Journal
TRENDS IN MICROBIOLOGY
Volume 30, Issue 7, Pages 612-614Publisher
CELL PRESS
DOI: 10.1016/j.tim.2022.05.006
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Through whole-genome sequencing and machine-learning analysis, researchers discovered that recurrent infections are often caused by different strains and developed a personalized algorithm to reduce antimicrobial resistance at the individual-patient level.
Despite susceptibility testing, recurrent infections are common and are associated with resistance. Using whole-genome sequencing, Stracy et al. demonstrated that recurrence is often driven by a different strain than the original infection. By machine-learning analysis, they developed an algorithm for patient-specific recommendations to minimize antimicrobial resistance AMR) at the individual-patient level.
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