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Naveed M, Ali U, Aziz T, Naveed R, Mahmood S, Khan MM, Alharbi M, Albekairi TH, Alasmari AF. An Aedes-Anopheles Vaccine Candidate Supplemented with BCG Epitopes Against the Aedes and Anopheles Genera to Overcome Hypersensitivity to Mosquito Bites. Acta Parasitol 2024; 69:483-504. [PMID: 38194049 DOI: 10.1007/s11686-023-00771-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Skeeter syndrome is a severe local allergic response to mosquito bites that is accompanied by considerable inflammation and, in some cases, a systemic response like fever. People with the syndrome develop serious allergies, ranging from rashes to anaphylaxis or shock. The few available studies on mosquito venom immunotherapy have utilized whole-body preparations and small sample sizes. Still, owing to their little success, vaccination remains a promising alternative as well as a permanent solution for infections like Skeeter's. METHODS This study, therefore, illustrated the construction of an epitope-based vaccine candidate against Skeeter Syndrome using established immunoinformatic techniques. We selected three species of mosquitoes, Anopheles melas, Anopheles funestus, and Aedes aegypti, to derive salivary antigens usually found in mosquito bites. Our construct was also supplemented with bacterial epitopes known to elicit a strong TH1 response and suppress TH2 stimulation that is predicted to reduce hypersensitivity against the bites. RESULTS A quality factor of 98.9496, instability index of 38.55, aliphatic index of 79.42, solubility of 0.934747, and GRAVY score of -0.02 indicated the structural (tertiary and secondary) stability, thermostability, solubility, and hydrophilicity of the construct, respectively. The designed Aedes-Anopheles vaccine (AAV) candidate was predicted to be flexible and less prone to deformability with an eigenvalue of 1.5911e-9 and perfected the human immune response against Skeeter (hypersensitivity) and many mosquito-associated diseases as we noted the production of 30,000 Th1 cells per mm3 with little (insignificant production of Th2 cells. The designed vaccine also revealed stable interactions with the pattern recognition receptors of the host. The TLR2/vaccine complex interacted with a free energy of - 1069.2 kcal/mol with 26 interactions, whereas the NLRP3/vaccine complex interacted with a free energy of - 1081.2 kcal/mol with 16 molecular interactions. CONCLUSION Although being a pure in-silico study, the in-depth analysis performed herein speaks volumes of the potency of the designed vaccine candidate predicting that the proposition can withstand rigorous in-vitro and in-vivo clinical trials and may proceed to become the first preventative immunotherapy against mosquito bite allergy.
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Affiliation(s)
- Muhammad Naveed
- Department of Biotechnology, Faculty of Science and Technology, University of Central Punjab, Lahore, 54590, Punjab, Pakistan.
| | - Urooj Ali
- Department of Biotechnology, Quaid-I-Azam University Islamabad, Islamabad, 45320, Pakistan
| | - Tariq Aziz
- Department of Agriculture, University of Ioannina Arta, 47100, Arta, Greece.
| | - Rida Naveed
- Department of Biotechnology, Faculty of Science and Technology, University of Central Punjab, Lahore, 54590, Punjab, Pakistan
| | - Sarmad Mahmood
- Department of Biotechnology, Faculty of Science and Technology, University of Central Punjab, Lahore, 54590, Punjab, Pakistan
| | - Muhammad Mustajab Khan
- Department of Biotechnology, Quaid-I-Azam University Islamabad, Islamabad, 45320, Pakistan
| | - Metab Alharbi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
| | - Thamer H Albekairi
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
| | - Abdullah F Alasmari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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Stafie CS, Sufaru IG, Ghiciuc CM, Stafie II, Sufaru EC, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics (Basel) 2023; 13:1995. [PMID: 37370890 DOI: 10.3390/diagnostics13121995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.
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Affiliation(s)
- Celina Silvia Stafie
- Department of Preventive Medicine and Interdisciplinarity, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Irina-Georgeta Sufaru
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Cristina Mihaela Ghiciuc
- Department of Morpho-Functional Sciences II-Pharmacology and Clinical Pharmacology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Ingrid-Ioana Stafie
- Endocrinology Residency Program, Sf. Spiridon Clinical Emergency Hospital, Independentei 1, 700111 Iasi, Romania
| | | | - Sorina Mihaela Solomon
- Department of Periodontology, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
| | - Monica Hancianu
- Pharmacognosy-Phytotherapy, Grigore T. Popa University of Medicine and Pharmacy Iasi, Universitatii Street 16, 700115 Iasi, Romania
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Pandey A, Madan R, Singh S. Immunology to Immunotherapeutics of SARS-CoV-2: Identification of Immunogenic Epitopes for Vaccine Development. Curr Microbiol 2022; 79:306. [PMID: 36064873 PMCID: PMC9444117 DOI: 10.1007/s00284-022-03003-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 08/16/2022] [Indexed: 11/21/2022]
Abstract
The emergence of COVID19 pandemic caused by SARS-CoV-2 virus has created a global public health and socio-economic crisis. Immunoinformatics-based approaches to investigate the potential antigens is the fastest way to move towards a multiepitope-based vaccine development. This review encompasses the underlying mechanisms of pathogenesis, innate and adaptive immune signaling along with evasion pathways of SARS-CoV-2. Furthermore, it compiles the promiscuous peptides from in silico studies which are subjected to prediction of cytokine milieu using web-based servers. Out of the 434 peptides retrieved from all studies, we have identified 33 most promising T cell vaccine candidates. This review presents a list of the most potential epitopes from several proteins of the virus based on their immunogenicity, homology, conservancy and population coverage studies. These epitopes can form a basis of second generation of vaccine development as the first generation vaccines in various stages of trials mostly focus only on Spike protein. We therefore, propose them as most potential candidates which can be taken up immediately for confirmation by experimental studies.
