Evidence map›Paper›PMID 41963727›Full record

ArticleBulletin of mathematical biology2026

A Mathematical Model for Smooth Muscle Cell Phenotype Switching In Atherosclerotic Plaque.

Joseph P Ndenda, Michael G Watson, Ashish Misra, Mary R Myerscough

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Joseph P NdendaSchool of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia.
Michael G WatsonSchool of Mathematics and Statistics, University of New South Wales, Sydney, NSW, 2052, Australia. michael.watson1@unsw.edu.au.ORCID http://orcid.org/0000-0002-1938-0440
Ashish MisraHeart Research Institute, 7 Eliza Street, Newtown, NSW, 2042, Australia.
Mary R MyerscoughSchool of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia.

Funding

Australian Research Council DP200102071
6 · The paper itself

Abstract

Smooth muscle cells (SMCs) play a fundamental role in the development of atherosclerotic plaques. They ingest lipids in a similar way to monocyte-derived macrophages (MDMs) in the plaque. This can stimulate SMCs to undergo a phenotypic switch to a macrophage-like phenotype. We formulate an ordinary differential equation (ODE) model for the populations of SMCs, MDMs and smooth muscle cell-derived macrophages (SDMs) and the internalised lipid load in each population. We use this model to explore the effect on plaque fate of SMC phenotype switching. We find that when SMCs switch to a macrophage-like phenotype, there is an increase in the lipid quantity in the model plaque that is internalised inside cells. Additionally, removal of SMCs from the model plaque via phenotype switching reduces the number of SMCs in the fibrous cap, increases the lipid in the necrotic core, and increases plaque inflammation. These features are hallmarks of vulnerable plaques, whose rupture can cause heart attacks or strokes. When SDMs are highly proliferative or resistant to cell death, the model plaque becomes increasingly pathological. The model suggests that the switch of SMCs to a macrophage-like phenotype may drive the development of unstable and pathological plaques.

Indexed as

Models, CardiovascularMyocytes, Smooth MusclePlaque, AtheroscleroticAnimalsAtherosclerosisComputer SimulationHumansLipid MetabolismMacrophagesMathematical ConceptsPhenotypeAtherosclerosisOrdinary differential equation modelPhenotype switchSmooth muscle cell

Identifiers

PMID41963727
PMCPMC13068761

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Registered trials

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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.