\\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:"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"},{slug:"intechopen-identified-as-one-of-the-most-significant-contributor-to-oa-book-growth-in-doab-20210809",title:"IntechOpen Identified as One of the Most Significant Contributors to OA Book Growth in DOAB"}]},book:{item:{type:"book",id:"1611",leadTitle:null,fullTitle:"Nonlinear Optics",title:"Nonlinear Optics",subtitle:null,reviewType:"peer-reviewed",abstract:"Rapid development of optoelectronic devices and laser techniques poses an important task of creating and studying, from one side, the structures capable of effectively converting, modulating, and recording optical data in a wide range of radiation energy densities and frequencies, from another side, the new schemes and approaches capable to activate and simulate the modern features. It is well known that nonlinear optical phenomena and nonlinear optical materials have the promising place to resolve these complicated technical tasks. The advanced idea, approach, and information described in this book will be fruitful for the readers to find a sustainable solution in a fundamental study and in the industry approach. The book can be useful for the students, post-graduate students, engineers, researchers and technical officers of optoelectronic universities and companies.",isbn:null,printIsbn:"978-953-51-0131-4",pdfIsbn:"978-953-51-4964-4",doi:"10.5772/2073",price:119,priceEur:129,priceUsd:155,slug:"nonlinear-optics",numberOfPages:238,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"1996056f2eafba51ac41d52290731f92",bookSignature:"Natalia Kamanina",publishedDate:"February 29th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/1611.jpg",numberOfDownloads:22375,numberOfWosCitations:32,numberOfCrossrefCitations:1,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:7,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:40,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 2nd 2011",dateEndSecondStepPublish:"May 30th 2011",dateEndThirdStepPublish:"October 4th 2011",dateEndFourthStepPublish:"November 3rd 2011",dateEndFifthStepPublish:"March 2nd 2012",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"28159",title:"Dr.",name:"Natalia",middleName:"Vladimirovna",surname:"Kamanina",slug:"natalia-kamanina",fullName:"Natalia Kamanina",profilePictureURL:"https://mts.intechopen.com/storage/users/28159/images/system/28159.png",biography:"Dr. Sci. PhD. Natalia Vladimirovna Kamanina was born in Kaliningrad, Russian Federation, 1957. She graduated with an Honor Diploma from Leningrad Polytechnical Institute (1981), St. Petersburg, Russia, and received a PhD (Physics & Mathematics) at Vavilov State Optical Institute, St.-Petersburg, Russia (1995), as well as a Dr. Sci. (Physics & Mathematics) at the same institution (2001). She is currently a Head of the Lab for “Photophysics of media with nanoobjects” at Vavilov State Optical Institute St.-Petersburg, Russia and has been involved in collaboration research with many researchers and scientists all over the world since 1995, publishing about 200 technical papers. Her pioneer ideas and experienced contributions were applied in technique and were certificated by 13 Russian Patents. Dr.Sci. N.V. Kamanina has current interest in the areas of investigations on organic conjugated materials, liquid crystals, inorganic soft materials of the UV and IR range, laser-matter interaction, fullerenes and biological objects. She has an experience in nanoparticles doping process of organics, in recording of amplitude-phase thin gratings in thin films as well as in developing of LC cells and spatial light modulators; she has an experience in optical limiting effect of laser radiation over visible and infrared spectral ranges, in medical applications of LC structures to orient human blood cell. In parallel to her scientific activity, she has also been lecturing from 2001, as a Professor of Electronic Department at St. Petersburg Electrotechnical University( “LETI”).\nScopus Author ID: 55980751700. h-index in Scopus is 17, in RINC is 19.",institutionString:"St. Petersburg Electrotechnical University (“LETI”)",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"State Optical Institute named after SI Vavilov",institutionURL:null,country:{name:"Russia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1226",title:"Optoelectronics",slug:"optics-and-lasers-optoelectronics"}],chapters:[{id:"29950",title:"Overview of Nonlinear Optics",doi:"10.5772/37416",slug:"overview-of-nonlinear-optics",totalDownloads:4011,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:1,abstract:null,signatures:"Elsa Garmire",downloadPdfUrl:"/chapter/pdf-download/29950",previewPdfUrl:"/chapter/pdf-preview/29950",authors:[{id:"106724",title:"Prof.",name:"Elsa",surname:"Garmire",slug:"elsa-garmire",fullName:"Elsa Garmire"}],corrections:null},{id:"29951",title:"Stimulated Raman Scattering in Quantum Dots and Nanocomposite Silicon Based Materials",doi:"10.5772/34915",slug:"stimulated-raman-scattering-in-quantum-dots-and-nanocomposite-silicon-based-materials",totalDownloads:2618,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:null,signatures:"M. 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Also, drug-loaded ligand anchored liposomes are used to induce selective apoptosis in cells of monocyte and macrophage lineage. Therefore this book aims to cover all spheres of drug and antigen delivery for developing therapeutic interventions and vaccines against infectious diseases and beyond.
\r\n\r\n\tThis book will shed light on various methods for the preparation and characterization of liposomes and their engineered versions. Technological advancements have enabled us to develop newer methods of formulating liposomes. Over time, liposomes have been modified to a larger extent and engineered to meet our growing needs for sustained and controlled delivery for developing therapeutic interventions vaccines. Further, this book will throw light on the various methods of preparation and characterization of liposomes, and discuss several biochemical and indirect methods to understand the biological and physicochemical mechanisms of action of liposomes that decide their efficiency in vivo.
\r\n\tDespite their limitations such as encapsulation efficiency, liposomes are a well-established choice for a number of unconventional and conventional biological applications. The versatility of these lipid-based vesicles presents the importance of these nanoparticles in the future applications of nanotechnology besides targeted drug delivery. Overall, this book provides the necessary and relevant information about various aspects of liposomes and their use in nanomedicine.
\r\n\t
Neuroblastoma is the most common extracranial solid tumor to present in children [1]. Neuroblastoma arises from bipotential sympathoadrenal progenitor cells of the trunk neural crest. During normal development, sympathoadrenal progenitors differentiate to form the sympathetic ganglia and adrenal medulla, however in neuroblastoma they form tumors at these sites instead [1, 2].
Retinoic acid is now employed in multi-modal therapy to treat high-risk neuroblastoma and eradicate minimal residual disease [3]. All-trans retinoic acid (ATRA) diminishes MYCN oncogene expression, arrests proliferation, and induces differentiation of neuroblastoma cells [4, 5].
Gap junctional intercellular communication (GJIC) and connexins (Cx) have been implicated in carcinogenesis and differentiation. GJIC is often perturbed and connexins are typically downregulated or aberrantly localized in cancer cells, including IMR-32 neuroblastoma cells [6, 7]. Many connexins have been identified as tumor suppressors when overexpressed in cancer cells [6]. For example, overexpression of Cx43 resulted in growth suppression of communication deficient Neuro-2A murine neuroblastoma cells [8]. In addition, connexins have been shown to enhance the differentiation of cancer cell lines [8, 9]. For example, overexpression of Cx32 and Cx43 resulted in enhanced nerve growth factor induced neurite outgrowth in PC12 cells [10].
Gap junctions are membrane channels that allow intercellular communication between adjacent cells [11]. GJIC involves the passage of ions, second messengers, and metabolites less than 1kDa in size between cells [11, 12]. Gap junctions and their constituent proteins, connexins, regulate cellular processes such as homeostasis, growth, and differentiation [6, 13]. Gap junctions are formed from the docking of two connexons or hemichannels contributed by adjacent cells. Each connexon in turn is comprised of a hexamer of connexin subunits [11]. There are 21 known members of the human connexin family, most with unique spatial and temporal expression patterns [14].
Cx36 is a recently identified connexin that is expressed in neurons and pancreatic beta cells [15-17]. Cx36 is highly expressed in the developing nervous system, however its expression is decreased or lost by the second post-natal week in many structures [18, 19]. In adults, Cx36 is the predominant connexin involved in electrical synapses [20].
The differentiating agent, retinoic acid, increases GJIC and Cx43 expression in multiple cell types [21-23]. Interestingly, the mouse
In this study, we explored the effect of the neuronal connexin, Cx36, on the differentiation of SH-SY5Y human neuroblastoma cells. SH-SY5Y cells are often utilized as a model system for studies of neural disease, as well as in studies of differentiation [25, 26]. The effect of ATRA on
The neuroblastoma cell line, SH-SY5Y was obtained from American Type Culture Collection. SH-SY5Y is a proliferative cell line with a documented doubling time of 48 hours. SH-SY5Y cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM, Invitrogen) with high glucose, and supplemented with 10% fetal bovine serum (FBS, HyClone), 100 U/ml penicillin and streptomycin (Invitrogen) and 2 mM L-glutamine (Invitrogen). For cell passage, cells were washed with Hank’s Buffered Salt Solution (HBSS, Sigma), treated with 0.25% trypsin and incubated for 5 minutes at 37°C to disperse the cells. Five ml of growth medium was added to the cells, and they were resuspended in a 10 cm dish in fresh growth medium after pipetting several times.
