Comparison of the effects of nobiletin and letrozole on the activity and expression of aromatase in the MCF-7 breast cancer cell line
Bibliographic record
Abstract
Nobiletin (NOB) is one of the polymethoxyflavones mainly found in citrus fruits. Aromatase or cytochrome P450 (CYP19) enzyme catalyzes the last and rate-limiting step in estrogen biosynthesis. This study was carried out to investigate the effect of NOB on the activity and expression of aromatase, and to compare this property with letrozole (LET) as aromatase inhibitor in the MCF-7 breast cancer cell line. Cell viability was assessed with 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazolium bromide (MTT) assays. Aromatase enzyme activity based on the conversion of androgenic substrate testosterone into 17β-estradiol was determined. CYP19 gene expression was measured by quantitative real-time PCR. MTT assays demonstrated that NOB at a concentration of 100 μmol/L decreased cell viability in a time-dependent manner (P < 0.05). NOB significantly inhibited aromatase at the concentration of 0.1 μmol/L (P = 0.013), whereas other concentrations had no effect. Treatment with 10 μmol/L and 1 μmol/L of NOB for 48 h significantly increased (P = 0.001) and decreased (P = 0.02) relative aromatase expression, respectively. The combination of LET and NOB had no effect on aromatase. This study showed for the first time that NOB decreases the activity and expression of aromatase at low concentrations in MCF-7 breast cancer cells.
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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".