Evidence map›Paper›PMID 31575664›Full record

ArticlemSystems2019

Lying in Wait: Modeling the Control of Bacterial Infections via Antibiotic-Induced Proviruses.

Sara M Clifton, Ted Kim, Jayadevi H Chandrashekhar, George A O'Toole, Zoi Rapti, Rachel J Whitaker

Open access · goldAbstract read
In one paragraph

Article in mSystems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

3 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

6 authors at 2 institutions in 1 country.

Sara M CliftonDepartment of Mathematics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA smc567@illinois.edu.ORCID https://orcid.org/0000-0001-5949-068X
Ted KimDepartment of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Jayadevi H ChandrashekharDepartment of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
George A O'TooleDepartment of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.
Zoi RaptiDepartment of Mathematics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Rachel J WhitakerDepartment of Microbiology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
University of Illinois Urbana-Champaign · USDartmouth College · US

Funding

cdiGMP regulation of Biofilm FormationR37AI083256 · NIAID · DARTMOUTH COLLEGE · PI O'TOOLE, GEORGE A. · 2014 to 2023
$4.7M
NIAID NIH HHS R37 AI083256
6 · The paper itself

Abstract

Most bacteria and archaea are infected by latent viruses that change their physiology and responses to environmental stress. We use a population model of the bacterium-phage relationship to examine the role that latent phage play in the bacterial population over time in response to antibiotic treatment. We demonstrate that the stress induced by antibiotic administration, even if bacteria are resistant to killing by antibiotics, is sufficient to control the infection under certain conditions. This work expands the breadth of understanding of phage-antibiotic synergy to include both temperate and chronic viruses persisting in their latent form in bacterial populations.

Indexed as

antibiotic resistancebacteriabacteriophagechroniccystic fibrosislatentlatent infectionlysogeniclyticmathematical modelmathematical modelingphagepopulation dynamicsPseudomonas aeruginosaresistancetemperate

Identifiers

PMID31575664
PMCPMC6774016
OpenAlexW2978060396

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.