Penetration and drug delivery of albumin nanoparticles into pancreatic multicellular tumor spheroids
Bibliographic record
Abstract
Albumin-based nanoparticles have been exploited as a useful carrier for the efficient delivery of anti-cancer drugs. In this study, albendazole was encapsulated into bovine serum albumin (BSA)-polycaprolactone (PCL) conjugates and the formed nanoparticles with a size about 100 nm were used to treat pancreatic carcinoma cells. In addition, two more types of albendazole-loaded BSA nanoparticles, 10 nm and 200 nm ones, were prepared using a desolvation method. The albendazole-loaded BSA nanoparticles were evaluated with both 2D cultured AsPC-1 cells and 3D multicellular tumor spheroids (MCTS). Their anti-tumor effects were also compared. BSA-PCL nanoparticles and 200 nm BSA nanoparticles showed noticeable cytotoxicity to 2D cultured AsPC-1 cells when compared to the free drug. The penetration of BSA-PCL nanoparticles and 200 nm BSA nanoparticles, especially the BSA-PCL nanoparticles, enabled effective delivery of albendazole into pancreatic MCTS. BSA-PCL nanoparticles also showed a better inhibition effect on the growth of pancreatic MCTS than the 200 nm counterpart. Although 10 nm BSA nanoparticles inhibited the growth of MCTS, the inhibitory effect was even less than that of free albendazole. In addition, it is also found that SPARC protein facilitates the penetration and drug delivery of albumin nanoparticle since treatment using anti-SPARC antibody decreased the efficacy of drug loaded BSA nanoparticles.
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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".