Role of Resveratrol in Modulating microRNAs in Human Diseases: From Cancer to Inflammatory Disorder
Bibliographic record
Abstract
Cancer and inflammatory disorders are two important public health issues worldwide with significant socio.economic impacts. Despite several efforts, the current therapeutic platforms are associated with severe limitations. Therefore, developing new therapeutic strategies for the treatment of these diseases is a top priority. Besides current therapies, the utilization of natural compounds has emerged as a new horizon for the treatment of cancer and inflammatory disorders as well. Such natural compounds could be used either alone or in combination with the standard cancer therapeutic modalities such as chemotherapy, radiotherapy, and immunotherapy. Resveratrol is a polyphenolic compound that is found in grapes as well as other foods. It has been found that this medicinal agent displays a wide pharmacological spectrum, including anti-cancer, anti-inflammatory, anti-microbial, and antioxidant activities. Recently, clinical and pre-clinical studies have highlighted the anti-cancer and anti-inflammatory effects of resveratrol. Increasing evidence revealed that resveratrol exerts its therapeutic effects by targeting various cellular and molecular mechanisms. Among cellular and molecular targets that are modulated by resveratrol, microRNAs (miRNAs) have appeared as key targets. MiRNAs are short non-coding RNAs that act as epigenetic regulators. These molecules are involved in many processes that are involved in the initiation and progression of cancer and inflammatory disorders. Herein, we summarized various miRNAs that are directly/indirectly influenced by resveratrol in cancer and inflammatory disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".