Effects of p53-knockout in vascular smooth muscle cells on atherosclerosis in mice
Bibliographic record
Abstract
In vitro and in vivo evidence has indicated that the tumor suppressor, p53, may play a significant role in the regulation of atherosclerotic plaque formation. In vivo studies using global knockout mice models, however, have generated inconclusive results that do not address the roles of p53 in various cell types involved in atherosclerosis. In this study, we have specifically ablated p53 in vascular smooth muscle cells (VSMC) in the ApoE-/- mouse model to investigate the roles of p53 in VSMC in atherosclerotic plaque formation and stability. We found that p53 deficiency in VSMC alone did not affect the overall size of atherosclerotic lesions. However, there was a significant increase in the number of p53-/- VSMC in the fibrous caps of atherosclerotic plaques in the early stages of plaque development. Loss of p53 results in migration of VSMC at a faster rate using wound healing assays and augments PDGF-induced formation of circular dorsal ruffles (CDR), known to be involved in cell migration and internalization of surface receptors. Furthermore, aortic VSMC from ApoE-/- /p53-/- mice produce significantly more podosomes and are more invasive. We conclude that p53-/- VSMC are enriched in the fibrous caps of lesions at early stages of plaque formation, which is caused in part by an increase in VSMC migration and invasion as shown by p53-/- VSMC in culture having significantly higher rates of migration and producing more CDRs and invasive podosomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".