Role of long non-coding RNA in inflammatory bowel disease.

Inflammatory bowel disease (IBD) is a group of recurrent chronic inflammatory diseases, including Crohn's disease (CD) and ulcerative colitis (UC). Although IBD has been extensively studied for decades, its cause and pathogenesis remain unclear. Existing research suggests that IBD may be the result of an interaction between genetic factors, environmental factors and the gut microbiome. IBD is closely related to non-coding RNAs (ncRNAs). NcRNAs are composed of microRNA(miRNA), long non-coding RNA(lnc RNA) and circular RNA(circ RNA). Compared with miRNA, the role of lnc RNA in IBD has been little studied. Lnc RNA is an RNA molecule that regulates gene expression and regulates a variety of molecular pathways involved in the pathbiology of IBD. Targeting IBD-associated lnc RNAs may promote personalized treatment of IBD and have therapeutic value for IBD patients. Therefore, this review summarized the effects of lnc RNA on the intestinal epithelial barrier, inflammatory response and immune homeostasis in IBD, and summarized the potential of lnc RNA as a biomarker of IBD and as a predictor of therapeutic response to IBD in the future.

Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage.

BACKGROUND: Increased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had significantly impacting patient prognosis. Identifying optimal genes associated with OS could enhance the understanding of OS after ICH. METHODS: We employed single-cell RNA sequencing (scRNA-seq) to investigate the heterogeneity of OS across various cellular tiers following ICH, aiming to acquire biological insights into ICH. We utilized AUCell, Ucell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, to identify hub genes influencing high OS post-ICH. Furthermore, we employed four machine learning algorithms, eXtreme Gradient Boosting, Boruta, Random Forest, and Least Absolute Shrinkage and Selection Operator, to identify the optimal feature genes. To validate the accuracy of our analysis, we conducted validation in ICH animal experiments. RESULTS: After analyzing the scRNA-seq dataset using various algorithms, we found that OS activity exhibited heterogeneity across different cellular layers following ICH, with particularly heightened activity observed in monocytes. Further integration of bulk data and machine learning algorithms revealed that ANXA2 and COTL1 were closely associated with high OS after ICH. Our animal experiments demonstrated an increase in OS expression post-ICH. Additionally, the protein expression of ANXA2 and COTL1 was significantly elevated and co-localized with microglia. Pearson correlation coefficient analysis revealed a significant correlation between ANXA2 and OS, indicating strong consistency (r = 0.84, p < 0.05). Similar results were observed for COTL1 and OS (r = 0.69, p < 0.05). CONCLUSIONS: Following ICH, ANXA2 and COTL1 might penetrate the brain via monocytes, localize within microglia, and enhance OS activity. This might help us better understand OS after ICH.

Orthostatic hypotension is associated with malnutrition diagnosed by GLIM in elderly hypertensive patients.

BACKGROUND: Orthostatic Hypotension (OH) and malnutrition, are common health problems in elderly hypertensive patients. This study aimed to analyze the relationship between malnutrition and OH in elderly hypertensive patients. METHODS: This is a cross-sectional single-center study. All participants underwent a Comprehensive Geriatric Assessment (CGA), in which malnutrition was defined according to the Global Leadership Initiative on Malnutrition (GLIM) criteria based on four different methods of diagnosing muscle mass loss. Furthermore, the accuracy of these methods was verified by Receiver Operating Characteristic (ROC) analysis. Univariate and multivariate logistic regression analyses were used to identify risk factors for OH in elderly hypertensive patients. RESULTS: For GLIM criteria, when Fat-Free Mass Index (FFMI) was the gold standard for muscle mass loss, the Area Under ROC Curve (AUC) values for Upper Arm Circumference (UAC), Calf Circumference (CC), and Hand Grip Strength (HGS) were 0.784, 0.805, and 0.832, with moderate accuracy in diagnosing malnutrition. Multivariate analysis showed that females, Diabetes Mellitus (DM), diuretics, and malnutrition diagnosed by GLIM-UAC were risk factors for OH in elderly hypertensive patients. CONCLUSION: Prompt detection of malnutrition in the elderly and attention to changes in UAC may be critical. Similarly, we should strengthen medication and disease management in elderly hypertensive patients.