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Chao Wan¹, Huijuan Yuan¹, Chenxi Dai¹

¹The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

Analytical chemistry

|March 11, 2026

概括

一个新的,低成本的微流体芯片在不到一个小时内提供了快速的,无培养的抗生素敏感性测试 (AST). 这项技术克服了资源有限的地区的障碍,帮助打击抗菌素耐药性 (AMR).

科学领域:

生物医学工程生物医学工程
微流体学微流体学
诊断诊断诊断诊断

背景情况:

抗菌素耐药性 (AMR) 是一个全球性的健康危机,由于滥用抗生素而加剧.
目前的抗生素敏感性测试 (AST) 方法缓慢,需要广泛的基础设施,限制在资源有限的环境中使用它们.
迫切需要快速,可访问和可靠的诊断,以有效打击抗药性耐药性.

研究的目的:

开发一种低成本,自我修复的微流体芯片,用于快速,无培养的AST.
将细菌丰富,抗生素渐变生成和自动化结果分析集成到一个平台中.
为AST提供一个变革性的工具,特别是在资源有限的地区.

主要方法:

一个带有自我修复门 (μCFC-shv) 的离心微流体芯片被设计和制造,成本为0.62美元.
该芯片集成了1000倍的细菌丰富,抗生素梯度生成和AST.
开发了一种机器学习模型,用于自动化结果分析,减轻光干扰和主观解释.

主要成果:

微CFC-shv在缩后5分钟内实现了病原体检测,具有100%的灵敏度和特异性.
在306个临床病例中,无培养的AST在30分钟的抗生素暴露时间内完成,准确率为97.39%.
使用机器学习模型进行自动化分析,准确率达到了98.83%.

结论:

CFC-shv为AST提供了一个快速,经济有效和可靠的解决方案,克服了当前的技术限制.
该平台特别适合资源有限的地区,能够更快地做出临床决策,改善抗菌药物耐药性管理.
微流体,自愈门和机器学习的整合代表了全球卫生挑战诊断技术的重大进步.

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