Ulcerative colitis (UC), a type of inflammatory bowel disease (IBD), is a chronic inflammatory disorder of the colon. Although UC is generally treated with anti-inflammatory drugs or immunosuppressants, most of these treatments often prove to be inadequate. Rosmarinic acid (RA) is a phenolic ester included in various medicinal herbs such as Salvia miltiorrhiz and Perilla frutescens. Although RA has many biological and pharmacological activities, the anti-inflammatory effect of RA in colonic tissue remains unclear. In this study, we investigated the anti-inflammatory effects and underlying molecular mechanism of RA in mice with dextran sulphate sodium (DSS)-induced colitis. In the DSS-induced colitis model, RA significantly reduced the severity of colitis, as assessed by disease activity index (DAI) scores, colonic damage, and colon length. In addition, RA resulted in the reduction of the inflammatory-related cytokines, such as IL-6, IL-1β, and IL-22, and protein levels of COX-2 and iNOS in mice with DSS-induced colitis. Furthermore, RA effectively and pleiotropically inhibited nuclear factor-kappa B and signal transducer and activator of transcription 3 activation, and subsequently reduced the activity of pro-survival genes that depend on these transcription factors. These results demonstrate that RA has an ameliorative effect on colonic inflammation and thus a potential therapeutic role in colitis.
The CRISPR-Cas system is the RNA-guided immune defense mechanism in bacteria and archaea. Csm1 belongs to the Cas10 family, which is the common signature protein of the type III system. Csm1 is the largest subunit of the Csm interference complex in the type III-A subtype, which targets foreign DNA or RNA. Here, we report crystallographic and biochemical analyses of Thermococcus onnurineus Csm1, revealing a five-domain organization and single-stranded DNA (ssDNA)-specific nuclease activity associated with the N-terminal HD domain. This domain folds into permuted secondary structures in comparison with the HD domain of Cas3 and contains all the catalytically important residues. It exhibited both endo- and exonuclease activities in an Ni(2+) or Mn(2+)-dependent manner. The narrow width of the active-site cleft appears to restrict the substrate specificity to ssDNA and thus to prevent Csm1 from cleaving double-stranded chromosomal DNA. These data suggest that Csm1 may function in DNA interference by the Csm effector complex.
BackgroundThere are no widely used models in clinical care to predict outcome in acute lower gastro-intestinal bleeding (ALGIB). If available these could help triage patients at presentation to appropriate levels of care/intervention and improve medical resource utilisation. We aimed to apply a state-of-the-art machine learning classifier, gradient boosting (GB), to predict outcome in ALGIB using non-endoscopic measurements as predictors.MethodsNon-endoscopic variables from patients with ALGIB attending the emergency departments of two teaching hospitals were analysed retrospectively for training/internal validation (n=170) and external validation (n=130) of the GB model. The performance of the GB algorithm in predicting recurrent bleeding, clinical intervention and severe bleeding was compared to a multiple logic regression (MLR) model and two published MLR-based prediction algorithms (BLEED and Strate prediction rule).ResultsThe GB algorithm had the best negative predictive values for the chosen outcomes (>88%). On internal validation the accuracy of the GB algorithm for predicting recurrent bleeding, therapeutic intervention and severe bleeding were (88%, 88% and 78% respectively) and superior to the BLEED classification (64%, 68% and 63%), Strate prediction rule (78%, 78%, 67%) and conventional MLR (74%, 74% 62%). On external validation the accuracy was similar to conventional MLR for recurrent bleeding (88% vs. 83%) and therapeutic intervention (91% vs. 87%) but superior for severe bleeding (83% vs. 71%).ConclusionThe gradient boosting algorithm accurately predicts outcome in patients with acute lower gastrointestinal bleeding and outperforms multiple logistic regression based models. These may be useful for risk stratification of patients on presentation to the emergency department.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.