Cells were plated at a density of 1x106 cells per well in a 6-well dish. The following day, 4 µg of Cx36myc-DDK (Origene) or EGFP pcDNA3.1 were diluted in OPTI-MEM (Invitrogen) and mixed with 2.5 µl of lipofectamine 2000 (Invitrogen) in OPTI-MEM. The DNA and lipofectamine were mixed and the resulting complexes added to the cells after 20 minutes, which were also in OPTI-MEM. The cells and complexes were incubated for 6 hours and then returned to normal growth medium. The following day, transfected cells were passaged onto 10 cm dishes. Kill curves were performed on wild-type cells to determine optimal antibiotic concentrations. For stable cell lines, 800 µg/ml G418 was added to the cells, and all viable cells were passaged to a 10 cm dish following two weeks of selection, and G418 was reduced to 200 µg/ml. However, cells were treated with 800 µg/ml G418 monthly to ensure purity of the culture.
shRNA knockdown: Cells were plated at a density of 5x105 cells per well in a 6-well dish. The following day, 1 µg of one of four distinct shRNAs targeting Cx36 or non-effective (scrambled) control plasmids co-expressing GFP (Origene) were diluted in OPTI-MEM and mixed with 1.5 µl of lipofectamine 2000 in OPTI-MEM. The cells were then maintained as per the overexpression protocol. For stable cell lines, 800 ng/ml puromycin was added to the cells, and all viable cells were passaged to a 10 cm dish following two weeks of selection, and puromycin was further increased to 1.5 µg/ml. In addition, cells were treated with 2.5 µg/ml puromycin monthly to maintain purity of the culture due to the occasional appearance of non-fluorescent cells.
SHSY-5Y cells were plated on rat-tail collagen coated 6-well or 60 mm plates, at a density of 10 000 cells/cm2 in growth medium. The following day, cells were treated with 10 µM of all trans-retinoic acid (Sigma) for 4 days, with medium and treatment refreshed every 2 days. Cells were then washed with HBSS and treated with 20 ng/ml of brain derived neurotrophic factor (BDNF) in serum-free DMEM for 6 days, with medium and BDNF replaced every 2 days (modified from [26]). Medium for transfected cells contained the appropriate antibiotic. RNA and protein were extracted at Days 0, 2, 4 and 10 of differentiation, and connexin and differentiation marker expression were analyzed. Images were taken at each timepoint with a Leica inverted epifluorescent microscope. The images were analyzed with the NeuronJ plugin for ImageJ [27, 28]. For each image, neurites within the 0.16 mm2 field of view were traced. Neurites were classified as cell projections longer than one cell body length. For analysis of average neurite length, the mean length of all neurites in the field of view was computed. In order to calculate maximum neurite length, the length of the longest neurite in the field of view was analyzed. Finally, for neurite density, the number of neurites was divided by the number of cell bodies in the field of view.
RNA was extracted and purified using the Qiagen RNeasy mini kit, and 0.5 µg of RNA was reverse transcribed using the qScript cDNA synthesis kit (Quanta Biosciences). Connexin primers were designed using Primer-Blast yielding the following sense and anti-sense sequences: Cx36, 5’-AAG GCA TCT CCC GCT TCT ACA - 3’ and 5’- GCC AAC CAG GAA CCC AAT TT- 3’; Cx45, 5’-CTG GAG GCT CTG CAG CGG GA- 3’ and 5’-TCT CCC GGG GAC CAT GAG GG- 3’; Cx43, 5’-GGT TAC ACT TGC AAA AGA GAT C- 3’, and 5’-GAG CAG CCA TTG AAA TAA GC- 3’. Differentiation markers: Neuropeptide Y (NPY), 5’- TCC AGC CCA GAG ACA CTG ATT-3’ and 5’-AGG GTC TTC AAG CCG AGT TCT-3’; Growth associated protein 43 (Gap43), 5’-ACG ACC AAA AGA TTG AAC AAG ATG-3’ and 5’-TCC ACG GAA GCT AGC CTG AA-3’ (Origene). Adhesion molecule: Neural cell adhesion molecule (NCAM), 5’- CAT CAC CTG GAG GAC TTC TAC C-3’ and 5’- CAG TGT ACT GGA TGC TCT TCA GG- 3’ (Origene). Inhibitor of DNA Binding 2 (ID2), 5’-TTGTCAGCCTGCATCACCAGAG-3’ and 5’-AGCCACACAGTGCTTTGCTGTC-3’ (Origene). Nestin, 5’-TCAAGATGTCCCTCAGCCTGGA-3’ and 5’-AAGCTGAGGGAAGTCTTGGAGC-3’ (Origene). Values were normalized to those of the PPIA reference gene: cyclophillin A (PPIA), 5’-AGA CAA GGT CCC AAA GAC-3’ and 5’ACC ACC CTG ACA CAT AAA-3’. All primer pairs were tested using standard curves with 10-fold serial dilutions, and selected only if the effiencies were within the 95-110% range over a minimum of 3 points. Three technical repeats were included for each biological replicate. The qPCR was performed in the CFX96 Real-Time PCR Detection System (Bio-Rad) using Perfecta Sybr Green Fastmix (Quanta Biosciences), and the data analyzed with CFX Manager software (Bio-Rad).
Protein was extracted using 18 mM Tris, 123mM NaCl, 10% glycerol, 1% NP40, and protease inhibitor cocktail (Calbiochem). Cell lysates were kept on ice for 5 minutes with frequent agitation, sonicated for 10 seconds, and lysates were cleared in a cold centrifuge at 10,000 rpm for 10 minutes. The supernatant was removed and used for western blotting. Protein concentration was measured using the BCA Protein Assay (Thermo Scientific). Thirty µg of protein were electrophoresed on 10% polyacrylamide gels and transferred to a nitrocellulose membrane. The membrane was blocked for one hour in 3% BSA in PBS with 0.05% Tween-20 (PBST), followed by incubation with primary antibodies: rabbit polyclonal Cx43 (Sigma) 1:2000 overnight at 4°C; mouse monoclonal Gap43 (Invitrogen) 1:500 overnight at 4°C; mouse monoclonal N-cadherin (BD) 1:2500 2 hours at room temperature; mouse monoclonal GAPDH (Millipore) 1:5000 for 2 hours at room temperature. Membranes were washed with PBST and incubated with secondary antibodies for one hour: horseradish peroxidase conjugated goat anti-mouse at a 1:5000 dilution (Thermo Scientific). Membranes were then washed with PBST and developed using enhanced chemiluminscence (Thermo Scientific). Zymed, Diatheva, Santa Cruz, and Sigma Cx36 antibodies were tested on blots with both positive and negative controls for Cx36. However, none of these antibodies were able to detect Cx36. Therefore, Cx36 expression was measured at the gene expression level. Protein expression was quantified with Quantity One software (Bio-Rad) by densitometric analysis. Protein levels were normalized to GAPDH.
Cells were plated at 20, 000 cells per well in a 48-well plate. At 0, 2, 4, and 6 days, 20 µl of MTT (Invitrogen) solution was added to each well and incubated for 4 hours. 200 µl of SDS-HCl was then added to each well and incubated for 14 hours. The absorbance was measured at 570 nm on a μQuant Biomolecular spectrophotometer (Bio-Tek). Increased absorbance indicates higher cell numbers. Four technical repeats were included in each biological replicate.
Student’s t-test was conducted for aggregate and neuritogenesis analysis between EGFP and Cx36 overexpressing cells. One-way analysis of variance (ANOVA) was conducted for the following experiments: gene expression analysis between wild-type cells, and neuritogenesis analysis between non-effective shRNA and Cx36 shRNAs. Tukey’s multiple comparison post-hoc test was used to assess differences in means between groups.
Two-way ANOVA was conducted for the following experiments: All gene and protein expression analysis and proliferation assays between transfected cells (overexpression and knockdown). Bonferroni’s post-hoc test was conducted to assess differences in means between groups. p<0.05 was considered significant for all tests. All statistical analysis was performed with GraphPad Prism 4.0 software.
SH-SY5Y human neuroblastoma cells were induced to differentiate with all-trans retinoic acid to determine whether neuronal connexins are regulated by retinoic acid. Neuritogenesis, and Gap43 and NPY expression were assessed in order to evaluate the differentiation status of cells. Neuritogenesis is the process of morphological differentiation of neuronal cells whereby the cytoskeleton reorganizes and forms neurites, which are extensions of the cell body that serve as precursors of axons and dendrites [29]. Gap43 is found at high concentrations in the growth cone during neuritogenesis [30] and consequently it is often used as marker of neuronal differentiation [25, 31]. NPY is a sympathetic peptide neurotransmitter and is also often used as a marker for differentiated neurons [31, 32].
