Effect of particle aspect ratio in targeted drug delivery in abdominal aortic aneurysm
Bibliographic record
Abstract
Aneurysm is a permanent irreversible bulge in the artery that can occur with higher prevalence among elderly individuals. Although invasive surgical procedures can prevent their development, they come with considerable side effects . Recently, treatments based on targeted drug delivery have gained a lot of attention to suppress aneurysm growth. Numerical simulations have been shown to be of great role in the prediction of blood hemodynamics and vascular wall behaviour in the case of an aneurysm. Moreover, the utilization of high-fidelity approaches such as the Lagrangian frame of reference can address the motion characteristics of microbubble (MB) contrast agents in particulate flows. This study aims to investigate the effect of particle aspect ratio on the adhesion of oblate spheroid particles to the vascular wall. Accordingly, a two-way fluid–structure interaction (FSI) method consisting of a hyperelastic material model for the vessel along with a non-Newtonian, compressible model for blood was employed to simulate an abdominal aortic aneurysm (AAA). Moreover, the ligand–receptor binding concept has been utilized to address the quantification of MBs adhesion. Five sets of aspect ratios ranging from 1 to 9 have been investigated and results indicated that with the increase of the aspect ratio the rate of adhesion decreases. Two drastic changes in the particle number occurred due to the diastolic peak and negative velocity profile , respectively. However, it was concluded that the hydrodynamic of the MBs in terms of velocity and wall distance is rather insensible to the particle shape.
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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) | 0.000 | 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".