Abstract 15318: A Dene- and Drug-Eluting Peripheral Stent Using a Baculovirus-Eluting PVA Hydrogel Coating
Bibliographic record
Abstract
Introduction: Over 20 % of people over 60 have peripheral artery disease (PAD), the narrowing of peripheral arteries due to atherosclerosis. More recent data suggests a surge in PAD in younger people (affecting up to 4 % of people). Atherosclerosis is complex, initially involving fat deposition and endothelial cell dysfunction, leading to inflammation, hyperproliferation of cells, plaque development, and calcification. This can lead to significant pain, discomfort, limb ischemia, stroke, and increases the risk of major cardiovascular events such as myocardial infarction and coronary artery disease. Often a bare-metal or drug-eluting stent will force the artery open. These stents are highly effective but come with complications such as restenosis, thrombosis, and enhanced endothelial dysfunction leading to patency as low as 60 % one-year post-implantation. Hypothesis: A gene- and drug-eluting hydrogel stent coating can promote endothelial recovery while the long-term drug elution prevents restenosis and inflammation, improving the clinical outcome of PAD (Figure 1). Methods & results: The gene encoding nitric oxide had been cloned into a baculovirus, a highly efficient and safe gene delivery vector to arterial cells. Nitric oxide inhibits smooth muscle cell proliferation, promotes endothelial cell proliferation, promotes angiogenesis, and prevents reactive oxygen species production within the diseased artery. The 10-day gene elution has the potential to leave behind a healthy endothelial lining, and the 100-day Everolimus drug elution can prevent restenosis and inflammation. Moreover, the gene- and drug-eluting stent is stable during crimping and expansion, has a high tensile strength, is non-cytotoxic, and is anti-thrombotic by preventing platelet adhesion. Conclusion: Overall, the novel stent exhibits beneficial mechanical and cellular properties with the potential to mitigate current stenting complications using dual gene and drug 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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".