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Affiliation(s)
- Apoorva Pandey
- Indian Council of Medical Research, V. Ramalingaswami Bhawan, Ansari Nagar, P.O. Box No. 4911, New Delhi, 110029 India
| | - Riya Madan
- Indian Institute of Science Education and Research (IISER) Mohali, Knowledge City, Sector 81, Sahibzada Ajit Singh Nagar, Punjab 140306 India
| | - Swati Singh
- Department of Zoology, University of Delhi, Delhi, 110007 India
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Abbasi BA, Saraf D, Sharma T, Sinha R, Singh S, Sood S, Gupta P, Gupta A, Mishra K, Kumari P, Rawal K. Identification of vaccine targets & design of vaccine against SARS-CoV-2 coronavirus using computational and deep learning-based approaches. PeerJ 2022; 10:e13380. [PMID: 35611169 PMCID: PMC9124463 DOI: 10.7717/peerj.13380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 04/13/2022] [Indexed: 01/13/2023] Open
Abstract
An unusual pneumonia infection, named COVID-19, was reported on December 2019 in China. It was reported to be caused by a novel coronavirus which has infected approximately 220 million people worldwide with a death toll of 4.5 million as of September 2021. This study is focused on finding potential vaccine candidates and designing an in-silico subunit multi-epitope vaccine candidates using a unique computational pipeline, integrating reverse vaccinology, molecular docking and simulation methods. A protein named spike protein of SARS-CoV-2 with the GenBank ID QHD43416.1 was shortlisted as a potential vaccine candidate and was examined for presence of B-cell and T-cell epitopes. We also investigated antigenicity and interaction with distinct polymorphic alleles of the epitopes. High ranking epitopes such as DLCFTNVY (B cell epitope), KIADYNKL (MHC Class-I) and VKNKCVNFN (MHC class-II) were shortlisted for subsequent analysis. Digestion analysis verified the safety and stability of the shortlisted peptides. Docking study reported a strong binding of proposed peptides with HLA-A*02 and HLA-B7 alleles. We used standard methods to construct vaccine model and this construct was evaluated further for its antigenicity, physicochemical properties, 2D and 3D structure prediction and validation. Further, molecular docking followed by molecular dynamics simulation was performed to evaluate the binding affinity and stability of TLR-4 and vaccine complex. Finally, the vaccine construct was reverse transcribed and adapted for E. coli strain K 12 prior to the insertion within the pET-28-a (+) vector for determining translational and microbial expression followed by conservancy analysis. Also, six multi-epitope subunit vaccines were constructed using different strategies containing immunogenic epitopes, appropriate adjuvants and linker sequences. We propose that our vaccine constructs can be used for downstream investigations using in-vitro and in-vivo studies to design effective and safe vaccine against different strains of COVID-19.
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Alyasseri ZAA, Al‐Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Alomari OA, Abdulkareem KH, Adam A, Damasevicius R, Mohammed MA, Zitar RA. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. EXPERT SYSTEMS 2022; 39:e12759. [PMID: 34511689 PMCID: PMC8420483 DOI: 10.1111/exsy.12759] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 05/17/2021] [Accepted: 06/07/2021] [Indexed: 05/02/2023]
Abstract
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
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Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
- ECE Department‐Faculty of EngineeringUniversity of KufaNajafIraq
| | - Mohammed Azmi Al‐Betar
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Information TechnologyAl‐Huson University College, Al‐Balqa Applied UniversityIrbidJordan
| | - Iyad Abu Doush
- Computing Department, College of Engineering and Applied SciencesAmerican University of KuwaitSalmiyaKuwait
- Computer Science DepartmentYarmouk UniversityIrbidJordan
| | - Mohammed A. Awadallah
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Computer ScienceAl‐Aqsa UniversityGazaPalestine
| | - Ammar Kamal Abasi
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Faculty of Information TechnologyMiddle East UniversityAmmanJordan
| | | | | | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
| | | | - Mazin Abed Mohammed
- College of Computer Science and Information TechnologyUniversity of AnbarAnbarIraq
| | - Raed Abu Zitar
- Sorbonne Center of Artificial IntelligenceSorbonne University‐Abu DhabiAbu DhabiUnited Arab Emirates
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Almustafa KM. Covid19-Mexican-Patients' Dataset (Covid19MPD) Classification and Prediction Using Feature Importance. CONCURRENCY AND COMPUTATION : PRACTICE & EXPERIENCE 2022; 34:e6675. [PMID: 34899078 PMCID: PMC8646298 DOI: 10.1002/cpe.6675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 09/15/2021] [Accepted: 09/24/2021] [Indexed: 06/04/2023]
Abstract
Coronavirus disease, Covid19, pandemic has a great effect on human heath worldwide since it was first detected in late 2019. A clear understanding of the structure of the available Covid19 datasets might give the healthcare provider a better understanding of identifying some of the cases at an early stage. In this article, we will be looking into a Covid19 Mexican Patients' Dataset (Covid109MPD), and we will apply number of machine learning algorithms on the dataset to select the best possible classification algorithm for the death and survived cases in Mexico, then we will study the performance of the enhancement of the specified classifiers in term of their features selection in order to be able to predict sever, and or death, cases from the available dataset. Results show that J48 classifier gives the best classification accuracy with 94.41% and RMSE = 0.2028 and ROC = 0.919, compared to other classifiers, and when using feature selection method, J48 classifier can predict a surviving Covid19MPD case within 94.88% accuracy, and by using only 10 out of the total 19 features.
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Affiliation(s)
- Khaled Mohamad Almustafa
- Department of Information Systems, College of Computer and Information SystemsPrince Sultan UniversityRiyadhKingdom of Saudi Arabia
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8
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Naz SS, Munir I. An Outline of Contributing Vaccine Technologies for SARS CoV2 Advancing in Clinical and Preclinical Phase-Trials. Recent Pat Biotechnol 2022; 16:122-143. [PMID: 35040422 DOI: 10.2174/1872208316666220118094344] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 10/11/2021] [Accepted: 11/27/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV2) is an RNA virus involving 4 structural and 16 non-structural proteins, and exhibiting high transmission potential and fatality. The emergence of this newly encountered beta coronavirus-SARS CoV2 has brought over 2 million people to death, and more than 10 billion people got infected across the globe as yet. Consequently, the global scientific community has contributed to the synthesis and design of effective immunization technologies to combat this virus. OBJECTIVES This literature review was intended to gather an update on published reports of the vaccines advancing in the clinical trial phases or preclinical trials, to summarize the foundations and implications of contributing vaccine candidates inferring their impact in the pandemic repression. In addition, this literature review distinctly facilitates an outline of the overall vaccine effectiveness at current doses. METHODS The reported data in this review was extracted from research articles, review articles and patents published from January 2020 to July 2021, available on Google Scholar, Pubmed, Pubmed Central, Research Gate, Science direct, and Free Patent Online Database by using combination of keywords. Moreover, some information is retrieved from native web pages of vaccine manufacturing companies' due to progressing research and unavailability of published research papers. CONCLUSION Contributing vaccine technologies include: RNA (Ribonucleic acid) vaccines, DNA (Deoxyribonucleic acid) vaccines, viral vector vaccines, protein-based vaccines, inactivated vaccines, viruses-like particles, protein superglue, and live-attenuated vaccines. Some vaccines are prepared by establishing bacterial and yeast cell lines and as self-assembling adenovirus- derived multimeric protein-based self-assembling nanoparticle (ADDOmer). On May 19, WHO has issued an emergency use sanction of Moderna, Pfizer, Sinopharm, AstraZeneca, and Covishield vaccine candidates on account of clinical credibility from experimental data.
