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\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
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\\n\\nNote: Edited in October 2021
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\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
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\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
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\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
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Tsuzuki",dateSubmitted:null,dateReviewed:"May 25th 2021",datePrePublished:"July 6th 2021",datePublished:null,book:{id:"10627",title:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",subtitle:null,fullTitle:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",slug:null,publishedDate:null,bookSignature:"Dr. Marcos Sales Guerra Tsuzuki and Prof. Rehab O. 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Fundamentally, with Descartes and the introduction of coordinates, a line or a plane becomes via coordinates an algebraic object, more precisely an equation.
\r\n\r\n\tIn general, any coordinates replace geometry by algebra and we get a two-dimensional correspondence between the study of space and the study of equations. This process is a shift from geometry to numbers at a basic level. The coordinatization process has been used well before mathematicians accepted it as a method.
\r\n\r\n\tThe manifolds are precisely those spaces that can be piecewise provided with coordinates by means of a smooth correspondence on overlaps, and the book will intend to study these structures in mathematics, as well as the impact and applications to a variety of other areas of mathematics. Recently, there have been very deep insights into the subject, and it is intended this the book will provide readers with an interest in the subject a clear review of advances and consequences in this area of investigation.
",isbn:"978-1-80356-231-5",printIsbn:"978-1-80356-230-8",pdfIsbn:"978-1-80356-232-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"eca1aa784f719310820d6bb2cf5a7b20",bookSignature:"Prof. Paul Bracken",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11502.jpg",keywords:"Covariant Derivative, Connection, Elliptic, Boundary Value Problem, Hodge Decomposition, Differential Form, Curvature, Metric, Spin Structure, Bundle, Local Index Theorem, Clifford Algebra",numberOfDownloads:24,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 9th 2022",dateEndSecondStepPublish:"April 12th 2022",dateEndThirdStepPublish:"June 11th 2022",dateEndFourthStepPublish:"August 30th 2022",dateEndFifthStepPublish:"October 29th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"4 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Paul Bracken obtained his BSc degree from the University of Toronto and holds a Ph.D. from the University of Waterloo in Canada. His research interests include mathematical problems from the area of quantum mechanics and quantum field theory, differential geometry, a study of partial differential equations as well as their overlap with other problems in physics. He has published more than 180 papers in journals and books and has given many talks at different levels over the years.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"92883",title:"Prof.",name:"Paul",middleName:null,surname:"Bracken",slug:"paul-bracken",fullName:"Paul Bracken",profilePictureURL:"https://mts.intechopen.com/storage/users/92883/images/system/92883.jpg",biography:"Professor Paul Bracken is currently a Professor in the Department of Mathematics, at the University of Texas RGV in Edinburg, TX. He obtained his BSc degree from the University of Toronto and holds a Ph.D. from the University of Waterloo in Canada. 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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors, and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"53332",title:"Beamforming in Wireless Networks",doi:"10.5772/66399",slug:"beamforming-in-wireless-networks",body:'\nRecently, cooperative communication has become one of the appealing techniques that can be used in 5G wireless relay networks to achieve spatial diversity and multiplexing, which overcomes the channel impairments caused by several fading effects and destructive interference. Though various cooperative communication schemes exist [1, 2], the AF scheme is more attractive due to its simplicity since the relays simply forward the amplitude phase-adjusted version of received signals to destinations. In Ref. [2], a distributed beamforming relay system with a single transmitter-receiver pair, and several relaying nodes have been proposed. The authors assumed that perfect channel state information (CSI) is available at all relay nodes. Although the same scenario is investigated in Ref. [3], the second-order statistics of all channel coefficients are assumed to be available at the relays. Furthermore, the beamforming weights are obtained in order to maximize the signal-to-noise ratio (SNR) at destination subject to holding the relay power above a certain threshold value.
\nIn the past three decades, code-division-multiple-access (CDMA) systems have been extensively investigated as the one of the important candidates for transmitting data over single channels while sharing a fixed bandwidth among a large number of users [4]. The design of receivers to increase the number of supported users, in these systems, has been explored in Ref. [5, 6]. In Ref. [6], joint channel estimation and data detection based on an expectation-maximization (EM) algorithm [7] is proposed. The authors have shown that the proposed receiver achieves a near-optimum performance with modest complexity. Furthermore, the authors in Ref. [5] designed a double stage linear-detection receiver to increase the number of supported users on the system. This design requires complex processing at the receiver’s side instead of using a precoding scheme at the transmitter where more hardware complexity is tolerable. Therefore, the authors in Ref. [8] studied a MIMO CDMA system implementing zero-forcing beamforming (ZFBF) as an efficient precoding technique.
Though various complex multiuser detection techniques that can be used in CDMA systems [9], the unconventional matched filter receiver is chosen at destination nodes due to the intractability of the precoding design when other forms of detectors are used. In this article, we have focused on the optimization of the beamforming weights applied to the outputs of matched filter banks to minimize the total relay transmit power subject to a target SINR of all destinations. Our proposed distributed CDMA-relay network can easily overcome the other multiplexing schemes such as space division-multiple access (SDMA), time division-multiple access (TDMA) or frequency division-multiple access (FDMA). The SDMA schemes [10] in which sources, destinations and relays are distributed in the space, have two disadvantages. First, these schemes should have a significant number of relays in proportion to their users to be able to overcome channel impairments at destinations. Although the SDMA scheme with the limited number of relays cannot compensate the interference power, our CDMA schemes can easily satisfy the network QoS due to their ability to decrease the interference effect at destinations. So, the second disadvantage of SDMA is the inefficient use of hardware communication resources. In the SDMA scheme, if the number of users increases, the network data rate can significantly decrease. Therefore, the number of relays should be considerably increased to be able to satisfy the QoS constraints, which is costly for the network operator.
Consider a wireless relay network with
In this section, a peer-to-peer MIMO-relay network with
A MIMO-relay network (from M.H. Golbon et al. [
Let
where
where
The received signal in MIMO relay has been processed by the beamforming weights, that is,
The
where
The three last terms of Eq. (5) are the desired received signal, interference and noise at the
where
First, using Eq. (3), the total relay transmit power can be calculated as
where
For any conforming matrices
Therefore, Eq. (7) can be rewritten as the following quadratic form:
where
Using Eq. (5), the desired signal power at the
where
Also, using Eq. (5), the received noise power at
where
Finally, the power of the received interference at the
where
In this case, the instantaneous SINR for
Since
In this problem, if all the matrices
This optimization problem is non-convex, because the Rank(
The optimal value of the relaxed problem is a lower bound of the optimal value of SDP problem (Eq. 16).Well-known semi-definite problem solvers such as SeDuMi or CVX can solve the above problem in polynomial time using interior point methods. If the optimal value of Eq. (17), that is,
Then, solving problem Eq. (16) from
In the above algorithm, the acceptable scaling factors are those that satisfy
Consequently, the approximate solution of problem (Eq. 16) is
For examination, we assumed that channel state information is known at a processing center and the beamforming weights are optimized and spreaded to the nodes from this processing Center [17]. In each simulation snapshot, the channel coefficients
Figures 2 and 3 show the minimum MIMO-relay transmit power
In Figures 4 and 5, we examine the network performance by changing the number of MIMO-relay antennas and number of source-destination pairs. As expected, more power saving will be obtained by increasing the number of MIMO antennas and/or decreasing the number of user nodes.
