Influencing Selectivity to Cancer Cells with Mixed Nanoparticles Prepared from Albumin–Polymer Conjugates and Block Copolymers
Bibliographic record
Abstract
Albumin-based nanoparticles are widely used to delivery anticancer drug because they promote the accumulation of drugs in tumor sites. Nanoparticles with surface immobilized albumin are widely described in literature, although mixed nanoparticles with systematically modified ratios between albumin and PEG-based material are less common. In this work, hybrid nanoparticles were prepared by coassembly of a PEG-based amphiphilic block copolymer together with a polymer-protein conjugate. Poly(oligo(ethylene glycol) methyl ether acrylate)-poly(ε-caprolactone) (POEGMEA-PCL) was prepared by a combination of ring-opening polymerization and reversible addition-fragmentation chain transfer (RAFT) polymerization, while the polymer-protein conjugate was obtained by reacting poly(ε-caprolactone) with bovine serum albumin (BSA-PCL). Co-assembly of both amphiphiles at different ratios, with and without curcumin as a drug, led to hybrid nanoparticles with various amount of albumin on the particle surface. The resulting hybrid nanoparticles were similar in size (100-120 nm), but increasing the amount of albumin on the surface led to a more-negative ζ potential. The cytotoxicity of the curcumin-loaded nanoparticles was examined on several cell lines. The curcumin-loaded nanoparticles with high amount of albumin led to high cytotoxicity against breast cancer cell lines (MDA-MB-231 and MCF-7), which coincided with high cellular uptake. However, the cytotoxicity of the curcumin-loaded nanoparticles against CHO cells and RAW264.7 cells was reduced, suggesting that albumin can facilitate selectivity toward cancer cells.
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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".