Evidence map›Paper›PMID 36361754›Full record

ReviewInternational journal of molecular sciences2022

Non-Mouse Models of Atherosclerosis: Approaches to Exploring the Translational Potential of New Therapies.

Danielle Kamato, Iqra Ilyas, Suowen Xu, Peter J Little

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed, 13 citations in OpenAlex.

  1. Frontiers in microbiology · 2026
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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

4 authors at 3 institutions in 2 countries.

Danielle KamatoDiscovery Biology, Griffith Institute for Drug Discovery, School of Environment and Science, Griffith University, Brisbane, QLD 4111, Australia.ORCID 0000-0002-1089-2829
Iqra IlyasLaboratory of Metabolics and Cardiovascular Diseases, University of Science and Technology of China, Hefei 230027, China.ORCID 0000-0002-0800-9590
Suowen XuLaboratory of Metabolics and Cardiovascular Diseases, University of Science and Technology of China, Hefei 230027, China.ORCID 0000-0002-5488-5217
Peter J LittlePharmacy Australia Centre of Excellence, School of Pharmacy, University of Queensland, Woolloongabba, QLD 4102, Australia.ORCID 0000-0002-0335-3835
University of Science and Technology of China · CNGriffith University · AUThe University of Queensland · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease is the largest single cause of disease-related mortality worldwide and the major underlying pathology is atherosclerosis. Atherosclerosis develops as a complex process of vascular lipid deposition and retention by modified proteoglycans, endothelial dysfunction and unresolved chronic inflammation. There are a multitude of current therapeutic agents, most based on lowering plasma lipid levels, but, overall, they have a lower than optimum level of efficacy and many deaths continue to arise from cardiovascular disease world-wide. To identify and evaluate potential novel cardiovascular drugs, suitable animal models that reproduce human atherosclerosis with a high degree of fidelity are required as essential pre-clinical research tools. Commonly used animal models of atherosclerosis include mice (ApoE

Indexed as

AtherosclerosisCardiovascular DiseasesPlaque, AtheroscleroticAnimalsDisease Models, AnimalHumansRabbitsRatsSwineZebrafishanimal modelsdisease mechanismkey atherosclerosispathologytherapeutics

Identifiers

PMID36361754
PMCPMC9656683
OpenAlexW4307301589

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.