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Alsulimani A, Akhter N, Jameela F, Ashgar RI, Jawed A, Hassani MA, Dar SA. The Impact of Artificial Intelligence on Microbial Diagnosis. Microorganisms 2024; 12:1051. [PMID: 38930432 PMCID: PMC11205376 DOI: 10.3390/microorganisms12061051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 05/19/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.
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Affiliation(s)
- Ahmad Alsulimani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Naseem Akhter
- Department of Biology, Arizona State University, Lake Havasu City, AZ 86403, USA;
| | - Fatima Jameela
- Modern American Dental Clinic, West Warren Avenue, Dearborn, MI 48126, USA;
| | - Rnda I. Ashgar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Arshad Jawed
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
| | - Mohammed Ahmed Hassani
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia; (A.A.); (M.A.H.)
| | - Sajad Ahmad Dar
- College of Nursing, Jazan University, Jazan 45142, Saudi Arabia; (R.I.A.); (A.J.)
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2
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Farzan R. Artificial intelligence in Immuno-genetics. Bioinformation 2024; 20:29-35. [PMID: 38352901 PMCID: PMC10859949 DOI: 10.6026/973206300200029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/16/2024] Open
Abstract
Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.
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Affiliation(s)
- Raed Farzan
- Department of Clinical Laboratory Sciences, College of Applied Medical Scienecs, King Saud University, Riyadh - 11433, Saudi Arabia
- Center of Excellence in Biotechnology Research, King Saud University, Riyadh - 11433, Saudi Arabia
- Medical and Molecular Genetics Research, King Saud University, Riyadh-11433, Saudi Arabia
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3
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Shelke YP, Badge AK, Bankar NJ. Applications of Artificial Intelligence in Microbial Diagnosis. Cureus 2023; 15:e49366. [PMID: 38146579 PMCID: PMC10749263 DOI: 10.7759/cureus.49366] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/24/2023] [Indexed: 12/27/2023] Open
Abstract
The diagnosis is an important factor in healthcare care, and it is essential to identify microorganisms that cause infections and diseases. The application of artificial intelligence (AI) systems can improve disease management, drug development, antibiotic resistance prediction, and epidemiological monitoring in the field of microbial diagnosis. AI systems can quickly and accurately detect infections, including new and drug-resistant strains, and enable early detection of antibiotic resistance and improved diagnostic techniques. The application of AI in bacterial diagnosis focuses on the speed, precision, and identification of pathogens and the ability to predict antibiotic resistance.
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Affiliation(s)
- Yogendra P Shelke
- Microbiology, Bhaktshreshtha Kamalakarpant Laxmanrao Walawalkar Rural Medical College, Ratnagiri, IND
| | - Ankit K Badge
- Microbiology, Datta Meghe Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
| | - Nandkishor J Bankar
- Microbiology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (Deemed to Be University), Wardha, IND
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4
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The Epidemiology of Infectious Diseases Meets AI: A Match Made in Heaven. Pathogens 2023; 12:pathogens12020317. [PMID: 36839589 PMCID: PMC9963936 DOI: 10.3390/pathogens12020317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/17/2023] Open
Abstract
Infectious diseases remain a major threat to public health [...].
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5
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Hu H, Xu J, Liu M, Lim MK. Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning. JOURNAL OF BUSINESS RESEARCH 2023; 156:113480. [PMID: 36506475 PMCID: PMC9718486 DOI: 10.1016/j.jbusres.2022.113480] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 11/15/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
Vaccination offers health, economic, and social benefits. However, three major issues-vaccine quality, demand forecasting, and trust among stakeholders-persist in the vaccine supply chain (VSC), leading to inefficiencies. The COVID-19 pandemic has exacerbated weaknesses in the VSC, while presenting opportunities to apply digital technologies to manage it. For the first time, this study establishes an intelligent VSC management system that provides decision support for VSC management during the COVID-19 pandemic. The system combines blockchain, internet of things (IoT), and machine learning that effectively address the three issues in the VSC. The transparency of blockchain ensures trust among stakeholders. The real-time monitoring of vaccine status by the IoT ensures vaccine quality. Machine learning predicts vaccine demand and conducts sentiment analysis on vaccine reviews to help companies improve vaccine quality. The present study also reveals the implications for the management of supply chains, businesses, and government.
