\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"287",leadTitle:null,fullTitle:"Liver Biopsy",title:"Liver Biopsy",subtitle:null,reviewType:"peer-reviewed",abstract:"Liver biopsy is recommended as the gold standard method to determine diagnosis, fibrosis staging, prognosis and therapeutic indications in patients with chronic liver disease. However, liver biopsy is an invasive procedure with a risk of complications which can be serious. This book provides the management of the complications in liver biopsy. Additionally, this book provides also the references for the new technology of liver biopsy including the non-invasive elastography, imaging methods and blood panels which could be the alternatives to liver biopsy. The non-invasive methods, especially the elastography, which is the new procedure in hot topics, which were frequently reported in these years. In this book, the professionals of elastography show the mechanism, availability and how to use this technology in a clinical field of elastography. The comprehension of elastography could be a great help for better dealing and for understanding of liver biopsy.",isbn:null,printIsbn:"978-953-307-644-7",pdfIsbn:"978-953-51-6471-5",doi:"10.5772/811",price:139,priceEur:155,priceUsd:179,slug:"liver-biopsy",numberOfPages:416,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"9856c3e2c382494e27f34c5264f50fd4",bookSignature:"Hirokazu Takahashi",publishedDate:"September 6th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/287.jpg",numberOfDownloads:142600,numberOfWosCitations:70,numberOfCrossrefCitations:33,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:78,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:181,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 2nd 2010",dateEndSecondStepPublish:"November 30th 2010",dateEndThirdStepPublish:"April 6th 2011",dateEndFourthStepPublish:"May 6th 2011",dateEndFifthStepPublish:"July 5th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"40534",title:"Dr",name:"Hirokazu",middleName:null,surname:"Takahashi",slug:"hirokazu-takahashi",fullName:"Hirokazu Takahashi",profilePictureURL:"https://mts.intechopen.com/storage/users/40534/images/1859_n.jpg",biography:"Dr. Takahashi is a graduate of Saga Medical School, in Saga, Japan. He is a Hepatology specialist and is currently working as an Assistant Professor in the Department of Hepatology, Diabetes, Metabolism and Endocrinology at Saga Medical School. \nIn 2008, dr. Takahashi received a Travel Award for the 18th Annual Meeting of United European Gastroenterology. He was 2009 the recipient of the Young Investigator’s Award from The Japan Society of Ultrasonics in Medicine at their 19th Annual Meeting. \nDr. Takahashi is an active board member of Japanese Society of Gastroenterology, Japan Society of Hepatology and Japan Society of Ultrasonics in Medicine.\nDr. Takahashi has featured as a guest editor for numerous journals and has been a reviewer for the Journal of Gastroenterology, Liver International, the European Journal of Ultrasound and Hepatology International.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1102",title:"Hepatology",slug:"medicine-pathology-hepatology"}],chapters:[{id:"18764",title:"The Role of Liver Biopsy in the Non-Invasive Methods Era and Liver Stiffness Measurement Using Transient Elastography",doi:"10.5772/19561",slug:"the-role-of-liver-biopsy-in-the-non-invasive-methods-era-and-liver-stiffness-measurement-using-trans",totalDownloads:3097,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:null,signatures:"C. 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Several theories exist on how tumor cells alter their neighboring cells and matrix ultimately changing their behavior into an invasive one. This typically would involve the transport of materials from tumor cells to their adjacent surroundings. These materials include a wide range of soluble cytokines, RNA species, enzymes, and proteins. Most of which are carried in nano-sized carriers such as EVs. EVs are classified according to their size and the mechanism of genesis. The first class of EVs known as MVs or when secreted from cancer cells, are called oncosomes [1]. MVs formation is originated by the outward budding of the cell surface at specific regions along the plasma membrane enriched with high concentrations of lipids, such as cholesterol and glycosphingolipids, and proteins such as Flotillin-1 and 2 [2]. Exo represent the second major class of EVs [3]. They are formed when multivesicular bodies (MVBs) in the endo-lysosomal pathway accumulate intraluminal vesicles (ILVs) that consist of proteins and nucleic acids. Exo are smaller in size and range from 30 to 50 nm.
