ArticleScience advances2026
A rapid, inexpensive diagnostic for bacterial pathogen and resistance detection in resource-limited settings.
Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Antimicrobial resistance is a major global health threat, with disproportionate impact in regions with limited diagnostic infrastructure. To address this challenge, we developed BADLOCK (Bacterial and AMR Detection by SHERLOCK), a rapid, low-cost molecular diagnostic platform for direct detection of bacterial pathogens and resistance genes from clinical samples. BADLOCK operates as a one-pot CRISPR-Cas13a reaction capable of detecting nine bacterial species and four major resistance genes directly from positive blood culture. It requires only a heat block and a basic fluorometer for operation. We validated BADLOCK on 194 sequentially collected clinical blood culture specimens growing Gram-negative rods, supplemented with 69 mock samples generated from banked isolates enriched for targeted resistance genes. In all, we conducted 2224 individual reactions, achieving 97.6% accuracy (2171 of 2224) at the reaction level. At the assay level, 89.5% (274 of 306) showed perfect or partial concordance with gold-standard species and resistance gene detection, including 255 assays with perfect concordance and 19 with partial concordance (correct detection of at least one pathogen). This included an evaluation of BADLOCK as a potential culture-free diagnostic for urinary tract infections (UTIs), achieving 98.0% reaction-level accuracy. At the assay level, 95.3% (41 of 43) were partially or perfectly concordant with gold-standard detection of both species and resistance genes. To our knowledge, this represents the first demonstration of the CRISPR-Cas13a diagnostic platform on clinical bloodstream infections to date and supports BADLOCK's potential as a practical and scalable solution for rapid pathogen and resistance gene detection in resource-constrained settings.
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