Growth Independent Morphometric Machine Learning Workflow for Single-Cell Antimicrobial Susceptibility Testing of <i>Klebsiella pneumoniae</i> to Meropenem.

ABSTRACT: Multidrug-resistant Enterobacteriaceae are among the most urgent global public health threats associated with various life-threatening infections. In the absence of a rapid method to identify antimicrobial susceptibility, empirical use of broad-spectrum antimicrobials such as carbapenem monotherapy has led to the spread of resistant organisms. Rapid determination of antimicrobial resistance is urgently needed to overcome this issue. By capturing dynamic single-cell morphological features, including growth-independent, antibiotic-induced changes, of cells from nineteen strains of Klebsiella pneumoniae, we evaluated strategies based on time and concentration differentials for classifying its susceptibility to a commonly used carbapenem, meropenem, and predicting their minimum i

SUBMITTER: Tjandra KC 

PROVIDER: S-EPMC13618438 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature