List of 446 significantly differentially expressed genes in Alzheimer’s gene expression datasets.
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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
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IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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The book provides practical information about the screening approach to vasculitis by laboratory analysis, histopathology and advanced image techniques, current standard treatment along with new and more specific interventions including biologic agents, reparative surgery and experimental therapies, as well as miscellaneous issues such as the extra temporal manifestations of "temporal arteritis" or the diffuse alveolar hemorrhage syndrome. The editor and each of the authors invite you to share this journey by one of the most exciting fields of the medicine, the world of Vasculitis.',isbn:null,printIsbn:"978-953-307-786-4",pdfIsbn:"978-953-51-6556-9",doi:"10.5772/1767",price:139,priceEur:155,priceUsd:179,slug:"advances-in-the-diagnosis-and-treatment-of-vasculitis",numberOfPages:380,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"d67c36edbb7168b5e5d5d9d3880ba83f",bookSignature:"Luis M. 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Amezcua-Guerra is a Professor of Rheumatology and Immunology at the Instituto Nacional de Cardiología in Mexico City, Mexico. He is a postgraduate in Rheumatology and Internal Medicine at the School of Medicine, Universidad Nacional Autónoma de México.\nHis work has been seminal to identify and characterize the immune mechanisms that underlie erosive arthritis in systemic lupus erythematosus, especially those related to the abnormal behavior of citrulline and C-reactive protein. He has also been involved in the study of kidney damage in Takayasu’s arteritis as well as in the existence of subclinical tissue damage in patients with asymptomatic hyperuricemia. Dr. Amezcua-Guerra teaches immunology to postgraduate students in Rheumatology at the Universidad Nacional Autónoma de México and to undergraduate students at the School of Medicine, La Salle University.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Instituto Nacional de Cardiología",institutionURL:null,country:{name:"Mexico"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1042",title:"Intravascular Immunity",slug:"immunology-allergology-and-rheumatology-intravascular-immunity"}],chapters:[{id:"22427",title:"ANCA Diagnostics in Clinical Practice: New Developments",doi:"10.5772/21837",slug:"anca-diagnostics-in-clinical-practice-new-developments",totalDownloads:2301,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:null,signatures:"Jan Damoiseaux, Jos Austen and Jan Willem Cohen Tervaert",downloadPdfUrl:"/chapter/pdf-download/22427",previewPdfUrl:"/chapter/pdf-preview/22427",authors:[{id:"45052",title:"Dr.",name:"Jan",surname:"Damoiseaux",slug:"jan-damoiseaux",fullName:"Jan Damoiseaux"},{id:"52773",title:"Prof.",name:"Jan Willem",surname:"Cohen Tervaert",slug:"jan-willem-cohen-tervaert",fullName:"Jan Willem Cohen Tervaert"},{id:"86822",title:"Mr.",name:"Jos",surname:"Austen",slug:"jos-austen",fullName:"Jos Austen"}],corrections:null},{id:"22428",title:"Histopathology of Cutaneous Vasculitis",doi:"10.5772/20047",slug:"histopathology-of-cutaneous-vasculitis",totalDownloads:4299,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:null,signatures:"Ko-Ron Chen",downloadPdfUrl:"/chapter/pdf-download/22428",previewPdfUrl:"/chapter/pdf-preview/22428",authors:[{id:"37246",title:"Dr.",name:"Ko-Ron",surname:"Chen",slug:"ko-ron-chen",fullName:"Ko-Ron Chen"}],corrections:null},{id:"22429",title:"FDG-PET in Large Vessel Vasculitis",doi:"10.5772/21263",slug:"fdg-pet-in-large-vessel-vasculitis",totalDownloads:4913,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Quinn K.T. 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Advances in the development of high-throughput technologies have enabled researchers to identify and quantify a large range of gene expression differences in many diseases. The number of gene expression studies has been increasing over the past decades as a result of advanced technologies. Several of them examine and address the same biological questions, even they have been implemented under different experimental conditions. Meta-analysis of gene expression data, therefore, has become a widely applied approach in combining results from multiple studies to identify common expression patterns that sometimes cannot be detected in individual studies. The meta-analytic approach has been shown to increase statistical power, accuracy, and generalizability of results [1, 2, 3, 4]. The use of meta-analysis techniques depends on the type of response and study objectives and most analyses in microarray studies have emphasized identifying differentially expressed (DE) genes or genes that distinguish the group of samples.