EVs can function in an autocrine, paracrine, and even endocrine fashion, and were shown to impact various cancer cell phenotypes, increasing their cell growth and promoting metastasis [4]. This secretome is released into the microenvironment and acts as cell-cell communicators. Tumor derived Exo (TDE) has appeared as imperative facilitators in cancer initiation, progression, metastasis, host immune suppression, and drug resistance [5]. TDE typically consists of high sphingolipids and cholesterol contents that contain major histocompatibility complex (MHC) molecules, heat shock proteins, and tetraspanin (CD63, CD81, and CD9). Additionally, tumor antigens such as Mart1, gp100, TRP, and Her2-neu have been discovered in TDE [5]. TDE also contains surface and soluble proteins and RNA species such as mRNAs and miRNAs. mRNAs conveyed in EVs result in proteins synthesis in target cells, while miRNAs alter their gene expression [6]. The
Tumor development is a multistep process that starts by cellular reprogramming of cells to acquire the hallmarks of cancer cells to gain and maintain abnormal growth and invasive capacity [8]. The complex process of tumor formation and spreading additionally requires a rewiring of the surrounding stromal cells. This can be induced by intrinsic cell events such as genetic or epigenetic aberrations or by external factors from direct or indirect cell communication [9]. In cancer, EVs especially Exo, have been shown to be essential for various steps during tumor initiation and progression. EVs disrupt signaling and gene expression regulation in the recipient cell by horizontally transferring bioactive chemicals between cancer cells and the surrounding stroma. As a result, malignant cells can change the phenotype of surrounding benign cells to one that supports tumor growth and metastasis, creating a favorable environment for cancer progression and spread. EVs play several roles in priming the surrounding environment preparing it for metastasis and invasion. The role of EVs in promoting tumor progression has been elucidated in studies on mixed populations of EVs. The function of EVs largely depends on their bioactive cargo, in particular the shuttling of tumor-specific proteins to the surrounding cells. While researchers have mainly studied the RNA content of EVs, however, the focus is starting to shift towards the EVs proteome [10].
The protein content of MVs within mixed populations of EVs was discovered to be significantly diverse from that of the Exo proteome, and is supplemented in proteins involved in microtubule, actin, and cytoskeleton networks, ARF6, its effector phospholipase D2, and parts of the endosomal sorting complex required for transport family (ESCRT-I) [11]. By transporting these molecules, MVs can impact nearby tumor cells and stromal cells.
One example in which MVs shed by the cancer cells were shown to enhance tumor cell proliferation is in multiple myeloma. This effect was shown to be related to the amelioration of the Extracellular Matrix Metalloproteinase Inducer (EMMPRIN/CD147) on the tumor MVs. This protein is known to be overexpressed in solid tumors, some lymphomas, and leukemias [12]. Another study in breast cancer cells found that the highly glycosylated version of EMMPRIN exists in high quantities in breast cancer cell-derived MVs and enhances tumor invasion through activation of p38/MAPK signaling [11]. Interestingly, it was found that MVs from patient Blood with metastatic breast cancer had a similar high-EMMPRIN expression, along with the tumor marker Mucin-1 (MUC1/CA 15-3) [11]. Additionally, the truncated oncogenic form of the epidermal growth factor receptor (EGFR), EGFRvIII, commonly expressed in aggressive brain tumor cells, is associated with pro-tumorigenic tumor–tumor crosstalk via MVs. It was discovered that EGFRvIII was present in MVs released by U373 glioma cells, allowing them to transfer malignant features from highly aggressive tumor cells to the more benign tumor cells, EGFRvIII-negative, thereby facilitating their oncogenic transformation [11]. Hence, MVs are convenient communicators within the TME, as they can either mediate the horizontal transfer of oncogenic material or activate oncogenic signaling pathways in neighboring cancer cells, enhancing their survival, proliferative, and angiogenic potential and triggering their transformation into an aggressive phenotype.
Alongside the tumor–tumor communication, MVs were proven to facilitate the crosstalk between the tumor and its surrounding stroma and immune cells which ultimately leads to cancer immune evasion. In breast cancer cells, the secretion of both tumor MV and TDE induced the expression of Wnt5a in tumor-associated macrophages. Macrophage Wnt5a promoted ß-catenin-independent Wnt signaling in breast cancer cells when delivered by macrophage-derived MVs and Exo, resulting in enhanced tumor invasion. This shows how EV-based cell-cell communication can drive tumor-associated immune cells to stimulate tumor growth [11]. MVs-enriched preparations induced the differentiation of monocytes producing anti-inflammatory cytokines such as IL-10. In line with this, early stimulation with tumor MV triggered macrophage polarization towards an anti-inflammatory phenotype with decreased anti-tumor cytotoxic potential. Additionally, as T cells represent the first line of the immune defense, tumor cells appear to suppress T cell activity and diminish antitumoral immune response via MVs-mediated cell-cell communication. For instance, leukemia-derived MVs deliver miRNAs to T cells, which alters T cell phenotype [13] (Figure 1). Moreover, MVs released by irradiated breast cancer cells were shown to carry abundant immune-suppressive proteins, such as programmed cell death ligand 1 (PD-L1) which inhibited cytotoxic T cell activity and enabled tumor growth (Figure 1)[15].
