Main components of pomegranate, ellagic acid and luteolin, inhibit metastasis of ovarian cancer by down-regulating MMP2 and MMP9
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ovarian cancer is the third most common cancer in the female reproductive organs and epithelial ovarian cancer has the highest lethality of all gynecological cancers. Pomegranate fruit juice (PFJ) has been shown to inhibit the growth of several types of cancer other than ovarian cancer. In this study, we exposed the ovarian cancer cell line A2780 to PFJ and two of its components (ellagic acid and luteolin). MTT and wound healing assays demonstrated that all three treatments suppressed the proliferation and migration of the ovarian cancer cells. In addition, western blotting and ELISA assays showed that the expression levels of MMP2 and MMP9 gradually decreased after treatment with increasing concentrations of ellagic acid and luteolin. To confirm our findings in the in vitro experiments, we used another ovarian cancer cell line, ES-2, in nude mice experiments. All three treatments inhibited tumor growth without obvious side-effects. Furthermore, compared with the control group, the expression levels of MMP2 and MMP9 were depressed. Ellagic acid induced a greater effect than luteolin, suggesting that ellagic acid might be a promising candidate for further preclinical testing for treatment of human ovarian cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it