使用机器学习辅助的纳米运动技术平台进行准确和快速的抗生素敏感性测试
Alexander Sturm1, Grzegorz Jóźwiak2, Marta Pla Verge2
1Resistell AG, Hofackerstrasse 40, 4132, Muttenz, Switzerland. alex.sturm@resistell.com.
Nature communications
|March 19, 2024
概括
快速抗生素敏感性测试 (AST) 对于打击抗菌素耐药性 (AMR) 是至关重要的. 这项研究引入了一个纳米运动技术平台,用于快速,准确的细菌识别,改善治疗结果.
科学领域:
- 微生物学 微生物学
- 生物技术是生物技术.
- 公共卫生 公共卫生
背景情况:
- 抗菌素耐药性 (AMR) 构成了全球重大健康挑战,限制了有效的治疗选择.
- 缺乏快速抗生素敏感性测试 (AST) 阻碍了对细菌感染的及时和明智的临床决策.
研究的目的:
- 开发和验证一个快速,独立于生长的表型AST.
- 评估纳米运动技术平台与机器学习结合用于细菌敏感性测试的有效性.
主要方法:
- 利用纳米运动技术平台来测量细菌振动,这是一个独立于生长的表型标记.
- 应用机器学习分析了来自1180个尖端阳性血液培养的2762个纳米运动记录.
- 在暴露于和诺的埃舍里希亚大肠杆菌和Klebsiella pneumoniae分离物上进行了测试.
主要成果:
- 机器学习模型在训练中实现了90.5100%的准确性.
- 独立测试表明,在预测易感性和耐药性方面,准确率为89.598.9%.
- 该平台成功地识别了细菌对抗生素的反应,而不依赖细菌生长.
结论:
- 纳米运动平台为快速表型AST提供了一个有希望的方法.
- 这项技术有可能显著改善细菌感染的管理和打击AMR.
- 进一步开发可能会导致更快的诊断,为重症监护设置.
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