血免疫分析与机器学习相结合,有助于对活跃肺结核的诊断和预后
Fusheng Yao1, Ruiqi Zhang1, Qiao Lin2
1National Clinical Research Center for Infectious Diseases, Shenzhen Third People's Hospital, Southern University of Science and Technology, Shenzhen, People's Republic of China.
Emerging microbes & infections
|June 18, 2024
概括
结核病 (TB) 诊断可以使用新的血液检测来改善. 该测试识别了九种免疫生物标志物,为结核病患者提供了准确的检测和结果预测.
科学领域:
- 免疫学 免疫学 免疫学
- 蛋白质组学是指蛋白质组学.
- 机器学习 机器学习
背景情况:
- 结核病 (TB) 是全球主要的健康威胁,延迟诊断阻碍了控制工作.
- 目前的诊断方法通常依赖于液,限制了可访问性,导致诊断延迟.
- 炎症是结核病的关键特征,表明免疫标记物可以帮助诊断.
研究的目的:
- 为了确定新的血生物标志物,以准确诊断结核病.
- 通过机器学习评估这些生物标志物的诊断潜力.
- 评估生物标志物预测结核病严重程度和治疗结果的能力.
主要方法:
- 从健康对照组,疾病对照组 (肺炎),结核病患者和治疗结核病例中收集了222个血样本.
- 使用多重近距离延伸试验 (PEA) 来量化92种免疫蛋白.
- 应用差分分析,与结核病严重程度的相关性,以及7个机器学习算法.
主要成果:
- 识别了9个血生物标志物的签名 (CXCL9,PDL1,CDCP1,CCL28,CCL23,CCL19,MMP1,IFNγ,TRANCE). 它们的生物标志物包括:
- 实现了高诊断准确度 (AUROC 0.89-0.99,灵敏度82-93%,特异性88-92%),使结核病与对照群区分开来.
- 在识别严重结核病例方面表现卓越 (AUROC > 0.95,敏感性/特异性 > 93.3%).
结论:
- 一个9-血蛋白签名显示了准确的结核病诊断显著的潜力.
- 这种生物标志物板有助于预测临床结果和疾病严重程度.
- 针对性蛋白质组学与机器学习相结合,为结核病诊断提供了一个有希望的方法.
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