Random-effects (RE) meta-analysis models are commonly applied in combining effect sizes from individual gene expression studies. However, study heterogeneity is unknown and may arise from the variation of sample quality and experimental conditions and the study heterogeneity can decrease the statistical power of the models. To maintain power, we can increase the number of studies [5] or apply an appropriate estimation method for incorporating study heterogeneity into the models. Typically, the classical RE models assume studies are independently and uniformly sampled from a population of studies. However, studies are possibly designed based on the results of previous studies. The independence assumption and an infinite population of studies may not exist. Bayesian random-effects (BRE) models have been applied to handle the uncertainty of parameters in the models. The uncertainty is incorporated through a prior distribution. A summary of evidence after the data have been observed is described by the likelihood of the models. Multiplying the prior distribution and the likelihood function will provide a posterior distribution of the parameters [6, 7].
Sample quality has a substantial impact on results of gene expression studies [8, 9]. Low heterogeneity can be found in meta-analyses containing good-quality samples, while poor-quality samples can induce high heterogeneity of effect sizes. We recently evaluated the relationships between DE and heterogeneous genes in meta-analyses of Alzheimer’s gene expression data. Some overlapped DE and heterogeneous genes were detected in meta-analyses containing borderline- or poor-quality samples, while no heterogeneous genes were identified in meta-analyses containing good-quality samples [10]. The data obtained from borderline- or poor-quality samples can increase study heterogeneity and decrease the efficiency of meta-analyses [11, 12].
Small samples in gene expression studies may limit the application of classical RE models and its results may be biased toward the null or the observed value is closer to the null hypothesis than the true value. The BRE model can be used to avoid the approximation and the biases. We introduced a meta-analytic approach that included a sample-quality weight to adjust study heterogeneity in the BRE model [13]. The gene expression data therefore would include both up-weighted good-quality samples and down-weighted borderline-quality samples. Therefore, we first reviewed the classical RE models, the BRE model, and the weighted BRE model in the methods section and then illustrated an application of the methods in Alzheimer’s gene expression studies. Our results are then compiled in the results section and followed by discussion and conclusions.
Choi et al. [14] introduced an unbiased standardized mean difference in expression between groups for each gene
where
Generally, an unbiased estimator for
where
The standard random-effects model currently estimates the between-study variance
In contrast to the classical RE model, the data and model parameters in the BRE model are considered to be random quantities [21]. We applied the BRE model to allow for the uncertainty of the between-study variance in this study. The model for gene
The kernel of the posterior distribution can be written as
where
The choice of prior distributions for scale parameters can affect analysis results, particularly in small samples. With scale parameters, the distributional form and the location of the prior distributions are obtained [22]. Uniform distributions are appropriate non-informative priors for
The quality control (QC) criteria for identifying poor-quality samples in this study were the 3′:5′ GAPDH ratio greater than 3 and/or percent of present calls less than 30% for Affymetrix arrays; and detection rate less than 30% for Illumina Bead Arrays, in addition to data visualizations [8, 23]. Poor-quality samples were excluded before data preprocessing. Furthermore, the inverse of the within-study variance is considered an optimal weight for meta-analysis. The variance of weighted mean (
where
We adjusted the between-study variance in the BRE model (Eq. (9)) by multiplying with an average weight over the total sample in the
