Evidence of Bariatric Surgery Benefits Cardiac Function in Non-HFpEF Patients with Obesity: a Meta-Analysis.

BACKGROUND/OBJECTIVE: Nowadays, increasing clinical evidence on metabolic and weight-loss effects of bariatric surgery on improving cardiac structure in obese patients, but its application in improving the cardiac function of HF (heart failure) patients remains controversial. The objective of this meta-analysis was to assess the effects of BS on cardiac function by quantifying the changes of LVEF (left ventricular ejection fraction) and NYHA (New York Heart Association classification) after operations in non-HFpEF (heart failure and preserved ejection fraction) patients. METHODS: Articles were searched using PubMed and Embase from inception to December 9, 2022, and the Minors scale was used for quality assessments. The included patients should be non-HFpEF and clinically severely obese, and their pre-operative and post-operative values of LVEF or NYHA should be reported. RESULT: Nine studies involving 146 patients were eventually included with a final result showing that the cardiac functional parameters were improved in non-HFpEF patients. After a weighted mean follow-up time of 15.8 months, the mean NYHA decreased by 0.59 (I2 = 0; 95% CI 0.27 ~ 0.92; p = 0.003), and the mean LVEF increased by 7.49% (I2 = 0; 95% CI - 9.99 ~ - 4.99; p < 0.00001). CONCLUSION: Bariatric surgery offers beneficial cardiac effects on non-HFpEF patients with obesity but failed to show a significant improvement in the pooled analysis for the changes of cardiac parameters. The improving degree may be related to the baseline BMI, the extent of BMI loss, and the baseline age. Future studies should focus on finding out the influencing factors of effectivenesses and defining the suitable crowd.

Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains.

Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms.