N-Cadherin Upregulation and Function in Response of Smooth Muscle Cells to Arterial Injury
Bibliographic record
Abstract
OBJECTIVE: Smooth muscle cell migration is critical to neointimal formation after arterial injury. The purpose of this study was to elucidate the regulation and functional significance of cell-cell adhesion via adherens junctions during this process. METHODS AND RESULTS: Using balloon catheter injury of rat carotid artery, we showed that neointimal formation is accompanied by dramatic but transient upregulation of intimal N-cadherin and associated catenins, proteins that mediate adhesion at adherens junctions. Upregulation was demonstrated by immunofluorescence microscopy and by immunoblotting, and it coincided with evidence of phenotypic modulation of smooth muscle cells. Similar upregulation was observed when postconfluent cultures of porcine aortic smooth muscle cells were subjected to linear denuding injuries. Furthermore, treatment of wounded cultures with a blocking antibody against the extracellular domain of the N-cadherin protein significantly suppressed the repair of wounds. CONCLUSIONS: N-cadherin and associated proteins are dynamically regulated during neointimal formation and provide evidence that this regulation is important for migratory repair. Therefore, N-cadherin may provide a novel target for therapies that are directed toward intimal proliferative disorders, including restenosis and vascular bypass graft failure.
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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".