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Kombo DC, LaMarche MJ, Konkankit CC, Rackovsky S. Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences. Proteins 2024. [PMID: 38808365 DOI: 10.1002/prot.26704] [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: 02/23/2024] [Revised: 05/07/2024] [Accepted: 05/13/2024] [Indexed: 05/30/2024]
Abstract
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed previously, which encodes the intrinsic dynamic properties of the naturally occurring amino acids. We Fourier analyze the resulting sequences. It is demonstrated that classification models built using several different supervised learning methods are able to successfully distinguish folded from intrinsically disordered proteins from sequence alone. It is further shown that the most important sequence property for this discrimination is the sequence mobility, which is the sequence averaged value of the residue-specific average alpha carbon B factor. This is in agreement with previous work, in which we have demonstrated the central role played by the sequence mobility in protein dynamic bioinformatics and biophysics. This finding opens a path to the application of dynamic bioinformatics, in combination with machine learning algorithms, to a range of significant biomedical problems.
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Affiliation(s)
- David C Kombo
- Department of Medicinal Chemistry, Integrated Drug Discovery, Cambridge, Massachusetts, USA
| | - Matthew J LaMarche
- Department of Medicinal Chemistry, Integrated Drug Discovery, Cambridge, Massachusetts, USA
| | - Chilaluck C Konkankit
- Department of Chemistry and Chemical Biology, Baker Laboratory, Cornell University, Ithaca, New York, USA
| | - S Rackovsky
- Department of Chemistry and Chemical Biology, Baker Laboratory, Cornell University, Ithaca, New York, USA
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Israr J, Alam S, Kumar A. Drug repurposing for respiratory infections. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:207-230. [PMID: 38942538 DOI: 10.1016/bs.pmbts.2024.03.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2024]
Abstract
Respiratory infections such as Coronavirus disease 2019 are a substantial worldwide health challenge, frequently resulting in severe sickness and death, especially in susceptible groups. Conventional drug development for respiratory infections faces obstacles such as extended timescales, substantial expenses, and the rise of resistance to current treatments. Drug repurposing is a potential method that has evolved to quickly find and reuse existing medications for treating respiratory infections. Drug repurposing utilizes medications previously approved for different purposes, providing a cost-effective and time-efficient method to tackle pressing medical needs. This chapter summarizes current progress and obstacles in repurposing medications for respiratory infections, focusing on notable examples of repurposed pharmaceuticals and their probable modes of action. The text also explores the significance of computational approaches, high-throughput screening, and preclinical investigations in identifying potential candidates for repurposing. The text delves into the significance of regulatory factors, clinical trial structure, and actual data in confirming the effectiveness and safety of repurposed medications for respiratory infections. Drug repurposing is a valuable technique for quickly increasing the range of treatments for respiratory infections, leading to better patient outcomes and decreasing the worldwide disease burden.
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Affiliation(s)
- Juveriya Israr
- Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Barabanki, Uttar Pradesh, India; Department of Biotechnology, Era University, Lucknow, Uttar Pradesh, India
| | - Shabroz Alam
- Department of Biotechnology, Era University, Lucknow, Uttar Pradesh, India
| | - Ajay Kumar
- Department of Biotechnology, Faculty of Engineering and Technology, Rama University, Mandhana, Kanpur, Uttar Pradesh, India.
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Yadav AK, Basavegowda N, Shirin S, Raju S, Sekar R, Somu P, Uthappa UT, Abdi G. Emerging Trends of Gold Nanostructures for Point-of-Care Biosensor-Based Detection of COVID-19. Mol Biotechnol 2024:10.1007/s12033-024-01157-y. [PMID: 38703305 DOI: 10.1007/s12033-024-01157-y] [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: 01/16/2024] [Accepted: 03/26/2024] [Indexed: 05/06/2024]
Abstract
In 2019, a worldwide pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged. SARS-CoV-2 is the deadly microorganism responsible for coronavirus disease 2019 (COVID-19), which has caused millions of deaths and irreversible health problems worldwide. To restrict the spread of SARS-CoV-2, accurate detection of COVID-19 is essential for the identification and control of infected cases. Although recent detection technologies such as the real-time polymerase chain reaction delivers an accurate diagnosis of SARS-CoV-2, they require a long processing duration, expensive equipment, and highly skilled personnel. Therefore, a rapid diagnosis with accurate results is indispensable to offer effective disease suppression. Nanotechnology is the backbone of current science and technology developments including nanoparticles (NPs) that can biomimic the corona and develop deep interaction with its proteins because of their identical structures on the nanoscale. Various NPs have been extensively applied in numerous medical applications, including implants, biosensors, drug delivery, and bioimaging. Among them, point-of-care biosensors mediated with gold nanoparticles (GNPSs) have received great attention due to their accurate sensing characteristics, which are widely used in the detection of amino acids, enzymes, DNA, and RNA in samples. GNPS have reconstructed the biomedical application of biosensors because of its outstanding physicochemical characteristics. This review provides an overview of emerging trends in GNP-mediated point-of-care biosensor strategies for diagnosing various mutated forms of human coronaviruses that incorporate different transducers and biomarkers. The review also specifically highlights trends in gold nanobiosensors for coronavirus detection, ranging from the initial COVID-19 outbreak to its subsequent evolution into a pandemic.
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Affiliation(s)
- Akhilesh Kumar Yadav
- Department of Environmental Engineering and Management, Chaoyang University of Technology, Taichung, 413310, Taiwan
- Department of Mining Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
| | - Nagaraj Basavegowda
- Department of Biotechnology, Yeungnam University, Gyeongsan, 38451, Republic of Korea
| | - Saba Shirin
- Department of Mining Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India
- Department of Environmental Science, School of Vocational Studies and Applied Sciences, Gautam Buddha University, Greater Noida, 201312, India
| | - Shiji Raju
- Bioengineering and Nano Medicine Group, Faculty of Medicine and Health Technology, Tampere University, 33720, Tampere, Finland
| | - Rajkumar Sekar
- Department of Chemistry, Karpaga Vinayaga College of Engineering and Technology, GST Road, Chinna Kolambakkam, Chengalpattu, Tamil Nadu, 603308, India
| | - Prathap Somu
- Department of Biotechnology and Chemical Engineering, School of Civil, Biotechnology and Chemical Engineering, Manipal University Jaipur, Dehmi Kalan, Off. Jaipur-Ajmeer Expressway, Jaipur, Rajasthan, 303007, India.
| | - U T Uthappa
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, 518055, China
- Department of Bioengineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, India
| | - Gholamreza Abdi
- Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr, 75169, Iran.
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Chafai N, Bonizzi L, Botti S, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 2024; 61:140-163. [PMID: 37815417 DOI: 10.1080/10408363.2023.2259466] [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: 06/19/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.
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Affiliation(s)
- Narjice Chafai
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
| | - Luigi Bonizzi
- Department of Biomedical, Surgical and Dental Science, University of Milan, Milan, Italy
| | - Sara Botti
- PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy
| | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laâyoune, Morocco
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Pisano F, Cannas B, Fanni A, Pasella M, Canetto B, Giglio SR, Mocci S, Chessa L, Perra A, Littera R. Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19. Front Med (Lausanne) 2023; 10:1230733. [PMID: 37601789 PMCID: PMC10433226 DOI: 10.3389/fmed.2023.1230733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 07/19/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals. Methods This study developed a machine learning model to predict inherent risk of severe symptoms after contracting SARS-CoV-2. Using a Decision Tree trained on 153 Alpha variant patients, demographic, clinical and immunogenetic markers were considered. Model performance was assessed on Alpha and Delta variant datasets. Key risk factors included age, gender, absence of KIR2DS2 gene (alone or with HLA-C C1 group alleles), presence of 14-bp polymorphism in HLA-G gene, presence of KIR2DS5 gene, and presence of KIR telomeric region A/A. Results The model achieved 83.01% accuracy for Alpha variant and 78.57% for Delta variant, with True Positive Rates of 80.82 and 77.78%, and True Negative Rates of 85.00% and 79.17%, respectively. The model showed high sensitivity in identifying individuals at risk. Discussion The present study demonstrates the potential of AI algorithms, combined with demographic, epidemiologic, and immunogenetic data, in identifying individuals at high risk of severe COVID-19 and facilitating early treatment. Further studies are required for routine clinical integration.
