Specific and blind docking studies with AutoDock were repeated three times.
\\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:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"9428",leadTitle:null,fullTitle:"New Trends in the Use of Artificial Intelligence for the Industry 4.0",title:"New Trends in the Use of Artificial Intelligence for the Industry 4.0",subtitle:null,reviewType:"peer-reviewed",abstract:"Industry 4.0 is based on the cyber-physical transformation of processes, systems and methods applied in the manufacturing sector, and on its autonomous and decentralized operation. Industry 4.0 reflects that the industrial world is at the beginning of the so-called Fourth Industrial Revolution, characterized by a massive interconnection of assets and the integration of human operators with the manufacturing environment. In this regard, data analytics and, specifically, the artificial intelligence is the vehicular technology towards the next generation of smart factories.Chapters in this book cover a diversity of current and new developments in the use of artificial intelligence on the industrial sector seen from the fourth industrial revolution point of view, namely, cyber-physical applications, artificial intelligence technologies and tools, Industrial Internet of Things and data analytics. This book contains high-quality chapters containing original research results and literature review of exceptional merit. Thus, it is in the aim of the book to contribute to the literature of the topic in this regard and let the readers know current and new trends in the use of artificial intelligence for the Industry 4.0.",isbn:"978-1-83880-142-7",printIsbn:"978-1-83880-141-0",pdfIsbn:"978-1-83880-466-4",doi:"10.5772/intechopen.86015",price:119,priceEur:129,priceUsd:155,slug:"new-trends-in-the-use-of-artificial-intelligence-for-the-industry-4-0",numberOfPages:212,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"9e089eec484ce8e9eb32198c2d8b34ea",bookSignature:"Luis Romeral Martínez, Roque A. Osornio Rios and Miguel Delgado Prieto",publishedDate:"March 25th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/9428.jpg",numberOfDownloads:8425,numberOfWosCitations:0,numberOfCrossrefCitations:20,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:37,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:57,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 25th 2019",dateEndSecondStepPublish:"October 3rd 2019",dateEndThirdStepPublish:"December 10th 2019",dateEndFourthStepPublish:"February 20th 2020",dateEndFifthStepPublish:"April 20th 2020",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!0,featuredMarkup:null,editors:[{id:"86501",title:"Dr.",name:"Luis",middleName:null,surname:"Romeral Martinez",slug:"luis-romeral-martinez",fullName:"Luis Romeral Martinez",profilePictureURL:"https://mts.intechopen.com/storage/users/86501/images/system/86501.jpeg",biography:"Dr. Luis Romeral was born in Asturias, Spain, in 1960. He received his electrical engineering degree and the Ph.D degree from the Technical University of Catalonia (UPC) in 1985 and 1995, respectively. In 1988 he joined the Electronic Engineering Department of the UPC, where he is currently Associate Professor. His research interests include electric machines, power electronics converters and modulation strategies, variable-speed drive systems and fault detection algorithms. He has authored more than 250 peer reviewed scientific papers published in technical journals and conference proceedings. Over the last ten years, 10 Ph.D. dissertations have been completed under his supervision. He participates as a partner or responsible in European-Union funded projects, and research projects funded by Spanish agencies. He has also participated as engineer or project manager for research and development projects funded by local private companies, in the areas of control automation, industrial networking and supervision and data acquisition.",institutionString:"Technical University of Catalonia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Universitat Politècnica de Catalunya",institutionURL:null,country:{name:"Spain"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"165604",title:"Dr.",name:"Roque A.",middleName:null,surname:"Osornio-Rios",slug:"roque-a.-osornio-rios",fullName:"Roque A. Osornio-Rios",profilePictureURL:"https://mts.intechopen.com/storage/users/165604/images/system/165604.jpeg",biography:"Dr. Roque A. Osornio Rios received the Ph.D. degree in mechatronics from the Autonomous University of Queretaro, Queretaro, Mexico, in 2007. Dr. Osornio-Rios is a National Researcher level 2 with the Mexican Council of Science and Technology, CONACYT. He is currently a Head Professor with the University of Queretaro, Mexico. He is advisor to more than 80 theses, and a coauthor of more than 100 technical papers published in international journals and conferences. His fields of interest include hardware signal processing and mechatronics. Dr. Osornio-Rios is fellow of the Mexican Academy of Engineering. He is part of the editorial board of Journal and Scientific and Industrial Research.",institutionString:"Autonomous University of Queretaro",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},coeditorTwo:{id:"234568",title:"Dr.",name:"Miguel",middleName:null,surname:"Delgado Prieto",slug:"miguel-delgado-prieto",fullName:"Miguel Delgado Prieto",profilePictureURL:"https://mts.intechopen.com/storage/users/234568/images/system/234568.jpeg",biography:"Dr. M. Delgado-Prieto was born in Barcelona, Spain, in 1983. He received the M.S. and Ph.D. degrees in Electronics Engineering from the UPC, Barcelona, Spain, in 2007 and 2012, respectively. In 2008 he joined the MCIA Research Center of the UPC. In 2021 he joined the Automatic Control Department of the UPC as Assistant Professor. His activity includes research, technology transfer and project management on condition based monitoring and internet of things for the transport, industry and energy sectors, trough national and international cooperation with companies, research centers and universities. His research interests include condition monitoring, predictive maintenance, fault detection algorithms, machine learning, signal processing methods and embedded systems.",institutionString:"Universitat Politècnica de Catalunya",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Universitat Politècnica de Catalunya",institutionURL:null,country:{name:"Spain"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"289",title:"Industrial Engineering",slug:"technology-industrial-engineering"}],chapters:[{id:"71278",title:"Trends of Digital Transformation in the Shipbuilding Sector",doi:"10.5772/intechopen.91164",slug:"trends-of-digital-transformation-in-the-shipbuilding-sector",totalDownloads:1183,totalCrossrefCites:5,totalDimensionsCites:6,hasAltmetrics:0,abstract:"The new paradigms of Industry 4.0 force all the industrial sectors to face a deep digital transformation in order to be on the edge in a competitive and globalized scenario. Following this trend, the shipbuilding industry has to establish its own path to adapt itself to the digital era. This chapter aims to explore this challenge and give an outlook on the multiple transformative technologies that are involved. For that reason, a case of study is presented as a starting point, in which the digital technologies that can be applied are easily recognized. A social network analysis (SNA) is developed among these key enabling technologies (KETs), in order to stress their correlations and links. As a result, artificial intelligence (AI) can be highlighted as a support to the other technologies, such as vertical integration of naval production systems (e.g., connectivity, Internet of things, collaborative robotics, etc.), horizontal integration of value networks (e.g., cybersecurity, diversification, etc.), and life cycle reengineering (e.g., drones, 3D printing (3DP), virtual and augmented reality, remote sensing networks, robotics, etc.).",signatures:"Alejandro Sánchez-Sotano, Alberto Cerezo-Narváez, Francisco Abad-Fraga, Andrés Pastor-Fernández and Jorge Salguero-Gómez",downloadPdfUrl:"/chapter/pdf-download/71278",previewPdfUrl:"/chapter/pdf-preview/71278",authors:[{id:"61296",title:"Prof.",name:"Andres",surname:"Pastor-Fernandez",slug:"andres-pastor-fernandez",fullName:"Andres Pastor-Fernandez"},{id:"210963",title:"Dr.",name:"Alberto",surname:"Cerezo-Narváez",slug:"alberto-cerezo-narvaez",fullName:"Alberto Cerezo-Narváez"},{id:"312939",title:"Ph.D. Student",name:"Alejandro",surname:"Sánchez-Sotano",slug:"alejandro-sanchez-sotano",fullName:"Alejandro Sánchez-Sotano"},{id:"312941",title:"Dr.",name:"Jorge",surname:"Salguero",slug:"jorge-salguero",fullName:"Jorge Salguero"},{id:"312943",title:"Dr.",name:"Francisco",surname:"Abad-Fraga",slug:"francisco-abad-fraga",fullName:"Francisco Abad-Fraga"}],corrections:null},{id:"68858",title:"Energy Infrastructure of the Factory as a Virtual Power Plant: Smart Energy Management",doi:"10.5772/intechopen.88861",slug:"energy-infrastructure-of-the-factory-as-a-virtual-power-plant-smart-energy-management",totalDownloads:803,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Smart energy factories are crucial for the development of upcoming energy markets in which emissions, energy use and network congestions are to be decreased. The virtual power plant (VPP) can be implemented in an industrial site with the aim of minimizing costs, emissions and total energy usage. A VPP considers the future situation forecasting and the situation of all energy assets, including renewable energy generation units and energy storage systems, to optimize the total cost of the plant, considering the possibility to trade with the energy market. For a VPP to be constructed, a proper communication system is essential. The energy management system (EMS) enables the monitoring, management and control of the different energy devices and permits the transference of the decisions made by the VPP to the different energy assets. VPP concept is explained together with the methods used for forecasting the future situation and the energy flow inside the facility. To reach its benefits, the optimization of the VPP is assessed. After that, the communication technologies that enable the VPP implementation are also introduced, and the advantages/disadvantages regarding their deployment are stated. With the tools introduced, the VPP can face the challenges of energy markets efficiently.",signatures:"Eva M. Urbano and Víctor Martínez Viol",downloadPdfUrl:"/chapter/pdf-download/68858",previewPdfUrl:"/chapter/pdf-preview/68858",authors:[{id:"304500",title:"M.Sc.",name:"Eva",surname:"Urbano",slug:"eva-urbano",fullName:"Eva Urbano"},{id:"304586",title:"M.Sc.",name:"Victor",surname:"Martinez-Viol",slug:"victor-martinez-viol",fullName:"Victor Martinez-Viol"}],corrections:null},{id:"69429",title:"Novel Methods Based on Deep Learning Applied to Condition Monitoring in Smart Manufacturing Processes",doi:"10.5772/intechopen.89570",slug:"novel-methods-based-on-deep-learning-applied-to-condition-monitoring-in-smart-manufacturing-processe",totalDownloads:536,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The Industry 4.0 is the recent trend of automation and the rotating machinery takes a role of great relevance when it comes to meet the demands and challenges of smart manufacturing. Condition-based monitoring (CBM) schemes are the most prominent tool to cover the task of predictive diagnosis. With the current demand of the industry and the increasing complexity of the systems, it is vital to incorporate CBM methodologies that