Impact of Stent Surface on Thrombogenicity and Vascular Healing
Bibliographic record
Abstract
BACKGROUND: Emerging drug-eluting stent technologies are evolving toward the elimination of polymeric component used as the method for modulating drug delivery. Although this technological approach seems to be biologically appealing, the impact of durable polymers and metallic stent surfaces on vascular healing remains unclear. In the present study, we aimed to compare the independent effect of a durable polymer and a metallic stent surface on thrombogenicity and endothelial cell coverage using different in vitro and in vivo experimental models. METHODS AND RESULTS: Platinum chromium (PtCr) and polyvinylidene fluoride-co-hexafluoropropene (PVDF-HFP)-coated surfaces were evaluated in this study. Thrombogenicity was assessed by exposing all surfaces to human blood under shear flow conditions. The inflammatory potential of the material was evaluated by measuring cytokine release from THP-1 cells exposed to all surfaces for 24 hours. Endothelial cell coverage was evaluated by detection of CD31 after the stents were exposed to human coronary artery endothelial cells for ≤ 14 days. Platelet adhesion (P<0.01) and activation (P=0.03) on PVDF-HFP were greater than on PtCr. In vivo, PVDF-HFP revealed more neointimal area (P<0.01) and residual parastrut fibrin (P=0.01) at 30 days compared with PtCr. PtCr displayed higher endothelialization rates and higher vascular endothelial-cadherin expression at 7 and 14 days (P=0.02) compared with PVDF-HFP. CONCLUSIONS: Thrombogenicity and vascular healing differ among metallic and polymeric stent surfaces. PVDF-HFP exhibits higher degrees of platelet activation-adhesion and thrombus accumulation in vivo compared with PtCr. PtCr displayed higher degrees of endothelial surface coverage compared with PVDF-HFP surfaces.
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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 | 0.001 |
| 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) | 0.002 | 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".