Matrix-Binding, N-Cadherin–Targeting Chimeric Peptide Inhibits Intimal Thickening but Not Endothelial Repair in Balloon-Injured Carotid Arteries
Bibliographic record
Abstract
BACKGROUND: Treatment of occluded vessels can involve angioplasty, stenting, and bypass grafting, which can be limited by restenosis and thrombosis. Drug-eluting stents attenuate restenosis, but the current drugs used are cytotoxic, causing smooth muscle cell (SMC) and endothelial cell (EC) death that may lead to late thrombosis. N-cadherin is a junctional protein expressed by SMCs, which promotes directional SMC migration contributing to restenosis. We propose that engaging N-cadherin with mimetic peptides can act as a cell type-selective therapeutic strategy to inhibit polarization and directional migration of SMCs without negatively impacting ECs. METHODS: . This peptide was tested in SMC and EC culture assays of migration, viability, and apoptosis. Rat carotid arteries were balloon injured and treated with the N-cadherin peptide. RESULTS: Treating scratch-wounded SMCs with the N-cadherin-targeting peptide inhibited migration and reduced polarization of wound-edge cells. The peptide colocalized with fibronectin. Importantly, EC junction, permeability, or migration was not impacted by peptide treatment in vitro. We also demonstrated that the chimeric peptide persisted for 24 hours after transient delivery in the balloon-injured rat carotid artery. Treatment with the N-cadherin-targeting chimeric peptide reduced intimal thickening in balloon-injured rat carotid arteries at 1 and 2 weeks after injury. Reendothelialization of injured vessels after 2 weeks was unimpaired by peptide treatment. CONCLUSIONS: These studies show that an N-cadherin-binding and fibronectin-binding chimeric peptide is effective in inhibiting SMC migration in vitro and in vivo and limiting neointimal hyperplasia after balloon angioplasty without affecting EC repair. These results establish the potential of an advantageous SMC-selective strategy for antirestenosis therapy.
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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 | 0.001 |
| 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".