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Signal Transduct Target Ther ; 6(1): 427, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1795805


Abnormal glucose and lipid metabolism in COVID-19 patients were recently reported with unclear mechanism. In this study, we retrospectively investigated a cohort of COVID-19 patients without pre-existing metabolic-related diseases, and found new-onset insulin resistance, hyperglycemia, and decreased HDL-C in these patients. Mechanistically, SARS-CoV-2 infection increased the expression of RE1-silencing transcription factor (REST), which modulated the expression of secreted metabolic factors including myeloperoxidase, apelin, and myostatin at the transcriptional level, resulting in the perturbation of glucose and lipid metabolism. Furthermore, several lipids, including (±)5-HETE, (±)12-HETE, propionic acid, and isobutyric acid were identified as the potential biomarkers of COVID-19-induced metabolic dysregulation, especially in insulin resistance. Taken together, our study revealed insulin resistance as the direct cause of hyperglycemia upon COVID-19, and further illustrated the underlying mechanisms, providing potential therapeutic targets for COVID-19-induced metabolic complications.

COVID-19/blood , Hyperglycemia/blood , Insulin Resistance , Lipid Metabolism , Lipids/blood , SARS-CoV-2/metabolism , Adult , Aged , Biomarkers/blood , COVID-19/complications , Female , Humans , Hyperglycemia/etiology , Male , Middle Aged , Retrospective Studies
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