Recent studies have found gut microbiota to be closely associated with onset and perpetuation of UC. Currently, studies about gut microbiota have mainly covered samples collected from the intestinal lumen. However, the luminal flora is only part of the gut microbiota. Studies of the changes in mucosal flora under pathological conditions have been lacking. In this study, we investigated the correlation between the onset of UC and flora changes in different intestinal layers. The dextran sulfate sodium(DSS)-induced UC model was established by exposing mice to cycles of DSS. The luminal contents, an inner mucus layer, and outer mucus layer were harvested under sterile conditions. The samples were then analyzed using high-throughput sequencing of 16S rRNA V3 + V4 amplicons. The colonic microbiota composition and diversity were analyzed and compared using MetaStat, LefSe, multivariate analysis of variance, and spatial statistics. The DSS-induced UC mouse model was successfully established. The diversity of the microbiota from luminal content, the outer mucus layer, and inner mucus layer were significantly different in both control and UC model groups. The statistically different OTUs belonged to Lachnospiraceae and Ruminococcaceae families within the order Clostridiales were mainly localized to the outer mucus layer. The alterations in flora composition and diversity mainly occurred in the colonic outer mucus layer. The change of flora in the colonic mucus layers is of great significance in the understanding of common features of gut flora in IBD and the understanding of the relationship between gut flora and disease progression.

Background: Ulcerative colitis (UC) is a modern refractory disease, and its etiology has been difficult to discern. Studies have shown that UC is closely associated with the gut microbiota. Garidisan is composed of wild poppy and Artemisia frigida Willd and is commonly used for the treatment of UC in Inner Mongolia, China. In clinical settings, Garidisan has been found to treat UC effectively, with low recurrence. Previous studies have shown that Garidisan has a good therapeutic effect on mice with UC, but the therapeutic mechanism is still unclear. In this study, we investigated the regulatory effect of Garidisan on dysbiosis of the gut microbiota in a UC mouse model and explored the possible mechanism of the therapeutic effect of Garidisan on UC. Methods: The UC mouse model was established by the dextran sulfate sodium (DSS) circulating free water drinking method, and the luminal contents were sampled under sterile conditions. High-throughput sequencing of the 16S rRNA gene V3 + V4 region of the luminal contents of the control group, model group, and Garidisan group was conducted, and clustering of operational taxonomic units (OTUs) and species annotation were performed. The differences in species composition and microbial community structure between individual groups of samples were analyzed using MetaStat, LefSe, rank sum test, and Bayesian causal network analysis. Results: The UC mouse model was successfully established and the sequencing results were of adequate quality. There were significant differences in the diversity of luminal contents between the control group, model group, and Garidisan group, and the differences between groups were greater than those within any group. The therapeutic effect of Garidisan on UC is attributed to the direct effect on the Lachnospiraceae family of bacteria. Conclusion: Garidisan has a good regulatory effect on the gut microbiota, and Lachnospiraceae could be an important direct target of Garidisan for the treatment of UC.

Background: Early assessment and management of cerebral edema and hematoma following aneurysmal subarachnoid hemorrhage (a-SAH) can significantly impact clinical cognitive outcomes. However, current clinical practices lack predictive models to identify early structural brain abnormalities affecting cognition. To address this gap, the authors propose the development of a predictive model termed the a-SAH Early Brain Edema/Hematoma Compression Neural (Structural Brain) Networks Score System (SEBE-HCNNSS). Methods: In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or MRI scans within 24 h of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), the authors evaluated the association of clinically relevant factors and various traditional scale ratings with cognitive impairment (CI). Risk factors with the highest area under the curve (AUC) values were included in the multivariate analysis and least absolute shrinkage and selection operator (LASSO) analysis or Cox regression analysis. Results: A total of 177 patients were enrolled in the study, and 43 patients were classified with a high SEBE-HCNNSS grade (3–5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and three control subjects developed incident CI. The CT interobserver reliability of the SEBE-HCNNSS scale was high, with a Kappa value of 1. Furthermore, ROC analysis identified the SEBE-HCNNSS scale (OR 3.322, 95% CI: 2.312–7.237, P=0.00025) as an independent predictor of edema, CI, and unfavorable prognosis. These results were also replicated in a validation cohort. Conclusion: Overall, the SEBE-HCNNSS scale represents a simple assessment tool with promising predictive value for CI and clinical outcomes post-a-SAH. Our findings indicate its practical utility as a prognostic instrument for risk evaluation after a-SAH, potentially facilitating early intervention and treatment.