:
\nAs the experiments had shown that the inputs net interest margin (\n
Furthermore, in this model insignificant variables debt/assets (\n
Each of the constructed probit models was checked on the test sample of 50 banks. The results of application of all probit models are presented in Table 26.
\nInput data | \nTesting sample | \nFirst type of errors | \nSecond type of errors | \nTotal number of errors | \n% of errors | \n
---|---|---|---|---|---|
All variables (eight) | \n50 | \n5 | \n2 | \n7 | \n14 | \n
Six variables | \n50 | \n5 | \n2 | \n7 | \n14 | \n
Four variables | \n50 | \n6 | \n3 | \n9 | \n18 | \n
Forecasting results of probit models.
As one may see from Table 26, the application of all the probit models gives relative error 14–18%, which is much worse than results obtained by fuzzy neural networks. It is worth to mention the decrease of model forecasting quality after exclusion of insignificant variables.
\nIn the final series of experiments, investigations and detailed analysis of various methods for forecasting bankruptcy risk were performed. The following methods were investigated: FNN ANFIS, FNN TSK, FGMDH, regression models, logit models, and probit models.
\nPeriod of input data was 2007 (1 year before possible bankruptcy).
\nComparative analysis of all the forecasting methods is presented in Table 27.
\nMethod | \nTotal number of errors | \n% of errors | \nFirst type of errors | \nSecond type of errors | \n
---|---|---|---|---|
ANFIS | \n4 | \n8 | \n0 | \n4 | \n
TSK | \n1 | \n2 | \n0 | \n1 | \n
FGMDH | \n2 | \n4 | \n0 | \n2 | \n
ARMA | \n9 | \n18 | \n4 | \n5 | \n
Logit | \n8 | \n16 | \n2 | \n6 | \n
Probit | \n7 | \n14 | \n2 | \n5 | \n
Comparative analysis of methods for banks bankruptcy forecasting.
As one may see from this table, fuzzy methods and models show much better results than crisp methods: ARMA, logit models, and probit models. When forecasting by one year prior to current date, fuzzy neural network TSK shows better results than FGMDH. But when forecasting for longer intervals (several years), FGMDH is the best method.
\nIn a whole, the conclusions of experiments with European banks completely confirmed the conclusions of experiments with Ukrainian banks.
\nThe problem of banks bankruptcy risk forecasting under uncertainty was considered.
\nFor its solution, the application of novel methods of computational intelligence, fuzzy neural networks ANFIS and TSK and fuzzy GMDH, was suggested.
The experimental investigation of FNN TSK, ANFIS, and GMDH application in the problem of bankruptcy risk forecasting was carried out for Ukrainian and European banks.
The comparison of forecasting efficiency of FNN TSK and ANFIS with Fuzzy GMDH and conventional statistical methods ARMA, logit, and probit models was performed.
The experimental results show that FNN and FGMDH have much better accuracy than statistical methods. When forecasting by one year prior to current date, fuzzy neural network TSK shows better results than FGMDH. But when forecasting for longer intervals (several years), FGMDH is the best method.
While in experimental investigations, the best sets of financial indicators for bankruptcy forecasting were found for Ukrainian and European banks as well.
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Prof.",name:"Muhammad",middleName:null,surname:"Zubair",slug:"muhammad-zubair",fullName:"Muhammad Zubair"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7687",title:"Reliability and Maintenance",subtitle:"An Overview of Cases",isOpenForSubmission:!1,hash:"14790fdcb395faea44e1351e45cb20a5",slug:"reliability-and-maintenance-an-overview-of-cases",bookSignature:"Leo Kounis",coverURL:"https://cdn.intechopen.com/books/images_new/7687.jpg",editedByType:"Edited by",editors:[{id:"111582",title:"Dr.",name:"Leo",middleName:"Dimitrios",surname:"Kounis",slug:"leo-kounis",fullName:"Leo Kounis"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9373",title:"Engineering Failure 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by",editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5317",title:"Concise Reliability for Engineers",subtitle:null,isOpenForSubmission:!1,hash:"8dd29c0cfec89eb0c272c374e903b3da",slug:"concise-reliability-for-engineers",bookSignature:"Jaroslav Mencik",coverURL:"https://cdn.intechopen.com/books/images_new/5317.jpg",editedByType:"Authored by",editors:[{id:"142710",title:"Prof.",name:"Jaroslav",middleName:null,surname:"Menčík",slug:"jaroslav-mencik",fullName:"Jaroslav Menčík"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"3",chapterContentType:"chapter",authoredCaption:"Authored by"}}],booksByTopicTotal:7,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"57936",doi:"10.5772/intechopen.71926",title:"Power System Reliability: Mathematical Models and Applications",slug:"power-system-reliability-mathematical-models-and-applications",totalDownloads:2802,totalCrossrefCites:5,totalDimensionsCites:10,abstract:"This chapter deals with power systems reliability including technical, economical, and decisional aspects. Knowing that almost 90% of failures occur in the distribution systems, great interest was dedicated to this part of the system, and the first work was oriented to reliability indices defined as objectives to attempt and as performance measures in the electricity market. Some works deal with the managers’ behavior, and the customers reactions are modeled using economic criteria in uncertain future and inspired from game theory. When studying components, degradation models were introduced and combined with the effects of socks to study the reliability changing during system operation. In some works, the correlation between maintenance policies and reliability aspects was highlighted. In a recent work, considering the importance of new technologies integration and renewable energy insertion to power systems, it was revealed that reliability aspects and energy sustainability are two fundamental issues of progress in a given society.",book:{id:"6024",slug:"system-reliability",title:"System Reliability",fullTitle:"System Reliability"},signatures:"Rabah Medjoudj, Hassiba Bediaf and Djamil Aissani",authors:[{id:"182165",title:"Dr.",name:"Rabah",middleName:null,surname:"Medjoudj",slug:"rabah-medjoudj",fullName:"Rabah Medjoudj"},{id:"182167",title:"Prof.",name:"Djamil",middleName:null,surname:"Aissani",slug:"djamil-aissani",fullName:"Djamil Aissani"},{id:"208149",title:"Ms.",name:"Hassiba",middleName:null,surname:"Bediaf",slug:"hassiba-bediaf",fullName:"Hassiba Bediaf"}]},{id:"56062",doi:"10.5772/intechopen.69721",title:"A Decision Support System for Planning and Operation of Maintenance and Customer Services in Electric Power Distribution Systems",slug:"a-decision-support-system-for-planning-and-operation-of-maintenance-and-customer-services-in-electri",totalDownloads:1705,totalCrossrefCites:4,totalDimensionsCites:7,abstract:"This