Gene Transfer of the Serine Elastase Inhibitor Elafin Protects Against Vein Graft Degeneration
Bibliographic record
Abstract
Background —Leukocyte infiltration and serine elastase activity lead to smooth muscle cell proliferation in association with posttransplant coronary arteriopathy and may also be involved in vein graft neointimal formation. A number of therapies have targeted cellular proliferation, but the inhibition of serine elastase–mediated extracellular matrix remodeling has not been investigated as a potential strategy to prevent neointimal formation and subsequent atherosclerotic degeneration in vein grafts. Methods and Results —We studied jugular vein grafts 48 hours after interposition into the carotid arteries of rabbits and demonstrated inflammatory cell infiltration and elevated serine elastase activity, a stimulus for matrix remodeling and deposition of elastin. Therefore, elastolytic activity in vein grafts was targeted through transient expression of the selective serine elastase inhibitor elafin with hemagglutinating virus of Japan liposome–mediated gene transfer. Elafin transfection reduced inflammation by 60% at 48 hours and neointimal formation by ≈50% at 4 weeks after implantation. At 3 months, a 74% decrease in neointimal elastin deposition correlated with protection against cholesterol-induced macrophage infiltration and lipid accumulation, which were both reduced by ≈50% in elafin-transfected grafts relative to controls. Conclusions —Gene transfer of the selective serine elastase inhibitor elafin in vein grafts is effective in reducing the early inflammatory response. Although transient expression of elafin delays neointimal formation, it is also sufficient to cause an alteration in elastin content of the extracellular matrix, making it relatively resistant to atherosclerotic degeneration.
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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 | 0.001 |
| 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".