Enterolactone promotes efficacy of gemcitabine on epithelial ovarian cancer and ameliorates gut dysbacteriosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The effectiveness of conventional treatments for epithelial ovarian cancer (EOC) is very limited and their side effects are serious. Previous research has demonstrated the inhibitory effects of enterolactone (ENL) on EOC by inhibiting malignant angiogenesis. Gemcitabine (Gem) is a chemotherapeutic agent commonly used for the treatment of EOC with limited efficacy. In this study, we aimed to explore the combined inhibitory effects of ENL and Gem on EOC. EXPERIMENTAL APPROACH: We detected the proliferation ability of EOC cells after ENL/Gem/ENL + Gem by CCK8, crystal violet assays, migration and invasion ability by wound healing and transwell assays, in vivo evaluation of the anti-neovascularisation efficacy of zebrafish and in vitro tube formation assays to detect angiogenesis, network pharmacology, Western-blot and immunohistochemistry to analyse molecular pathways, and in vivo animal experiments on tumour progression. KEY RESULTS: Our results demonstrated that the ENL and Gem combination synergistically inhibited the proliferation, migration and invasion of EOC. Tube formation and zebrafish neovascularization assays showed potent anti-angiogenic activity of the ENL + Gem combination. In animal experiments, the combined use of ENL and Gem also synergistically inhibited tumour growth and in the meantime markedly reduced the side effects of Gem. ENL ameliorated gut dysbacteriosis of ovarian cancer animals, which significantly enhanced the synergistic anti-cancer effect of ENL and Gem. CONCLUSIONS AND IMPLICATIONS: ENL and Gem synergistically inhibit the proliferation, migration, invasion, and angiogenesis of EOC by modulating the Akt-Bax and Akt-MMP9-VEGFR-2 pathways and ameliorating gut dysbacteriosis.
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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 | 0.001 |
| 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".