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Abstract Small bowel vascular malformation disease (SBVM) commonly causes obscure gastrointestinal bleeding (OGIB). However, the pathogenetic mechanism and the role of lncRNAs in SBVM remain largely unknown. Here, we found that hypoxia and low-glucose environments co-augment angiogenesis and existed in SBVM. Mechanistically, hypoxia and low-glucose environments supported angiogenesis via activation of hypoxia and glucose deprivation-induced lncRNA (HGDILnc1) transcription by increasing binding of the NeuroD1 transcription factor to the HGDILnc1 promoter. Raised HGDILnc1 acted as a suppressor of α-Enolase 1 (ENO1) small ubiquitin-like modifier modification (SUMOylation)-triggered ubiquitination, and an activator of transcription of Aldolase C (ALDOC) via upregulation of Histone H2B lysine 16 acetylation (H2BK16ac) level in the promoter of ALDOC, and consequently promoting glycolysis and angiogenesis. Moreover, HGDILnc1 was clinically positively correlated with Neurogenic differentiation 1 (NeuroD1), ENO1, and ALDOC in SBVM tissues, and could function as a biomarker for SBVM diagnosis and therapy. These findings suggest that hypoxia and low-glucose environments were present in SBVM tissues, and co-augmented angiogenesis. Hypoxia and low-glucose environments co-induced HGDILnc1, which is higher expressed in SBVM tissue compared with normal tissue, could promoted glycolysis and angiogenesis.

Summary: Artificial intelligence (AI) has been found to assist in optical differentiation of hyperplastic and adenomatous colorectal polyps. We investigated whether AI can improve the accuracy of endoscopists’ optical diagnosis of polyps with advanced features. We introduced our AI system distinguishing polyps with advanced features with more than 0.870 of accuracy in the internal and external validation datasets. All 19 endoscopists with different levels showed significantly lower diagnostic accuracy (0.410–0.580) than the AI. Prospective randomized controlled study involving 120 endoscopists into optical diagnosis of polyps with advanced features with or without AI demonstration identified that AI improved endoscopists’ proportion of polyps with advanced features correctly sent for histological examination (0.960 versus 0.840, p

Abstract Background Endoscopic treatment methods for early colorectal cancer (ECRC) mainly depend on the size and morphology. It is unclear whether different endoscopic resection methods could achieve curative resection for ECRC confined in the mucosa. The study was designed to compare the rate of positive vertical margin (VM) of ECRC with advanced adenomas (AAs) including adenoma > 1 cm, villous adenoma, high-grade intraepithelial neoplasia/dysplasia stratified by different endoscopic resection methods. Methods Rate of positive VM for 489 ECRCs including Intramucosal (pTis) and superficial submucosal invasion (pT1) carcinomas were compared with those of 753 AAs stratified by different endoscopic resection methods using Chi-squared test. Multivariate logistic model was performed to investigate the risk factors of positive VM for different endoscopic resection methods. Results The pTis ECRC exhibited a similar rate of positive VM as that of AAs for en bloc hot snare polypectomy (HSP, 0% Vs. 0.85%, P = 0.617), endoscopic mucosal resection (EMR, 0.81% vs. 0.25%, P = 0.375) and endoscopic submucosal dissection (ESD, 1.82% Vs. 1.02%, P = 0.659). The pTis carcinoma was not found to be a risk factor for positive VM by en bloc EMR (P = 0.349) or ESD (P = 0.368). The en bloc resection achieved for pT1a carcinomas exhibited similar to positive VM achieved through ESD (2.06% Vs. 1.02%, P = 1.000) for AAs. Nonetheless, EMR resulted in higher risk of positive VM (5.41% Vs. 0.25%, P

Background: Log odds of positive lymph nodes (LODDS) classification showed superiority over 8th edition N staging in predicting survival of small bowel adenocarcinoma (SBA) patients. The aim of this study was to develop and validate the Tumor, LODDS, and Metastasis (TLM) staging of SBA. Methods: Totally 1789 SBA patients from the Surveillance, Epidemiology, and End Results (SEER) database between 1988–2010, 437 patients from SEER database between 2011–2013 and 166 patients from multicenters were categorized into development, validation and test cohort, respectively. The TLM staging was developed in the development cohort using Ensemble Algorithm for Clustering Cancer Data (EACCD) method. C-index was used to assess the performance of the TLM staging in predicting cancer-specific survival (CSS) and was compared with the traditional 8th edition TNM staging. Findings: Four-category TLM staging designed for the development cohort showed higher discriminatory power than TNM staging in predicting CSS in the development cohort (0.682 vs. 0.650, P