BRCA interacting proteins
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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.
\\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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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.
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"3582",leadTitle:null,fullTitle:"Advances in Optical and Photonic Devices",title:"Advances in Optical and Photonic Devices",subtitle:null,reviewType:"peer-reviewed",abstract:"The title of this book, Advances in Optical and Photonic Devices, encompasses a broad \r\nrange of theory and applications which are of interest for diverse classes of optical and \r\nphotonic devices. 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\r\n\tThe number of people is rising steadily and the human impact on the environment is approaching its critical level, requiring ultimate effort to reduce the footprint on the surrounding environment. To make justified and timely decisions for sustainable existence, reliable and detailed information about environmental parameters is inevitable.
\r\n\tWe need such information of the environmental indicators day and night, from the crowded cities and the most remote locations. Therefore the study, development, and application of automated sensing systems have been booming during the last decades and the progress in this field is really fast.
\r\n\tThe current book intends to provide the reader with the most recent trends in the development of sensing technologies for environmental control and monitoring, application of these novel technologies for the detection and monitoring of different environmental indicators, but also identification of hazardous chemical compounds and pathogens, and to introduce various aspects of using the online sensing data for decision-making in different fields of social life.
\r\n\t
Cancer is perhaps the cruelest of deadly diseases in our era. So many factors play a role in cancer and these features were characterized in 2011 as belonging to eight categories: evasion of apoptosis, excessive growth signalling, insensitivity to anti-growth signals, maintained angiogenesis, endless replicative potential, metastasis, reprogramming of energy metabolism and avoidance of immune destruction. Types of cancer may be put in different categories (or combinations of these) according to symptoms and pathogenesis, therefore revealing many relationships.
Breast cancer is the most commonly diagnosed cancer type among women. There are similarities between breast and ovarian cancer such as similar mutations (tumor suppressors, proto-oncoges), changes in hormone regulation and microenvironment, etc. In 2014, approximately 235,030 new cases are expected, and it is estimated that 40,430 deaths from breast cancer will occur. Also, an estimated 21,980 new cases of ovarian cancer will be diagnosed in 2014, with an estimated 14,270 deaths. Statistical results and similarities raise the question of whether metastasis of breast cancer is related to the occurrence of ovarian cancer.
Several mutations in growth control genes can trigger the development of tumors in the body. The specific causes of the mutations that lead to cancer are not fully known. Recent studies have tried to uncover these unknown relationships between breast and ovarian cancer. Understanding of the correlations between different types of cancers provide knowledge to us about the disease process. Recent studies focus on common mutations, tumor micro-environment, receptor inactivation, Trastuzumab resistance, etc. Thanks to these studies, new therapeutic techniques have been developed such as using miRNA as therapeutic targets or improvement of nanodrug delivery systems. Also, mathematical modeling has been used in attempts to understand changes in metabolic pathways and metastasis.
Briefly, understanding of the associations between breast and ovarian cancers provide opportunities for the prevention of metastasis and allow development of new ways to cure cancer.
Despite intense studies about breast and ovarian cancer, these cancer types are the most significant cause of death in women in our century. Recent studies have tried to identify different types of mutations for certain genes and determine changes in copy numbers, expression profiles, etc. by using high-throughput technologies [1]. Identifying variations among breast and ovarian cancers will hopefully uncover associations between them, thus possibly revealing methods for early disease screening and allow understanding of the mechanism(s) of metastasis between these two cancer types.
Several studies have continued to find a common point for breast and ovarian cancer; all studies have defined certain mutations in BRCA1/BRCA2 for these types of cancer. The statistics show that 60-80% of BRCA1/BRCA2 gene mutation carriers will develop breast cancer and 20-40% will develop ovarian cancer. Some cases of HBOC indicate a connection with constitutive epimutations or other susceptibility genes such as several gene clusters including the Fanconi anemia (FA) cluster (FANCD2, FANCA and FANCC), mismatch repair (MMR) cluster (MLH1, MSH2, PMS1, PMS2 and MSH6), NA repair cluster (ATM, ATR and CHK1/2), and tumor suppressor cluster (TP53, SKT11 and PTEN). If a patient does not have any mutations in the BRCA genes but their cancer has a phenotype characteristic of those with BRCA mutations and a dysfunction in a DNA repair system, it is known as ‘BRCAness’;[1]. In conclusion,mutations that occur in some DNA repair mechanisms can increase the risk of developing breast and ovarian cancer.
The inactivation of BRCA1 and BRCA2 genes are germline mutations and trigger breast and ovarian cancer. This phenomenon was confirmed by high throughput technologies used for molecular diagnostics such as next generation sequencing (NGS). By using NGS, the DNA of 59 patients harbouring SNVs that include indels or large genomic rearrangements of BRCA1 or BRCA2 was analyzed. Also, 168 patients were used as blind study to compare NGS versus Sanger sequencing or MLPA analyses of BRCA1 and BRCA2. Then, by using three different capture methods, 708 consecutive patients were monitored. A total of 69 deleterious germline alterations within BRCA1 and BRCA2, and 4 TP53 mutations were detected in 468 patients. In addition to this, 36 variations that include either a premature codon stop or a splicing defect among other genes were found (
In the past, full coding exon sequencing was challenging, because researchers had to analyse dozens of coding genes using the traditional method of Sanger sequencing. It is a very time consuming and labor intensive method. Thus, complicated genetic analysis was not possible. However, new techniques have made such research easy. Also, parallel sequencing allows for complicated genetic analysis in a short time. This technique is now reliable for genomic research, but applying this in the clinic is still difficult due to the requirement of complex equipment and highly trained staff [3]. In clinical applications, several library preparation methods have been used to demonstrate a novel capture method. Targeting coding sequences of genes have high coverage in every captured region. In order to streamline the number of germline mutation variants, further whole exon sequencing studies and confirmations are required in order to provide a gold standard for the investigation of germline variants.Nowadays, clinical decisions that include molecular diagnoses have a significant impact on the determination of treatments such as chemotherapy and prophylactic surgery. The association between breast and ovarian cancer try to depend on high or low penetrance of genes that are observable in both cancer types. The most common susceptibility genes in this field are BRCA1/ BRCA2. If any mutations are present in either of these genes, it translates to a 60-85% lifetime risk of developing breast or ovarian cancer [4].