Minimum MIMO-relay transmit power
Minimum MIMO-relay transmit power
Minimum MIMO relay transmit power
Minimum MIMO relay transmit power
In the last section, we obtained the optimal beamforming weights for a MIMO relay network. Here, in addition to the multiple antenna technique, CDMA is applied to the network to increase the order of multiuser multiplexing. CDMA systems can share a fixed bandwidth among a large number of users without the need of frequency division or time division between nodes. CDMA introduces a diverse range of trade-off between receiver complexity and system performance.
As shown in Figure 6, a two-step AF protocol is used for this MIMO-relay network. In the first step, each source user broadcasts its precoded signal (i.e.
MIMO-relay multiuser network (from M.H. Golbon et al. [
Let
The vector form of Eq. (20) can be written as:
where
By denoting the cross correlation between
where
where
The output of the matched filter in each relay has been processed by the beamforming weights
Another filter bank is applied to the output of each MIMO antenna, which generates
The
where
where
The object of the network beamforming is to minimize the total relay transmit power subject to maintaining every destination SINR above a pre-defined threshold value
First, using Eq. (27), the total MIMO-relay transmit power can be obtained as:\n
where
For simplicity, Q can be represented by the following quadratic formml:
The kernel of the above form can be expressed as a Kronecker products as follows:\n
where
where
Also, the instantaneous desired signal power at the
By defining
where
Also, the received noise power at
where
Finally, the power of the received interference at the
The instantaneous SINR for
By defining
We solve this optimization problem in a same way as the previous section. The first simulation scenario was carried out to consider the total MIMO-relay transmit power versus destination SINR threshold value, for different values of users’ correlation factors. The averaged results are shown in Figure 7. The network consists of two source-destination pairs and four MIMO-relay antennas. Figure 7 shows that the total MIMO-relay transmit power in all cases increases by raising
Minimum MIMO-relay transmit power
When
Figures 8 and 9 display the minimum relay transmit power versus
Minimum relay transmit power
Minimum relay transmit power
In this section, we considered a distributed relays network, instead of MIMO-relay. The optimization problem is defined to find the relay beamforming coefficients that minimize the total relay transmit power by keeping the SINR of all destinations above a certain threshold value.
Consider a wireless relay network with
Distributed relay network.
where
where the following definitions have been used:
The output of the matched filter in each relay has been processed by the beamforming weights
Another filter bank is applied to the output of each relay, which generates
The vector forms of Eq. (47) can be written as
The
where
where:
The three last terms of Eq. (50) are the desired received signal, interference and noise at the
The object of the network beamforming is to minimize the total relay transmit power subject to maintaining every destination SINR above a pre-defined threshold value
where
Note that using Eqs. (48) and (44),
Using Eq. (50), the desired signal power at the
where
where
The relay noises are assumed to be zero-mean and independent with the equal noise power. So, we have
Finally, the power of the received interference at the
where
In this case, the instantaneous SINR for
Since
In this problem, if all the matrices
The problem is non-convex, because the Rank(
This optimization problem has been solved in a same way as the previous sections. Figure 11 shows the total relay transmit power versus destination SINR threshold value, for different values of users’ correlation factors. The network consists of two source-destination pairs and four relays. Figure 11 shows that the total relay transmit power in all cases increases by raising
Minimum relay transmit power
Figure 12 displays the minimum relay transmit power versus
Minimum relay transmit power
Figure 13 shows the minimum relay transmit power versus the network data rate (
Minimum relay transmits power versus
Also, it can be seen from Figure 13 that the minimum relay transmitted power increases with the increase of
Since the CDMA relay systems have a heavy computational complexity, the aim of this section is to analyze the computational form of related algorithms used in practice [20]. Here, the computational complexity of a standard SDP is introduced and extended to our case. The standard SDP problem with equality constraint is given as:
where
where
It is shown in Ref. [22] that small update interior point methods (IPMs) are restricted to unacceptably slow progress, while large-update IPMs are more efficient for faster. Also, large update IPMs perform much more efficiently in practice, however, they often have somewhat worse complexity bounds. The complexity order of solving standard SDP problem is polynomial time.
For evaluating the complexity of our SDP problem with inequality constraints, we have to calculate the dimension parameter
Similarly, we can obtain the above conclusion for
Next, a new variable
As a result, the following standard form will be attained.
where\n
As a result of the above representation form,
Also, we can use the same procedure to calculate
Therefore, the complexity for problems (16), (42) and (63) for MIMO, MIMO-CDMA, and distributed-relay networks are as follows:
while a SDMA relay network has the complexity order of
Distinct diseases have different etiology pattern and this chapter covers the chromosomal diseases, cancer, neurodegenerative diseases, pulmonary diseases, obesity-induced insulin resistance, lymphoblastic leukemia, viral immunology and infectious diseases. These communicable and non-communicable diseases negatively affect structure-function of the organism and specific symptoms are associated with these conditions. Pathogens or internal dysfunctions may lead these diseases. The chapter provides pathology of selected diseases from each class along with the molecular mechanisms.
\nDown syndrome (DS) is the most common chromosomal genetic disorder. The disease is caused by the trisomy of human chromosome 21 (HSA21) and is also the most genetic mental disability [1]. The HSA21 mosaic can also lead to DS. Maternity age is an important aspect in the formation of an individual with DS [2]. The main cause of this disease is the absence of normal chromosome separation during meiosis and the production of gametes with two copies of chromosome copies instead of a single copy. As a result, DS individuals have trisomy 21 in some body cells, and a normal number of chromosomes in others. This is called mosaicism and is seen in approximately 4% of DS individuals. The term mosaicism was first reported in 1961 [3] and can occur in two ways: either a normal zygote is exposed to an early mitotic error following fertilization, which results in trisomy 21 in some cells, or an early mitotic error in some cells allows it to return to normal karyotype [4].
\nHSA21 is the most studied human chromosome, and since the long arm of chromosome 21 has been fully sequenced, a significant progress has been made in understanding its functional genomic units. HSA21 is the smallest chromosome and the overall gene density per megabase is about 15 genes per Mb (for the human genome) [5]. HSA21 is also very rich in long encoding RNA (lncRNA) genes, and, one of the poorest for genes encoding microRNA (miRNA). Also, the gene density is average for pseudogenes encoding the protein per Mb [6]. HSA21 is a weak chromosome in non-encoding RNAs (ncRNAs) and long nuclear elements (LINE). Interestingly, HSA21 shows significant enrichment for proteins found in cytoskeleton structures. These cytoskeletal proteins are known to play a role in neurological disorders, especially Alzheimer’s neuropathology [7].
\nIndividuals with DS occasionally develop the myeloproliferative disorder (TMD), a disease that is mostly unique to DS. Almost all TMD cases were found to contain somatic mutations on the X chromosome, in the GATA1 transcription factor [8]. Certain features of DS contain genes on other chromosomes causing gene and trisomy mutations and working together to reveal the disorder in HSA21. Studies have shown that the formation of Trisomy 21 precedes the formation of GATA1 mutations [1]. This may indicate that Trisomy 21 either increases genomic discrepancy leading to GATA1 mutations, or it supplies a selected medium for hematopoietic cells containing GATA1 mutations.