are capable of facing the variability and complexity of manufacturing processes. In recent years, various deep learning techniques have been applied successfully in different areas of research, such as image recognition, robotics, and the detection of abnormalities in clinical studies; some of these techniques have been approaching to the diagnosis of the condition in rotating machinery, promising great results in the Industry 4.0 era. In this chapter, some of the deep learning techniques that promise to make important advances in the field of intelligent fault diagnosis in industrial electromechanical systems will be addressed.",signatures:"Francisco Arellano Espitia and Lucia Ruiz Soto",downloadPdfUrl:"/chapter/pdf-download/69429",previewPdfUrl:"/chapter/pdf-preview/69429",authors:[{id:"305128",title:"Ph.D. Student",name:"Francisco",surname:"Arellano Espitia",slug:"francisco-arellano-espitia",fullName:"Francisco Arellano Espitia"},{id:"305311",title:"BSc.",name:"Lucia",surname:"Ruiz Soto",slug:"lucia-ruiz-soto",fullName:"Lucia Ruiz Soto"}],corrections:null},{id:"69804",title:"Smart Monitoring Based on Novelty Detection and Artificial Intelligence Applied to the Condition Assessment of Rotating Machinery in the Industry 4.0",doi:"10.5772/intechopen.89945",slug:"smart-monitoring-based-on-novelty-detection-and-artificial-intelligence-applied-to-the-condition-ass",totalDownloads:454,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The application of condition monitoring strategies for detecting and assessing unexpected events during the operation of rotating machines is still nowadays the most important equipment used in industrial processes; thus, their appropriate working condition must be ensured, aiming to avoid unexpected breakdowns that could represent important economical loses. In this regard, smart monitoring approaches are currently playing an important role for the condition assessment of industrial machinery. Hence, in this work an application is presented based on a novelty detection approach and artificial intelligence techniques for monitoring and assessing the working condition of gearbox-based machinery used in processes of the Industry 4.0. The main contribution of this work lies in modeling the normal working condition of such gearbox-based industrial process and then identifying the occurrence of faulty conditions under a novelty detection framework.",signatures:"Saucedo-Dorantes Juan Jose, Jaen-Cuellar Arturo Yosimar and Elvira-Ortiz David Alejandro",downloadPdfUrl:"/chapter/pdf-download/69804",previewPdfUrl:"/chapter/pdf-preview/69804",authors:[{id:"305195",title:"Dr.",name:"Juan Jose",surname:"Saucedo-Dorantes",slug:"juan-jose-saucedo-dorantes",fullName:"Juan Jose Saucedo-Dorantes"},{id:"305305",title:"Dr.",name:"Arturo Yosimar",surname:"Jaen-Cuellar",slug:"arturo-yosimar-jaen-cuellar",fullName:"Arturo Yosimar Jaen-Cuellar"},{id:"305315",title:"Dr.",name:"David Alejandro",surname:"Elvira-Ortiz",slug:"david-alejandro-elvira-ortiz",fullName:"David Alejandro Elvira-Ortiz"}],corrections:null},{id:"69975",title:"AI for Improving the Overall Equipment Efficiency in Manufacturing Industry",doi:"10.5772/intechopen.89967",slug:"ai-for-improving-the-overall-equipment-efficiency-in-manufacturing-industry",totalDownloads:1161,totalCrossrefCites:3,totalDimensionsCites:6,hasAltmetrics:1,abstract:"Industry 4.0 has emerged as the perfect scenario for boosting the application of novel artificial intelligence (AI) and machine learning (ML) solutions to industrial process monitoring and optimization. One of the key elements on this new industrial revolution is the hatching of massive process monitoring data, enabled by the cyber-physical systems (CPS) distributed along the manufacturing processes, the proliferation of hybrid Internet of Things (IoT) architectures supported by polyglot data repositories, and big (small) data analytics capabilities. Industry 4.0 paradigm is data-driven, where the smart exploitation of data is providing a large set of competitive advantages impacting productivity, quality, and efficiency key performance indicators (KPIs). Overall equipment efficiency (OEE) has emerged as the target KPI for most manufacturing industries due to the fact that considers three key indicators: availability, quality, and performance. This chapter describes how different AI and ML solutions can enable a big step forward in industrial process control, focusing on OEE impact illustrated by means of real use cases and research project results.",signatures:"Francesc Bonada, Lluís Echeverria, Xavier Domingo and Gabriel Anzaldi",downloadPdfUrl:"/chapter/pdf-download/69975",previewPdfUrl:"/chapter/pdf-preview/69975",authors:[{id:"305235",title:"M.Sc.",name:"Xavier",surname:"Domingo",slug:"xavier-domingo",fullName:"Xavier Domingo"},{id:"305238",title:"M.Sc.",name:"Francesc",surname:"Bonada",slug:"francesc-bonada",fullName:"Francesc Bonada"},{id:"305244",title:"Mr.",name:"Lluis",surname:"Echeverria Rovira",slug:"lluis-echeverria-rovira",fullName:"Lluis Echeverria Rovira"},{id:"305247",title:"MSc.",name:"Gabriel",surname:"Anzaldi",slug:"gabriel-anzaldi",fullName:"Gabriel Anzaldi"}],corrections:null},{id:"69392",title:"Decision Support Models for the Selection of Production Strategies in the Paradigm of Digital Manufacturing, Based on Technologies, Costs and Productivity Levels",doi:"10.5772/intechopen.89535",slug:"decision-support-models-for-the-selection-of-production-strategies-in-the-paradigm-of-digital-manufa",totalDownloads:570,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"Digital manufacturing has opened a new window in the way to approach the manufacture of parts. The possible switch from manufacturing and holding physical stock to manoeuvring with a fully-digital one is promising but still has not been undertaken‑or only in a small proportion‑by the majority of the manufacturing companies. What are the cost and productivity frontiers that halt the transformation taking place so far? When does it make sense, in terms of production volume and costs, to undertake this transformation? What level of savings could be achieved and what investments would be favourable? The base line of the present chapter is to depict quantitative tools to address the potential impact of endeavouring digital transformation in manufacturing environments, considering costing and production variables, as well as technological decision-making parameters. Keeping the modelling of the demand very basic, some exploration on the degree of postponement of the production is discussed. Also, decision support systems (DSSs) for manufacturing selection are reviewed. Finally, a case study serves to apply the mathematical framework presented and to quantify the results in a realistic industrial case. Using this case, the chapter outlines and describes how to apply artificial intelligence (AI) techniques to implement the DSSs.",signatures:"Joaquim Minguella-Canela and Irene Buj Corral",downloadPdfUrl:"/chapter/pdf-download/69392",previewPdfUrl:"/chapter/pdf-preview/69392",authors:[{id:"304981",title:"Prof.",name:"Joaquim",surname:"Minguella-Canela",slug:"joaquim-minguella-canela",fullName:"Joaquim Minguella-Canela"},{id:"310040",title:"Prof.",name:"Irene",surname:"Buj Corral",slug:"irene-buj-corral",fullName:"Irene Buj Corral"}],corrections:null},{id:"70073",title:"Developing Cognitive Advisor Agents for Operators in Industry 4.0",doi:"10.5772/intechopen.90211",slug:"developing-cognitive-advisor-agents-for-operators-in-industry-4-0",totalDownloads:496,totalCrossrefCites:3,totalDimensionsCites:8,hasAltmetrics:0,abstract:"Human cyber-physical systems (CPS) are an important component in the development of Industry 4.0. The paradigm shift of doing to thinking has allowed the emergence of cognition as a new perspective for intelligent systems. Currently, different platforms offer several cognitive solutions. Within this space, user assistance systems become increasingly necessary not as a tool but as a function that amplifies the capabilities of the operator in the work environment. There exist different perspectives of cognition. In this study cognition is introduced from the point of view of joint cognitive systems (JCSs); the synergistic combination of different technologies such as artificial intelligence (AI), the Internet of Things (IoT) and multi-agent systems (MAS) allows the operator and the process to provide the necessary conditions to do their work effectively and efficiently.",signatures:"Alejandro Chacón, Cecilio Angulo and Pere Ponsa",downloadPdfUrl:"/chapter/pdf-download/70073",previewPdfUrl:"/chapter/pdf-preview/70073",authors:[{id:"132919",title:"Prof.",name:"Pere",surname:"Ponsa",slug:"pere-ponsa",fullName:"Pere Ponsa"},{id:"133666",title:"Prof.",name:"Cecilio",surname:"Angulo",slug:"cecilio-angulo",fullName:"Cecilio Angulo"},{id:"305250",title:"MSc.",name:"Alejandro",surname:"Chacón",slug:"alejandro-chacon",fullName:"Alejandro Chacón"}],corrections:null},{id:"69835",title:"Current Transducer for IoT Applications",doi:"10.5772/intechopen.90000",slug:"current-transducer-for-iot-applications",totalDownloads:680,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The evolution of communication technology and the reduction of its costs have driven several advances in measurement systems. Points that could not be measured before can now be monitored. Points with difficulty to reach or with major security restrictions can begin to have their quantities measured and informed to control centers. This chapter presents one of these evolutions showing a current transducer (CT), which can measure this magnitude, make an initial treatment of the signal, and transmit it to a panel or control center. Besides, this current transducer does not require an energy source to operate, being self-powered by the current it is measuring. Because it is inexpensive, it can be spread through the facilities, supplying the current at various points of the observed electrical network. With signal treatment, useful information can be inserted in this device so that it informs already preprocessed elements to reading devices, becoming part of the world of IoT. This article presents its use in motor condition monitoring at the Pimental hydroelectric power plant.",signatures:"Erik Leandro Bonaldi, Levy Ely de Lacerda de Oliveira, Germano Lambert-Torres, Luiz Eduardo Borges da Silva and Vitor Almeida Bernardes",downloadPdfUrl:"/chapter/pdf-download/69835",previewPdfUrl:"/chapter/pdf-preview/69835",authors:[{id:"112971",title:"Prof.",name:"Germano",surname:"Lambert-Torres",slug:"germano-lambert-torres",fullName:"Germano Lambert-Torres"},{id:"117413",title:"Dr.",name:"Erik",surname:"Bonaldi",slug:"erik-bonaldi",fullName:"Erik Bonaldi"},{id:"117672",title:"Dr.",name:"Levy Ely",surname:"Oliveira",slug:"levy-ely-oliveira",fullName:"Levy Ely Oliveira"},{id:"310798",title:"Dr.",name:"Vitor",surname:"Almeida Bernardes",slug:"vitor-almeida-bernardes",fullName:"Vitor Almeida Bernardes"},{id:"312497",title:"Prof.",name:"Luiz Eduardo",surname:"Borges Da Silva",slug:"luiz-eduardo-borges-da-silva",fullName:"Luiz Eduardo Borges Da Silva"}],corrections:null},{id:"70886",title:"How the Data Provided by IIoT Are Utilized in Enterprise Resource Planning: A Multiple-Case Study of Three Change Projects",doi:"10.5772/intechopen.90953",slug:"how-the-data-provided-by-iiot-are-utilized-in-enterprise-resource-planning-a-multiple-case-study-of-",totalDownloads:401,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"An extreme increase in data production has taken place over the past few decades with a large number of sensor and