Therapeutic improvements of nifedipine controlled-release tablets combined with sacubitril valsartan on patients with diabetic nephropathy complicated with hypertension.

This study aimed to evaluate the efficacy of nifedipine controlled-release tablets combined with sacubitril valsartan in diabetic nephropathy (DN) patients with hypertension. One hundred and twelve DN patients with hypertension were enrolled. They were randomly divided into the control group (treated with nifedipine controlled-release tablets combined with valsartan) and the observation group (treated with nifedipine controlled-release tablets combined with sacubitril valsartan). Renal function, endothelial function and inflammatory response were examined. After three-months treatment, the levels of clinical indexes (glycosylated hemoglobin, fasting blood glucose, systolic and diastolic blood pressure), renal function indicators (urinary albumin excretion rate, blood urea nitrogen, serum creatinine and cystatin C), endothelial function indicators (microalbumin, angiotensin II, thrombomodulin and cartilage oligomeric matrix protein) and inflammatory response factors (interleukin-6 and tumor necrosis factor-α) in the observation group were significantly lower than those in the control group. Nifedipine controlled-release tablets combined with sacubitril valsartan could effectively alleviate the progression of DN combined with hypertension.

Predicting biological functions of compounds based on chemical-chemical interactions.

Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.