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Affiliation(s)
- Sheikh Saba Naz
- Department of Microbiology, Jinnah University for Women, Pakistan
| | - Iqra Munir
- Department of Microbiology, Jinnah University for Women, Pakistan
- National Nanotechnology Research Center-UNAM, Bilkent University, Turkey
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AIM and Evolutionary Theory. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_41] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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10
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Equbal A, Masood S, Equbal I, Ahmad S, Khan NZ, Khan ZA. Artificial Intelligence against COVID-19 Pandemic: A Comprehensive Insight. Curr Med Imaging 2022; 19:1-18. [PMID: 34607548 DOI: 10.2174/1573405617666211004115208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/11/2021] [Accepted: 08/30/2021] [Indexed: 11/22/2022]
Abstract
COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present. Several vaccination drives using emergency use authorisation vaccines have been held across many countries; however, their long-term efficacy and side-effects studies are yet to be studied. Various analytical and statistical models have been developed, however, their outcome rate is prolonged. Thus, modern science stresses the application of state-of-the-art methods to combat COVID-19. This paper aims to provide a deep insight into the comprehensive literature about AI and AI-driven tools in the battle against the COVID-19 pandemic. The high efficacy of these AI systems can be observed in terms of highly accurate results, i.e., > 95%, as reported in various studies. The extensive literature reviewed in this paper is divided into five sections, each describing the application of AI against COVID-19 viz. COVID-19 prevention, diagnostic, infection spread trend prediction, therapeutic and drug repurposing. The application of Artificial Intelligence (AI) and AI-driven tools are proving to be useful in managing and fighting against the COVID-19 pandemic, especially by analysing the X-Ray and CT-Scan imaging data of infected subjects, infection trend predictions, etc.
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Affiliation(s)
- Azhar Equbal
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
| | - Sarfaraz Masood
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
| | - Iftekhar Equbal
- Department of Rural Management, Xavier Institute of Social Service, Jharkhand, India
| | - Shafi Ahmad
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
| | - Noor Zaman Khan
- National Institute of Technology Srinagar, Hazratbal, Srinagar, Jammu, and Kashmir, India
| | - Zahid A Khan
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
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Huang PC, Goru R, Huffman A, Yu Lin A, Cooke MF, He Y. Cov19VaxKB: A Web-based Integrative COVID-19 Vaccine Knowledge Base. Vaccine X 2021; 10:100139. [PMID: 34981039 PMCID: PMC8716025 DOI: 10.1016/j.jvacx.2021.100139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 11/09/2021] [Accepted: 12/22/2021] [Indexed: 12/23/2022] Open
Abstract
The development of SARS-CoV-2 vaccines during the COVID-19 pandemic has prompted the emergence of COVID-19 vaccine data. Timely access to COVID-19 vaccine information is crucial to researchers and public. To support more comprehensive annotation, integration, and analysis of COVID-19 vaccine information, we have developed Cov19VaxKB, a knowledge-focused COVID-19 vaccine database (http://www.violinet.org/cov19vaxkb/). Cov19VaxKB features comprehensive lists of COVID-19 vaccines, vaccine formulations, clinical trials, publications, news articles, and vaccine adverse event case reports. A web-based query interface enables comparison of product information and host responses among various vaccines. The knowledge base also includes a vaccine design tool for predicting vaccine targets and a statistical analysis tool that identifies enriched adverse events for FDA-authorized COVID-19 vaccines based on VAERS case report data. To support data exchange, Cov19VaxKB is synchronized with Vaccine Ontology and the Vaccine Investigation and Online Information Network (VIOLIN) database. The data integration and analytical features of Cov19VaxKB can facilitate vaccine research and development while also serving as a useful reference for the public.
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Key Words
- AE, adverse event
- CDC, Centers for Disease Control and Prevention
- COVID-19
- COVID-19 vaccine
- COVID-19, Coronavirus disease 2019
- Cov19VaxKB
- FDA, Food and Drug Administration
- MERS-CoV, Middle Eastern Respiratory Syndrome
- NCBI, National Center for Biotechnology Information
- OWL, Web Ontology Language
- PMID, PubMed identification number
- PRR, Proportional Reporting Ratio
- SARS-CoV, Severe Acute Respiratory Syndrome Coronavirus
- SARS-CoV-2
- SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2
- VAERS
- VAERS, Vaccine Adverse Event Reporting System
- VIOLIN, Vaccine Investigation and Online Information Network
- VO, Vaccine Ontology
- WHO, World Health Organization
- adverse event
- bioinformatics
- database
- knowledge base
- ontology
- vaccine
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Affiliation(s)
- Philip C. Huang
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rohit Goru
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anthony Huffman
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Asiyah Yu Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Michael F. Cooke
- School of Information, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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Kaur J, Kaur P. Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 29:2351-2382. [PMID: 34690493 PMCID: PMC8525064 DOI: 10.1007/s11831-021-09667-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 10/01/2021] [Indexed: 06/13/2023]
Abstract
From the month of December-19, the outbreak of Coronavirus (COVID-19) triggered several deaths and overstated every aspect of individual health. COVID-19 has been designated as a pandemic by World Health Organization. The circumstances placed serious trouble on every country worldwide, particularly with health arrangements and time-consuming responses. The increase in the positive cases of COVID-19 globally spread every day. The quantity of accessible diagnosing kits is restricted because of complications in detecting the existence of the illness. Fast and correct diagnosis of COVID-19 is a timely requirement for the prevention and controlling of the pandemic through suitable isolation and medicinal treatment. The significance of the present work is to discuss the outline of the deep learning techniques with medical imaging such as outburst prediction, virus transmitted indications, detection and treatment aspects, vaccine availability with remedy research. Abundant image resources of medical imaging as X-rays, Computed Tomography Scans, Magnetic Resonance imaging, formulate deep learning high-quality methods to fight against the pandemic COVID-19. The review presents a comprehensive idea of deep learning and its related applications in healthcare received over the past decade. At the last, some issues and confrontations to control the health crisis and outbreaks have been introduced. The progress in technology has contributed to developing individual's lives. The problems faced by the radiologists during medical imaging techniques and deep learning approaches for diagnosing the COVID-19 infections have been also discussed.