chapter aims to present the design and development of a decision support system (DSS) for the analysis, simulation, planning, and operation of maintenance and customer services in electric power distribution system (EPDS). The main objective of the DSS is to improve the decision‐making processes through visualization tools and simulation of real cases in the EPDS, in order to allow better planning in the short, medium, and long term. Therefore, the DSS helps managers and decision‐makers to reduce maintenance and operational costs, to improve system reliability, and to analyze new scenarios and conditions for system expansion planning. First, we introduce the key challenges faced by the decision‐makers in the planning and operation of maintenance and customer services in EPDS. Next, we discuss the benefits and the requirements for the DSS design and development, including use cases modeling and the software architecture. Afterwards, we present the capabilities of the DSS and discuss important decisions made during the implementation phases. We conclude the chapter with a discussion about the obtained results, pointing out the possible enhancements of the DSS, future extensions, and new use cases that may be addressed.",book:{id:"6024",slug:"system-reliability",title:"System Reliability",fullTitle:"System Reliability"},signatures:"Carlos Henrique Barriquello, Vinícius Jacques Garcia, Magdiel\nSchmitz, Daniel Pinheiro Bernardon and Júlio Schenato Fonini",authors:[{id:"180154",title:"Dr.",name:"Daniel",middleName:"P",surname:"Bernardon",slug:"daniel-bernardon",fullName:"Daniel Bernardon"},{id:"180657",title:"Dr.",name:"Vinicius Jacques",middleName:"Jacques",surname:"Garcia",slug:"vinicius-jacques-garcia",fullName:"Vinicius Jacques Garcia"},{id:"203699",title:"Dr.",name:"Carlos",middleName:null,surname:"Barriquello",slug:"carlos-barriquello",fullName:"Carlos Barriquello"},{id:"206560",title:"Mr.",name:"Magdiel",middleName:null,surname:"Schmitz",slug:"magdiel-schmitz",fullName:"Magdiel Schmitz"},{id:"206562",title:"BSc.",name:"Júlio",middleName:null,surname:"Schenato Fonini",slug:"julio-schenato-fonini",fullName:"Júlio Schenato Fonini"}]},{id:"66913",doi:"10.5772/intechopen.85571",title:"Reliability Evaluation of Power Systems",slug:"reliability-evaluation-of-power-systems",totalDownloads:2019,totalCrossrefCites:3,totalDimensionsCites:7,abstract:"Reliability evaluation of electric power systems is an essential and vital issue in the planning, designing, and operation of power systems. An electric power system consists of a set of components interconnected with each other in some purposeful and meaningful manner. The object of a reliability evaluation is to derive suitable measures, criteria, and indices of reliable and dependable performance based on component outage data and configuration. For evaluating generated reliability, the components of interest are the generating units and system configuration, which refer to the specific unit(s) operated to serve the present or future load. The indices used to measure the generated reliability are probabilistic estimates of the ability of a particular generation configuration to supply the load demand. These indices are better understood as an assessment of system-wide generation adequacy and not as absolute measures of system reliability. The indices are sensitive to basic factors like unit size and unit availability and are most useful when comparing the relative reliability of different generation configurations. The system is deemed to operate successfully if there is enough generation capacity (adequate reserve) to satisfy the peak load (maximum demand). Firstly, generation model and load model are convolved (mutually combined) to yield the risk of supply shortages in the system. Secondly, probabilistic estimates of shortage risk are used as indices of bulk power system reliability evaluation for the considered configuration.",book:{id:"7687",slug:"reliability-and-maintenance-an-overview-of-cases",title:"Reliability and Maintenance",fullTitle:"Reliability and Maintenance - An Overview of Cases"},signatures:"Abdullah M. Al-Shaalan",authors:[{id:"274935",title:"Prof.",name:"Abdullah",middleName:"Mohammed",surname:"Al-Shaalan",slug:"abdullah-al-shaalan",fullName:"Abdullah Al-Shaalan"}]},{id:"56115",doi:"10.5772/intechopen.69440",title:"Mixed-Mode Delamination Failures of Quasi-Isotropic Quasi- Homogeneous Carbon/Epoxy Laminated Composite",slug:"mixed-mode-delamination-failures-of-quasi-isotropic-quasi-homogeneous-carbon-epoxy-laminated-composi",totalDownloads:1358,totalCrossrefCites:5,totalDimensionsCites:5,abstract:"This chapter characterised the delamination behaviour of a quasi-isotropic quasi-homogeneous (QIQH) multidirectional carbon/epoxy-laminated composite. The delaminated surface constituted of 45°//0 layers. Specimens were tested using mode I double cantilever beam (DCB), mode II end-notched flexure (ENF) and mixed-mode I+II mixed-mode flexure (MMF) tests at constant crosshead speed of 1 mm/min. Results showed that the fracture toughness increased with the mode II component. Specifically, the mode I, mode II and mixed-mode I+II fracture toughness were 508.17, 1676.26 and 927.52 N/m, respectively. When the fracture toughness values were fitted using the Benzeggagh-Kenane (BK) criterion, it was found that the best-fit material parameter, η, was attained at 1.21. Furthermore, fibre bridging was observed in DCB specimens, where the steady-state fracture toughness was approximately 80% higher compared to the mode I fracture toughness. Finally, through scanning electron micrographs, it was found that there was resin-rich region at the crack tip of the specimens. In addition, fibre debonding of the 45°layer was found to be dominant in the DCB specimens. Significant shear cusps were noticed in the ENF specimens. As for the MMF specimens, matrix cracking and fibre debonding of the 0°layer were observed to be the major failure mechanisms.",book:{id:"5720",slug:"failure-analysis-and-prevention",title:"Failure Analysis and Prevention",fullTitle:"Failure Analysis and Prevention"},signatures:"Mahzan Johar, King Jye Wong and Mohd Nasir Tamin",authors:[{id:"196884",title:"Prof.",name:"Mohd Nasir",middleName:null,surname:"Tamin",slug:"mohd-nasir-tamin",fullName:"Mohd Nasir Tamin"},{id:"197028",title:"Dr.",name:"King Jye",middleName:null,surname:"Wong",slug:"king-jye-wong",fullName:"King Jye Wong"},{id:"203971",title:"Dr.",name:"Mahzan",middleName:null,surname:"Johar",slug:"mahzan-johar",fullName:"Mahzan Johar"}]},{id:"55938",doi:"10.5772/intechopen.69286",title:"Imperfect Maintenance Models, from Theory to Practice",slug:"imperfect-maintenance-models-from-theory-to-practice",totalDownloads:2159,totalCrossrefCites:3,totalDimensionsCites:6,abstract:"The role of maintenance in the industrial