Resveratrol against Cervical Cancer: Evidence from In Vitro and In Vivo Studies
Bibliographic record
Abstract
Cervical cancer affects many women worldwide, with more than 500,000 cases diagnosed and approximately 300,000 deaths each year. Resveratrol is a natural substance of the class of phytoalexins with a basic structure of stilbenes and has recently drawn scientific attention due to its anticancer properties. The purpose of this review is to examine the effectiveness of resveratrol against cervical cancer. All available in vitro and in vivo studies on cervical cancer were critically reviewed. Many studies utilizing cervical cancer cells in culture reported a reduction in proliferation, cell cycle arrest, and induction of apoptosis. Apart from apoptosis, induction of autophagy was seen in some studies. Importantly, many studies have shown a reduction in the HPV oncoproteins E6 and E7 and increased levels of the tumor suppressor p53 with resveratrol treatment. A few studies examined the effects of resveratrol administration in mice ectopic-xenografted with cervical cancer cells showing reduced tumor volume and weight. Overall, the scientific data show that resveratrol has the ability to target/inhibit certain signaling molecules (EGFR, VEGFR, PKC, JNK, ERK, NF-kB, and STAT3) involved in cervical cancer cell proliferation and survival. Further in vivo experiments and clinical studies are required to better understand the potential of resveratrol against cervical cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".