Evidence map›Paper›PMID 32983077›Full record

ArticleFrontiers in microbiology2020

Predicting Antibiotic-Associated Virulence of

Marwa M Hassan, Niamh E Harrington, Esther Sweeney, Freya Harrison

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

12 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Recreating chronic respiratory infectionsEuropean respiratory review : an official journal of the European Respiratory Society · 2024
    Review
  4. Article
  5. Article
  6. ComparativeAIMS microbiology · 2023
    Article
  7. The biogeography of infection revisited.Nature reviews. Microbiology · 2022
    Review
  8. Article
  9. Article
  10. Speciality Grand Challenge for "Biofilms".Frontiers in cellular and infection microbiology · 2021
    Article
  11. Article
  12. Predicting Antibiotic-Associated Virulence ofFrontiers in 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

4 authors at 2 institutions in 1 country.

Marwa M HassanSchool of Life Sciences, University of Warwick, Coventry, United Kingdom.
Niamh E HarringtonSchool of Life Sciences, University of Warwick, Coventry, United Kingdom.
Esther SweeneySchool of Life Sciences, University of Warwick, Coventry, United Kingdom.
Freya HarrisonSchool of Life Sciences, University of Warwick, Coventry, United Kingdom.
University of Warwick · GBUniversity of Surrey · GB

Funding

Medical Research Council MR/R001898/1
6 · The paper itself

Abstract

backgroundBacterial biofilms are known to have high antibiotic tolerance which directly affects clearance of bacterial infections in people with cystic fibrosis (CF). Current antibiotic susceptibility testing methods are either based on planktonic cells or do not reflect the complexity of biofilms

methodsIn this study, we use an

resultsThe results demonstrated minimal effect on the bacterial load with therapeutically relevant concentrations of ciprofloxacin and meropenem, with the latter causing an increased production of proteases and pyocyanin. A combination of meropenem and tobramycin did not show any additional decrease in bacterial load but demonstrated a slight decrease in total proteases and pyocyanin production.

conclusionIn this initial study of six clinical isolates of

Indexed as

antibiotic susceptibility testingantibiotic toleranceantimicrobial resistancebacterial biofilmcystic fibrosis

Identifiers

PMID32983077
PMCPMC7492588
OpenAlexW3082250550

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.