Exosome PD-L1 (similar to tumor PD-L1) can bind to PD-1 on T cells, induce T cell apoptosis, and inhibit T cell activation and proliferation [
TDEs, through their miRNAs proteins, DNAs, mRNAs lncRNAs, initiate the transformation of epithelial cells to mesenchymal cells. This transformation was due to the loss of epithelial E-cadherin expression, cell-cell adhesion and cell polarity, and gaining of vimentin expression [16].
The complex and heterogeneous microenvironment of both primary or metastatic tumor is comprised of a network of cellular and acellular constituents. The cellular compartment consists of tumor cells and assorted non-transformed cells, such as cancer-associated fibroblasts (CAFs), macrophages, and endothelial cells. The non-cellular part is formed by secreted factors and components of the ECM. The tumor microenvironment modulates tumor progression by providing inhibitory or stimulatory growth signals [17]. Thus pre-metastatic niche refers to the microenvironment, that is primed to allow tumor cells to colonize in and disseminate to distant sites. The main machineries of the premetastatic niche formation include tumor-derived secreted factors (TDSFs), EVs bone marrow-derived cells (BMDCs), suppressive immune cells and host stromal cells [4], and inflammation. Chronic inflammation is a driving force for tumor development and metastasis. Thus, the local inflammatory microenvironment is one of the essential factors for the pre-metastatic niche formation and driving force for metastasis.
Tumor development and metastasis are aided by chronic inflammation. As a result, one of the most important variables in the establishment of a pre-metastatic niche is the local inflammatory microenvironment. Tumor cells can be induced to create TDSFs such as vascular endothelial growth factor (VEGF), tumor necrosis factor alpha (TNF-α), transforming growth factor (TGF-β), and interleukin-2 by the local inflammatory microenvironment. These TDSFs then exert a paracrine effect on myeloid cells, initiating their migration to potential pre-metastatic niche formation sites [18]. Host stromal cells in the pre-metastatic niche may upregulate the expression of inflammatory factors in response to TDSF activation. The recruitment of BMDCs or immune cells to the pre-metastatic niche speeds up the release of inflammatory factors. Exo from tumors also transport inflammatory substances into the bloodstream, where they reach the pre-metastatic niche. In the pre-metastatic niche, an inflammatory milieu supportive to tumors is eventually generated [18].
In a study conducted by Hoshino, he showed that the proinflammatory cytokine s100 was upregulated up to four folds when Kupffer cells were treated with integrin intact Exo, as compared to those treated with integrin knocked out Exo. Hoshino speculated that the activation of Src, and its phosphorylation might be a causative pathway [19].
TDE and MV were also shown to modify fibroblasts in the tumor stroma. When normal human fibroblasts were exposed to oral squamous carcinoma derived MV [20] the fibroblasts were altered into a cancer phenotype. This switch to CAFs was largely mediated via metabolic reprogramming of the fibroblasts to aerobic glycolysis, with an increase in glucose uptake and lactate secretion. Some TDEs contain surface TGF-β along with betaglycan, which could trigger SMAD-dependent signaling and regulate the differentiation of fibroblasts to myofibroblasts [21]. This was further proved by co-culturing the generated CAF with cancer cells which led to enhanced cancer cell invasion and migration, creating a bidirectional cross-talk that favors tumor promotion and spread. The MVs-induced fibroblast activation and spreading seem to occur in the matrix milieu in the tumor periphery [22]. In prostate cancer, TDE were shown to induce the expression of RANKL and Metalloproteinases in CAFs, through miR-100, -21, and -139, further promoting its metastasis [23]. Hypoxia seems to stimulate prostate cancer cells to release protein-rich Exo which further induces activation of CAFs [24], promotes epithethelial mesenchymal transition (EMT), stemness, and angiogenesis by prostate cancer cells.
