A Wyss Institute team reported dSHERLOCK, a digital CRISPR‑based diagnostic that quantifies Candida auris and detects resistance mutations from swab samples with high sensitivity and speed. The approach couples SHERLOCK CRISPR chemistry with single‑molecule microarray readouts and machine learning to produce real‑time, quantitative signals. A related study described advanced CRISPR diagnostics for rapid resistance profiling in mixed fungal populations, offering clinicians a faster path to actionable antifungal susceptibility data. Both efforts respond to urgent public health calls for better tools amid hospital outbreaks of multidrug‑resistant C. auris. The platforms promise to improve outbreak control and patient management by shortening time to accurate detection and enabling targeted antifungal therapy decisions at the point of care.