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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".