Evidence map›Paper›PMID 32825096›Full record

ReviewToxins2020

Interaction of Macrophages and Cholesterol-Dependent Cytolysins: The Impact on Immune Response and Cellular Survival.

Roshan Thapa, Sucharit Ray, Peter A Keyel

Open access · goldAbstract readReview
In one paragraph

Review in Toxins, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

16 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Mechanistic Landscape and Farm to Fork Control Strategies forTransboundary and emerging diseases · 2026
    Review
  6. Article
  7. Article
  8. Review
  9. Article
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  11. Review
  12. Review
  13. Article
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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 1 institution in 1 country.

Roshan ThapaDepartment of Biological Sciences, Texas Tech University, Lubbock, TX 79409, USA.
Sucharit RayDepartment of Biological Sciences, Texas Tech University, Lubbock, TX 79409, USA.
Peter A KeyelDepartment of Biological Sciences, Texas Tech University, Lubbock, TX 79409, USA.ORCID 0000-0001-8637-8538
Texas Tech University · US

Funding

American Heart Association 16SDG30200001
6 · The paper itself

Abstract

Cholesterol-dependent cytolysins (CDCs) are key virulence factors involved in many lethal bacterial infections, including pneumonia, necrotizing soft tissue infections, bacterial meningitis, and miscarriage. Host responses to these diseases involve myeloid cells, especially macrophages. Macrophages use several systems to detect and respond to cholesterol-dependent cytolysins, including membrane repair, mitogen-activated protein (MAP) kinase signaling, phagocytosis, cytokine production, and activation of the adaptive immune system. However, CDCs also promote immune evasion by silencing and/or destroying myeloid cells. While there are many common themes between the various CDCs, each CDC also possesses specific features to optimally benefit the pathogen producing it. This review highlights host responses to CDC pathogenesis with a focus on macrophages. Due to their robust plasticity, macrophages play key roles in the outcome of bacterial infections. Understanding the unique features and differences within the common theme of CDCs bolsters new tools for research and therapy.

Indexed as

AnimalsCell MembraneCell SurvivalCholesterolCytotoxinsHumansImmunityMacrophagesCholesterolCytotoxinsClostridium perfringensperfringolysin Opore-forming toxinStreptococcus pyogenesstreptolysin O

Identifiers

PMID32825096
PMCPMC7551085
OpenAlexW3081187716

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.