Germline mutations in BRCA1 and BRCA2 can be inherited by offspring and thus are known as constitutional mutations. The mutations may have complete or partial gene deletions, large insertions, duplications, splicing, frameshifts, missense and nonsense mutations. Insertions and deletions may occur at the same position in the sequence and induce gene shuffling, which in turn leads to abnormal gene structure, function,etc. The rate of these mutations changes from population to population. According to data from the Breast Cancer Information Core website, approximately 3500 mutations have been reported for both genes. For instance, female breast cancer patients of Ashkenazi Jewish descent have a 10 – 12 % frequency of mutations in these genes. Frequency of this mutation is higher than in the rest of the Caucasian population, because the female Ashkenazi Jewish population harbors ancient BRCA1 / BRCA 2 mutant alleles. The 5266dup, BRCA2999del5 and BRCA1delexon17 mutations have been defined in some populations such as Slavic, Finnish, Icelandic and German [4].
In addition, the penetrance of mutations is important for genomic rearrangements to develop into a detectable trait. Detection of high penetrance genes is easier than lower ones, because they form symptoms and are always apparent in an individual carrying the allele. However, several variations in low penetrance alleles are more common, and these low penetrance alleles could increase risk to develop cancer and its progression [5]. Some researchers have focused on identification of new genes to explain the missing heritability in BRCA negative cancer patients, including targeted genes that may interact with BRCA pathways and proteins.
Nowadays, several studies have focused on finding these candidate genes and mutations using NGS technologies. According to these studies, additional high penetrance alleles have been found for breast/ovarian cancers; for instance, TP53, STK11,etc. Also, moderate penetrance alleles such as PALB2, BRIP1, RAD51C have a role in cancer via their alteration in pathways like Fanconi Anemia [6],[7]. In addition, ATM and CHEK2 have the same penetrance level and are involved in the homologous recombination repair pathway [8]. Detection of mutations and penetrance within genes other than BRCA1 and BRCA2 has shed light on the genetic heterogeneity of HBOC.
BRCA1 and BRCA2 genes are expressed in epithelial cells of breast and ovarian tissues. They regulate the repair of some types of DNA damage and are involved in cell fate decision; if DNA damage is too excessive and cannot be repaired efficiently, the cell will be directed to be destroyed. Briefly, BRCA1 and BRCA2 genes are tumor supressor genes that are essential in homologous recombination repair of double strand breaks [9], [10]. If any mutations or damage occurs in BRCA1/BRCA2, DNA damage cannot be properly repaired and this increases the risk of developing breast cancer [11]. However, BRCA1/2 are not oncogenes.They are normal but their mutations are abnormal and cause formation of breast cancer. Chromosomal arrangements may result from errors in the DNA damage response mechanism. It might lead to genomic instability. If genomic rearrangements are large, they may escape detection. The problem is that standard genetic testing is not capable of identifying large rearrangements and therefore next generation and whole exon sequencing technologies must be used to detect these gene modifications/changes [12].
Some studies have focused on solving the mechanisms of BRCA1 and BRCA2. According to biochemical, genetic and cytological studies, the lack of BRCA1 results in cell death because BRCA1 regulates stem/progenitor cell proliferation and differentiation. Apicobasal polarity is regulated by BRCA1 and RHAMM (hyaluronan-mediated motility receptor), AURKA (aurora kinase A) and TPX2 (microtubule-associated, homolog). This gene complex can change the miotic spindle promoting activity of RHAMM which may control tumor progression. In addition to this, BRCA1 binds and regulates AURKA which plays a role in the cell cycle as a kinase and appears to be strongly involved in centrosome regulation. Therefore, variations of the AURKA gene may contribute to breast cancer progression [13]. BRCA1 causes an accumulation of TPX2 and is required for mitotic spindle- pole assembly. Not only DNA damage response and repair, but also cell differentiation requires the BRCA core complex proteins for functional integrity.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
RAD51 | \n\t\t\tDSB repair | \n\t\t\tExon 11 (758-1064) | \n\t\t\t[14] | \n\t\t
RAD50 | \n\t\t\tDSB repair | \n\t\t\tExon 11(341- 748) | \n\t\t\t[15] | \n\t\t
BRCA2 | \n\t\t\tDSB repair | \n\t\t\tBRCT domain (1314-1863) | \n\t\t\t[14] | \n\t\t
BASC (QTM,BLM,MSH2,MSH6,MLH1,RCF) | \n\t\t\tMismatch repair | \n\t\t\tBRCA part of complex | \n\t\t\t[16] | \n\t\t
p53 | \n\t\t\tTranscription Factor, tumor supressor | \n\t\t\tExon 11 and BRCT domain (224 – 500 and 1760-1863) | \n\t\t\t[17], [18] | \n\t\t
pRB | \n\t\t\tCell cycle regulator | \n\t\t\tExon 11 and BRCT domain (304-394 and 15336-1863) | \n\t\t\t[19] | \n\t\t
c-Myc | \n\t\t\tTF,oncogene | \n\t\t\tN-terminus and exon11 (175-303 and 433-511) | \n\t\t\t[20] | \n\t\t
ZBRK1 | \n\t\t\tTF,represses GADD45 | \n\t\t\tExon 11 (341-748) | \n\t\t\t[21] | \n\t\t
ATF | \n\t\t\tTF | \n\t\t\tRING (1-101) | \n\t\t\t[22] | \n\t\t
STAT1 | \n\t\t\tSignal transducer,TF | \n\t\t\tExon 11(502-802) | \n\t\t\t[23] | \n\t\t
E2F | \n\t\t\tTF, cell cycle regulator | \n\t\t\tN-terminus (1-76) | \n\t\t\t[24] | \n\t\t
