Department of Laboratory Medicine, Nanjing First Hospital, China Pharmaceutical University, Nanjing, 210006, China.
Department of Clinical Pharmacy, School of Basic Medicine and Clinical Pharmacy, Pharmaceutical University, Nanjing, 211198, China.
Arch Microbiol. 2024 Sep 20;206(10):409. doi: 10.1007/s00203-024-04131-z.
The widespread spread of bacterial antimicrobial resistance (AMR) and multidrug-resistant bacteria poses a significant threat to global public health. Traditional methods for detecting bacterial AMR are simple, reproducible, and intuitive, requiring long time incubation and high labor intensity. To quickly identify and detect bacterial AMR is urgent for clinical treatment to reduce mortality rate, and many new methods and technologies were required to be developed. This review summarizes the current phenotypic and genotypic detection methods for bacterial AMR. Phenotypic detection methods mainly include antimicrobial susceptibility tests, while genotypic detection methods have higher sensitivity and specificity and can detect known or even unknown drug resistance genes. However, most of the current tests are either genotypic or phenotypic and rarely combined. Combining the advantages of phenotypic and genotypic methods, combined with the joint application of multiple rapid detection methods may be the trend for future AMR testing. Driven by rapid diagnostic technology, big data analysis, and artificial intelligence, detection methods of bacterial AMR are expected to constantly develop and innovate. Adopting rational detection methods and scientific data analysis can better address the challenges of bacterial AMR and ensure human health and social well-being.
细菌抗菌药物耐药性(AMR)和多药耐药菌的广泛传播对全球公共卫生构成重大威胁。传统的细菌 AMR 检测方法简单、可重复且直观,需要长时间孵育和高劳动强度。快速识别和检测细菌 AMR 对于临床治疗以降低死亡率非常紧迫,需要开发许多新的方法和技术。本综述总结了当前用于检测细菌 AMR 的表型和基因型检测方法。表型检测方法主要包括抗菌药物敏感性试验,而基因型检测方法具有更高的灵敏度和特异性,可以检测已知甚至未知的耐药基因。然而,目前的大多数检测方法要么是表型的,要么是基因型的,很少将两者结合起来。结合表型和基因型方法的优势,结合多种快速检测方法的联合应用,可能是未来 AMR 检测的趋势。在快速诊断技术、大数据分析和人工智能的推动下,细菌 AMR 的检测方法有望不断发展和创新。采用合理的检测方法和科学的数据分析可以更好地应对细菌 AMR 的挑战,确保人类健康和社会福祉。