Enhanced susceptibility to apoptosis and growth arrest of human breast carcinoma cells treated with silica nanoparticles loaded with monohydroxy flavone compounds
Bibliographic record
Abstract
The treatment of drug-resistant cancer is a clinical challenge, hence screening for novel anticancer drugs is critically important. In this study, we investigated the anti-tumor potential of three plant-derived flavone compounds: 3-hydroxy flavone (3-HF), 6-hydroxy flavone (6-HF), and 7-hydroxy flavone (7-HF), either alone or combined with silica nanoparticles (3-HF + NP, 6-HF + NP, and 7-HF + NP), on the human breast carcinoma cell lines MDA-MB-231 and MCF-7, as well as on non-tumorigenic normal breast epithelial cells (MCF-10). The IC50 values of these flavone compounds loaded with NP (flavones + NP) in these cell lines were determined to be 1.5 μg/mL without affecting the viability of normal MCF-10 cells. Additionally, using annexin V – propidium iodide double-staining followed by flow cytometry analysis, we found that the combination of flavones with NP significantly induced apoptosis in MCF-7 and MDA-MB-231 cancer cells. Furthermore, flavones + NP increased the expression of cytochrome c and caspase-9, mediating the growth arrest of these cancer cells. Most importantly, the combination of flavones with NP significantly abolished the expression of ATF-3, which is responsible for the proliferation and invasion of bone-metastatic breast cancer cells. Our data revealed the potential therapeutic effects of these flavones in fighting breast cancer cells, and provide the first insights concerning the underlying molecular mechanisms.
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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) | 0.001 | 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".