Modeling Drug Eluting Stents for Coronary Artery Bifurcation Considering Non-Newtonian Effects
Bibliographic record
Abstract
Drug Eluting Stents (DES) are commonly used for the treatment of stenotic arteries. Restenosis can be treated by delivering anti-thrombotic and anti-proliferative drugs to the arterial wall. The main mechanism of the drug eluting stent is to allow diffusion of the drug from the coating on the stent, into the arterial wall over a prolonged period of time. Investigation of blood flow hemodynamics and shear stress are of great importance in understanding the transport of drugs through the circulatory systems and predicting the performance of drug eluting stents. While drug eluting stent effectively reduces restenosis rate, the conventional drug eluting stent should be optimized to be used in the bifurcation stenting. Various flow patterns due to specific designs of drug eluting stent influence drug delivery. Numerical simulation techniques are appropriate approaches to study such phenomena which can be used to optimize the design of drug eluting stents for bifurcations. In this paper, the complexity of drug eluting stent function in the bifurcation is presented by employing computational fluid dynamics analysis for various stent strut designs. Drug transportation through the lumen and determination of local drug concentrations in arterial wall is carried out for both Newtonian and non-Newtonian flow conditions. It is, to the author’s best knowledge, the first investigation of drug dispersion in arterial bifurcation considering the effects of both the blood rheological properties and stent strut design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".