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Affiliation(s)
- Jaspreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab India
| | - Prabhpreet Kaur
- Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, Punjab India
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Pooventhiran T, Marondedze EF, Govender PP, Bhattacharyya U, Rao DJ, Aazam ES, Kuthanapillil JM, E TJ, Thomas R. Energy and reactivity profile and proton affinity analysis of rimegepant with special reference to its potential activity against SARS-CoV-2 virus proteins using molecular dynamics. J Mol Model 2021; 27:276. [PMID: 34480634 PMCID: PMC8416574 DOI: 10.1007/s00894-021-04885-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 08/18/2021] [Indexed: 12/23/2022]
Abstract
Rimegepant is a new medicine developed for the management of chronic headache due to migraine. This manuscript is an attempt to study the various structural, physical, and chemical properties of the molecules. The molecule was optimized using B3LYP functional with 6-311G + (2d,p) basis set. Excited state properties of the compound were studied using CAM-B3LYP functional with same basis sets using IEFPCM model in methanol for the implicit solvent atmosphere. The various electronic descriptors helped to identify the reactivity behavior and stability. The compound is found to possess good nonlinear optical properties in the gas phase. The various intramolecular electronic delocalizations and non-covalent interactions were analyzed and explained. As the compound contain several heterocyclic nitrogen atoms, they have potential proton abstraction features, which was analyzed energetically. The most important result from this study is from the molecular docking analysis which indicates that rimegepant binds irreversibly with three established SARS-CoV-2 proteins with ID 6LU7, 6M03, and 6W63 with docking scores − 9.2988, − 8.3629, and − 9.5421 kcal/mol respectively. Further assessment of docked complexes with molecular dynamics simulations revealed that hydrophobic interactions, water bridges, and π–π interactions play a significant role in stabilizing the ligand within the binding region of respective proteins. MMGBSA-free energies further demonstrated that rimegepant is more stable when complexed with 6LU7 among the selected PDB models. As the pharmacology and pharmacokinetics of this molecule are already established, rimegepant can be considered as an ideal candidate with potential for use in the treatment of COVID patients after clinical studies.
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Affiliation(s)
- T Pooventhiran
- Department of Chemistry, St Berchmans College (Autonomous), Mahatma Gandhi University, Changanassery, Kerala, India
| | - Ephraim Felix Marondedze
- Department of Chemical Sciences, Doornfontein Campus, University of Johannesburg, P. O. Box 17011, Johannesburg, 2028, South Africa
| | - Penny Poomani Govender
- Department of Chemical Sciences, Doornfontein Campus, University of Johannesburg, P. O. Box 17011, Johannesburg, 2028, South Africa
| | - Utsab Bhattacharyya
- Department of Chemistry, St Berchmans College (Autonomous), Mahatma Gandhi University, Changanassery, Kerala, India
| | - D Jagadeeswara Rao
- Department of Physics, Dr. Lankapalli Bullayya College, Visakhapatnam, Andhra Pradesh, India
| | - Elham S Aazam
- Chemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, 23622, Saudi Arabia
| | - Jinesh M Kuthanapillil
- Department of Chemistry, St Berchmans College (Autonomous), Mahatma Gandhi University, Changanassery, Kerala, India
| | - Tomlal Jose E
- Department of Chemistry, St Berchmans College (Autonomous), Mahatma Gandhi University, Changanassery, Kerala, India
| | - Renjith Thomas
- Department of Chemistry, St Berchmans College (Autonomous), Mahatma Gandhi University, Changanassery, Kerala, India.
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14
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Rawal K, Sinha R, Abbasi BA, Chaudhary A, Nath SK, Kumari P, Preeti P, Saraf D, Singh S, Mishra K, Gupta P, Mishra A, Sharma T, Gupta S, Singh P, Sood S, Subramani P, Dubey AK, Strych U, Hotez PJ, Bottazzi ME. Identification of vaccine targets in pathogens and design of a vaccine using computational approaches. Sci Rep 2021; 11:17626. [PMID: 34475453 PMCID: PMC8413327 DOI: 10.1038/s41598-021-96863-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.
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Affiliation(s)
- Kamal Rawal
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India.
| | - Robin Sinha
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Bilal Ahmed Abbasi
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Amit Chaudhary
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Swarsat Kaushik Nath
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Priya Kumari
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - P Preeti
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Devansh Saraf
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Shachee Singh
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Kartik Mishra
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Pranjay Gupta
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Astha Mishra
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Trapti Sharma
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Srijanee Gupta
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Prashant Singh
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Shriya Sood
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Preeti Subramani
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Aman Kumar Dubey
- Centre for Computational Biology and Bioinformatics, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, India
| | - Ulrich Strych
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Peter J Hotez
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
| | - Maria Elena Bottazzi
- Texas Children's Hospital Center for Vaccine Development, Departments of Pediatrics and Molecular Virology and Microbiology, National School of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
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15
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Babcock S, Beverley J, Cowell LG, Smith B. The Infectious Disease Ontology in the age of COVID-19. J Biomed Semantics 2021; 12:13. [PMID: 34275487 PMCID: PMC8286442 DOI: 10.1186/s13326-021-00245-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 06/21/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules. RESULTS To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents - organisms with an infectious disposition - and infectious structures - acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core's content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices. CONCLUSIONS IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.
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Affiliation(s)
- Shane Babcock
- Department of Philosophy, Niagara University, Lewiston, NY, USA.
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA.
| | - John Beverley
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Department of Philosophy, Northwestern University, Evanston, IL, USA
| | - Lindsay G Cowell
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Cowell Lab, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Barry Smith
- National Center for Ontological Research, University at Buffalo, Buffalo, NY, USA
- Department of Philosophy, University at Buffalo, Buffalo, NY, USA
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16
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Cuspoca AF, Díaz LL, Acosta AF, Peñaloza MK, Méndez YR, Clavijo DC, Yosa Reyes J. An Immunoinformatics Approach for SARS-CoV-2 in Latam Populations and Multi-Epitope Vaccine Candidate Directed towards the World's Population. Vaccines (Basel) 2021; 9:vaccines9060581. [PMID: 34205992 PMCID: PMC8228945 DOI: 10.3390/vaccines9060581] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 04/21/2021] [Accepted: 04/28/2021] [Indexed: 12/15/2022] Open
Abstract
The coronavirus pandemic is a major public health crisis affecting global health systems with dire socioeconomic consequences, especially in vulnerable regions such as Latin America (LATAM). There is an urgent need for a vaccine to help control contagion, reduce mortality and alleviate social costs. In this study, we propose a rational multi-epitope candidate vaccine against SARS-CoV-2. Using bioinformatics, we constructed a library of potential vaccine peptides, based on the affinity of the most common major human histocompatibility complex (HLA) I and II molecules in the LATAM population to predict immunological complexes among antigenic, non-toxic and non-allergenic peptides extracted from the conserved regions of 92 proteomes. Although HLA-C, had the greatest antigenic peptide capacity from SARS-CoV-2, HLA-B and HLA-A, could be more relevant based on COVID-19 risk of infection in LATAM countries. We also used three-dimensional structures of SARS-CoV-2 proteins to identify potential regions for antibody production. The best HLA-I and II predictions (with increased coverage in common alleles and regions evoking B lymphocyte responses) were grouped into an optimized final multi-epitope construct containing the adjuvants Beta defensin-3, TpD, and PADRE, which are recognized for invoking a safe and specific immune response. Finally, we used Molecular Dynamics to identify the multi-epitope construct which may be a stable target for TLR-4/MD-2. This would prove to be safe and provide the physicochemical requirements for conducting experimental tests around the world.