environment changed a lot in recent years, and today, it is a key function for long-term profitability in an organization. Many contributions were recently written by researchers on this topic. A lot of models were proposed to optimize maintenance activities while ensuring availability and high-quality requirements. In addition to the well-known classification of maintenance activities—preventive and corrective—in the last decades, a new classification emerged in the literature regarding the degree of system restoration after maintenance actions. Among them, the imperfect maintenance is one of the most studied maintenance types: it is defined as an action after which the system lies in a state somewhere between an “as good as new” state and its pre-maintenance condition “as bad as old.” Most of the industrial companies usually operate with imperfect maintenance actions, even if the awareness in actual industrial context is limited. On the practical definition side, in particular, there are some real situations of imperfect maintenance: three main specific cases were identified, both from literature analysis and from experience. Considering these three implementations of imperfect maintenance actions and the main models proposed in the literature, we illustrate how to identify the most suitable model for each real case.",book:{id:"6024",slug:"system-reliability",title:"System Reliability",fullTitle:"System Reliability"},signatures:"Filippo De Carlo and Maria Antonietta Arleo",authors:[{id:"161657",title:"Dr.",name:"Filippo",middleName:null,surname:"De Carlo",slug:"filippo-de-carlo",fullName:"Filippo De Carlo"},{id:"171361",title:"Dr.",name:"Maria Antonietta",middleName:null,surname:"Arleo",slug:"maria-antonietta-arleo",fullName:"Maria Antonietta Arleo"}]}],mostDownloadedChaptersLast30Days:[{id:"50094",title:"Reliability of Systems",slug:"reliability-of-systems",totalDownloads:3450,totalCrossrefCites:1,totalDimensionsCites:0,abstract:"Many objects consist of more components. The mutual arrangement of the individual elements influences the resultant reliability. The formulae are shown for the resultant reliability of series arrangement, as well as for parallel and combined arrangement. The possibility of reliability increasing by means of redundancy is explained, and also the principle of optimal allocation of reliabilities to individual elements. Everything is illustrated on examples.",book:{id:"5317",slug:"concise-reliability-for-engineers",title:"Concise Reliability for Engineers",fullTitle:"Concise Reliability for Engineers"},signatures:"Jaroslav Menčík",authors:[{id:"142710",title:"Prof.",name:"Jaroslav",middleName:null,surname:"Menčík",slug:"jaroslav-mencik",fullName:"Jaroslav Menčík"}]},{id:"50095",title:"Time to Failure of Deteriorating Objects",slug:"time-to-failure-of-deteriorating-objects",totalDownloads:1690,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This chapter explains the prediction of the time to failure in the following cases: fatigue of metallic components under cyclic loading or in the presence of cracks, static fatigue, wear and creep, variable loading (damage accumulation). Prediction of the time to failure based on monitoring of the changing response. Probabilistic aspects of the lifetime prediction. The determination of the time to failure is illustrated on examples.",book:{id:"5317",slug:"concise-reliability-for-engineers",title:"Concise Reliability for Engineers",fullTitle:"Concise Reliability for Engineers"},signatures:"Jaroslav Menčík",authors:[{id:"142710",title:"Prof.",name:"Jaroslav",middleName:null,surname:"Menčík",slug:"jaroslav-mencik",fullName:"Jaroslav Menčík"}]},{id:"66913",title:"Reliability Evaluation of Power Systems",slug:"reliability-evaluation-of-power-systems",totalDownloads:2013,totalCrossrefCites:3,totalDimensionsCites:6,abstract:"Reliability evaluation of electric power systems is an essential and vital issue in the planning, designing, and operation of power systems. An electric power system consists of a set of components interconnected with each other in some purposeful and meaningful manner. The object of a reliability evaluation is to derive suitable measures, criteria, and indices of reliable and dependable performance based on component outage data and configuration. For evaluating generated reliability, the components of interest are the generating units and system configuration, which refer to the specific unit(s) operated to serve the present or future load. The indices used to measure the generated reliability are probabilistic estimates of the ability of a particular generation configuration to supply the load demand. These indices are better understood as an assessment of system-wide generation adequacy and not as absolute measures of system reliability. The indices are sensitive to basic factors like unit size and unit availability and are most useful when comparing the relative reliability of different generation configurations. The system is deemed to operate successfully if there is enough generation capacity (adequate reserve) to satisfy the peak load (maximum demand). Firstly, generation model and load model are convolved (mutually combined) to yield the risk of supply shortages in the system. Secondly, probabilistic estimates of shortage risk are used as indices of bulk power system reliability evaluation for the considered configuration.",book:{id:"7687",slug:"reliability-and-maintenance-an-overview-of-cases",title:"Reliability and Maintenance",fullTitle:"Reliability and Maintenance - An Overview of Cases"},signatures:"Abdullah M. Al-Shaalan",authors:[{id:"274935",title:"Prof.",name:"Abdullah",middleName:"Mohammed",surname:"Al-Shaalan",slug:"abdullah-al-shaalan",fullName:"Abdullah Al-Shaalan"}]},{id:"58172",title:"X-Ray Techniques",slug:"x-ray-techniques",totalDownloads:2443,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"This chapter reviewed existing X-ray techniques that can be used for the analysis of materials, inclusive of those used as engineering and structural components. These techniques are X-ray fluorescence (XRF) spectrometry, proton-induced X-ray emission (PIXE) spectrometry, and X-ray diffraction (XRD). These analytical techniques provide qualitative and quantitative information on the composition and structure of materials with precision. XRD gives information on the crystalline forms and amorphous content of materials, which could be quite useful in failure analysis if the type of failure brings about morphological changes in the material under investigation. PIXE and XRF provide information on the types of elements present in a sample material and their concentrations. PIXE is however preferable to XRF due to its higher sensitivity to trace elements and lower atomic number elements as well as its faster analysis. XRF and XRD are more commonly used than