\nMany hypotheses have been proposed to explain the genotype–phenotype relationship in DS. One of these is the ‘gene dosage effect’ hypothesis putting forward that the phenotypes arise directly from the dosage imbalance of the genes. Overlapping this hypothesis, the ‘DS Critical Region’ (DSCR) was announced in the 1990s. [9, 10]. Many of the DS features can be called into a subset of the critical genes in the DSCR region, suggesting that DS phenotypes are mainly caused by the dosage imbalance of only a few genes on HSA21. Genomic regions affecting the presence of certain DS phenotypes have been identified and high-resolution genetic maps of DS features have been created [11]. Olson et al. studied the DSCR regions in mice to test its hypothesis. They concluded that dosage imbalance of some individual genes on HSA21 directly affects certain phenotypes, but they stated that more studies are needed [12].
\nThe “amplified developmental instability” hypothesis suggests that dosage imbalance of the HSA21 gene leads to a non-specific impairment of cellular homeostasis [10]. Extra chromosome materials may also contribute to phenotypes by disrupting chromosomal regions. Some data on monozygotic twins for TS21 suggest that differential expression between normal and trisomic twins can be regulated across chromosome domains. This study shows that some DS phenotypes can be enlightened by the modification of the chromatin structure in the nucleus [13]. Monozygotic twins affected by DS but showing incompatible phenotypes have been reported in some cases, suggesting the role of epigenetics in the phenotypic variability of DS. For example, DNA methylation (controlling gen expression) has been shown to change in Trizomy of chromosome 21 (TS21) samples [14].
\nTurner syndrome (TS) is a disorder in mosaic karyotypes associated with complete or partial loss of the X chromosome. Seen especially in women, TS is associated with short stature, delayed puberty, ovarian dysgenesis, infertility, congenital malformations of the heart, type 1 and type 2 diabetes mellitus, osteoporosis, and autoimmune disorders. It occurs in almost every 2500 live female births. Fetuses affected by TS are 99% estimated to result in fetal death. Approximately half have monosomy X (45, X) and 10% have a repeat (isochromosome) of the long arm of the X chromosome. Most of the rest has a mosaic in more cell lines for 45X. TS, which is associated with a missing X chromosome, was first identified about 100 years ago [15].
\nRelated genes: Shox gene (short length homeobox protein-coding) located on X and Y chromosomes, it is a gene responsible for TS phenotype. This gene does not undergo X inactivation, and a decrease in the expression of SHOX explains some of the TS-related growth deficits. The gene product controls the expression of natriuretic peptide B (NPBB) and FGFR3 (fibroblast growth factor receptor 3) and regulates the proliferation and of chondrocytes, and also cooperates with SOX5, SOX6 and SOX9 and some other genes [16].
\nThe TS genome is hypo-methylated with less hypermethylation sites and there are RNA expression changes that affect the X chromosome genes and autosomal genes compared to women who are 46 XX. Known escape genes are expressed differently in individuals with TS and other X chromosome genes such as RPS4X and JPX (CD40LG and KDM5C) in particularly, KDM5C (encoding lysine-specific demethylase 5C) can participate in the transcriptional profile of neuronal genes and play role in different neurocognitive profiles [17]. 40S ribosomal protein S4 (RPS4X) also plays an important role in TS, bringing together multiple protein complexes. In addition, the Y paralog of RPS4X (RPS4Y) may also have a role since it is normally expressed as duplicates [18].
\nMany different studies show that women with TS have increased mortality compared to the pool of a wide variety of related diseases [19]. The most obvious increase in morbidity is caused by autoimmunities like diabetes mellitus or thyroiditis, osteoporosis, cardiovascular diseases, hypertension, congenital malformations, especially endocrine diseases including heart diseases, digestive system and anemia [20].
\nIt is still unclear which chromosomal regions or genes make up the phenotypical properties of TS. The physical symptoms of TS were thought to be due to the absence of normal sex chromosomes before inactivation of the X chromosome, or the haplo-insensitivity of the genes in the pseudo-autosomal regions of the aneuploidy [21]. It is thought that a complete phenotype results in the loss of short arm (Xp) in the X chromosome. Aneuploidy itself can cause growth failure. Loss of a region in Xp22.3 was found to be related to neurocognitive problems in TS [22]. Loss of the SRY gene locus in the short arm of the Y chromosome leads to the phenotype of TS, even if it does not cause a population of 45 X cells. It has also been suggested that an area in Xp11.4 is important for the development of lymphedema [23].
\nCancer can be defined as the uncontrolled cell growth with the most basic explanation. Cell stacks that grow uncontrollably are called tumors. Benign tumors grow much slower and usually do not metastasize, while malignant tumors can spread to other organs through metastasis, and lead to multiple organ damage and eventually death. Tumor cells acquire characteristic features such as sustaining growth signals in the process of cancer, avoiding growth suppressors, resisting cell death, ensuring replicative immortality, initiating angiogenesis, and activating invasion and metastasis [24].
\nCancer cells acquire these abilities in the process due to genetic instability and inflammation caused by environmental and hereditary effects. Many studies show that viruses, in addition to many environmental factors such as radiation and chemicals, induce cancer. Chronic inflammation has been shown to trigger oncogenic mutations, genetic instability, tumor growth, and angiogenesis through angiogenesis and cause local immunosuppression [25].
\nTwo types of gene groups involved in cancer are oncogenes, which trigger cellular growth and uncontrolled proliferation, causing increased genetic instability with increased expression and tumor suppressor genes that cause cancer as a result of decreased control of their expression, cell division, and growth. Proto-oncogenes include RAS, WNT, MYC, ERK, and TRK genes. A mutation that may occur on a proto-oncogene or a regulatory region of the gene (e.g., promoter region) can cause an increase in the amount of protein with the change in protein structure [26]. Expressions of oncogenes can also be regulated with miRNAs [27]. Mutations occurring in these regulatory miRNAs can cause activation of oncogenes [28]. Cancer cells increase cell growth-division by activation of oncogenes, as well as suppress preventive control mechanisms of tumor suppressor genes that control this process.
\nMutations in tumor suppressor genes cause loss of function. Therefore, they occur in both alleles. To inactivate the gene and its protein, wide-ranging effects, such as deletions, frame-shift mutations, insertions, should be seen rather than point mutations [29]. Tumor suppressor genes include retinoblastoma (RB) [30], TP53, BRCA1, BRCA2, APC, and PTEN. Many side factors such as transcription complexes, changes in cellular metabolism, microenvironment can guide the course of cancer [31].
\nThe development of cancer is a multi-stage process consisting of initiation, promotion, and progression. Cancer-inducing events are usually caused by genetic mutations. Mutant cell proliferates rapidly in the promotion stage and acquires features that allow malignant behavior in the progression stage. Production of telomerase and expression of p53 are examples of malignant behavior [32]. Then, the process proceeds in the form of dysplasia formation, where new blood vessels are formed (angiogenesis) with cellular transformation. Angiogenesis facilitates the intravasation of cancer cells after undergoing an epithelial-mesenchymal transition (EMT) [33]. EMT gives an invasive phenotype to cancer cells and is managed by various transcription factors (such as SNAI, SLUG, ZEB2, ETS1, TWIST) [34]. These transcription factors also regulate each other for the protection of EMT [35].