RNA Pol II holoenzyme *(RPH) | \n\t\t\tTranscription | \n\t\t\tBRCT domain (1650-1800) | \n\t\t\t[25], [26] | \n\t\t
RNA helicase A | \n\t\t\tComponent of RPH | \n\t\t\tBRCT domain (1650-1800) | \n\t\t\t[27] | \n\t\t
Estrogen receptor | \n\t\t\tLigand responsive TF | \n\t\t\tN-terminus (1-300) | \n\t\t\t[28, 29] | \n\t\t
Androgen receptor | \n\t\t\tLigand responsive TF | \n\t\t\tExon 11;BRCT domain (758-1064 and 1314-1863) | \n\t\t\t[30] | \n\t\t
CtIP | \n\t\t\tTranscriptional co-repressor | \n\t\t\tBRCT domain (1651-1863) | \n\t\t\t[31, 32] | \n\t\t
p300/CBP | \n\t\t\tTranscriptional coactivator | \n\t\t\tRING and BRCT domain (1-303 and 1314-1863) | \n\t\t\t[33] | \n\t\t
HDAC1 and 2 | \n\t\t\tHistone deacetylation; chromation remodeling | \n\t\t\tBRCT domain (1563 - 1863) | \n\t\t\t[34] | \n\t\t
Centrosome (p53,Prb,Nm23) | \n\t\t\tChromosome segregation | \n\t\t\t*BRCA1 part of the complex | \n\t\t\t[35] | \n\t\t
BRAP2 | \n\t\t\tCytoplasmic retention | \n\t\t\tNLS (303-701) | \n\t\t\t[36] | \n\t\t
Vasolin- containing protein, VCP | \n\t\t\tATPase | \n\t\t\tExon 11 ( 303- 625) | \n\t\t\t[37] | \n\t\t
BARD1 | \n\t\t\tUbiquitination | \n\t\t\tRING (1-101) | \n\t\t\t[38] | \n\t\t
BAP1 | \n\t\t\tDeubiquitinating enzyme | \n\t\t\tRING (1-100) | \n\t\t\t[39] | \n\t\t
Importin α | \n\t\t\tNuclear transport | \n\t\t\tNLS (303-701) | \n\t\t\t[40] | \n\t\t
BRCA2 interacting protein or complex | \n\t\t\t- | \n\t\t\tInteracting domain (a.a. residues) on BRCA2 | \n\t\t\t[41] | \n\t\t
BRCA interacting proteins
Many biochemical studies have shed light on a multitude of proteins with defined interactions with BRCA1 and BRCA2. These proteins are involved in control mechanisms of DNA double strand breaks. Within several minutes after damage, H2AX, a member of the histone H2A family of proteins, becomes phosphorylated and foci form at the site of DNA double strand breaks [42]. BRCA1 is recruited with this area within several hours. Subsequently, RAD50 and RAD51 interact with the strand breaks. This situation shows that BRCA1 and H2AX can initiate repair mechanisms of local chromatin structure, thus DNA repair proteins can access damaged sites.
If BRCA1 and BRCA2 genes are absent from the cell, chromosomal abnormalities, breaks, aneuploidy and centrosome amplification occurs. The pathogenic tumor formation in breast and ovarian tissue may depend on chromosomal instability that is the result of deficiency of BRCA1 and BRCA2 genes. In order to reveal this relation, researchers monitored sporadic breast and ovarian tumors. 50 – 70 % of them were found to have lost an BRCA1 allele and 30 – 50 % were found to have lost an BRCA2 allele [43],[44].
Genomic instability of BRCA1 and BRCA2 genes result from the repetitive DNA elements that are of high density. 42% of BRCA1 consists of Alu sequences and 5% non-Alu repeats. The genomic region of BRCA2 consists of 47% repetitive DNA [45]. BRCA1 and BRCA2 are rare genes that include high density repetitive DNA regions. Multiple diseases are mediated by genetic rearrangements of Alu sequences. According to the given density of repeat elements in BRCA1 and BRCA2, careful analysis of these genes can reveal the risk of breast and ovarian cancer due to these susceptibility genes.
The source of the large deletions depends on repetitive regions on genes. One mechanism that manages the large deletions observed around the BRCA1 and BRCA2 that are inherited and sporadic tumors in breast and ovarian cancer (Figure1). These repeat regions may be far apart from the linear DNA but physically close in the nucleus. For instance, if a chromosome break occurs near a replication fork during replication, it might be repaired by HR to a replication fork at a nearby anchorage point.
A mechanism for the formation of deletion by loss of a chromatin loop at different stages. Deletions of phase 1 occur in S phase, when the same repetitive sequences are physically brought together by MAR (blue ellipse). Breaks in DNA, and their repair, might lead to deletion of a chromatin loop (red). Deletions of type 2 and 3 occur by the same mechanism but occur later during DNA synthesis in the replication cycle. (Adapted from Piri et al [
Double strand breaks such as exposure to ionizing radiation or certain kinds of DNA-damaging agents. Genetic defects in DNA damage response genes and/or down-regulation of the DNA repair mechanisms induces genomic instability, and this can lead to carcinogenesis [46]. Among the many DNA repair pathways available in mammalian cells are homologous repair, non-homologous end-joining and single-strand annealing [47]. There are several ways that cells can repair double strand breaks. A number of signaling pathways are involved in the detection of DSBs and regulate DNA repair or apoptotic cell death. The main DNA damage recognition molecule is ATM [48], a checkpoint kinase that phosphorylates a number of proteins in response to DNA damage, including p53 and BRCA1 [Figure2].
p53 plays a critical role in preventing cancer development. Generally, p53 gene is mutated in cancer tissue, so it cannot protect the genetic integrity of cells. In physiological conditions, p53 is activated when DNA damage occurs. The failure of DNA damage response results in p53 mediated cell apoptosis [49]. Several mechanisms regulate p53 activity. p21WAF-1 has been shown to play an important role in both p53-dependent [50] and -independent pathways [51]. p21WAF-1 prevents cell cycle progression via interaction with the cyclin-dependent kinase (CDK) complex. Therefore, p53 plays a role in the most important part of providing stability to the genome by using cell cycle checkpoints, DNA repair and apoptosis.