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Key Words
- BILSTM, Bidirectional Long-Short Term Memory
- Blockchain
- CNN, Convolutional Neural Network
- COVID-19 pandemic
- DTs, Digital Technologies
- GRU, Gate Recurrent Unit
- IPFS, Interplanetary File System
- Intelligent system
- Internet of things
- IoT, Internet of Things
- LSTM, Long-Short Term Memory
- Machine learning
- RFID, Radio Frequency Identification
- RNN, Recurrent Neural Network
- VSC, Vaccine Supply Chain
- Vaccine supply chain
- dApp, Decentralized Application
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Affiliation(s)
- Hui Hu
- Economic Development Research Centre, Wuhan University, China
- School of Economics and Management, Wuhan University, China
| | - Jiajun Xu
- School of Economics and Management, Wuhan University, China
| | - Mengqi Liu
- Business School, Hunan University, China
| | - Ming K Lim
- Adam Smith Business School, University of Glasgow, UK
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6
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Wang X, He X, Wei J, Liu J, Li Y, Liu X. Application of artificial intelligence to the public health education. Front Public Health 2023; 10:1087174. [PMID: 36703852 PMCID: PMC9872201 DOI: 10.3389/fpubh.2022.1087174] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023] Open
Abstract
With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.
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Affiliation(s)
- Xueyan Wang
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiujing He
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiawei Wei
- Research Center for Nano-Biomaterials, Analytical and Testing Center, Sichuan University, Chengdu, Sichuan, China
| | - Jianping Liu
- The First People's Hospital of Yibin, Yibin, Sichuan, China
| | - Yuanxi Li
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaowei Liu
- Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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Gao Y, Gao H, Xiao H, Yao F. Vaccine supply chain coordination using blockchain and artificial intelligence technologies. COMPUTERS & INDUSTRIAL ENGINEERING 2023; 175:108885. [PMID: 36505091 PMCID: PMC9722244 DOI: 10.1016/j.cie.2022.108885] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 10/24/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Currently, the global spread of COVID-19 is taking a heavy toll on the lives of the global population. There is an urgent need to improve and strengthen the coordination of vaccine supply chains in response to this severe pandemic. In this study, we consider a vaccine supply chain based on a combination of artificial intelligence and blockchain technologies and model the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The study focuses on the applicability and effectiveness of the two technologies in the vaccine supply chain and provides management insights. The impact of the application of the technologies on environmental performance is also considered in the model. We also examine factors such as the number of people vaccinated, positive and side effects of vaccines, vaccine decay rate, revenue-sharing/cost-sharing ratio, and commission ratio. The results are as follows: the correlation between the difficulty in obtaining certified vaccines and the profit of a vaccine manufacturer is not monotonous; the vaccine manufacturer is more sensitive to changes in the vaccine attenuation rate. The study's major conclusions are as follows: First, the vaccine supply chain should estimate the level of consumers' difficulty in obtaining a certified vaccine source and the magnitude of the production planning and demand forecasting error terms before adopting the two technologies. Second, the application of artificial intelligence (AI) technology is meaningful in the vaccine supply chain when the error terms satisfy a particular interval condition.
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Affiliation(s)
- Ye Gao
- School of Business, Qingdao University, Qingdao, 266071, PR China
| | - Hongwei Gao
- School of Mathematics and Statistics, Qingdao University, Qingdao, 266071, PR China
| | - Han Xiao
- School of Business, Qingdao University, Qingdao, 266071, PR China
| | - Fanjun Yao
- School of Business, Qingdao University, Qingdao, 266071, PR China
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8
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Saldivar-Espinoza B, Macip G, Garcia-Segura P, Mestres-Truyol J, Puigbò P, Cereto-Massagué A, Pujadas G, Garcia-Vallve S. Prediction of Recurrent Mutations in SARS-CoV-2 Using Artificial Neural Networks. Int J Mol Sci 2022; 23:ijms232314683. [PMID: 36499005 PMCID: PMC9736107 DOI: 10.3390/ijms232314683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 11/26/2022] Open
Abstract
Predicting SARS-CoV-2 mutations is difficult, but predicting recurrent mutations driven by the host, such as those caused by host deaminases, is feasible. We used machine learning to predict which positions from the SARS-CoV-2 genome will hold a recurrent mutation and which mutations will be the most recurrent. We used data from April 2021 that we separated into three sets: a training set, a validation set, and an independent test set. For the test set, we obtained a specificity value of 0.69, a sensitivity value of 0.79, and an Area Under the Curve (AUC) of 0.8, showing that the prediction of recurrent SARS-CoV-2 mutations is feasible. Subsequently, we compared our predictions with updated data from January 2022, showing that some of the false positives in our prediction model become true positives later on. The most important variables detected by the model's Shapley Additive exPlanation (SHAP) are the nucleotide that mutates and RNA reactivity. This is consistent with the SARS-CoV-2 mutational bias pattern and the preference of some host deaminases for specific sequences and RNA secondary structures. We extend our investigation by analyzing the mutations from the variants of concern Alpha, Beta, Delta, Gamma, and Omicron. Finally, we analyzed amino acid changes by looking at the predicted recurrent mutations in the M-pro and spike proteins.