\nNormal cells only use anaerobic glycolysis when oxygen is absent or limited, while cancer cells can convert glucose to lactate in the presence of oxygen. Otto Warburg discovered that cancer cells exhibit a differentiated metabolism ability [36]. Warburg effect is biochemical properties that help identify cancer cells. On the other side, cancer cells are generally highly glucose-dependent. Glucose intake of cells is enabled by overexpression of different isoforms of membrane glucose transporters in cancer cells [37]. It has been shown that the benefit of the Warburg effect for cancer cells is not just the formation of glycolytic ATP, but also the production of many glycolytic intermediates before anabolic processes such as NADPH and amino acids [38]. Cancer cells are also able to metabolize glutamine to synthesize some amino acids they need, use it as a nitrogen source and for fatty acid synthesis in hypoxic conditions [39]. Therefore, blood glutamine levels increase in some cancer cases [40]. Lactic acid is used to produce citric acid and maintain cancer progression in neighboring cancer cells. This is called the “Reverse Warburg effect” [41].
\nTumor micro-environment, consisting of fibroblasts, adipocytes, endothelial cells, and macrophages, is a good source for tumor growth. Tumors “steal” energy-rich metabolites from their micro-environment [42]. Monocarboxylate carriers (MCTs) are used for L-lactate transfer between cancer cells and their microenvironment [43]. Tumors have heterogeneous structures with hypoxic and aerobic regions. A “metabolic symbiosis” behavior has recently been found between the two regions [44]. Lactate is produced by glycolysis in hypoxic tumor cells. This product is obtained by aerobic cancer cells by MCT1. Aerobic cells convert lactate to pyruvate with lactate dehydrogenase isoform B (LDH-B) enzyme.
\nWhen glucose consumption is not enough to meet the energy need of cancer cells, they begin the fatty acid oxidation (FAO) [45]. For example, prostate cancer, leukemia, and large B-cell lymphoma, increasing palmitate and FAO uptake in cells are among the most commonly used bioenergetic pathways [46, 47, 48]. Normal cells usually receive fatty acids by diet, while tumors show an increase in de novo fatty acid synthesis [45].
\nPyruvate plays a pivotal role in the regulation of metabolic reprogramming, especially in tumors [49]. Pyruvate dehydrogenase (PDH) converts cytosolic pyruvate into mitochondrial acetyl-CoA, which is the first substrate of the Krebs cycle. Pyruvate dehydrogenase kinase (PDK) negatively regulates PDH. This reaction slides glucose from oxidative to glycolytic metabolism [50]. Lactate dehydrogenase (LDH) is the primary metabolic enzyme converting pyruvate into lactate. LDH plays an important role in arranging food interchange between stroma and tumor. Studies have shown that inhibition of LDH is important for treating advanced carcinomas [51]. Mitochondrial hyperpolarization is a mutual property of several tumor cells [52]. Tumor cells, which have more negative mitochondrial structures, are more selective targets in drug therapies [53].
\nBrain tumors are cancer tissues that grow abnormally and prevent the brain or central spinal system from performing its normal functions. Primary brain tumors originating from brain tissue can usually spread only to other parts of the brain, and occasionally to other organs. Tumors that form in another tissue in the body migrate to the brain are called metastatic or secondary brain tumors. These types of tumors occur more frequently than primary brain tumors. They are termed after their tissue of origin [54].
\nThe most prevalent primary tumor types in adults are glioma, astrocytomas, oligodendroglioma, meningioma, schwannoma, pituitary tumors, and central nervous system (CNS) lymphoma.
\nRetinoblastoma mutations are found in almost 75% of brain tumors and are mostly associated with glioblastoma, and Tp53 mutations are found in more than 80% of advanced gliomas [55]. Primary glioblastomas have EGFR tyrosine kinase mutations, tumor suppressor PTEN gene mutations, DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT) protein abnormalities [56, 57]. While IDH1 mutations in the control mechanism of the citric acid cycle are seen in advanced glioblastomas, IDH2 mutations are usually shown in oligodendroglioma [58]. Mutations in the BRAF oncogene are common in pilocytic astrocytomas, pleomorphic xanthoastrocytomas, and gangliogliomas [55]. In some glioblastoma tumors, telomere length is maintained by mutations in the TERT promoter and ATRX gene [59].
\nWHO groups glioma patients based on the presence of two genetic changes; first, mutations [60] in the family of genes encoding isocitrate dehydrogenase (IDH), and second, loss of two specific parts of the genome (1p and 19q co-deletion) [61]. The presence or absence of these changes gives a clue about the patient’s prognosis and appropriateness of various kinds of treatments.
\nApproximately 40% of people with astrocytoma, oligodendroglioma, or IDH mutation bear a hereditary variation. This variation is a single nucleotide polymorphism (SNP) in the 8q24 region of the genome [62]. There is another SNP in the 11q23 region, which enhances the risk of IDH-mutant brain cancer. Approximately 5–8% of gliomas are familial, POT1 gene mutations have been found in 6 of 300 families with glioma [63].
\nNon-coding RNAs (ncRNAs) play important roles in regulating tumor malignancy in glioma [64, 65, 66]. According to healthy brain tissue, mir-21 expression increases in glioma and mir-21 acts as an oncogene [67, 68]. It has been reported that mir-124 and mir-137 act as tumor suppressors in glioblastoma multiform cells [69]. Hotair, SOX2ot, CRNDE, Malat1, H19, GAS are lncRNAs that have been recently shown to regulate glioma [70, 71]. Glioma cells also express the circRNAs, for example, circBRAF, bircFBXW7, circSMARCA5. These regulate proliferation, migration, and invasion of glioma cells [72, 73, 74]. The exosomal ncRNAs, mir-21, mir-148a, lncRNA PU03F3, lncRNACCAT2 can be used as circulating biomarkers of glioma patients [75, 76, 77, 78]. circRNAs and the exosomal ncRNAs were also reported as potential biomarkers for the diagnosis and prognosis of glioma patients.
\nAs a characteristic of almost all neurodegenerative diseases, abnormal protein assembly gathers these diseases under the prion concept [79]. Prion protein, known as PrP, was introduced to define protein pathogens and distinguish them from viruses and was identified as a proteinaceous infectious particle known to resist inactivation. Even back at that time, its importance was foreseen in terms of shedding light on the etiologies of chronic degenerative diseases [80]. Self-propagation is an important characteristic of prions that is also observed in abnormal protein assembly in Alzheimer’s Disease (AD) [81, 82]. Aggregation of proteins in neurodegenerative diseases was believed to occur spontaneously in autonomous cells, however, it was later understood that this aggregation begins in a particular region and propagates across other regions developing the disease further. Transmission of these prion proteins across neuronal cells takes place trans-synaptically [82].