Schematic representation and overview of the DNA repair and checkpoint regulation of cell cycle
BRCA1 also involves a gold standard for a tumor suppressor gene that is needed to prevent cancer development and progression. BRCA1 / BRCA2 related breast and ovarian cancers are have defects in a DNA repair pathway [52]. Studies have shed light on the functional roles of BRCA1/BRCA2 genes in DNA repair, cell cycle checkpoints and DNA damage signaling pathways [53]. BRCA1 interacts with several cyclins and CDKs, triggers the activation of the CDK inhibitor, p21WAF-1, and p53, thus it can control the cell cycle. The main function of BRCA1 depends on its phosphorylation status, so if the gene becomes hyper-phosphorylated following any damage or exposure to DNA damaging agents, it becomes non-functional[54].
Also, BRCA1 and BRCA2 genes are not only responsible for DNA damage response but also their proteins interact with the estrogen and androgen receptors [55]. These genes inhibit estrogen receptor-α activity and stimulate androgen receptors. In this way, BRCA1 mutations are associated with hormone responsive cancer. In other words, the cancer risk of BRCA1 mutation carriers will increase via hormonal factors.
Estrogen, progesterone and androgen hormones control the initiation of carcinogenesis by using special hormone receptors. Moreover, hormonal therapies frequently regulate hormone-mediated diseases such as cancer. A number of candidate genes have been identified as biomarkers for ovarian and breast cancers [56].
Frequently, damage in the DNA repair system induce growth arrest and cell death. BRCA deficient mice die in the early stages of embryogenesis. The first question that arises is why BRCA deficient breast or ovarian epithelial cells develop tumors instead of undergoing apoptosis? What is special to breast and ovarian epithelial cells that allows them to escape apoptosis or response to the DNA damage response system? Finally, how are BRCA1 and BRCA2 genes associated with estrogen levels?
The transition of the hormone independence induces the progression of breast and ovarian cancer because of DNA repair defects. The estrogen-bound receptor dimerizes and associates with chromatin. The estrogen response elements that are present on a DNA sequence motif bind directly to the receptor dimers. There are two kind of estrogen receptors:estrogen receptor-α and estrogen receptor-β. Estrogen receptor-α plays a role in proliferation, and the activation of estrogen receptor-β controls apoptosis [57]. An increase in estrogen receptor-β levels might be related with a reduction in breast cancer risk [58]. Estrogen receptor-β may prevent cellular proliferation by action opposite to that of estrogen receptor-α.
Schematic representation of interaction between BRCA1 and estrogen receptor (ER)-α
A woman exposed to estrogen either endogenously or exogenously, has an increased risk of developing breast or ovarian cancer. BRCA1 and BRCA2 expression levels are highest during pregnancy and puberty, when estrogen levels are increased [59].
If estrogens triggers cell proliferation [60], increased estrogens promotes the probability of developing random genetic rearrangements and errors. Metabolic processes produce reactive oxygen species (ROS) that cause oxidative damage to genomic DNA. In addition, some hormone oxidative metabolites catalyzed by cytochrome p450 enzymes can form unstable adducts in DNA which then leads to mutations [61].
Connection of the hormone endocrine, immune, DNA damage and DNA repair systems in cancer
A long period of exposure to estrogen is strongly associated with an increased risk of developing breast and ovarian cancer. However, activation of DNA damage response mechanisms may be triggered via androgen signaling [62]. The estrogen receptor-mediated pathways are inhibited by BRCA1 and BRCA2 proteins which function as a suppressor in mammary epithelial cell proliferation. Also, the estrogen receptor complex regulates the transcription of BRCA1 and BRCA2 under the condition of estrogen stimulation. In addition, estrogens are not only essential for mammary growth and differentiation, but also enhance the activity of the p53 tumor suppressor protein [63].
Cancer susceptibility genes increase the risk of malignancy as a result of mutations in tumor suppressor or oncogenes that control different pathways. The KRAS variants are active at the site of the 3’-untranslated region of the complementary site of let-7 miRNA. miRNAs are 22-nucleotide long noncoding RNAs that are conserved regions. They are a novel class of oncogenes and tumor supressors that are upregulated in cancers [64]. Recent studies showed that SNPs that are present in miRNA binding sites can be powerful markers of cancer risk [65]. Ratner et al. reported that KRAS is associated with 61% of cases of breast and ovarian cancer syndrome. In another study, KRAS variants were observed to be increased within women with triple-negative breast cancer [66]. A study at Yale University, involving 58 hereditary breast and ovarian syndrome patients tested for the presence of the KRAS variant. The KRAS-variant was identified in 60% of HBOC patients who lacked BRCA1 or BRCA2 mutations. These findings strongly support the hypothesis that the KRAS-variant is a genetic marker of an increased risk of developing ovarian cancer[67].
The KRAS variant might be a new biomarker for breast and ovarian cancer. Therefore, preventing or identifying cancer in early steps may be possible by using this biomarker.
FEN1 is a kind of flap structure endonuclease that is critical for DNA repair processing. It is involved in long patch base excision repair (LP-BER) and Okazaki fragment maturation during replication. In addition, it plays a role in rescue delayed in replication forks, managing of telomere stability and apoptotic formation of DNA [68] [69]. Fen1 is also a main actor in posttranslational modifications such as acetylation, phosphorylation, sumoylation, methylation and ubiquitylation which control nuclease activities [68] [69].
FEN1 has a role in tumor formation. A FEN1 E160D mutant mouse model shows alteration in DNA repair [70] [71]. These changes trigger an increased frequency of cancer development. Polymorphic variations of FEN1 in humans may be associated with high frequency cancer susceptibility [72, 73].
FEN1 has an impact on breast tumors. It affects BRCA1, PARP1, XRCC1 and TOP2A genes. There is an association between high FEN1 and ATM expression. FEN1 may regulate the ER-induced transcriptional response with interaction of estrogen response elements [74]. There is a complex network between ER, FEN1 and ATM in breast cancer cells. Similarly, in ovarian cancer, FEN1 expression is linked to an aggressive phenotype and poor survival [75]. Abdel-Fatah et al. demonstrated that FEN1 overexpression is associated with an aggressive phenotype and poor survival in breast and ovarian cancer.