We implemented two chains each with 20,000 iterations, a 15,000 burn-in period, and a thinning of 3 in the Bayesian model, and assessed the convergence of the model using the Gelman and Rubin diagnostic [26]. The posterior mean was standardized by posterior standard deviation as the posterior distribution was symmetric and normal. We then applied a Benjamini and Hochberg (BH) procedure to control the false discovery rate (FDR) for multiple gene testing. The performance of several BRE models for unweighted and weighted data, Gibbs and Metropolis-Hastings sampling algorithms, weighted common effect, and weighted between-study variance and classical RE models for unweighted and weighted data were evaluated in simulation studies [10, 13]. The classical RE and BRE meta-analysis models were implemented using programs from
We reviewed publicly available gene expression data from the NCBI GEO database. Twelve series of RNA expression profiling in the GEO database were selected for initial review. Eligible criteria for data acquisition were as follows: (1) the datasets were publicly accessible, (2) the samples were from human brain regions, (3) the series included samples from healthy controls, (4) the datasets included phenotypic data and published manuscripts describing the data, (5) the datasets without redundant samples, and (6) the raw or normalized intensity data were defined as gene expression levels. For each study we reviewed the minimum information about a microarray experiment (MIAME) from the GEO website, including research methods and results described in the manuscripts, and data summaries of the phenotypic data. This presented study included four publicly available Alzheimer’s disease (AD) gene expression datasets of post-mortem brain samples. The Gene Expression Omnibus accession numbers: GSE1297 [30], GSE5281 [31], GSE29378 [32], and GSE48350 [33] containing the gene expression and phenotypic data were included in this presented study. Some of these accession numbers (GSE5281, GSE13214, and GSE48350) include samples from multiple brain regions; we restricted our attention to only those samples acquired from hippocampus and to AD and control subjects in each dataset. The QC criteria for identifying poor-quality samples were having a 3′/5′ glyceraldehyde-3-phosphate dehydrogenase (GAPDH) ratio greater than three and/or percent of present calls less than 30% [23]. We then conducted within study data preprocessing, quantile normalization, and data aggregating. Our meta-analysis was therefore performed on 12,037 target genes in 131 subjects (68 AD cases and 63 controls) from the four studies using the Affymetrix and Illumina platforms (Figure 1). We then considered five ways of metadata sets and primarily examined the strength of study heterogeneity (
Study profile for meta-analysis in Alzheimer’s gene expression datasets.
Barplots on strength of study heterogeneity measuring by random effects coefficient determination (
Distribution of unbiased standardized mean difference of gene expression (x-axis) between Alzheimer’s and control groups in GSE1297, GSE5281, GSE29378, and GSE48350 datasets.
Percentage of present calls and 3′/5′ GAPDH ratio of GSE5281samples.
In this meta-analysis on the metadata of the four AD gene expression datasets, 1766 DE genes were identified by the classical RE model, while 466 DE genes were identified by the weighted BRE model. Almost all the DE genes identified by the weighted BRE model were genes among the significant DE genes identified by the classical RE model. Figure 5 presents the heatmap of 1766 DE up-regulated and down-regulated genes detected in the AD samples. There was no trend apparently toward more up-regulated genes or down-regulated genes on the AD samples as compared to the control samples. Meanwhile, there was a trend toward more down-regulated genes on the AD samples as compared to the control samples in the heatmap of the 466 identified DE genes (Figure 6). The 446 genes could potentially be down-regulated genes that may contribute to the good classification of Alzheimer’s samples (Table 1).
Heatmaps of expression patterns of 1766 differentially expressed genes in hippocampus in Alzheimer’s and control samples. The differentially expressed genes were detected by the classical random-effect meta-analysis model on the metadata of four Alzheimer’s gene expression datasets: GSE1297, GSE5281, GSE29378, and GSE48350.
Heatmaps of expression patterns of 446 differentially expressed genes in hippocampus in Alzheimer’s and control samples. The differentially expressed genes were detected by the classical random-effect meta-analysis model on the metadata of four Alzheimer’s gene expression datasets: GSE1297, GSE5281, GSE29378, and GSE48350.
