Oleuropein induces apoptosis via abrogating NF‐κB activation cascade in estrogen receptor–negative breast cancer cells
Bibliographic record
Abstract
Oleuropein is one of the most abundant phenolic compounds found in olives. Epidemiological studies have indicated that an increasing intake of olive oil can significantly reduce the risk of breast cancer. However, the potential effect(s) of oleuropein on estrogen receptor (ER)-negative breast cancer is not fully understood. This study aims to understand the anticancer effects and underlying mechanism(s) of oleuropein on ER-negative breast cancer cells in vitro. The effect of oleuropein on the viability of breast cancer cell lines was examined by mitochondrial dye-uptake assay, apoptosis by flow cytometric analysis, nuclear factor-κB (NF-κB) activation by DNA binding/reporter assays and protein expression by Western blot analysis. In the present report, thiazolyl blue tetrazolium bromide assay results indicated that oleuropein inhibited the viability of breast cancer cells, and its effects were more pronounced on MDA-MB-231 as compared with MCF-7 cells. It was further found that oleuropein increased the level of reactive oxygen species and also significantly inhibited cellular migration and invasion. In addition, the activation of NF-κB was abrogated as demonstrated by Western blot analysis, NF-κB-DNA binding, and luciferase assays. Overall, the data indicates that oleuropein can induce substantial apoptosis via modulating NF-κB activation cascade in 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".