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Affiliation(s)
- Fabio Pisano
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Barbara Cannas
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Alessandra Fanni
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | - Manuela Pasella
- Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy
| | | | - Sabrina Rita Giglio
- Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Medical Genetics, R. Binaghi Hospital, Local Public Health and Social Care Unit (ASSL) of Cagliari, Cagliari, Italy
- Centre for Research University Services (CeSAR, Centro Servizi di Ateneo per la Ricerca), University of Cagliari, Cagliari, Monserrato, Italy
| | - Stefano Mocci
- Medical Genetics, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Centre for Research University Services (CeSAR, Centro Servizi di Ateneo per la Ricerca), University of Cagliari, Cagliari, Monserrato, Italy
| | - Luchino Chessa
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Liver Unit, Department of Internal Medicine, University Hospital of Cagliari, Cagliari, Italy
| | - Andrea Perra
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Unit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
| | - Roberto Littera
- AART-ODV (Association for the Advancement of Research on Transplantation), Cagliari, Italy
- Medical Genetics, R. Binaghi Hospital, Local Public Health and Social Care Unit (ASSL) of Cagliari, Cagliari, Italy
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Karbasi Z, Gohari SH, Sabahi A. Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies. Health Sci Rep 2023; 6:e1244. [PMID: 37152228 PMCID: PMC10158785 DOI: 10.1002/hsr2.1244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/11/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
Background and Aims One such strategy is citation analysis used by researchers for research planning an article referred to by another article receives a "citation." By using bibliometric analysis, the development of research areas and authors' influence can be investigated. The current study aimed to identify and analyze the characteristics of 100 highly cited articles on the use of artificial intelligence concerning COVID-19. Methods On July 27, 2022, this database was searched using the keywords "artificial intelligence" and "COVID-19" in the topic. After extensive searching, all retrieved articles were sorted by the number of citations, and 100 highly cited articles were included based on the number of citations. The following data were extracted: year of publication, type of study, name of journal, country, number of citations, language, and keywords. Results The average number of citations for 100 highly cited articles was 138.54. The top three cited articles with 745, 596, and 549 citations. The top 100 articles were all in English and were published in 2020 and 2021. China was the most prolific country with 19 articles, followed by the United States with 15 articles and India with 10 articles. Conclusion The current bibliometric analysis demonstrated the significant growth of the use of artificial intelligence for COVID-19. Using these results, research priorities are more clearly defined, and researchers can focus on hot topics.
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Affiliation(s)
- Zahra Karbasi
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| | - Sadrieh H. Gohari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Azam Sabahi
- Department of Health Information Technology, Ferdows School of Health and Allied Medical SciencesBirjand University of Medical SciencesBirjandIran
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Kumar Y, Koul A, Kaur S, Hu YC. Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations' COVID-19 Pandemic. SN COMPUTER SCIENCE 2023; 4:91. [PMID: 36532634 PMCID: PMC9748400 DOI: 10.1007/s42979-022-01493-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 11/03/2022] [Indexed: 12/15/2022]
Abstract
In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R 2 score values.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat India
| | - Apeksha Koul
- Department of Computer Engineering, Punjabi University, Patiala, India
| | - Sukhpreet Kaur
- Department of Computer Science and Engineering, Chandigarh Engineering College, Landran, Mohali India
| | - Yu-Chen Hu
- Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, ROC
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Benarous L, Benarous K, Muhammad G, Ali Z. Deep learning application detecting SARS-CoV-2 key enzymes inhibitors. CLUSTER COMPUTING 2023; 26:1169-1180. [PMID: 35874186 PMCID: PMC9295888 DOI: 10.1007/s10586-022-03656-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 04/28/2022] [Accepted: 06/17/2022] [Indexed: 05/14/2023]
Abstract
The fast spread of the COVID-19 over the world pressured scientists to find its cures. Especially, with the disastrous results, it engendered from human life losses to long-term impacts on infected people's health and the huge financial losses. In addition to the massive efforts made by researchers and medicals on finding safe, smart, fast, and efficient methods to accurately make an early diagnosis of the COVID-19. Some researchers focused on finding drugs to treat the disease and its symptoms, others worked on creating effective vaccines, while several concentrated on finding inhibitors for the key enzymes of the virus, to reduce its spreading and reproduction inside the human body. These enzymes' inhibitors are usually found in aliments, plants, fungi, or even in some drugs. Since these inhibitors slow and halt the replication of the virus in the human body, they can help fight it at an early stage saving the patient from death risk. Moreover, if the human body's immune system gets rid of the virus at the early stage it can be spared from the disastrous sequels it may leave inside the patient's body. Our research aims to find aliments and plants that are rich in these inhibitors. In this paper, we developed a deep learning application that is trained with various aliments, plants, and drugs to detect if a component contains SARS-CoV-2 key inhibitor(s) intending to help them find more sources containing these inhibitors. The application is trained to identify various sources rich in thirteen coronavirus-2 key inhibitors. The sources are currently just aliments, plants, and seeds and the identification is done by their names.
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Affiliation(s)
- Leila Benarous
- LIM Laboratory (Laboratoire d’informatique Et de Mathématique), Department of Computer Science, Faculty of Science, University of Amar Telidji, Laghouat, Algeria
- LISSI-Tinc-NET Laboratory, University of Paris-Est Creteil, 94400 Vitry-sur-Seine, France
| | - Khedidja Benarous
- Science Fundamental Laboratory, Department of Biology, Faculty of Sciences, University of Amar Telidji, Laghouat, Algeria
| | - Ghulam Muhammad
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
| | - Zulfiqar Ali
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ UK
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Arowolo MO, Ogundokun RO, Misra S, Agboola BD, Gupta B. Machine learning-based IoT system for COVID-19 epidemics. COMPUTING 2023; 105. [PMCID: PMC8886203 DOI: 10.1007/s00607-022-01057-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The planet earth has been facing COVID-19 epidemic as a challenge in recent time. It is predictable that the world will be fighting the pandemic by taking precautions steps before an operative vaccine is found. The IoT produces huge data volumes, whether private or public, through the invention of IoT devices in the form of smart devices with an improved rate of IoT data generation. A lot of devices interact with each other in the IoT ecosystem through the cloud or servers. Various techniques have been presented in recent time, using data mining approach have proven help detect possible cases of coronaviruses. Therefore, this study uses machine learning technique (ABC and SVM) to predict COVID-19 for IoT data system. The system used two machine learning techniques which are Artificial Bee Colony algorithm with Support Vector Machine classifier on a San Francisco COVID-19 dataset. The system was evaluated using confusion matrix and had a 95% accuracy, 95% sensitivity, 95% specificity, 97% precision, 96% F1 score, 89% Matthews correlation coefficient for ABC-L-SVM and 97% accuracy, 95% sensitivity, 100% specificity, 100% precision, 97% F1 score, 93.1% Matthews correlation coefficient for ABC-Q-SVM. In conclusion, the system shows that the process of dimensionality reduction utilizing ABC feature extraction techniques can boost the classification production for SVM. It was observed that fetching relevant information from IoT systems before classification is relatively beneficial.
