Mechanistic understanding of in vivo protein corona formation on polymeric nanoparticles and impact on pharmacokinetics
Bibliographic record
Abstract
Abstract In vitro incubation of nanomaterials with plasma offer insights on biological interactions, but cannot fully explain the in vivo fate of nanomaterials. Here, we use a library of polymer nanoparticles to show how physicochemical characteristics influence blood circulation and early distribution. For particles with different diameters, surface hydrophilicity appears to mediate early clearance. Densities above a critical value of approximately 20 poly(ethylene glycol) chains (MW 5 kDa) per 100 nm 2 prolong circulation times, irrespective of size. In knockout mice, clearance mechanisms are identified for nanoparticles with low and high steric protection. Studies in animals deficient in the C3 protein showed that complement activation could not explain differences in the clearance of nanoparticles. In nanoparticles with low poly(ethylene glycol) coverage, adsorption of apolipoproteins can prolong circulation times. In parallel, the low-density-lipoprotein receptor plays a predominant role in the clearance of nanoparticles, irrespective of poly(ethylene glycol) density. These results further our understanding of nanopharmacology.
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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 | 0.000 | 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".