Effect of disturbed flow on nanoparticle uptake in endothelial cells
Bibliographic record
Abstract
n recent years, the focus of nanotechnology research has shifted from industrial applications, such as cosmetic or oil & gas (1,2), to those with a greater impact on medicine. Researchers are attempting to harness the ability of nanoparticles (NPs) to target specific areas of the body for the purpose of drug delivery or medical imaging (3,4). In order to predict how nanoparticles will accumulate once they enter the blood stream, a greater understanding of the effect of fluid flow on cellular interactions with NPs is required. Current research examines the effect of shape, size, and density on NP uptake (5), but very little attention is paid to the way in which flow disturbances affect how NPs accumulate. Disturbed flow regimes are characteristic in many biological systems in areas of vascular branching or curvature, regions of new vessel growth or in the presence of atherosclerotic plaques (6). In this study, a sudden expansion parallel plate flow chamber was used to examine the effects of flow rate (shear stress) as well as flow pattern on the uptake of NP by human umbilical vein endothelial cells (HUVEC). Fluorescence microscopy was used to image and quantify the presence of NPs following 30 minutes of exposure. As previous findings suggest (7), an increase in shear stress resulted in a decrease in NP uptake. Statically grown cells subject to short term flow and NP exposure exhibited equal accumulation in regions of disturbed and laminar flow, while preconditioning of HUVEC to flow for 24 hrs resulted in a difference in uptake between the two flow regimes. The results suggest that prolonged exposure to specific flow patterns may cause physiological changes that affect NP uptake. Such observations are important to ensure that in vitro studies are accurate predictors of NP behavior in biological models and warrant further exploration.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".