Antiproliferative effect of resveratrol in pancreatic cancer cells
Bibliographic record
Abstract
To investigate resveratrol, one of the food derived polyphenols that might be partially responsible for the beneficial effect on cancer, the in vitro antitumor activity of resveratrol against pancreatic cancer cell lines (PANC-1, BxPC-3 and AsPC-1) was examined, together with the mechanisms involved. The effects of resveratrol on the growth inhibition, apoptosis and cell cycle were assayed. The activity of caspases and the expression of Bcl-2, Bcl-xL, XIAP and Bax protein were detected. The results showed that resveratrol inhibited the proliferation of pancreatic cancer cells in a dose- and time-dependent manner. Resveratrol inhibited the cell growth of PANC-1, BxPC-3 and AsPC-1 cells with IC(50) values of 78.3 ± 9.6 μmol/L, 76.1 ± 7.8 μmol/L and 123.1 ± 6.5 μmol/L at 48 h, respectively. Incubation of pancreatic cancer cells with resveratrol resulted in cell apoptosis and cell cycle arrests. Resveratrol induced activation of caspases. Simultaneously, resveratrol regulated the expression of the antiapoptotic proteins Bcl-2, Bcl-xL and XIAP and the proapoptotic protein Bax. PANC-1 and BxPC-3 cells were more chemosensitive to resveratrol than AsPC-1 cells. In conclusion, resveratrol inhibited the proliferation of pancreatic cancer cells by inducing apoptotic cell death. There was different sensitivity to resveratrol in different pancreatic cancer cell lines.
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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".