Despite the more intense studies about breast and ovarian cancer, these cancer types are the most significant cause of death in women in our century. Recent studies have tried to streamline the number of mutations for specific genes and identify changes in copy number, expression profiles, etc. by using high-throughput technologies for identification of variations. Identification of all kinds of variations will uncover associations between breast and ovarian cancer, and thus reveal potential disease screening methods and provide an understanding of the mechanism of metastasis between these two cancer types. In this chapter, we aimed to gather the current knowledge about susceptibility genes BRCA1 and BRCA2 which are highly connected with breast and ovarian cancer. Also, mechanisms and hormones (estrogen) that induce cancer associated with BRCA1/BRCA2 have been discussed. Finally, new biomarkers including FEN1 and KRAS for breast and ovarian cancer have been discussed.
For an effective training, supervised learning requires a decent amount of labeled data, which is expensive. Unlabeled and inexpensive data (e.g. text and images on the Internet) is considerably more than the limited size datasets labeled by humans. We can use unlabeled data and perform a training on a pretext task, which is a
Although its origins date as back as 1990s [1, 2], contrastive learning has recently gained popularity due to its achievements in self-supervised learning, especially in computer vision. In
Self-supervised (left) vs. supervised (right) contrastive learning. Training results in an embedding space such that similar sample pairs stay close to each other while dissimilar ones are far apart. Figure is reproduced based on [
Since a self-supervised model does not know the actual labels corresponding to the inputs, its success depends on the design of the pretext tasks to generate the pseudo-labels from part of the input data. With these pseudo-labels, training on pretext task is performed with a ‘supervised’ loss function. Final performance on the pretext task is not important, but we hope that the learned intermediate representations can capture good information and be beneficial to a variety of downstream tasks.
Especially in computer vision and natural language processing (NLP), deep learning has become the most popular machine learning approach [5]. In parallel, self-supervised learning studies in computer vision have employed CNNs. Figure 2 shows the knowledge transfer from a self-supervised training to a supervised one in a deep learning setting. We save convolutional layers which are assumed to produce learned representations. We change/add fully connected layers, place a classifier head and train with the limited amount of labeled data for a downstream task like image classification or object detection.
A model is first trained with a pretext task with unlabeled data, then fine-tuned on the downstream task with limited amount of labeled data. Usually convolution layers, which are mostly responsible of learning representations, are transferred. A few fully-connected layers towards the end are changed or retrained.
The remainder of this chapter is structured as follows. Pretext tasks that are common in literature are reviewed in Section 2. Section 3 has detailed information about recent self-supervised learning models that use Siamese architectures. Section 4 provides our own experimental study where self-supervised contrastive learning is employed to learn representations of semantic segmentation masks, which is followed by the conclusions in Section 5.
Several random transformations applied to a patch from the unlabeled dataset to be used for self-supervised learning. Original sample is in top-left. The idea was first used by [
This is not only one of the first pretext tasks but also a very popular one. We will see in Section 3 that the mentioned type of augmentations have succeeded in learning useful representations and have achieved state-of-the-art results in transfer learning for downstream computer vision tasks.
Self-supervised representation learning by rotating input images, implemented in [
A model is trained to predict the colorized version of grayscale images (obtaining the dataset is inexpensive).
Last two pretext tasks (image colorization and inpainting) and some other GANs (e.g. image super-resolution [12]) are generation-based methods, where a missing info in the content is generated from available input. Whereas distortion, rotation and jigsaw are context-based self-supervision methods. For more detailed literature on pretext tasks, we refer the readers to the review in [13].
In our study, we concentrate on the context-based approach. Taking advantage of contrastive learning, this approach nowadays achieves state-of-the-art performance [14, 15, 16, 17]. We will go into details, especially the models with Siamese architecture, in Section 3. The generation-based and context-based method distinction also exists for video representation learning. In [18], an encoder network is used to learn video representations. Then, a decoder uses the representations to predict future frames. Differently, Qian
The rest of our chapter will consider works on image data. Before proceeding, let us give a few examples where contrastive learning is used for image-text pairs. Contrastive Language-Image Pre-training (CLIP, [20]) is a pretext task, where a text encoder and an image encoder are jointly trained to match captions with images. Training set consists of 400 million (image,text) pairs and an inter-modal contrastive loss is defined such that image and text embeddings of same objects will be closer to each other. Then, this pretraining is employed for a downstream task of zero-shot class prediction from images. Li
The goal of contrastive learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Implemented using Siamese networks, recent approaches create two different augmentations of samples and feed into the networks for contrastive learning. While SimCLR [14] and MoCo [15] use the negative samples directly along with the positive ones, BYOL [16] and SimSiam [17] achieved similar performance just with the positive samples. Differently, SwAV [23] forced consistency between cluster assignments of augmentations, instead of comparing features directly. Shortly after, vision transformers were included in self-supervised learning architectures [24, 25]. According to the results, not only image classification, but also object detection and semantic segmentation as downstream tasks benefit from self-supervised contrastive learning. Let us briefly explain some of these main approaches.
Let us describe SimCLR [14] first, then we will describe other methods by comparing to previous ones. SimCLR uses both positive and negative samples, but being positive or negative does not correspond to actual class labels. Augmented versions of the anchor are taken as positives, whereas samples belong to different instances are taken as negatives (Figure 6).
Let
A base feature encoder
Each representation
A batch of
SimCLR framework [
SimCLR uses the contrastive loss given in Eq. (1). This is a categorical cross-entropy loss to identify the positive sample among a set of negative samples (inspired from InfoNCE [27]).
A common protocol to evaluate self-supervised model efficiency is to place a linear classifier on top of (frozen) layers learnt by self-supervised training and train it for the downstream task with the labeled data. If the performance gap between this self-supervised encoder + linear classifier and a fully-supervised model is small, then the self-supervised training considered as efficient. An alternative evaluation protocol uses semi-supervised learning, i.e. pretrained network is re-trained as a whole with a certain percentage of available labels. Experiments reveal that re-training with only 10% of the labeled data achieves a performance (92.8%) very close to fully-supervised training performance on the whole dataset (94.2%) as reported in [14] (performances are top-5 classification accuracy on ImageNet dataset for ResNet-50).