SH-SY5Y cells were exposed to retinoic acid and BDNF treatment to induce cell differentiation. Neurite outgrowth was observed in treated cells beginning at day 2 (Figure 1A). Neuritogenesis continued through day 4, and within 10 days extensive neurite networking was observed (Figure 1A). Untreated cells began to cluster and form aggregates by day 2, and continued to form large aggregates by day 10 (Figure 1A). Thus, retinoic acid and BDNF treatment induces the morphological differentiation of SH-SY5Y cells.
In order to determine whether retinoic acid regulates the expression of neuronal connexins, the effect of the differentiation treatment on
Cx36 overexpressing cells were induced to differentiate in order to determine the effect of Cx36 on neuritogenesis. EGFP and Cx36 overexpressing cells were seeded for differentiation, and imaged at 0, 2, 4, and 10 days following differentiation treatment. EGFP expressing cells showed intense neurite networking by day 10 of differentiation treatment, while Cx36 overexpression diminished neuritogenesis (Figure 3A). There was no significant difference between EGFP and Cx36 overexpressing cells in average neurite length (Figure 3B). However, Cx36 overexpressing cells had significantly reduced neurite density compared to EGFP expressing cells (Figure 3C). Thus, our findings indicate that Cx36 downregulates retinoic acid induced neuritogenesis.
To further demonstrate that Cx36 negatively regulates neuritogenesis, the effect of Cx36 knockdown on neuritogenesis was determined. An identical experimental design as the overexpression study was conducted between non-effective shRNA and Cx36 shRNA expressing cells. In these experiments, Cx36 shRNA expressing cells showed enhanced neuritogenesis (Figure 4A). There was no significant difference in average neurite length between Cx36 shRNA expressing and non-effective shRNA expressing cells (Figure 4B). However, neurite density was significantly increased in Cx36 shRNA expressing cells compared to non-effective shRNA expressing cells (Figure 4C). Therefore, knockdown of Cx36 enhanced retinoic acid induced neuritogenesis. These findings confirm that Cx36 diminishes retinoic acid induced neuritogenesis of SH-SY5Y cells.
In order to determine whether Cx36 also negatively regulates other indicators of differentiation, the effect of Cx36 on the expression of molecular differentiation markers, Gap43 and NPY, was assessed. Transcript expression of the
The impact of Cx36 knockdown on expression of differentiation markers was assessed to confirm the effect of Cx36 on differentiation. Non-effective shRNA and Cx36 shRNA expressing cells were subjected to the 10 day differentiation treatment, and RNA was isolated at days 0, 4, and 10. Cx36 shRNA expressing cells exhibited significantly increased levels of
Cx36 knockdown enhances retinoic acid induced Gap43 expression and upregulates
An unexpected finding of our study was that Cx36 overexpression affected the adhesion and aggregation properties of cells on collagen coated dishes. Untreated EGFP and Cx36 overexpressing cells were plated on collagen coated plates for ten days. EGFP expressing cells behaved like wild-type cells, where they formed large cell aggregates over time, while Cx36 overexpressing cells formed cell clumps that were susceptible to lifting off the plate (Figure 3A). Cx36 overexpressing cells formed significantly smaller aggregates than EGFP expressing cells (Figure 3D) in addition to significantly higher numbers of cell clumps than EGFP expressing cells (Figure 3E). Expression levels of two distinct cell adhesion molecules,
In order to further explore the effect of Cx36 on cell clumping, the effect of Cx36 knockdown on aggregate formation was assessed in a comparable study between non-effective shRNA and Cx36 shRNA expressing SH-SY5Y cells. The untreated non-effective shRNA expressing cells formed large aggregates over ten days, similar to wild-type cells (Figure 4A). However, Cx36 shRNA expressing cells did not form aggregates, and maintained a dispersed phenotype (Figure 4A).
NCAM expression was reduced in Cx36 shRNA expressing cells and N-cadherin expression was only decreased in shRNA-4 expressing cells. RNA and protein were isolated from cells at day 0 and 10 of differentiation treatment, and real-time PCR was used to quantify mRNA expression and western blots to quantify protein expression. (A)
Neurons typically exit the cell cycle in order to terminally differentiate. Thus, cell proliferation was assessed in Cx36 overexpressing and knockdown cells to determine if changes in proliferation rate are a consequence of Cx36 manipulation of differentiation. The proliferation rates of EGFP and Cx36 overexpressing cells were assessed using the MTT proliferation assay. Absorbance was measured at 0, 2, 4, and 6 days in culture. At day 6, Cx36 overexpressing cells had significantly higher absorbance than EGFP expressing cells implying enhanced cell proliferation in these cells (Figure 9A).
The identical assay was performed for non-effective shRNA and Cx36 shRNA expressing cells to confirm that Cx36 stimulates cell proliferation. At day 6, Cx36 shRNA expressing cells had significantly reduced absorbance in comparison to non-effective shRNA expressing cells (Figure 9B). Therefore, our findings indicate that Cx36 promotes proliferation of SH-SY5Y neuroblastoma cells.
Since high proliferation and lack of differentiation are traits of stem cells, the effect of Cx36 overexpression and knockdown on stem cell marker expression was assessed. ID2 and Nestin mRNA expression were upregulated following Cx36 overexpression (Figure 10 A & B), but did not change upon Cx36 knockdown (Figure 10 C & D). These findings suggest that Cx36 overexpression may cause SH-SY5Y cells to adopt a stem-cell like phenotype.
Retinoic acid is often used as a therapeutic agent in the treatment of neuroblastoma to induce differentiation. In current neuroblastoma therapy, patients are given 13-
Cx36 appears to negatively regulate differentiation of SH-SY5Y human neuroblastoma cells. Accordingly, overexpression of Cx36 dimishes differentiation while knockdown of Cx36 enhances differentiation. This appears to be in contradiction with the upregulation of Cx36 upon retinoic acid induced differentiation. However, since we were unable to measure protein expression, it is possible that although
The effect of Cx36 on SH-SY5Y differentiation is in contrast with what Hartfield et al (2011) have recently shown on Cx36 and neuronal progenitor cells [37]. Progenitors isolated from the rat hippocampus and striatum and grown in neurospheres showed increased differentiation with Cx36 overexpression, and diminished differentiation with Cx36 knockdown [37]. Although neuroblastoma arises from progenitor cells, these cancer cells likely respond differently to Cx36 levels than healthy progenitors. It is also possible that neuronal cells of the peripheral nervous system react differently to Cx36 than those of the central nervous system. The effect of Cx36 on differentiation is also in contrast with other connexins, which are known to enhance differentiation [8, 9]. For example, overexpression of Cx43 and Cx32 enhanced nerve growth factor induced differentiation of PC12 cells [10]. Although connexin overexpression usually enhances differentiation, Todorova et al (2008) have shown that GJIC and Cx43 are required to maintain pluripotency of embryonic stem cells. Inhibition of GJIC or Cx43 knockdown resulted in reduced proliferation and stem cell marker expression, with a concomitant increase in differentiation marker expression [38]. Thus, in certain environments, as with our Cx36 overexpression study, connexin expression may be required to maintain the stem cell or cancer phenotype. This is further demonstrated in human breast cancer where gap junctions and GJIC are usually associated with tumor suppression however in aggressive breast cancers they may facilitate disease progression [39]. Our finding of Cx36 knockdown enhancing differentiation in fact parallels the natural expression patterns of Cx36, where it is highly expressed in many neural structures of the developing fetus but is subsequently downregulated during postnatal differentiation and maturation of the nervous system [18, 19]. This downregulation may be due in part to replacement of electrical synapses with chemical synapses [40], or it may indicate that Cx36 is actually detrimental in the process of terminal differentiation of certain tissues or cell types, as was shown in this study.
A surprising finding was that altering Cx36 expression dramatically changed the adhesion and aggregation properties of SH-SY5Y cells. Cx36 overexpression led to the formation of small cell clumps susceptible to lifting off the dish and growing in suspension. This type of growth is similar to that of neurospheres, which are suspended clusters of neural stem cells [41]. In fact, it is characteristic of the neuroblast subpopulations of SH-SY5Y cells to aggregate, lift off, and grow in suspension [42]. This neuroblastic phenotype appears to be stimulated by overexpression of Cx36, which is in accordance with our findings of Cx36 induced downregulation of differentiation. Interestingly, knockdown of Cx36 resulted in a dispersed cell phenotype.
There is a substantial linkage between connexin expression and cell growth and proliferation [6]. Our findings of increased cell proliferation with Cx36 overexpression, and diminished cell proliferation following knockdown of Cx36 are in contrast to other connexins, which typically act as tumor suppressors [6, 8]. However, in certain environments, connexins and gap junctional intercellular communication are required to maintain the proliferative ability of cells. For example, extravillous trophoblasts lost the ability to proliferate when GJIC was inhibited by a gap junction blocker or Cx40 antisense cDNA [45].