PIXE which is a powerful, high-tech method that is relatively new in the field of chemical research. In this chapter, the theory and principles of these analytical techniques are explained, and diagrams showing the components of spectrometers and diffractometers are provided with descriptions of how they function.",book:{id:"5720",slug:"failure-analysis-and-prevention",title:"Failure Analysis and Prevention",fullTitle:"Failure Analysis and Prevention"},signatures:"Clementina Dilim Igwebike-Ossi",authors:[{id:"219931",title:"Dr.",name:"Clementina",middleName:null,surname:"Igwebike-Ossi",slug:"clementina-igwebike-ossi",fullName:"Clementina Igwebike-Ossi"}]},{id:"57936",title:"Power System Reliability: Mathematical Models and Applications",slug:"power-system-reliability-mathematical-models-and-applications",totalDownloads:2799,totalCrossrefCites:5,totalDimensionsCites:10,abstract:"This chapter deals with power systems reliability including technical, economical, and decisional aspects. Knowing that almost 90% of failures occur in the distribution systems, great interest was dedicated to this part of the system, and the first work was oriented to reliability indices defined as objectives to attempt and as performance measures in the electricity market. Some works deal with the managers’ behavior, and the customers reactions are modeled using economic criteria in uncertain future and inspired from game theory. When studying components, degradation models were introduced and combined with the effects of socks to study the reliability changing during system operation. In some works, the correlation between maintenance policies and reliability aspects was highlighted. In a recent work, considering the importance of new technologies integration and renewable energy insertion to power systems, it was revealed that reliability aspects and energy sustainability are two fundamental issues of progress in a given society.",book:{id:"6024",slug:"system-reliability",title:"System Reliability",fullTitle:"System Reliability"},signatures:"Rabah Medjoudj, Hassiba Bediaf and Djamil Aissani",authors:[{id:"182165",title:"Dr.",name:"Rabah",middleName:null,surname:"Medjoudj",slug:"rabah-medjoudj",fullName:"Rabah Medjoudj"},{id:"182167",title:"Prof.",name:"Djamil",middleName:null,surname:"Aissani",slug:"djamil-aissani",fullName:"Djamil Aissani"},{id:"208149",title:"Ms.",name:"Hassiba",middleName:null,surname:"Bediaf",slug:"hassiba-bediaf",fullName:"Hassiba Bediaf"}]}],onlineFirstChaptersFilter:{topicId:"828",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:320,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:7,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:17,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"10",title:"Physiology",doi:"10.5772/intechopen.72796",issn:"2631-8261",scope:"Modern physiology requires a comprehensive understanding of the integration of tissues and organs throughout the mammalian body, including the cooperation between structure and function at the cellular and molecular levels governed by gene and protein expression. 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Badria",profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",institutionString:"Mansoura University",institution:{name:"Mansoura University",institutionURL:null,country:{name:"Egypt"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{type:"book",id:"9659",title:"Fibroblasts",subtitle:"Advances in Inflammation, Autoimmunity and Cancer",coverURL:"https://cdn.intechopen.com/books/images_new/9659.jpg",slug:"fibroblasts-advances-in-inflammation-autoimmunity-and-cancer",publishedDate:"December 22nd 2021",editedByType:"Edited by",bookSignature:"Mojca Frank Bertoncelj and Katja Lakota",hash:"926fa6446f6befbd363fc74971a56de2",volumeInSeries:25,fullTitle:"Fibroblasts - Advances in Inflammation, Autoimmunity and Cancer",editors:[{id:"328755",title:"Ph.D.",name:"Mojca",middleName:null,surname:"Frank Bertoncelj",slug:"mojca-frank-bertoncelj",fullName:"Mojca Frank Bertoncelj",profilePictureURL:"https://mts.intechopen.com/storage/users/328755/images/system/328755.jpg",institutionString:"BioMed X Institute",institution:{name:"University Hospital of Zurich",institutionURL:null,country:{name:"Switzerland"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{type:"book",id:"8977",title:"Protein Kinases",subtitle:"Promising Targets for Anticancer Drug Research",coverURL:"https://cdn.intechopen.com/books/images_new/8977.jpg",slug:"protein-kinases-promising-targets-for-anticancer-drug-research",publishedDate:"December 8th 2021",editedByType:"Edited by",bookSignature:"Rajesh Kumar Singh",hash:"6d200cc031706a565b554fdb1c478901",volumeInSeries:24,fullTitle:"Protein Kinases - Promising Targets for Anticancer Drug Research",editors:[{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{type:"book",id:"8018",title:"Extracellular Matrix",subtitle:"Developments and Therapeutics",coverURL:"https://cdn.intechopen.com/books/images_new/8018.jpg",slug:"extracellular-matrix-developments-and-therapeutics",publishedDate:"October 27th 2021",editedByType:"Edited by",bookSignature:"Rama Sashank Madhurapantula, Joseph Orgel P.R.O. and Zvi Loewy",hash:"c85e82851e80b40282ff9be99ddf2046",volumeInSeries:23,fullTitle:"Extracellular Matrix - Developments and Therapeutics",editors:[{id:"212416",title:"Dr.",name:"Rama Sashank",middleName:null,surname:"Madhurapantula",slug:"rama-sashank-madhurapantula",fullName:"Rama Sashank Madhurapantula",profilePictureURL:"https://mts.intechopen.com/storage/users/212416/images/system/212416.jpg",institutionString:"Illinois Institute of Technology",institution:{name:"Illinois Institute of Technology",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{type:"book",id:"9759",title:"Vitamin E in Health and Disease",subtitle:"Interactions, Diseases and Health Aspects",coverURL:"https://cdn.intechopen.com/books/images_new/9759.jpg",slug:"vitamin-e-in-health-and-disease-interactions-diseases-and-health-aspects",publishedDate:"October 6th 2021",editedByType:"Edited by",bookSignature:"Pınar Erkekoglu and Júlia Scherer Santos",hash:"6c3ddcc13626110de289b57f2516ac8f",volumeInSeries:22,fullTitle:"Vitamin E in Health and Disease - Interactions, Diseases and Health Aspects",editors:[{id:"109978",title:"Prof.",name:"Pınar",middleName:null,surname:"Erkekoğlu",slug:"pinar-erkekoglu",fullName:"Pınar Erkekoğlu",profilePictureURL:"https://mts.intechopen.com/storage/users/109978/images/system/109978.jpg",institutionString:"Hacettepe University",institution:{name:"Hacettepe University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Proteomics",value:18,count:4},{group:"subseries",caption:"Metabolism",value:17,count:6},{group:"subseries",caption:"Cell and Molecular Biology",value:14,count:9},{group:"subseries",caption:"Chemical Biology",value:15,count:13}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:8},{group:"publicationYear",caption:"2021",value:2021,count:7},{group:"publicationYear",caption:"2020",value:2020,count:12},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:2}],authors:{paginationCount:303,paginationItems:[{id:"313921",title:"Dr.",name:"Hassan M.",middleName:null,surname:"Heshmati",slug:"hassan-m.