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Affiliation(s)
| | | | - Sanjay Misra
- Department of Computer Science and Communication, Ostfold University College, Halden, Norway
| | | | - Brij Gupta
- Department of Computer Science and Information Engineering, Asia University, Taichung, 40704 Taiwan
- King Abdulaziz University, Jeddah, 21589 Saudi Arabia
- National Institute of Technology Kurukshetra, Kurukshetra, 136119 Haryana India
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de Souza AS, de Souza RF, Guzzo CR. Quantitative structure-activity relationships, molecular docking and molecular dynamics simulations reveal drug repurposing candidates as potent SARS-CoV-2 main protease inhibitors. J Biomol Struct Dyn 2022; 40:11339-11356. [PMID: 34370631 DOI: 10.1080/07391102.2021.1958700] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The current outbreak of COVID-19 is leading an unprecedented scientific effort focusing on targeting SARS-CoV-2 proteins critical for its viral replication. Herein, we performed high-throughput virtual screening of more than eleven thousand FDA-approved drugs using backpropagation-based artificial neural networks (q2LOO = 0.60, r2 = 0.80 and r2pred = 0.91), partial-least-square (PLS) regression (q2LOO = 0.83, r2 = 0.62 and r2pred = 0.70) and sequential minimal optimization (SMO) regression (q2LOO = 0.70, r2 = 0.80 and r2pred = 0.89). We simulated the stability of Acarbose-derived hexasaccharide, Naratriptan, Peramivir, Dihydrostreptomycin, Enviomycin, Rolitetracycline, Viomycin, Angiotensin II, Angiotensin 1-7, Angiotensinamide, Fenoterol, Zanamivir, Laninamivir and Laninamivir octanoate with 3CLpro by 100 ns and calculated binding free energy using molecular mechanics combined with Poisson-Boltzmann surface area (MM-PBSA). Our QSAR models and molecular dynamics data suggest that seven repurposed-drug candidates such as Acarbose-derived Hexasaccharide, Angiotensinamide, Dihydrostreptomycin, Enviomycin, Fenoterol, Naratriptan and Viomycin are potential SARS-CoV-2 main protease inhibitors. In addition, our QSAR models and molecular dynamics simulations revealed that His41, Asn142, Cys145, Glu166 and Gln189 are potential pharmacophoric centers for 3CLpro inhibitors. Glu166 is a potential pharmacophore for drug design and inhibitors that interact with this residue may be critical to avoid dimerization of 3CLpro. Our results will contribute to future investigations of novel chemical scaffolds and the discovery of novel hits in high-throughput screening as potential anti-SARS-CoV-2 properties.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Anacleto Silva de Souza
- Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Robson Francisco de Souza
- Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Cristiane Rodrigues Guzzo
- Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
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Naz R, Torrisi M. The Transmission Dynamics of a Compartmental Epidemic Model for COVID-19 with the Asymptomatic Population via Closed-Form Solutions. Vaccines (Basel) 2022; 10:vaccines10122162. [PMID: 36560572 PMCID: PMC9788203 DOI: 10.3390/vaccines10122162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/08/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Unlike previous viral diseases, COVID-19 has an "asymptomatic" group that has no symptoms but can still spread the disease to others at the same rate as symptomatic patients who are infected. In the literature, the mass action or standard incidence rates are considered for compartmental models with asymptomatic compartment for studying the transmission dynamics of COVID-19, but the quarantined adjusted incidence rate is not. To bridge this gap, we developed a Susceptible Asymptomatic Infectious Quarantined (SAIQ) model with a Quarantine-Adjusted (QA) incidence to investigate the emergence and containment of COVID-19. COVID-19 models are investigated using various methods, but only a few studies take into account closed-form solutions. The knowledge of closed-form solutions simplifies the construction of the various epidemic indicators that describe the epidemic phenomenon and makes the sensitivity analysis to variations in the data under consideration possible. The closed-form solutions of the systems of four nonlinear first-order ordinary differential equations (ODEs) are established. The Epidemic Peak (EP), Force of Infection (FOI) and Rate of Infection (ROI) are the important indicators for the control and prevention of disease. We examined these indicators using closed-form solutions and particular parameter values. Different disease control scenarios are thoroughly examined. The four scenarios to analyze COVID-19 propagation and containment are (i) lockdown, (ii) quarantine and other preventative measures, (iii) stabilizing the basic reproduction rate to a level where the pandemic can be contained and (iv) containing the epidemic through an appropriate combination of lockdown, quarantine and other preventative measures.
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Affiliation(s)
- Rehana Naz
- Department of Mathematics and Statistical Sciences, Lahore School of Economics, Lahore 53200, Pakistan
- Correspondence:
| | - Mariano Torrisi
- Dipartimento di Matematica ed Informatica, Università di Catania Viale A. Doria, 6, I-95125 Catania, Italy
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Pathfinder-Driven Chemical Space Exploration and Multiparameter Optimization in Tandem with Glide/IFD and QSAR-Based Active Learning Approach to Prioritize Design Ideas for FEP+ Calculations of SARS-CoV-2 PL pro Inhibitors. Molecules 2022; 27:molecules27238569. [PMID: 36500659 PMCID: PMC9741453 DOI: 10.3390/molecules27238569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/25/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
A global pandemic caused by the SARS-CoV-2 virus that started in 2020 and has wreaked havoc on humanity still ravages up until now. As a result, the negative impact of travel restrictions and lockdowns has underscored the importance of our preparedness for future pandemics. The main thrust of this work was based on addressing this need by traversing chemical space to design inhibitors that target the SARS-CoV-2 papain-like protease (PLpro). Pathfinder-based retrosynthesis analysis was used to generate analogs of GRL-0617 using commercially available building blocks by replacing the naphthalene moiety. A total of 10 models were built using active learning QSAR, which achieved good statistical results such as an R2 > 0.70, Q2 > 0.64, STD Dev < 0.30, and RMSE < 0.31, on average for all models. A total of 35 ideas were further prioritized for FEP+ calculations. The FEP+ results revealed that compound 45 was the most active compound in this series with a ΔG of −7.28 ± 0.96 kcal/mol. Compound 5 exhibited a ΔG of −6.78 ± 1.30 kcal/mol. The inactive compounds in this series were compound 91 and compound 23 with a ΔG of −5.74 ± 1.06 and −3.11 ± 1.45 kcal/mol. The combined strategy employed here is envisaged to be of great utility in multiparameter lead optimization efforts, to traverse chemical space, maintaining and/or improving the potency as well as the property space of synthetically aware design ideas.
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Lasker A, Obaidullah SM, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN COMPUTER SCIENCE 2022; 4:65. [PMID: 36467853 PMCID: PMC9702883 DOI: 10.1007/s42979-022-01464-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 10/18/2022] [Indexed: 11/26/2022]
Abstract
Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.
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Affiliation(s)
- Asifuzzaman Lasker
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Sk Md Obaidullah
- Department of Computer Science & Engineering, Aliah University, Kolkata, India
| | - Chandan Chakraborty
- Department of Computer Science & Engineering, National Institute of Technical Teachers’ Training & Research Kolkata, Kolkata, India
| | - Kaushik Roy
- Department of Computer Science, West Bengal State University, Barasat, India
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14
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Chen L, Lin D, Xu H, Li J, Lin L. WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19. Front Microbiol 2022; 13:1040252. [PMID: 36466666 PMCID: PMC9713947 DOI: 10.3389/fmicb.2022.1040252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/06/2022] [Indexed: 11/18/2022] Open
Abstract
The global coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV) has led to a huge health and economic crises. However, the research required to develop new drugs and vaccines is very expensive in terms of labor, money, and time. Owing to recent advances in data science, drug-repositioning technologies have become one of the most promising strategies available for developing effective treatment options. Using the previously reported human drug virus database (HDVD), we proposed a model to predict possible drug regimens based on a weighted reconstruction-based linear label propagation algorithm (WLLP). For the drug–virus association matrix, we used the weighted K-nearest known neighbors method for preprocessing and label propagation of the network based on the linear neighborhood similarity of drugs and viruses to obtain the final prediction results. In the framework of 10 times 10-fold cross-validated area under the receiver operating characteristic (ROC) curve (AUC), WLLP exhibited excellent performance with an AUC of 0.8828 ± 0.0037 and an area under the precision-recall curve of 0.5277 ± 0.0053, outperforming the other four models used for comparison. We also predicted effective drug regimens against SARS-CoV-2, and this case study showed that WLLP can be used to suggest potential drugs for the treatment of COVID-19.
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Affiliation(s)
- Langcheng Chen
- Center of Campus Network and Modern Educational Technology, Guangdong University of Technology, Guangzhou, China
| | - Dongying Lin
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Haojie Xu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Jianming Li
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Lieqing Lin
- Center of Campus Network and Modern Educational Technology, Guangdong University of Technology, Guangzhou, China
- *Correspondence: Lieqing Lin
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15
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Jamal QMS. Antiviral Potential of Plants against COVID-19 during Outbreaks-An Update. Int J Mol Sci 2022; 23:13564. [PMID: 36362351 PMCID: PMC9655040 DOI: 10.3390/ijms232113564] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/06/2022] [Accepted: 11/02/2022] [Indexed: 12/01/2023] Open
Abstract
Several human diseases are caused by viruses, including cancer, Type I diabetes, Alzheimer's disease, and hepatocellular carcinoma. In the past, people have suffered greatly from viral diseases such as polio, mumps, measles, dengue fever, SARS, MERS, AIDS, chikungunya fever, encephalitis, and influenza. Recently, COVID-19 has become a pandemic in most parts of the world. Although vaccines are available to fight the infection, their safety and clinical trial data are still questionable. Social distancing, isolation, the use of sanitizer, and personal productive strategies have been implemented to prevent the spread of the virus. Moreover, the search for a potential therapeutic molecule is ongoing. Based on experiences with outbreaks of SARS and MERS, many research studies reveal the potential of medicinal herbs/plants or chemical compounds extracted from them to counteract the effects of these viral diseases. COVID-19's current status includes a decrease in infection rates as a result of large-scale vaccination program implementation by several countries. But it is still very close and needs to boost people's natural immunity in a cost-effective way through phytomedicines because many underdeveloped countries do not have their own vaccination facilities. In this article, phytomedicines as plant parts or plant-derived metabolites that can affect the entry of a virus or its infectiousness inside hosts are described. Finally, it is concluded that the therapeutic potential of medicinal plants must be analyzed and evaluated entirely in the control of COVID-19 in cases of uncontrollable SARS infection.
