Effects of Diffusion Coefficients and Struts Apposition Using Numerical Simulations for Drug Eluting Coronary Stents
Bibliographic record
Abstract
In the context of drug eluting stent, we present two-dimensional numerical models of mass transport of the drug in the wall and in the lumen to study the effect of the drug diffusion coefficients in the three principal media (blood, vascular wall, and polymer coating treated as a three-compartment problem) and the impact of different strut apposition configurations (fully embedded, half embedded, and not embedded). The different conditions were analyzed in terms of their consequence on the drug concentration distribution in the arterial wall. We apply the concept of the therapeutic window to the targeted vascular wall region and derive simple metrics to assess the efficiency of the various stent configurations. Although most of the drug is dispersed in the lumen, variations in the blood flow rate within the physiological range of coronary blood flow and the diffusivity of the drug molecule in the blood were shown to have a negligible effect on the amount of drug in the wall. Our results reveal that the amount of drug cumulated in the wall depends essentially on the relative values of the diffusion coefficients in the polymer coating and in the wall. Concerning the strut apposition, it is shown that the fully embedded strut configuration would provide a better concentration distribution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".