高维空间蛋白质组学和新型机器学习管道识别了特定疾病的损伤状态
bioRxiv : the preprint server for biology
|February 6, 2026
概括
一个新的AI工具,脏损伤诊断 (RDDx),精确量化脏损伤在狼性炎和脏异种移植排斥. 它识别了特定的损伤模式,有助于理解疾病机制和寻找新的治疗方法.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 狼性炎 (LuN) 和异体移植排斥 (RAR) 通过炎症和纤维化导致功能衰竭.
- 在病中对空间损伤模式的定量评估对于了解疾病进展和开发向疗法至关重要.
研究的目的:
- 开发和验证一个计算管道,脏损伤诊断 (RDDx),用于脏活检的定量空间分析.
- 使用高维空间蛋白质组学数据在LuN和RAR中区分和量化损伤机制.
主要方法:
- 收集了来自23个LuN,33个RAR和8个控制 (KC) 活检的空间蛋白质组学数据.
- 开发了用于损伤诊断 (RDDx) 的三个神经网络,以在整个幻灯片图像中细分和分类结构 (管道,毛细血管,质细胞).
- 经验证的RDDx准确性,概括性和与组织病理学得分的相关性.
主要成果:
- RDDx精确地对脏结构进行了细分,并确定了管道和血管炎症.
- 无论是LuN还是RAR,都显示了管状/毛细血管区域的减少以及间歇空间的扩大.
- 卢恩呈现出不齐的压力管道;RAR显示出分散的损伤,具有T细胞介导排斥 (TCMR) 和抗体介导排斥 (ABMR) 的特定模式.
- RDDx指标与组织病理学得分相关,证明了临床相关性.
结论:
- 在LuN和RAR中,RDDx有效地识别和量化疾病特异性损伤机制.
- 应力管道和增殖毛细血管分别是LuN和ABMR的敏感标志物.
- 在脏疾病中,RDDx促进了机制研究和治疗点的发现.
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