Contrastive methods based on InfoNCE loss tend to work better with high number of negative examples since negative examples may represent underlying distribution more efficiently. SimCLR requires large batches (4096 samples) to ensure that there is enough negatives which demands high computation power (8 V100 GPUs in their study). To alleviate this need, MoCo [15] uses a dictionary of negative representations that is structured as a FIFO queue. This queue-based dictionary enables us to reuse representations of immediately preceding mini-batches of data. Thus, the main advantage of MoCo compared to SimCLR is that MoCo decouples the batch size from the number of negatives. SimCLR requires a large batch size and suffers performance drops when the batch size is reduced.
Given a query sample
MoCo framework [
Let us assume that there is a single positive key,
From the two encoders defined above, for
where
Later on, two design choices in SimCLR, namely MLP projection head and more stronger data augmentation were integrated into the approach resulting in MoCo-v2 [28].
Different from the approaches above, BYOL [16] achieves similar representation performance without using negative samples. It relies on two different neural networks (in contrast to SimCLR but similar to MoCo), referred to as online and target networks that interact. Online network has a predictor head. Target network has the same network architecture with the online network except for the predictor head (Figure 8). Parameters of the target network are not updated with back-propagation, but with a moving average of online network’s weights just as MoCo did for the momentum encoder.
Comparison of some Siamese architectures: SimCLR, BYOL and SimSiam. Dashed lines indicate back-propagation. Components colored in red are no more needed in SimSiam. Figure is reproduced based on [
It is curious that how the model escapes from collapsing (i.e. a trivial solution of fixed vector for each sample) when no negative samples are used. Authors of BYOL thought it is due to the momentum update, but later (with SimSiam [17]) it was discovered that using stop-gradient and predictor head is enough.
BYOL needs to maintain two copies of weights for the two separate networks which can be resource demanding. SimSiam [17] solves this problem with parameter sharing between the networks (with and w/o predictor head). The encoder
Finally, negative cosine similarity based total loss is computed in a symmetric fashion:
Figure 8 compares SimSiam with SimCLR and BYOL. SimSiam [17] does not use negative samples as SimCLR and MoCo did. Success with SimSiam also shows that momentum encoder (or any sort of moving average update of weights) is not needed. Stop-gradient operation and including predictor head are enough to prevent the model from collapsing.
SimSiam also presents transfer learning results for object detection and semantic segmentation downstream tasks. Results reveal that starting with a self-supervised pre-training on ImageNet outperforms image classification pre-training on ImageNet.
Caron
As a case study, we employ self-supervised contrastive learning to learn representations of semantically segmented images, i.e. semantic masks. This learning task is especially useful when two scenes are compared according to their semantic content. A use case would be image retrieval based localization, where standard approach extract features from RGB images and compare them to find the most similar image in the database [29, 30]. Recently, several studies showed that checking semantic resemblance between query and database images and using this similarity score while retrieving images improves localization accuracy [31, 32, 33]. The reason of improvement is that there is appearance difference between images taken at different times (query-database) due to illumination differences, viewpoint variations, seasonal changes. Although RGB image features are directly affected by those changes, semantic labels are stable most of the time (Figure 9).
The image on top-left was taken in 2008 and the image on top-right was taken in 2019 (source: Google street view) which respectively represent query and database for image retrieval. Observe illumination differences, viewpoint variations and changing objects. Bottom row shows their semantic segmentation results. Semantic similarity can help to verify/deny the localization result.
Given a semantic mask, obtaining the most similar result among the alternatives is not a trivial task. SIFT-like features do not exist to match. Moreover, two masks of the same scene are far from being identical not only because of changing content but also due to camera position and viewpoint variations. Thus, instead of employing a pixel-by-pixel label comparison score, a trainable semantic feature extractor is preferable.
Measuring semantic similarity to distinguish if two images belong to the same scene or not is a task especially suitable for self-supervised learning. Because datasets has to be prepared such that query and database are the same scene but different images (preferably long-term difference) is not easy. However, large amount of semantic masks can easily be obtained for a self-supervised training. We do not need groundtruth masks, since a successful estimation is enough to compute semantic similarity.
Our unsupervised learning dataset composed of 3484 images randomly taken from UCF dataset [34]. These are perspective images obtained from Google Street View panoramas which where taken in Pittsburgh, PA before 2014. Our supervised training and test datasets have query-database image pairs. Query images were also taken from UCF dataset (not coinciding with the 3484 images mentioned above). Database images were collected again from Google Street View panoramas at the same locations of query images but in 2019. This time gap results in seasonal changes and illumination variances. Also, a wide camera baseline between the database and query images conforms better to the long-term localization scenario [35]. Top row in Figure 9 shows an example of query-database image pair with time difference.
Since our aim to learn representations for semantic masks, we first automatically generated a semantic mask for each image in our dataset using a well-performing CNN model [36]. The CNN model we employed trained on Cityscapes [37], which is an urban scene understanding dataset consists of 30 visual classes. Examples are in Figure 9 (bottom row). After this point, we only have semantic masks in our dataset.
We used SimCLR [14] as our contrastive learning model and trained a ResNet-18 as the encoder. Encoder network (Figure 10) produces
Illustration of training a CNN model with self-supervised contrastive loss on a dataset that consists of semantically segmented masks. A positive pair is created from two randomly augmented views of the same mask, while negative pairs are created from views of other masks. All masks are encoded by the a shared encoder and projection heads before the representations are evaluated by the contrastive loss function.
CNN model, trained as explained above, is now ready to produce a similarity score when two semantic masks (one query and one database) are given. After self-supervised training, same network can be fine-tuned with a labeled dataset (query and database segmentation masks for the same scene). For this purpose, we prepared a dataset of 368 query images with their corresponding database images and extracted their semantic masks. Figure 9 shows an example of this preparation. Not surprisingly, this paired dataset is much smaller than the self-supervised training dataset. Here, common practice in literature is that the projection head (Figure 10) is removed after pretraining and a classifier head is added and trained with labeled data for the downstream task. However, our pretext and downstream tasks are the same. We learn semantic representations by treating each sample as its own class (exemplar-CNN [6], instance discrimination [7]). Thus, we do not place a classifier head, but we retrain the network (partially or full).