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Affiliation(s)
- Andrés Felipe Cuspoca
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Laura Lorena Díaz
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Alvaro Fernando Acosta
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Marcela Katherine Peñaloza
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Yardany Rafael Méndez
- Grupo de Investigación en Epidemiología Clínica de Colombia (GRECO), Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia; (A.F.C.); (L.L.D.); (A.F.A.); (M.K.P.); (Y.R.M.)
| | - Diana Carolina Clavijo
- Facultad de Ingeniería y Ciencias, Pontificia Universidad Javeriana Cali, Santiago de Cali 760031, Colombia;
| | - Juvenal Yosa Reyes
- Laboratorio de Simulación Molecular, Facultad de Ciencias Básicas y Biomédicas, Universidad Simón Bolívar, Barranquilla 080002, Colombia
- Correspondence:
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17
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Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, Prior F, Yu F. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Med Inform 2021; 9:e23811. [PMID: 33326405 PMCID: PMC7806275 DOI: 10.2196/23811] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/27/2020] [Accepted: 11/15/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic. OBJECTIVE The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression. METHODS A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI. RESULTS The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme. CONCLUSIONS In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.
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Affiliation(s)
- Hafsa Bareen Syeda
- Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Mahanazuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Kevin Wayne Sexton
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Surgery, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Health Policy and Management, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Shorabuddin Syed
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Salma Begum
- Department of Information Technology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Farhanuddin Syed
- College of Medicine, Shadan Institute of Medical Sciences, Hyderabad, India
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Feliciano Yu
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, United States
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18
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AIM and Evolutionary Theory. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_41-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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19
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Al-Emran M, Al-Kabi MN, Marques G. A Survey of Using Machine Learning Algorithms During the COVID-19 Pandemic. STUDIES IN SYSTEMS, DECISION AND CONTROL 2021:1-8. [DOI: 10.1007/978-3-030-67716-9_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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20
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Chintagunta AD, M SK, Nalluru S, N. S. SK. Nanotechnology: an emerging approach to combat COVID-19. EMERGENT MATERIALS 2021; 4:119-130. [PMID: 33615141 PMCID: PMC7883336 DOI: 10.1007/s42247-021-00178-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 01/27/2021] [Indexed: 05/04/2023]
Abstract
The recent outbreak of coronavirus disease (COVID-19) has challenged the survival of human existence in the last 1 year. Frontline healthcare professionals were struggling in combating the pandemic situation and were continuously supported with literature, skill set, research activities, and technologies developed by various scientists/researchers all over the world. To handle the continuously mutating severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requires amalgamation of conventional technology with emerging approaches. Nanotechnology is science, engineering, and technology dealing at the nanoscale level. It has made possible the development of nanomaterials, nano-biosensors, nanodrugs, and vaccines for diagnosis, therapy, and prevention of COVID-19. This review has elaborately highlighted the role of nanotechnology in developing various detection kits such as nanoparticle-assisted diagnostics, antibody assay, lateral flow immunoassay, nanomaterial biosensors, etc., in detection of SARS-CoV-2. Similarly, various advancements supervene through nanoparticle-based therapeutic drugs for inhibiting viral infection by blocking virus attachment/cell entry, multiplication/replication, and direct inactivation of the virus. Furthermore, information on vaccine development and the role of nanocarriers/nanoparticles were highlighted with a brief outlining of nanomaterial usage in sterilization and preventive mechanisms engineered to combat COVID-19 pandemic.
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Affiliation(s)
- Anjani Devi Chintagunta
- Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh 522213 India
| | - Sai Krishna M
- Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh 522213 India
| | - Sanjana Nalluru
- Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh 522213 India
| | - Sampath Kumar N. S.
- Department of Biotechnology, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh 522213 India
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21
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Catania LJ. SARS-CoV-2 and the COVID-19 pandemic. FOUNDATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE AND BIOSCIENCE 2021. [PMCID: PMC7691822 DOI: 10.1016/b978-0-12-824477-7.00004-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
I have written clinical and technical papers, journal articles, chapters and textbooks for over 40 years, but none ever created the pain I felt in writing this chapter. It was written in late April and May 2020 when the SARS-CoV-19 pandemic was ramping up and the deaths were dramatically mounting. When I wrote the paragraph on mortality rates in April 2020, total deaths in the U.S. were 1371. As I write this abstract now in late May, the total U.S. deaths have reached 104,000 souls, a 76-fold increase. I wept. This chapter will help you understand the clinical and technical aspects of the COVID-19 pandemic. It will provide a clear understanding of the background, the pathogenesis, the clinical aspects and the AI applications for COVID-19 (albeit dated by the time you read this), but it can’t begin to convey the pain we’re all sharing from this human tragedy.
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22
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Kose U, Deperlioglu O, Alzubi J, Patrut B. Future of Medical Decision Support Systems. DEEP LEARNING FOR MEDICAL DECISION SUPPORT SYSTEMS 2021. [PMCID: PMC7298991 DOI: 10.1007/978-981-15-6325-6_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Utku Kose
- Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey
| | - Omer Deperlioglu
- Department of Computer Technologies, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Jafar Alzubi
- Faculty of Engineering, Al-Balqa Applied University, Al-Salt, Jordan
| | - Bogdan Patrut
- Faculty of Computer Science, Alexandru Ioan Cuza University of Iasi, Iasi, Romania
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23
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Payandeh Z, Mohammadkhani N, Nabi Afjadi M, Khalili S, Rajabibazl M, Houjaghani Z, Dadkhah M. The immunology of SARS-CoV-2 infection, the potential antibody based treatments and vaccination strategies. Expert Rev Anti Infect Ther 2020; 19:899-910. [PMID: 33307883 DOI: 10.1080/14787210.2020.1863144] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a potentially fatal agent for a new emerging viral disease (COVID-19) is of great global public health emergency. Herein, we represented potential antibody-based treatments especially monoclonal antibodies (mAbs) that may exert a potential role in treatment as well as developing vaccination strategies against COVID-19.Areas covered: We used PubMed, Google Scholar, and clinicaltrials.gov search strategies for relevant papers. We demonstrated some agents with potentially favorable efficacy as well as favorable safety. Several therapies are under assessment to evaluate their efficacy and safety for COVID19. However, the development of different strategies such as SARS-CoV-2-based vaccines and antibody therapy are urgently required beside other effective therapies such as plasma, anticoagulants, and immune as well as antiviral therapies. We encourage giving more attention to antibody-based treatments as an immediate strategy. Although there has not been any approved specific vaccine until now, developing vaccination strategies may have a protective effect against COVID-19.Expert opinion: An antiviral mAbs could be a safe and high-quality therapeutic intervention which is greatly recommended for COVID-19. Additionally, the high sequence homology between the SARS-CoV-2 and SARS/MERS viruses could shed light on developing to design a vaccine against SARS-CoV-2.
