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Background:Helicobacter pylori (Hp) persistent infection is an important pathogenic factor for a series of chronic gastric diseases from chronic gastritis to gastric cancer. Genetic and epigenetic abnormalities of microRNAs may play a vital role in the pathological evolution of gastric mucosa in Helicobacter pylori-related gastric diseases (HPGD). This study aimed to investigate the relationship between miR-146a, miR-196a2, miR-149, miR-499 and miR-27a gene single nucleotide polymorphisms (SNPs) and their expressions with pathological changes in gastric mucosa, and to further analyze the interactions between SNPs and Hp.Methods: Subjects in this study included patients diagnosed with HPGD and healthy controls. MiR-146a rs2910164, miR-196a2 rs11614913, miR-149 rs2292832, miR-499 rs3746444 and miR-27a rs895819 were genotyped by direct sequencing. Fluorescence quantitative PCR was used to detect microRNA expressions. Gene-gene and gene-environment interactions were evaluated by multifactor dimensionality reduction (MDR) method.Results: we found that frequency distribution of miR-196a2 rs11614913 CT genotype in gastric precancerous lesion (GPL) group and gastric cancer (GC) group was significantly higher than normal control (NOR) group [adjusted OR = 6.16, 95%CI (1.46–26.03); adjusted OR = 11.83, 95%CI (1.65–84.72), respectively]. CT genotype and C allele of miR-27a rs895819 were associated with increased risk of GC [adjusted OR = 10.14, 95%CI (2.25–45.77); adjusted OR = 3.71, 95%CI(1.46–9.44), respectively]. The MDR analysis results showed that the interaction between miR-196a2 rs11614913 and Hp was associated with the risk of GPL (p = 0.004). Meanwhile, the expression level of miR-196a2 in GC group was significantly higher than NOR, chronic inflammation (CI) and early precancerous lesion (EPL) groups among Hp-positive subjects. And expressions of miR-499 and miR-27a in GC group were both higher than EPL group. Also, miR-27a expression in GC group was higher than CI and gastric atrophy (GA) groups.Conclusion: miR-196a2 rs11614913 and miR-27a rs895819 may affect the genetic susceptibility to GPL or GC. MiR-196a2 rs11614913 and Hp have a synergistic effect in the occurrence and development of GPL. The up-regulation of miR-499, miR-196a2 and miR-27a expression caused by Hp infection may be an important mechanism of gastric carcinogenesis.

Background & Aims: The protective intestinal mucosal barrier consists of multiple elements including mucus and epithelial layers and immune defense; nonetheless, barrier dysfunction is common in various disorders. The imprinted and developmentally regulated long noncoding RNA H19 is involved in many cell processes and diseases. Here, we investigated the role of H19 in regulating Paneth and goblet cells and autophagy, and its impact on intestinal barrier dysfunction induced by septic stress. Methods: Studies were conducted in H19-deficient (H19-/-) mice, mucosal tissues from patients with sepsis, primary enterocytes, and Caco-2 cells. Septic stress was induced by cecal ligation and puncture (CLP), and gut permeability was detected by tracer fluorescein isothiocyanate–dextran assays. The function of Paneth and goblet cells was examined by immunostaining for lysozyme and mucin 2, respectively, and autophagy was examined by microtubule-associated proteins 1A/1B light chain 3 II immunostaining and Western blot analysis. Intestinal organoids were isolated from H19-/- and control littermate mice and treated with lipopolysaccharide (LPS). Results: Intestinal mucosal tissues in mice 24 hours after exposure to CLP and in patients with sepsis showed high H19 levels, associated with intestinal barrier dysfunction. Targeted deletion of the H19 gene in mice enhanced the function of Paneth and goblet cells and promoted autophagy in the small intestinal mucosa. Knockout of H19 protected Paneth and goblet cells against septic stress, preserved autophagy activation, and promoted gut barrier function after exposure to CLP. Compared with organoids from control littermate mice, intestinal organoids isolated from H19-/- mice had increased numbers of lysozyme- and mucin 2–positive cells and showed increased tolerance to LPS. Conversely, ectopic overexpression of H19 in cultured intestinal epithelial cells prevented rapamycin-induced autophagy and abolished the rapamycin-induced protection of the epithelial barrier against LPS. Conclusions: In investigations of mice, human tissues, primary organoids, and intestinal epithelial cells, we found that increased H19 inhibited the function of Paneth and goblet cells and suppressed autophagy, thus potentially contributing to barrier dysfunction in intestinal pathologies.

Background & Aims: Gastric cancer is the common malignancies from cancer worldwide. Endoscopy is currently the most effective method to detect early gastric cancer (EGC). However, endoscopy is not infallible and EGC can be missed during endoscopy. Artificial intelligence (AI)-assisted endoscopic diagnosis is a recent hot spot of research. We aimed to quantify the diagnostic value of AI-assisted endoscopy in diagnosing EGC.Method: The PubMed, MEDLINE, Embase and the Cochrane Library Databases were searched for articles on AI-assisted endoscopy application in EGC diagnosis. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated, and the endoscopists' diagnostic value was evaluated for comparison. The subgroup was set according to endoscopy modality, and number of training images. A funnel plot was delineated to estimate the publication bias.Result: 16 studies were included in this study. We indicated that the application of AI in endoscopic detection of EGC achieved an AUC of 0.96 (95% CI, 0.94–0.97), a sensitivity of 86% (95% CI, 77–92%), and a specificity of 93% (95% CI, 89–96%). In AI-assisted EGC depth diagnosis, the AUC was 0.82(95% CI, 0.78–0.85), and the pooled sensitivity and specificity was 0.72(95% CI, 0.58–0.82) and 0.79(95% CI, 0.56–0.92). The funnel plot showed no publication bias.Conclusion: The AI applications for EGC diagnosis seemed to be more accurate than the endoscopists. AI assisted EGC diagnosis was more accurate than experts. More prospective studies are needed to make AI-aided EGC diagnosis universal in clinical practice.