Institute for Personalized Medicine, School of Biomedical Engineering , Shanghai Jiao Tong University , Shanghai 200030 , China.
Biodesign Center for Biosensors and Bioelectronics , Arizona State University , Tempe , Arizona 85287 , United States.
Anal Chem. 2018 May 15;90(10):6314-6322. doi: 10.1021/acs.analchem.8b01128. Epub 2018 May 3.
Timely determination of antimicrobial susceptibility for a bacterial infection enables precision prescription, shortens treatment time, and helps minimize the spread of antibiotic resistant infections. Current antimicrobial susceptibility testing (AST) methods often take several days and thus impede these clinical and health benefits. Here, we present an AST method by imaging freely moving bacterial cells in urine in real time and analyzing the videos with a deep learning algorithm. The deep learning algorithm determines if an antibiotic inhibits a bacterial cell by learning multiple phenotypic features of the cell without the need for defining and quantifying each feature. We apply the method to urinary tract infection, a common infection that affects millions of people, to determine the minimum inhibitory concentration of pathogens from human urine specimens spiked with lab strain E. coli (ATCC 43888) and an E. coli strain isolated from a clinical urine sample for different antibiotics within 30 min and validate the results with the gold standard broth macrodilution method. The deep learning video microscopy-based AST holds great potential to contribute to the solution of increasing drug-resistant infections.
及时确定细菌感染的药敏性可以实现精准处方，缩短治疗时间，并有助于最大限度地减少抗生素耐药性感染的传播。目前的抗菌药物敏感性测试（AST）方法通常需要数天时间，因此阻碍了这些临床和健康益处的实现。在这里，我们提出了一种 AST 方法，通过实时对尿液中自由移动的细菌细胞进行成像，并使用深度学习算法对视频进行分析。该深度学习算法通过学习细菌细胞的多个表型特征来确定抗生素是否抑制细菌细胞，而无需定义和量化每个特征。我们将该方法应用于尿路感染，这是一种影响数百万人的常见感染，以确定从含有实验室菌株大肠杆菌（ATCC 43888）和从临床尿液样本中分离出的大肠杆菌菌株的人尿液标本中添加的不同抗生素的最小抑菌浓度，结果在 30 分钟内用金标准肉汤微量稀释法进行验证。基于深度学习的视频显微镜 AST 具有很大的潜力，可以为解决日益增加的耐药性感染做出贡献。
