Synergistic effects of graphene quantum dots nanocarriers and folic acid targeting agent on enhanced killing of breast cancer cells by tamoxifen
Bibliographic record
Abstract
Abstract In this study, a new targeted and controlled‐release drug delivery system based on graphene quantum dots (GQDs) was fabricated. The fluorescent GQDs were synthesized via an environmentally‐friendly chemical oxidation method, using graphene oxide (GO) as a precursor and hydrogen peroxide and ammonia solution as oxidants. Folic acid (FA), as a targeting agent, was bound to GQDs through strong amide covalent bindings, and tamoxifen (TMX), as a hydrophobic anticancer drug, was non‐covalently attached to the nanocarrier via π–π stacking bonds. The as‐prepared GQDs and TMX/FA‐GQDs were characterized using field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR), UV/Vis spectroscopy (UV/Vis), and photoluminescence spectroscopy (PL). The resulting TMX/GQDs demonstrated pH‐sensitive release behaviour with an overall release of 85% and 58% at pH 5.5 and 7.4, respectively, after 120 h. Also, the MTT assay test performed on MCF‐7 cells presented negligible toxicity of the nanocarriers, even at high concentrations. At the same time, the targeted nanocarrier, TMX/FA‐GQDs, showed much more toxicity towards MCF‐7 cells than the non‐targeted one and free TMX.
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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".