Optimization of anti-cancer drugs and a targeting molecule on multifunctional gold nanoparticles
Bibliographic record
Abstract
Breast cancer is the most common and deadly cancer among women worldwide. Currently, nanotechnology-based drug delivery systems are useful for cancer treatment; however, strategic planning is critical in order to enhance the anti-cancer properties and reduce the side effects of cancer therapy. Here, we designed multifunctional gold nanoparticles (AuNPs) conjugated with two anti-cancer drugs, TGF-β1 antibody and methotrexate, and a cancer-targeting molecule, folic acid. First, optimum size and shape of AuNPs was selected by the highest uptake of AuNPs by MDA-MB-231, a metastatic human breast cancer cell line. It was 100 nm spherical AuNPs (S-AuNPs) that were used for further studies. A fixed amount (900 μl) of S-AuNP (3.8 × 10(8) particles/ml) was conjugated with folic acid-BSA or methotrexate-BSA. Methotrexate on S-AuNP induced cellular toxicity and the optimum amount of methotrexate-BSA (2.83 mM) was 500 μl. Uptake of S-AuNPs was enhanced by folate conjugation that binds to folate receptors overexpressed by MDA-MB-231 and the optimum uptake was at 500 μl of folic acid-BSA (2.83 mM). TGF-β1 antibody on S-AuNP reduced extracellular TGF-β1 of cancer cells by 30%. Due to their efficacy and tunable properties, we anticipate numerous clinical applications of multifunctional gold nanospheres in treating breast cancer.
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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".