smart devices acquired from distributed data sources. Industrial Internet of Things (IIoT) enables seamless processing of information by integrating physical and digital world devices that can be used ubiquitously. This multiple-case study analyzes how the data generated by the IIoT benefit enterprise resource planning. In the analyzed cases, IIoT has been produced using and integrating various digital services and software in the enterprise. Data produced by IIoT might be raw data or pre-analyzed by the IIoT service provider according to the enterprise’s needs. Services based on IIoT solutions ensure competitiveness within the enterprise since IIoT is flexible and easy to apply on future demands. IIoT generates increased amount of data and enterprises can utilize it to provide significant benefits to their operations. The cross-case conclusions emphasize that improving operational processes with data does not provide maximal benefit to the enterprise. Data-driven procedure and the entire change project (digital transformation) together with new procedures will provide most benefits to the enterprise.",signatures:"Jyri Rajamäki and Petra Tuppela",downloadPdfUrl:"/chapter/pdf-download/70886",previewPdfUrl:"/chapter/pdf-preview/70886",authors:[{id:"312663",title:"Dr.",name:"Jyri",surname:"Rajamäki",slug:"jyri-rajamaki",fullName:"Jyri Rajamäki"},{id:"312664",title:"Ms.",name:"Petra",surname:"Tuppela",slug:"petra-tuppela",fullName:"Petra Tuppela"}],corrections:null},{id:"69320",title:"Big Data Analytics and Its Applications in Supply Chain Management",doi:"10.5772/intechopen.89426",slug:"big-data-analytics-and-its-applications-in-supply-chain-management",totalDownloads:2143,totalCrossrefCites:6,totalDimensionsCites:11,hasAltmetrics:0,abstract:"In today’s competitive marketplace, development of information technology, rising customer expectations, economic globalization, and the other modern competitive priorities have forced organizations to change. Therefore, competition among enterprises is replaced by competition among enterprises and their supply chains. In current competitive environment, supply chain professionals are struggling in handling the huge data in order to reach integrated, efficient, effective, and agile supply chain. Hence, explosive growth in volume and different types of data throughout the supply chain has created the need to develop technologies that can intelligently and rapidly analyze large volume of data. Big data analytics capability (BDA) is one of the best techniques, which can help organizations to overcome their problem. BDA provides a tool for extracting valuable patterns and information in large volume of data. 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The objective of the book will be to highlight the importance of sex education and explain normal human sexuality. With the growing number of males and females reporting sexual dysfunction the need for a ready reckoner of sexual dysfunction may be relevant and necessary.
\r\n\r\n\tThe book will have chapters on normal human sexuality, sexual health, Sexual dysfunction in the male and female, sexual dysfunction disorders related to libido, orgasm, ejaculation, erection, and genetic or hormonal or developmental or sexuo-erotic orientation defects.
\r\n\r\n\tThe book will also highlight the importance of sex counselors and therapists.
\r\n\tThere will be a chapter on secondary causes of sexual dysfunction disorders related to diabetes, cardiovascular disease, and obesity. A chapter on remedial measures to enhance sexual activity and maintain human relationships will be discussed. As there is a growing number of cancer survivors a chapter on cancer-related sexual dysfunction will be welcomed for including it.
The molecular machines of the cell, i.e., proteins, are essential to many cellular processes such as signal transduction and cell regulation. Proteins seldom act alone in the cell, but they function through interacting with other small or macromolecules. Therefore, understanding protein interactions at the atomic level is critical to understanding biological processes [1]. Primary structure, i.e., amino acid sequence, of the interacting proteins is a necessary but insufficient source of information at the atomic level. After being synthesized, proteins fold and acquire a stable native structure, i.e., tertiary structure that can be defined in a three-dimensional (3D) plane in order to be functional. It is known that proteins with different sequence information can have similar functional structures, that is, different amino acid sequences can show similar folding trends in 3D space and structure is more conserved than sequence [2]. Therefore, it is crucial to understand the interaction details at the structural level. Proteins physically interact with their partners via non-covalent associations, namely H-bond, hydrophobic, and electrostatic interactions, with the exception of covalent disulfide bridges. These intermolecular physical forces also dominate the protein folding process.
The 3D structure of the macromolecules can be determined using the experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM and then deposited in the Protein Data Bank (PDB) (https://www.rcsb.org). However, there is a huge gap between the number of known protein sequences and structures [3, 4]. Computational modeling approaches that can predict 3D structures of macromolecules can help to bridge this gap. A recent machine learning algorithm developed by DeepMind, called AlphaFold [5], can predict 3D structures of proteins using the sequence information with high accuracy and has been accepted as a breakthrough in the structural biology field. In 1 year, approximately 1 million new structures have been predicted and deposited at AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/). In order to have a complete understanding of the proteome, computational techniques are not only needed for modeling single protein structures, but also the interactions between them.
Molecular docking is a method used to predict the structures of proteins in complex with other proteins, nucleic acids, or small molecules. It can be defined as predicting the appropriate low-energy binding pose of the ligand in complex with the target structure, by randomly colliding proteins and their potential partners in space, first creating a rigid complex structure model, and then focusing on the binding sites of that model with flexible interface refinement [6]. Energy minimization of randomly docked conformations in space requires a multidimensional calculation. Initially developed molecular docking method was treating ligands and receptors as rigid bodies without considering any conformational changes [7]. However, interactions between proteins can become quite complex even with small changes in the conformation of the structures [7], and docking algorithms may not physically solve this complex problem correctly [8]. The main factor that creates computational difficulties in docking algorithms is when the protein backbone changes its conformation significantly upon binding [9, 10]. To address this problem, different techniques that consider backbone flexibility have been successfully implemented in docking algorithms [10].
Many diseases today, such as cancer, are likely to be linked to problems in protein-protein interactions and targeting them can therefore enable the development of next-generation therapeutic methods [11]. Modeling the complex structures formed by proteins with other proteins or small molecules holds the key to understand many biological processes such that modeling enzyme-substrate or protein-drug interactions can reveal insights into binding sites/interface regions, function, and mechanism of action. The main protein–small-molecule docking applications in drug discovery include drug repositioning, structure- and ligand-based (pharmacophore−/shape-based) drug design approaches using virtual and reverse screening [11, 12, 13, 14]. Today, with the continuously developing technology; targeted drug design, drug target search, evaluation of the side effects of existing drugs, or finding new targets for these drugs can be achieved with the help of molecular modeling and machine learning methods [12]. Deep learning neural network models have strong computational ability on big data and attract attention in structural biology field [15]. There are antibiotic discovery studies using deep neural networks [16] and deep learning studies adapted to drug design [17].
In this chapter, we focus on the protein–small-molecule docking fundamentals and the steps of the docking algorithm and procedure in detail. We then give recent successful applications in drug design and discovery that use different docking approaches, namely virtual screening, reverse screening, and machine learning. Lastly, we comparatively analyze some of the available protein–small-molecule docking tools using the structure of SARS-CoV-2 main protease in complex with a non-covalent inhibitor Jun8-76-3A as a case study.
Protein–small-molecule interactions are essential for the sustainability of biological processes such as enzymatic catalysis and overall homeostasis in the body [18]. The engineering of protein–small-molecule interactions is one of the computational approaches used to solve critical problems in biology [18]. protein–small-molecule docking, i.e., modeling the interaction between chemical compounds and their target protein receptors at the atomic level, is an effective tool in drug design. In the structure-based design of small-molecule drugs, a good estimation of the binding pose is required to clearly demonstrate important interactions and design drugs with increased selectivity and efficacy [19]. The procedures that can be followed and the tools that can be used before, during, and after molecular docking are explained in the following subsections and summarized in Figure 1.
The procedures that can be followed and the tools that can be used before, during, and after protein-ligand molecular docking in drug design.
Before starting the docking studies, first of all, the most suitable protein and ligand structures should be selected [20]. There are databases to access the experimentally determined structures of target proteins such as PDB, Uniprot, and Therapeutic Target Database (TTD). If the experimental structure is not available, modeled structures can be obtained from AlphaFold Database or can be modeled using relevant structure modeling software. The most frequently used databases for getting the small-molecule ligand/chemical structures are: DrugBank [21], PubChem [22], ZINC [23], ChEMBL [24], and Chemspider [25] (Figure 1). DrugBank, Chemspider, and ZINC databases include more than 500,000, 100 million, and 230 million compounds/drug molecules, respectively.
The molecular docking algorithms may require preliminary preparation of the structures that are obtained in PDB format (lacking H atoms). There are tools available for such preliminary preparations such as Open Babel [26] and AutoDockTools (Figure 1) [27].
It is also of crucial importance to guide docking with preliminary information on the binding site. Otherwise, there are no binding site constraints, blind docking takes place, and it is more difficult to detect the correct binding poses when the ligand search space is large. There are various guiding algorithms for active site prediction that can be used when binding sites are not known. Some of them can be listed as: GRID [28], SurfNet [29], COACH [30], SCFbio [31], CASTp [32], DeepSite [33], and PUResNet (Figure 1) [34].