Additionally, TDE were also described as regulators of metabolism in the tumor microenvironment, for example, breast cancer tumors could suppress glucose uptake by lung fibroblasts, via secretion of Exo containing miR-122, increasing glucose availability and facilitating metastasis [25]. The cell-to-cell communication mediated by Exo is also affected by the genetic profile of the recipient fibroblasts. For example, fibroblasts lacking the BRCA1, a tumor suppressor gene, internalize larger amounts of serum-derived Exo when compared to BRCA1 containing fibroblasts [26]. Furthermore, these cells were found to undergo a malignant transformation when exposed to Exo derived from sera of cancer patients, implying that oncosuppressor genes can prevent exosome information from tumor cells from being integrated and thus shelter these cells from their pro-oncogenic signals [26].
Tumor MVs extravasate through the vessel wall in pancreatic cancer, reach the liver microcirculation and are picked up by perivascular macrophages to prime the liver metastatic niche in a CD36-dependent manner. Furthermore, tumor MVs produced from the B16F10 melanoma cell line was discovered to cause metastases in BALB/c mice, which are generally resistant to the B1610 tumor cell line [27]. TDEs also protect cancer cells from apoptosis by selectively effluxing apoptosis inducer proteins that are delivered by T cells or natural killer (NK) cells. TDEs also reduce the effects of therapy by preventing drug efflux or concealing the binding site of monoclonal antibodies, which could lead to the emergence of chemotherapy-resistant cell populations [28].
Exosome-derived programmed death receptor 1 (PD-1) and programmed death-ligand 1 have been linked to an immunological escape mechanism in recent years. PD-1 is mostly found on macrophages, activated T cells, and B cells, whereas PD-L1 is abundant in tumor tissues, antigen-presenting cells (APCs), and stromal cells [29]. T lymphocytes can recognize and destroy tumor cells in normal circumstances. When PD-1 attaches to PD-L1, however, it sends an inhibitory signal to T cells, causing them to die and inhibiting their activation and proliferation. As a result, blocking the PD-1/PD-L1 pathway may boost the immune response by increasing the killing effect of T cells [30]. T lymphocytes can recognize and destroy tumor cells in normal circumstances. When PD-1 attaches to PD-L1, however, it sends an inhibitory signal to T cells, causing them to die and inhibiting their activation and proliferation (Figure 1). As a result, blocking the PD-1/PD-L1 pathway may boost the immune response by increasing the killing effect of T cells. As a result, Exo containing PD-L1 suppress the immune system in the pre-metastatic milieu and promote the establishment of a pre-metastatic niche [31].
Angiogenesis within the primary tumor is also influenced by tumor MVs and TDE. Normal endothelial cells (ECs) were shown to endocytose tumor EVs, which triggered PI3K/Akt signaling and increased EC motility and tube formation ability [32]. Tumor MVs and TDE also release VEGF, a pro-angiogenic substance that stimulates ECs [33]. Similarly, MVs produced from multiple myeloma cells have been demonstrated to transfer CD138, a myeloma cell marker, to ECs, promoting their proliferation, invasion, and production of the angiogenic mediators IL-6 and VEGF, resulting in tube formation [50] (Figure 2). MVs change the environment around the main tumor and create pre-metastatic niches from afar. This was originally attributed to their procoagulant activity, which encouraged the production of microthrombi and facilitated the extravasation of trapped circulating tumor cells. ECs are important components of the tumor microenvironment because they provide a pathway for nutrients and trophic substances [34].
Possible mechanisms of pre-metastatic niche formation. The figure delineates how TDEs can modulate its surroundings of ECM, cancer-CAFs, immune cells, ECs, and MSCs all in favor of tumor support and progression. TDE can carry integrins to distant sites and create a pre-metastatic niche.
TDE enriched in vascular cell adhesion molecule (VCAM)-1 and intercellular adhesion molecule (ICAM)-1 has been demonstrated to regulate the process of neovascularization in myeloid leukemia [35]. Furthermore, enhanced vascularization has been linked to the packaging of miR-92a in Exo derived from leukemia[36] and CO-029/D6.1A Tetraspanin in Exo produced from pancreatic cancer [37]. Upregulation of Heparanase in tumor cells, such as myeloma and breast malignancies, has also been linked to increased exosome production and exosomal packing of Syndecan-1, VEGF, and hepatocyte growth factor, resulting in enhanced endothelial invasion through the ECM [38]. Exo produced from skin cancer can also enhance angiogenesis by transferring the EGFR [39] and miR-9 to ECs [26]. Furthermore, melanoma-derived Exo have been found to condition sentinel lymph nodes prior to the installation of melanoma cells and subsequent metastasis by upregulating Collagen 18 and Laminin 5, as well as producing angiogenic growth factors [26].
