Injury-induced expression of cytokeratins 8 and 18 by vascular smooth muscle cells requires concurrent activation of cytoskeletal and growth factor receptors
Bibliographic record
Abstract
Cytokeratins are not present in the vascular smooth muscle cells (VSMCs) of normal arteries, but they are detectable in the VSMCs of atherosclerotic lesions. A correlation between cytokeratin expression and VSMC phenotype is proposed, but an examination of VSMCs after mechanical injury has yet to be performed. Immunohistochemistry was used to monitor proteins in arterial sections. Western blotting enabled quantification of protein levels. Angioplasty of porcine femoral artery in vivo and porcine coronary artery in vitro served as models of vascular injury. Cytokeratins 8 and 18 were expressed by VSMCs in porcine femoral artery lesions 14 days after balloon angioplasty. Cytokeratins were also present in the neointima of porcine coronary artery segments placed into organ culture for 4 days. Cytokeratin expression was decreased in the presence of inhibitors that affect MAP kinase, PI3 kinase, Src kinase, and G protein, but not in the presence of an AT1 receptor antagonist. Cytokeratin expression also occurred when VSMCs were plated onto collagen in the presence of serum. We conclude that mechanical injury induces expression of cytokeratin 8 and 18 both in vitro and in vivo by synthetic VSMCs that migrate into the neointima. Furthermore, cytokeratin expression requires cellular attachment to extracellular matrix proteins in conjunction with mitogenic stimulation.
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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) | 0.000 | 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".