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Affiliation(s)
- Qazi Mohammad Sajid Jamal
- Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Al Bukayriyah 52741, Saudi Arabia
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16
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Nasim N, Sandeep IS, Mohanty S. Plant-derived natural products for drug discovery: current approaches and prospects. THE NUCLEUS 2022; 65:399-411. [PMID: 36276225 PMCID: PMC9579558 DOI: 10.1007/s13237-022-00405-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 09/29/2022] [Indexed: 11/19/2022] Open
Abstract
Nature has abundant source of drugs that need to be identified/purified for use as essential biologics, either individually or in combination in the modern medical field. These drugs are divided into small bio-molecules, plant-made biologics, and a recently introduced third category known as phytopharmaceutical drugs. The development of phytopharmaceutical medicines is based on the ethnopharmacological approach, which relies on the traditional medicine system. The concept of ‘one-disease one-target drug’ is becoming less popular, and the use of plant extracts, fractions, and molecules is the new paradigm that holds promising scope to formulate appropriate drugs. This led to discovering a new concept known as polypharmacology, where natural products from varying sources can engage with multiple human physiology targets. This article summarizes different approaches for phytopharmaceutical drug development and discusses the progress in systems biology and computational tools for identifying drug targets. We review the existing drug delivery methods to facilitate the efficient delivery of drugs to the targets. In addition, we describe different analytical techniques for the authentication and fingerprinting of plant materials. Finally, we highlight the role of biopharming in developing plant-based biologics.
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Affiliation(s)
- Noohi Nasim
- grid.412612.20000 0004 1760 9349Centre for Biotechnology, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751003 India
| | - Inavolu Sriram Sandeep
- grid.412612.20000 0004 1760 9349Centre for Biotechnology, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751003 India
| | - Sujata Mohanty
- grid.506052.40000 0004 4911 8595Department of Biotechnology, Rama Devi Women’s University, Vidya Vihar, Bhubaneswar, Odisha 751022 India
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17
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Muneer A, Fati SM, Arifin Akbar N, Agustriawan D, Tri Wahyudi S. iVaccine-Deep: Prediction of COVID-19 mRNA vaccine degradation using deep learning. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2022; 34:7419-7432. [PMID: 38620874 PMCID: PMC8513509 DOI: 10.1016/j.jksuci.2021.10.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/29/2021] [Accepted: 10/05/2021] [Indexed: 12/14/2022]
Abstract
Messenger RNA (mRNA) has emerged as a critical global technology that requires global joint efforts from different entities to develop a COVID-19 vaccine. However, the chemical properties of RNA pose a challenge in utilizing mRNA as a vaccine candidate. For instance, the molecules are prone to degradation, which has a negative impact on the distribution of mRNA among patients. In addition, little is known of the degradation properties of individual RNA bases in a molecule. Therefore, this study aims to investigate whether a hybrid deep learning can predict RNA degradation from RNA sequences. Two deep hybrid neural network models were proposed, namely GCN_GRU and GCN_CNN. The first model is based on graph convolutional neural networks (GCNs) and gated recurrent unit (GRU). The second model is based on GCN and convolutional neural networks (CNNs). Both models were computed over the structural graph of the mRNA molecule. The experimental results showed that GCN_GRU hybrid model outperform GCN_CNN model by a large margin during the test time. Validation of proposed hybrid models is performed by well-known evaluation measures. Among different deep neural networks, GCN_GRU based model achieved best scores on both public and private MCRMSE test scores with 0.22614 and 0.34152, respectively. Finally, GCN_GRU pre-trained model has achieved the highest AuC score of 0.938. Such proven outperformance of GCNs indicates that modeling RNA molecules using graphs is critical in understanding molecule degradation mechanisms, which helps in minimizing the aforementioned issues. To show the importance of the proposed GCN_GRU hybrid model, in silico experiments has been contacted. The in-silico results showed that our model pays local attention when predicting a given position's reactivity and exhibits interesting behavior on neighboring bases in the sequence.
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Affiliation(s)
- Amgad Muneer
- Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32160, Malaysia
| | - Suliman Mohamed Fati
- Information Systems Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
| | - Nur Arifin Akbar
- Research Department, Idenitive Mashable Prototyping, Banyumas 53124, Indonesia
| | - David Agustriawan
- Faculty of Bioinformatics, Indonesia International Institute for Life Sciences, Jakarta Timur 13210, Indonesia
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18
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Rotich AK, Takashima E, Yanow SK, Gitaka J, Kanoi BN. Towards identification and development of alternative vaccines against pregnancy-associated malaria based on naturally acquired immunity. FRONTIERS IN TROPICAL DISEASES 2022. [DOI: 10.3389/fitd.2022.988284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Pregnant women are particularly susceptible to Plasmodium falciparum malaria, leading to substantial maternal and infant morbidity and mortality. While highly effective malaria vaccines are considered an essential component towards malaria elimination, strides towards development of vaccines for pregnant women have been minimal. The leading malaria vaccine, RTS,S/AS01, has modest efficacy in children suggesting that it needs to be strengthened and optimized if it is to be beneficial for pregnant women. Clinical trials against pregnancy-associated malaria (PAM) focused on the classical VAR2CSA antigen are ongoing. However, additional antigens have not been identified to supplement these initiatives despite the new evidence that VAR2CSA is not the only molecule involved in pregnancy-associated naturally acquired immunity. This is mainly due to a lack of understanding of the immune complexities in pregnancy coupled with difficulties associated with expression of malaria recombinant proteins, low antigen immunogenicity in humans, and the anticipated complications in conducting and implementing a vaccine to protect pregnant women. With the accelerated evolution of molecular technologies catapulted by the global pandemic, identification of novel alternative vaccine antigens is timely and feasible. In this review, we discuss approaches towards novel antigen discovery to support PAM vaccine studies.
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19
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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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20
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Kanoi BN, Maina M, Likhovole C, Kobia FM, Gitaka J. Malaria vaccine approaches leveraging technologies optimized in the COVID-19 era. FRONTIERS IN TROPICAL DISEASES 2022. [DOI: 10.3389/fitd.2022.988665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Africa bears the greatest burden of malaria with more than 200 million clinical cases and more than 600,000 deaths in 2020 alone. While malaria-associated deaths dropped steadily until 2015, the decline started to falter after 2016, highlighting the need for novel potent tools in the fight against malaria. Currently available tools, such as antimalarial drugs and insecticides are threatened by development of resistance by the parasite and the mosquito. The WHO has recently approved RTS,S as the first malaria vaccine for public health use. However, because the RTS,S vaccine has an efficacy of only 36% in young children, there is need for more efficacious vaccines. Indeed, based on the global goal of licensing a malaria vaccine with at least 75% efficacy by 2030, RTS,S is unlikely to be sufficient alone. However, recent years have seen tremendous progress in vaccine development. Although the COVID-19 pandemic impacted malaria control, the rapid progress in research towards the development of COVID-19 vaccines indicate that harnessing funds and technological advances can remarkably expedite vaccine development. In this review, we highlight and discuss current and prospective trends in global efforts to discover and develop malaria vaccines through leveraging mRNA vaccine platforms and other systems optimized during COVID-19 vaccine studies.
