Antiproliferative Effects of Different Concentrations of Auraptene on MCF7 Cancer Cell Line
Bibliographic record
Abstract
Background: Auraptene is a coumarin derivative extracted from citrus species, such as lemon, grapefruit, and orange. To date, auraptene has shown antioxidant, antibacterial, anti-inflammatory, antiproliferative, antiapoptotic, and antitumor activities. Among these, antitumor activity has become more important over the recent years, while its underlying mechanism is not fully understood. The current study was conducted to evaluate the antiproliferative effect of auraptene and its mechanisms on MCF7 cell line.Method: This experimental study investigated whether hesperidin affected the proliferation of MCF-7 human breast cancer cells. MCF7 cells were cultured in DMEM medium with 10% fetal bovine serum, 100 μg/ml streptomycin, and 100 units/ml penicillin. The cells were incubated in order to be treated with different concentrations of auraptene and time points. Subsequently, the amount of cytotoxicity and apoptosis was measured utilizing MTT and PI staining.Results: The MTT assay revealed that auraptene had a significant effect on cell viability and induced apoptosis in MCF7 cells at concentrations of 75, 100, 130, 170, and 200 μM.Conclusion: In this study, through the induction of apoptosis, auraptene prevented the growth and inhibited the proliferation of MCF7 cells at high concentrations in a dose-dependent manner. However, further investigation is needed to reveal the mechanisms of auraptene concerning apoptosis induction.
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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 | 0.001 | 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) | 0.002 | 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".