\nAs described more than a 100 years ago, abnormal protein assembly forms the basis of neurodegeneration with AD being one of the most common neurodegenerative diseases. The pathology of abnormal protein assembly starts with misfolding of native proteins that gather to form seeds which eventually lead to aggregation and development of protein fibrils. The pathophysiology of AD involves amyloid plaque inclusions of β-amyloid (Aβ) peptides and neurofibrillary lesions of tau protein. Tau inclusions may also be characteristics of other neurodegenerative diseases, which do not necessarily show the same implications. Altering the native forms of this protein may contribute to its pathology and cause damage to its host cell.
\nMost cases of this disease are sporadic, while dominantly inherited mutations are also seen to a lesser extent. Back in the 1990s, missense mutations of APP, encoding amyloid precursor, were shown to cause AD [83, 84, 85, 86, 87]. Mutations in this gene also increase the aggregation tendency of encoded proteins. Many studies have demonstrated phenotypes associated with neurodegeneration when this protein is overexpressed.
\nThere are six isoforms of microtubule-associated protein tau ranging from 352 to 441 amino acids, encoded by the MAPT gene as a result of alternative mRNA splicing. One half has three repeats and the other has four repeats, altogether establishing the microtubule-binding domain and also the core of tau filaments in case of pathology [88]. All isoforms have been observed in the brains of AD patients. Diseases that have isoforms with only three or four repeats, but not both, lack the Aβ peptides seen in AD and therefore do not carry the symptoms specific to the disease [70]. Tau inclusions may be of a variety of conformations, which can also be caused by different mutations on the MAPT gene, explaining the existence of numerous tauopathies [89, 90, 91, 92, 93].
\nAβ peptides are encoded by the amyloid precursor protein gene, APP, and are widely expressed as type 1 transmembrane glycoproteins. As a result of alternative mRNA splicing, there are three major transcripts named APP695, APP751, and APP770 [94, 95]. β- and γ-secretase enzymes take part in the production of Aβ peptides in sequential endoproteolytic cleavage. β-secretase is responsible for cleaving the N-terminus of the peptide thus removing the portion that remains on the extracellular side. This cleaved peptide is endocytosed and intracellular aggregation builds up which is later released into the extracellular space [79]. γ-Secretase is a membrane-embedded enzyme that is able to cleave many transmembrane proteins including C-terminus of the Aβ peptide. It a complex enzyme of four proteins; presenilin (PS) forming the catalytic core, presenilin enhancer-2 (Pen-2) enabling maturation of PS, anterior pharynx-defective (Aph-1) stabilizing the complex, and nicastrin possibly being the receptor for the enzyme’s substrate [96, 97]. PS and Aph-1 each have two variants resulting in at least four different enzyme complexes, which give rise to various cleaved Aβ peptides. Additionally, γ-secretases cleave the peptide in three different sites. Different protein variants and cleavage sites produce Aβ peptides of different profiles, some of which may be more susceptible to aggregation [98].
\nOverall, it is important to target the pathways leading to abnormal protein assembly and only then treatments may be proposed based on these mechanisms. Once the first protein inclusion is formed, it is essential to keep an eye on the time frame until the disease symptoms come forth. When techniques sensitive enough to catch the first protein inclusion are developed, then tracking its transformation into filaments can be helpful in designing novel preventive approaches. Understanding this cascade will also contribute to planning more efficient therapeutic methods.
\nAsthma and chronic obstructive pulmonary disease (COPD) are common disorders characterized by progressive chronic inflammation in the lungs. They have unique characteristics with dissimilarly involved cells, mediators, and inflammation. They also have distinct responses to corticosteroid treatment. Roughly 15% of COPD patients have characteristics of asthma [99]. Also, a comparable ratio of asthma patients has traits of COPD that is currently the fifth leading cause of death worldwide [100]. Many risk factors are linked to COPD including smoking tobacco, air pollution, indoor cooking while tobacco smoking (including passive smoking) making up around 80% of the cases [101]. There are many types of cells and mediators that have a significant effect during the pathogenesis of asthma and COPD.
\nMacrophages have a crucial role in coordinating the inflammatory response activated by cigarette smoke extract in COPD cases [102]. They discharge inflammatory mediators including tumor necrosis factor (TNF)-α, IL-8, other CXC chemokines, monocyte chemotactic peptide (MCP)-1, LTB4 and reactive oxygen species (ROS) [103]. However, the role of macrophages in asthma is not certain. Allergens via low-affinity IgE receptors may activate macrophages causing an inflammatory response through the discharge of a definite arrangement of cytokines. On the other hand, macrophages also excrete anti-inflammatory mediators, such as IL-10 that is thought to decrease in subjects with intense asthma [104].
\nActivated neutrophils were shown to be enhanced in some subjects with severe asthma and COPD in their sputum and airways [105]. Among the serine proteases secreted by neutrophils are neutrophil elastase (NE), cathepsin G, proteinase-3, matrix metalloproteinase (MMP)-8 and MMP-9, leading to alveolar destruction [103]. The mechanisms of neutrophilic inflammation in asthma and COPD are not clear. Demonstration of priming in COPD occurs at neutrophils in the peripheral circulation. Many chemotactic signals exhibit the capacity for neutrophil recruitment in COPD. These include LTB4, IL-8 and related CXC chemokines, comprising GRO-α (growth-related oncoprotein) and ENA-78 (epithelial neutrophil activating protein of 78 kDa) which are enhanced in COPD airways [106]. Although the mentioned mediators might be sourced from alveolar macrophages and epithelial cells, neutrophils have the capacity of being a vital source of IL-8 [107].
\nAirway and alveolar epithelial cells in COPD can be a vital point of source of inflammatory mediators and proteases 5. Cigarette smoke activates epithelial cells which produce inflammatory mediators, including TNF-α, IL-1β, GM-CSF and IL-8 [108]. Epithelial cells play an important role in airways defense and tissue repair processes. Goblet cells, a type of epithelial cell, in mucus catch bacteria and inhaled particulates [109]. Epithelial cells release antioxidants and antiproteases. Immunoglobulin A is carried by epithelial cells, hence involved in adaptive immunity [110]. On a side note, native and adaptive immune reactions of the airway epithelium are triggered by cigarette smoke and damage by other harmful agents, increasing sensitivity to infection.
\nThe main role of dendritic cells is to introduce innate and adaptive immune reaction by activating macrophages, neutrophils, T and B lymphocytes among others [103].
\nLymphocytes are directly involved in the pathogenesis of both asthma and COPD. Both airway and parenchymal inflammation exist in asthma and COPD patients [111]. Most lung lymphocytes are T cells which are in the respiratory tract of ordinary humans. Activated T lymphocytes are characteristic in both asthma and COPD, but CD4+ type-2 T lymphocytes are the major player in asthma whereas CD8+ type-1 lymphocytes are specific to COPD [111]. CD4+ T lymphocytes can generate many cytokines involved in mediating cell functions and cell–cell communications. This is done through impressing physiologic cell properties such as proliferation, differentiation and activation of other immunocompetent cells, chemotaxis, and connective tissue metabolism [112]. On the other hand, CD8+ T lymphocytes exist in the respiratory mucosa and are activated in response to foreign antigens [111]. Specifically, the cells in the respiratory mucosa have an important role in anti-viral immunity. Another lymphocyte type is B cells which are the minority (<5%) lymphocytes. The main function of B cells located in the lungs is the production of immunoglobulins for local defense mechanisms [113].