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Affiliation(s)
- Bryan Saldivar-Espinoza
- Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Guillem Macip
- Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Pol Garcia-Segura
- Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Júlia Mestres-Truyol
- Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Pere Puigbò
- Department of Biology, University of Turku, 20500 Turku, Finland
- Department of Biochemistry and Biotechnology, Rovira i Virgili University, 43007 Tarragona, Spain
- Nutrition and Health Unit, Eurecat Technology Centre of Catalonia, 43204 Reus, Spain
| | - Adrià Cereto-Massagué
- EURECAT Centre Tecnològic de Catalunya, Centre for Omic Sciences (COS), Joint Unit Universitat Rovira i Virgili-EURECAT, Unique Scientific and Technical Infrastructures (ICTS), 43204 Reus, Spain
| | - Gerard Pujadas
- Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Spain
| | - Santiago Garcia-Vallve
- Research Group in Cheminformatics & Nutrition, Departament de Bioquímica i Biotecnologia, Campus de Sescelades, Universitat Rovira i Virgili, 43007 Tarragona, Spain
- Correspondence:
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9
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Niemann B, Puleo A, Stout C, Markel J, Boone BA. Biologic Functions of Hydroxychloroquine in Disease: From COVID-19 to Cancer. Pharmaceutics 2022; 14:pharmaceutics14122551. [PMID: 36559044 PMCID: PMC9787624 DOI: 10.3390/pharmaceutics14122551] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/05/2022] [Accepted: 11/10/2022] [Indexed: 11/23/2022] Open
Abstract
Chloroquine (CQ) and Hydroxychloroquine (HCQ), initially utilized in the treatment of malaria, have now developed a long list of applications. Despite their clinical relevance, their mechanisms of action are not clearly defined. Major pathways by which these agents are proposed to function include alkalinization of lysosomes and endosomes, downregulation of C-X-C chemokine receptor type 4 (CXCR4) expression, high-mobility group box 1 protein (HMGB1) inhibition, alteration of intracellular calcium, and prevention of thrombus formation. However, there is conflicting data present in the literature. This is likely the result of the complex overlapping pathways between these mechanisms of action that have not previously been highlighted. In fact, prior research has focused on very specific portions of particular pathways without describing these in the context of the extensive CQ/HCQ literature. This review summarizes the detailed data regarding CQ/HCQ's mechanisms of action while also providing insight into the overarching themes. Furthermore, this review provides clinical context to the application of these diverse drugs including their role in malaria, autoimmune disorders, cardiovascular disease, thrombus formation, malignancies, and viral infections.
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Affiliation(s)
- Britney Niemann
- Department of Surgery, West Virginia University, Morgantown, WV 26506, USA
- Correspondence: ; Tel.: +1-304-293-1254
| | - Amanda Puleo
- Department of Surgery, West Virginia University, Morgantown, WV 26506, USA
| | - Conley Stout
- Department of Surgery, West Virginia University, Morgantown, WV 26506, USA
| | - Justin Markel
- Department of Surgery, West Virginia University, Morgantown, WV 26506, USA
| | - Brian A. Boone
- Department of Surgery, West Virginia University, Morgantown, WV 26506, USA
- Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 26506, USA
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10
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Sun Y, Jiao Y, Shi C, Zhang Y. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5014-5027. [PMID: 36091720 PMCID: PMC9448712 DOI: 10.1016/j.csbj.2022.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/03/2022] [Accepted: 09/03/2022] [Indexed: 11/26/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.
