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      Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic

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          Abstract

          The world has never been prepared for global pandemics like the COVID-19, currently posing an immense threat to the public and consistent pressure on the global healthcare systems to navigate optimized tools, equipments, medicines, and techno-driven approaches to retard the infection spread. The synergized outcome of artificial intelligence paradigms and human-driven control measures elicit a significant impact on screening, analysis, prediction, and tracking the currently infected individuals, and likely the future patients, with precision and accuracy, generating regular international and national data on confirmed, recovered, and death cases, as the current status of 3,820,869 infected patients worldwide. Artificial intelligence is a frontline concept, with time-saving, cost-effective, and productive access to disease management, rendering positive results in physician assistance in high workload conditions, radiology imaging, computational tomography, and database formulations, to facilitate availability of information accessible to researchers all over the globe. The review tends to elaborate the role of industry 4.0 technology, fast diagnostic procedures, and convolutional neural networks, as artificial intelligence aspects, in potentiating the COVID-19 management criteria and differentiating infection in SARS-CoV-2 positive and negative groups. Therefore, the review successfully supplements the processes of vaccine development, disease management, diagnosis, patient records, transmission inhibition, social distancing, and future pandemic predictions, with artificial intelligence revolution and smart techno processes to ensure that the human race wins this battle with COVID-19 and many more combats in the future.

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          Most cited references50

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          Is Open Access

          CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database

          The Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca) is a manually curated resource containing high quality reference data on the molecular basis of antimicrobial resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR. CARD is ontologically structured, model centric, and spans the breadth of AMR drug classes and resistance mechanisms, including intrinsic, mutation-driven and acquired resistance. It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Its design allows the development of novel genome analysis tools, such as the Resistance Gene Identifier (RGI) for resistome prediction from raw genome sequence. Recent improvements include extensive curation of additional reference sequences and mutations, development of a unique Model Ontology and accompanying AMR detection models to power sequence analysis, new visualization tools, and expansion of the RGI for detection of emergent AMR threats. CARD curation is updated monthly based on an interplay of manual literature curation, computational text mining, and genome analysis.
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            Artificial intelligence in healthcare: past, present and future

            Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.
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              Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies

              The beginning of 2020 has seen the emergence of COVID-19 outbreak caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). There is an imminent need to better understand this new virus and to develop ways to control its spread. In this study, we sought to gain insights for vaccine design against SARS-CoV-2 by considering the high genetic similarity between SARS-CoV-2 and SARS-CoV, which caused the outbreak in 2003, and leveraging existing immunological studies of SARS-CoV. By screening the experimentally-determined SARS-CoV-derived B cell and T cell epitopes in the immunogenic structural proteins of SARS-CoV, we identified a set of B cell and T cell epitopes derived from the spike (S) and nucleocapsid (N) proteins that map identically to SARS-CoV-2 proteins. As no mutation has been observed in these identified epitopes among the 120 available SARS-CoV-2 sequences (as of 21 February 2020), immune targeting of these epitopes may potentially offer protection against this novel virus. For the T cell epitopes, we performed a population coverage analysis of the associated MHC alleles and proposed a set of epitopes that is estimated to provide broad coverage globally, as well as in China. Our findings provide a screened set of epitopes that can help guide experimental efforts towards the development of vaccines against SARS-CoV-2.
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                Author and article information

                Contributors
                tapanbehl31@gmail.com , tapan.behl@chitkara.edu.in
                Journal
                Environ Sci Pollut Res Int
                Environ Sci Pollut Res Int
                Environmental Science and Pollution Research International
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0944-1344
                1614-7499
                25 May 2021
                : 1-18
                Affiliations
                [1 ]GRID grid.428245.d, ISNI 0000 0004 1765 3753, Chitkara College of Pharmacy, , Chitkara University, ; Chandigarh, Punjab India
                [2 ]GRID grid.7459.f, ISNI 0000 0001 2188 3779, Chrono-Environment Laboratory, UMR CNRS 6249, , Bourgogne Franche-Comté University, ; Besançon, France
                [3 ]GRID grid.15444.30, ISNI 0000 0004 0470 5454, Department of Global Medical Science, Wonju College of Medicine, , Yonsei University, ; Seoul, South Korea
                [4 ]GRID grid.443031.1, ISNI 0000 0004 0371 4375, Department of Pharmacy, , Southeast University, ; Banani, Dhaka, 1213 Bangladesh
                Author notes

                Responsible Editor: Philipp Gariguess

                Author information
                http://orcid.org/0000-0003-0993-7708
                Article
                13823
                10.1007/s11356-021-13823-8
                8148397
                34036497
                a6b59c20-c952-4e51-afb2-8c3bb6de09f1
                © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 13 May 2020
                : 5 April 2021
                Categories
                Environmental Factors and the Epidemics of COVID-19

                General environmental science
                covid-19,techno-driven,radiology imaging,computational tomography,industry 4.0,disease management

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