Effect of Poly(<i>N</i>-vinyl-pyrrolidone)-<i>block</i>-poly(<scp>d</scp>,<scp>l</scp>-lactide) as Coating Agent on the Opsonization, Phagocytosis, and Pharmacokinetics of Biodegradable Nanoparticles
Bibliographic record
Abstract
The effect of the coating polymer poly(N-vinyl-pyrrolidone) (PVP) on the protein adsorption, phagocytosis, and pharmacokinetics of poly(D,L-lactide)-based nanoparticles was evaluated in vitro and in vivo. Control poly(ethylene glycol) (PEG)-coated nanoparticles were included for comparison. While no difference between PEG- and PVP-decorated nanoparticles in terms of amount of adsorbed protein was evident upon incubation in single protein solutions (BSA, IgG), incubation in serum revealed a protein adsorption pattern both quantitatively and qualitatively distinct. Larger amounts of complement components and immunoglobulins were found to adhere to PVP-coated particles, whereas PEG particles showed preferential adsorption of apolipoproteins. Furthermore, preopsonization in fresh rather than heat-inactivated serum enhanced uptake of both types of particles by murine RAW 264.7 macrophages. However, when isolated rat Kupffer cells were employed, activation of the complement system significantly enhanced the uptake of PVP-coated nanoparticles compared to PEG particles. Ultimately, PVP-coated nanoparticles exhibited considerably shorter circulation times compared to their PEG counterparts when administered intravenously to rats.
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.
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) | 0.000 | 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".