Another significant component in altering tumor-EC communication is hypoxia. Hypoxic glioblastoma cells, for example, release Exo that interact with ECs, promoting proliferation and angiogenesis both in vitro and in vivo [40], and also prompting tissue factor/Factor VIIa dependent activation of hypoxic ECs [26].
Exo from melanoma cause pulmonary vascular leakiness and upregulate tumor cell recruitment genes such Stabilin 1, Vitronectin, Integrins, and Ephrin receptor b4 in lymph nodes, forming pre-metastatic niches [41]. Furthermore, breast cancer-derived Exo enriched in miR-105 alter the expression of Claudin 5, Zonula Occludens protein 1, and Occludin, which promotes metastasis by disrupting vascular endothelial barriers [42]. Exo produced from brain tumors include miR-181c, which regulates EC actin dynamics and promotes the breakdown of the blood-brain barrier by three times. Protein Kinase-1 Degradation Requires Phosphoinositol [43]. Similarly, glioblastoma cells release Exo with high quantities of VEGF-A, which promote EC permeability and angiogenesis in vitro [44].
TDE can promote pro-tumorigenic microenvironments via promoting tumor-stem/progenitor cell contact, in addition to its well-known actions in differentiated cells. Melanoma-derived Exo, for example, stimulate BMDCs by transferring the oncoprotein MET, resulting in the mobilization of vasculogenic and hematopoietic bone marrow progenitor cells to ensure vascular proliferation and immunosuppression at pre-metastatic niches [45]. Communication between tumor stem/progenitor cells is also critical in bone metastasis. Exo from bone metastatic prostate cancer PC3 cells were found to influence the process of bone metastasis by modulating both osteoclast genesis and osteoblast proliferation. Exo generated from osteoblasts, on the other hand, have been demonstrated to stimulate PC3 prostate cancer cell proliferation [46].
TDE was also demonstrated to influence the development of myeloid precursor cells into myeloid-derived suppressor cells (MDSCs), which are known to aid tumor progression by permitting immune escape [47]. Exo produced from breast carcinomas have been found to be taken up by bone marrow cells and to convert these cells’ development pathways toward MDSCs via Prostaglandin E2 and TGF-β, boosting COX2, IL6, VEGF, and Arginase1 accumulation by MDSCs [48].
TDE can also cause alterations in mesenchymal stem cells (MSCs), which help to promote and maintain tumor-promoting inflammatory environments. For example, HSP70+ lung tumor-derived exosomes (TDEs) activate NF-kB and cause MSCs to secrete IL-6, IL-8, and MCP1 via TLR2-mediated signaling, causing MSCs to become more inflammatory and tumor supportive [49]. According to De Veirman et al. [50], myeloma-derived Exo transfer miR-146a to mesenchymal cells, stimulating them to secrete numerous cytokines and chemokines including CXCL1, IL6, IL-8, IP-10, MCP-1, and CCL-5 (Figure 2). Another example is Exo produced by KMBC cholangiocarcinoma cells, which cause MSCs to upregulate IL-6, and hence KMBC cell proliferation [51].
One of the proposed mechanisms of tumorigenicity of TDE is the induction of tumor cell proliferation. Studies involving various cancer cells such as, chronic myeloid leukemia and in human gastric cancer, showed that this proliferative potential is via an autocrine induction through the phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) and MAPK/ERK signaling pathways. Additionally, through the transference of lncRNAs (reviewed in [49]).
In addition, glioblastoma-derived Exo were shown to induce proliferation of the human glioma U87 cell line [40] in a mechanism dependent on the chloride intracellular channel protein 1 (CLIC1) [52]. In a more specific context linked to prostate cancer treatment, prostate cancer LNCaP cells grown in the presence of androgens generate Exo high in CD9, which enhance the growth of androgen-depleted LNCaP cells. Another example involves the promotion of in vivo growth of murine melanomas by systemic treatment of mice with melanoma-derived Exo, which accelerated growth and inhibited apoptosis of melanoma tumors in vivo [26].
