Resistell AG, Hofackerstrasse 40, 4132, Muttenz, Switzerland.
Hospital Universitario Ramón y Cajal, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Carretera de Colmenar Km 9,1, 28034, Madrid, Spain.
Nat Commun. 2024 Mar 18;15(1):2037. doi: 10.1038/s41467-024-46213-y.
Antimicrobial resistance (AMR) is a major public health threat, reducing treatment options for infected patients. AMR is promoted by a lack of access to rapid antibiotic susceptibility tests (ASTs). Accelerated ASTs can identify effective antibiotics for treatment in a timely and informed manner. We describe a rapid growth-independent phenotypic AST that uses a nanomotion technology platform to measure bacterial vibrations. Machine learning techniques are applied to analyze a large dataset encompassing 2762 individual nanomotion recordings from 1180 spiked positive blood culture samples covering 364 Escherichia coli and Klebsiella pneumoniae isolates exposed to cephalosporins and fluoroquinolones. The training performances of the different classification models achieve between 90.5 and 100% accuracy. Independent testing of the AST on 223 strains, including in clinical setting, correctly predict susceptibility and resistance with accuracies between 89.5% and 98.9%. The study shows the potential of this nanomotion platform for future bacterial phenotype delineation.
抗菌药物耐药性（AMR）是一个主要的公共卫生威胁，降低了感染患者的治疗选择。缺乏快速抗生素药敏试验（AST）会促进 AMR。加速 AST 可以及时、知情地确定有效的治疗抗生素。我们描述了一种快速生长非依赖性表型 AST，它使用纳米运动技术平台来测量细菌的振动。机器学习技术被应用于分析一个大数据集，其中包括 1180 个阳性血培养样本中 2762 个单独的纳米运动记录，涵盖了暴露于头孢菌素和氟喹诺酮类药物的 364 株大肠杆菌和肺炎克雷伯菌分离株。不同分类模型的训练性能达到了 90.5%到 100%的准确率。对包括临床环境中的 223 株菌株进行的 AST 独立测试，其敏感性和耐药性的准确率在 89.5%至 98.9%之间。该研究表明了这种纳米运动平台在未来细菌表型描述方面的潜力。