AACS, AASDHPPT, ABCA1, ACLY, ACOT7, ADAM22, ADAM23, ADARB1,AFF2, AGK, AMPH, ANGPT1, ANP32C, AP2S1, AP3B2, AP3D1, AP3M2, APBA2, APMAP, ARFGEF1, ARHGDIG, ARHGEF9, ARPC5L, ASIC2, ASNS, ASPHD1, ATAT1, ATP1A1, ATP1A3, ATP2A2, ATP2B2, ATP5B, ATP5C1, ATP5D, ATP5G1, ATP5H, ATP5L, ATP6AP1, ATP6V0B, ATP6V0E1, ATP6V1B2, ATP6V1E1, ATP6V1G2, ATP8A2, ATPIF1, ATR, ATRN, ATRNL1, ATXN7L3B, BCL2, BEX1, BEX4, BPGM, BSN, C10orf88, C12orf10, C14orf2, C16orf45, C1orf216, C2CD5, C2orf47, C5orf22, CA10, CABYR, CACNA2D3, CADPS, CALY, CAMK1, CAMK2N1, CAMKV, CAPRIN2, CCK, CDC40, CDC42EP4, CDK5, CDKN2D, CGREF1, CHGB, CHN1, CISD1, CLIP3, CLTA, CNR1, COPS3, COPS7A, COPZ2, COQ6, COX4I1, COX6C, CP, CREBBP, CRYM, CS, CUL2, CYCS, CYP4F12, DAP3, DCTN1, DDX41, DEAF1, DGUOK, DHRS11, DHRS3, DIRAS3, DLEC1, DLG2, DLGAP2, DMXL2, DNASE2, DNM1, DNM1L, DNM3, DOCK3, DOPEY1, DROSHA, DYNC1H1, DYNC1I1, ECM2, EEF1A2, EGFR, EHD3, ELF1, ELOVL4, ELOVL6, ENC1, ENO2, ENTPD2, ENTPD3, EPB41L1, EPS15, ERC2, FAM111A, FAM127A, FAM162A, FAM174B, FAM188A, FAM216A, FAM60A, FAM98A, FAR2, FGF12, FH, FHL2, FIBP, FKBP3, FMO2, FOCAD, FOXJ1, FOXO1, FRMPD4, FSD1, FXN, FYCO1, FYN, GABBR2, GABRG2, GAD, GAD2, GCC2, GLS2, GNAI2, GNG3, GNG4,GOT1, GPHN,GPI, GPRASP1, GRIA2, GRIN1, GRM1, GSTA4, GUCY1B3, GUK1v GYPC, HAGH, HARS, HERC1, HMGCR, HMP19, HN1, HNRNPUL1, HPRT1, HSPA12A, IGF1R, IMMT, IMP3, IMP4, INA, INPP5F, ITPKB, ITSN1, KAT6A, KCNN3, KCNQ2, KIAA0513, KIAA1324, KIF21B, KIFAP3, LARGE, LCMT1, LDB2, LEMD3, LGALS8, LPAR4, LPCAT4, LPIN1, LPP, LRPPRC, LRRC8B, LY6H, MAK16, MAP1A, MAP2K1, MAP2K4, MAP3K9, MAPK9, MAST3, MCF2, MCTS1, MDH1, MDH2, MICU1,MKKS, MLLT11, MOAP1, MPP1, MPPED2, MRPL15, MRPL17, MRPL35, MRPS11, MRPS17, MRPS22, MTMR11, MTSS1L, MTX2, MXI1, MYL12B, MYT1L, NAP1L2, NAP1L3, NCALD, NDN, NDRG3, NDRG4, NDUFA10, NDUFA3, NDUFA4, NDUFA8, NDUFA9, NDUFS3, NDUFS5, NDUFV2, NECAP1, NEDD8, NEFL, NEFM, NELL1, NETO2, NFIB, NIPSNAP3B, NLK, NME1, NMNAT2, NOVA1, NREP, NRGN, NRIP3, NRN1, NSF, NSG1, NUPL2, OGDHL, OPA1, ORC5, P4HTM, PAGE1, PAX6, PDCD1LG2, PEX11B, PIN1, PLCD1, PLCE1, PLCL2, PLD3, PLEC, PLEKHA4, PLK2, PLSCR4, PLXNB2, PMFBP1, PNMAL1, PNO1, PODXL2, POLB, POLRMT, POP7, PPFIA4, PPIA, PPIP5K1, PPM1H, PPME1, PPP1R13L, PPP2CA, PPP3CB, PREP, PREPL, PRKCZ, PRMT1, PRPF40A, PSD4, PSMD8, PTDSS1, PTGES2, PTPRE, PTPRR, PTRH2, PTS, PVRL3, RAB11A, RAB26, RAB27A, RAB2A, RAB6A, RAD51C, RAP1GDS1, RARS, RBFOX2, RGS17, RGS7, RHOQ, RIMBP2, RIT2, RND2, RNF123, RNF41, RNFT2, RNMT, RNPS1, RPH3A, RPP40, RPS6KC1, RUNDC3B, RWDD2A, RXRA, SCAMP2, SCG5, SCN2A, SCN3B, SDHA, SEC22A, SEC61A2, SEH1L, SEPT6, SERPINI2, SEZ6L2, SLC12A5, SLC25A11, SLC25A12, SLC25A14, SLC25A4, SLC4A1AP, SLIRP, SLITRK3, SMARCA4, SMO, SMOX, SMYD2, SNAP25