The capabilities of docking algorithms can differ from each other, and in this respect, it is important to carefully choose the algorithm to use in accordance with the purpose of the study before starting the docking.
There are many approaches and algorithms for molecular docking, based on different parameters, and they aim to perform the protein-ligand docking with the best performance [12]. The steps of molecular docking algorithms can be summarized as follows: molecule flexibility, conformational search algorithms (ligand sampling), and scoring functions (Figure 2) [12, 35].
Methods for protein-ligand molecular docking.
During molecular docking, structures can be considered rigid or flexible. Rigid docking takes into account only the translation and rotation degrees of freedom. Providing flexibility means also considering the rotation about single bonds so that they have the same bond lengths and angles but different torsion angles. Although flexible docking approach is more realistic than rigid docking, when there are many rotatable bonds, the ligand conformational search space becomes so large that it is difficult to find the correct binding pose with the lowest binding free energy (global minimum solution). Some algorithms, such as HADDOCK [36], first treat the structures as rigid to increase time efficiency and then perform flexibility improvements on the poses of molecules with the best energy scores. Molecular docking software can be grouped according to the flexibility treatments of molecules such as Rigid Docking, Semi-Flexible Docking, and Soft Docking [35, 37].
In rigid docking, protein and ligand molecules are treated as rigid entities [37, 38]. During docking, the positions of the molecules change without losing their shape [37], i.e., only translation and rotation but no conformational degrees of freedom are considered.
Semi-flexible docking is based on the principle of keeping the protein structure rigid and letting the ligand structure be flexible by allowing rotatable bonds. Thus, various conformational poses of the ligand on the protein are sampled [35, 37, 38]. It gives more accurate results than rigid docking [37].
In soft docking, van der Waals interactions between atoms are softened, making the structures of both receptor and ligand molecules implicitly flexible as overlap is allowed to a small extent [39, 40]. Soft docking process is carried out realistically by ensuring that both the protein and the ligand are rotatable as in their natural states [37, 38]. It is an advantageous method due to its computational efficiency and ease of application [35, 37].
Conformational search algorithms can identify different conformational orientations (poses) of the ligand sampled around the experimentally determined active site or other binding sites on the protein [35, 41, 42]. These algorithms are generally classified as: shape matching, systematic, stochastic, and simulation methods [35, 38, 43].
Shape matching algorithms have the advantage of speed over other algorithms [35, 44] and adopt a sampling principle in which the conformation of the ligand should be structurally complementary to the protein binding site [38]. It ensures that the ligand is positioned in such a way that best complements the molecular surface of the binding site on the protein [35]. Some example software using shape matching are: DOCK [45], FLOG [46], EUDOC [47], Surflex [48], LibDOCK [49], SANDOCK [50], and MDock [51].
Using systematic search algorithms, a large number of possible binding poses can be obtained by gradually changing the degrees of freedom of the ligands [35, 52] toward the direction of minimum energy. Systematic search algorithms can be divided into two as exhaustive search and fragmentation (incremental structure) [35, 41, 53]. Exhaustive search algorithm is based on systematically generating flexible ligand conformations by rotating the rotatable bonds in the ligand [35]. If the number of rotatable bonds is large, there is a combinatorial explosion in the number of poses, i.e., the search space, so that some filtering and optimization procedures are applied for practical purposes [35]. Glide [54] and FRED [55] are example docking software using exhaustive conformational search algorithms. In the fragmentation method, the ligand is divided into smaller fragments, each fragment is placed and augmented at the binding site gradually through covalent bonding to the previous one [35]. DOCK [56], LUDI [57], FlexX [58], and eHiTs [59] are example software using fragmentation.
The algorithms used in stochastic search methods are more efficient but do not guarantee an accurate result as they are based on generating random ligand conformations, and therefore, the docking process is iterative in these algorithms [41, 44]. Monte Carlo, swarm optimization, evolutionary algorithms, and Tabu search methods are among the most used stochastic algorithms [35, 38, 52]. Example software using stochastic conformational search method include AutoDock [60], GOLD [61], DockThor [62], and MolDock [63].
Simulations of the obtained ligand poses (simulation methods) represent protein and ligand flexibility better than the other algorithms but have a slow flow and can make insufficient sampling [38, 44]. For this reason, they are used as a complement to other conformational search methods [38].
In the previously described conformational search step, many structures are created and most of them should be eliminated by selecting the biologically appropriate structures. Therefore, the possible poses created by conformational search algorithms are evaluated and ranked by using a scoring function [35]. The scoring function is a measure to evaluate the docking poses obtained [35, 38, 52] in terms of their binding free energies [11, 44, 64].
With the scoring functions that estimate the binding energies of the created complex structures, various physicochemical properties should be evaluated in order to distinguish good results from the bad ones. These physicochemical properties can be intermolecular interactions, desolvation from solvent, electrostatic and entropic effects, etc. [65]. As the number of evaluated parameters increases, the accuracy of the scoring function will increase; but the computational load will also increase. Therefore, scoring functions with ideal efficiency, especially when working with large ligand sets, are those that are balanced in terms of accuracy and speed [11]. The scoring functions can be classified as: force-field-based, empirical, knowledge-based, and consensus scoring.
The Force Field Scoring Function (FFSF) is designed to work with multiple force fields such as AMBER [66], CHARMM [67], GROMOS [68], and OPLS [69] individually or in combination. The designed FFSFs estimate the free energy of ligand binding by considering van der Waals energy terms such as electrostatic interactions and hydrogen bonds [35, 38].
Empirical scoring functions use simpler energy terms to estimate the free energy of ligand binding such as hydrogen bonds and ionic interaction, and they can be calculated more easily and faster than FFSFs [35, 38, 52]. Some examples of empirical scoring functions are GlideScore [54], PLP [70], LigScore [71], LUDI [72], SCORE [73], and X-Score [74].
Knowledge-based scoring functions use statistical analysis of protein-ligand complex structures to derive protein-ligand distance [44]. These functions can show high performance in a short time [52]. They can also model some uncommon interactions, such as sulfur-aromatic, that other functions do not address [44].
Consensus scoring function, not a specific scoring system, aims at an effective scoring with a combination of multiple scoring functions with the idea of minimizing the possible error margins of existing scoring systems [35, 38, 44].
After performing protein-ligand docking studies, the accuracy of pose estimations needs to be evaluated [41, 52]. The best way to evaluate the docking algorithm is to compare the predicted binding pose of the ligand with position of the reference ligand in the experimentally determined structure, if possible. The structural comparison is quantified by using root mean squared deviation (RMSD) (Eq. 1), with the unit of Å [41, 75]. It is preferred that this value is between 2 and 4 Å or less for a good docking. RMSD calculations are simple, but this metric is not normalized to number of atoms and therefore should not be considered as an absolute measure [76]. As a more systematic approach, in order to ensure the consistency of the docking algorithm used, it should be checked whether the same poses are obtained by repeating the docking process [52] at least 50 times and clustering the poses of the side chains and references according to a certain threshold value [77]. With this method, whether the docking algorithm correctly and consistently creates a pose in the right position can be determined [41, 44, 78].
Eq. (1) Root mean squared deviation for the coordinates of two molecules, a and b, with N atoms.
Modeling successes and capabilities of docking algorithms are being evaluated in a competition called CAPRI (Critical Assessment of Protein Interactions) (https://www.capri-docking.org/) since 2001 [79, 80]. Experimentally determined complex structures that have not yet been published in PDB are submitted to CAPRI and without knowing the experimental structure of the complex, the participants try to predict the most similar structure to the experimentally determined complex structure through docking algorithms [79]. A solution set of 10 models is presented to the CAPRI committee for evaluation based on the geometry similarity and biological relevance of the predicted complex structures. The results of CAPRI show very good predictions for easy targets with simple conformational changes, but rather worse ones for difficult targets with conformational changes upon binding [9].
Computational methods have become an important part of the drug discovery process with increasing accuracy of algorithms. Various docking methods based on different algorithms are constantly being developed to determine the structural relationships of potential drug molecules and their targets [44]. In addition, studies in this area shed light on the candidate drugs in terms of the pharmacodynamic properties, affinity, and selectivity [11]. The main molecular docking applications in drug discovery include drug repositioning (repurposing), structure- and ligand-based drug design approaches using virtual and reverse screening [11, 12, 13, 14].
Drug repositioning seeks out new targets for natural compounds, drugs currently in use, or candidate ligands to reveal their unknown therapeutic potentials [81]. Many successful repositioning studies are available in the literature [81, 82, 83]. Virtual screening (VS) and reverse screening (RS) techniques are frequently used in drug discovery and repositioning. VS offers a more effective and rational approach compared with traditional methods [36]. The atomic-level analyzable results presented to us by virtual screening studies guide us in understanding the function of the target and in new drug discoveries [5, 36, 55]. In the RS approach, interest is on a single ligand molecule, and there is a search for a biological target for this molecule [12]. Unlike virtual screening (VS), the search library consists of potential target receptors. RS approach has the potential to lead studies such as testing toxicity or side effects of the existing drugs [38]. The potential side effects of a drug need to be evaluated in the drug discovery process. Molecular docking studies can offer an important perspective in this regard, and there are inverse (reverse) docking studies that provide bioactivity data by detecting off-target bindings [25]. Lastly, the subclasses of Artificial Intelligence (AI): Machine Learning (ML) and Deep Learning (DL) methods have significant contributions in pharmaceutical industry [84]. AI can be applied to different steps such as drug design with VS,
Virtual screening (VS) approach uses a target receptor and a library of small molecules. Libraries can be created manually, or already existing libraries can be used. The library consists of a large number of chemically diverse bioactive small molecules with a high probability of binding to the receptor. This virtual computing technique is considered as the
Ligand-based VS methods conduct research by identifying common properties of compound sequences, such as molecular volume and protonation state [11]. In addition to chemical similarity [88] and rule-based [89] software included in filtration strategies, there are also various software such as freely add-on pharmacophore and quantitative structure-activity relationship (QSAR) models [87, 90]. The most commonly used ligand-based virtual screening method is the QSAR method. Ligand-based VS does not contain structural information about the receptor, it only scans using receptor sites known to be active and tries to detect active ligand molecules [85].
