Nanoparticle-eluting stents for coronary intervention: formulation, characterization, and in vitro evaluation
Bibliographic record
Abstract
Coronary artery disease (CAD) is currently a leading cause of death worldwide. In the history of percutaneous coronary intervention for the treatment of CAD, a drug-eluting stent (DES) is recognized as a revolutionary technology that has the unique ability to significantly reduce restenosis and provide both mechanical and biological solutions simultaneously to the target lesion. The aim of the research work was to design and fabricate DES coated with a nanoparticulate drug formulation. Sirolimus, an inhibitor of the smooth muscle cell (SMC) proliferation and migration, was encapsulated in polymeric nanoparticles (NPs). The NP formulation was characterized for various physicochemical parameters. Cell viability and cell uptake studies were performed using human coronary artery smooth muscle cells (HCASMCs). The developed NP formulation showed enhanced efficacy compared to plain drug solution and exhibited time-dependent uptake into the HCASMCs. The developed NP formulation was coated on the Flexinnium™ ultra-thin cobalt-chromium alloy coronary stent platform. The NP-coated stents were characterized for morphology and residual solvent analysis. In vitro drug release was also evaluated. Ex vivo arterial permeation was carried out to evaluate the NP uptake from the surface of the stents. The characterization studies together corroborated that the developed NP coated stent can be a promising replacement of the current DESs.
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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 | 0.001 | 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 | 0.000 |
| 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".