Syal Karan, Mo Manni, Yu Hui, Iriya Rafael, Jing Wenwen, Guodong Sui, Wang Shaopeng, Grys Thomas E, Haydel Shelley E, Tao Nongjian
Center for Biosensors and Bioelectronics, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA.
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Theranostics. 2017 Apr 10;7(7):1795-1805. doi: 10.7150/thno.19217. eCollection 2017.
Infectious diseases caused by bacterial pathogens are a worldwide burden. Serious bacterial infection-related complications, such as sepsis, affect over a million people every year with mortality rates ranging from 30% to 50%. Crucial clinical microbiology laboratory responsibilities associated with patient management and treatment include isolating and identifying the causative bacterium and performing antibiotic susceptibility tests (ASTs), which are labor-intensive, complex, imprecise, and slow (taking days, depending on the growth rate of the pathogen). Considering the life-threatening condition of a septic patient and the increasing prevalence of antibiotic-resistant bacteria in hospitals, rapid and automated diagnostic tools are needed. This review summarizes the existing commercial AST methods and discusses some of the promising emerging AST tools that will empower humans to win the evolutionary war between microbial genes and human wits.
由细菌病原体引起的传染病是一项全球性负担。严重的细菌感染相关并发症，如败血症，每年影响超过一百万人，死亡率在30%至50%之间。与患者管理和治疗相关的关键临床微生物学实验室职责包括分离和鉴定致病细菌以及进行抗生素敏感性测试（ASTs），这些工作劳动强度大、复杂、不精确且速度慢（取决于病原体的生长速度，需要数天时间）。考虑到败血症患者危及生命的状况以及医院中抗生素耐药菌的日益流行，需要快速且自动化的诊断工具。本综述总结了现有的商业化AST方法，并讨论了一些有前景的新兴AST工具，这些工具将使人类能够在微生物基因与人类智慧之间的进化战争中获胜。
