Cationic Albumin Nanoparticles for Enhanced Drug Delivery to Treat Breast Cancer: Preparation and <i>In Vitro</i> Assessment
Bibliographic record
Abstract
Most anticancer drugs are greatly limited by the serious side effects that they cause. Doxorubicin (DOX) is an antineoplastic agent, commonly used against breast cancer. However, it may lead to irreversible cardiotoxicity, which could even result in congestive heart failure. In order to avoid these harmful side effects to the patients and to improve the therapeutic efficacy of doxorubicin, we developed DOX-loaded polyethylenimine- (PEI-) enhanced human serum albumin (HSA) nanoparticles. The formed nanoparticles were ~137 nm in size with a surface zeta potential of ~+15 mV, prepared using 20 μg of PEI added per mg of HSA. Cytotoxicity was not observed with empty PEI-enhanced HSA nanoparticles, formed with low-molecular weight (25 kDa) PEI, indicating biocompatibility and safety of the nanoparticle formulation. Under optimized transfection conditions, approximately 80% of cells were transfected with HSA nanoparticles containing tetramethylrhodamine-conjugated bovine serum albumin. Conclusively, PEI-enhanced HSA nanoparticles show potential for developing into an effective carrier for anticancer drugs.
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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 | 0.001 | 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".