To be able to measure the capability of representing semantic masks, we conduct experiments that compare the retrieval accuracies of three training schemes. First is the CNN model which is trained with the supervised training set (368 query-database pairs). This is the baseline model that does not exploit self-supervised training at all. Second is the CNN model that is trained in a self-supervised fashion with 3484 individual semantic masks (no matching pairs). Lastly, the model with self-supervised training is retrained with the supervised training set. Two versions exist:
Trained models are tested on a test set which consists of 120 query-database pairs (different from 368 pairs used in training). Performances are compared with Recall@N metric. According to this metric, for a query image, the retrieval is considered successful if any of top-N retrieved database images is a correct match. In other words, Recall@1 is the recall when only the top-most retrieval is checked.
We observe in Table 1 that, only supervised training is not very successful. In fact, for certain N values self-supervised training managed to outperform supervised training alone. This shows the power of self-supervised learning when a large dataset is provided. Our unlabeled dataset is much larger than the labeled dataset (3484
Training methods | Retrieval accuracy (Recall@N) | ||||
---|---|---|---|---|---|
N = 1 | N = 2 | N = 3 | N = 4 | N = 5 | |
Only supervised training | 0.500 | 0.608 | 0.767 | 0.808 | 0.817 |
Only self-supervised training | 0.567 | 0.692 | 0.733 | 0.775 | 0.800 |
Dense layers were replaced and trained | 0.542 | 0.675 | 0.767 | 0.825 | 0.850 |
All layers were fine-tuned |
Only supervised training is compared with self-supervised training and fine-tuned versions of it.
Each row shows a retrieval result for a given query (left column). Examples show the cases where only supervised training (middle column) fails at Recall@1, but utilizing self-supervised training and then fine-tuning on the labeled dataset (query-database pairs) correctly retrieves (last column).
Table 2 presents the effect of minimum crop ratio parameter used in data augmentation module. Since it is an important parameter to represent the variation in our semantic masks, we compare the performance for minimum crop ratio from 0.9 to 0.1. Apart from individual Recall@N values, we also compute and plot mean recall (mean of all N values) in Table 2 last column and in Figure 12. We observe that it is highest around 0.6 and 0.7. Performance gradually drops as we increase or decrease the minimum crop ratio. A minimum random crop parameter of 0.6 means that cropped mask covers at least 60% area of the original mask. Since query and database masks in our training and test datasets have a considerable overlap ratio, it is reasonable that 0.6 or higher overlaps serve best. This result is also in accordance with the finding in [38] that there is a reverse U-shape relationship between the performance and the mutual information within augmented views. When crops are close to each other (high mutual information, e.g. crop ratio = 0.9) the model does not benefit from them much. On the other hand, for low crop ratios (low mutual information) model can not learn well since views look quite different from each other. Peak performance stays somewhere in between.
Crop ratio | Retrieval accuracy (Recall@N) | |||||
---|---|---|---|---|---|---|
N = 1 | N = 2 | N = 3 | N = 4 | N = 5 | mean | |
0.90 | 0.608 | 0.708 | 0.758 | 0.817 | 0.858 | 0.750 |
0.80 | 0.617 | 0.733 | 0.800 | 0.848 | 0.867 | 0.773 |
0.70 | 0.617 | 0.742 | 0.782 | |||
0.60 | 0.808 | 0.867 | ||||
0.50 | 0.617 | 0.700 | 0.767 | 0.808 | 0.833 | 0.745 |
0.40 | 0.575 | 0.692 | 0.717 | 0.783 | 0.825 | 0.718 |
0.30 | 0.567 | 0.675 | 0.742 | 0.767 | 0.808 | 0.712 |
0.20 | 0.542 | 0.633 | 0.717 | 0.767 | 0.783 | 0.688 |
0.10 | 0.525 | 0.608 | 0.675 | 0.742 | 0.783 | 0.667 |
Effect of the minimum crop ratio parameter in data augmentation at the stage of retraining of the self-supervised model.
Mean Recall@N values for varying min. Crop ratio parameter. Observe the reverse U-shape with a peak at 0.6.
In this chapter, we presented the main concepts in self-supervised contrastive learning and reviewed the approaches that attracted attention due to their success in computer vision. Contrastive learning that aims to end up in an embedding space where similar samples stay close to each other was implemented successfully with Siamese neural networks. Necessity on huge computation power was also alleviated with the most recent models. Currently, for common downstream tasks of computer vision such as object detection and semantic segmentation, self-supervised pre-training is a better alternative than using a model trained on ImageNet for image classification.
We also presented a case study where self-supervised contrastive learning is applied to learn representations of semantic masks of images. Performance was evaluated on an image retrieval task where the most similar semantic mask is retrieved from the database for a given query. In compliance with the results on other vision tasks in the literature, fine-tuning the self-supervised model with available labeled data gave better results than the supervised training alone.
This work was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant No. 120E500 and also under 2214-A International Researcher Fellowship Programme.
