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.
ACS Sens. 2022 Aug 26;7(8):2262-2272. doi: 10.1021/acssensors.2c00788. Epub 2022 Aug 5.
Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1-2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.
即时护理点（POC）的细菌感染疾病诊断，为即时开始抗菌治疗和减少非细菌性感染中不必要抗生素的过度/误用提供了临床益处。我们在此提出一种 POC 兼容的方法，可在 10 分钟内快速检测细菌感染。我们使用大体积溶液散射成像（LVSi）系统，低放大倍数（1-2×），可视化临床样本中的细菌，从而消除了基于培养的分离和富集的需要。我们在短时间视频中跟踪了单个细胞的多个内在表型特征。通过用简单的机器学习算法对这些特征进行聚类，我们可以区分与类似大小聚苯乙烯珠的差异，区分不同形状的细菌，并区分与尿液颗粒的差异。我们将该方法应用于 104 例患者尿液样本中的尿路感染检测，与临床培养结果相比，其 30 秒 LVSi 视频的准确率为 92.3%。这项技术为在 POC 环境下进行快速细菌感染诊断提供了机会。
