Current topics in medicinal chemistry
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The advancement of cardiac surgery benefits from the continual technological progress of cardiopulmonary bypass (CPB). Every improvement in the CPB technology requires further clinical and laboratory tests to prove its safety and effectiveness before it can be widely used in clinical practice. In order to reduce the priming volume and eliminate a separate arterial filter in the CPB circuit, several manufacturers developed novel hollow-fiber membrane oxygenators with integrated arterial filters (IAF). ⋯ It can be easily set up and managed, simplifying the CPB circuit without sacrificing safety. An oxygenator with IAF is expected to be more beneficial to the patients with low body weight and when using a minimized extracorporeal circulation system. The aim of this review manuscript was to discuss briefly the concept of integration, the current oxygenators with IAF, and the in-vitro / in-vivo performance of the oxygenators with IAF.
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Type 2 diabetes mellitus is one of the most common forms of the disease worldwide. Hyperglycemia and insulin resistance play key roles in type 2 diabetes mellitus. Renal glucose reabsorption is an essential feature in glycaemic control. ⋯ Through glucosuria, SGLT2 inhibitors reduce body weight and body fat, and shift substrate utilisation from carbohydrates to lipids and, possibly, ketone bodies. These pleiotropic effects of SGLT2 inhibitors are likely to have contributed to the results of the EMPA-REG OUTCOME trial in which the SGLT2 inhibitor, empagliflozin, slowed down the progression of chronic kidney disease and reduced major adverse cardiovascular events in high-risk individuals with type 2 diabetes. This review discusses the role of SGLT2 in the physiology and pathophysiology of renal glucose reabsorption and outlines the unexpected logic of inhibiting SGLT2 in the diabetic kidney.
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The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an indispensable tool for drug designers to mine chemical information from large compound databases for developing drugs at a much faster rate as never before. The applications of AI have gone beyond bioactivity predictions and have shown promise in addressing diverse problems in drug discovery like de novo molecular design, synthesis prediction and biological image analysis. In this article, we provide an overview of all the algorithms under the umbrella of AI, enlist the tools/frameworks required for implementing these algorithms as well as present a compendium of web servers, databases and open-source platforms implicated in drug discovery, Quantitative Structure-Activity Relationship (QSAR), data mining, solvation free energy and molecular graph mining.