Additionally, Cx36 has been implicated in retinal cell survival following injury [46]. In light of the role of Cx36 in cell survival and its high expression during development when neural precursors are proliferating, it is not surprising to find that Cx36 serves to increase proliferation. It is also important to consider that Cx36 is regulated in a different manner from other connexins. For example, Cx36 channels show differential pH gating and experience very low sensitivity to voltage gating [47, 48]. This pH gating may be important under pathological conditions such as ischemia, where Cx36 expression has been shown to be upregulated and involved in cell survival [49]. Therefore, the differential regulation mechanisms of Cx36 channels may suggest a different role for Cx36 in key cellular processes from that of other connexins.
In relation to differentiation, as neurons do not retain a capacity to divide, cells must exit the cell cycle and stop proliferating in order to terminally differentiate into neurons. Retinoic acid also exerts part of its differentiation inducing effects by inhibiting cell proliferation [50]. Therefore, the enhanced differentiation observed in Cx36 shRNA expressing cells may be partially due to their decreased proliferative ability.
SH-SY5Y cells overexpressing Cx36 adopt several characteristics of stem cells, including aggregation, high proliferation, and lack of differentiation. To determine whether these cells were also more stem cell like at the molecular level, the expression levels of ID2 and Nestin were assessed. ID2 is expressed in neural crest cells and is also known to increase proliferation and inhibit differentiation of neuroblastoma cells [51]. Nestin is often used as a neural stem cell marker as it is highly expressed in progenitor cells during development, but is downregulated in adults [52]. The upregulation of both ID2 and Nestin upon Cx36 overexpression suggests that increased levels of Cx36 push SH-SY5Y cells towards a stem cell phenotype.
This study is the first to determine the effects of Cx36 in a cancer cell context. We have identified
However, it is unknown whether Cx36 mediates these changes in a gap junctional intercellular communication dependent manner or through interaction of Cx36 with binding partners, and thus downstream signaling pathways. Therefore, further research is required to elucidate the mechanisms underlying Cx36 mediated regulation of differentiation.
Supported by the National Sciences and Engineering Research Council of Canada to DJB.
Posttraumatic stress disorder (PTSD) is an often debilitating mental disorder that may occur following trauma exposure [1]. PTSD is characterized by four diagnostic clusters—(1) the re-experiencing the traumatic event (e.g., recurrent memories, dreams, or flashbacks); (2) symptoms of avoidance (e.g., efforts to evade trauma reminders); (3) arousal (e.g., hypervigilance, sleep disruptions); and (4) negative cognitions and mood (e.g., self-blame) [1]. Substance use disorder (SUD) is another psychiatric disorder characterized by 11 possible symptoms which involve negative consequences arising from one’s substance use and inability to control one’s substance use [1]. In the last version of the
PTSD often co-occurs with SUD. Research has documented high rates of comorbidity between PTSD and alcohol use disorder (AUD) [2], cannabis use disorder (CUD) [3, 4], and other SUDs [5]. The prognosis of comorbid PTSD-SUD is worse than either disorder alone [6] with comorbid PTSD-SUD leading to greater functional impairment in comparison to those with only PTSD or a SUD [7].
It has been suggested that PTSD and SUD are likely functionally related to one another [8] in comorbid individuals. While the precise underlying mechanisms are not well understood, there are several learning theories that may help explain the high rates of substance misuse in people with trauma histories and help us understand the high comorbidity of PTSD with SUD. The first is the two-factor learning theory which was originally developed by Mowrer to explain the acquisition and maintenance of phobias [9] and which has more recently been applied by Stasiewicz [10] to the acquisition and maintenance of SUDs. Two-factor learning theory applies a combination of classical conditioning and operant conditioning mechanisms to the development and maintenance phases of these disorders, respectively. Applying this theory to the co-occurrence of PTSD and SUDs in traumatized individuals, trauma-relevant cues that were paired with the original traumatic experience (e.g., loud noises of gunfire paired with witnessing a comrade fatally injured in wartime) are thought to come to elicit negative affect through the process of classical conditioning [10]. Future exposures to the trauma cue alone (e.g., loud noises alone) motivate avoidance/escape behavior, including substance misuse, to reduce the associated negative affect and thereby experience relief [10]. Avoidance/escape behaviors like substance misuse are thus negatively reinforced in individuals with trauma histories/PTSD as they remove the aversive experience of negative affect. Therefore, substance misuse is maintained as a self-medication type of coping response through operant conditioning processes where behavior is repeated when it is followed by desirable consequences, in this case, relief from negative affect.
Another theory that is relevant to understanding the links of trauma/PTSD with SUD involves the role of classical conditioning in the development of conditioned craving—a strong urge to use the substance in response to exposure to the conditioned cues. It has long been known that drug-related stimuli that are frequently paired with drug-taking can come to elicit a conditioned craving response through the process of classical conditioning [11]. For example, a needle and other drug use paraphernalia that are frequently paired with heroin use can come to elicit craving when presented alone, for an injection drug user. Similarly, for a substance user with a trauma history/PTSD, the frequent pairing of trauma cues (e.g., intrusive memories of the trauma, exposure to external trauma reminders) with substance use, as explained by the two-factor learning theory above [10], can come to create strong associations between trauma cues and substance use [12]. The result is that such trauma cues can become conditioned stimuli that elicit a conditioned craving response when presented on their own [13]. For example, if a young woman with sexual assault-related PTSD drinks alcohol each time she has an intrusive memory about her sexual assault, such trauma cues can come to elicit a strong craving for a drink, which may motivate her alcohol seeking and maintain her alcohol use.
The study of the above putative mechanisms under controlled, laboratory conditions is crucial for a better understanding of the intertwined relationships between trauma/PTSD and substance misuse. Specifically, the use of cue-reactivity paradigms allows researchers to examine how substance-related and trauma cues may come to elicit craving and/or negative affective responses through the conditioning processes described above.
The cue-reactivity paradigm is broadly defined as a lab-based method in which participants are exposed to a set of stimuli meant to elicit a “reactivity” response—that is, a change from baseline in response to the stimulus [14]. In the context of addictions research, stimuli may be substance-related cues, such as a syringe or other drug-related paraphernalia for an injection drug user [15]; these stimuli serve as analogs for real-life stimuli which may evoke a craving response outside of the lab. Indeed, research in this area has shown that relevant drug-related cues presented in the lab can elicit a heightened craving response among substance users [16, 17]. More recently, cue-reactivity paradigms have been used to study conditioned craving as a possible mechanism underlying the relationship between trauma/PTSD and SUD [18, 19]. Indeed, extant research has shown that in-lab exposure to cues representing trauma reminders (e.g., a video of a violent crime) activates both substance-related craving responses as well as increased negative affect [20].
Craving has been measured in a number of ways in substance- and trauma-related cue-reactivity research, including with subjective self-report measures, such as the Desire for Drug Questionnaire [21], and measures specific to the substances used, such as the Alcohol Urge Questionnaire [22] and the Marijuana Craving Questionnaire [23]. Craving has also been measured more objectively in cue-reactivity studies, albeit less commonly than via self-report. Specifically, physiological measures, such as salivary flow and heart rate monitoring, are often used as a more objective proxy measure of craving [24]. Craving has also been further differentiated into reward-related craving (i.e., a desire for reward or stimulation from a substance) and relief-related craving (i.e., a desire for a reduction in tension or negative affect from using a substance) using certain self-report measures [23].
While cue exposure paradigms are homogenous in their goal to elicit some form of reactivity (e.g., change from baseline in craving or emotional state in response to the stimulus), the types of cues and paradigms used in this area of research have varied widely. For example, cues may be standardized across participants in the study or may be personalized to the individual’s own trauma history details; cues may be presented through the use of script-driven imagery (i.e., audio recordings, such as a retelling of a traumatic event) or
Indeed, it is evident that cue-reactivity paradigms vary widely in design, are used in an expansive variety of contexts and with a wide range of populations, with many different outcomes used to capture cue-reactivity effects. Thus, in this chapter, we intend to scope the extant cue-reactivity literature in the context of PTSD-SUD comorbidity research to identify patterns and variations in methodology, measures, and outcomes used in this growing field.
Our first aim was to examine how cue-reactivity paradigms have been used in samples of substance users with trauma histories. Specifically, we were interested in how these studies lead to a further understanding of the mechanisms underlying comorbid PTSD-SUD. Second, we intended to examine the different types of cues used within the cue-reactivity paradigm as well as the specific effects, strengths, and weaknesses of variations in paradigm design. Specifically, we compared the merit of personalized vs. non-personalized cues, as well as other cue variations, in PTSD-SUD cue-reactivity research (e.g.,
The present scoping review followed preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines appropriate for a scoping review. Specifically, we used the PRISMA Scoping Review checklist [25].
