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通过将MALDI-TOF质谱与人工神经网络相结合,加速检测Clostridioides difficile37型序列

Liqian Wang^1,2, Keqing Zhang³, Junjie Lao²

¹Department of Laboratory Medicine of The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

Microbiology spectrum

|November 13, 2025

概括

使用MALDI-TOF MS和机器学习快速识别Clostridioides困难序列类型37 (ST37). 这种具有成本效益的方法显著减少了诊断时间,有助于对严重感染及时进行临床干预.

科学领域:

微生物学微生物学
传染性疾病传染性疾病
计算生物学计算生物学

背景情况:

Clostridioides difficile序列37型 (ST37),也称为RT017,是一种与严重感染和抗生素耐药性相关的毒性菌株.
目前用于ST37识别的方法耗时且昂贵,延迟了关键的临床决策.
快速识别对于有效的感染控制和患者管理至关重要.

研究的目的:

开发一种快速,具有成本效益的方法来识别C. difficile ST37,使用MALDI-TOF MS与机器学习相结合.
评估用于ST37检测的人工神经网络 (ANN) 模型的诊断性能.
为了识别特定于C. difficile ST37.37的潜在蛋白质生物标志物.

主要方法:

从385个C. difficile分离物中收集和分析了1,155个质谱,包括118个ST37菌株.
开发了一个ANN模型,对80%的数据进行训练,并对剩余的20%进行验证.
利用MALDI-TOF MS数据进行光谱分析和生物标志物识别.

主要成果:

该ANN模型实现了高诊断准确性,在验证集中用于ST37检测的AUC为0.96 (ROC曲线) 和0.94 (精度回忆曲线).
确定了15种C. difficile ST37菌株的潜在生物标志物.
该模型可以在物种识别后大约10秒内对ST37进行分类.

结论:

开发的ANN模型为C. difficile ST37鉴定提供了一个快速,可靠和经济的方法.
这种方法显著减少了周转时间,促进了更快的临床诊断和治疗.
这种工具的实施可以帮助减轻严重C. difficile感染的风险.

关键词:

克洛斯特里迪奥伊德斯困难菌人工神经网络的人工神经网络机器学习是机器学习.多个位置的序列类型化

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