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Affiliation(s)
- Zahra Payandeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Hospital of Xi'an Jiaotong University (Xibei Hospital), 710004 Xi'an, China
| | - Niloufar Mohammadkhani
- Department of Clinical Biochemistry, School of Medicine, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Mohsen Nabi Afjadi
- Institute of Biochemistry and Biophysics, Tehran University, Tehran, Iran
| | - Saeed Khalili
- Department of Biology Sciences, Shahid Rajaee Teacher Training University, Tehran, Iran
| | - Masoumeh Rajabibazl
- Department of Clinical Biochemistry, School of Medicine, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Houjaghani
- Department of Pharmacy Education, EMUPSS, Eastern Mediterranean University, Famagusta, N.Cyprus
| | - Masoomeh Dadkhah
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.,Department of Pharmacology, School of Pharmacy, Ardabil University of Medical Sciences, Ardabil, Iran
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24
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DNA vaccines against COVID-19: Perspectives and challenges. Life Sci 2020; 267:118919. [PMID: 33352173 PMCID: PMC7749647 DOI: 10.1016/j.lfs.2020.118919] [Citation(s) in RCA: 144] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/01/2020] [Accepted: 12/13/2020] [Indexed: 12/23/2022]
Abstract
The coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is associated with several fatal cases worldwide. The rapid spread of this pathogen and the increasing number of cases highlight the urgent development of vaccines. Among the technologies available for vaccine development, DNA vaccination is a promising alternative to conventional vaccines. Since its discovery in the 1990s, it has been of great interest because of its ability to elicit both humoral and cellular immune responses while showing relevant advantages regarding producibility, stability, and storage. This review aimed to summarize the current knowledge and advancements on DNA vaccines against COVID-19, particularly those in clinical trials.
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Abd-Alrazaq A, Alajlani M, Alhuwail D, Schneider J, Al-Kuwari S, Shah Z, Hamdi M, Househ M. Artificial Intelligence in the Fight Against COVID-19: Scoping Review. J Med Internet Res 2020; 22:e20756. [PMID: 33284779 PMCID: PMC7744141 DOI: 10.2196/20756] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 07/26/2020] [Accepted: 07/29/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. OBJECTIVE This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. METHODS A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. RESULTS We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome-related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. CONCLUSIONS The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.
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Affiliation(s)
- Alaa Abd-Alrazaq
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Mohannad Alajlani
- Institute of Digital Healthcare, University of Warwick, Coventry, United Kingdom
| | - Dari Alhuwail
- Information Science Department, College of Life Sciences, Kuwait University, Kuwait, Kuwait
| | - Jens Schneider
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Saif Al-Kuwari
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Mounir Hamdi
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Mowafa Househ
- Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Rastogi M, Pandey N, Shukla A, Singh SK. SARS coronavirus 2: from genome to infectome. Respir Res 2020; 21:318. [PMID: 33261606 PMCID: PMC7706175 DOI: 10.1186/s12931-020-01581-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 11/22/2020] [Indexed: 02/06/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) belongs to the group of Betacoronaviruses. The SARS-CoV-2 is closely related to SARS-CoV-1 and probably originated either from bats or pangolins. SARS-CoV-2 is an etiological agent of COVID-19, causing mild to severe respiratory disease which escalates to acute respiratory distress syndrome (ARDS) or multi-organ failure. The virus was first reported from the animal market in Hunan, Hubei province of China in the month of December, 2019, and was rapidly transmitted from animal to human and human-to-human. The human-to-human transmission can occur directly or via droplets generated during coughing and sneezing. Globally, around 53.9 million cases of COVID-19 have been registered with 1.31 million confirmed deaths. The people > 60 years, persons suffering from comorbid conditions and immunocompromised individuals are more susceptible to COVID-19 infection. The virus primarily targets the upper and the lower respiratory tract and quickly disseminates to other organs. SARS-CoV-2 dysregulates immune signaling pathways which generate cytokine storm and leads to the acute respiratory distress syndrome and other multisystemic disorders.
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Affiliation(s)
- Meghana Rastogi
- Molecular Biology Unit, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India
| | - Neha Pandey
- Molecular Biology Unit, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India
| | - Astha Shukla
- Molecular Biology Unit, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India
| | - Sunit K Singh
- Molecular Biology Unit, Institute of Medical Sciences, Banaras Hindu University, Varanasi, 221005, India.
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Muhammad LJ, Algehyne EA, Usman SS, Ahmad A, Chakraborty C, Mohammed IA. Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset. ACTA ACUST UNITED AC 2020; 2:11. [PMID: 33263111 PMCID: PMC7694891 DOI: 10.1007/s42979-020-00394-7] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 11/05/2020] [Indexed: 12/15/2022]
Abstract
COVID-19 or 2019-nCoV is no longer pandemic but rather endemic, with more than 651,247 people around world having lost their lives after contracting the disease. Currently, there is no specific treatment or cure for COVID-19, and thus living with the disease and its symptoms is inevitable. This reality has placed a massive burden on limited healthcare systems worldwide especially in the developing nations. Although neither an effective, clinically proven antiviral agents' strategy nor an approved vaccine exist to eradicate the COVID-19 pandemic, there are alternatives that may reduce the huge burden on not only limited healthcare systems but also the economic sector; the most promising include harnessing non-clinical techniques such as machine learning, data mining, deep learning and other artificial intelligence. These alternatives would facilitate diagnosis and prognosis for 2019-nCoV pandemic patients. Supervised machine learning models for COVID-19 infection were developed in this work with learning algorithms which include logistic regression, decision tree, support vector machine, naive Bayes, and artificial neutral network using epidemiology labeled dataset for positive and negative COVID-19 cases of Mexico. The correlation coefficient analysis between various dependent and independent features was carried out to determine a strength relationship between each dependent feature and independent feature of the dataset prior to developing the models. The 80% of the training dataset were used for training the models while the remaining 20% were used for testing the models. The result of the performance evaluation of the models showed that decision tree model has the highest accuracy of 94.99% while the Support Vector Machine Model has the highest sensitivity of 93.34% and Naïve Bayes Model has the highest specificity of 94.30%.
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Affiliation(s)
- L J Muhammad
- Department of Mathematics and Computer Science, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria
| | - Ebrahem A Algehyne
- Department of Mathematics, University of Tabuk, Tabuk, 71491 Saudi Arabia
| | - Sani Sharif Usman
- Department of Biological Sciences, Faculty of Science, Federal University of Kashere, P.M.B. 0182, Gombe, Nigeria
| | - Abdulkadir Ahmad
- Department of Computer Science, Kano University of Science and Technology, Wudil, Kano Nigeria
| | - Chinmay Chakraborty
- Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, Jharkhand India
| | - I A Mohammed
- Computer Science Department, Yobe StateUniversity, Damaturu, Yobe State Nigeria
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28
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Affiliation(s)
- M Sreepadmanabh
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
| | - Amit Kumar Sahu
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
| | - Ajit Chande
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
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Chen W. Promise and challenges in the development of COVID-19 vaccines. Hum Vaccin Immunother 2020; 16:2604-2608. [PMID: 32703069 PMCID: PMC7733917 DOI: 10.1080/21645515.2020.1787067] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 06/08/2020] [Indexed: 12/23/2022] Open
Abstract
The pandemic outbreak of COVID-19, caused by coronavirus SARS-CoV-2, created an unprecedented challenge to global public health system and biomedical community. Vaccination is an effective way to prevent viral infection, stop its transmission, and develop herd immunity. Rapid progress and advances have been made to date in the development of COVID-19 vaccines. Currently, more than 115 vaccine candidates have been developed from different technology platforms with several of them in clinical trials. Most of those vaccine candidates are developed based on the experience with other coronaviruses with an aim to induce neutralizing antibodies against the viral spike protein or its different receptor binding domains. Here, we discuss the promise, potential scientific challenges, and future directions for the development of a safe and effective COVID-19 vaccine. We also emphasize the importance of a better understanding of the infection pathogenesis and host defense mechanisms against SARS-CoV-2 infection.