CNN | Convolutional neural network |
RGB | Red green blue |
NLP | Natural language processing |
LSTM | Long short term memory |
GAN | Generative adversarial network |
MoCo | Momentum contrast |
BYOL | Bootstrap your own latent |
MLP | Multi-layer perceptron |
FIFO | First in first out |
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His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. 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In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/15648_n.jpg",biography:"Dr. Mohd Aftab Siddiqui is currently working as Assistant Professor in the Faculty of Pharmacy, Integral University, Lucknow for the last 6 years. He has completed his Doctor in Philosophy (Pharmacology) in 2020 from Integral University, Lucknow. He completed his Bachelor in Pharmacy in 2013 and Master in Pharmacy (Pharmacology) in 2015 from Integral University, Lucknow. He is the gold medalist in Bachelor and Master degree. He qualified GPAT -2013, GPAT -2014, and GPAT 2015. His area of research is Pharmacological screening of herbal drugs/ natural products in liver and cardiac diseases. He has guided many M. Pharm. research projects. He has many national and international publications.",institutionString:"Integral University",institution:null},{id:"333824",title:"Dr.",name:"Ahmad Farouk",middleName:null,surname:"Musa",slug:"ahmad-farouk-musa",fullName:"Ahmad Farouk Musa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333824/images/22684_n.jpg",biography:"Dato’ Dr Ahmad Farouk Musa\nMD, MMED (Surgery) (Mal), Fellowship in Cardiothoracic Surgery (Monash Health, Aust), Graduate Certificate in Higher Education (Aust), Academy of Medicine (Mal)\n\n\n\nDato’ Dr Ahmad Farouk Musa obtained his Doctor of Medicine from USM in 1992. He then obtained his Master of Medicine in Surgery from the same university in the year 2000 before subspecialising in Cardiothoracic Surgery at Institut Jantung Negara (IJN), Kuala Lumpur from 2002 until 2005. He then completed his Fellowship in Cardiothoracic Surgery at Monash Health, Melbourne, Australia in 2008. He has served in the Malaysian army as a Medical Officer with the rank of Captain upon completing his Internship before joining USM as a trainee lecturer. He is now serving as an academic and researcher at Monash University Malaysia. He is a life-member of the Malaysian Association of Thoracic & Cardiovascular Surgery (MATCVS) and a committee member of the MATCVS Database. He is also a life-member of the College of Surgeons, Academy of Medicine of Malaysia; a life-member of Malaysian Medical Association (MMA), and a life-member of Islamic Medical Association of Malaysia (IMAM). Recently he was appointed as an Interim Chairperson of Examination & Assessment Subcommittee of the UiTM-IJN Cardiothoracic Surgery Postgraduate Program. As an academic, he has published numerous research papers and book chapters. He has also been appointed to review many scientific manuscripts by established journals such as the British Medical Journal (BMJ). He has presented his research works at numerous local and international conferences such as the European Association for Cardiothoracic Surgery (EACTS) and the European Society of Cardiovascular Surgery (ESCVS), to name a few. He has also won many awards for his research presentations at meetings and conferences like the prestigious International Invention, Innovation & Technology Exhibition (ITEX); Design, Research and Innovation Exhibition, the National Conference on Medical Sciences and the Annual Scientific Meetings of the Malaysian Association for Thoracic and Cardiovascular Surgery. He was awarded the Darjah Setia Pangkuan Negeri (DSPN) by the Governor of Penang in July, 2015.",institutionString:null,institution:{name:"Monash University Malaysia",country:{name:"Malaysia"}}},{id:"30568",title:"Prof.",name:"Madhu",middleName:null,surname:"Khullar",slug:"madhu-khullar",fullName:"Madhu Khullar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/30568/images/system/30568.jpg",biography:"Dr. Madhu Khullar is a Professor of Experimental Medicine and Biotechnology at the Post Graduate Institute of Medical Education and Research, Chandigarh, India. She completed her Post Doctorate in hypertension research at the Henry Ford Hospital, Detroit, USA in 1985. She is an editor and reviewer of several international journals, and a fellow and member of several cardiovascular research societies. Dr. Khullar has a keen research interest in genetics of hypertension, and is currently studying pharmacogenetics of hypertension.",institutionString:"Post Graduate Institute of Medical Education and Research",institution:{name:"Post Graduate Institute of Medical Education and Research",country:{name:"India"}}},{id:"223233",title:"Prof.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/223233/images/system/223233.png",biography:"Xianquan Zhan received his MD and Ph.D. in Preventive Medicine at West China University of Medical Sciences. He received his post-doctoral training in oncology and cancer proteomics at the Central South University, China, and the University of Tennessee Health Science Center (UTHSC), USA. He worked at UTHSC and the Cleveland Clinic in 2001–2012 and achieved the rank of associate professor at UTHSC. Currently, he is a full professor at Central South University and Shandong First Medical University, and an advisor to MS/PhD students and postdoctoral fellows. He is also a fellow of the Royal Society of Medicine and European Association for Predictive Preventive Personalized Medicine (EPMA), a national representative of EPMA, and a member of the American Society of Clinical Oncology (ASCO) and the American Association for the Advancement of Sciences (AAAS). He is also the editor in chief of International Journal of Chronic Diseases & Therapy, an associate editor of EPMA Journal, Frontiers in Endocrinology, and BMC Medical Genomics, and a guest editor of Mass Spectrometry Reviews, Frontiers in Endocrinology, EPMA Journal, and Oxidative Medicine and Cellular Longevity. He has published more than 148 articles, 28 book chapters, 6 books, and 2 US patents in the field of clinical proteomics and biomarkers.",institutionString:"Shandong First Medical University",institution:{name:"Affiliated Hospital of Shandong Academy of Medical Sciences",country:{name:"China"}}},{id:"297507",title:"Dr.",name:"Charles",middleName:"Elias",surname:"Assmann",slug:"charles-assmann",fullName:"Charles Assmann",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/297507/images/system/297507.jpg",biography:"Charles Elias Assmann is a biologist from Federal University of Santa Maria (UFSM, Brazil), who spent some time abroad at the Ludwig-Maximilians-Universität München (LMU, Germany). He has Masters Degree in Biochemistry (UFSM), and is currently a PhD student at Biochemistry at the Department of Biochemistry and Molecular Biology of the UFSM. His areas of expertise include: Biochemistry, Molecular Biology, Enzymology, Genetics and Toxicology. He is currently working on the following subjects: Aluminium toxicity, Neuroinflammation, Oxidative stress and Purinergic system. Since 2011 he has presented more than 80 abstracts in scientific proceedings of national and international meetings. Since 2014, he has published more than 20 peer reviewed papers (including 4 reviews, 3 in Portuguese) and 2 book chapters. 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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 28th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:317,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",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. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. 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. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/48121",hash:"",query:{},params:{id:"48121"},fullPath:"/chapters/48121",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()