Barrier healing represents a novel therapeutic target in ulcerative colitis (UC), although its assessment remains challenging and lacks standardisation. This exploratory study evaluates the ability of ultra-high magnification endocytoscopy to guide tissue sampling and drive automated quantification of tight junction (TJ) proteins to assess intestinal barrier integrity and predict major adverse outcomes (MAOs). 34 UC patients in clinical remission prospectively underwent assessment with endocytoscopy and machine learning-enabled intestinal barrier protein evaluation. The combination of endocytoscopy with Claudin-2 expression showed promise in accurately predicting MAOs over 12 months. This integrative approach holds promise in identifying deep healing and enhancing treat-to-target strategy in UC. Barrier healing is attracting fresh attention as a therapeutic target in UC.1 2 However, its evaluation is subjective and not standardised. It has generally depended on probe permeability with considerable variability, thus highlighting an unmet need for novel tools to accurately and objectively assess deep healing and predict clinical outcomes, including endocytoscopy, histology and intestinal barrier proteins. Endocytoscope (Olympus, Japan) is a commercially available endoscope capable of achieving up to 520-fold magnification, enabling real-time, in vivo assessment of intestinal cellular components and accurately guiding tissue sampling.3 Furthermore, automated spatial multispectral imaging pathology is promising for precisely and objectively quantifying intestinal barrier proteins.4 This exploratory study aims to combine endocytoscope with intestinal barrier proteins assessment through machine learning-enabled multispectral spatial imaging (MSI) (figure 1) to assess the ability of this integrative approach to define deep healing and predict MAOs over a 12-month follow-up. Figure 1 Ultra-high magnification endocytoscopy driving machine learning-enabled multispectral spatial intestinal barrier protein imaging. Created with BioRender.com. Patients with an established diagnosis of UC in clinical remission, defined as a partial Mayo score ≤3 without any subscore ≥1 and undergoing surveillance colonoscopy at two tertiary referral centres were prospectively enrolled (online supplemental table 1). In …

中文翻译：

---

具有自动多光谱肠屏障病理学成像的内吞细胞镜用于评估深度愈合以预测溃疡性结肠炎的结果


屏障愈合代表了溃疡性结肠炎（UC）的一个新的治疗靶点，尽管其评估仍然具有挑战性并且缺乏标准化。这项探索性研究评估了超高放大倍率内吞细胞镜引导组织取样和驱动紧密连接 (TJ) 蛋白自动定量的能力，以评估肠道屏障完整性并预测主要不良后果 (MAO)。 34 名临床缓解的 UC 患者前瞻性地接受了内吞细胞镜检查和机器学习支持的肠屏障蛋白评估。细胞内镜检查与 Claudin-2 表达相结合，有望在 12 个月内准确预测 MAO。这种综合方法有望在 UC 中确定深度愈合并增强治疗目标策略。屏障愈合作为 UC 的治疗目标引起了新的关注。1 2 然而，其评估是主观的且不标准化。它通常依赖于具有相当大变异性的探针渗透性，从而凸显了对新工具的未满足需求，以准确、客观地评估深度愈合和预测临床结果，包括内吞细胞镜、组织学和肠屏障蛋白。 Endocytoscope（奥林巴斯，日本）是一种市售内窥镜，能够实现高达 520 倍的放大倍数，能够实时、体内评估肠道细胞成分并准确指导组织取样。3 此外，自动空间多光谱成像病理学在精确、客观地量化肠道屏障蛋白。4 这项探索性研究旨在通过机器学习支持的多光谱空间成像 (MSI) 将内吞镜与肠道屏障蛋白评估结合起来（图 1），以评估这种综合方法在 12 个月的随访中定义深度愈合和预测 MAO 的能力。向上。图 1 超高放大倍率内吞细胞镜驱动机器学习支持的多光谱空间肠屏障蛋白成像。使用 BioRender.com 创建。前瞻性纳入临床缓解期 UC 诊断明确的患者（定义为部分 Mayo 评分≤3，且任何单项评分≥1）并在两个三级转诊中心接受结肠镜监测监测（在线补充表 1）。在 …