Evidence map›Paper›PMID 31872964›Full record

ArticleProteomics. Clinical applications2020

Detection of Antimicrobial Resistance Using Proteomics and the Comprehensive Antibiotic Resistance Database: A Case Study.

Chih-Yu Chen, Clifford G Clark, Stacie Langner, David A Boyd, Amrita Bharat, Stuart J McCorrister, Andrew G McArthur, Morag R Graham, Garrett R Westmacott, Gary Van Domselaar

Open access · hybridAbstract read
In one paragraph

Article in Proteomics. Clinical applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.7field-weighted citation impact, top 8% of its field
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

20 citing papers in PubMed, 43 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Detection of Genes Related to Antibiotic Resistance inTropical medicine and infectious disease · 2024
    Article
  8. Article
  9. Article
  10. Alternate Antimicrobial Therapies and Their Companion Tests.Diagnostics (Basel, Switzerland) · 2023
    Review
  11. Review
  12. Article
  13. Genomic insight intoFrontiers in veterinary science · 2023
    Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Article
  20. WGS-Based Prediction and Analysis of Antimicrobial Resistance inFrontiers in cellular and infection microbiology · 2020
    Article
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

10 authors at 2 institutions in 1 country.

Chih-Yu ChenNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.ORCID 0000-0003-4790-0025
Clifford G ClarkNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
Stacie LangnerNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
David A BoydNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
Amrita BharatNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
Stuart J McCorristerNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
Andrew G McArthurM. G. DeGroote Institute for Infectious Disease Research, Department of Biochemistry and Biomedical Sciences, DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada.
Morag R GrahamNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
Garrett R WestmacottNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.
Gary Van DomselaarNational Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada.ORCID 0000-0003-1139-4458
Public Health Agency of Canada · CAMcMaster University · CA

Funding

CIHR PJT-156214
6 · The paper itself

Abstract

purposeAntimicrobial resistance (AMR), especially multidrug resistance, is one of the most serious global threats facing public health. The authors proof-of-concept study assessing the suitability of shotgun proteomics as an additional approach to whole-genome sequencing (WGS) for detecting AMR determinants. EXPERIMENTAL

designPreviously published shotgun proteomics and WGS data on four isolates of Campylobacter jejuni are used to perform AMR detection by searching the Comprehensive Antibiotic Resistance Database, and their detection ability relative to genomics screening and traditional phenotypic testing measured by minimum inhibitory concentration is assessed.

resultsBoth genomic and proteomic approaches identify the wild-type and variant molecular determinants responsible for resistance to tetracycline and ciprofloxacin, in agreement with phenotypic testing. In contrast, the genomic method identifies the presence of the β-lactamase gene, bla

Indexed as

Anti-Bacterial AgentsCampylobacter jejuniCiprofloxacinDrug Resistance, BacterialMicrobial Sensitivity TestsProteomicsTetracyclineWhole Genome SequencingAnti-Bacterial AgentsCiprofloxacinTetracyclineantimicrobial resistanceCampylobacter jejuniComprehensive Antibiotic Resistance Databaseshotgun proteomicswhole genome sequencing

Identifiers

PMID31872964
PMCPMC7378939
OpenAlexW2998366577

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.