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Affiliation(s)
- Yao Sun
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yanqi Jiao
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Chengcheng Shi
- State Key Lab of Urban Water Resource and Environment, School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
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11
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Kumar S, Kumar GS, Maitra SS, Malý P, Bharadwaj S, Sharma P, Dwivedi VD. Viral informatics: bioinformatics-based solution for managing viral infections. Brief Bioinform 2022; 23:6659740. [PMID: 35947964 DOI: 10.1093/bib/bbac326] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/26/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
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Affiliation(s)
- Sanjay Kumar
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | - Geethu S Kumar
- Department of Life Science, School of Basic Science and Research, Sharda University, Greater Noida, Uttar Pradesh, India.,Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India
| | | | - Petr Malý
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Shiv Bharadwaj
- Laboratory of Ligand Engineering, Institute of Biotechnology of the Czech Academy of Sciences v.v.i., BIOCEV Research Center, Vestec, Czech Republic
| | - Pradeep Sharma
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, India
| | - Vivek Dhar Dwivedi
- Center for Bioinformatics, Computational and Systems Biology, Pathfinder Research and Training Foundation, Greater Noida, India.,Institute of Advanced Materials, IAAM, 59053 Ulrika, Sweden
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12
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Ouyang Y, Huang J, Wang Y, Tang F, Hu Z, Zeng Z, Zhang S. Bioinformatic analysis of RNA-seq data from TCGA database reveals prognostic significance of immune-related genes in colon cancer. Medicine (Baltimore) 2022; 101:e29962. [PMID: 35945793 PMCID: PMC9351934 DOI: 10.1097/md.0000000000029962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The tumor immune microenvironment is of crucial importance in cancer progression and anticancer immune responses. Thus, systematic exploration of the expression landscape and prognostic significance of immune-related genes (IRGs) to assist in the prognosis of colon cancer is valuable and significant. The transcriptomic data of 470 colon cancer patients were obtained from The Cancer Genome Atlas database and the differentially expressed genes were analyzed. After an intersection analysis, the hub IRGs were identified and a prognostic index was further developed using multivariable Cox analysis. In addition, the discriminatory ability and prognostic significance of the constructed model were validated and the characteristics of IRGs associated overall survival were analyzed to elucidate the underlying molecular mechanisms. A total of 465 differentially expressed IRGs and 130 survival-associated IRGs were screened. Then, 46 hub IRGs were identified by an intersection analysis. A regulatory network displayed that most of these genes were unfavorable for the prognosis of colon cancer and were regulated by transcription factors. After a least absolute shrinkage and selection operator regression analysis, 14 hub IRGs were ultimately chose to construct a prognostic index. The validation results illustrated that this model could act as an independent indicator to moderately separate colon cancer patients into low- and high-risk groups. This study ascertained the prognostic significance of IRGs in colon cancer and successfully constructed an IRG-based prognostic signature for clinical prediction. Our results provide promising insight for the exploration of diagnostic markers and immunotherapeutic targets in colon cancer.
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Affiliation(s)
- Yan Ouyang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
| | - Jiangtao Huang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
| | - Yun Wang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
| | - Fuzhou Tang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
| | - Zuquan Hu
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education of China, Guizhou Medical University, Guiyang, China
- *Correspondence: Zuquan Hu, Department of Medical Biotechnology, School of Biology and Engineering, Guizhou Medical University, Guiyang, Guizhou, China (e-mail: )
| | - Zhu Zeng
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
- State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Center of Cellular Immunotherapy of Guizhou Province, Guizhou Medical University, Guiyang, China
| | - Shichao Zhang
- Key Laboratory of Infectious Immune and Antibody Engineering of Guizhou Province/Immune Cells and Antibody Engineering Research Center of Guizhou Province, School of Biology and Engineering/School of Basic Medical Sciences, Guizhou Medical University, Guiyang, China
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13
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Sharma A, Virmani T, Pathak V, Sharma A, Pathak K, Kumar G, Pathak D. Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7205241. [PMID: 35845955 PMCID: PMC9279074 DOI: 10.1155/2022/7205241] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/15/2022] [Indexed: 12/12/2022]
Abstract
The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.