-heshmati",fullName:"Hassan M. Heshmati",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313921/images/system/313921.jpg",biography:"Dr. Hassan Massoud Heshmati is an endocrinologist with 46 years of experience in clinical research in academia (university-affiliated hospitals, Paris, France; Mayo Foundation, Rochester, MN, USA) and pharmaceutical companies (Sanofi, Malvern, PA, USA; Essentialis, Carlsbad, CA, USA; Gelesis, Boston, MA, USA). His research activity focuses on pituitary tumors, hyperthyroidism, thyroid cancers, osteoporosis, diabetes, and obesity. He has extensive knowledge in the development of anti-obesity products. Dr. Heshmati is the author of 299 abstracts, chapters, and articles related to endocrinology and metabolism. He is currently a consultant at Endocrinology Metabolism Consulting, LLC, Anthem, AZ, USA.",institutionString:"Endocrinology Metabolism Consulting, LLC",institution:null},{id:"76477",title:"Prof.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/76477/images/system/76477.png",biography:"Dr. Mirza Hasanuzzaman is a Professor of Agronomy at Sher-e-Bangla Agricultural University, Bangladesh. He received his Ph.D. in Plant Stress Physiology and Antioxidant Metabolism from Ehime University, Japan, with a scholarship from the Japanese Government (MEXT). Later, he completed his postdoctoral research at the Center of Molecular Biosciences, University of the Ryukyus, Japan, as a recipient of the Japan Society for the Promotion of Science (JSPS) postdoctoral fellowship. He was also the recipient of the Australian Government Endeavour Research Fellowship for postdoctoral research as an adjunct senior researcher at the University of Tasmania, Australia. Dr. Hasanuzzaman’s current work is focused on the physiological and molecular mechanisms of environmental stress tolerance. Dr. Hasanuzzaman has published more than 150 articles in peer-reviewed journals. He has edited ten books and written more than forty book chapters on important aspects of plant physiology, plant stress tolerance, and crop production. According to Scopus, Dr. Hasanuzzaman’s publications have received more than 10,500 citations with an h-index of 53. He has been named a Highly Cited Researcher by Clarivate. He is an editor and reviewer for more than fifty peer-reviewed international journals and was a recipient of the “Publons Peer Review Award” in 2017, 2018, and 2019. He has been honored by different authorities for his outstanding performance in various fields like research and education, and he has received the World Academy of Science Young Scientist Award (2014) and the University Grants Commission (UGC) Award 2018. He is a fellow of the Bangladesh Academy of Sciences (BAS) and the Royal Society of Biology.",institutionString:"Sher-e-Bangla Agricultural University",institution:{name:"Sher-e-Bangla Agricultural University",country:{name:"Bangladesh"}}},{id:"187859",title:"Prof.",name:"Kusal",middleName:"K.",surname:"Das",slug:"kusal-das",fullName:"Kusal Das",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBDeQAO/Profile_Picture_1623411145568",biography:"Kusal K. Das is a Distinguished Chair Professor of Physiology, Shri B. M. Patil Medical College and Director, Centre for Advanced Medical Research (CAMR), BLDE (Deemed to be University), Vijayapur, Karnataka, India. Dr. Das did his M.S. and Ph.D. in Human Physiology from the University of Calcutta, Kolkata. His area of research is focused on understanding of molecular mechanisms of heavy metal activated low oxygen sensing pathways in vascular pathophysiology. He has invented a new method of estimation of serum vitamin E. His expertise in critical experimental protocols on vascular functions in experimental animals was well documented by his quality of publications. He was a Visiting Professor of Medicine at University of Leeds, United Kingdom (2014-2016) and Tulane University, New Orleans, USA (2017). For his immense contribution in medical research Ministry of Science and Technology, Government of India conferred him 'G.P. Chatterjee Memorial Research Prize-2019” and he is also the recipient of 'Dr.Raja Ramanna State Scientist Award 2015” by Government of Karnataka. He is a Fellow of the Royal Society of Biology (FRSB), London and Honorary Fellow of Karnataka Science and Technology Academy, Department of Science and Technology, Government of Karnataka.",institutionString:"BLDE (Deemed to be University), India",institution:null},{id:"243660",title:"Dr.",name:"Mallanagouda Shivanagouda",middleName:null,surname:"Biradar",slug:"mallanagouda-shivanagouda-biradar",fullName:"Mallanagouda Shivanagouda Biradar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243660/images/system/243660.jpeg",biography:"M. S. Biradar is Vice Chancellor and Professor of Medicine of\nBLDE (Deemed to be University), Vijayapura, Karnataka, India.\nHe obtained his MD with a gold medal in General Medicine and\nhas devoted himself to medical teaching, research, and administrations. He has also immensely contributed to medical research\non vascular medicine, which is reflected by his numerous publications including books and book chapters. Professor Biradar was\nalso Visiting Professor at Tulane University School of Medicine, New Orleans, USA.",institutionString:"BLDE (Deemed to be University)",institution:{name:"BLDE University",country:{name:"India"}}},{id:"289796",title:"Dr.",name:"Swastika",middleName:null,surname:"Das",slug:"swastika-das",fullName:"Swastika Das",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/289796/images/system/289796.jpeg",biography:"Swastika N. Das is Professor of Chemistry at the V. P. Dr. P. G.