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Affiliation(s)
- Wangxue Chen
- Human Health Therapeutics Research Center (HHT), National Research Council Canada, Ottawa, Ontario, Canada
- Department of Biology, Brock University, St. Catharines, Ontario, Canada
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Bird JJ, Barnes CM, Premebida C, Ekárt A, Faria DR. Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach. PLoS One 2020; 15:e0241332. [PMID: 33112931 PMCID: PMC7592809 DOI: 10.1371/journal.pone.0241332] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 10/13/2020] [Indexed: 12/23/2022] Open
Abstract
In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as 'low', 'medium-low', 'medium-high', and 'high'. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding.
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Affiliation(s)
- Jordan J. Bird
- Aston Robotics, Vision, and Intelligent Systems Lab (ARVIS), School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom
| | - Chloe M. Barnes
- Aston Robotics, Vision, and Intelligent Systems Lab (ARVIS), School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom
| | - Cristiano Premebida
- Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal
| | - Anikó Ekárt
- Aston Robotics, Vision, and Intelligent Systems Lab (ARVIS), School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom
| | - Diego R. Faria
- Aston Robotics, Vision, and Intelligent Systems Lab (ARVIS), School of Engineering and Applied Science, Aston University, Birmingham, United Kingdom
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31
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Chen J, See KC. Artificial Intelligence for COVID-19: Rapid Review. J Med Internet Res 2020; 22:e21476. [PMID: 32946413 PMCID: PMC7595751 DOI: 10.2196/21476] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 07/25/2020] [Accepted: 09/15/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. OBJECTIVE To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. METHODS We performed an extensive search of the PubMed and EMBASE databases for COVID-19-related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. RESULTS In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. CONCLUSIONS In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.
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Affiliation(s)
- Jiayang Chen
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Kay Choong See
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Hospital, Singapore, Singapore
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32
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Kavadi DP, Patan R, Ramachandran M, Gandomi AH. Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19. CHAOS, SOLITONS, AND FRACTALS 2020; 139:110056. [PMID: 32834609 PMCID: PMC7315984 DOI: 10.1016/j.chaos.2020.110056] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 06/23/2020] [Indexed: 05/18/2023]
Abstract
The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.
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Affiliation(s)
- Durga Prasad Kavadi
- Department of Information Technology, B V Raju Institute of Technology, Narsapur, Telangana, India
| | - Rizwan Patan
- Department of Computing Science & Engineering, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada 520007, India
| | | | - Amir H Gandomi
- Faculty of Engineering & Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
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33
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Smitha T, Thomas A. A brief outlook on the current emerging trends of COVID 19 vaccines. J Oral Maxillofac Pathol 2020; 24:206-211. [PMID: 33456225 PMCID: PMC7802844 DOI: 10.4103/jomfp.jomfp_334_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 08/06/2020] [Indexed: 11/04/2022] Open
Affiliation(s)
- T Smitha
- Department of Oral and Maxillofacial Pathology, VSDCH, Bengaluru, Karnataka, India
| | - Anela Thomas
- Department of Oral and Maxillofacial Pathology, VSDCH, Bengaluru, Karnataka, India
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34
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Campos EVR, Pereira AES, de Oliveira JL, Carvalho LB, Guilger-Casagrande M, de Lima R, Fraceto LF. How can nanotechnology help to combat COVID-19? Opportunities and urgent need. J Nanobiotechnology 2020; 18:125. [PMID: 32891146 PMCID: PMC7474329 DOI: 10.1186/s12951-020-00685-4] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 08/28/2020] [Indexed: 12/11/2022] Open
Abstract
Incidents of viral outbreaks have increased at an alarming rate over the past decades. The most recent human coronavirus known as COVID-19 (SARS-CoV-2) has already spread around the world and shown R0 values from 2.2 to 2.68. However, the ratio between mortality and number of infections seems to be lower in this case in comparison to other human coronaviruses (such as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV)). These outbreaks have tested the limits of healthcare systems and have posed serious questions about management using conventional therapies and diagnostic tools. In this regard, the use of nanotechnology offers new opportunities for the development of novel strategies in terms of prevention, diagnosis and treatment of COVID-19 and other viral infections. In this review, we discuss the use of nanotechnology for COVID-19 virus management by the development of nano-based materials, such as disinfectants, personal protective equipment, diagnostic systems and nanocarrier systems, for treatments and vaccine development, as well as the challenges and drawbacks that need addressing.
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Affiliation(s)
- Estefânia V R Campos
- Human and Natural Sciences Center, Federal University of ABC. Av. dos Estados, 5001. Bl. A, T3 Lab. 503-3. Bangú, Santo André, SP, Brazil
| | - Anderson E S Pereira
- São Paulo State University-UNESP, Institute of Science and Technology, Sorocaba, SP, Brazil
| | | | | | | | - Renata de Lima
- Universidade de Sorocaba, Rodovia Raposo Tavares km 92,5, Sorocaba, São Paulo, Brazil.
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35
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Keshavarzi Arshadi A, Webb J, Salem M, Cruz E, Calad-Thomson S, Ghadirian N, Collins J, Diez-Cecilia E, Kelly B, Goodarzi H, Yuan JS. Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development. Front Artif Intell 2020; 3:65. [PMID: 33733182 PMCID: PMC7861281 DOI: 10.3389/frai.2020.00065] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 07/17/2020] [Indexed: 12/31/2022] Open
Abstract
SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.
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Affiliation(s)
- Arash Keshavarzi Arshadi
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, United States
| | - Julia Webb
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, United States
| | - Milad Salem
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, United States
| | | | | | - Niloofar Ghadirian
- Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ, United States
| | - Jennifer Collins
- Burnett School of Biomedical Sciences, University of Central Florida, Orlando, FL, United States
| | | | | | - Hani Goodarzi
- Department of Biochemistry and Biophysics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, United States
| | - Jiann Shiun Yuan
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, United States
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36
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Bansal A, Padappayil RP, Garg C, Singal A, Gupta M, Klein A. Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review. J Med Syst 2020; 44:156. [PMID: 32740678 PMCID: PMC7395799 DOI: 10.1007/s10916-020-01617-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/15/2020] [Indexed: 01/07/2023]
Abstract
The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.
