Evidence map›Paper›PMID 29219107›Full record

ReviewMicrobiology spectrum2017

Molecular Methods for Detection of Antimicrobial Resistance.

Muna F Anjum, Ea Zankari, Henrik Hasman

Abstract readReview
In one paragraph

Review in Microbiology spectrum, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.

0numbers the graph read from it
0cells of the map it votes in
83citing 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

83 citing papers in PubMed.

  1. Article
  2. Review
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  7. Review
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  9. Review
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  11. Polymerase Chain Reaction-Lateral Flow Strip for DetectingAntibiotics (Basel, Switzerland) · 2025
    Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
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  18. Review
  19. Article
  20. Review

23 more citing papers are in PubMed but not listed here.

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.

Muna F AnjumDepartment of Bacteriology, Animal and Plant Health Agency, Surrey, United Kingdom.
Ea ZankariNational Food Institute, Technical University of Denmark, Lyngby, Denmark.
Henrik HasmanNational Food Institute, Technical University of Denmark, Lyngby, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increase in bacteria harboring antimicrobial resistance (AMR) is a global problem because there is a paucity of antibiotics available to treat multidrug-resistant bacterial infections in humans and animals. Detection of AMR present in bacteria that may pose a threat to veterinary and public health is routinely performed using standardized phenotypic methods. Molecular methods are often used in addition to phenotypic methods but are set to replace them in many laboratories due to the greater speed and accuracy they provide in detecting the underlying genetic mechanism(s) for AMR. In this article we describe some of the common molecular methods currently used for detection of AMR genes. These include PCR, DNA microarray, whole-genome sequencing and metagenomics, and matrix-assisted laser desorption ionization-time of flight mass spectrometry. The strengths and weaknesses of these methods are discussed, especially in the context of implementing them for routine surveillance activities on a global scale for mitigating the risk posed by AMR worldwide. Based on current popularity and ease of use, PCR and single-isolate whole-genome sequencing seem irreplaceable.

Indexed as

Genetic TechniquesAnimalsAnti-Bacterial AgentsBacteriaBacteriological TechniquesDNA, BacterialDrug Resistance, BacterialDrug Resistance, Multiple, BacterialHumansMetagenomicsOligonucleotide Array Sequence AnalysisPolymerase Chain ReactionSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationWhole Genome SequencingAnti-Bacterial AgentsDNA, Bacterial

Identifiers

PMID29219107
PMCPMC11687549

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.