\nHalakatti College of Engineering and Technology, BLDE (Deemed\nto be University), Vijayapura, Karnataka, India. She obtained an\nMSc, MPhil, and PhD in Chemistry from Sambalpur University,\nOdisha, India. Her areas of research interest are medicinal chemistry, chemical kinetics, and free radical chemistry. She is a member\nof the investigators who invented a new modified method of estimation of serum vitamin E. She has authored numerous publications including book\nchapters and is a mentor of doctoral curriculum at her university.",institutionString:"BLDEA’s V.P.Dr.P.G.Halakatti College of Engineering & Technology",institution:{name:"BLDE University",country:{name:"India"}}},{id:"248459",title:"Dr.",name:"Akikazu",middleName:null,surname:"Takada",slug:"akikazu-takada",fullName:"Akikazu Takada",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248459/images/system/248459.png",biography:"Akikazu Takada was born in Japan, 1935. After graduation from\nKeio University School of Medicine and finishing his post-graduate studies, he worked at Roswell Park Memorial Institute NY,\nUSA. He then took a professorship at Hamamatsu University\nSchool of Medicine. In thrombosis studies, he found the SK\npotentiator that enhances plasminogen activation by streptokinase. He is very much interested in simultaneous measurements\nof fatty acids, amino acids, and tryptophan degradation products. By using fatty\nacid analyses, he indicated that plasma levels of trans-fatty acids of old men were\nfar higher in the US than Japanese men. . He also showed that eicosapentaenoic acid\n(EPA) and docosahexaenoic acid (DHA) levels are higher, and arachidonic acid\nlevels are lower in Japanese than US people. By using simultaneous LC/MS analyses\nof plasma levels of tryptophan metabolites, he recently found that plasma levels of\nserotonin, kynurenine, or 5-HIAA were higher in patients of mono- and bipolar\ndepression, which are significantly different from observations reported before. In\nview of recent reports that plasma tryptophan metabolites are mainly produced by\nmicrobiota. He is now working on the relationships between microbiota and depression or autism.",institutionString:"Hamamatsu University School of Medicine",institution:{name:"Hamamatsu University School of Medicine",country:{name:"Japan"}}},{id:"137240",title:"Prof.",name:"Mohammed",middleName:null,surname:"Khalid",slug:"mohammed-khalid",fullName:"Mohammed Khalid",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/137240/images/system/137240.png",biography:"Mohammed Khalid received his B.S. in Chemistry in July 2000, and his Ph.D. in Physical Chemistry in 2007 from the University of Khartoum, Sudan. In 2009 he joined the Dr. Ron Clarke research group at the School of Chemistry, Faculty of Science, University of Sydney, Australia as a postdoctoral fellow where he worked on the Interaction of ATP with the phosphoenzyme of the Na+, K+-ATPase, and Dual mechanisms of allosteric acceleration of the Na+, K+-ATPase by ATP. He then worked as Assistant Professor at the Department of Chemistry, University of Khartoum, and in 2014 was promoted to Associate Professor ranking. In 2011 he joined the staff of the Chemistry Department at Taif University, Saudi Arabia, where he is currently active as an Assistant Professor. His research interests include:\r\n(1) P-type ATPase Enzyme Kinetics and Mechanisms; (2) Kinetics and Mechanism of Redox Reactions; (3) Autocatalytic reactions; (4) Computational enzyme kinetics; (5) Allosteric acceleration of P-type ATPases by ATP; (6) Exploring of allosteric sites of ATPases and interaction of ATP with ATPases located in the cell membranes.",institutionString:"Taif University",institution:{name:"Taif University",country:{name:"Saudi Arabia"}}},{id:"63810",title:"Prof.",name:"Jorge",middleName:null,surname:"Morales-Montor",slug:"jorge-morales-montor",fullName:"Jorge Morales-Montor",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/63810/images/system/63810.png",biography:"Dr. Jorge Morales-Montor was recognized with the Lola and Igo Flisser PUIS Award for best graduate thesis at the national level in the field of parasitology. He received a fellowship from the Fogarty Foundation to perform postdoctoral research stay at the University of Georgia. He has 153 journal articles to his credit. He has also edited several books and published more than fifty-five book chapters. He is a member of the Mexican Academy of Sciences, Latin American Academy of Sciences, and the National Academy of Medicine. He has received more than thirty-five awards and has supervised numerous bachelor’s, master’s, and Ph.D. students. Dr. Morales-Montor is the past president of the Mexican Society of Parasitology.",institutionString:"National Autonomous University of Mexico",institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"217215",title:"Dr.",name:"Palash",middleName:null,surname:"Mandal",slug:"palash-mandal",fullName:"Palash Mandal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217215/images/system/217215.jpeg",biography:null,institutionString:"Charusat University",institution:null},{id:"49739",title:"Dr.",name:"Leszek",middleName:null,surname:"Szablewski",slug:"leszek-szablewski",fullName:"Leszek Szablewski",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49739/images/system/49739.jpg",biography:"Leszek Szablewski is a professor of medical sciences. He received his M.S. in the Faculty of Biology from the University of Warsaw and his PhD degree from the Institute of Experimental Biology Polish Academy of Sciences. He habilitated in the Medical University of Warsaw, and he obtained his degree of Professor from the President of Poland. Professor Szablewski is the Head of Chair and Department of General Biology and Parasitology, Medical University of Warsaw. Professor Szablewski has published over 80 peer-reviewed papers in journals such as Journal of Alzheimer’s Disease, Biochim. Biophys. Acta Reviews of Cancer, Biol. Chem., J. Biomed. Sci., and Diabetes/Metabol. Res. Rev, Endocrine. He is the author of two books and four book chapters. He has edited four books, written 15 scripts for students, is the ad hoc reviewer of over 30 peer-reviewed journals, and editorial member of peer-reviewed journals. Prof. Szablewski’s research focuses on cell physiology, genetics, and pathophysiology. He works on the damage caused by lack of glucose homeostasis and changes in the expression and/or function of glucose transporters due to various diseases. He has given lectures, seminars, and exercises for students at the Medical University.",institutionString:"Medical University of Warsaw",institution:{name:"Medical University of Warsaw",country:{name:"Poland"}}},{id:"173123",title:"Dr.",name:"Maitham",middleName:null,surname:"Khajah",slug:"maitham-khajah",fullName:"Maitham Khajah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/173123/images/system/173123.jpeg",biography:"Dr. Maitham A. Khajah received his degree in Pharmacy from Faculty of Pharmacy, Kuwait University, in 2003 and obtained his PhD degree in December 2009 from the University of Calgary, Canada (Gastrointestinal Science and Immunology). Since January 2010 he has been assistant professor in Kuwait University, Faculty of Pharmacy, Department of Pharmacology and Therapeutics. His research interest are molecular targets for the treatment of inflammatory bowel disease (IBD) and the mechanisms responsible for immune cell chemotaxis. He cosupervised many students for the MSc Molecular Biology Program, College of Graduate Studies, Kuwait University. Ever since joining Kuwait University in 2010, he got various grants as PI and Co-I. He was awarded the Best Young Researcher Award by Kuwait University, Research Sector, for