Evidence map›Paper›PMID 40832394›Full record

ArticlemedRxiv : the preprint server for health sciences2025

BADLOCK: A Rapid, Portable, Inexpensive Diagnostic for Bacterial Pathogen and Resistance Detection in Resource-Limited Settings.

David J Roach, Bhrus Pun Sangruji, Shriya Bhat, Selama Tesfamariam, Ishay Ben-Zion, Miriam Bern, Josephine Bagnall, Noam Shoresh, Lisa Milien, Roby P Bhattacharyya

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026
    Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

David J RoachDivision of Infectious Diseases, Brigham and Women's Hospital, Boston, MA.
Bhrus Pun SangrujiTufts University, Boston, MA.
Shriya BhatHarvard University, Boston, MA.
Selama TesfamariamKaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA.
Ishay Ben-ZionInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Boston, MA.
Miriam BernInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Boston, MA.
Josephine BagnallInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Boston, MA.
Noam ShoreshInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Boston, MA.
Lisa MilienDivision of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA.
Roby P BhattacharyyaInfectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Boston, MA.ORCID 0000-0001-6955-5088

Funding

Antibacterial Resistance Leadership Group (ARLG)UM1AI104681 · NIAID · DUKE UNIVERSITY · PI Vance G. Fowler, Henry F HENRY CHAMBERS · 2013 to 2026
$180.8M
INFECTIOUS DISEASE AND BASIC MICROBIOLOGICAL MECHANISMST32AI007061 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI Marcia B Goldberg · 1985 to 2026
$12.1M
NIAID NIH HHS T32 AI007061NIAID NIH HHS UM1 AI104681
6 · The paper itself

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 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 BADLOCK's potential as a practical and scalable solution for rapid pathogen and resistance gene detection in resource-constrained settings.

Identifiers

PMID40832394
PMCPMC12363746

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.