Structure-based VS methods are often used when the receptor has different conformations. The aim is to predict receptor binding affinity by processing structural information using a variety of techniques, such as binding site similarity and pharmacophore mapping. By estimating the different binding modes, the molecules are sorted for evaluation [11]. Analysis of the predicted poses can be done manually using visualization programs. It has been reported that nAPOLI, a web server developed in recent years, analyzes results automatically [91].
Structure-based pharmacophore generation is one of the most frequently used methods for small molecules in the virtual screening method. Here, 3D pharmacophore model interfaces of the scaffolds of the ligands are created, and ligands that will adapt to the binding site and provide the desired bioactivity are selected. Some of the programs that use pharmacophore modeling are HipHop [92], PHASE [93], MOE, which are commercial, SCAMPI [94], PharmaGist [95], ALADDIN [96], which are suitable for academic use.
A recent example of VS application on the non-structural protein of SARS-CoV-2, nsp1, one of the virulence factors causing viral infection, is by G. O. Timo
Reverse screening (RS) is also called inverse docking, reverse docking, inverse virtual screening, or target screening. Libraries are more limited for target hunting and profiling [12] and can be created manually using the most common accessible databases such as PDB [98] and TTD [12, 99]. But this process requires a long preparation time and effort. There are various algorithms used to detect interactions by reverse screening. Some web platforms (INVDOCK [100], idTarget [101], ACTP [102], etc.) have been developed for reverse docking, which use libraries prepared for specific diseases and docked using programs such as standard AutoDock and AutoDock Vina [12].
A recently developed Consensus Reverse Docking System (CRDS) detects potential binding sites by screening approximately 5200 candidate proteins for the ligand molecule using three different scoring methods [103]. In another example, Stepanova
Machine learning techniques take information from biological data and make predictions about them, thus contributing to building a structural model [9]. Once a model is built, it must be improved so that the state with the lowest potential energy (global minimum) can be reached. Global minimum means a stable and sterically acceptable structure, and reaching it without being stuck at the local minima is very important in the field of bioinformatics and computational structural biology. A recent machine learning algorithm developed by DeepMind, called AlphaFold [5], implements deep learning and can predict 3D structures of proteins using the sequence information with high accuracy and has been accepted as a breakthrough in the structural biology field.
Machine learning makes classifications by learning on datasets and needs human intervention to evaluate possible outcomes. Deep learning is a more advanced model having the neural network with ability to decide the right result without human intervention (Figure 3). Machine learning can use supervised or unsupervised learning. Supervised learning performs machine learning on datasets that we know about, whereas unsupervised learning detects and labels similarities and orientations in a created cluster [38, 90].
Schematic illustration of artificial intelligence subfields: Machine learning and deep learning.
The training set used in machine learning constitutes the performance of the algorithm. Machine learning studies in the field of virtual screening are generally focused on improving the performance of the scoring function [85]. Studies have shown that working with small subsets of the same family, which consists of similar structures, gives better scoring results rather than working with large data from different complexes [105]. Working with subsets of interest is also a better approach in terms of computational requirements [38].
Machine learning and deep learning can describe more diverse data than other computational systems and can be representative of structural biology. Nonparametric machine learning has great potential to be the next step in computer-based programming to improve the accuracy of molecular docking studies [41]. Machine learning can be used to refine predetermined function data as well as provide high-quality data to complement pharmaceutical discovery research and development.
As a case study for comparing different protein-ligand docking tools, the crystal structure of the SARS-CoV-2 (COVID-19) main protease in complex with its non-covalent inhibitor Jun8-76-3A (PDB ID: 7KX5) is used as the experimental reference structure to evaluate the accuracies of the complex structures predicted using AutoDock Vina, HADDOCK, and SwissDock programs and changing some of the parameters to test their effects on prediction capabilities. The inhibitor in the experimental protein structure is removed and then molecular docking is performed using the initial coordinates of the main protease structure of SARS-CoV-2 and its inhibitor Jun8-76-3A, separately.
AutoDock is a free software that predicts the binding compatibility of small ligands to macromolecule targets with a flexible-rigid (semi-flexible) docking approach [27]. It uses a grid-based method to place the ligand in the active region determined on the macromolecule [106]. AutoDockTools (http://mgltools.scripps.edu/downloads) is the user interface to produce and examine grid information required for the preparation of the protein and ligand structures in the relevant format and the configuration file [27].
As a docking input in AutoDock Vina, a configuration file, which contains the coordinate information of the protein and ligand structures and the ligand-binding region on the receptor, is required. For docking the case study ligand to the receptor using AutoDock Vina, the structure file was downloaded from RCSB PDB database (https://www.rcsb.org) in .pdb format (PDB ID:7KX5). AutoDockTools (v1.5.6) interface was used to prepare input files, such that, water molecules in the relevant protein structure were deleted, polar H bonds were added to the structure and both the receptor and ligand structures were saved in .pdbqt file format. After preparing the ligand and protein structures, the most important input information for AutoDock is the docking parameter. The docking parameter involves determining the coordinates of the ligand-binding region on the target protein. While determining the docking parameter, if the binding region on the protein is not known, blind docking can be performed by putting the whole protein in the grid box (Figure 4A), or a small grid box can be placed in the specific known/predicted ligand-binding region on the protein (Figure 4B). Lastly, after determining the region on the protein where the ligand is to be bound by using the “grid box” in AutoDockTools, the protein coordinates were specified in the input configuration file. Preparing all the required inputs, docking was performed using AutoDock Vina by repeating each docking process three times in order to observe the consistency of the algorithm (Table 1).
Grid box usage in docking: (A) blind docking with a grid box of size:
Mode | Specific docking | Blind docking | ||||||
---|---|---|---|---|---|---|---|---|
Affinity (kcal/mol) | Affinity (kcal/mol) | |||||||
Rep1 | Rep 2 | Rep 3 | AVG | Rep1 | Rep2 | Rep3 | AVG | |
1 | −8.9 | −8.8 | −8.9 | −8.9 | −8.9 | −8.9 | −9.0 | −8.9 |
2 | −7.3 | −8.7 | −7.3 | −7.8 | −8.2 | −8.2 | −8.3 | −8.2 |
3 | −7.2 | −7.2 | −7.3 | −7.2 | −8.1 | −8.0 | −8.1 | −8.1 |
4 | −6.8 | −7.0 | −7.0 | −6.9 | −7.9 | −7.8 | −8.0 | −7.9 |
5 | −6.8 | −6.9 | −7.0 | −6.9 | −7.9 | −7.5 | −8.0 | −7.8 |
6 | −6.8 | −6.8 | −6.9 | −6.8 | −7.7 | −7.4 | −7.8 | −7.6 |
7 | −6.7 | −6.5 | −6.8 | −6.7 | −7.7 | −7.4 | −7.8 | −7.6 |
8 | −6.4 | −6.4 | −6.8 | −6.5 | −7.6 | −7.2 | −7.6 | −7.5 |
9 | −6.3 | −6.4 | −6.7 | −6.4 | −7.5 | −6.9 | −7.4 | −7.3 |
Specific and blind docking studies with AutoDock were repeated three times.
In order to examine the accuracy of the docking results, the poses obtained from AutoDock Vina were aligned with the original PDB structure by using the PyMol program [107]. When the energies of the poses predicted with specific docking (i.e., using specific grid on the binding site) and blind docking are compared, although the energy scores of the blind docking results are better, the comparison of the poses with the reference ligand shows that the most accurate binding is achieved with specific docking (Figure 5). Alignment of the first poses (with the lowest energy score) predicted with specific docking (green) and blind docking studies (blue) with the reference ligand (red) shows that the specific docking pose was in a more similar position with the reference ligand (green vs. red), than the blind docking pose (blue vs. red).
Crystal SARS-CoV-2 main protease structure (white, PDB ID: 7KX5_chain (A) in complex with the blind docking (blue), specific docking (green) poses predicted with AutoDock Vina and the reference ligand Jun8-76-3A inhibitor (red, PDB ID: 7KX5_chain B). This figure was drawn with PyMol 2.5.2.
An integrative platform called High Ambiguity-Driven biomolecular DOCKing (HADDOCK) is used for molecular docking of two or more molecules [108] and is a popular algorithm [36]. Although it is mainly suitable for protein-protein interactions, it can also be applied to model the protein–small-molecule complexes [109]. HADDOCK automatically decides the most suitable configuration of the ligand according to the given restrictions [108]. Protein-protein docking is more complex than protein–small-molecule docking, as the proteins are flexible and the conformational space is larger [110].
HADDOCK does not require CPU and allows the user to see all the docking steps from start to finish. It should be noted that the success of HADDOCK studies is directly related with the amount of data entered into the system [36]. HADDOCK allows processing different types of molecules with the help of different platforms such as WHATIF, ProDRG, PDB. There is no need to create different conformer sequences as the system selects the most compatible conformers based on the shape constraints. With restriction files, we can set clear target sites, binding distances, or select active or passive residues (areas that are likely to interact). Defining semi-flexible regions is also allowed.
HADDOCK algorithm consists of three stages: rigid-body minimization and randomization of orientations (it0), semi-flexible simulated annealing in torsion angle space (it1), and refinement in 3D space with explicit solvent (water) (https://www.bonvinlab.org/education/HADDOCK-protein-protein-basic/). it0 stage treats structures as rigid solids and 1000 poses with the best score are selected. it1 optimizes orientations by allowing different docking poses from it0 to have different flexible regions defined. Two-hundred models with the best energy pass to the final stage. In the final step, a complex solvent medium (DMSO or water) is considered to improve the interaction energy and the final models are automatically aggregated.
To dock the case study inhibitor-protein complex (PDB ID:7XK5), the guideline tutorial (HADDOCK small-molecule binding site screening protocol) [111] was followed and two different approaches were tested: (i) using an unambiguous (distance) restraint file, indicating the target that should bind the ligand, (ii) by defining the active and passive residues. This case study consists of a pre-docking for the detection of the binding region and a second docking for the detection of binding pose.
