Effects of Resveratrol on Adipocytes: Evidence from In Vitro and In Vivo Studies
Bibliographic record
Abstract
Obesity, a prevalent global health issue, arises from an imbalance between caloric intake and energy expenditure, leading to the expansion of adipose tissue and metabolic dysfunction. White adipose tissue (WAT) stores energy as lipids, while brown adipose tissue (BAT) plays a pivotal role in energy dissipation through adaptive thermogenesis. Recent research initiatives have focused on finding strategies to decrease adipogenesis and fat mass accumulation and increase thermogenesis. Finding chemicals with anti-obesity properties would be beneficial. Resveratrol, a polyphenolic compound abundantly found in the skin of grapes and red wine, possesses anti-oxidant, anti-inflammatory, anti-cancer, and anti-obesity properties. This literature review examines the effects of resveratrol on adipocytes in culture and adipose tissue in animal models of obesity. The existing evidence indicates that resveratrol may exert its anti-obesity effects by inhibiting adipogenesis, promoting the apoptosis of mature adipocytes, reducing lipid accumulation, and increasing thermogenesis. Further research utilizing animal and clinical studies is required to understand in detail the anti-obesity potential of resveratrol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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".