TDE can alter the migratory behavior of recipient malignant cells. Exo produced from nasopharyngeal cancer-bearing EMT-inducing signals such as TGF-β and hypoxia-inducible factor 1 alpha (HIF1a) [53], matrix metalloproteinases (MMPs) Notch1, LMP1 Casein Kinase II and Annexin A2, were shown to enhance the migratory capacity of the tumor recipient cells. Another example involves Exo derived from hypoxic prostate cancer cells, which prompted invasiveness and motility of naïve human prostate cancer cells (reviewed in [26]) through the neighboring stroma and to nearby cells.
Exo have been found to have a role in tumor-tumor communication by transferring chemoresistance. Exo have been linked to the transfer of Docetaxel resistance in prostate cancer since Corcoran and colleagues first discovered it [54]. The transfer of cisplatin resistance in lung cancer is achieved by donor resistant cells producing Exo with low levels of miR-100-5p, which leads to enhanced expression of the mammalian target of rapamycin (mTOR) protein and chemoresistance in recipient cells [55].
MiRNA packed in Exo from drug-resistant cells can modulate the expression of specific target genes in breast cancer, such as miR-23a targeting Sprouty2, miR-222 targeting PTEN, miR-452 targeting APC4, and miR-24 targeting p27, thereby modulating chemoresistance in recipient cells that integrate these Exo. In fact, exosomal miR-222 plays a key role in this process, as the silencing of miR-221/222 prevents the transmission of resistance [56].
In addition to miRNAs, the transfer of exosomal mRNAs that encode drug-resistant proteins may result in chemoresistance in the receiving cell. GSTP1 exosomal mRNA from Adriamycin-resistant breast cancer cells, for example, confers resistance to previously susceptible cells. The presence of GSTP1 in circulating Exo from patients’ peripheral blood was linked to a worse outcome in breast cancer patients receiving Adriamycin [57]. A supporting stroma is required for an optimum metabolic and physiological environment for tumor growth. Fibroblasts are the most abundant cells in most solid tissues, participating in environmental cue responses and being a common target of tumor-derived signals [58].
Integrins are a wide family of cell adhesion receptor proteins such as alpha3beta1, alpha6beta1, alpha6beta4, and alpha7beta1. Their roles have been implicated in tumor metastasis and mesenchymal transformation. TDE carry these integrins from primary tumor sites to distant sites such as lung, lymph nodes, brain, and bone creating pre-metastatic niches (Figure 2) [59].
TDEs are involved in the advancement of several forms of cancer. Because of their abundance, TDEs may serve as noninvasive diagnostic and prognostic tools for various cancers. Additionally, blocking exosome secretion can slow the growth of some malignancies. Hence, Exo have been a popular target for developing cancer treatment techniques because of this property. Decreasing the expression of the exosomal proteins, Rab27a and Rab27b, inhibit exosome secretion without matching changes in soluble proteins secretions [60]. Several drugs used in the pharmaceutical industry such as Ketoconazole (an anti-fungal) sphingomyelinase (a hydrolase enzyme that is responsible for degrading sphingomyelin) [61], are additionally Rab27a inhibitors. These drugs can be re-directed as cancer modulators for their possible effects on attenuating TDE tumor progressive effects.
Furthermore, TDE owing to its small size, cancer-homing, and nontoxic nature, TDE can be re-directed to serve as a drug delivery system. Exo have been proven in several investigations to act as drug delivery vehicles, transporting anti-cancer chemicals to target cells [62]. For example, adriamycin and paclitaxel, target cancer cells via exosomal encapsulation and have low toxicity and immunogenicity [63].
EVs modulate the environment that favors tumor growth and progression. EVs provide a method of cell-cell communication, and through their rich cargo of ECM proteins, cell adhesion proteins, tyrosine kinases, chaperones, signaling proteins, DNA and RNA binding proteins, they create a pre-metastatic niche. By priming nearby and distant cells into becoming cancerous, they promote tumor metastasis. Several mechanisms have been discovered for their actions including, promotion of migratory behavior, chemoresistance, anti-apoptosis, vascular leakage, and immune modulation. Understanding how TDE and MVs create a pre-metastatic niche and how halting the trafficking of such vesicles can produce a revolutionizing new era in the field of cancer therapeutics. By preventing TDE-promoted metastasis and tumor progression, coupled with conventional radio and chemotherapy, the survival rates of cancer patients can significantly improve.
The author declares no conflicts of interest.
