Evidence map›Paper›PMID 41988391›Full record

ReviewInfection and drug resistance2026

Applications and Challenges of CRISPR-Cas Technology for the Detection of Antimicrobial Resistance Genes.

Wenting Tang, Ninglei Yang, Meijia Shi

Abstract readReview
In one paragraph

Review in Infection and drug resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

3 authors.

Wenting Tang *Department of Laboratory Medicine, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People's Republic of China.
Ninglei Yang *Department of Laboratory Medicine, Shanghai First Rehabilitation Hospital, Shanghai, People's Republic of China.
Meijia ShiDepartment of Laboratory Medicine, The Second Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global rise of antimicrobial resistance (AMR) demands rapid and precise diagnostic tools capable of providing actionable results at the point of care. Traditional methods, including bacterial culture and PCR, face critical limitations: culture-based approaches require days to weeks for results, potentially delaying life-saving treatment decisions, while PCR-based methods, though faster, require expensive instrumentation, trained personnel, and are typically confined to centralized laboratory settings. CRISPR-Cas systems offer a promising approach for detecting bacterial drug resistance genes with high specificity, speed, and sensitivity. By utilizing Cas enzymes (eg, Cas12 and Cas13) and guide RNAs, CRISPR enables targeted recognition of resistance markers, typically requiring upstream nucleic acid amplification (eg, recombinase polymerase amplification or loop-mediated isothermal amplification) to achieve clinically relevant sensitivity. Platforms such as SHERLOCK and DETECTR facilitate rapid identification of markers associated with pathogens like MRSA and CRE directly from clinical samples, supporting point-of-care use. Integration with microfluidics and AI further expands its potential for personalized therapy and AMR surveillance. Despite ethical and regulatory challenges, CRISPR-Cas technology holds significant promise for advancing antimicrobial stewardship and public health responses to AMR.

Indexed as

antimicrobial resistanceCRISPR-Cas systemsmolecular diagnosticspoint-of-care testingresistance gene markers

Identifiers

PMID41988391
PMCPMC13077583

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.