Apolipoprotein A-I vascular gene therapy reduces vein-graft atherosclerosis
Bibliographic record
Abstract
Coronary artery venous bypass grafts typically fail because of atherosclerosis driven by lipid and macrophage accumulation. Therapy for vein-graft atherosclerosis is limited to statin drugs, which are only modestly effective. We hypothesized that transduction of vein-graft endothelium of fat-fed rabbits with a helper-dependent adenovirus expressing apolipoprotein AI (HDAdApoAI) would reduce lipid and macrophage accumulation. Fat-fed rabbits received bilateral external jugular vein-to-carotid artery interposition grafts. Four weeks later, one graft per rabbit (n = 23 rabbits) was infused with HDAdApoAI and the contralateral graft with HDAdNull. Grafts were harvested 12 weeks later. Paired analyses of grafts were performed, with vein graft cholesterol, intimal lipid, and macrophage content as the primary endpoints. HDAd genomes were detected in all grafts. APOAI mRNA was median 63-fold higher in HDAdApoAI grafts versus HDAdNull grafts (p < 0.001). HDAdApoAI grafts had a mean 15% lower total cholesterol (by mass spectrometry; p = 0.003); mean 19% lower intimal lipid (by oil red O staining; p = 0.02); and mean 13% lower expression of the macrophage marker CD68 (by reverse transcriptase-mediated quantitative PCR; p = 0.008). In vivo transduction of vein-graft endothelium achieves persistent APOAI expression and reduces vein-graft cholesterol, intimal lipid, and CD68 expression. Vascular gene therapy with APOAI has promise for preventing vein-graft failure caused by atherosclerosis.
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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".