The Role of Probiotics in Managing Glucose Homeostasis in Adults with Prediabetes: A Systematic Review and Meta-Analysis.

Methods: The Preferred Reporting Items for Systematic Reviews and Analysis checklist was used. A comprehensive literature search of the PubMed, Embase, and Cochrane Library databases was conducted through August 2022 to assess the impact of probiotics on blood glucose, lipid, and inflammatory markers in adults with prediabetes. Data were pooled using a random effects model and were expressed as standardized mean differences (SMDs) and 95% confidence interval (CI). Heterogeneity was evaluated and quantified as I2. Results: Seven publications with a total of 550 patients were included in the meta-analysis. Probiotics were found to significantly reduce the levels of glycosylated hemoglobin (HbA1c) (SMD -0.44; 95% CI -0.84, -0.05; p = 0.03; I2 = 76.13%, p < 0.001) and homeostatic model assessment of insulin resistance (HOMA-IR) (SMD -0.27; 95% CI -0.45, -0.09; p < 0.001; I2 = 0.50%, p = 0.36) and improve the levels of high-density lipoprotein cholesterol (HDL) (SMD -8.94; 95% CI -14.91, -2.97; p = 0.003; I2 = 80.24%, p < 0.001), when compared to the placebo group. However, no significant difference was observed in fasting blood glucose, insulin, total cholesterol, triglycerides, low-density lipoprotein cholesterol, interleukin-6, tumor necrosis factor-α, and body mass index. Subgroup analyses showed that probiotics significantly reduced HbA1c in adults with prediabetes in Oceania, intervention duration of ≥3 months, and sample size <30. Conclusions: Collectively, our meta-analysis revealed that probiotics had a significant impact on reducing the levels of HbA1c and HOMA-IR and improving the level of HDL in adults with prediabetes, which indicated a potential role in regulating blood glucose homeostasis. However, given the limited number of studies included in this analysis and the potential for bias, further large-scale, higher-quality randomized controlled trials are needed to confirm these findings. This trial is registered with CRD42022358379.

A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction.

Combination therapy is a promising clinical treatment strategy for cancer and other complex diseases. Multiple drugs can target multiple proteins and pathways, greatly improving the therapeutic effect and slowing down drug resistance. To narrow the search space of synergistic drug combinations, many prediction models have been developed. However, drug combination datasets always have the characteristics of class imbalance. Synergistic drug combinations receive the most attention in clinical application but are in small numbers. To predict synergistic drug combinations in different cancer cell lines, in this study, we propose a genetic algorithm-based ensemble learning framework, GA-DRUG, to address the problems of class imbalance and high dimensionality of input data. The cell-line-specific gene expression profiles under drug perturbations are used to train GA-DRUG, which contains imbalanced data processing and the search of global optimal solutions. Compared to 11 state-of-the-art algorithms, GA-DRUG achieves the best performance and significantly improves the prediction performance in the minority class (Synergy). The ensemble framework can effectively correct the classification results of a single classifier. In addition, the cellular proliferation experiment performed on several previously unexplored drug combinations further confirms the predictive ability of GA-DRUG.