Studies were included if they used an experimental design if they utilized a cue-reactivity paradigm and if self-reported craving was assessed following the cue-reactivity paradigm. Furthermore, the population of interest had to include individuals who had experienced a traumatic event consistent with Criterion A of a DSM-5 PTSD diagnosis [1]. Alternatively, PTSD symptoms must have been assessed for each participant. Additionally, it was required that participants report on their substance use.
We excluded studies that were not written in English, or if humans were not the research participants. We did not exclude gray literature. Specifically, we included theses and dissertations to gather the full scope of research in this area and to reduce publication bias.
The databases PsycInfo, PubMed, and PTSDPubs were searched to identify studies of interest. Each search was conducted using a Boolean search logic and relevant keywords: (“PTSD” OR “post traumatic stress disorder” OR “posttraumatic stress disorder” OR “post-traumatic stress disorder” OR “trauma”) AND (“cue” OR “cue exposure” OR “cue-reactivity” OR “conditioned response” OR “stimuli”) AND (“substance” OR “substances” OR “alcohol” OR “drug” OR “drugs” OR “cocaine” OR “cannabis” OR “marijuana” OR “opioids” OR “opiates” OR “tobacco” OR “nicotine”) AND (“craving” OR “urge”). There were no search restrictions based on year of publication or language.1
One hundred fifty-eight studies were initially imported into Covidence, a literature screening software. After duplicates were removed by Covidence, 128 studies remained. Abstracts of all studies were screened by two independent raters (SDG and CS) who removed all irrelevant studies; a moderate rate of agreement of 74% was achieved [26]. A third screener (PRS) aided in resolving any conflicts between the two raters. A total of 28 studies met our final inclusion criteria (Figure 1).
PRISMA flowchart of literature search and screening.
The data were extracted into a spreadsheet, including information on the study sample, sample characteristics, outcome measures, cue-reactivity methodology, hypotheses/aims, outcomes of interest, and general findings. A quality assessment and risk of bias assessment were not conducted, as these are not typical in scoping reviews [27]. The extracted data were then synthesized into common categories by the first author to further examine themes in the scoped research.
Script-driven imagery cues were the most common cue paradigm used in the present sample of studies (n = 20). These were often paired with a substance-related
All studies used subjective self-report measures of craving as a measure of reactivity (n = 28); this was an inclusion criterion for this scoping review. However, many did include objective craving measures in addition to subjective measures (i.e., salivation, heart rate; n = 9). Other reactivity measures assessed included affect (n = 14), subjective stress (n = 6), objective stress (i.e., cortisol; n = 3), attentional/memory tasks (n = 3), and neural activation (n = 3).
Types of substances used/misused by participants in the study were alcohol (n = 17), cocaine (n = 6), nicotine (n = 3), heroin (n = 1), opioids (n = 1), and any substance (n = 4). It is important to note that some studies (n = 4) allowed for combinations of specific drugs (i.e., individuals who use alcohol and/or cocaine were recruited for one study).
Studies identified in the present scoping review employed the use of several types of cues. Specifically, neutral cues (n = 24; e.g., brushing your teeth), trauma cues (n = 23; e.g., a physical assault), substance cues (n = 14; i.e., cannabis paraphernalia), stress cues (n = 5; a presentation at work), and social cues (n = 1; speaking with a friend; [33]) were used. The average number of cue types used per study was 2.36 (SD = .731).
The majority of studies utilized pre-cue baseline as a comparator for their measures of reactivity (n = 22); a minority only compared reactivity data across cue types (i.e., comparing neutral vs. trauma responses; n = 6). However, many studies used a combination of comparators by comparing to baseline data and across cue types as well (n = 12).
While we have summarized the key components of included studies here, a full summary of each study across the coded variables of interest is available in Appendix A.
Populations of interest were largely adults who were assessed for PTSD symptoms/diagnoses and/or trauma history, and substance use. Participants across studies were more often male (
All studies included participants with either PTSD (n = 14) or those who had been exposed to a lifetime trauma (n = 10), or both with PTSD and/or trauma histories assessed continuously (n = 4). PTSD was assessed but not required for some studies, with others requiring trauma exposure but not a PTSD diagnosis (see [44, 45]). To assess for PTSD, most studies used some form of a validated structured interview (n = 25), such as the MINI [46], the SCID-5-RV [47], and the Clinician-Administered PTSD Scale [48]. Those studies examining trauma-exposed individuals typically administered a questionnaire to assess trauma history (n = 3), such as the Trauma History Questionnaire [49] or the Life Events Checklist [50], as well as continuous measures of PTSD symptoms, such as the PTSD Checklist for DSM-5 [51].
Substance use among the study populations was similarly measured. Specifically, the majority of studies (n = 18) required an SUD as inclusion criteria [18, 52], with some using inpatients receiving treatment for PTSD, SUD, or both (n = 12; [32, 53]). Fewer studies required less extreme forms of substance use, such as occasional drinking (n = 6) (see [13, 54]) and other cut-off points for use of various substances (n = 3; [55]). To assess for the presence of a SUD, most studies (n = 18) used structured interviews, such as the C-DIS IV [56] or the SCID-5-RV [47], but others used shorter self-report measures, such as the Alcohol Use Disorders Identification Test (AUDIT; n = 10) [57].
Many of the studies employed personalized cues within their cue-reactivity paradigms, either through interviews where they obtained information about a participant’s worst traumatic experience and transcribed the interview into an imagery-based cue [58] or utilized the participants’ preferred substance as part of an
Studies that utilized photographic cues as part of task-based cue paradigms found to support that their paradigms functioned as effective cue-reactivity paradigms, even though craving was not the primary outcome of interest. For example, Garland and colleagues [28] showed participants trauma-related images and asked them to either simply view the photos or reappraise the photos by reinterpreting the photo’s meaning to regulate their emotions in reaction to the photo. Following this task, relief craving increased; this increase was associated with the number of adverse childhood experiences to which participants reported having been exposed. Similarly, Beckham et al. [30] utilized a Stroop color-naming attentional [31] task with trauma-related words with a veteran sample of cigarette smokers. Results demonstrated trauma words, relative to neutral words, led to greater cigarette craving as well as more withdrawal symptoms.
One of our inclusion criteria was the measurement of craving following a cue-reactivity paradigm. Accordingly, all studies included a measure of craving, with all studies including a measure of subjective craving. Many studies measured craving using a Visual Analog Scale (VAS) or various Likert-type rating scales. Among those who examined craving changes from baseline by cue type, subjective craving responses were highest following trauma-related cues compared to substance, stress, and/or neutral cues (n = 9). Studies that did not use trauma cues found substance-related cues elicited greater craving compared to neutral cues (n = 3). In those studies that used trauma cues, substance cues, and neutral cues (n = 9), typically trauma cues elicited the greatest craving, followed by substance cues, and then neutral cues. Interestingly, studies, where trauma imagery cues were paired with
While our inclusion criteria did not specifically require an objective assessment of craving, the frequent use of salivation, heart rate, and other measures of physiological reactivity warrants a brief summary of this work. Most studies that included physiological/objective craving measures did so by measuring salivary flow (n = 5). Coffey et al. [18] found a significant increase in salivation following trauma and alcohol cues relative to neutral cues. Nosen et al. [59] found an increase in salivation following alcohol
Seven studies examined outcomes of pharmacological or psychotherapeutic treatment in clinical populations, utilizing cue reactivity as a secondary outcome measure or adjunct to symptom measures. Two studies examined the effectiveness of pharmaceuticals as a treatment for comorbid PTSD-SUD. Specifically, in a pre-clinical lab-based study, Stauffer et al. [35] examined the use of intranasal oxytocin (20 IU and 40 IU) vs. placebo in males with comorbid PTSD-AUD. Each participant took part in each condition across three counterbalanced sessions. Following drug or placebo administration, participants were exposed to
Five studies examined the effects of several psychotherapeutic interventions on cue-elicited craving as well as distress, PTSD symptoms, and resilience. Coffey and colleagues [18] examined the effects of trauma-based imaginal exposure vs. relaxation using a cue-reactivity paradigm to assess trauma cue-reactivity (i.e., craving), showing a decrease in craving to the trauma-alcohol cue combination only among those enrolled in prolonged exposure (PE) therapy but not among those in the relaxation condition. However, craving following the trauma-only cue decreased relative to baseline among both intervention groups. Similarly, two studies ([53, 61]) assessed the merits of PE therapy in comparison to a health/lifestyle therapy using a craving to a cue-reactivity paradigm as an outcome measure; one study [53] found both healthy lifestyle (control) and trauma cue-exposure treatments led to a decrease in craving responses to trauma imagery and
Three studies combined fMRI and a cue-reactivity paradigm. One study [34] examined neural activation during the presentation of stress, neutral, and substance-related cues among cocaine-dependent individuals with and without childhood maltreatment histories. The degree of craving to the stress cues predicted activation of the rostral anterior cingulate cortex to a lesser extent in the participants with maltreatment histories. The authors interpreted this to suggest that childhood maltreatment interferes with a key mechanism for resolving conflict and responding adaptively to stress [34]. Conversely, the degree of craving to the substance-related cues was associated with activation of the supplemental motor area and the visual cortex to a greater extent in those with maltreatment histories. The authors interpreted this latter finding to suggest that childhood maltreatment enhances the anticipatory reward response to substance cue exposure [34]. Further, during substance cue presentation, another study [35] found childhood trauma histories among substance users were significantly associated with increased activation of the frontal striatal circuit and the amygdala. However, a third study [32] did not find any psychological correlates of neural activation during the presentation of substance-related vs. neutral stimuli in a sample of adults with comorbid PTSD-AUD. It is difficult to know if this failure to observe an effect of cue exposure on neural activation was due to an ineffective manipulation since craving responses were not measured.
Fourteen studies included a measure of effect as part of their evaluation of cue-reactivity. Eleven of such studies examined both positive and negative affect, and three examined negative affects only. The Positive and Negative Affect Schedule or PANAS [63] was overwhelmingly used as the standardized measure of this variable (n = 10), although other measures were used as well, such as the Affect Grid [64] (n = 2). Among the majority of studies (n = 9), negative affect increased following stress and trauma-related cues [38]. In those studies which also examined positive affect, positive affect tended to decrease following stress and trauma-related cues [42, 33] but this was not always consistent. For example, Coffey et al. [39] did not find any statistically significant differences in positive affect across cue types. Interestingly, one study reported that substance-related cue exposure increased both positive and negative affect, and this ambivalent response was associated with the strongest substance cravings [55].
The primary aim of this scoping review was to map the use of cue-reactivity paradigms in PTSD-SUD research among substance users with trauma histories and/or PTSD. Specifically, we sought to summarize the characteristics of the samples, examine outcomes measured followed the cue-reactivity paradigm (e.g., subjective/objective craving, negative affect), and map how such paradigms vary across the literature on several dimensions (e.g., cue type, personalization/standardization, cue presentation). Furthermore, we aimed to assess the consequences of methodological differences in cue-reactivity research. While prior literature has summarized cue-reactivity methodology in substance use research [65] and one group has briefly summarized cue-reactivity research in a comorbid PTSD-AUD population as part of a broader review of mechanisms involved in this form of comorbidity [66], we aimed to map the use of cue-reactivity paradigms in a way which could lead to further understanding of conditioned craving as a mechanism in the maintenance of comorbid PTSD-SUD. Specifically, our systematic scoping of the literature identified 28 studies that assessed craving following a cue-reactivity paradigm in a population of substance users with trauma histories and/or PTSD.
Our scoping review revealed wide variations in methodologies used to examine cue-induced craving. Specifically, characteristics of study samples, the methodological details of the cue-reactivity paradigm, and the types of outcomes assessed, all varied broadly. We have identified four themes in the studies through our scoping of the literature that may help elucidate commonalities and important distinctions across the identified studies—(1) increases in craving following trauma cue presentation; (2) the use of methodological subtypes of cue-reactivity paradigms; (3) affect as an outcome and possible mediator of craving in cue-reactivity research; and (4) the cue-reactivity paradigm as an adjunct outcome measure in intervention research.
From the above literature review, it is evident that craving has been repeatedly shown to increase following exposure to certain cues in substance users with trauma histories and/or PTSD. In particular, trauma cues tend to elicit the greatest increase from baseline in craving responses when compared to substance-related and neutral cues. This was true across studies using both personalized [43] and standardized cues [54]. However, this effect was typically magnified when a combination of trauma-related imagery and
Second, the cue-reactivity methodologies used in the studies identified through our scoping review tended to vary widely. While the majority of studies utilized imagery-based audio cues to elicit cue-reactivity craving responses, some used combinations of imagery-based trauma and substance-related
Third, while we did not systematically aim to include effect as an outcome in the present scoping review, we decided to cover this outcome as many of the studies included in the review (50%) did include a measure of effect as an additional outcome alongside craving. Our findings elucidated the importance of effect in helping explain the relationship between trauma cue-reactivity and craving. To elaborate, negative affect has quite consistently been shown to increase following trauma cue exposure [44, 59]. This is consistent with suggestions that conditioned
Finally, it is important to note that seven studies utilized cue-reactivity paradigms as an additional outcome in trauma and/or substance-related therapeutic interventions. Notably, neither of the two pharmacological studies found an effect of the respective medications (oxytocin and neurokinin-1 receptor antagonist aprepitant) relative to placebo as a means of reducing either PTSD symptoms or stress cue- or substance cue-induced craving (see [32, 35]). Conversely, all studies examining the efficacy of PE therapy for PTSD or PTSD-SUD found that trauma cue-induced craving, as well as other forms of cue-reactivity (e.g., salivation, distress), decreased over time in those who received PE when compared to patients who received a control intervention [36, 37, 39, 53, 61]. Indeed, behavioral interventions seem to be more efficacious than pharmacological interventions in reducing reactivity to both trauma and substance-related cues among trauma-exposed substance users, at least for the few pharmacotherapies that have been investigated with this paradigm thus far, and at least in comparison to PE therapy. Furthermore, the use of cue-reactivity paradigms as a secondary outcome in randomized controlled trials of therapeutic interventions speaks to the multifaceted functionality of the cue-reactivity paradigm in the PTSD/trauma-exposed population, offering a mechanism-based outcome that informs beyond the decrease of symptoms.
First, it is important to note that no formal examination of the study quality of the included literature was completed, as this step is not typical for scoping reviews [27]. It should also be noted that our choice to include unpublished theses and dissertations in the present review may have led to the inclusion of some studies with poor methodological quality, although it does help ensure that our conclusions are not hampered by publication bias.
To further assess the studies included in the present scoping review, we recommend a formal analysis of methodological quality be completed in the future to better understand how methodological variations in cue-reactivity may influence relevant outcomes. Additionally, the use of cue-reactivity paradigms as secondary outcomes in the context of behavioral and pharmacological intervention trials is an interesting research direction that should be studied further, as this may lead to important implications for understanding the breadth of response to the use of psychotherapeutic or pharmacological interventions in this population, and may point to potential mechanisms of action. We also recommend that a formal systematic review and meta-analysis be conducted to quantify the magnitude of trauma cue-induced craving responses in this population, and to identify significant moderators of this response in terms of sample characteristics (e.g., percentage of the sample with PTSD), and methodological variables (e.g., personalized vs. standardized cues). Providing that relevant data could be obtained from published papers or authors, novel techniques, such as two-step meta-analytic structural equation modeling (TS-MASEM; [68]) could also be employed to examine theorized mediational pathways (e.g., that trauma cue exposure leads to activation of negative affect which in turn activates craving). Finally, meta-analyses could also quantify the degree of reduction in trauma cue-induced craving that is achieved with various forms of treatment for PTSD, SUD, and their comorbidity, and its relations to treatment efficacy (i.e., symptom reduction).
Our scoping review maps the use of cue-reactivity paradigms across the trauma-exposed, substance-using population with and without PTSD, and summarizes methodological variations in cue-reactivity paradigms across this body of literature, as well as the results of identified studies. Cue-reactivity paradigms have proven efficacious in eliciting cue-induced craving responses in populations of trauma-exposed individuals who use substances. Cue-reactivity research may help increase understanding of the learning processes that are involved in the development, maintenance, or exacerbation of a SUD among trauma-exposed individuals with and without PTSD who use substances. Furthermore, cue-reactivity paradigms may be used as an important means of assessing whether behavioral and/or pharmacological treatments for PTSD and/or SUD have had an impact on the ability of trauma cues to elicit a conditioned craving response in this population.