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Affiliation(s)
- Agam Bansal
- Internal Medicine, Cleveland Clinic, Cleveland, OH USA
| | | | - Chandan Garg
- Deptartment of Statistics, Columbia University, New York, NY USA
| | - Anjali Singal
- Deptartment of Anatomy, All India Institute of Medical Sciences, Bathinda, India
| | - Mohak Gupta
- All India Institute of Medical Sciences, New Delhi, India
| | - Allan Klein
- Deptartment of Cardiology, Cleveland Clinic, Cleveland, OH USA
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37
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Bhatnager R, Bhasin M, Arora J, Dang AS. Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach. J Biomol Struct Dyn 2020; 39:5690-5705. [PMID: 32619134 DOI: 10.1080/07391102.2020.1787227] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
World is witnessing exponential growth of SARS-CoV2 and fatal outcomes of COVID 19 has proved its pandemic potential already by claiming more than 3 lakhs deaths globally. If not controlled, this ongoing pandemic can cause irreparable socio-economic and psychological impact worldwide. Therefore a safe and effective vaccine against COVID 19 is exigent. Recent advances in immunoinformatics approaches could potentially decline the attrition rate and accelerate the process of vaccine development in these unprecedented times. In the present study, a multivalent subunit vaccine targeting S2 subunit of the SARS-CoV2 S glycoprotein has been designed using open source, immunoinformatics tools. Designed construct comprises of epitopes capable of inducing T cell, B cell (Linear and discontinuous) and Interferon γ. physiologically, vaccine construct is predicted to be thermostable, antigenic, immunogenic, non allergen and non toxic in nature. According to population coverage analysis, designed multiepitope vaccine covers 99.26% population globally. 3D structure of vaccine construct was designed, validated and refined to obtain high quality structure. Refined structure was docked against Toll like receptors to confirm the interactions between them. Vaccine peptide sequence was reverse transcribed, codon optimized and cloned in pET vector. Our in-silico study suggests that proposed vaccine against fusion domain of virus has the potential to elicit an innate as well as humoral immune response in human and restrict the entry of virus inside the cell. Results of the study offer a framework for in-vivo analysis that may hasten the process of development of therapeutic tools against COVID 19.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Richa Bhatnager
- Centre for Medical Biotechnology, M.D. University, Rohtak, Haryana, India
| | - Maheshwar Bhasin
- Department of Neonatology, Lady Hardinge Medical College and associated hospital, New Delhi, India
| | - Jyoti Arora
- Centre for Medical Biotechnology, M.D. University, Rohtak, Haryana, India
| | - Amita S Dang
- Centre for Medical Biotechnology, M.D. University, Rohtak, Haryana, India
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38
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He Y, Yu H, Ong E, Wang Y, Liu Y, Huffman A, Huang HH, Beverley J, Hur J, Yang X, Chen L, Omenn GS, Athey B, Smith B. CIDO, a community-based ontology for coronavirus disease knowledge and data integration, sharing, and analysis. Sci Data 2020; 7:181. [PMID: 32533075 PMCID: PMC7293349 DOI: 10.1038/s41597-020-0523-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 05/19/2020] [Indexed: 11/15/2022] Open
Abstract
The Coronavirus Infectious Disease Ontology (CIDO) is a community-based ontology that supports coronavirus disease knowledge and data standardization, integration, sharing, and analysis.
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Affiliation(s)
- Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
| | - Hong Yu
- People's Hospital of Guizhou Province, Guiyang, Guizhou, 550002, China
- Guizhou University Medical College, Guiyang, Guizhou, 550025, China
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Yang Wang
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- People's Hospital of Guizhou Province, Guiyang, Guizhou, 550002, China
- Guizhou University Medical College, Guiyang, Guizhou, 550025, China
| | - Yingtong Liu
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Anthony Huffman
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Hsin-Hui Huang
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- National Yang-Ming University, Taipei, 112-21, Taiwan
| | | | - Junguk Hur
- University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, 58203, USA
| | - Xiaolin Yang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences (CAMS) & School of Basic Medicine, Peking Union Medical College (PUMC), Beijing, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, 200031, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
| | - Gilbert S Omenn
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Brian Athey
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Barry Smith
- University at Buffalo, Buffalo, NY, 14260, USA
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Abd-alrazaq A, Alajlani M, Alhuwail D, Schneider J, Al-kuwari S, Shah Z, Hamdi M, Househ M. Artificial Intelligence in the Fight Against COVID-19: Scoping Review (Preprint).. [DOI: 10.2196/preprints.20756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
BACKGROUND
In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts.
OBJECTIVE
This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation.
METHODS
A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data.
RESULTS
We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine.
CONCLUSIONS
The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.
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Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X. Artificial intelligence and machine learning to fight COVID-19. Physiol Genomics 2020; 52:200-202. [PMID: 32216577 PMCID: PMC7191426 DOI: 10.1152/physiolgenomics.00029.2020] [Citation(s) in RCA: 214] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Affiliation(s)
- Ahmad Alimadadi
- Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | - Sachin Aryal
- Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | - Ishan Manandhar
- Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | - Patricia B Munroe
- Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
- Clinical Pharmacology, William Harvey Research Institute, National Institute of Health Research Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Bina Joe
- Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | - Xi Cheng
- Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
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Sreepadmanabh M, Sahu AK, Chande A. COVID-19: Advances in diagnostic tools, treatment strategies, and vaccine development. J Biosci 2020; 45:148. [PMID: 33410425 PMCID: PMC7683586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/15/2020] [Indexed: 09/18/2023]
Abstract
An unprecedented worldwide spread of the SARS-CoV-2 has imposed severe challenges on healthcare facilities and medical infrastructure. The global research community faces urgent calls for the development of rapid diagnostic tools, effective treatment protocols, and most importantly, vaccines against the pathogen. Pooling together expertise across broad domains to innovate effective solutions is the need of the hour. With these requirements in mind, in this review, we provide detailed critical accounts on the leading efforts at developing diagnostics tools, therapeutic agents, and vaccine candidates. Importantly, we furnish the reader with a multidisciplinary perspective on how conventional methods like serology and RT-PCR, as well as cutting-edge technologies like CRISPR/Cas and artificial intelligence/machine learning, are being employed to inform and guide such investigations. We expect this narrative to serve a broad audience of both active and aspiring researchers in the field of biomedical sciences and engineering and help inspire radical new approaches towards effective detection, treatment, and prevention of this global pandemic.
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Affiliation(s)
- M Sreepadmanabh
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
| | - Amit Kumar Sahu
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
| | - Ajit Chande
- Molecular Virology Laboratory, Indian Institute of Science Education and Research, Bhopal, India
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