In ACS sensors
Rapid antibacterial susceptibility testing (RAST) methods which measure change of a bacterial phenotype in response to a given treatment are of significant importance in healthcare, as they can assist care-givers in the timely administration of correct treatments. Various RAST techniques have been reported for tracking bacterial phenotypes, including size, shape, motion, and redox state. However, they still require bulky and expensive instruments - which hinders their application in resource-limited environments - and/or utilize labeling reagents which can interfere with antibiotics and add to cost. Furthermore, the existing RAST methods do not address the potential gradual adaptation of bacteria to antibiotics, which can lead to a false diagnosis. In this work, we present a RAST approach by leveraging machine learning to analyze time-resolved dynamic laser speckle imaging (DLSI) results. DLSI captures change of bacterial motion/division in response to antibiotic treatments. Our method accurately predicts the minimum inhibitory concentration (MIC) of ampicillin and gentamicin for a model strain of Escherichia coli (E. coli K-12) in 60 minutes, compared to 6 hours using the currently FDA-approved phenotype-based RAST technique. In addition to ampicillin (a β-lactam) and gentamicin (an aminoglycoside), we studied the effect of ceftriaxone (a third-generation clinical cephalosporin) on E. coli K-12. The machine learning algorithm was trained and validated using the overnight results of a gold standard AST method enabling prediction of MIC with a similarly high accuracy, yet substantially faster.
Zhou Keren, Zhou Chen, Sapre Anjali, Pavlock Jared Henry, Weaver Ashley, Muralidharan Ritvik, Noble Joshua, Chung Taejung, Kovac Jasna, Liu Zhiwen, Ebrahimi Aida