Kristin Hegstad

This paper uses machine learning to predict the prevalence of ST131-C in UTIs and BSIs using antimicrobial susceptibility data, finding a link between UTI prevalence and BSI burden.

The paper introduces CPPT, a method to profile and trace phenotypic subpopulations in isogenic bacterial communities, revealing clinically relevant biomarkers for antibiotic resistance.

This study uses ATP-interacting protein profiling to analyze S. aureus-host interactions in macrophages and keratinocytes, revealing cell-type specific adaptations and metabolic shifts.

This study examines TCSs in S. aureus USA300, identifying virulence regulatory pathways and specificity determinants through mutant analysis and structural modeling.

The paper introduces a pipeline for genomic epidemiology using mixed samples, showing comparable results to colony picks in a nosocomial setting.