Suppressive activities of mangiferin on human epithelial ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Epithelial carcinoma is a subtype of ovarian cancers, with the highest lethality among all ovarian cancer subtypes. Hitherto surgical excision combined with chemotherapy has been the most extensively employed method in clinical treatment. However, the disease relapses very frequently, calling for more effective therapies. Mangiferin, a natural xanthone glucoside, has displayed promising anti-cancer activities by in vitro studies, but its therapeutic value in epithelial ovarian cancer treatment, either by in vivo or in vitro studies, remained to be known. PURPOSE: This study aimed to determine the suppressive activities of mangiferin on human epithelial ovarian cancer and elucidate the underlying molecular mechanisms. STUDY DESIGN AND METHODS: ) values of mangiferin with paclitaxel as a positive control and the inhibitory effects of mangiferin on the proliferation of two human epithelial ovarian cancer cell lines. Wound healing and Transwell assays were used to determine anti-metastastic activities of mangiferin. ES-2 xenograft nude mouse model was used for the in vivo experiments. Western blotting, enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) assays were carried out for evaluating the expression level of matrix metalloproteinase 2 (MMP2) and matrix metalloproteinase 9 (MMP9). RESULTS: In the present study, we demonstrated by both in vitro and in vivo assays that mangiferin suppressed the progress of epithelial ovarian cancer in a dose-dependent manner. In the animals treated with mangiferin, the tumor volume and weight were reduced significantly. Analyses of involved molecular events demonstrated that mangiferin down-regulated the expression of metastasis-associated proteins MMP2 and MMP9. CONCLUSION: Mangiferin strongly inhibited the progression of human epithelial ovarian cancer by down-regulating MMP2 and MMP9.
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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".