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Affiliation(s)
- Ashwani Sharma
- School of Pharmaceutical Sciences, MVN University, Haryana 121102, India
| | - Tarun Virmani
- School of Pharmaceutical Sciences, MVN University, Haryana 121102, India
| | - Vipluv Pathak
- GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India
| | | | - Kamla Pathak
- Uttar Pradesh University of Medical Sciences, Etawah, Uttar Pradesh 206001, India
| | - Girish Kumar
- School of Pharmaceutical Sciences, MVN University, Haryana 121102, India
| | - Devender Pathak
- Rajiv Academy for Pharmacy, NH. #2, Mathura Delhi Road P.O, Chhatikara, Mathura, Uttar Pradesh 281001, India
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14
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Hesse BW, Aronoff‐Spencer E, Ahern DK, Mullett TW, Gibbons C, Chih M, Hubenko A, Koop B. “Don't drop the patient:” Health information in a postpandemic world. WORLD MEDICAL & HEALTH POLICY 2022. [DOI: 10.1002/wmh3.527] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Bradford W. Hesse
- National Cancer Institute (Retired) Health Communications and Research Branch Kailua‐Kona Hawaii USA
| | - Eliah Aronoff‐Spencer
- Department of Division of Infectious Diseases and Global Public Health, School of Medicine, University of California San Diego La Jolla California USA
| | - David K. Ahern
- Department of Psychiatry, School of Medicine, Brigham and Women's Hospital Boston Massachusetts USA
| | | | - Chris Gibbons
- Department of Medicine, School of Medicine, Johns Hopkins University Baltimore Maryland USA
| | - Ming‐Yuan Chih
- Markey Cancer Center, University of Kentucky Lexington Kentucky USA
| | - Alexandra Hubenko
- Department of Division of Infectious Diseases and Global Public Health, School of Medicine, University of California San Diego La Jolla California USA
| | - Barbara Koop
- Philips, Usability – Design Practice and Centre of Excellence Amsterdam Netherlands
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15
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Medicine and machines. MEDICINE, HEALTH CARE AND PHILOSOPHY 2022; 25:165-166. [PMID: 35366171 PMCID: PMC8976455 DOI: 10.1007/s11019-022-10080-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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16
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Olarte Carrillo I, Ramos Peñafiel CO, Mendoza Salas I, Cerón Maldonado R, García Laguna AI, De la Cruz Rosas A, Martinez Murrillo C, Martinez Tovar A. Impact of the ABCB1 Drug Resistance Gene on the Risk Factors of Patients with COVID-19 and Its Relationship with the Drugs Used. Infect Drug Resist 2022; 15:2661-2669. [PMID: 35637928 PMCID: PMC9147417 DOI: 10.2147/idr.s358389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/29/2022] [Indexed: 11/29/2022] Open
Abstract
Objective In the last two years progress was made in molecular, physio pathological understanding and the form of transmission of COVID-19, and different therapeutic strategies have been explored to deal with the situation of the pandemic. However, the evaluation of certain genes that participate in the metabolism and transport of these drugs has not been fully explored. A lack of response to treatment and a lower survival have been observed that may be due to the presence of the ABCB1 drug resistance gene. Our research group analyzed whether the expression levels of the ABCB1 gene are associated with comorbidities, treatments, overall survival and risk of death in patients with severe COVID-19. Methods The expression levels of the ABCB1 gene were analyzed by RT-qPCR in 61 patients diagnosed with COVID-19. The association between the levels of expression, the risk variables and different treatments were determined by the Chi-Square test and the Fisher’s exact test. Global Survival (GS) was determined by the Kaplan–Meier method. The impact of high levels of expression and the risk of death was performed by odds ratio. Results The different risk variables showed that patients with either high or absent levels of ABCB1 gene expression presented a greater risk of death (OR 3.08, 95%, CI 1.02–9.26) as well as need for ventilatory support (OR 2.8, 95%, CI 0.98 −8.5). Patients with diabetes and COVID-19, treated with metformin, were associated with a lower risk of death (OR 1.11, 95%, CI 0.38–3.22). OS with respect to high or absent levels of expression of the ABCB1 gene was lower. Conclusion High levels or null expression of the ABCB1 gene are associated with a higher risk of death or progression of the disease, the use of metformin in patients with COVID-19 confers a lower risk of death.
