Researchers at the Wyss Institute developed dSHERLOCK, a digital CRISPR‑based diagnostic that quantifies Candida auris load and resistance mutations from swabs in real time. The platform integrates SHERLOCK chemistry with single‑molecule microarrays and AI‑driven signal analysis to provide rapid, quantitative detection and allele‑level resistance profiling. Published in Nature Biomedical Engineering, the method aims to give hospitals a fast, low‑infrastructure tool to both detect outbreaks and measure antifungal resistance heterogeneity in mixed populations—capabilities public health labs and infection control teams have identified as urgent for Candida auris containment.