Resveratrol: Biological and pharmaceutical properties as anticancer molecule
Bibliographic record
Abstract
There is ample evidence that shows an inverse relationship between consumption of fruit/vegetable-rich diets and the risk of cancer at various anatomical sites. In this review, we will assess and summarize recent advances on cancer prevention by resveratrol, a natural stilbenoid present in red grapes, peanuts, some common drinks, and dietary supplements. We will focus on data published within the past few years on in vivo model tumor animal studies that reinforce the chemopreventive efficacy of resveratrol against a multitude of cancers, as well as on its sensitization/enhancing activities against tumor cells when used in combination with established chemotherapeutic and pharmaceutical agents. In addition, we will review examples resveratrol-target proteins, denoted RTPs, including the 24-kDa cytosolic protein quinone reductase 2 (NQO2) discovered in our laboratory that may confer resveratrol responsiveness to cancer cells. We will discuss the possible role of NQO2 in mediating cancer prevention by resveratrol. Our analysis of published data strengthen support that resveratrol displays novel roles in various cellular processes, and help to establish an expanded molecular framework for cancer prevention by resveratrol in vivo.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".