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21
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Naik N, Hameed BMZ, Sooriyaperakasam N, Vinayahalingam S, Patil V, Smriti K, Saxena J, Shah M, Ibrahim S, Singh A, Karimi H, Naganathan K, Shetty DK, Rai BP, Chlosta P, Somani BK. Transforming healthcare through a digital revolution: A review of digital healthcare technologies and solutions. Front Digit Health 2022; 4:919985. [PMID: 35990014 PMCID: PMC9385947 DOI: 10.3389/fdgth.2022.919985] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/08/2022] [Indexed: 01/08/2023] Open
Abstract
The COVID-19 pandemic has put a strain on the entire global healthcare infrastructure. The pandemic has necessitated the re-invention, re-organization, and transformation of the healthcare system. The resurgence of new COVID-19 virus variants in several countries and the infection of a larger group of communities necessitate a rapid strategic shift. Governments, non-profit, and other healthcare organizations have all proposed various digital solutions. It's not clear whether these digital solutions are adaptable, functional, effective, or reliable. With the disease becoming more and more prevalent, many countries are looking for assistance and implementation of digital technologies to combat COVID-19. Digital health technologies for COVID-19 pandemic management, surveillance, contact tracing, diagnosis, treatment, and prevention will be discussed in this paper to ensure that healthcare is delivered effectively. Artificial Intelligence (AI), big data, telemedicine, robotic solutions, Internet of Things (IoT), digital platforms for communication (DC), computer vision, computer audition (CA), digital data management solutions (blockchain), digital imaging are premiering to assist healthcare workers (HCW's) with solutions that include case base surveillance, information dissemination, disinfection, and remote consultations, along with many other such interventions.
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Affiliation(s)
- Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal, Karnataka, India
| | - B. M. Zeeshan Hameed
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal, Karnataka, India
- Department of Urology, Father Muller Medical College, Mangalore, Karnataka, India
- *Correspondence: B. M. Zeeshan Hameed
| | | | | | - Vathsala Patil
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Komal Smriti
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Janhavi Saxena
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Milap Shah
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal, Karnataka, India
- Robotics and Urooncology, Max Hospital and Max Institute of Cancer Care, New Delhi, India
| | - Sufyan Ibrahim
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal, Karnataka, India
- Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Anshuman Singh
- Department of Urology, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Hadis Karimi
- Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | | | - Dasharathraj K. Shetty
- Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
- Dasharathraj K. Shetty
| | - Bhavan Prasad Rai
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal, Karnataka, India
- Department of Urology, Freeman Hospital, Newcastle upon Tyne, United Kingdom
| | - Piotr Chlosta
- Department of Urology, Jagiellonian University in Krakow, Kraków, Poland
| | - Bhaskar K. Somani
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal, Karnataka, India
- Department of Urology, University Hospital Southampton NHS Trust, Southampton, United Kingdom
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22
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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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23
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Dasgupta A, Bakshi A, Mukherjee S, Das K, Talukdar S, Chatterjee P, Mondal S, Das P, Ghosh S, Som A, Roy P, Kundu R, Sarkar A, Biswas A, Paul K, Basak S, Manna K, Saha C, Mukhopadhyay S, Bhattacharyya NP, De RK. Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2022; 12:e1462. [PMID: 35942397 PMCID: PMC9350133 DOI: 10.1002/widm.1462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 03/28/2022] [Accepted: 04/28/2022] [Indexed: 05/02/2023]
Abstract
World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.
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Affiliation(s)
- Abhijit Dasgupta
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Abhisek Bakshi
- Department of Information TechnologyBengal Institute of TechnologyKolkataWest BengalIndia
| | - Srijani Mukherjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Kuntal Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Soumyajeet Talukdar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pratyayee Chatterjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Sagnik Mondal
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Puspita Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Subhrojit Ghosh
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Archisman Som
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pritha Roy
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Rima Kundu
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Akash Sarkar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Arnab Biswas
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Karnelia Paul
- Department of BiotechnologyUniversity of CalcuttaKolkataWest BengalIndia
| | - Sujit Basak
- Department of Physiology and BiophysicsStony Brook UniversityStony BrookNew YorkUSA
| | - Krishnendu Manna
- Department of Food and NutritionUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Chinmay Saha
- Department of Genome Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Satinath Mukhopadhyay
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Nitai P. Bhattacharyya
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Rajat K. De
- Machine Intelligence UnitIndian Statistical InstituteKolkataWest BengalIndia
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24
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Oliva G, Schlueter M, Munetomo M, Scala A. Dynamical intervention planning against COVID-19-like epidemics. PLoS One 2022; 17:e0269830. [PMID: 35700170 PMCID: PMC9197046 DOI: 10.1371/journal.pone.0269830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/30/2022] [Indexed: 11/28/2022] Open
Abstract
COVID-19 has got us to face a new situation where, for the lack of ready-to-use vaccines, it is necessary to support vaccination with complex non-pharmaceutical strategies. In this paper, we provide a novel Mixed Integer Nonlinear Programming formulation for fine-grained optimal intervention planning (i.e., at the level of the single day) against newborn epidemics like COVID-19, where a modified SIR model accounting for heterogeneous population classes, social distancing and several types of vaccines (each with its efficacy and delayed effects), allows us to plan an optimal mixed strategy (both pharmaceutical and non-pharmaceutical) that takes into account both the vaccine availability in limited batches at selected time instants and the need for second doses while keeping hospitalizations and intensive care occupancy below a threshold and requiring that new infections die out at the end of the planning horizon. In order to show the effectiveness of the proposed formulation, we analyze a case study for Italy with realistic parameters.
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Affiliation(s)
- Gabriele Oliva
- Unit of Automatic Control, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- * E-mail: (GO); (AS)
| | - Martin Schlueter
- Information Initiative Center, Hokkaido University, Sapporo, Japan
| | | | - Antonio Scala
- CNR-ISC, Applico Lab, Roma, Italy
- Centro Ricerche Enrico Fermi, Roma, Italy
- Big Data in Health Society, Roma, Italy
- Global Health Security Agenda - GHSA, Roma, Italy
- * E-mail: (GO); (AS)
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25
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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26
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Vasighi M, Romanova J, Nedyalkova M. A multilevel approach for screening natural compounds as an antiviral agent for COVID-19. Comput Biol Chem 2022; 98:107694. [PMID: 35576744 PMCID: PMC9090871 DOI: 10.1016/j.compbiolchem.2022.107694] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 04/27/2022] [Accepted: 05/06/2022] [Indexed: 01/11/2023]
Abstract
The COVID-19 has a worldwide spread, which has prompted concerted efforts to find successful drug treatments. Drug design focused on finding antiviral therapeutic agents from plant-derived compounds which may disrupt the attachment of SARS-CoV-2 to host cells is with a pivotal need and role in the last year. Herein, we provide an approach based on drug design methods combined with machine learning approaches to classify and discover inhibitors for COVID-19 from natural products. The spike receptor-binding domain (RBD) was docked with database of 125 ligands. The docking protocol based on several steps was performed within Autodock Vina to identify the high-affinity binding mode and to reveal more insights into interaction between the phytochemicals and the RBD domain. A protein-ligand interaction analyzer has been developed. The drug-likeness properties of explored inhibitors are analyzed in the frame of exploratory data analyses. The developed computational protocol yielded a comprehensive pipeline for predicting the inhibitors to prevent the entry RBD region.
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Affiliation(s)
- Mahdi Vasighi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), 45137-66731 Zanjan, Iran
| | - Julia Romanova
- Department of Inorganic Chemistry, Sofia University “St. Kl. Ohridski”, Sofia, Bulgaria
| | - Miroslava Nedyalkova
- Department of Inorganic Chemistry, Sofia University “St. Kl. Ohridski”, Sofia, Bulgaria,Chemistry Department, University of Fribourg, Fribourg, Switzerland,Corresponding author at: Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), 45137-66731 Zanjan, Iran
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27
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ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data. JOURNAL OF THE INSTITUTION OF ENGINEERS (INDIA): SERIES B 2022. [PMCID: PMC8724598 DOI: 10.1007/s40031-021-00696-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.
