VSMCs in atherosclerosis: Implications on the role of inflammation and extracellular matrix remodelling
Bibliographic record
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of mortality and morbidity worldwide. Lipid-lowering drugs, such as statins and proprotein convertase subtilisin/kexin type 9 inhibitors, are effective in reducing plasma low-density lipoprotein cholesterol levels and the risk of ASCVD. However, the residual risk of ASCVD remains very high. Therefore, new strategies to treat ASCVD are urgently needed. Vascular smooth muscle cells (VSMCs) are essential contributors to atherosclerosis development and progression, with more than 50 % of atherosclerotic foam cells originating from VSMCs. VSMCs are characterized by their plasticity and ability to switch phenotype in response to the changing environment of atherosclerotic lesions, starting from the early stage of intimal thickening to the most advanced atherosclerotic lesions. However, VSMCs do not act independently, they interact with neighbouring cells and respond to the surrounding growth factors and cytokines by modulating their protein expression and changing their phenotype. Therefore, the main functions of VSMCs in atherosclerosis will be influenced, including the production of extracellular matrix (ECM) proteins and the maintenance of atherosclerotic plaque stability. In this review, we summarize the current understanding of VSMCs in atherosclerosis, focusing on their origin, plasticity, phenotype switching, and role at different stages of atherosclerosis. Furthermore, we highlight the influence of growth factors and cytokines on VSMC behaviour in atherosclerosis and discuss the role of ECM remodelling, specifically by integrins and matrix metalloproteinases, on VSMCs in atherosclerosis. Finally, we focus on current therapeutic strategies and options to target VSMCs in atherosclerosis management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".