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基于机器学习的识别和表征质细胞在埃索诺菲尔性食道炎

Simin Zhang¹, Julie M Caldwell², Mark Rochman²

¹Division of Rheumatology, Allergy, and Immunology, Department of Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio.

The Journal of allergy and clinical immunology

|February 23, 2024

概括

性食道炎 (EoE) 涉及食道中的巨细胞 (MCs). 机器学习显示,在活跃的EoE期间,乳头MCs下降,上皮MCs增加,与疾病严重程度相关.

科学领域:

胃肠病学胃肠病学
免疫学免疫学免疫学
计算病理学计算病理学

背景情况:

生性食道炎 (EoE) 诊断依赖于生细胞计数,但巨细胞的参与尚未得到充分研究.
食道巨细胞症是EoE的一个显著的,但不太了解的特征.

研究的目的:

利用机器学习来定位和表征食道巨细胞 (MCs).
为了研究食道巨细胞在EoE病理学中的潜在作用.

主要方法:

来自EoE患者和对照者的食道活检被染色为巨细胞 (MCs).
使用了免疫光成像和高分辨率全组织组件.
机器学习软件 (MC-AI) 开发用于识别,列举和描述MCs.

主要成果:

MC-AI准确地列出了MCs,显示在活跃的EOE中表皮MCs增加和lamina propriaMCs减少.
乳头和上皮的MC密度与炎症,增生和纤维化相关.
在活跃的EoE中观察到更大的MC脱粒化,MCs比eosinophils更接近底层膜.

结论:

鉴定出了一种独特的食道乳头MC群体.
在活跃的EoE期间,乳头MCs减少,降粒,并与组织学特征相关联.
MC-AI提供了一种新的方法来理解MC参与EoE和相关疾病.

关键词:

埃索诺菲尔性食道炎是什么?人工智能的人工智能是人工智能.机器学习是机器学习.巨细胞巨细胞它们的乳头是papillae.

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