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Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.",book:{id:"8141",slug:"social-media-and-machine-learning",title:"Social Media and Machine Learning",fullTitle:"Social Media and Machine Learning"},signatures:"Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Kosai Raoof, Mohamed Ali Mahjoub and Catherine Cleder",authors:[{id:"247090",title:"Ph.D. Student",name:"Leila",middleName:null,surname:"Kerkeni",slug:"leila-kerkeni",fullName:"Leila Kerkeni"}]},{id:"63164",title:"Introduction to Kalman Filter and Its Applications",slug:"introduction-to-kalman-filter-and-its-applications",totalDownloads:10954,totalCrossrefCites:25,totalDimensionsCites:64,abstract:"We provide a tutorial-like description of Kalman filter and extended Kalman filter. This chapter aims for those who need to teach Kalman filters to others, or for those who do not have a strong background in estimation theory. Following a problem definition of state estimation, filtering algorithms will be presented with supporting examples to help readers easily grasp how the Kalman filters work. Implementations on INS/GNSS navigation, target tracking, and terrain-referenced navigation (TRN) are given. In each example, we discuss how to choose, implement, tune, and modify the algorithms for real world practices. Source codes for implementing the examples are also provided. 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Genomics studies of large populations are producing a huge amount of data, giving rise to computational issues around the storage, transfer, and analysis of the data. Fortunately, cloud computing has recently emerged as a viable option to quickly and easily acquire the computational resources for large-scale NGS data analyses. Some cloud-based applications and resources have been developed specifically to address the computational challenges of working with very large volumes of data generated by NGS technology. In this chapter, we will review some cloud-based systems and solutions for NGS data analysis, discuss the practical hurdles and limitations in cloud computing, including data transfer and security, and share the lessons we learned from the implementation of Rainbow, a cloud-based tool for large-scale genome sequencing data analysis.",book:{id:"5416",slug:"cloud-computing-architecture-and-applications",title:"Cloud Computing",fullTitle:"Cloud Computing - Architecture and Applications"},signatures:"Shanrong Zhao, Kirk Watrous, Chi Zhang and Baohong Zhang",authors:[{id:"176364",title:"Dr.",name:"Shanrong",middleName:null,surname:"Zhao",slug:"shanrong-zhao",fullName:"Shanrong Zhao"}]}],onlineFirstChaptersFilter:{topicId:"9",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82351",title:"Building an Online Ecosystem for English Teaching and Learning in the Times of Covid-19 Pandemic and Beyond",slug:"building-an-online-ecosystem-for-english-teaching-and-learning-in-the-times-of-covid-19-pandemic-and",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.105651",abstract:"Online education is a revolutionary trend of educational technology today, particularly, since the Covid-19 pandemic, online classes have become the cornerstone of modern education. 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Data deluge brings substantial challenges in the collection and management of massive amounts of unstructured data towards decision making. Likewise, unprecedented production of information exceeds the ability of authoritative bodies to create regulations and policies that can keep up with these transformations in the nature of work. We explicate the impact of well-timed policies (fiscal and monetary), prediction of long-term structural changes in the industrial sector, industrial strategy formulation practices, and examine the economic studies and analysis of sustainable development in these areas.",book:{id:"11198",title:"Digital Transformation",coverURL:"https://cdn.intechopen.com/books/images_new/11198.jpg"},signatures:"Kinda R. Dahlan, Ahmed A. 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The knowledge presented in this chapter may provide tips and inspiration for some other game projects, and practical and useful notes on the advantages or disadvantages of some systems will be interesting and useful.",book:{id:"11192",title:"Computer Game Development",coverURL:"https://cdn.intechopen.com/books/images_new/11192.jpg"},signatures:"Branislav Sobota, Marián Hudák and Emília Pietriková"},{id:"82300",title:"An Effective Method for Secure Data Delivery in IoT",slug:"an-effective-method-for-secure-data-delivery-in-iot",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.104663",abstract:"The Internet of Things (IoT) has become very popular recently due to its important features that contribute to many aspects of our lives such as health and transportation. It consists of a vast number of different projects such as sensors, tags, actuators, and mobile devices, which can communicate and collaborate without human interactions. These devices carry small memory and low-energy battery, which affects their performance and lead to many issues. In this work, we are going to focus on the efficiency and security issues. We will propose a secure and efficient routing protocol for data delivery in order to improve its performance. The proposed technique will