对于单细胞亚细胞结构表型抗菌素敏感性测试的时间缩短深度学习
Wenwen Jing1, Tianran Zhang1, Xi Chen2
1Key Laboratory of Medical Molecular Virology, MOE & NHC, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
Analytical chemistry
|November 24, 2025
概括
一种新的快速表型抗微生物敏感性测试 (AST) 方法使用显微镜和深度学习在20分钟内提供结果. 这种方法可以准确地识别抗菌素耐药性 (AMR),并揭示单细胞异质性,有助于及时做出临床决定.
科学领域:
- 微生物学与传染病的研究
- 生物技术和医学成像技术
- 医疗保健中的人工智能
背景情况:
- 抗菌素耐药性 (AMR) 构成了全球健康的重大威胁,增加了疾病的严重程度和医疗保健成本.
- 传统的抗微生物敏感性测试 (AST) 方法是缓慢的 (24小时至几天) 并依赖于培养.
- 基因型检测仅限于已知的耐药性基因,不能检测出新型变异.
研究的目的:
- 开发一个快速,准确的表型AST平台,以克服当前方法的局限性.
- 利用结构化照明显微镜 (SIM) 和深度学习进行亚细胞细菌表型评估.
- 为了使AST更快,更有效地解决及时的临床决策和AMR制.
主要方法:
- 开发一个快速的表型AST平台,集成SIM成像和深度学习.
- 七个深度学习架构 (ResNet-50,C3D,DenseNet-121等) 的培训 在细菌图像数据集上.
- 应用单细胞分析在最小抑制度 (MIC) 附近,以评估细菌反应.
主要成果:
- 对于大肠杆菌,ResNet-50在不到20分钟的时间内实现了AST结果的87%准确性,对于M. smegmatis,在4小时内,对于BCG. smegmatis,在15小时内.
- 深度学习平台表现出与传统的AST测试有很强的一致性.
- 单细胞分析揭示了细菌对抗生素反应的异质性,掩盖了人口水平的MIC.
结论:
- 开发的方法能够在没有培养要求的情况下快速,亚细胞水平的表型AST.
- 这个平台准确地评估了抗生素的有效性,并揭示了单细胞异质性,这对于理解耐药性至关重要.
- 这些发现支持及时的临床决策,并提供了一种打击AMR传播的工具.
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