First, we tested HADDOCK’s accuracy of binding site detection. Two different binding sites were detected in the top 10 clusters with the best energy scores and 70% (7 out of 10) of the clusters were in the correct binding site (Figure 6A). Secondly, an ambiguous and unambiguous restraint file was created by identifying the region with the highest number of interactions between the ligand and the receptor. The restraint files can be created manually or using the link in the protocol. However, it may be necessary to make corrections in the distance restraints. The structure with the best energy is visualized in Figure 6B. Secondly, active and passive residues were defined on the system, and the pose with the best energy result is visualized in Figure 6C. HADDOCK results are summarized in Table 2.
Crystal SARS-CoV-2 main protease structure (gray, PDB ID: 7KX5_chain (A) in complex with the docking poses (blue) predicted with HADDOCK and reference ligand Jun8-76-3A inhibitor (red, PDB ID: 7KX5_chain B). A. Top 10 clusters for binding site determination. B. Pose with the best energy using ambiguous/unambiguous restraints.
Binding site detection | Ambiguous/Unambiguous restraints | Active/passive restraints | |
---|---|---|---|
HADDOCK score | −53.4 ± 1.5 | −52.1 ± 0.5 | −21.9 ± 2.7 |
Cluster size | 69 | 5 | 13 |
RMSD from the overall lowest-energy structure | 0.3 ± 0.2 | 0.1 ± 0.1 | 0.2 ± 0.0 |
Van der Waals energy | −40.3 ± 1.2 | −41.6 ± 0.2 | −32.4 ± 4.5 |
Electrostatic energy | −22.1 ± 1.9 | −15.2 ± 6.0 | −25.8 ± 7.3 |
Desolvation energy | −10.9 ± 2.5 | −9.0 ± 0.2 | −6.7 ± 0.3 |
Restraints violation energy | 0.0 ± 0.00 | 0.7 ± 0.2 | 198.5 ± 78.0 |
Buried Surface Area | 795.4 ± 21.9 | 781.6 ± 5.2 | 783.0 ± 9.4 |
Z-Score | −1.7 | −2.4 | −1.3 |
HADDOCK results.
Comparison of the results shows that HADDOCK is successful in detecting the binding site. However, according to the results obtained in the second stage, the algorithm was not successful enough to find the correct conformation of the ligand in binding site. Defining ambiguous/unambiguous restraint files or selecting active and passive residues did not make a significant contribution in detecting the correct binding pose (Figure 6B and C). Docking with both approaches was repeated several times and no significant similarity was detected.
SwissDock is a database to improve protein–small-molecule docking using amino acid sequence information from genome projects. Moreover, it is a web browser and programmatic interface that enables creating three-dimensional protein models from protein amino acid sequences [112]. It also has user interfaces such as Swiss-Pdb Viewer (DeepView) to simultaneously analyze several proteins [113]. Using the SwissDock web server, the starting crystal structures of the target proteins can be searched and fetched from protein and ligand structure databases. If there is no crystal structure available to compare, it provides homology modeling of the studied protein. During the docking process, the user does not have to do any calculations because all calculations are handled by the server side [112]. As a docking constraint, the ligand binding region can be defined or blind docking can be applied with no information.
Using the case study, both specific and blind dockings were performed on the SwissDock server, and the results were compared. The server presented 256 poses. The best scores obtained by specific docking (green) blind docking (blue) were −9.88 and −9.35 kcal/mol, respectively (Figure 7). Although both of the predicted poses did not show the same conformation with the reference ligand, it was observed that the pose obtained from the specific docking (green) was more similar to the reference ligand (red) (Figure 7).
Crystal SARS-CoV-2 main protease structure (white, PDB ID: 7KX5_chain (A) in complex with the blind docking (blue), specific docking (green) poses predicted by SwissDock and the reference ligand Jun8-76-3A inhibitor (red, PDB ID: 7KX5_chain B). This figure was drawn with PyMol 2.5.2.
Molecular docking is a computational method that predicts the 3D structures of receptor-ligand complexes. Modeling the atomic details of the ligand pose with the receptor protein by molecular docking can assist in understanding protein structure-function relationship and in drug design studies in several ways. Computational modeling approaches complement and/or lead experiments by eliminating irrelevant drug candidates and selecting the ones with the best binding properties. With the continuously developing technology, there are many different approaches and algorithms for molecular docking studies, and they are successfully used in therapeutic applications such as targeted drug design, drug target search, evaluation of the side effects of existing drugs, or finding new targets for these drugs.
The crystal structure of the SARS-CoV-2 (COVID-19) main protease in complex with its non-covalent inhibitor Jun8-76-3A (PDB ID: 7KX5) was used as an experimental reference case study to compare and evaluate the prediction accuracies of AutoDock Vina, HADDOCK, and SwissDock programs as well as to test the effects of some parameters on their prediction capabilities. One of the main observations is that the ligand poses with the lowest binding energy scores are not necessarily the best solution. Therefore, docking results should always be evaluated in terms of biological relevance. Moreover, when
In summary, before starting the molecular docking, it is of crucial importance to obtain detailed information on the target protein and ligand from various sources and servers and to decide which docking algorithm to use. Moreover, the top predicted poses with the best scores should not be unquestioningly accepted as the best solutions but further structural analyses and evaluations should be incorporated in the decision process.
We would like to thank dear Merve DEMİR AKYÜZ and Merve YÜCETÜRK (a.k.a Merves), who are fourth-year undergraduate students at Molecular Biology and Genetics Department of Istanbul Medeniyet University, for their contribution to the writing of the introduction section.
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Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. 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peripheral nerve injury after trauma is a common occurrence in both human and veterinary medicine and has severe consequences for the survival and quality of life of the patients. Despite the continuous efforts and the creation of diverse medical and surgical techniques, the harmful effects of this type of injury are far from being overcome. Regenerative medicine has been growing in the scientific milieu as a new therapeutic approach for different situations. Among the cell-based therapies explored, the mesenchymal stem cells are evidenced by their features, versatility and potential applications. The olfactory mucosa mesenchymal stem cells, components of the olfactory system and identified in the lamina propria, were newly identified and are still undergoing characterization, appearing as a new promise in the regenerative therapy of several tissues but with special emphasis on the nervous system in general and the peripheral nervous system in particular, for which they appear to have special regenerative aptitude.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Rui Damásio Alvites, Ana Rita Caseiro Santos, Artur Severo Proença\nVarejão and Ana Colette Pereira de Castro Osório Maurício",authors:[{id:"56285",title:"Prof.",name:"Ana Colette",middleName:null,surname:"Maurício",slug:"ana-colette-mauricio",fullName:"Ana Colette Maurício"},{id:"56291",title:"Dr.",name:"Artur",middleName:null,surname:"Varejão",slug:"artur-varejao",fullName:"Artur Varejão"},{id:"188034",title:"Dr.",name:"Rita",middleName:null,surname:"Caseiro",slug:"rita-caseiro",fullName:"Rita Caseiro"},{id:"196044",title:"Dr.",name:"Rui",middleName:"Damásio",surname:"Alvites",slug:"rui-alvites",fullName:"Rui Alvites"}]},{id:"55537",doi:"10.5772/intechopen.68172",title:"Current Applications of Mesenchymal Stem Cells for Cartilage Tissue Engineering",slug:"current-applications-of-mesenchymal-stem-cells-for-cartilage-tissue-engineering",totalDownloads:1461,totalCrossrefCites:3,totalDimensionsCites:5,abstract:"Articular cartilage injuries caused by traumatic/mechanical progressive degeneration result in joint pain, swelling, the consequent loss of joint function, and eventually osteoarthritis. Articular tissue possesses a poor ability to regenerate that further complicates the therapeutic approaches. Mesenchymal stem cells (MSCs) have emerged as a promising alternative treatment. Recently, it has been reported that a wide variety of strategies ranging from merely using cells in the injured area to employ biofunctional substitutes in which cells are harmonizing with scaffolding and growth factors to create an engineered cartilage tissue.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Lizeth Fuentes-Mera, Alberto Camacho, Nidia K. Moncada-Saucedo\nand Víctor Peña-Martínez",authors:[{id:"196376",title:"Dr.",name:"Lizeth",middleName:null,surname:"Fuentes-Mera",slug:"lizeth-fuentes-mera",fullName:"Lizeth Fuentes-Mera"},{id:"198410",title:"Dr.",name:"Alberto",middleName:null,surname:"Camacho",slug:"alberto-camacho",fullName:"Alberto Camacho"},{id:"204534",title:"Dr.",name:"Nidia K.",middleName:null,surname:"Moncada-Saucedo",slug:"nidia-k.-moncada-saucedo",fullName:"Nidia K. Moncada-Saucedo"},{id:"205441",title:"Dr.",name:"Victor",middleName:null,surname:"Peña-Martinez",slug:"victor-pena-martinez",fullName:"Victor Peña-Martinez"}]},{id:"55767",doi:"10.5772/intechopen.69392",title:"Characteristics of Full-Term Amniotic Fluid-Derived Mesenchymal Stem Cells in Different Culture Media",slug:"characteristics-of-full-term-amniotic-fluid-derived-mesenchymal-stem-cells-in-different-culture-medi",totalDownloads:1354,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"Amniotic fluid contains precious therapeutic stem cells with ideal features such as they are broadly multipotent, genetically stable, and non-tumorigenic. One of the stem cells that is abundantly found in amniotic fluid is mesenchymal stem cells. Human amniotic fluid mesenchymal stem cells (hAFMSCs) had been successfully isolated from amniotic fluid obtained from second or third trimester amniocentesis. However, studies on hAFMSCs obtained during full-term delivery are still lacking. Furthermore, suitable culture media to propagate hAFMSCs for therapeutic purposes have not been fully established. Basal medium supplemented with fetal bovine serum is commonly used, and unfortunately, this condition has been associated with the risk of transmission of animal pathogens and xenogenic immune reaction. An efficient isolation and expansion method together with suitable culture conditions is essential in establishing a specific homogenous cell population, such as full-term hAFMSCs, of clinical grade. In this chapter we briefly describe the feasibility of generating hAFMSCs from full-term amniotic fluid obtained during cesarean section using serum-free medium as opposed to the conventional serum containing media. These findings would be very useful in utilizing stem cells for bench side application from a source that is accessible and devoid of ethical and safety concerns.