the Year 2013–2014. He was a member in the organizing committee for three conferences organized by Kuwait University, Faculty of Pharmacy, as cochair and a member in the scientific committee (the 3rd, 4th, and 5th Kuwait International Pharmacy Conference).",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"195136",title:"Dr.",name:"Aya",middleName:null,surname:"Adel",slug:"aya-adel",fullName:"Aya Adel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/195136/images/system/195136.jpg",biography:"Dr. Adel works as an Assistant Lecturer in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. Dr. Adel is especially interested in joint attention and its impairment in autism spectrum disorder",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"94911",title:"Dr.",name:"Boulenouar",middleName:null,surname:"Mesraoua",slug:"boulenouar-mesraoua",fullName:"Boulenouar Mesraoua",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94911/images/system/94911.png",biography:"Dr Boulenouar Mesraoua is the Associate Professor of Clinical Neurology at Weill Cornell Medical College-Qatar and a Consultant Neurologist at Hamad Medical Corporation at the Neuroscience Department; He graduated as a Medical Doctor from the University of Oran, Algeria; he then moved to Belgium, the City of Liege, for a Residency in Internal Medicine and Neurology at Liege University; after getting the Belgian Board of Neurology (with high marks), he went to the National Hospital for Nervous Diseases, Queen Square, London, United Kingdom for a fellowship in Clinical Neurophysiology, under Pr Willison ; Dr Mesraoua had also further training in Epilepsy and Continuous EEG Monitoring for two years (from 2001-2003) in the Neurophysiology department of Zurich University, Switzerland, under late Pr Hans Gregor Wieser ,an internationally known epileptologist expert. \n\nDr B. Mesraoua is the Director of the Neurology Fellowship Program at the Neurology Section and an active member of the newly created Comprehensive Epilepsy Program at Hamad General Hospital, Doha, Qatar; he is also Assistant Director of the Residency Program at the Qatar Medical School. \nDr B. Mesraoua's main interests are Epilepsy, Multiple Sclerosis, and Clinical Neurology; He is the Chairman and the Organizer of the well known Qatar Epilepsy Symposium, he is running yearly for the past 14 years and which is considered a landmark in the Gulf region; He has also started last year , together with other epileptologists from Qatar, the region and elsewhere, a yearly International Epilepsy School Course, which was attended by many neurologists from the Area.\n\nInternationally, Dr Mesraoua is an active and elected member of the Commission on Eastern Mediterranean Region (EMR ) , a regional branch of the International League Against Epilepsy (ILAE), where he represents the Middle East and North Africa(MENA ) and where he holds the position of chief of the Epilepsy Epidemiology Section; Dr Mesraoua is a member of the American Academy of Neurology, the Europeen Academy of Neurology and the American Epilepsy Society.\n\nDr Mesraoua's main objectives are to encourage frequent gathering of the epileptologists/neurologists from the MENA region and the rest of the world, promote Epilepsy Teaching in the MENA Region, and encourage multicenter studies involving neurologists and epileptologists in the MENA region, particularly epilepsy epidemiological studies. \n\nDr. Mesraoua is the recipient of two research Grants, as the Lead Principal Investigator (750.000 USD and 250.000 USD) from the Qatar National Research Fund (QNRF) and the Hamad Hospital Internal Research Grant (IRGC), on the following topics : “Continuous EEG Monitoring in the ICU “ and on “Alpha-lactoalbumin , proof of concept in the treatment of epilepsy” .Dr Mesraoua is a reviewer for the journal \"seizures\" (Europeen Epilepsy Journal ) as well as dove journals ; Dr Mesraoua is the author and co-author of many peer reviewed publications and four book chapters in the field of Epilepsy and Clinical Neurology",institutionString:"Weill Cornell Medical College in Qatar",institution:{name:"Weill Cornell Medical College in Qatar",country:{name:"Qatar"}}},{id:"282429",title:"Prof.",name:"Covanis",middleName:null,surname:"Athanasios",slug:"covanis-athanasios",fullName:"Covanis Athanasios",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/282429/images/system/282429.jpg",biography:null,institutionString:"Neurology-Neurophysiology Department of the Children Hospital Agia Sophia",institution:null},{id:"190980",title:"Prof.",name:"Marwa",middleName:null,surname:"Mahmoud Saleh",slug:"marwa-mahmoud-saleh",fullName:"Marwa Mahmoud Saleh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/190980/images/system/190980.jpg",biography:"Professor Marwa Mahmoud Saleh is a doctor of medicine and currently works in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. She got her doctoral degree in 1991 and her doctoral thesis was accomplished in the University of Iowa, United States. Her publications covered a multitude of topics as videokymography, cochlear implants, stuttering, and dysphagia. She has lectured Egyptian phonology for many years. Her recent research interest is joint attention in autism.",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259190/images/system/259190.png",biography:"Dr. Naqvi is a radioanalytical chemist and is working as an associate professor of analytical chemistry in the Department of Chemistry, Government College University, Faisalabad, Pakistan. Advance separation techniques, nuclear analytical techniques and radiopharmaceutical analysis are the main courses that he is teaching to graduate and post-graduate students. In the research area, he is focusing on the development of organic- and biomolecule-based radiopharmaceuticals for diagnosis and therapy of infectious and cancerous diseases. Under the supervision of Dr. Naqvi, three students have completed their Ph.D. degrees and 41 students have completed their MS degrees. He has completed three research projects and is currently working on 2 projects entitled “Radiolabeling of fluoroquinolone derivatives for the diagnosis of deep-seated bacterial infections” and “Radiolabeled minigastrin peptides for diagnosis and therapy of NETs”. He has published about 100 research articles in international reputed journals and 7 book chapters. Pakistan Institute of Nuclear Science & Technology (PINSTECH) Islamabad, Punjab Institute of Nuclear Medicine (PINM), Faisalabad and Institute of Nuclear Medicine and Radiology (INOR) Abbottabad are the main collaborating institutes.",institutionString:"Government College University",institution:{name:"Government College University, Faisalabad",country:{name:"Pakistan"}}},{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.png",biography:"Gyula Mózsik MD, Ph.D., ScD (med), is