, SNAP91, SNCB, SOX2, SPAG7, SPIN2A, SPINT2, SRM, SRPR, SS18L1, SSPN, STAU2, STMN2, STX6, STXBP1, SULT4A1, SUSD4, SV2B, SYDE1, SYN1, SYN2, SYNGR1, SYNJ1, SYT1, TAGLN3, TAZ, TBC1D31, TBC1D9, TBCC, TBCE, TBL1X, TBPL1, TCEA2, TCF7L2, TERF2IP, TGFBR3, THOC5, TMEM151B, TMEM160, TMEM246, TMEM59L, TMEM70, TMEM97, TNPO1, TOMM34, TOMM70A, TOR1A, TPD52, TPI1, TRAP1, TRAPPC2, TRIM37, TRIM9, TRIOBP, TSPAN13, TSPAN7, TSSC1, TUBA1B, TUBA4A, TUBB3, TUBG1, TUBG2, TXNDC9, UBE2M, UBE2S, UCHL1, UCHL3, UQCC1, UTP11L, VSNL1, WDR47, WDR7, WFDC1, XK, YWHAH, ZFP36L1, ZNF365, ZNHIT3 |
List of 446 significantly differentially expressed genes in Alzheimer’s gene expression datasets.
Note: The differentially expressed genes were detected by the weighted Bayesian random-effect meta-analysis models on the metadata of four Alzheimer’s gene expression datasets: GSE1297, GSE5281, GSE29378, and GSE48350.
We then performed gene network analysis using a publicly available web interface, GeneMANIA [34]. The 446 DE genes identified by the weighted BRE model participate in 146 significant pathways at a false discovery rate of 1%. The first-thirty highly significant pathways with more than twenty identified DE genes in each network included cellular respiration, oxidative phosphorylation, mitochondrial protein complex, inner mitochondrial membrane, protein complex, ATP metabolic process, respiratory electron transport chain, ATP synthesis coupled electron transport, electron transport chain, mitochondrial ATP synthesis coupled, electron transport, mitochondrial inner membrane, energy derivation by oxidation of organic compounds, respiratory chain complex, respirasome NADH dehydrogenase activity, NADH dehydrogenase (quinone) activity, NAD(P)H dehydrogenase (quinone) activity, mitochondrial respirasome, oxidoreductase activity, acting on NAD(P)H, quinone or similar compound as acceptor, respiratory chain complex I, NADH dehydrogenase complex, proton transmembrane transporter activity, aerobic respiration, presynapse, postsynapse, NADH dehydrogenase, complex assembly, oxidoreductase complex, proton-transporting two-sector ATPase complex, mitochondrial proton-transporting ATP synthase complex, ATPase-coupled cation transmembrane transporter activity, synaptic vesicle recycling, inner mitochondrial membrane organization, and cellular response to peptide. GeneMANIA overall retrieved the genes with known coexpression (51.98%), consolidated pathways (25.08%), physical interactions (27.73%), colocalization (10.79%), genetic interactions (5.79%), predicted interactions (2.65%), pathway (0.86%), and share protein domain (0.20%). More details can be found on www.genemania.org.
