使用由卷积神经网络增强的拉曼光谱,快速识别血液感染病原体和耐药性
Haiquan Kang1,2, Ziling Wang2, Jingfang Sun1
1Department of Clinical Laboratory, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Frontiers in microbiology
|July 30, 2024
概括
这项研究将深度学习与SERS结合起来,以快速识别血液感染病原体及其抗生素耐药性. 不同离心法在细菌隔离方面被证明是优越的,能够准确检测耐药细菌.
科学领域:
- 生物技术是生物技术.
- 医学诊断 医学诊断 医学诊断
- 微生物学 微生物学
背景情况:
- 由于高发病率和死亡率,血流感染 (BSI) 在诊断和治疗方面存在重大挑战.
- 目前的诊断方法,如血液培养,在检测病原体耐药性的过程中是缓慢和有限的.
- 快速识别病原体和抗菌素敏感性对于改善患者的治疗结果至关重要.
研究的目的:
- 将深度学习 (DL) 与表面增强拉曼散射 (SERS) 整合起来,以快速识别常见的BSI病原体.
- 评估DL-SERS在检测细菌病原体中抗菌素耐药性的有效性.
- 为了比较细菌隔离方法,以优化血液样本中基于SERS的检测.
主要方法:
- 从血培养中分离的细菌采集了SERS光谱,使用红细胞溶解和微分离心.
- 使用深度学习算法分析光谱数据,特别是卷积神经网络 (CNN).
- 评估DL-CNN模型用于病原体识别和药物耐药性检测的准确性.
主要成果:
- 不同离心法在细菌隔离方面比红细胞溶解更有效.
- 在识别致病细菌方面取得了98.68%的准确性.
- 在检测耐卡巴胺的Klebsiella肺炎时达到99.85%的准确性.
结论:
- 结合DL-SERS方法为诊断BSI提供了一个快速而准确的方法.
- 这种创新技术有可能显著改善患者的预后和医疗保健效率.
- 该研究证明了SERS与DL相结合的可行性,用于实时的病原体和耐药性概况.
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