Nanoparticle localization in blood vessels: dependence on fluid shear stress, flow disturbances, and flow-induced changes in endothelial physiology
Bibliographic record
Abstract
Nanoparticles in the bloodstream are subjected to complex fluid forces as they move through the curves and branches of healthy or tumor vasculature. While nanoparticles are known to preferentially accumulate in angiogenic vessels, little is known about the flow conditions in these vessels and how these conditions may influence localization. Here, we report a methodology which combines confocal imaging of nanoparticle-injected transgenic zebrafish embryos, 3D modeling of the vasculature, particle mapping, and computational fluid dynamics, to quantitatively assess the effects of fluid forces on nanoparticle distribution in vivo. Six-fold lower accumulation was found in zebrafish arteries compared to the lower velocity veins. Nanoparticle localization varied inversely with shear stress. Highest accumulation was present in regions of disturbed flow found at branch points and curvatures in the vasculature. To further investigate cell-particle association under flow, human endothelial cells were exposed to nanoparticles under hemodynamic conditions typically found in human vessels. Physiological adaptations of endothelial cells to 20 hours of flow enhanced nanoparticle accumulation in regions of disturbed flow. Overall our results suggest that fluid shear stress magnitude, flow disturbances, and flow-induced changes in endothelial physiology modulate nanoparticle localization in angiogenic vessels.
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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".