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28
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Hognon C, Marazzi M, García-Iriepa C. Atomistic-Level Description of the Covalent Inhibition of SARS-CoV-2 Papain-like Protease. Int J Mol Sci 2022; 23:ijms23105855. [PMID: 35628665 PMCID: PMC9143025 DOI: 10.3390/ijms23105855] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/13/2022] [Accepted: 05/21/2022] [Indexed: 12/25/2022] Open
Abstract
Inhibition of the papain-like protease (PLpro) of SARS-CoV-2 has been demonstrated to be a successful target to prevent the spreading of the coronavirus in the infected body. In this regard, covalent inhibitors, such as the recently proposed VIR251 ligand, can irreversibly inactivate PLpro by forming a covalent bond with a specific residue of the catalytic site (Cys111), through a Michael addition reaction. An inhibition mechanism can therefore be proposed, including four steps: (i) ligand entry into the protease pocket; (ii) Cys111 deprotonation of the thiol group by a Brønsted-Lowry base; (iii) Cys111-S- addition to the ligand; and (iv) proton transfer from the protonated base to the covalently bound ligand. Evaluating the energetics and PLpro conformational changes at each of these steps could aid the design of more efficient and selective covalent inhibitors. For this aim, we have studied by means of MD simulations and QM/MM calculations the whole mechanism. Regarding the first step, we show that the inhibitor entry in the PLpro pocket is thermodynamically favorable only when considering the neutral Cys111, that is, prior to the Cys111 deprotonation. For the second step, MD simulations revealed that His272 would deprotonate Cys111 after overcoming an energy barrier of ca. 32 kcal/mol (at the QM/MM level), but implying a decrease of the inhibitor stability inside the protease pocket. This information points to a reversible Cys111 deprotonation, whose equilibrium is largely shifted toward the neutral Cys111 form. Although thermodynamically disfavored, if Cys111 is deprotonated in close proximity to the vinylic carbon of the ligand, then covalent binding takes place in an irreversible way (third step) to form the enolate intermediate. Finally, due to Cys111-S- negative charge redistribution over the bound ligand, proton transfer from the initially protonated His272 is favored, finally leading to an irreversibly modified Cys111 and a restored His272. These results elucidate the selectivity of Cys111 to enable formation of a covalent bond, even if a weak proton acceptor is available, as His272.
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Affiliation(s)
- Cécilia Hognon
- Grupo de Reactividad y Estructura Molecular (RESMOL), Departamento de Química Analítica, Química Física e Ingeniería Química, Universidad de Alcalá, Alcalá de Henares, 28801 Madrid, Spain;
| | - Marco Marazzi
- Grupo de Reactividad y Estructura Molecular (RESMOL), Departamento de Química Analítica, Química Física e Ingeniería Química, Universidad de Alcalá, Alcalá de Henares, 28801 Madrid, Spain;
- Instituto de Investigación Química “Andrés M. del Río” (IQAR), Universidad de Alcalá, Alcalá de Henares, 28801 Madrid, Spain
- Correspondence: (M.M.); (C.G.-I.)
| | - Cristina García-Iriepa
- Grupo de Reactividad y Estructura Molecular (RESMOL), Departamento de Química Analítica, Química Física e Ingeniería Química, Universidad de Alcalá, Alcalá de Henares, 28801 Madrid, Spain;
- Instituto de Investigación Química “Andrés M. del Río” (IQAR), Universidad de Alcalá, Alcalá de Henares, 28801 Madrid, Spain
- Correspondence: (M.M.); (C.G.-I.)
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29
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Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106731. [PMID: 35286874 PMCID: PMC8897838 DOI: 10.1016/j.cmpb.2022.106731] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/28/2022] [Accepted: 03/03/2022] [Indexed: 05/05/2023]
Abstract
Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.
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Affiliation(s)
- Haseeb Hassan
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China
| | - Zhaoyu Ren
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Chengmin Zhou
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China
| | - Muazzam A Khan
- Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Yi Pan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China
| | - Jian Zhao
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
| | - Bingding Huang
- College of Big data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
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30
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Drug Repurposing for COVID-19: A Review and a Novel Strategy to Identify New Targets and Potential Drug Candidates. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27092723. [PMID: 35566073 PMCID: PMC9099573 DOI: 10.3390/molecules27092723] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/14/2022] [Accepted: 04/21/2022] [Indexed: 02/01/2023]
Abstract
In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19) was first identified in the province of Wuhan, China. Since then, there have been over 400 million confirmed cases and 5.8 million deaths by COVID-19 reported worldwide. The urgent need for therapies against SARS-CoV-2 led researchers to use drug repurposing approaches. This strategy allows the reduction in risks, time, and costs associated with drug development. In many cases, a repurposed drug can enter directly to preclinical testing and clinical trials, thus accelerating the whole drug discovery process. In this work, we will give a general overview of the main developments in COVID-19 treatment, focusing on the contribution of the drug repurposing paradigm to find effective drugs against this disease. Finally, we will present our findings using a new drug repurposing strategy that identified 11 compounds that may be potentially effective against COVID-19. To our knowledge, seven of these drugs have never been tested against SARS-CoV-2 and are potential candidates for in vitro and in vivo studies to evaluate their effectiveness in COVID-19 treatment.
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31
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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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32
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Panahi Y, Dadkhah M, Talei S, Gharari Z, Asghariazar V, Abdolmaleki A, Matin S, Molaei S. Can anti-parasitic drugs help control COVID-19? Future Virol 2022. [PMID: 35359702 PMCID: PMC8940209 DOI: 10.2217/fvl-2021-0160] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 02/28/2022] [Indexed: 01/18/2023]
Abstract
Novel COVID-19 is a public health emergency that poses a serious threat to people worldwide. Given the virus spreading so quickly, novel antiviral medications are desperately needed. Repurposing existing drugs is the first strategy. Anti-parasitic drugs were among the first to be considered as a potential treatment option for this disease. Even though many papers have discussed the efficacy of various anti-parasitic drugs in treating COVID-19 separately, so far, no single study comprehensively discussed these drugs. This study reviews some anti-parasitic recommended drugs to treat COVID-19, in terms of function and in vitro as well as clinical results. Finally, we briefly review the advanced techniques, such as artificial intelligence, that have been used to find effective drugs for the treatment of COVID-19.
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Affiliation(s)
- Yasin Panahi
- Department of Pharmacology & Toxicology, School of Pharmacy, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Masoomeh Dadkhah
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Sahand Talei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Gharari
- Department of Biotechnology, Faculty of Biological Sciences, Al-Zahra University, Tehran, Iran
| | - Vahid Asghariazar
- Deputy of Research & Technology, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Arash Abdolmaleki
- Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran.,Bio Science & Biotechnology Research center (BBRC), Sabalan University of Advanced Technologies (SUAT), Namin, Iran
| | - Somayeh Matin
- Department of Internal Medicine, Imam Khomeini Hospital, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Soheila Molaei
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.,Zoonoses Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
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33
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Application of Neural Networks and Regression Modelling to Enable Environmental Regulatory Compliance and Energy Optimisation in a Sequencing Batch Reactor. SUSTAINABILITY 2022. [DOI: 10.3390/su14074098] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Real-time control of wastewater treatment plants (WWTPs) can have significant environmental and cost advantages. However, its application to small and decentralised WWTPs, which typically have highly varying influent characteristics, remains limited to date due to cost, reliability and technical restrictions. In this study, a methodology was developed using numerical models that can improve sustainability, in real time, by enhancing wastewater treatment whilst also optimising operational and energy efficiency. The methodology leverages neural network and regression modelling to determine a suitable soft sensor for the prediction of ammonium-nitrogen trends. This study is based on a case-study decentralised WWTP employing sequencing batch reactor (SBR) treatment and uses pH and oxidation-reduction potential sensors as proxies for ammonium-nitrogen sensors. In the proposed method, data were pre-processed into 15 input variables and analysed using multi-layer neural network (MLNN) and regression models, creating 176 soft sensors. Each soft sensor was then analysed and ranked to determine the most suitable soft sensor for the WWTP. It was determined that the most suitable soft sensor for this WWTP would achieve a 67% cycle-time saving and 51% electricity saving for each treatment cycle while meeting the criteria set for ammonium discharges. This proposed soft sensor selection methodology can be applied, in full or in part, to existing or new WWTPs, potentially increasing the adoption of real-time control technologies, thus enhancing their overall effluent quality and energy performance.