an emeritus professor of Medicine at the First Department of Medicine, Univesity of Pécs, Hungary. He was head of this department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition, and dietetics. His research fields are biochemical pharmacological examinations in the human gastrointestinal (GI) mucosa, mechanisms of retinoids, drugs, capsaicin-sensitive afferent nerves, and innovative pharmacological, pharmaceutical, and nutritional (dietary) research in humans. He has published about 360 peer-reviewed papers, 197 book chapters, 692 abstracts, 19 monographs, and has edited 37 books. He has given about 1120 regular and review lectures. He has organized thirty-eight national and international congresses and symposia. He is the founder of the International Conference on Ulcer Research (ICUR); International Union of Pharmacology, Gastrointestinal Section (IUPHAR-GI); Brain-Gut Society symposiums, and gastrointestinal cytoprotective symposiums. He received the Andre Robert Award from IUPHAR-GI in 2014. Fifteen of his students have been appointed as full professors in Egypt, Cuba, and Hungary.",institutionString:"University of Pécs",institution:{name:"University of Pecs",country:{name:"Hungary"}}},{id:"277367",title:"M.Sc.",name:"Daniel",middleName:"Martin",surname:"Márquez López",slug:"daniel-marquez-lopez",fullName:"Daniel Márquez López",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/277367/images/7909_n.jpg",biography:"Msc Daniel Martin Márquez López has a bachelor degree in Industrial Chemical Engineering, a Master of science degree in the same área and he is a PhD candidate for the Instituto Politécnico Nacional. His Works are realted to the Green chemistry field, biolubricants, biodiesel, transesterification reactions for biodiesel production and the manipulation of oils for therapeutic purposes.",institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a Ph.D. in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes, and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. He is the co-editor of The Open Biology Journal and associate editor for Oxidative Medicine and Cellular Longevity.",institutionString:"Universidad Nacional de La Plata",institution:{name:"National University of La Plata",country:{name:"Argentina"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",slug:"francisco-javier-martin-romero",fullName:"Francisco Javier Martin-Romero",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",biography:"Francisco Javier Martín-Romero (Javier) is a Professor of Biochemistry and Molecular Biology at the University of Extremadura, Spain. He is also a group leader at the Biomarkers Institute of Molecular Pathology. Javier received his Ph.D. in 1998 in Biochemistry and Biophysics. At the National Cancer Institute (National Institute of Health, Bethesda, MD) he worked as a research associate on the molecular biology of selenium and its role in health and disease. After postdoctoral collaborations with Carlos Gutierrez-Merino (University of Extremadura, Spain) and Dario Alessi (University of Dundee, UK), he established his own laboratory in 2008. The interest of Javier's lab is the study of cell signaling with a special focus on Ca2+ signaling, and how Ca2+ transport modulates the cytoskeleton, migration, differentiation, cell death, etc. He is especially interested in the study of Ca2+ channels, and the role of STIM1 in the initiation of pathological events.",institutionString:null,institution:{name:"University of Extremadura",country:{name:"Spain"}}},{id:"198499",title:"Dr.",name:"Daniel",middleName:null,surname:"Glossman-Mitnik",slug:"daniel-glossman-mitnik",fullName:"Daniel Glossman-Mitnik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/198499/images/system/198499.jpeg",biography:"Dr. Daniel Glossman-Mitnik is currently a Titular Researcher at the Centro de Investigación en Materiales Avanzados (CIMAV), Chihuahua, Mexico, as well as a National Researcher of Level III at the Consejo Nacional de Ciencia y Tecnología, México. His research interest focuses on computational chemistry and molecular modeling of diverse systems of pharmacological, food, and alternative energy interests by resorting to DFT and Conceptual DFT. He has authored a coauthored more than 270 peer-reviewed papers, 32 book chapters, and 4 edited books. He has delivered speeches at many international and domestic conferences. He serves as a reviewer for more than eighty international journals, books, and research proposals as well as an editor for special issues of renowned scientific journals.",institutionString:null,institution:null},{id:"217323",title:"Prof.",name:"Guang-Jer",middleName:null,surname:"Wu",slug:"guang-jer-wu",fullName:"Guang-Jer Wu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217323/images/8027_n.jpg",biography:null,institutionString:null,institution:null},{id:"148546",title:"Dr.",name:"Norma Francenia",middleName:null,surname:"Santos-Sánchez",slug:"norma-francenia-santos-sanchez",fullName:"Norma Francenia Santos-Sánchez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/148546/images/4640_n.jpg",biography:null,institutionString:null,institution:null},{id:"272889",title:"Dr.",name:"Narendra",middleName:null,surname:"Maddu",slug:"narendra-maddu",fullName:"Narendra Maddu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/272889/images/10758_n.jpg",biography:null,institutionString:null,institution:null},{id:"242491",title:"Prof.",name:"Angelica",middleName:null,surname:"Rueda",slug:"angelica-rueda",fullName:"Angelica Rueda",position:"Investigador Cinvestav 3B",profilePictureURL:"https://mts.intechopen.com/storage/users/242491/images/6765_n.jpg",biography:null,institutionString:null,institution:null},{id:"88631",title:"Dr.",name:"Ivan",middleName:null,surname:"Petyaev",slug:"ivan-petyaev",fullName:"Ivan Petyaev",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Lycotec (United Kingdom)",country:{name:"United Kingdom"}}},{id:"428313",title:"Dr.",name:"Sambangi",middleName:null,surname:"Pratyusha",slug:"sambangi-pratyusha",fullName:"Sambangi Pratyusha",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"CGIAR",country:{name:"France"}}},{id:"423869",title:"Ms.",name:"Smita",middleName:null,surname:"Rai",slug:"smita-rai",fullName:"Smita Rai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}},{id:"424024",title:"Prof.",name:"Swati",middleName:null,surname:"Sharma",slug:"swati-sharma",fullName:"Swati Sharma",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}},{id:"439112",title:"MSc.",name:"Touseef",middleName:null,surname:"Fatima",slug:"touseef-fatima",fullName:"Touseef Fatima",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Integral University",country:{name:"India"}}}]}},subseries:{item:{id:"15",type:"subseries",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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