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Comput Methods Programs Biomed ; 218: 106731, 2022 May.
Article in English | MEDLINE | ID: covidwho-1719551


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.

COVID-19 , Deep Learning , Artificial Intelligence , COVID-19/diagnostic imaging , COVID-19 Testing , Humans , SARS-CoV-2 , Tomography, X-Ray Computed/methods
Cell Metab ; 34(3): 424-440.e7, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1676683


Coronavirus disease 2019 (COVID-19) represents a systemic disease that may cause severe metabolic complications in multiple tissues including liver, kidney, and cardiovascular system. However, the underlying mechanisms and optimal treatment remain elusive. Our study shows that impairment of ACE2 pathway is a key factor linking virus infection to its secondary metabolic sequelae. By using structure-based high-throughput virtual screening and connectivity map database, followed with experimental validations, we identify imatinib, methazolamide, and harpagoside as direct enzymatic activators of ACE2. Imatinib and methazolamide remarkably improve metabolic perturbations in vivo in an ACE2-dependent manner under the insulin-resistant state and SARS-CoV-2-infected state. Moreover, viral entry is directly inhibited by these three compounds due to allosteric inhibition of ACE2 binding to spike protein on SARS-CoV-2. Taken together, our study shows that enzymatic activation of ACE2 via imatinib, methazolamide, or harpagoside may be a conceptually new strategy to treat metabolic sequelae of COVID-19.

COVID-19/drug therapy , Imatinib Mesylate/therapeutic use , Metabolic Diseases/drug therapy , Methazolamide/therapeutic use , SARS-CoV-2/drug effects , Angiotensin-Converting Enzyme 2/drug effects , Angiotensin-Converting Enzyme 2/metabolism , Animals , COVID-19/complications , COVID-19/metabolism , COVID-19/virology , Cells, Cultured , Chlorocebus aethiops , Down-Regulation/drug effects , HEK293 Cells , Human Umbilical Vein Endothelial Cells , Humans , Imatinib Mesylate/pharmacology , Male , Metabolic Diseases/metabolism , Metabolic Diseases/virology , Methazolamide/pharmacology , Mice , Mice, Inbred C57BL , Mice, Obese , Mice, Transgenic , SARS-CoV-2/physiology , Vero Cells , Virus Internalization/drug effects
Comput Biol Med ; 141: 105123, 2022 02.
Article in English | MEDLINE | ID: covidwho-1588037


This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.

Artificial Intelligence , COVID-19 , COVID-19 Testing , Computers , Humans , SARS-CoV-2 , Tomography, X-Ray Computed