Alexander Sturm, Grzegorz Jóźwiak, Marta Pla Verge, Laura Munch, Gino Cathomen, Anthony Vocat, Amanda Luraschi-Eggemann, Clara Orlando, Katja Fromm, Eric Delarze, Michał Świątkowski, Grzegorz Wielgoszewski, Roxana M Totu, María García-Castillo, Alexandre Delfino, Florian Tagini, Sandor Kasas, Cornelia Lass-Flörl, Ronald Gstir, Rafael Cantón, Gilbert Greub, Danuta Cichocka
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...
March 18, 2024: Nature Communications