First author (year) | Sample characteristics and context | Cue reactivity paradigm and method | Outcome(s) of interest | Craving measure | Relevant findings |
---|---|---|---|---|---|
Elton et al. [34] | 38 cocaine-dependent males with (n = 20) and without (n = 18) childhood maltreatment histories. | Script-driven imagery. All participants listened to a personalized neutral, stress, and cocaine-related audio cue whilst in an fMRI scanner. | Brain region activation, anxiety, and subjective craving response. | Cue-induced cocaine craving was measured using the visual analog scale (VAS) from 0 to 10. | Stress-Neutral: The interaction between maltreatment severity and craving responses was associated with activation of the left premotor cortex and right cerebellum. Substance-Neutral: The interaction between maltreatment severity and craving responses was associated with activation of the bilateral occipital cortex, caudal pre-supplementary motor area [SMA], and cuneus. Findings suggest that childhood maltreatment alters neural correlates of cue-induced substance craving. |
Dutton [33] | 46 hazardous drinkers who met DSM-5 criterion A (trauma exposure) of a PTSD diagnosis | Script-driven imagery. Participants listened to a personalized neutral cue followed by either a neutral-social (n = 24) or a social conflict cue (n = 22). Each cue was 1 minute long followed by a 30 second visualization period. | State PTSD symptoms, subjective craving response, affect, and alcohol approach bias. | Cue-induced alcohol craving was measured using a VAS from 0 to 100. | Following the social conflict cue but not the neutral social cue, state PTSD symptoms increased. There were no differences in alcohol approach bias, affect, or craving between cues. |
Trautmann et al. [54] | 95 healthy occasional drinkers who had experienced childhood trauma. | Standardized video. Participants watched either a 15-minute trauma film (n = 47) or a 15-minute neutral film (n = 48). | Subjective craving response, anxiety, and physiological reactivity (i.e., skin conductance, heart rate, and saliva cortisol levels) | Cue-induced alcohol craving was measured using the Alcohol Craving Questionnaire-Short Form [69]. | In females, the trauma film elicited greater craving responses compared to the neutral film. In males, the number of childhood traumas positively moderated the relationship between film condition and craving responses. In males, childhood trauma was associated with increases in skin conductance, heart rate, and cortisol levels; only skin conductance was related to craving responses. |
Stauffer et al. [35]* | 47 males with comorbid PTSD-AUD and 37 healthy control males. | Effects of oxytocin as a treatment for comorbid PTSD-AUD, subjective craving responses, and heart rate variability. | Cue-induced alcohol craving was measured using a VAS from 0 to 100. | Craving responses and heart rate were higher following the alcohol cues compared to neutral cues. No effects of oxytocin compared to placebo on cue-induced craving or heart rate. | |
Ralevski et al. [38]* | 25 veterans with comorbid PTSD-AUD. | Script-driven imagery. All participants listened to personalized trauma, stress, and neutral audio cues. | Subjective craving responses, blood pressure, heart rate, negative affect, and salivary cortisol. | Cue-induced alcohol craving was measured using the Alcohol Craving Questionnaire-Short Form [69] and a VAS. | Craving responses, cardiovascular reactivity, and negative affect were highest following the trauma cue but were also high following the stress cue, both compared to the neutral cue. |
Winokur [60] | 95 individuals with (n = 31) and without (n = 39) trauma histories who were heroin (n = 25) or nicotine (n = 70) dependent. | Standardized video. Participants watched standardized video cues related to either heroin or nicotine use, and a neutral video cue. | Subjective craving responses. | Cue-induced heroin or nicotine craving was measured using a Within Sessions Rating Scale (0–9). | Post substance cue-craving responses increased among both the opiate and nicotine-dependent groups, but were highest in the opiate-dependent group, and only among those with trauma histories. |
Coffey et al. [39]* | 43 SUD inpatients with comorbid PTSD-AUD. 75% of participants who completed at least one clinical session were randomly assigned to receive six sessions of either imaginal exposure therapy (n = 12) or relaxation (control) condition (n = 12). However, only 17 participants completed the study. | Script-driven imagery and Trial 1: All participants listened to personalized neutral and trauma cues. Trial 2: All participants listened to a personalized trauma cue followed by the presentation of either alcohol or water. | Subjective craving responses, affect, and emotional distress. | Cue-induced alcohol craving was measured using a VAS from 0 to 10. | Craving responses decreased from pre- to post-treatment among those in the imaginal exposure condition following the trauma-alcohol cue (trial 2) and did not change in the relaxation condition. Craving responses also decreased in both groups following the trauma cue (trial 1). Negative affect was highest in trial 2. |
Read et al. [13] | 232 undergraduate students with PTSD (n = 28), with trauma exposure but no PTSD (n = 113), or no trauma history (n = 91) taking part in a clinical trial. | Script-driven imagery. Participants listened to either a personalized trauma (n = 111) or neutral cue (n = 121). Participants wrote about the event while continuing to imagine the scene. | Subjective craving, affect, and performance on a Stroop attentional task with trauma and alcohol-specific stimuli. | Cue-induced alcohol craving was measured using a 10-point Likert scale rating urge to drink. | Participants with PTSD in the trauma cue condition showed a slowed response in the Stroop task. This effect was associated with an urge to drink only among those with PTSD in the trauma cue condition. |
Kaag et al. [29] | 117 adults, half cocaine users (n = 59) and half healthy controls (n = 58) | Event-related cue-reactivity paradigm. All participants viewed substance-related photos, neutral photos, and photos of animals. They were instructed to press a button when photos of animals were presented. | Subjective craving and neural activation. | Cue-induced cocaine craving was measured using the Desire for Drug Questionnaire [21] at baseline and following the cue-reactivity paradigm. | Only among substance users, the presentation of cocaine cues led to neural activation in the frontal striatal circuit and the amygdala. Amygdala-striatal connectivity was associated with childhood trauma among substance users. |
Coffey et al. [58] | 75 individuals receiving SUD treatment with PTSD who were cocaine (n = 30) or alcohol-dependent (n = 45) | Script-driven imagery and | Subjective craving. | Cue-induced craving was measured using the Cocaine Craving Questionnaire-Now (CCQ-Now) [70] and Alcohol Craving Questionnaire-Now (ACQ-Now) [71] | Both alcohol-dependent and cocaine-dependent participants evidenced greater cravings following the trauma- and substance-related cues compared to the neutral cues. |
McHugh et al. [44] | 194 individuals with PTSD receiving treatment for a comorbid SUD. | Script-driven imagery. All participants listened to a personalized trauma and neutral cue, counterbalanced across two sessions, followed by a 1-minute visualization period. | Subjective craving and affect. | Cue-induced substance craving was measured on an 11-point scale. Ratings ranged from 0 (no cravings) to 11 (very strong cravings). | Craving and negative emotional reactivity were greater following the trauma cue compared to the neutral cue. Anxiety sensitivity was associated with greater emotional reactivity following the trauma cue, but there was no association between anxiety sensitivity and craving response. |
McGuire et al. [36] | 29 veterans receiving treatment for comorbid PTSD-SUD. | Interview. All participants provided a detailed verbal description of their most traumatic lifetime event. | Subjective craving, resilience, and PTSD symptoms. | Cue-induced craving for alcohol and/or other substances was measured using the Alcohol Craving Questionnaire Short Form-Revised [71] | Posttreatment, participants evidenced a decrease in cue-induced craving and fewer PTSD symptoms, as well as increased resiliency, relative to pre-treatment baseline. |
Saladin et al. [45] | 124 individuals with trauma histories receiving SUD treatment who were alcohol- (n = 70) or cocaine-dependent (n = 54). | Script-driven imagery and | Subjective craving. | Cue-induced substance craving was measured using a 21-point VAS. | Craving was greater following the trauma- and substance-related cues in comparison to the neutral cues. PTSD symptom severity predicted greater craving responses, but only following the trauma + substance cue pairing. |
Coffey et al. [18] | 40 individuals with comorbid PTSD-AUD receiving inpatient SUD treatment. | Script-driven imagery and | Subjective and objective craving responses; emotional distress. | Cue-induced craving was measured using a VAS from 0 to 10 and salivary flow. | Subjective craving responses, distress, and salivary flow were greater following substance and trauma cues compared to the neutral cue. |
Vujanovic et al. [43] | 58 low-income inner-city adults. | Script-driven imagery. All participants listened to personalized trauma, substance, and neutral audio cues. | Subjective craving responses. | Cue-induced craving was measured using a VAS from 0 to 100. | Lower distress tolerance was a significant predictor of higher craving responses following the trauma cue. |
Rodriguez et al. [40] | 305 undergraduate students with no trauma (n = 127), trauma exposure (n = 106), and PTSD (n = 72). | Script-driven imagery. Participants were instructed to close their eyes and imagine their most traumatic event as if it was happening to them. Participants then wrote about the scene while continuing to imagine the scene. | Subjective craving responses and affect. | Cue-induced craving was measured using the Urge to Drink Questionnaire [22], on a scale from 1 to 10. | Emotional responses to the trauma cue mediated the relationship between trauma exposure and the urge to drink. |
Bing-Canar et al. [41] | 184 young adults with trauma histories | Script-driven imagery and | Subjective and objective craving responses. | Cue-induced craving was measured using a three-item Alcohol Craving Scale [72] and salivation levels. | Depressive symptoms did not have any effect or interaction with the cue-reactivity paradigm to predict increased craving or salivation. |
