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基于视频的目标散射强度检测的临床尿液样本快速抗菌药物敏感性测试。

Rapid Antimicrobial Susceptibility Testing on Clinical Urine Samples by Video-Based Object Scattering Intensity Detection.

机构信息

Biodesign Center for Bioelectronics and Biosensors, Arizona State University, Tempe, Arizona 85287, United States.

Biosensor National Special Laboratory, Key Laboratory for Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, PR China.

出版信息

Anal Chem. 2021 May 11;93(18):7011-7021. doi: 10.1021/acs.analchem.1c00019. Epub 2021 Apr 28.

DOI:10.1021/acs.analchem.1c00019
PMID:33909404
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8152505/
Abstract

To combat the ongoing public health threat of antibiotic-resistant infections, a technology that can quickly identify infecting bacterial pathogens and concurrently perform antimicrobial susceptibility testing (AST) in point-of-care settings is needed. Here, we develop a technology for point-of-care AST with a low-magnification solution scattering imaging system and a real-time video-based object scattering intensity detection method. The low magnification (1-2×) optics provides sufficient volume for direct imaging of bacteria in urine samples, avoiding the time-consuming process of culture-based bacterial isolation and enrichment. Scattering intensity from moving bacteria and particles in the sample is obtained by subtracting both spatial and temporal background from a short video. The time profile of scattering intensity is correlated with the bacterial growth rate and bacterial response to antibiotic exposure. Compared to the image-based bacterial tracking and counting method we previously developed, this simple image processing algorithm accommodates a wider range of bacterial concentrations, simplifies sample preparation, and greatly reduces the computational cost of signal processing. Furthermore, development of this simplified processing algorithm eases implementation of multiplexed detection and allows real-time signal readout, which are essential for point-of-care AST applications. To establish the method, 130 clinical urine samples were tested, and the results demonstrated an accuracy of ∼92% within 60-90 min for UTI diagnosis. Rapid AST of 55 positive clinical samples revealed 98% categorical agreement with both the clinical culture results and the on-site parallel AST validation results. This technology provides opportunities for prompt infection diagnosis and accurate antibiotic prescriptions in point-of-care settings.

摘要

为了应对当前抗生素耐药性感染这一公共卫生威胁,我们需要一种能够在即时检测环境中快速识别感染细菌病原体并同时进行抗生素药敏试验(AST)的技术。在这里,我们开发了一种即时检测 AST 技术,该技术使用低倍率溶液散射成像系统和基于实时视频的物体散射强度检测方法。低倍率(1-2×)光学系统提供了足够的体积,可直接对尿液样本中的细菌进行成像,避免了耗时的基于培养的细菌分离和富集过程。通过从短视频中减去时空背景,获得来自样本中移动细菌和颗粒的散射强度。散射强度的时间曲线与细菌的增长率和细菌对抗生素暴露的反应相关。与我们之前开发的基于图像的细菌跟踪和计数方法相比,这种简单的图像处理算法可适应更广泛的细菌浓度范围,简化了样品制备过程,并大大降低了信号处理的计算成本。此外,这种简化处理算法的开发简化了多重检测的实施,并允许实时信号读出,这对于即时检测 AST 应用至关重要。为了建立该方法,我们测试了 130 份临床尿液样本,结果表明在 60-90 分钟内,尿路感染诊断的准确率约为 92%。对 55 份阳性临床样本的快速 AST 分析显示,与临床培养结果和现场并行 AST 验证结果相比,其分类一致性达到 98%。该技术为即时检测环境中的快速感染诊断和准确抗生素处方提供了机会。

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