Flow Rate Affects Nanoparticle Uptake into Endothelial Cells
Bibliographic record
Abstract
Nanoparticles are commonly administered through systemic injection, which exposes them to the dynamic environment of the bloodstream. Injected nanoparticles travel within the blood and experience a wide range of flow velocities that induce varying shear rates to the blood vessels. Endothelial cells line these vessels, and have been shown to uptake nanoparticles during circulation, but it is difficult to characterize the flow-dependence of this interaction in vivo. Here, a microfluidic system is developed to control the flow rates of nanoparticles as they interact with endothelial cells. Gold nanoparticle uptake into endothelial cells is quantified at varying flow rates, and it is found that increased flow rates lead to decreased nanoparticle uptake. Endothelial cells respond to increased flow shear with decreased ability to uptake the nanoparticles. If cells are sheared the same way, nanoparticle uptake decreases as their flow velocity increases. Modifying nanoparticle surfaces with endothelial-cell-binding ligands partially restores uptake to nonflow levels, suggesting that functionalizing nanoparticles to bind to endothelial cells enables nanoparticles to resist flow effects. In the future, this microfluidic system can be used to test other nanoparticle-endothelial cell interactions under flow. The results of these studies can guide the engineering of nanoparticles for in vivo medical applications.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".