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34
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Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J Med Syst 2022. [PMID: 35348909 DOI: 10.1101/2021.04.06.21254997] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Affiliation(s)
- Theresa Roland
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.
| | - Carl Böck
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Thomas Tschoellitsch
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | | | - Sepp Hochreiter
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Jens Meier
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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35
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Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J Med Syst 2022; 46:23. [PMID: 35348909 PMCID: PMC8960704 DOI: 10.1007/s10916-022-01807-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 02/10/2022] [Indexed: 12/23/2022]
Abstract
AbstractMany previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Affiliation(s)
- Theresa Roland
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria.
| | - Carl Böck
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Thomas Tschoellitsch
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | | | - Sepp Hochreiter
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
| | - Jens Meier
- Department of Anesthesiology and Critical Care Medicine, Kepler University Hospital GmbH, Johannes Kepler University Linz, Linz, Austria
| | - Günter Klambauer
- ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Linz, Austria
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36
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Fritsch S, Sharafutdinov K, Schuppert A, Bickenbach J. [Usage of Artificial Intelligence in the Combat against the COVID-19 Pandemic]. Anasthesiol Intensivmed Notfallmed Schmerzther 2022; 57:185-197. [PMID: 35320841 DOI: 10.1055/a-1423-8039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The COVID-19 pandemic is a global health emergency of historic dimension. In this situation, researchers worldwide wanted to help manage the pandemic by using artificial intelligence (AI). This narrative review aims to describe the usage of AI in the combat against COVID-19. The addressed aspects encompass AI algorithms for analysis of thoracic X-rays or CTs, prediction models for severity and outcome of the disease, AI applications in development of new drugs and vaccines as well as forecasting models for spread of the virus. The review shows, which approaches were pursued, and which were successful.
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37
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Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2. Viruses 2022; 14:v14030605. [PMID: 35337012 PMCID: PMC8953050 DOI: 10.3390/v14030605] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 03/08/2022] [Accepted: 03/10/2022] [Indexed: 02/06/2023] Open
Abstract
We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.
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38
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Keshavarzi Arshadi A, Salem M, Firouzbakht A, Yuan JS. MolData, a molecular benchmark for disease and target based machine learning. J Cheminform 2022; 14:10. [PMID: 35255958 PMCID: PMC8899453 DOI: 10.1186/s13321-022-00590-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/13/2022] [Indexed: 12/25/2022] Open
Abstract
Deep learning’s automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge are necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChEMBL offer the screening data for millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological descriptions which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset is collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveils valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https://GitHub.com/Transilico/MolData as well as within the additional files.
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Abusalah MAH, Khalifa M, Al-Hatamleh MAI, Jarrar M, Mohamud R, Chan YY. Nucleic Acid-Based COVID-19 Therapy Targeting Cytokine Storms: Strategies to Quell the Storm. J Pers Med 2022; 12:386. [PMID: 35330388 PMCID: PMC8948998 DOI: 10.3390/jpm12030386] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/21/2022] [Accepted: 02/28/2022] [Indexed: 02/07/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) has shaken the world and triggered drastic changes in our lifestyle to control it. Despite the non-typical efforts, COVID-19 still thrives and plagues humanity worldwide. The unparalleled degree of infection has been met with an exceptional degree of research to counteract it. Many drugs and therapeutic technologies have been repurposed and discovered, but no groundbreaking antiviral agent has been introduced yet to eradicate COVID-19 and restore normalcy. As lethality is directly correlated with the severity of disease, hospitalized severe cases are of the greatest importance to reduce, especially the cytokine storm phenomenon. This severe inflammatory phenomenon characterized by elevated levels of inflammatory mediators can be targeted to relieve symptoms and save the infected patients. One of the promising therapeutic strategies to combat COVID-19 is nucleic acid-based therapeutic approaches, including microRNAs (miRNAs). This work is an up-to-date review aimed to comprehensively discuss the current nucleic acid-based therapeutics against COVID-19 and their mechanisms of action, taking into consideration the emerging SARS-CoV-2 variants of concern, as well as providing potential future directions. miRNAs can be used to run interference with the expression of viral proteins, while endogenous miRNAs can be targeted as well, offering a versatile platform to control SARS-CoV-2 infection. By targeting these miRNAs, the COVID-19-induced cytokine storm can be suppressed. Therefore, nucleic acid-based therapeutics (miRNAs included) have a latent ability to break the COVID-19 infection in general and quell the cytokine storm in particular.
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Affiliation(s)
- Mai Abdel Haleem Abusalah
- Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu 16150, Kelantan, Malaysia;
| | - Moad Khalifa
- School of Health Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia;
| | - Mohammad A. I. Al-Hatamleh
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu 16150, Kelantan, Malaysia; (M.A.I.A.-H.); (R.M.)
| | - Mu’taman Jarrar
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia;
- Medical Education Department, King Fahd Hospital of the University, Al-Khobar 34445, Saudi Arabia
| | - Rohimah Mohamud
- Department of Immunology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu 16150, Kelantan, Malaysia; (M.A.I.A.-H.); (R.M.)
| | - Yean Yean Chan
- Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Kota Bharu 16150, Kelantan, Malaysia;
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40
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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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Bent OE, Wachira C, Remy SL, Ogallo W, Walcott-Bryant A. A Framework for Inferring Epidemiological Model Parameters using Bayesian Nonparametrics. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:217-226. [PMID: 35308928 PMCID: PMC8861664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The use of epidemiological models for decision-making has been prominent during the COVID-19 pandemic. Our work presents the application of nonparametric Bayesian techniques for inferring epidemiological model parameters based on available data sets published during the pandemic, towards enabling predictions under uncertainty during emerging pandemics. We present a methodology and framework that allows epidemiological model drivers to be integrated as input into the model calibration process. We demonstrate our methodology using the stringency index and mobility data for COVID-19 on an SEIRD compartmental model for selected US states. Our results directly compare the use of Bayesian nonparametrics for model predictions based on best parameter estimates with results of inference of parameter values across the US states. The proposed methodology provides a framework for What-If analysis and sequential decision-making methods for disease intervention planning and is demonstrated for COVID-19, while also applicable to other infectious disease models.
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42
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Primary Factors Influencing the Decision to Vaccinate against COVID-19 in the United States: A Pre-Vaccine Analysis. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031026. [PMID: 35162050 PMCID: PMC8834206 DOI: 10.3390/ijerph19031026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/09/2022] [Accepted: 01/11/2022] [Indexed: 02/01/2023]
Abstract
Because vaccine hesitancy is a dynamic trait, it is critical to identify and compare the contributing factors at the different stages of a pandemic. The prediction of vaccine decision making and the interpretation of the analytical relationships among variables that encompass public perceptions and attitudes towards the COVID-19 pandemic have been extensively limited to the studies conducted after the administration of the first FDA-approved vaccine in December of 2020. In order to fill the gap in the literature, we used six predictive models and identified the most important factors, via Gini importance measures, that contribute to the prediction of COVID-19 vaccine acceptors and refusers using a nationwide survey that was administered in November 2020, before the widespread use of COVID-19 vaccines. Concerns about (re)contracting COVID-19 and opinions regarding mandatory face covering were identified as the most important predictors of vaccine decision making. By investigating the vaccine acceptors and refusers before the introduction of COVID-19 vaccines, we can help public health officials design and deliver individually tailored and dynamic vaccination programs that can increase the overall vaccine uptake.
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43
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Vasiliauskaite V, Antulov-Fantulin N, Helbing D. On some fundamental challenges in monitoring epidemics. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210117. [PMID: 34802270 PMCID: PMC8607144 DOI: 10.1098/rsta.2021.0117] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/13/2021] [Indexed: 05/22/2023]
Abstract
Epidemic models often reflect characteristic features of infectious spreading processes by coupled nonlinear differential equations considering different states of health (such as susceptible, infectious or recovered). This compartmental modelling approach, however, delivers an incomplete picture of the dynamics of epidemics, as it neglects stochastic and network effects, and the role of the measurement process, on which the estimation of epidemiological parameters and incidence values relies. In order to study the related issues, we combine established epidemiological spreading models with a measurement model of the testing process, considering the problems of false positives and false negatives as well as biased sampling. Studying a model-generated ground truth in conjunction with simulated observation processes (virtual measurements) allows one to gain insights into the fundamental limitations of purely data-driven methods when assessing the epidemic situation. We conclude that epidemic monitoring, simulation, and forecasting are wicked problems, as applying a conventional data-driven approach to a complex system with nonlinear dynamics, network effects and uncertainty can be misleading. Nevertheless, some of the errors can be corrected for, using scientific knowledge of the spreading dynamics and the measurement process. We conclude that such corrections should generally be part of epidemic monitoring, modelling and forecasting efforts. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
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Affiliation(s)
| | | | - Dirk Helbing
- Computational Social Science, ETH Zürich, Zürich, Switzerland
- Complexity Science Hub Vienna, Wien, Austria
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44
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A step toward better sample management of COVID-19: On-spot detection by biometric technology and artificial intelligence. COVID-19 AND THE SUSTAINABLE DEVELOPMENT GOALS 2022. [PMCID: PMC9334987 DOI: 10.1016/b978-0-323-91307-2.00017-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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45
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AI and Immunoinformatics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_113] [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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46
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Antonarelli G, Corti C, Tarantino P, Ascione L, Cortes J, Romero P, Mittendorf E, Disis M, Curigliano G. Therapeutic cancer vaccines revamping: technology advancements and pitfalls. Ann Oncol 2021; 32:1537-1551. [PMID: 34500046 PMCID: PMC8420263 DOI: 10.1016/j.annonc.2021.08.2153] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/21/2021] [Accepted: 08/29/2021] [Indexed: 12/12/2022] Open
Abstract
Cancer vaccines (CVs) represent a long-sought therapeutic and prophylactic immunotherapy strategy to obtain antigen (Ag)-specific T-cell responses and potentially achieve long-term clinical benefit. However, historically, most CV clinical trials have resulted in disappointing outcomes, despite promising signs of immunogenicity across most formulations. In the past decade, technological advances regarding vaccine delivery platforms, tools for immunogenomic profiling, and Ag/epitope selection have occurred. Consequently, the ability of CVs to induce tumor-specific and, in some cases, remarkable clinical responses have been observed in early-phase clinical trials. It is notable that the record-breaking speed of vaccine development in response to the coronavirus disease-2019 pandemic mainly relied on manufacturing infrastructures and technological platforms already developed for CVs. In turn, research, clinical data, and infrastructures put in place for the severe acute respiratory syndrome coronavirus 2 pandemic can further speed CV development processes. This review outlines the main technological advancements as well as major issues to tackle in the development of CVs. Possible applications for unmet clinical needs will be described, putting into perspective the future of cancer vaccinology.
