Lycium barbarum polysaccharide alleviates DSS-induced chronic ulcerative colitis by restoring intestinal barrier function and modulating gut microbiota.

PURPOSE: This study examined the protective effects and mechanism of Lycium barbarum polysaccharides (LBP) in the context of intestinal barrier function and intestinal microbiota in mice with dextran sulfate sodium (DSS)-induced chronic ulcerative colitis (UC). METHODS: C57BL/6J male mice were assigned to a standard normal diet without DSS (control group), a normal diet with DSS (DSS group, 2% DSS given discontinuously for 3 weeks) or a normal diet supplemented with LBP (1% dry feed weight, LBP group, 2% DSS given discontinuously for 3 weeks) for a total of 8 weeks, at which point colonic tissues and caecal contents were collected. RESULTS: LBP exerted a significant effect against colitis by increasing body weight, colon length, DAI and histopathological scores. LBP inhibited proinflammatory cytokines (IL-1ß, IL-6, iNOS and TNF-α) expression, improved anti-inflammatory cytokine (IL-10) expression, promoted the expression of tight junction proteins (Occludin and ZO-1) via nuclear factor erythroid 2-related factor 2 (Nrf2) activation and decreased Claudin-2 expression to maintain the intestinal mucosal barrier. In addition, the abundances of some probiotics (Ruminococcaceae, Lactobacillus, Butyricicoccus, and Akkermansia) were decreased with DSS treatment but increased obviously with LBP treatment. And LBP reduced the abundance of conditional pathogens associated with UC (Mucispirillum and Sutterella). Furthermore, LBP improved the production of short-chain fatty acids (SCFAs), including acetic acid, propionic acid, butyric acid and isobutyric acid. CONCLUSION: LBP can alleviate DSS-induced UC by regulating inflammatory cytokines and tight junction proteins. Moreover, LBP promotes probiotics, suppresses conditional pathogens and increases SCFAs production, showing a strong prebiotic effect.

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study.

OBJECTIVE: To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC). METHODS: A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed. RESULT: Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model. CONCLUSION: The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.