Evidence map›Paper›PMID 34621252›Full record

ReviewFrontiers in microbiology2021

A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions.

Kathryn M Styles, Aidan T Brown, Antonia P Sagona

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
4.5field-weighted citation impact, top 5% 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, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Bacteriophage density influences the rate of resistance evolution.bioRxiv : the preprint server for biology · 2025
    Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Mathematical Model of Intrinsic Drug Resistance in Lung Cancer.International journal of molecular sciences · 2023
    Article
  13. Automating Predictive Phage Therapy Pharmacology.Antibiotics (Basel, Switzerland) · 2023
    Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. 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

3 authors at 2 institutions in 1 country.

Kathryn M StylesSchool of Life Sciences, University of Warwick, Coventry, United Kingdom.
Aidan T BrownSchool of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom.
Antonia P SagonaSchool of Life Sciences, University of Warwick, Coventry, United Kingdom.
University of Warwick · GBUniversity of Edinburgh · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the 'post antibiotic era.' Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in

Indexed as

antibiotic alternativebacteriophagecommunicable diseaseheterogeneitymathematical modellingphage therapysimulationsstochasticity

Identifiers

PMID34621252
PMCPMC8490754
OpenAlexW3199361650

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.