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Affiliation(s)
- Irma Olarte Carrillo
- Laboratorio de Biología Molecular, Servicio de Hematología, Hospital General de México, “Dr. Eduardo Liceaga”, Ciudad de México, Mexico
| | | | - Iveth Mendoza Salas
- Servicio de Hematología, Hospital General de México, “Dr. Eduardo Liceaga”, Ciudad de México, Mexico
| | - Rafael Cerón Maldonado
- Laboratorio de Biología Molecular, Servicio de Hematología, Hospital General de México, “Dr. Eduardo Liceaga”, Ciudad de México, Mexico
| | - Anel Irais García Laguna
- Laboratorio de Biología Molecular, Servicio de Hematología, Hospital General de México, “Dr. Eduardo Liceaga”, Ciudad de México, Mexico
| | - Adrián De la Cruz Rosas
- Laboratorio de Biología Molecular, Servicio de Hematología, Hospital General de México, “Dr. Eduardo Liceaga”, Ciudad de México, Mexico
| | - Carlos Martinez Murrillo
- Servicio de Hematología, Hospital General de México, “Dr. Eduardo Liceaga”, Ciudad de México, Mexico
| | - Adolfo Martinez Tovar
- Laboratorio de Biología Molecular, Servicio de Hematología, Hospital General de México, “Dr. Eduardo Liceaga”, Ciudad de México, Mexico
- Correspondence: Adolfo Martinez Tovar, Labortororio de Biología Molecular, Servicio de Hematología, Hospital General de Mexico “Dr. Eduardo Liceaga”, Ciudad de México, Mexico, Email
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Aruleba RT, Adekiya TA, Ayawei N, Obaido G, Aruleba K, Mienye ID, Aruleba I, Ogbuokiri B. COVID-19 Diagnosis: A Review of Rapid Antigen, RT-PCR and Artificial Intelligence Methods. Bioengineering (Basel) 2022; 9:153. [PMID: 35447713 PMCID: PMC9024895 DOI: 10.3390/bioengineering9040153] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 12/15/2022] Open
Abstract
As of 27 December 2021, SARS-CoV-2 has infected over 278 million persons and caused 5.3 million deaths. Since the outbreak of COVID-19, different methods, from medical to artificial intelligence, have been used for its detection, diagnosis, and surveillance. Meanwhile, fast and efficient point-of-care (POC) testing and self-testing kits have become necessary in the fight against COVID-19 and to assist healthcare personnel and governments curb the spread of the virus. This paper presents a review of the various types of COVID-19 detection methods, diagnostic technologies, and surveillance approaches that have been used or proposed. The review provided in this article should be beneficial to researchers in this field and health policymakers at large.
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Affiliation(s)
- Raphael Taiwo Aruleba
- Department of Molecular and Cell Biology, Faculty of Science, University of Cape Town, Cape Town 7701, South Africa;
| | - Tayo Alex Adekiya
- Department of Pharmacy and Pharmacology, School of Therapeutic Science, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, 7 York Road, Parktown 2193, South Africa;
| | - Nimibofa Ayawei
- Department of Chemistry, Bayelsa Medical University, Yenagoa PMB 178, Bayelsa State, Nigeria;
| | - George Obaido
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093-0404, USA
| | - Kehinde Aruleba
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Ibomoiye Domor Mienye
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Idowu Aruleba
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa; (I.D.M.); (I.A.)
