通过深度学习实现的SlipChip上的快速单细胞表型抗真菌敏感性测试
Yan'an Ren1, Juanxiu Qin2, Min Li2,3,4
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Analytical chemistry
|January 22, 2026
概括
这项研究引入了一种快速的单细胞抗真菌敏感性测试 (AFST) 方法,使用微流体和人工智能. 它在4小时内产生结果,比传统的真菌感染方法快得多.
科学领域:
- 医学微生物学 医学微生物学
- 生物技术是生物技术.
- 计算生物学 计算生物学
背景情况:
- 真菌感染是全球主要的健康威胁,每年造成超过150万人的死亡.
- 目前的抗真菌敏感性测试 (AFST) 方法耗时,需要24-48小时,这延迟了关键的治疗决定.
- 迫切需要快速准确的AFST方法来指导抗真菌疗法.
研究的目的:
- 开发和验证一个快速,无标签,单细胞AFST (sc-AFST) 平台.
- 将AFST所需的时间从几天大幅缩短为几个小时.
- 为了实现高分辨率的单细胞生长分析和人口水平药物反应概况.
主要方法:
- 将SlipChip微流体设备与基于深度学习的图像分析 (ResNet-34分类器和U-Net细分模型) 集成.
- 将真菌细胞分成picoliter滴滴,以暴露在抗真菌渐变剂中.
- 从明亮场图像中自动识别和量化细胞生长.
主要成果:
- 在细胞检测中达到>98.8%的灵敏度和>93%的细分精度.
- 在10个临床隔离物中,与黄金标准微稀释试验100%的分类一致.
- 在4小时内成功确定最小抑制度 (MICs).
结论:
- 开发的sc-AFST平台为抗真菌敏感性测试提供了一个可扩展,快速和临床可行的工具.
- 这项技术使得精确的单细胞分析和人口水平药物反应评估成为可能.
- sc-AFST的快速周转时间可以通过使真菌感染的及时治疗决定显著改善患者的治疗结果.
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