Zambrano-Vazquez et al. [61] | 85 individuals with comorbid PTSD-SUD and current alcohol dependence receiving SUD treatment. Only 66 participants who completed 8 or more prolonged exposure treatment sessions were included in the analyses. | Script-driven imagery and | Subjective and objective (salivation) craving, subjective distress, and domains of functioning. | Cue-induced craving was measured using the Alcohol Craving Questionnaire-Now [69] and salivation levels. | Severity in all domains of functional impairment (Negative Valence, Arousal, and Cognitive) decreased from pre to post-treatment, and this change was associated with a decrease from pre-treatment baseline in self-reported craving and salivation post-treatment following alcohol and trauma cue exposure. |
Garland et al. [28]* | 36 opioid-treated chronic pain patients at risk for opioid use disorder, with adverse childhood experiences (ACEs). | Emotional Regulation Task. Participants were shown trauma-related images and were asked to both views or reappraise the images (dependent on the trial block) to regulate the emotions elicited by the image. | Subjective craving, heart rate variability, and negative affect. | Cue-induced opioid craving was measured using a 5-point scale, with 1 indicating no craving and 5 indicating very strong cravings. | Following the emotional regulation task, craving increased from the pre-task baseline. This change was related to the number of ACE exposures. ACEs and duration of opioid use also predicted a blunted heart rate variability when regulating negative emotions. |
Zaso et al. [42] | 611 college students with PTSD (n = 50), with trauma exposure but no PTSD (n = 325), and no trauma (n = 236) who drink alcohol | Script-driven imagery. Participants were randomized to listen to either a personalized trauma or neutral cue followed by a 2-minute writing period relating to the cues. | Subjective craving response and affect. | Cue-induced craving was measured using a 10-point scale, with 1 indicating no urge to drink and 10 indicating a very strong urge to drink. | Following the trauma cue, but not the neutral cue, participants reported greater cravings and negative affect relative to baseline, which was associated with coping drinking motives. |
Kwako et al. [32]* | 53 individuals with comorbid PTSD-AUD receiving inpatient SUD treatment. Participants received either aprepitant (n = 26) or a placebo (n = 27) prior to cue exposure. | Script-driven imagery, | Subjective craving, blood cortisol, and neural activation. | Cue-induced alcohol craving was measured using the Alcohol Urge Questionnaire [22] | Alcohol and stress cues induced more cravings compared to neutral cues. There was no significant neural activation following the substance-related relative to the neutral stimuli. |
Nosen et al. [59] | 108 adults with comorbid PTSD-AUD who were receiving residential treatment for SUD. | Script-driven imagery and | Subjective and objective (salivation) craving and affect. | Cue-induced alcohol craving was measured using a three-item alcohol craving scale [72] and salivation levels. | Trauma and substance cue pairings elicited the greatest subjective craving responses, negative affect, and salivation vs. all other cue combinations. Ambivalent affective responses predicted the strongest craving. |
Tull et al. [19] | 60 cocaine-dependent individuals with (n = 30) and without PTSD (n = 30) in treatment for a SUD | Script-driven imagery. Across two sessions, all participants listened to a personalized cue (trauma or neutral; 1 min) followed by a visualization period (1 min). | Subjective craving response and affect. | Cue-induced cocaine craving was measured using an 11-point scale, with 0 indicating no cravings and 10 indicating very strong cravings. | PTSD was associated with greater craving and negative affect following the trauma cue, but not the neutral cue. Among men, this relationship was mediated by self-conscious emotions. |
Nosen et al. [53] | 120 individuals with comorbid PTSD-AUD in treatment for a SUD. Participants were assigned to receive exposure therapy (n = 52) or health and lifestyle treatment (n = 35); only those who completed treatment (n = 87) were included in analyses. | Script-driven imagery and | Subjective and objective (salivation) craving response, distress, and affect. | Cue-induced craving was measured using a three-item alcohol craving scale [72] and salivation levels. | Pre-treatment, the trauma + substance cue-elicited the strongest craving responses, negative affect, and distress. Post-treatment, trauma cues no longer elicited greater craving compared to substance cues alone. Both treatments led to a decrease in salivation and subjective craving following cue exposure. |
Badour et al. [37]* | 54 veterans with comorbid PTSD-SUD taking part in a COPE RCT. | Participants were presented with personalized | Subjective craving and distress. | Cue-induced craving for participants’ preferred substance was measured using a VAS (0–100). | Between-session reduction of substance cue-induced craving and distress responses were associated with a decrease in PTSD symptom severity. |
Tull et al. [52] | 133 individuals with trauma histories in treatment for a SUD. | Script-driven imagery. Participants listened to a personalized trauma cue (1 min) followed by a visualization period (1 min). | Subjective craving, emotional regulation, negative affect, and salivary cortisol. | Cue-induced craving for participants’ preferred substance was measured using an 11-point scale, with 0 indicating no cravings and 11 indicating very strong cravings. | Following the trauma cue, craving increased relative to the pre-cue baseline. This change was associated with greater PTSD symptom severity. PTSD symptom severity was related to both adaptive and maladaptive emotional regulation strategies. |
Beckham et al. [55] | 129 smokers with (n = 82) and without PTSD (n = 47) were randomly assigned to either a nicotine or a non-nicotine smoking condition. | Script-driven imagery. Participants listened to either a personalized trauma, neutral, or stress cue (30 sec) followed by a visualization period (30 sec) both before and after smoking a nicotine or denicotinized cigarette. | Subjective craving and affect. | Cue-induced craving to smoke was measured using the Questionnaire on Smoking Urges [73]. | Trauma-related cues produced greater cravings and negative affect compared to stress scripts and neutral scripts. This effect was most pronounced among those with PTSD. Smoking either the nicotine or non-nicotine cigarettes reduced craving, negative affect, and PTSD symptoms following the trauma and stress script relative to the neutral script. |
Beckham et al. [30] | 25 veterans receiving PTSD treatment who smoke cigarettes. | Stroop task with combat/trauma-related words. Participants named the ink color of three blocks of trauma-related and three blocks of neutral words. | Subjective craving, affect somatic symptoms, and alertness. | Cue-induced craving to smoke was measured using a modified Smoking Withdrawal Questionnaire Short Form-Revised [71] | Craving, negative affect, somatic symptoms, and lack of alertness were all greater following the presentation of trauma-related words compared to neutral words. |
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Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:null,institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:120,paginationItems:[{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:null},{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:"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:"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:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{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:null},{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. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. Anderson",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}},{id:"351159",title:"BSc.",name:"Kalum J.",middleName:null,surname:"Ost",slug:"kalum-j.-ost",fullName:"Kalum J. Ost",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calgary",country:{name:"Canada"}}},{id:"325029",title:"Dr.",name:"Prem Chand",middleName:null,surname:"Jain",slug:"prem-chand-jain",fullName:"Prem Chand Jain",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Shiv Nadar University",country:{name:"India"}}},{id:"357275",title:"Dr.",name:"Thomas",middleName:null,surname:"Mih",slug:"thomas-mih",fullName:"Thomas Mih",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Buea",country:{name:"Cameroon"}}},{id:"305305",title:"Dr.",name:"Arturo Yosimar",middleName:null,surname:"Jaen-Cuellar",slug:"arturo-yosimar-jaen-cuellar",fullName:"Arturo Yosimar Jaen-Cuellar",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Autonomous University of Queretaro",country:{name:"Mexico"}}},{id:"305315",title:"Dr.",name:"David Alejandro",middleName:null,surname:"Elvira-Ortiz",slug:"david-alejandro-elvira-ortiz",fullName:"David Alejandro Elvira-Ortiz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Autonomous University of Queretaro",country:{name:"Mexico"}}},{id:"344374",title:"Dr.",name:"Manuel",middleName:null,surname:"Toledano-Ayala",slug:"manuel-toledano-ayala",fullName:"Manuel Toledano-Ayala",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Autonomous University of Queretaro",country:{name:"Mexico"}}}]}},subseries:{item:{id:"27",type:"subseries",title:"Multi-Agent Systems",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering",scope:"Multi-agent systems are recognised as a state of the art field in Artificial Intelligence studies, which is popular due to the usefulness in facilitation capabilities to handle real-world problem-solving in a distributed fashion. The area covers many techniques that offer solutions to emerging problems in robotics and enterprise-level software systems. Collaborative intelligence is highly and effectively achieved with multi-agent systems. Areas of application include swarms of robots, flocks of UAVs, collaborative software management. Given the level of technological enhancements, the popularity of machine learning in use has opened a new chapter in multi-agent studies alongside the practical challenges and long-lasting collaboration issues in the field. It has increased the urgency and the need for further studies in this field. We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11423,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. 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We welcome chapters presenting research on the many applications of multi-agent studies including, but not limited to, the following key areas: machine learning for multi-agent systems; modeling swarms robots and flocks of UAVs with multi-agent systems; decision science and multi-agent systems; software engineering for and with multi-agent systems; tools and technologies of multi-agent systems.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",keywords:"Collaborative Intelligence, Learning, Distributed Control System, Swarm Robotics, Decision Science, Software Engineering"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:{title:"Artificial Intelligence",id:"14"},selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/44050",hash:"",query:{},params:{id:"44050"},fullPath:"/chapters/44050",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()