A Wyss Institute team reported dSHERLOCK, a digital CRISPR-based assay that quantifies Candida auris load and resistance mutations from swab samples using single-molecule microarrays and a machine-learning readout. The method couples SHERLOCK CRISPR detection to parallel fluorescence microarrays and AI analysis to provide rapid, quantitative measurements of pathogen abundance and antifungal-resistance markers in mixed fungal populations. This digital approach addresses urgent clinical needs for fast, accurate detection and resistance profiling in outbreaks, enabling clinicians and infection-control teams to measure colonization and resistance with greater speed and precision than many existing methods.