Enterolactone has stronger effects than enterodiol on ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer is one of the three leading gynecological malignancies, characterized by insidious growth, highly frequent metastasis, and quick development of drug resistance. As a result, this disease has low 5-year survival rates. Estrogen receptor inhibitors were commonly used for the treatment, but only 7% to 18% of patients respond to anti-estrogen therapies. Therefore, more effective therapies to inhibit estrogen-related tumors are urgently needed. Recently, phytoestrogens, such as lignans with estrogen-like biological activities, have attracted attention for their potential effects in the prevention or treatment of estrogen-related diseases. Enterodiol (END) and enterolactone (ENL) are mammalian lignans, which can reduce the risk of various cancers. However, the effects of END and ENL on ovarian cancer are not adequately documented. METHODS: We used in vitro assays on the ES-2 cell line to evaluate the inhibiting effects of END and ENL on ovarian cancer cell proliferation, invasion and migration ability and in vivo xenograft experiments on nude mice to validate the anticancer effects of END and ENL. RESULTS: The in vitro assays demonstrated that high-dose END and ENL could obviously inhibit ovarian malignant properties, including cancerous proliferation, invasion, and metastasis. Compared to END, ENL behaved in a better time-dose dependent manner on the cancer cells. The in vivo experiments showed that END (1 mg/kg), ENL (1 mg/kg) and ENL (0.1 mg/kg) suppressed tumor markedly, and there were statistically significant differences between the experimental and control groups in tumor weight and volume. Compared to END, which have serious side effects to the animals at high concentration such as 1 mg/kg, ENL had higher anticancer activities and less side effects in the animals than END at the same concentrations, so it would be a better candidate for drug development. CONCLUSION: END and ENL both have potent inhibitory effects on ovarian cancer but ENL possesses a more effective anti-cancer capability and less side effects than END. Findings in this work provide novel insights into ovarian cancer therapeutics with phytoestrogens and encourage their clinical applications.
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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) | 0.004 | 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".