\nApart from these mentioned cells, there are crucial molecular mediators in the pathogenesis of asthma and COPD. The first family of mediators is transforming growth factor (TGF) family. The TGF-β subfamily is composed of five parts that exhibits plenty of effects pertaining to asthma and COPD. A recent study shows that overexpression of TGF-β1 in mice causes Smad3-dependent pulmonary expression of procollagen, antiproteases and fibrosis [114]. TGF-β exhibits chemotactic signatures for monocytes, macrophages and mast cells. Research shows an abnormal pulmonary expression of TGF-β1 in subjects suffering from COPD. Protein and mRNA expression of TGF- β1 are abundant in the lung tissue, including airway epithelial cells, of mild to moderate COPD patients. TGF-β1 has the role in pathogenesis of COPD because of its increased expression in parallel to the number of macrophages [115].
\nAnother mediator family is the fibroblast growth factor (FGF) family with 23 members in humans. Their functional receptors are named from FGFR1 to FGFR5 [116]. FGFs have many functions such as development, tissue homeostasis, and repair. In addition to further growth factors, FGF-1, FGF-2, and FGF-7 and their receptors FGFR1 and FGFR2, are located abundantly in the lungs [101]. Research shows that increased expression degrees of FGF-1, FGF-2, and FGFR1 were detected in vascular and epithelial areas in the lungs of COPD patients. FGF-1 causes higher collagenase expression and lower collagen I expression in lung fibroblasts which prompt tissue remodeling.
\nAnother family of mediators is the vascular endothelial growth factor (VEGF) family. There are seven units in this family capable of attaching to related cellular receptors. VEGFs have many functions including paracrine acting, angiogenic factors, prompting mitogenesis, emigration, and permeabilization of the vascular endothelium [101]. VEGF and its receptors assist in tissue remodeling as well as disease intensity in incessant lung diseases such as asthma [117]. COPD patients have increased pulmonary VEGF expression in bronchial and alveolar epithelial located around the vascular smooth muscle and alveolar macrophages. Additionally, unlike healthy subjects, COPD patients exhibit elevated levels of VEGFR-1 and VEGFR-2 expression inside the endothelium [118]. Furthermore, VEGFR-2 and VEGF expressions are decreased in COPD patients. Compared to VEGFR-2, VEGFR-1 has a higher affinity for VEGF which leads to VEGFR-1 scavenging VEGF from VEGFR-2. This phenomenon culminates VEGFR-1 activation and in the case of endothelial apoptosis, increased MMP activity as well as vascular and alveolar decimation [101]. This suggests the importance of harmony among VEGF, VEGFR-1, and VEGFR-2 during the pathogenesis of COPD subordinary types.
\nFinally, cytokines and chemokines are mediators supplying a chemotactic gradient which has the potential to activate macrophages, CD8+ T cells and neutrophils for COPD patients. It is known that inflammatory cells of both native and gained immune systems are significant in the COPD pathophysiology. This is where cytokines and chemokines are the key drivers [103, 119]. Different types of cytokines arrange chronic inflammation in asthma and COPD. T2 cytokines which are IL-4, IL-5, IL-9 and IL-13 interfere with allergic inflammation. Other types of cytokines including TNF-α and IL-1β accelerate the inflammatory response [120]. In asthma and COPD patients, chemokines are instrumental in drawing inflammatory cells from the circulation into the lungs [121].
\nObesity is a serious health problem that has become epidemic all over the world, especially in developed countries. It is characterized by hypertrophied adipocytes that secrete various adipokines and hormones, chronic inflammation in all tissues, and systemic insulin resistance resulting in type 2 diabetes, hypertension, and hyperlipidemia. In addition to these metabolic diseases, it can cause diseases such as cancer, atherosclerosis, obstructive sleep apnea syndrome, steatohepatitis, and musculoskeletal problems [122]. The obesity rate is 20% in women and 18% in men in developed countries [123]. It affects complex metabolic pathways in all tissues as a result of chronic and progressive inflammation, leads to insulin resistance, endothelial dysfunction and lipotoxicity.
\nThe pathophysiology of obesity includes complex interactions of numerous adipokines, hormones and pro-inflammatory cytokines with the central nervous system and metabolic organs (such as liver, pancreas, and muscle) as a result of genetic-environmental interactions.
\nGenetic etiology: Obesity is generally present in a polygenic etiology. Many studies have investigated the genetic background of body mass index (BMI) and waist/hip ratio (WHR), which are the best measurements of obesity. The results of these studies have been presented collectively in genome-wide association studies (GWAS) [124]. Although, single gene defects (monogenic) are rare in obesity, including especially melanocortin-4 receptor, leptin and leptin receptor genes [125].
\nDysregulation in hypothalamic control: The center of food intake and energy regulation in the central nervous system is the arcuate nucleus (ARC) in the hypothalamus besides the autonomic nervous system and brain stem. The balance between the opposing effects of orexigenic and anorexigenic neurons is important. Agouti-related protein (AgRP) and neuropeptide Y (NPY) (AgRP/NPY) neurons are orexigenic that promotes appetite and eating. Pro-opiomelanocortin–producing (POMC) peptide and cocaine-and-amphetamine–regulated transcript (CART), collectively known as POMC/CART neurons are anorexigenic that suppress appetite and eating. Oxygenic pathways that increase energy balance become more effective in obesity [126].
\nAdipose tissue dysfunction and systemic inflammation; The most important pathophysiological mechanisms of obesity and obesity-related insulin resistance are adipocyte dysfunction (visceral adipose tissue; VAT) and low-grade chronic systemic inflammation. In particular, white adipocyte tissue in obese subjects contributes to the regulation of food intake, energy metabolism and other functions by secreting adipokines from adipose tissue, which provide the necessary signals to the central nervous system, hypothalamus, liver, pancreas, muscle tissue, and other systems to regulate appetite, food intake, and energy balance [125]. Leptin is the most important adipokine that stimulates anorexigenic POMC/CART neurons and induces production of pro-inflammatory cytokines (TNF-alpha and IL-6) by macrophages and monocytes. In the case of hyperleptinemia, leptin resistance develops by the inhibition of the JAK2/STAT3 signaling pathway, which later increases oxidative stress and inflammation, causing insulin resistance, hyperlipidemia and hypertension [127]. Resistin is a pro-inflammatory adipokine produced by the resistin gene (RETN), which activates SOCS3, causing the insulin signaling pathway to be inhibited and consequently induces insulin resistance [128]. Other adipokines like Retinol binding protein 4 (RBP4), Angiopoietin-like protein 2 (ANGPTL2), Visfantin, Adiponectin, Lipocalin 2, Serum Amyloid A, Angiotensinogen, Renin, Angiotensin-Converting Enzyme, Acylation-Stimulating Protein, and Vaspin, are increased, and adiponectin, and Apelin are decreased in obesity, altogether stimulating inflammation, lipolysis, releasing free fatty acid (FFA) and causing insulin resistance as a result [129].