| | - Blessing Ogbuokiri
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;
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18
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Aghamirza Moghim Aliabadi H, Eivazzadeh‐Keihan R, Beig Parikhani A, Fattahi Mehraban S, Maleki A, Fereshteh S, Bazaz M, Zolriasatein A, Bozorgnia B, Rahmati S, Saberi F, Yousefi Najafabadi Z, Damough S, Mohseni S, Salehzadeh H, Khakyzadeh V, Madanchi H, Kardar GA, Zarrintaj P, Saeb MR, Mozafari M. COVID-19: A systematic review and update on prevention, diagnosis, and treatment. MedComm (Beijing) 2022; 3:e115. [PMID: 35281790 PMCID: PMC8906461 DOI: 10.1002/mco2.115] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 01/09/2023] Open
Abstract
Since the rapid onset of the COVID-19 or SARS-CoV-2 pandemic in the world in 2019, extensive studies have been conducted to unveil the behavior and emission pattern of the virus in order to determine the best ways to diagnosis of virus and thereof formulate effective drugs or vaccines to combat the disease. The emergence of novel diagnostic and therapeutic techniques considering the multiplicity of reports from one side and contradictions in assessments from the other side necessitates instantaneous updates on the progress of clinical investigations. There is also growing public anxiety from time to time mutation of COVID-19, as reflected in considerable mortality and transmission, respectively, from delta and Omicron variants. We comprehensively review and summarize different aspects of prevention, diagnosis, and treatment of COVID-19. First, biological characteristics of COVID-19 were explained from diagnosis standpoint. Thereafter, the preclinical animal models of COVID-19 were discussed to frame the symptoms and clinical effects of COVID-19 from patient to patient with treatment strategies and in-silico/computational biology. Finally, the opportunities and challenges of nanoscience/nanotechnology in identification, diagnosis, and treatment of COVID-19 were discussed. This review covers almost all SARS-CoV-2-related topics extensively to deepen the understanding of the latest achievements (last updated on January 11, 2022).
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Affiliation(s)
- Hooman Aghamirza Moghim Aliabadi
- Protein Chemistry LaboratoryDepartment of Medical BiotechnologyBiotechnology Research CenterPasteur Institute of IranTehranIran
- Advance Chemical Studies LaboratoryFaculty of ChemistryK. N. Toosi UniversityTehranIran
| | | | - Arezoo Beig Parikhani
- Department of Medical BiotechnologyBiotechnology Research CenterPasteur InstituteTehranIran
| | | | - Ali Maleki
- Department of ChemistryIran University of Science and TechnologyTehranIran
| | | | - Masoume Bazaz
- Department of Medical BiotechnologyBiotechnology Research CenterPasteur InstituteTehranIran
| | | | | | - Saman Rahmati
- Department of Medical BiotechnologyBiotechnology Research CenterPasteur InstituteTehranIran
| | - Fatemeh Saberi
- Department of Medical BiotechnologySchool of Advanced Technologies in MedicineShahid Beheshti University of Medical SciencesTehranIran
| | - Zeinab Yousefi Najafabadi
- Department of Medical BiotechnologySchool of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
- ImmunologyAsthma & Allergy Research InstituteTehran University of Medical SciencesTehranIran
| | - Shadi Damough
- Department of Medical BiotechnologyBiotechnology Research CenterPasteur InstituteTehranIran
| | - Sara Mohseni
- Non‐metallic Materials Research GroupNiroo Research InstituteTehranIran
| | | | - Vahid Khakyzadeh
- Department of ChemistryK. N. Toosi University of TechnologyTehranIran
| | - Hamid Madanchi
- School of MedicineSemnan University of Medical SciencesSemnanIran
- Drug Design and Bioinformatics UnitDepartment of Medical BiotechnologyBiotechnology Research CenterPasteur Institute of IranTehranIran
| | - Gholam Ali Kardar
- Department of Medical BiotechnologySchool of Advanced Technologies in MedicineTehran University of Medical SciencesTehranIran
- ImmunologyAsthma & Allergy Research InstituteTehran University of Medical SciencesTehranIran
| | - Payam Zarrintaj
- School of Chemical EngineeringOklahoma State UniversityStillwaterOklahomaUSA
| | - Mohammad Reza Saeb
- Department of Polymer TechnologyFaculty of ChemistryGdańsk University of TechnologyGdańskPoland
| | - Masoud Mozafari
- Department of Tissue Engineering & Regenerative MedicineIran University of Medical SciencesTehranIran
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19
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Abubaker Bagabir S, Ibrahim NK, Abubaker Bagabir H, Hashem Ateeq R. Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery. J Infect Public Health 2022; 15:289-296. [PMID: 35078755 PMCID: PMC8767913 DOI: 10.1016/j.jiph.2022.01.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 01/11/2022] [Accepted: 01/12/2022] [Indexed: 02/07/2023] Open
Abstract
