Automated real-time imaging of intestinal barrier integrity and molecular profiling for early outcome prediction in inflammatory bowel disease: endo-histo-barrier-omics study

利用自动化实时成像技术检测肠道屏障完整性并进行分子谱分析,以预测炎症性肠病早期预后:内皮-组织-屏障组学研究

阅读:2

Abstract

BACKGROUND: Barrier healing is an emerging therapeutic target in inflammatory bowel disease (IBD), though its assessment remains challenging. We evaluated automated advanced imaging for real-time barrier assessment, its correlation with epithelial/vascular barrier markers, and ability to predict adverse outcomes. METHODS: IBD patients and healthy controls undergoing endoscopic assessment were prospectively enrolled. The intestinal barrier was evaluated using ultra-high-magnification endocytoscopy and probe-based confocal laser endomicroscopy. Targeted biopsies were obtained from inflamed and non-inflamed segments. Epithelial and vascular barriers were assessed through automated multiplex immunofluorescence for Claudin-2, ZO-1, E-cadherin, PV-1, and CD31. Gene expression profiling was performed in epithelial and lamina propria compartments. Artificial intelligence (AI)-based analysis was employed for automated evaluation of barrier features captured by advanced imaging. RESULTS: In total, 103 patients were included (38 ulcerative colitis [UC], 54 Crohn's disease [CD], 11 healthy controls). Advanced imaging revealed barrier healing in 21% (8/38) of UC and 30% (16/54) of CD patients. In UC, Claudin-2 moderately correlated with abnormal crypt architecture (ρ = 0.49), goblet cell depletion (ρ = 0.5), and overall endocytoscopy activity (ρ = 0.49). In CD, PV-1 moderately correlated with altered blood flow (ρ = 0.41) and vessel architecture (ρ = 0.40). An integrated assessment of advanced imaging, combined with Claudin-2 and PV-1 expression, effectively predicted adverse outcomes in UC and CD, respectively. AI tools accurately classified epithelial and vascular barrier features captured by advanced imaging. Finally, gene expression confirmed upregulation of Claudin-2 and PV-1 in IBD. CONCLUSION: Automated advanced imaging enables real-time barrier assessment in IBD and correlates with markers of epithelial and vascular barrier impairment. AI integration can enhance standardization toward broader clinical applicability.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。