BADLOCK: A Rapid, Portable, Inexpensive Diagnostic for Bacterial Pathogen and Resistance Detection in Resource-Limited Settings
Roach, D. J.; Sangruji, B. P.; Bhat, S.; Tesfamariam, S.; Ben-Zion, I.; Bern, M.; Bagnall, J.; Shoresh, N.; Milien, L.; Bhattacharyya, R. P.
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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 supports both fluorescence and paper-based lateral flow readouts. We validated BADLOCK on a prospectively collected clinical cohort of 194 blood culture specimens, supplemented with 69 mock samples generated from banked isolates enriched for targeted resistance genes. Across all cohorts, we conducted 2,224 individual reactions, achieving 97.6% accuracy (2,171/2,224) at the reaction level. At the assay level, 89.5% (274/306) 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, 90.7% (41/43) were perfectly concordant with gold-standard detection of both species and resistance genes, with 2 additional assays showing partial concordance. To our knowledge, this represents the first demonstration of the CRISPR-Cas13a diagnostic platform on clinical bloodstream infections to date and supports BADLOCKs potential as a practical and scalable solution for rapid pathogen and resistance gene detection in resource-constrained settings.
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