Effect of polymer architecture on surface properties, plasma protein adsorption, and cellular interactions of pegylated nanoparticles
Bibliographic record
Abstract
The aim of the present study was to evaluate the cellular interaction of nanoparticles (NPs) prepared from different pegylated polymers and elucidate the effect of polymer architecture, for instance, grafted versus block copolymer on their cellular uptake. Fluorescein-labeled NPs of four different polymers, viz., poly(D,L-lactide) (PLA), poly(ethylene glycol)(1%)-graft-poly(D,L-lactide) (PEG(1%)-g-PLA), poly(ethylene glycol)(5%)-graft-poly(D,L-lactide) (PEG(5%)-g-PLA), and (poly(D,L-lactide)-block-poly(ethylene glycol)-block-poly(D,L-lactide))(n) multiblock copolymer (PLA-PEG-PLA)(n) were prepared. These NPs were characterized for their size, zeta-potential, and surface morphology. XPS studies revealed possibility of chemical interaction between PLA-COOH groups and PVA-OH groups, thus making it difficult to be washed off the NP surface completely. Grafted polymer NPs showed more surface PEG coverage than (PLA-PEG-PLA)(n) despite of their comparatively lower PEG content. The results of surface properties were translated into protein binding showing least amount of proteins bound to grafted copolymer NPs as against multiblock copolymer NPs. NPs showed no toxicity to RAW 264.7 cells. Cellular uptake of NPs was temperature and concentration-dependent as well as involved clathrin-mediated processes. Thus, this study confirms the importance of polymer architecture in determining the surface properties and hence, protein binding and cellular interactions of NPs. Also, it was shown that grafted copolymer NPs reduced macrophage uptake as compared to multiblock copolymer although mechanisms different than phagocytosis were involved.
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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) | 0.000 | 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".