Cytotoxicity of natural extract from Tegillarca granosa on ovarian cancer cells is mediated by multiple molecules
Bibliographic record
Abstract
PURPOSE: To determine the cellular and molecular mechanism of cytotoxicity induced by Haishengsu (HSS), nature extract from Tegillarca granosa, toward human ovarian cancer cell lines SKOV-3 and OVCAR-3. METHODS: The cytotoxic effects of HSS on two ovarian cancer cell lines were tested by XTT assay. Cell apoptosis and cell cycle arrest induced by HSS were demonstrated by DNA ladder assay and flow cytometric analysis, respectively. RT-PCR or flow cytometric analysis was used to investigate the expression of bcl-2, caspase-3, p53, beta-catenin, E-cadherin, CD24, and CD44. RESULTS: Continuous exposure to HSS for 48 h produced cytotoxic effects on both cell lines in a concentration dependent manner, which was accompanied by apoptosis and cell cycle arrest. Apoptosis associated gene bcl-2 and caspase-3, tumor metastasis associated gene ?-catenin, but not E-cadherin, and CD24, but not CD44, were involved in the effect of growth inhibition induced by HSS. Although p53 mediated apoptosis induced by HSS in OVCAR-3 cells, it was not required in SKOV-3 cells. CONCLUSION: HSS has a potential cytotoxic effect on human ovarian cancer cells, which was mediated by multiple signal molecules including bcl-2, caspase-3, beta-catenin, and CD24. These findings will provide a theoretical basis for HSS's potential clinical application as a novel marine anti-cancer agent.
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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".