Endothelial nanoparticle binding kinetics are matrix and size dependent
Bibliographic record
Abstract
Nanoparticles are increasingly important in medical research for application to areas such as drug delivery and imaging. Understanding the interactions of nanoparticles with cells in physiologically relevant environments is vital for their acceptance, and cell-particle interactions likely vary based on the design of the particle including its size, shape, and surface chemistry. For this reason, the kinetic interactions of fluorescent nanoparticles of sizes 20, 100, 200, and 500 nm with human umbilical vein endothelial cells (HUVEC) were determined by (1) measuring nanoparticles per cell at 37 and 4°C (to inhibit endocytosis) and (2) modeling experimental particle uptake data with equations describing particle attachment, detachment, and internalization. Additionally, the influence of cell substrate compliance on nanoparticle attachment and uptake was investigated. Results show that the number of binding sites per cell decreased with increasing nanoparticle size, while the attachment coefficient increased. By comparing HUVEC grown on either a thin coating of collagen or on top of three-dimensional collagen hydrogel, nanoparticle attachment and internalization were shown to be influenced significantly by the substrate on which the cells are cultured. This study concludes that both particle size and cell culture substrate compliance appreciably influence the binding of nanoparticles; important factors in translating in vitro studies of nanoparticle interactions to in vivo studies focused on therapeutic or diagnostic 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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".