Flow Rate Affects Nanoparticle Uptake into Endothelial Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
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 it