Response of shear‐activated nanotherapeutic particles in a clot‐obstructed blood vessel by <scp>CFD‐DEM</scp> simulations
Bibliographic record
Abstract
Abstract In recent years, targeted drug delivery systems have been regarded as a promising solution to enhance the efficiency of treatments against clots in blood vessels. In this context, shear‐activated nanotherapeutics (SANTs) have been recently proposed. These are micrometric clusters of polymeric nanoparticles coated with a clot lysing agent. These drug carriers are stable under normal blood flow conditions, but they can be designed to undergo breakup right on the clot in response to the local increase in the hydrodynamic stress caused by the lumen restriction, effectively concentrating the active agent at the point of need. The aim of this work is to investigate the mechanical response of three potential drug carrier morphologies to the pathological flow field stress, typically encountered in obstructed blood vessels. Computational fluid dynamics simulations have been used to compare the viscous stress in arterial obstructions with the one in a microfluidic device, suitable for in vitro experimental tests. Discrete element method simulations built upon Stokesian dynamics were conducted to estimate the tensile stress distribution acting inside isostatic, random close packing, and hollow drug carriers. The results herein presented constitute a platform for a future experimental campaign and aim at establishing SANTs as a robust and broadly applicable targeting strategy.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".