\nGastrointestinal hormones and microbiota: Gastrointestinal hormones and gut microbiota play a significant role in the complex pathophysiology of obesity. Ghrelin produced in the stomach induces starvation and food intake by stimulating orexigenic AgPR/NPY neurons in the hypothalamus. Although the effect of ghrelin cannot be fully explained, it is thought to increase in obesity, stimulate growth hormone release (GH), increase gastrointestinal motility and insulin secretion [130]. Decreased GLP-1, Peptide YY, pancreatic polypeptide, and increased amylin and cholecystokinin cause appetite inhibition and gastric emptying delay, resulting in excess energy [129]. Besides hormones in the gastrointestinal tract, changes in microbiota-gut-brain axis and their effects on metabolic organs are also important. Occurring as a result of nutrition and gene–environment interactions; chronic systemic inflammation resulting from intestinal microbiota dysbiosis (increase in Firmicutes-Bacteroides ratio), microbial fermentation products, increase in short-chain fatty acid formation and intestinal permeability, decrease in butyrate-producing bacteria rate, leads to an increase in proinflammatory response in metabolic organs, impaired fat metabolism and glucose metabolism [131, 132].
\nİmpaired insulin sensitivity and oxidative stress; The beginning of insulin resistance is the first step in the pathophysiology of T2D. Anabolic effects such as glycogen and protein synthesis, glucose transport, adipogenesis are formed by phosphatidylinositol-3-kinase (PI3K)/Akt pathway activation as a result of insulin binding to its receptor (INSR) synthesized in the pancreas [133]. On the other hand, insulin shows mitogenic effects with mitogen-activated protein kinases/Ras pathway (MAPK/Ras).
\nAdipokines, FFA’s, pro-inflammatory cytokines (TNF-a, IL-18, IL-1β, IL-6), synthesized as a result of inflammation in adipose tissue in obesity, also cause systemic inflammation in metabolic tissues such as liver and muscle. As a result, decreased GLUT-4 expression, activation of Ser/Thr kinases with insulin receptor substrate (IRS) phosphorylation, production of ceramides and proinflammatory cytokines, suppressing of cytokine signaling-3 (SOCS-3) expression, insulin pathways and effects. On the other hand, increased production of reactive oxygen radicals and production of toxic doses NO with inducible nitric oxide synthase (iNOS) activation, affect mitochondrial and endoplasmic reticulum functions. Activation of pro-inflammatory pathways increased oxidative stress, mitochondrial dysfunction, ER stress affects lipid metabolism, insulin mechanisms of action and other metabolic pathways, causing insulin resistance, Type 2 diabetes, hypertension, and hyperlipidemia [134].
\nBeta-cell dysfunction: In addition to peripheral insulin resistance in obesity, serious reductions in beta cell function are also observed. An increase in fat accumulation in islet cells due to chronic lipotoxicity disrupts the function of beta cells by blocking calcium channels. Chronic hyperglycemia due to disruption in glucose metabolism and systemic inflammation due to an increase in oxidative damage and lipotoxicity, disrupt insulin secretion pathways and cause changes in apoptosis gene expression. Hyperinsulinemia in obesity, impaired insulin signaling pathway, oxidative stress, lipotoxicity in islet cells, loss of beta-cell function and apoptosis may lead to the formation of type 2 diabetes [122, 135].
\nObesity has become a pandemic all over the world as a result of rapidly changing lifestyles and genetic heritage in the last century. Despite the findings in recent studies on the development and complications of obesity, it is difficult to say that the subject of etiology and pathophysiology is still not fully understood. Especially omics technologies, big data on environmental gene interactions, neuroendocrinology, and neuropsychological studies will reveal findings that open up different horizons. However, due to its complications from deadly metabolic diseases to cancer, rapid preventive measures should be taken, and effective treatment models should be developed.
\nAcute lymphoblastic leukemia (ALL) is a heterogeneous malignancy emerging from lymphoid precursors. It is characterized by the proliferation of immature lymphoid cells with somatic mutations including chromosomal rearrangements, and aneuploidy [136]. ALL has two peak points; first point occurs at ~5 years of age (80%), and the second point occurs at the age of ~50 (20%) [137]. The basic mechanism underlying the development of ALL is similar in children and adults, while they have the frequency of different genetic subtypes. Molecular analysis of genetic changes in leukemia disease provides a great advantage in order to understand prognosis and pathogenesis of ALL [138].
\nThe diagnosis of ALL depends on the presence of at least 20% lymphoblast in bone marrow. Immunophenotyping by flow cytometry (FCM) identifies the subtype of ALL that may be B-cell precursor (BCP), mature B-cell types, or T-cell ALL. Chromosomal abnormalities are a characteristic of lymphoblastic leukemia, which are found in B or T cell lineage. The most common abnormality found in adult B precursor ALL is the t(9;22) BCR-ABL translocation, while the t(12;21)(p13;q22) TEL-AML1 translocation is most commonly found in childhood B precursor ALL [139]. On the other hand, the discovery of mutations in the receptor tyrosine kinase FLT3 contributes to the understanding of leukemogenesis mechanism in hyperdiploid ALL (20% of cases). Based on this finding, targeting specific tyrosine kinase inhibition may be useful in the management of leukemia [140].
\nSmall-molecule kinase inhibitors have a clear benefit in the treatment of many cancer types including leukemia. Imatinib mesylate, a small-molecule inhibitor of BCR-ABL kinase, is highly effective in the treatment of chronic myelogenous leukemia (CML) [141]. Although the single kinase inhibitor is a remarkable treatment option in a different type of leukemia, it will need to be combined with either other targeted therapy or chemotherapy because of the resistance to small-molecule inhibitor [142]. Unlike ALL, Chronic Lymphocytic Leukemia (CLL) is defined the accumulation of monoclonal B cell with a special immunophenotype in the bone marrow, blood, and other lymphoid organs where B lymphocytes express CD19, CD23, CD5, low-level CD20 and surface immunoglobulins [143]. The standard treatment procedure of ALL and CLL includes consolidation therapy following chemotherapy in pediatric patients. For adult patients, unlike pediatric patients, the allogeneic hematopoietic stem cell transplantation is frequently preferred as consolidation therapy [144]. Because the patients resistant to chemotherapy are not respond to treatment well enough, novel therapy approaches such as Chimeric antigen receptor-modified T cell (CAR-T) therapy have developed in order to overcome chemotherapy resistance and improving the outcome of patients [145]. CAR-T cells, as immunotherapeutic tools, are genetically engineered to express a chimeric antigen receptor recognizing an antigen that is located in the special cells such as a tumor [146]. CD19 antigen on B lymphocytes was considered the initial target for CAR-T cell therapy. However, specific antigen loss might cause the failure of CAR-T cell therapy in CLL. CD19–20 co-targeting CAR-T cells were designed to kill both CD19-positive and CD19-negative CLL and it was shown that these cells were very effective in killing CLL cells. In one of the first reported in pediatric ALL the clinical trials, CAR-T cells targeted the CD19 antigen of B cells are designed with CD3ζ and CD28 costimulatory domain [147].