In this study, we developed a meta-analytic approach for incorporating sample-quality information into the BRE meta-analysis model using an efficient weight identified by a series of simulation studies [10, 13] to adjust the study heterogeneity in the model. We illustrated the weighted Bayesian approach as compared to the classical RE model through an application in Alzheimer’s gene expression studies. We have seen the results of Alzheimer’s gene expression varied by the sample qualities [13]. The variation of sample quality restricted meta-analysis techniques to properly detect DE genes [35, 36]. Meanwhile, the BRE meta-analysis model allows flexibility in calculating
Additionally, the classical RE model tended to estimate
This meta-analytic approach with the sample-quality weight can increase the precision and accuracy of the Bayesian random-effects models in gene expression meta-analysis. The performance of the weighted Bayesian random-effects model may be varied depending on data feature, levels of sample quality, and adjustment of parameter estimates.
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He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. 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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. 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Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. 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A dynamic career research platform which is based on the thematic areas of comparative vertebrate physiology, stress endocrinology, reproductive endocrinology, animal health and welfare, and conservation biology. \nEdward has supervised 40 research students and published over 60 peer reviewed research.",institutionString:null,institution:{name:"University of Queensland",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null,series:{id:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517"},editorialBoard:[{id:"258334",title:"Dr.",name:"Carlos Eduardo",middleName:null,surname:"Fonseca-Alves",slug:"carlos-eduardo-fonseca-alves",fullName:"Carlos Eduardo Fonseca-Alves",profilePictureURL:"https://mts.intechopen.com/storage/users/258334/images/system/258334.jpg",institutionString:null,institution:{name:"Universidade Paulista",institutionURL:null,country:{name:"Brazil"}}},{id:"191123",title:"Dr.",name:"Juan José",middleName:null,surname:"Valdez-Alarcón",slug:"juan-jose-valdez-alarcon",fullName:"Juan José Valdez-Alarcón",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBfcQAG/Profile_Picture_1631354558068",institutionString:"Universidad Michoacana de San Nicolás de Hidalgo",institution:{name:"Universidad Michoacana de San Nicolás de Hidalgo",institutionURL:null,country:{name:"Mexico"}}},{id:"161556",title:"Dr.",name:"Maria Dos Anjos",middleName:null,surname:"Pires",slug:"maria-dos-anjos-pires",fullName:"Maria Dos Anjos Pires",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS8q2QAC/Profile_Picture_1633432838418",institutionString:null,institution:{name:"University of Trás-os-Montes and Alto Douro",institutionURL:null,country:{name:"Portugal"}}},{id:"209839",title:"Dr.",name:"Marina",middleName:null,surname:"Spinu",slug:"marina-spinu",fullName:"Marina Spinu",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRLXpQAO/Profile_Picture_1630044895475",institutionString:null,institution:{name:"University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca",institutionURL:null,country:{name:"Romania"}}},{id:"92185",title:"Dr.",name:"Sara",middleName:null,surname:"Savic",slug:"sara-savic",fullName:"Sara Savic",profilePictureURL:"https://mts.intechopen.com/storage/users/92185/images/system/92185.jfif",institutionString:'Scientific Veterinary Institute "Novi Sad"',institution:{name:'Scientific Veterinary Institute "Novi Sad"',institutionURL:null,country:{name:"Serbia"}}}]},onlineFirstChapters:{paginationCount:10,paginationItems:[{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:5,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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