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Karuppiah Thilakavathy, Norshariza Nordin, Rajesh Ramasamy,\nPeyman Ghoraishizadeh, Izanwati Mohd Rawi Rohayu and Gurbind\nSingh",authors:[{id:"195911",title:"Associate Prof.",name:"Thilakavathy",middleName:null,surname:"Karuppiah",slug:"thilakavathy-karuppiah",fullName:"Thilakavathy Karuppiah"},{id:"201873",title:"Dr.",name:"Rajesh",middleName:null,surname:"Ramasamy",slug:"rajesh-ramasamy",fullName:"Rajesh Ramasamy"},{id:"205278",title:"Dr.",name:"Norshariza",middleName:null,surname:"Nordin",slug:"norshariza-nordin",fullName:"Norshariza Nordin"},{id:"205279",title:"Dr.",name:"Gurbind",middleName:null,surname:"Singh",slug:"gurbind-singh",fullName:"Gurbind Singh"},{id:"205280",title:"Ms.",name:"Rohayu Izanwati",middleName:null,surname:"Mohd Rawi",slug:"rohayu-izanwati-mohd-rawi",fullName:"Rohayu Izanwati Mohd Rawi"},{id:"205281",title:"Mr.",name:"Peyman",middleName:null,surname:"Ghoraishizadeh",slug:"peyman-ghoraishizadeh",fullName:"Peyman Ghoraishizadeh"}]},{id:"57478",doi:"10.5772/intechopen.68516",title:"The Proangiogenic Potential of Mesenchymal Stem Cells and Their Therapeutic Applications",slug:"the-proangiogenic-potential-of-mesenchymal-stem-cells-and-their-therapeutic-applications",totalDownloads:1255,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Mesenchymal stem cells (MSCs) can be isolated from many tissue types and following in vitro culture expansion, large numbers of patient-specific or allogenic cells can be produced for clinical applications. MSCs exhibit anti-inflammatory and immunomodulatory properties and are identified as lacking major histocompatibility complex (MHC) class II molecules. Cellular-based approaches using MSCs to enhance new blood vessel formation have shown promise in preclinical models and preliminary clinical trials. Transplantation of MSCs in vivo has significantly enhanced the formation of new blood vessels and promoted the healing of chronic wounds. The proangiogenic potential of MSCs can be further enhanced through gene delivery such as vascular endothelial growth factor (VEGF) or endothelial nitric oxide synthase (eNOS) providing long-term therapeutic expression. In this chapter, we review recent advances on the isolation and characterization of MSCs and in vivo applications for promoting angiogenesis. Enhancement of angiogenesis is also required for improved healing in myocardial infarction and cerebral ischemia, and the use of MSCs in these areas will also be reviewed. Furthermore, the combination of MSCs with biomaterials has greatly improved their survival and potency with improved vascularization of tissue-engineered constructs and integration within the host. In summary, this chapter provides an overview of both the basic science supporting the proangiogenic properties of MSCs and their translational use.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Nadeeka Bandara, Shiang Lim, Haiying Chen, Shuangfeng Chen, Le-\nXin Wang and Padraig Strappe",authors:[{id:"179037",title:"Dr.",name:"Padraig",middleName:null,surname:"Strappe",slug:"padraig-strappe",fullName:"Padraig Strappe"}]},{id:"54643",doi:"10.5772/intechopen.68173",title:"Stem Cells from Human Exfoliated Deciduous Teeth: Biology and Therapeutic Potential",slug:"stem-cells-from-human-exfoliated-deciduous-teeth-biology-and-therapeutic-potential",totalDownloads:1643,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Stem cells isolated from human exfoliated deciduous teeth (SHEDs) are a type of mesenchymal stem cells (MSCs), widely investigated for regenerative treatment. They are isolated from dental pulp tissues remaining in physiologically shedding human deciduous teeth. Thus, SHEDs are easy to access and not required invasive procedure to obtain cells. SHEDs are multipotent mesenchymal stem cells; however, they possess distinct properties when compared to other MSCs. In this regard, SHEDs exhibit higher proliferative rate than bone marrow‐derived MSCs and greater osteogenic differentiation potency than human dental pulp stem cells. This chapter reviews the isolation technique and basic characteristics of SHEDs. Moreover, the intracellular signalling involved in the stemness regulation and differentiation ability of SHEDs is discussed, particularly on fibroblast growth factor, Notch, and Wnt signalling. Finally, the potential regenerative therapeutic application of SHEDs is also described.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Waleerat Sukarawan and Thanaphum Osathanon",authors:[{id:"195871",title:"Associate Prof.",name:"Thanaphum",middleName:null,surname:"Osathanon",slug:"thanaphum-osathanon",fullName:"Thanaphum Osathanon"},{id:"195872",title:"Dr.",name:"Waleerat",middleName:null,surname:"Sukarawan",slug:"waleerat-sukarawan",fullName:"Waleerat Sukarawan"}]}],mostDownloadedChaptersLast30Days:[{id:"55235",title:"Olfactory Mucosa Mesenchymal Stem Cells and Biomaterials: A New Combination to Regenerative Therapies after Peripheral Nerve Injury",slug:"olfactory-mucosa-mesenchymal-stem-cells-and-biomaterials-a-new-combination-to-regenerative-therapies",totalDownloads:1901,totalCrossrefCites:3,totalDimensionsCites:5,abstract:"The peripheral nerve injury after trauma is a common occurrence in both human and veterinary medicine and has severe consequences for the survival and quality of life of the patients. Despite the continuous efforts and the creation of diverse medical and surgical techniques, the harmful effects of this type of injury are far from being overcome. Regenerative medicine has been growing in the scientific milieu as a new therapeutic approach for different situations. Among the cell-based therapies explored, the mesenchymal stem cells are evidenced by their features, versatility and potential applications. The olfactory mucosa mesenchymal stem cells, components of the olfactory system and identified in the lamina propria, were newly identified and are still undergoing characterization, appearing as a new promise in the regenerative therapy of several tissues but with special emphasis on the nervous system in general and the peripheral nervous system in particular, for which they appear to have special regenerative aptitude.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Rui Damásio Alvites, Ana Rita Caseiro Santos, Artur Severo Proença\nVarejão and Ana Colette Pereira de Castro Osório Maurício",authors:[{id:"56285",title:"Prof.",name:"Ana Colette",middleName:null,surname:"Maurício",slug:"ana-colette-mauricio",fullName:"Ana Colette Maurício"},{id:"56291",title:"Dr.",name:"Artur",middleName:null,surname:"Varejão",slug:"artur-varejao",fullName:"Artur Varejão"},{id:"188034",title:"Dr.",name:"Rita",middleName:null,surname:"Caseiro",slug:"rita-caseiro",fullName:"Rita Caseiro"},{id:"196044",title:"Dr.",name:"Rui",middleName:"Damásio",surname:"Alvites",slug:"rui-alvites",fullName:"Rui Alvites"}]},{id:"54431",title:"Transportation of Mesenchymal Stem Cells for Clinical Applications",slug:"transportation-of-mesenchymal-stem-cells-for-clinical-applications",totalDownloads:1670,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Cell‐processing procedures are conducted in accordance with Good Manufacturing Practices, and clinical procedures are performed by highly optimised methods. A high‐quality transportation system is essential for safe and effective handling of mesenchymal stem cells (MSCs) between cell‐processing and transplantation stages. For MSC transportation, either frozen cell or non‐frozen cell transportation is performed. There are many requirements for transporting a package by either type of transportation. In frozen cell transportation, some issues have yet to be resolved: the primary receptacle and cryoprotectant reagents. In non‐frozen cell transportation, control of cell metabolism and protection from environmental changes are more serious problems. Stabilisation of temperature, shock resistance, gas control, and an ultraviolet radiation (UVR) shielding technology should be considered. The transportation system should be established in compliance with the guidelines. Both development of a high‐quality transportation package and establishment of a high‐quality transportation system are important for the effective use of MSCs in clinical applications.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Tomoki Aoyama",authors:[{id:"126501",title:"Dr.",name:"Tomoki",middleName:null,surname:"Aoyama",slug:"tomoki-aoyama",fullName:"Tomoki Aoyama"}]},{id:"54643",title:"Stem Cells from Human Exfoliated Deciduous Teeth: Biology and Therapeutic Potential",slug:"stem-cells-from-human-exfoliated-deciduous-teeth-biology-and-therapeutic-potential",totalDownloads:1641,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Stem cells isolated from human exfoliated deciduous teeth (SHEDs) are a type of mesenchymal stem cells (MSCs), widely investigated for regenerative treatment. They are isolated from dental pulp tissues remaining in physiologically shedding human deciduous teeth. Thus, SHEDs are easy to access and not required invasive procedure to obtain cells. SHEDs are multipotent mesenchymal stem cells; however, they possess distinct properties when compared to other MSCs. In this regard, SHEDs exhibit higher proliferative rate than bone marrow‐derived MSCs and greater osteogenic differentiation potency than human dental pulp stem cells. This chapter reviews the isolation technique and basic characteristics of SHEDs. Moreover, the intracellular signalling involved in the stemness regulation and differentiation ability of SHEDs is discussed, particularly on fibroblast growth factor, Notch, and Wnt signalling. Finally, the potential regenerative therapeutic application of SHEDs is also described.