针对结核病的下一代测序和机器学习:对于杆状肺结核和结核性脑膜炎的超敏感诊断策略
Suting Chen1, Congli Wang2, Yijun Zou2
1National Clinical Laboratory on Tuberculosis, Beijing Key Laboratory for Drug-resistant Tuberculosis Research, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China.
Clinica chimica acta; international journal of clinical chemistry
|December 25, 2023
概括
使用下一代测序和机器学习的新超敏感结核病 (TB) 诊断策略显示出高灵敏度. 无血细胞DNA (cfDNA) 为诊断结核病提供了一个有希望的,易于采集的样本,包括结核性脑膜炎和儿科肺结核.
科学领域:
- 基因组学和生物信息学
- 传染病诊断 传染病诊断 传染病诊断
- 机器学习在医学中的应用
背景情况:
- 由于测试灵敏度低和样本采集困难,诊断结核病 (TB) 是具有挑战性的.
- 目前用于检测结核病的方法,特别是其不太常见的形式,需要改进以获得更好的患者结果.
研究的目的:
- 开发和验证一种超灵敏的诊断策略,用于治疗乳结核病.
- 通过先进的测序和机器学习,评估血无细胞DNA (cfDNA) 和脑脊液 (CSF) DNA 在结核病诊断中的实用性.
主要方法:
- 集成的TB目标下一代测序与机器学习模型.
- 在临床队列中验证了诊断策略,分析了结核性脑膜炎 (TBM) 和儿科肺结核 (PPTB) 患者的血cfDNA和CSFDNA.
主要成果:
- 在CSFDNA (97.01%,95.65%) 和血cfDNA (82.61%,86.36%) 检测中实现了高灵敏度和特异性.
- 在TBM患者的血cfDNA和CSFDNA之间显示出高一致性 (90.91%).
- 观察到血cfDNA比Xpert测定在儿科肺结核中具有更高的灵敏度 (28.57%对比15.38%).
结论:
- 开发的结核病诊断策略显著提高了对 paucibacillary TB 的检测灵敏度.
- 血cfDNA被确定为一个合适且易于获得的样本,用于诊断肺结核和TBM.
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