OBJECTIVES To clarify the work done by using AI for identifying the genomic sequences, development of drugs and vaccines for COVID-19 and to recognize the advantages and challenges of using such technology. METHODS A non-systematic review was done. All articles published on Pub-Med, Medline, Google, and Google Scholar on AI or digital health regarding genomic sequencing, drug development, and vaccines of COVID-19 were scrutinized and summarized. RESULTS The sequence of SARS- CoV-2 was identified with the help of AI. It can help also in the prompt identification of variants of concern (VOC) as delta strains and Omicron. Furthermore, there are many drugs applied with the help of AI. These drugs included Atazanavir, Remdesivir, Efavirenz, Ritonavir, and Dolutegravir, PARP1 inhibitors (Olaparib and CVL218 which is Mefuparib hydrochloride), Abacavir, Roflumilast, Almitrine, and Mesylate. Many vaccines were developed utilizing the new technology of bioinformatics, databases, immune-informatics, machine learning, and reverse vaccinology to the whole SARS-CoV-2 proteomes or the structural proteins. Examples of these vaccines are the messenger RNA and viral vector vaccines. AI provides cost-saving and agility. However, the challenges of its usage are the difficulty of collecting data, the internal and external validation, ethical consideration, therapeutic effect, and the time needed for clinical trials after drug approval. Moreover, there is a common problem in the deep learning (DL) model which is the shortage of interpretability. CONCLUSION The growth of AI techniques in health care opened a broad gate for discovering the genomic sequences of the COVID-19 virus and the VOC. AI helps also in the development of vaccines and drugs (including drug repurposing) to obtain potential preventive and therapeutic agents for controlling the COVID-19 pandemic.
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Affiliation(s)
- Sali Abubaker Bagabir
- Medical Laboratory Technology Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Nahla Khamis Ibrahim
- Community Medicine Department, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia; Epidemiology Department, High Institute of Public Health, Alexandria University, Alexandria, Egypt.
| | - Hala Abubaker Bagabir
- Medical Physiology Department, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi Arabia
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20
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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21
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Pouresmaieli M, Ekrami E, Akbari A, Noorbakhsh N, Moghadam NB, Mamoudifard M. A comprehensive review on efficient approaches for combating coronaviruses. Biomed Pharmacother 2021; 144:112353. [PMID: 34794240 PMCID: PMC8531103 DOI: 10.1016/j.biopha.2021.112353] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/14/2021] [Accepted: 10/19/2021] [Indexed: 02/07/2023] Open
Abstract
Almost 80% of people confronting COVID-19 recover from COVID-19 disease without any particular treatments. They experience heterogeneous symptoms; a wide range of respiratory symptoms, cough, dyspnea, fever, and viral pneumonia. However, some others need urgent intervention and special treatment to get rid of this widespread disease. So far, there isn't any unique drug for the potential treatment of COVID 19. However, some available therapeutic drugs used for other diseases seem beneficial for the COVID-19 treatment. On the other hand, there is a robust global concern for developing an efficient COVID-19 vaccine to control the COVID-19 pandemic sustainably. According to the WHO report, since 8 October 2021, 320 vaccines have been in progress. 194 vaccines are in the pre-clinical development stage that 126 of them are in clinical progression. Here, in this paper, we have comprehensively reviewed the most recent and updated information about coronavirus and its mutations, all the potential therapeutic approaches for treating COVID-19, developed diagnostic systems for COVID- 19 and the available COVID-19 vaccines and their mechanism of action.
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Affiliation(s)
- Mahdi Pouresmaieli
- Department of Industrial and Environmental Biotechnology, National Institute for Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran,Faculty of Mining, Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran
| | - Elena Ekrami
- Department of Industrial and Environmental Biotechnology, National Institute for Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Ali Akbari
- Department of Industrial and Environmental Biotechnology, National Institute for Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran,Department of Cell and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Negin Noorbakhsh
- Department of Industrial and Environmental Biotechnology, National Institute for Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran,Faculty of Medical Science and Technologies, Islamic Azad University Science and Research, Tehran, Iran
| | - Negin Borzooee Moghadam
- Department of Industrial and Environmental Biotechnology, National Institute for Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Matin Mamoudifard
- Department of Industrial and Environmental Biotechnology, National Institute for Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran.
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