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Waleerat Sukarawan and Thanaphum Osathanon",authors:[{id:"195871",title:"Associate Prof.",name:"Thanaphum",middleName:null,surname:"Osathanon",slug:"thanaphum-osathanon",fullName:"Thanaphum Osathanon"},{id:"195872",title:"Dr.",name:"Waleerat",middleName:null,surname:"Sukarawan",slug:"waleerat-sukarawan",fullName:"Waleerat Sukarawan"}]},{id:"55767",title:"Characteristics of Full-Term Amniotic Fluid-Derived Mesenchymal Stem Cells in Different Culture Media",slug:"characteristics-of-full-term-amniotic-fluid-derived-mesenchymal-stem-cells-in-different-culture-medi",totalDownloads:1352,totalCrossrefCites:2,totalDimensionsCites:5,abstract:"Amniotic fluid contains precious therapeutic stem cells with ideal features such as they are broadly multipotent, genetically stable, and non-tumorigenic. One of the stem cells that is abundantly found in amniotic fluid is mesenchymal stem cells. Human amniotic fluid mesenchymal stem cells (hAFMSCs) had been successfully isolated from amniotic fluid obtained from second or third trimester amniocentesis. However, studies on hAFMSCs obtained during full-term delivery are still lacking. Furthermore, suitable culture media to propagate hAFMSCs for therapeutic purposes have not been fully established. Basal medium supplemented with fetal bovine serum is commonly used, and unfortunately, this condition has been associated with the risk of transmission of animal pathogens and xenogenic immune reaction. An efficient isolation and expansion method together with suitable culture conditions is essential in establishing a specific homogenous cell population, such as full-term hAFMSCs, of clinical grade. In this chapter we briefly describe the feasibility of generating hAFMSCs from full-term amniotic fluid obtained during cesarean section using serum-free medium as opposed to the conventional serum containing media. These findings would be very useful in utilizing stem cells for bench side application from a source that is accessible and devoid of ethical and safety concerns.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Karuppiah Thilakavathy, Norshariza Nordin, Rajesh Ramasamy,\nPeyman Ghoraishizadeh, Izanwati Mohd Rawi Rohayu and Gurbind\nSingh",authors:[{id:"195911",title:"Associate Prof.",name:"Thilakavathy",middleName:null,surname:"Karuppiah",slug:"thilakavathy-karuppiah",fullName:"Thilakavathy Karuppiah"},{id:"201873",title:"Dr.",name:"Rajesh",middleName:null,surname:"Ramasamy",slug:"rajesh-ramasamy",fullName:"Rajesh Ramasamy"},{id:"205278",title:"Dr.",name:"Norshariza",middleName:null,surname:"Nordin",slug:"norshariza-nordin",fullName:"Norshariza Nordin"},{id:"205279",title:"Dr.",name:"Gurbind",middleName:null,surname:"Singh",slug:"gurbind-singh",fullName:"Gurbind Singh"},{id:"205280",title:"Ms.",name:"Rohayu Izanwati",middleName:null,surname:"Mohd Rawi",slug:"rohayu-izanwati-mohd-rawi",fullName:"Rohayu Izanwati Mohd Rawi"},{id:"205281",title:"Mr.",name:"Peyman",middleName:null,surname:"Ghoraishizadeh",slug:"peyman-ghoraishizadeh",fullName:"Peyman Ghoraishizadeh"}]},{id:"55815",title:"Physical versus Immunological Purification of Mesenchymal Stem Cells",slug:"physical-versus-immunological-purification-of-mesenchymal-stem-cells",totalDownloads:1596,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"A prerequisite before dealing with any cell type is to identify it and isolate it from the heterogeneous cell population that it belongs to. Mesenchymal stem cells (MSC) can be found in nearly all tissues and are mostly located in perivascular niches.",book:{id:"5770",slug:"mesenchymal-stem-cells-isolation-characterization-and-applications",title:"Mesenchymal Stem Cells",fullTitle:"Mesenchymal Stem Cells - Isolation, Characterization and Applications"},signatures:"Radwa Ali Mehanna",authors:[{id:"182118",title:"Dr.",name:"Radwa Ali",middleName:null,surname:"Mehanna",slug:"radwa-ali-mehanna",fullName:"Radwa Ali Mehanna"}]}],onlineFirstChaptersFilter:{topicId:"390",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"3",title:"Dentistry",doi:"10.5772/intechopen.71199",issn:"2631-6218",scope:"\r\n\tThis book series will offer a comprehensive overview of recent research trends as well as clinical applications within different specialties of dentistry. Topics will include overviews of the health of the oral cavity, from prevention and care to different treatments for the rehabilitation of problems that may affect the organs and/or tissues present. The different areas of dentistry will be explored, with the aim of disseminating knowledge and providing readers with new tools for the comprehensive treatment of their patients with greater safety and with current techniques. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This series of books will focus on various aspects of the properties and results obtained by the various treatments available, whether preventive or curative.
",coverUrl:"https://cdn.intechopen.com/series/covers/3.jpg",latestPublicationDate:"August 14th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"419588",title:"Ph.D.",name:"Sergio",middleName:"Alexandre",surname:"Gehrke",slug:"sergio-gehrke",fullName:"Sergio Gehrke",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038WgMKQA0/Profile_Picture_2022-06-02T11:44:20.jpg",biography:"Dr. Sergio Alexandre Gehrke is a doctorate holder in two fields. The first is a Ph.D. in Cellular and Molecular Biology from the Pontificia Catholic University, Porto Alegre, Brazil, in 2010 and the other is an International Ph.D. in Bioengineering from the Universidad Miguel Hernandez, Elche/Alicante, Spain, obtained in 2020. In 2018, he completed a postdoctoral fellowship in Materials Engineering in the NUCLEMAT of the Pontificia Catholic University, Porto Alegre, Brazil. He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. In addition, he is also a Collaborating Professor in several Postgraduate programs at different universities all over the world.",institutionString:null,institution:{name:"Universidad Católica San Antonio de Murcia",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",isOpenForSubmission:!0,editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",slug:"tomas-jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",biography:"Tomasz Jarzembowski was born in 1968 in Gdansk, Poland. He obtained his Ph.D. degree in 2000 from the Medical University of Gdańsk (UG). After specialization in clinical microbiology in 2003, he started studying biofilm formation and antibiotic resistance at the single-cell level. In 2015, he obtained his D.Sc. degree. His later study in cooperation with experts in nephrology and immunology resulted in the designation of the new diagnostic method of UTI, patented in 2017. He is currently working at the Department of Microbiology, Medical University of Gdańsk (GUMed), Poland. Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. He is also a reviewer and a member of editorial boards of a number of international journals.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",slug:"katarzyna-garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",biography:"Katarzyna Maria Garbacz, MD, is an Associate Professor at the Medical University of Gdańsk, Poland and she is head of the Department of Oral Microbiology of the Medical University of Gdańsk. She has published more than 50 scientific publications in peer-reviewed journals. She has been a project leader funded by the National Science Centre of Poland. Prof. Garbacz is a microbiologist working on applied and fundamental questions in microbial epidemiology and pathogenesis. Her research interest is in antibiotic resistance, host-pathogen interaction, and therapeutics development for staphylococcal pathogens, mainly Staphylococcus aureus, which causes hospital-acquired infections. Currently, her research is mostly focused on the study of oral pathogens, particularly Staphylococcus spp.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",isOpenForSubmission:!0,editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",isOpenForSubmission:!0,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},{id:"6",title:"Viral Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",isOpenForSubmission:!0,editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:20,paginationItems:[{id:"83065",title:"Interventions and Practical Approaches to Reduce the Burden of Malaria on School-Aged Children",doi:"10.5772/intechopen.106469",signatures:"Andrew Macnab",slug:"interventions-and-practical-approaches-to-reduce-the-burden-of-malaria-on-school-aged-children",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Andrew",surname:"Macnab"}],book:{title:"Malaria - Recent Advances, and New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11576.jpg",subseries:{id:"5",title:"Parasitic Infectious Diseases"}}},{id:"82804",title:"Psychiatric Problems in HIV Care",doi:"10.5772/intechopen.106077",signatures:"Seggane Musisi and Noeline Nakasujja",slug:"psychiatric-problems-in-hiv-care",totalDownloads:1,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Future Opportunities and Tools for Emerging Challenges for HIV/AIDS Control",coverURL:"https://cdn.intechopen.com/books/images_new/11575.jpg",subseries:{id:"6",title:"Viral Infectious Diseases"}}},{id:"82827",title:"Epidemiology and Control of Schistosomiasis",doi:"10.5772/intechopen.105170",signatures:"Célestin Kyambikwa Bisangamo",slug:"epidemiology-and-control-of-schistosomiasis",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"New Horizons for Schistosomiasis Research",coverURL:"https://cdn.intechopen.com/books/images_new/10829.jpg",subseries:{id:"5",title:"Parasitic Infectious Diseases"}}},{id:"82817",title:"Perspective Chapter: Microfluidic Technologies for On-Site Detection and Quantification of Infectious Diseases - The Experience with SARS-CoV-2/COVID-19",doi:"10.5772/intechopen.105950",signatures:"Andres Escobar and Chang-qing Xu",slug:"perspective-chapter-microfluidic-technologies-for-on-site-detection-and-quantification-of-infectious",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"SARS-CoV-2 Variants - Two Years After",coverURL:"https://cdn.intechopen.com/books/images_new/11573.jpg",subseries:{id:"6",title:"Viral Infectious Diseases"}}}]},overviewPagePublishedBooks:{paginationCount:13,paginationItems:[{type:"book",id:"6667",title:"Influenza",subtitle:"Therapeutics and Challenges",coverURL:"https://cdn.intechopen.com/books/images_new/6667.jpg",slug:"influenza-therapeutics-and-challenges",publishedDate:"September 19th 2018",editedByType:"Edited by",bookSignature:"Shailendra K. Saxena",hash:"105e347b2d5dbbe6b593aceffa051efa",volumeInSeries:1,fullTitle:"Influenza - Therapeutics and Challenges",editors:[{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}]},{type:"book",id:"7064",title:"Current Perspectives in Human Papillomavirus",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7064.jpg",slug:"current-perspectives-in-human-papillomavirus",publishedDate:"May 2nd 2019",editedByType:"Edited by",bookSignature:"Shailendra K. Saxena",hash:"d92a4085627bab25ddc7942fbf44cf05",volumeInSeries:2,fullTitle:"Current Perspectives in Human Papillomavirus",editors:[{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}]},{type:"book",id:"7123",title:"Current Topics in Neglected Tropical Diseases",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7123.jpg",slug:"current-topics-in-neglected-tropical-diseases",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Alfonso J. 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