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https://www.selleckchem.com/products/AV-951.html Malnutrition has been shown to be related to adverse clinical outcomes in patients with heart failure, hypertension, atrial fibrillation and other cardiovascular diseases. However, in the patients with coronary artery disease (CAD) undergoing percutaneous coronary interventions (PCI), especially in the elderly, the association of nutritional state and all-cause mortality remains unknown. We aimed to investigate the association of malnutrition with all-cause mortality in the elder patients undergoing PCI. Based on the largest retrospective and observational cohort study from January 2007 to December 2017, the Controlling Nutritional Status (CONUT) score was applied to 21,479 consecutive patients with age ≥60 who undergoing PCI for nutritional assessment. Participants were classified as absent, mild, moderate and severe malnutrition by CONUT score. The Kaplan-Meier method was used to compare all-cause mortality among the above four groups. Multivariable Cox proportional hazard regression analyses were perfoate the efficacy of nutritional interventions. Malnutrition is prevalent among elderly patients with CAD undergoing PCI, and is strongly related to the all-cause mortality increasing. For elderly patients with CAD undergoing PCI, it is necessary to assess the status of nutrition, and evaluate the efficacy of nutritional interventions.Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the

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