拉曼光谱与深度学习相结合,用于精确识别耐卡巴尼姆的肠道细菌
Wen Wang1, Xin Wang1, Ya Huang2
1Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Guangdong Medical University Dongguan First Affiliated Hospital, School of Medical Technology, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
表面增强的拉曼光谱 (SERS) 与深度学习相结合,可以准确地识别耐卡巴胺的肠杆菌 (CRE),并预测有效的抗生素. 这种新的方法有助于对抗CRE感染和抗生素耐药性.
科学领域:
- 微生物学 微生物学
- 频谱学是一种光谱学.
- 人工智能的人工智能
背景情况:
- 耐卡巴胺的肠道细菌 (CRE) 由于有效治疗方法有限,对全球健康构成重大威胁.
- 快速准确地识别CRE及其耐药性机制对于有效的临床管理至关重要.
研究的目的:
- 开发一种快速,独立于培养的方法来识别CRE菌株.
- 根据产生酶的亚型对CRE进行分类,并使用SERS和深度学习预测敏感的抗生素.
主要方法:
- 使用表面增强的拉曼光谱法 (SERS) 与金银复合基板分析22种CRE菌株.
- 采用了带有注意力机制的剩余网络来分类SERS频谱.
- 对细菌类型,产生酶的亚型和抗生素敏感性的评估分类准确性.
主要成果:
- SERS光谱是可重复和一致的,显示了与产生酶的亚型相关的物种特异性差异.
- 注意力机制提高了ResNet模型对CRE分类的准确性 (菌株为94.0%,亚型为96.13%).
- 在预测CRE敏感抗生素组合时获得了93.9%的准确性.
结论:
- 与深度学习相结合的SERS提供了一种有希望的方法,可以在没有文化的情况下快速识别CRE.
- 这种方法可以指导对CRE感染的抗生素选择,有助于抵抗管理.
- 证明了用于CRE检测和治疗指导的新临床诊断工具的潜力.
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