机器学习辅助MALDI质谱仪用于临床实验中快速预测抗菌素耐药性
Weibo Gao1, Hang Li2, Jingxian Yang3
1Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Analytical chemistry
|August 3, 2024
概括
快速识别使用矩阵辅助激光吸附离子化质谱法 (MALDI-MS) 和机器学习的抗菌素耐药Enterococcus faecium (VRE) 感染. 这种方法显著加快了抗菌素敏感性测试 (AST),改善了患者的治疗.
科学领域:
- 微生物学 微生物学
- 质谱测量质量谱测量
- 机器学习 机器学习
- 抗微生物耐药性 抗微生物耐药性
背景情况:
- 传统的抗微生物敏感性测试 (AST) 需要长达72小时才能获得结果.
- 对于有效治疗感染,快速识别抗万科素的Enterococcus faecium (VRE) 非常重要.
- 抗万科胺素的Enterococcus faecium (VRE) 构成了全球重大健康挑战.
研究的目的:
- 开发一种用于VRE检测的快速查策略.
- 整合矩阵辅助激光吸附离子化质谱 (MALDI-MS) 与机器学习用于VRE预测.
- 为了加速细菌抗生素耐药性的确定.
主要方法:
- 使用MALDI-MS.分析了400多个VRE和易受菌素影响的E. faecium (VSE) 隔离物.
- 创建一个包含2388个质谱的数据集.
- 在临床样本上对机器学习分类模型 (SVM,后勤回归,MLP) 的培训和验证.
主要成果:
- 机器学习模型的预测准确度从78.07%到80.54%不等,AUROC高于0.80.
- 确定了33个有影响力的群体区域,区分VRE和VSE.
- 暂时确定了有助于抵抗的特定核糖体蛋白质.
结论:
- 机器学习与MALDI-MS的整合使得VRE能够快速确定.
- 这一策略显著加快了对抗菌素敏感性测试的速度.
- 更快的结果有助于及时给予适当的抗生素.
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