Local Delivery of Therapeutics for Percutaneous Coronary Intervention
Bibliographic record
Abstract
Percutaneous coronary intervention (PCI) has become a highly effective alternative for the treatment of coronary artery disease. The use of stents has reduced the rates of restenosis by preventing elastic recoil and negative remodeling, however neointima formation still remains an issue. Local drug delivery is an attractive option to maintain effective drug concentrations at the site of arterial injury without risking systemic toxicity. Drug-eluting stents (DESs) are implanted to provide local drug delivery to combat neointima formation by slowing cell proliferation and migration. However, problems still remain with DES use including the non-specificity of therapeutics, incomplete endothelialization leading to late thrombosis, necessity for longer term anti-platelet drug use, and local hypersensitivity to polymer delivery matrices. This review describes recent advances in local drug delivery for the prevention of restenosis. Many different drug therapeutics have been considered, as well as the material properties of the drug delivery systems. Systems for delivery include DESs, balloon catheters, polymeric cuffs and nanoparticles. Our own experience designing a controlled release device for a new therapeutic agent, Serp-1, an anti-inflammatory protein, is briefly presented. The release of Serp-1 can be extended using diffusion controlled release from physically crosslinked poly(vinyl alcohol) hydrogels, where its release properties can be tuned by the processing parameters of the hydrogel.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".