be evaluated in an implemented platform with appropriate case study. The expected outcome of this study will be a reference design and its practical implementation to support efficiency and security in IoT.",book:{id:"11197",title:"Internet of Things - New Trends, Challenges and Hurdles",coverURL:"https://cdn.intechopen.com/books/images_new/11197.jpg"},signatures:"Mnar Alnaghes, Nickolas Falkner and Hong Shen"},{id:"82267",title:"Methods for Speech Signal Structuring and Extracting Features",slug:"methods-for-speech-signal-structuring-and-extracting-features",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.104634",abstract:"The preliminary stage of the biometric identification is speech signal structuring and extracting features. For calculation of the fundamental tone are considered and in number investigated the following methods – autocorrelation function (ACF) method, average magnitude difference function (AMDF) method, simplified inverse filter transformation (SIFT) method, method on a basis a wavelet analysis, method based on the cepstral analysis, harmonic product spectrum (HPS) method. For speech signal extracting features are considered and in number investigated the following methods – the digital bandpass filters bank; spectral analysis; homomorphic processing; linear predictive coding. This methods make it possible to extract linear prediction coefficients (LPC), reflection coefficients (RC), linear prediction cepstral coefficients (LPCC), log area ratio (LAR) coefficients, mel-frequency cepstral coefficients (MFCC), barkfrequency cepstral coefficients (BFCC), perceptual linear prediction coefficients (PLPC), perceptual reflection coefficients (PRC), perceptual linear prediction cepstral coefficients (PLPCC), perceptual log area ratio (PLAR) coefficients, reconsidered perceptual linear prediction coefficients (RPLPC), reconsidered perceptual reflection coefficients (RPRC), reconsidered perceptual linear prediction cepstral coefficients (RPLPCC), reconsidered perceptual log area ratio (RPLAR) coefficients. The largest probability of identification (equal 0.98) and the smallest number of coefficients (4 coefficients) are provided by coding of a vocal of the speech sound from the TIMIT based on PRC.",book:{id:"10992",title:"Speech Recognition - New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/10992.jpg"},signatures:"Eugene Fedorov, Tetyana Utkina and Tetiana Neskorodieva"},{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalDimensionsCites:0,doi:"10.5772/intechopen.104944",abstract:"Face recognition across aging emerges as a significant area among researchers due to its applications such as law enforcement, security. However, matching human faces with different age gaps is still bottleneck due to face appearance variations caused by aging process. In regard to mitigate such inconsistency, this chapter offers five sequential processes that are Image Quality Evaluation (IQE), Preprocessing, Pose Normalization, Feature Extraction and Fusion, and Feature Recognition and Retrieval. Primarily, our method performs IQE process in order to evaluate the quality of image and thus increases the performance of our Age Invariant Face Recognition (AIFR). In preprocessing, we carried out two processes that are Illumination Normalization and Noise Removal that have resulted in high accuracy in face recognition. Feature extraction adopts two descriptors such as Convolutional Neural Network (CNN) and Scale Invariant Heat Kernel Signature (SIHKS). CNN extracts texture feature, and SIHKS extracts shape and demographic features. These features plays vital role in improving accuracy of AIFR and retrieval. Feature fusion is established using Canonical Correlation Analysis (CCA) algorithm. Our work utilizes Support Vector Machine (SVM) to recognize and retrieve images. We implement these processes in FG-NET database using MATLAB2017b tool. 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\r\n\tThe environment is subject to severe anthropic effects. Among them are those associated with pollution, resource extraction and overexploitation, loss of biodiversity, soil degradation, disorderly land occupation and planning, and many others. These anthropic effects could potentially be caused by any inadequate management of the environment. However, ecosystems have a resilience that makes them react to disturbances which mitigate the negative effects. It is critical to understand how ecosystems, natural and anthropized, including urban environments, respond to actions that have a negative influence and how they are managed. It is also important to establish when the limits marked by the resilience and the breaking point are achieved and when no return is possible. The main focus for the chapters is to cover the subjects such as understanding how the environment resilience works, the mechanisms involved, and how to manage them in order to improve our interactions with the environment and promote the use of adequate management practices such as those outlined in the United Nations’ Sustainable Development Goals.
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",coverUrl:"https://cdn.intechopen.com/series_topics/covers/38.jpg",keywords:"Human activity, Pollutants, Reduced risks, Population growth, Waste disposal, Remediation, Clean environment"},{id:"41",title:"Water Science",scope:"