Mogroside Alleviates Diabetes Mellitus and Modulates Intestinal Microflora in Type 2 Diabetic Mice.

Mogroside, the main component of Siraitia grosvenorii (Swingle) C. Jeffrey (Cucurbitaceae) is a natural product with hypoglycemic and intestinal microbiota regulating properties. However, whether the alteration of intestinal microbiota is associated with the antidiabetic effect of mogroside remains poorly understood. This study investigated the mechanism underlying the hypoglycemic effect of mogroside in regulating intestinal flora and attenuating metabolic endotoxemia. Kunming mice with type 2 diabetes mellitus (T2DM) induced by high-fat diet and intraperitoneal injection of streptozotocin were randomly divided into model, pioglitazone (2.57 mg/kg) and mogroside (200, 100, and 50 mg/kg) groups. After 28 d of administration, molecular changes related to glucose metabolism and metabolic endotoxemia in mice were evaluated. The levels of insulin receptor substrate-1 (IRS-1), cluster of differentiation 14 (CD14) and toll-like receptor 4 (TLR4) mRNAs were measured, and the composition of intestinal microflora was determined by 16s ribosomal DNA (rDNA) sequencing. The results showed that mogroside treatment significantly improved hepatic glucose metabolism in T2DM mice. More importantly, mogroside treatment considerably reduced plasma endotoxin (inhibition rate 65.93%, high-dose group) and inflammatory factor levels, with a concomitant decrease in CD14 and TLR4 mRNA levels. Moreover, mogroside treatment reduced the relative abundance of Firmicutes and Proteobacteria (the inhibition rate of Proteobacteria was 85.17% in the low-dose group) and increased the relative abundance of Bacteroidetes (growth rate up to 40.57%, high-dose group) in the intestines of diabetic mice. This study reveals that mogroside can relieve T2DM, regulating intestinal flora and improving intestinal mucosal barrier, indicating that mogroside can be a potential therapeutic agent or intestinal microbiota regulator in the treatment of T2DM.

Prognostic nomogram in patients with right-sided colon cancer after colectomy: a surveillance, epidemiology, and end results-based study.

Objective: This study aimed to develop and validate a nomogram for predicting overall survival (OS) in patients undergoing surgery for right-sided colon cancer (RCC). Methods: We collected 25,203 patients with RCC from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided them into 7:3 training and internal validation set. Utilizing the Cox proportional hazards regression model, we constructed a nomogram based on prognostic risk factors. Furthermore, for external validation, we retrospectively followed up with 228 patients from Jiaxing First Hospital and assessed and calibrated the nomogram using the C-index and calibration curves. Results: After identifying independent prognostic factors through univariate and multivariate analyses, a nomogram was developed. The c-index values of this nomogram differed as follows: 0.851 (95% CI: 0.845-0.857) in the training set, 0.860 (95% CI: 0.850-0.870) in the internal validation set, and 0.834 (95% CI: 0.780-0.888) in the external validation set, indicating the model's strong discriminative ability. Calibration curves for 1-year, 3-year, and 5-year overall survival (OS) probabilities exhibited a high level of consistency between predicted and actual survival rates. Furthermore, Decision Curve Analysis (DCA) demonstrated that the new model consistently outperformed the TNM staging system in terms of net benefit. Conclusion: We developed and validated a survival prediction model for patients with RCC. This novel nomogram outperforms the traditional TNM staging system and can guide clinical practitioners in making optimal clinical decisions.