Effects of ferrohydrodynamics on drug transport and retention in drug eluting stents
Bibliographic record
Abstract
This study examines the transport of magnetized drug particles (MPs) in drug-eluting stents (DESs). The governing equations for multi-species transport in a two-domain consisting of a polymer and media are formulated and solved using the finite volume method. The effects of an external magnetic field (MF) on the distribution of different drug species are analyzed. The MF was found to increase MP concentrations in the tissue and, unexpectedly, in the polymer at the same time. This counterintuitive finding was explained by analyzing the rates of transport through the polymer topcoat and the media top-layer. It was revealed that the rates of transport into and out of the media layer initially decrease and then increase with the MF, with an intermediate regime where the dynamics resemble those without MF. The maximum averaged free drug concentration in the tissue and that of the dissolved drug in the polymer were observed to increase exponentially with the MF implying on the fact that drug delivery becomes more sensitive to the MF at its larger strength. Tracking the drug center of mass revealed a nonmonotonic variation with time consisting of two linear regimes on a time log scale. The slopes of the first regime decreases with the MF while that of the second one is unaffected by the MF. The transition time was shown to increase almost linearly with the MF. The results of this study have promising applications in palliating the tendency for low drug retention from which current DES suffers.
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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) | 0.000 | 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) | 0.000 | 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".