| Literature DB >> 28837780 |
Kenneth P Smith1, David L Richmond2, Thea Brennan-Krohn1,3, Hunter L Elliott2, James E Kirby1.
Abstract
Antibiotic resistance is compromising our ability to treat bacterial infections. Clinical microbiology laboratories guide appropriate treatment through antimicrobial susceptibility testing (AST) of patient bacterial isolates. However, increasingly, pathogens are developing resistance to a broad range of antimicrobials, requiring AST of alternative agents for which no commercially available testing methods are available. Therefore, there exists a significant AST testing gap in which current methodologies cannot adequately address the need for rapid results in the face of unpredictable susceptibility profiles. To address this gap, we developed a multicomponent, microscopy-based AST (MAST) platform capable of AST determinations after only a 2 h incubation. MAST consists of a solid-phase microwell growth surface in a 384-well plate format, inkjet printing-based application of both antimicrobials and bacteria at any desired concentrations, automated microscopic imaging of bacterial replication, and a deep learning approach for automated image classification and determination of antimicrobial minimal inhibitory concentrations (MICs). In evaluating a susceptible strain set, 95.8% were within ±1 and 99.4% were within ±2, twofold dilutions, respectively, of reference broth microdilution MIC values. Most (98.3%) of the results were in categorical agreement. We conclude that MAST offers promise for rapid, accurate, and flexible AST to help address the antimicrobial testing gap.Entities:
Keywords: antimicrobials; inkjet printing; machine learning; susceptibility testing
Mesh:
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Year: 2017 PMID: 28837780 PMCID: PMC5744253 DOI: 10.1177/2472630317727721
Source DB: PubMed Journal: SLAS Technol ISSN: 2472-6303 Impact factor: 3.047