Hubei University of Chinese Medicine

BackgroundUlcerative colitis (UC) is a chronic and relapsing inflammatory bowel disease (IBD), that lacks specific therapeutic drugs. Prim-O-glucosylcimifugin (POG), a furanochromone glycoside derived from Saposhnikovia divaricata, exhibits anti-inflammatory, antioxidant, and anti-inflammasome activities. It has also been shown to inhibit the activation of the NLR family pyrin domain containing 3 (NLRP3) inflammasome. Given that ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by intestinal barrier dysfunction and NLRP3 activation, this study aims to investigate the therapeutic effects and underlying mechanisms of POG in a murine model of UC induced by dextran sulfate sodium (DSS), and to evaluate its potential translational significance for UC intervention.MethodsA DSS-induced UC mouse model was employed to evaluate POG’s therapeutic efficacy. Disease activity index, colon length, spleen index, and histopathological alterations of the colonic mucosa were assessed by H&E and AB-PAS staining, transmission electron microscopy, and immunofluorescence. Proinflammatory cytokines (TNF-α, IL-6, IL-18, IL-1β) were measured by ELISA, and NLRP3 inflammasome–related proteins were analyzed by Western blot.ResultsPOG treatment alleviated weight loss, colon shortening, splenomegaly, and inflammatory infiltration. It also restored goblet cell mucin droplets and tight junction proteins (Occludin, ZO-1, Claudin-1, MUC2), and reduced villi exfoliation. Serum levels of TNF-α, IL-6, IL-1β, and IL-18 were decreased. Western blot and ELISA revealed that POG suppressed NLRP3 inflammasome formation, inhibited GSDMD activation, and reduced cleavage and release of IL-1β and IL-18. Collectively, these findings indicate that POG not only attenuates inflammatory injury but also protects epithelial barrier structure and function, supporting its potential value in mechanism-based UC therapy development.ConclusionPOG ameliorates experimental UC by inhibiting NLRP3 inflammasome activation, thereby mitigating colonic inflammation, preserving mucosal barrier integrity, and blocking downstream inflammatory responses. POG shows promise as a therapeutic agent for UC and provides a rationale for further preclinical optimization and future clinical translation.

BackgroundEarly and accurate identification of mild cognitive impairment (MCI) is crucial for timely intervention and preventing further cognitive decline. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, portable tool for clinical screening, but its diagnostic accuracy is often constrained by single-paradigm approaches and small sample sizes. To address this limitation, this study aimed to develop and validate an efficient early MCI screening model by integrating large-sample fNIRS data from resting-state and 1-back task paradigms using ensemble machine learning, thereby enhancing the accuracy and reliability of early MCI diagnosis.MethodsA total of 462 right-handed participants (185 MCI patients and 277 healthy controls, aged 58 -87 years) were included in the final analysis after screening, with MCI diagnosis jointly determined by two experienced neurologists based on Petersen’s criteria. fNIRS signals were collected during resting-state and 1-back task sessions; after preprocessing in MATLAB, features were extracted from oxygenated hemoglobin (HbO) signals of both paradigms.ResultsFeature selection was performed via a gradient boosting classifier based on feature importance scores, resulting in 108 selected features. Five classifiers were trained and evaluated using 10-fold cross-validation. The integrated dataset combining resting-state and 1-back task features outperformed the single-paradigm datasets: the Neural Network model on this integrated dataset achieved an accuracy of 86.49%, sensitivity of 94.74%, specificity of 77.78%, and Area Under the Curve (AUC) of 93.49%. In contrast, the Nearest Neighbor model on the resting-state dataset and the Decision Tree model on the 1-back task dataset yielded accuracies of 70.27% and 75.68%, respectively. Group classification using MoCA scores achieved an accuracy of 86.55%, which was comparable to single-paradigm machine learning models but inferior to the integrated model.DiscussionThis study demonstrates the value of a large-sample, data-driven approach and multi-paradigm feature integration in fNIRS-based MCI screening, providing an efficient diagnostic model for clinical application.Clinical trial registrationhttps://www.chictr.org.cn/showprojEN.html?proj=192047.