Evidence map›Paper›PMID 42088273›Full record

ArticleFrontiers in microbiology2026

BCS2.0: a capture sequencing platform for rapid differential diagnosis of bacterial infections and antimicrobial resistance.

Amit Ranjan, Cheng Guo, Thomas Briese, William Donovan, Vishal Kapoor, Alamelu Chandrasekaran, Mahesh M Mansukhani, Gregory J Berry, W Ian Lipkin

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Amit Ranjan *Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, United States.
Cheng Guo *Center for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, United States.
Thomas BrieseCenter for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, United States.
William DonovanCenter for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, United States.
Vishal KapoorCenter for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, United States.
Alamelu ChandrasekaranDepartment of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.
Mahesh M MansukhaniDepartment of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.
Gregory J BerryDepartment of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, United States.
W Ian LipkinCenter for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The "Golden Hour" is the period immediately after trauma, stroke or a cardiac event when rapid intervention is critical to reducing morbidity and mortality. The same principle should also apply to infectious diseases. Rapid, sensitive detection of infectious agents, enabling targeted interventions, has the potential to reduce mortality, morbidity, and costs of infectious diseases, and to decrease the inappropriate use of antibiotics that drives the evolution of antimicrobial resistance (AMR). Methods: Probes were designed to represent the MetaPhlAn4 database covering 894 known or potential pathogenic bacterial species, 16S rRNA sequences from the SILVA database comprising 1,325 potentially pathogenic bacterial species, genes in the Virulence Factor Database and antimicrobial resistance determinants in the Comprehensive Antibiotic Resistance Database. Target sequences were tiled with 120 nucleotide probes distributed at 60 nt intervals and clustered at 99% sequence identity. Performance measures included limits of detection (LOD) and assay reproducibility in plasma and urine using contrived and clinical samples. Results: Analytical validation using 20 bacterial strains in contrived plasma and urine samples confirmed an LOD of 5 colony-forming units per milliliter and detection of mixed infections. Results obtained with urine and blood cultures were concordant with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI), and Blood Culture Identification (BCID) assays. Discussion: BCS2.0 enables sensitive detection of bacterial species and AMR genes and has the potential to expedite rapid, efficient infectious disease management.

Indexed as

antimicrobial resistancebacteriacapture sequencingdiagnosticsinfectious disease

Identifiers

PMID42088273
PMCPMC13136086

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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.