Resveratrol and Resveratrol-Aspirin Hybrid Compounds as Potent Intestinal Anti-Inflammatory and Anti-Tumor Drugs
Bibliographic record
Abstract
Resveratrol (3,4,5-Trihydroxy-trans-stilbene) is a naturally occurring polyphenol that exhibits beneficial pleiotropic health effects. It is one of the most promising natural molecules in the prevention and treatment of chronic diseases and autoimmune disorders. One of the key limitations in the clinical use of resveratrol is its extensive metabolic processing to its glucuronides and sulfates. It has been estimated that around 75% of this polyphenol is excreted via feces and urine. To possibly alleviate the extensive metabolic processing and improve bioavailability, we have added segments of acetylsalicylic acid to resveratrol in an attempt to maintain the functional properties of both. We initially characterized resveratrol-aspirin derivatives as products that can inhibit cytochrome P450 Family 1 Subfamily A Member 1 (CYP1A1) activity, DNA methyltransferase (DNMT) activity, and cyclooxygenase (COX) activity. In this study, we provide a detailed analysis of how resveratrol and its aspirin derivatives can inhibit nuclear factor kappa B (NFκB) activation, cytokine production, the growth rate of cancer cells, and in vivo alleviate intestinal inflammation and tumor growth. We identified resveratrol derivatives C3 and C11 as closely preserving resveratrol bioactivities of growth inhibition of cancer cells, inhibition of NFκB activation, activation of sirtuin, and 5' adenosine monophosphate-activated protein kinase (AMPK) activity. We speculate that the aspirin derivatives of resveratrol would be more metabolically stable, resulting in increased efficacy for treating immune disorders and as an 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".