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Affiliation(s)
- G. Antonarelli
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy
| | - C. Corti
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy
| | - P. Tarantino
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy
| | - L. Ascione
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy
| | - J. Cortes
- International Breast Cancer Center (IBCC), Quironsalud Group, Barcelona, Spain,Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - P. Romero
- Department of Fundamental Oncology, University of Lausanne, Lausanne, Switzerland
| | - E.A. Mittendorf
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, USA,Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, USA
| | - M.L. Disis
- UW Medicine Cancer Vaccine Institute, University of Washington, Seattle, USA
| | - G. Curigliano
- Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Milan, Italy,Department of Oncology and Haematology (DIPO), University of Milan, Milan, Italy,Correspondence to: Prof. Giuseppe Curigliano, Division of Early Drug Development for Innovative Therapy, European Institute of Oncology, IRCCS, Via Ripamonti 435, 20141 Milan, Italy. Tel: +39-0257489599
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47
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Aronskyy I, Masoudi-Sobhanzadeh Y, Cappuccio A, Zaslavsky E. Advances in the computational landscape for repurposed drugs against COVID-19. Drug Discov Today 2021; 26:2800-2815. [PMID: 34339864 PMCID: PMC8323501 DOI: 10.1016/j.drudis.2021.07.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/30/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023]
Abstract
The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.
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Affiliation(s)
- Illya Aronskyy
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Corresponding authors
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA,Corresponding authors
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48
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Harigua-Souiai E, Heinhane MM, Abdelkrim YZ, Souiai O, Abdeljaoued-Tej I, Guizani I. Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules. Front Genet 2021; 12:744170. [PMID: 34912370 PMCID: PMC8667578 DOI: 10.3389/fgene.2021.744170] [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: 07/19/2021] [Accepted: 09/30/2021] [Indexed: 12/26/2022] Open
Abstract
Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.
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Affiliation(s)
- Emna Harigua-Souiai
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Mohamed Mahmoud Heinhane
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Yosser Zina Abdelkrim
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
| | - Oussama Souiai
- Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS)-LR20IPT09, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
| | - Ines Abdeljaoued-Tej
- Laboratory of BioInformatics BioMathematics and BioStatistics (BIMS)-LR20IPT09, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia
- Engineering School of Statistics and Information Analysis, University of Carthage, Ariana, Tunisia
| | - Ikram Guizani
- Laboratory of Molecular Epidemiology and Experimental Pathology-LR16IPT04, Institut Pasteur de Tunis, Université de Tunis El Manar, Tunis, Tunisia
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Sharma PP, Bansal M, Sethi A, Poonam, Pena L, Goel VK, Grishina M, Chaturvedi S, Kumar D, Rathi B. Computational methods directed towards drug repurposing for COVID-19: advantages and limitations. RSC Adv 2021; 11:36181-36198. [PMID: 35492747 PMCID: PMC9043418 DOI: 10.1039/d1ra05320e] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 10/07/2021] [Indexed: 12/19/2022] Open
Abstract
Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic status of countries. Although vaccines are now available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent for COVID-19, it continues to transmit and newer variants of concern have been consistently emerging world-wide. Computational strategies involving drug repurposing offer a viable opportunity to choose a medication from a rundown of affirmed drugs against distinct diseases including COVID-19. While pandemics impede the healthcare systems, drug repurposing or repositioning represents a hopeful approach in which existing drugs can be remodeled and employed to treat newer diseases. In this review, we summarize the diverse computational approaches attempted for developing drugs through drug repurposing or repositioning against COVID-19 and discuss their advantages and limitations. To this end, we have outlined studies that utilized computational techniques such as molecular docking, molecular dynamic simulation, disease-disease association, drug-drug interaction, integrated biological network, artificial intelligence, machine learning and network medicine to accelerate creation of smart and safe drugs against COVID-19.
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Affiliation(s)
- Prem Prakash Sharma
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
| | - Meenakshi Bansal
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
| | - Aaftaab Sethi
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER) Hyderabad India
| | - Poonam
- Department of Chemistry, Miranda House, University of Delhi Delhi 110007 India
| | - Lindomar Pena
- Department of Virology, Aggeu Magalhaes, Institute (IAM), Oswaldo Cruz Foundation (Fiocruz) Recife 50670-420 Pernambuco Brazil
| | - Vijay Kumar Goel
- School of Physical Sciences, Jawaharlal Nehru University New Delhi 110067 India
| | - Maria Grishina
- South Ural State University, Laboratory of Computational Modelling of Drugs Pr. Lenina 76 454080 Russia
| | - Shubhra Chaturvedi
- Division of Cyclotron and Radiopharmaceutical Sciences, Institute of Nuclear Medicine and Allied Sciences New Delhi 110054 India
| | - Dhruv Kumar
- Amity Institute of Molecular Medicine & Stem Cell Research (AIMMSCR), Amity University Uttar Pradesh Noida 201313 India
| | - Brijesh Rathi
- Laboratory For Translational Chemistry and Drug Discovery, Department of Chemistry, Hansraj College, University of Delhi Delhi 110007 India
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50
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Lee CY, Chen YPP. New Insights Into Drug Repurposing for COVID-19 Using Deep Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:4770-4780. [PMID: 34546931 PMCID: PMC8843052 DOI: 10.1109/tnnls.2021.3111745] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/20/2021] [Accepted: 09/08/2021] [Indexed: 05/21/2023]
Abstract
The coronavirus disease 2019 (COVID-19) has continued to spread worldwide since late 2019. To expedite the process of providing treatment to those who have contracted the disease and to ensure the accessibility of effective drugs, numerous strategies have been implemented to find potential anti-COVID-19 drugs in a short span of time. Motivated by this critical global challenge, in this review, we detail approaches that have been used for drug repurposing for COVID-19 and suggest improvements to the existing deep learning (DL) approach to identify and repurpose drugs to treat this complex disease. By optimizing hyperparameter settings, deploying suitable activation functions, and designing optimization algorithms, the improved DL approach will be able to perform feature extraction from quality big data, turning the traditional DL approach, referred to as a "black box," which generalizes and learns the transmitted data, into a "glass box" that will have the interpretability of its rationale while maintaining a high level of prediction accuracy. When adopted for drug repurposing for COVID-19, this improved approach will create a new generation of DL approaches that can establish a cause and effect relationship as to why the repurposed drugs are suitable for treating COVID-19. Its ability can also be extended to repurpose drugs for other complex diseases, develop appropriate treatment strategies for new diseases, and provide precision medical treatment to patients, thus paving the way to discover new drugs that can potentially be effective for treating COVID-19.
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Affiliation(s)
- Chun Yen Lee
- Department of Computer Science and Information TechnologyLa Trobe UniversityMelbourneVIC3086Australia
| | - Yi-Ping Phoebe Chen
- Department of Computer Science and Information TechnologyLa Trobe UniversityMelbourneVIC3086Australia
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