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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
Interdiscip Sci ; 13(2): 273-285, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1103577


Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in .

COVID-19/diagnostic imaging , Deep Learning , Diagnosis, Computer-Assisted , Lung/diagnostic imaging , Multidetector Computed Tomography , Pneumonia, Bacterial/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted , COVID-19/virology , Case-Control Studies , Diagnosis, Differential , Humans , Lung/microbiology , Lung/virology , Pneumonia, Bacterial/microbiology , Pneumonia, Viral/virology , Predictive Value of Tests , Reproducibility of Results