\nThe origin of a pathogen has a crucial role in developing vaccines and blocking transmission. This may last many years due to its elusiveness as seen in HIV-1, SARS, and MERS [148, 149, 150]. According to a recent report, it was emphasized that SARS-CoV-2 is able to infect T cells, which are targeted by HIV [151]. Another report alleged that the motif insertions of spike glycoprotein, similar to HIV-1, may help increase the range of host cells of SARS-CoV-2. HIV-1 envelope glycoprotein contains mutable insertions and deletions not necessary for biological function. Only 1 and 2 insertions are matched in only a few HIV-1 strains and this reveals that four insertions are scarce. Thus, HIV-1 cannot be assumed as the source for those insertion sequences in the SARS-CoV-2 genome due to their inefficient identities and scarceness in the HIV-1 sequences [152].
\nThe reported cases showed that there have been 3,162,284 COVID-19 cases in at least 212 countries and approximately 7.1% of which was resulted in death as of April 30, 2020 [153]. It is known that SARS-CoV, MERS-CoV, and SARS-CoV-2 are the members of coronoviridae family of the Nidovirales order, which comprises a relatively positive-sense, single-stranded RNA genome of around 26–32 kb [154]. 5o-methylguanosine cap at the beginning, a 3o-poly-A tail at the end, and a total of 6–10 genes in between exist in their genome [155, 156].
\nThis family has extremely expressive instability and recombination rate, which is similar to RNA viruses, so it is practically unfeasible to prevent their distribution among humans and animals worldwide; nevertheless, the fact that the virus is exceedingly pathogenic to humans is closely related to random genetic recombination in the host. Although there is a strict genetical relation between SARS-CoV-2 and SARS-CoV, it is explicit that SARS-CoV-2 has a unique feature providing rapidly spread worldwide [157].
\nSARS-CoV-2 genome sequence is much more resembles a SARS-like bat rather than SARS-CoV [158, 159]. Two open reading frames translating the replication- and transcription-related gene into two large non-structural polyproteins [156]. Ribosomal frameshifting contributes to translate two different but overlapping open reading frames. Besides these nonstructural proteins, the subgenomic RNA also encodes the viral genome packaging protein N (nucleocapsid), and the viral coating proteins M (membrane), E (envelope), and S (spike) as the structural proteins. Viral coating proteins, which interact with host surface receptors, is generally preferred as the therapeutic target blocking protein–protein interaction [160, 161]. TMPRSS2, the human serine protease, enables S Protein of both SARS-CoV and SARS-CoV-2 to prime, and these two viruses use the angiotensin-converting enzyme 2 (ACE2) receptor in order to bind the host cell as the first step of the viral entry mechanism. Unlike SARS-CoV and SARS-CoV-2, the cell entry of MERS-CoV depends on the binding of its own spike protein to DPP4 (dipeptidyl peptidase 4). The RT-PCR analysis of the throat swabs is essential to the diagnosis of COVID19 pneumonia, and it takes 3.5 h to provide the results [162].
\nClinical management puts emphasis on the importance of supportive care and prevention of complications due to a lack of specific treatment for COVID-19 pneumonia. On the other hand, potential antiviral therapies for the purpose of rapidly dealing with this pandemic are taking place on several clinical trials. These trials focused on three main targets that include enhancing the host immune system, blocking the virus spike protein-host cell surface receptor interaction, and vaccine development [163].
\nHPV genome, which is a double-stranded circular DNA, has the early (E) genes that are responsible for replication and transcription, and the late (L) genes that are responsible for viral capsid proteins. In the early stage of HPV infection, the highly expressed E1 and E2 proteins provide the maintaining of viral replication and transcription within the cervical cell [164].
\nHPVs, unlike SARS coronaviruses, are non-enveloped viruses and don’t have a specific host cell receptor that initiates the viral infection. Additionally, HPVs have many different genotypes such as HPV type 16 and type 18 which are known as the reason for cervical cancer. HPV infection may cause low-grade cytological changes on Papanicolaou smears, or low-grade squamous intraepithelial lesions [165]. When malignant conversion considered, viral oncoproteins E6 and E7 attach, respectively, tumor suppressor protein p53 and Rb have a crucial role [166]. Until today, many vaccine developments studies have been carried out to protect HPV malignant type 16 and 18. For example, the clinical vaccine Gardasil 9 provides effective protection against vaginal, cervical, and vulvar diseases caused by HPV type 16,18 and also its 5 other different types [167].
\nThe chapter outlined the unique mechanism of each disease. Depending of the origin of the disease; deficiency, hereditary, infectious and physiological diseases may be treated diversely but the perturbation effect can only be eliminated with proper intervention. Current amelioration may be improved by biochemical methods only if the molecular mechanism is clearly understood. Therefore, molecular medicine provides unique solutions to diagnose and treat disease by elucidating macromolecular interaction and abnormalities in cells and tissues. The chapter summarizes current findings and methods to alleviate and cure the diseases.
\nBYK, EK, KUC and ENYT acknowledge YOK100/2000 bursary and thanks to Turkish Council of Higher Education (YOK).
\nNone.
Thanks to Assistant Prof. Dr. Lütfi Tutar for carefully reading the manuscript.
\nThese Terms and Conditions outline the rules and regulations pertaining to the use of IntechOpen’s website www.intechopen.com and all the subdomains owned by IntechOpen located at 5 Princes Gate Court, London, SW7 2QJ, United Kingdom.
',metaTitle:"Terms and Conditions",metaDescription:"These terms and conditions outline the rules and regulations for the use of IntechOpen Website at https://intechopen.com and all its subdomains owned by Intech Limited located at 7th floor, 10 Lower Thames Street, London, EC3R 6AF, UK.",metaKeywords:null,canonicalURL:"/page/terms-and-conditions",contentRaw:'[{"type":"htmlEditorComponent","content":"By accessing the website at www.intechopen.com you are agreeing to be bound by these Terms of Service, all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. Use and/or access to this site is based on full agreement and compliance of these Terms. All materials contained on this website are protected by applicable copyright and trademark laws.
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\n\nThe following terminology applies to these Terms and Conditions, Privacy Statement, Disclaimer Notice, and any or all Agreements:
\n\n“Client”, “Customer”, “You” and “Your” refers to you, the person accessing this website and accepting the Company’s Terms and Conditions;
\n\n“The Company”, “Ourselves”, “We”, “Our” and “Us”, refers to our Company, IntechOpen;
\n\n“Party”, “Parties”, or “Us”, refers to both the Client and ourselves, or either the Client or ourselves.
\n\nAll Terms refer to the offer, acceptance, and consideration of payment necessary to provide assistance to the Client in the most appropriate manner, whether by formal meetings of a fixed duration, or by any other agreed means, for the express purpose of meeting the Client’s needs in respect of provision of the Company’s stated services/products, and in accordance with, and subject to, the prevailing laws of the United Kingdom.
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\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{},profiles:[{id:"396",title:"Dr.",name:"Vedran",middleName:null,surname:"Kordic",slug:"vedran-kordic",fullName:"Vedran Kordic",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/396/images/7281_n.png",biography:"After obtaining his Master's degree in Mechanical Engineering he continued his education at the Vienna University of Technology where he obtained his PhD degree in 2004. He worked as a researcher at the Automation and Control Institute, Faculty of Electrical Engineering, Vienna University of Technology until 2008. 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Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:{name:"Kobe College",institutionURL:null,country:{name:"Japan"}}}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-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. 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MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. 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