Role of p44/p42 MAP Kinase in the Age-Dependent Increase in Vascular Smooth Muscle Cell Proliferation and Neointimal Formation
Bibliographic record
Abstract
OBJECTIVE: Age-dependent increase in vascular smooth muscle cell (VSMC) proliferation is thought to contribute to the pathology of atherosclerotic diseases. In this study, we investigated the role of mitogen-activated protein kinases (MAPKs) on VSMC proliferation and neointimal formation in the context of aging. METHODS AND RESULTS: VSMCs were isolated from the aorta of young and old rabbits. The proliferative index after serum stimulation was significantly increased in old versus young VSMCs. This was associated with a significant and specific age-dependent increase in p44/p42 MAPK activation. Treatment with MEK inhibitor PD98059 successfully inhibited p44/p42 MAPK activities and VSMC proliferation. These results were confirmed in vivo using a model of balloon injury in rabbit iliac arteries. p44/p42 MAPK activities were rapidly induced by angioplasty in young and old animals. However, the levels of p44/p42 MAPK activities achieved in arteries of old rabbits were significantly higher than those of young rabbits. This was associated with a higher cellular proliferative index and a significant increase in neointimal formation in old animals. Local delivery of PD98059 in old rabbits successfully inhibited p44/p42 MAPK activities after angioplasty, which led to a significant reduction in cellular proliferation and neointimal formation in treated animals. CONCLUSIONS: Our study suggests for the first time that increased p44/p42 MAPK activation contributes to augmented VSMC proliferation and neointimal formation with aging. p44/p42 MAPK inhibition could represent a novel therapeutic avenue against atherosclerotic diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".