<i>Vitis vinifera</i> L. Bioactive Components Modulate Adipose Tissue Metabolic Markers of Healthy Rats in a Photoperiod‐Dependent Manner
Bibliographic record
Abstract
SCOPE: The beneficial health effects of (poly)phenol-rich foods such as red grapes mainly depend on both the type and concentration of (poly)phenols. Since fruit (poly)phenol content is influenced by growing conditions, the study examines the seasonal effects of red grapes (Vitis vinifera L.), grown under various cultivation conditions, on metabolic markers of adipose tissue in healthy rats. METHODS AND RESULTS: of either conventionally or organically grown red grapes for 10 weeks (n = 6). Seasonal consumption of organic grapes (OGs), which are richer in anthocyanins, increases energy expenditure (EE) of animals exposed to long photoperiod and enhances uncoupling protein 1 (UCP1) protein expression in brown adipose tissue of animals under standard photoperiod. Additionally, red grape consumption affects the gene expression profile of white adipose tissue (WAT), upregulating browning markers of subcutaneous WAT in 12 h light (L12) and 18 h light (L18) photoperiods, and downregulating adipogenic and lipolytic markers of visceral WAT in 6 h light (L6) and L12 photoperiods. CONCLUSIONS: These results clearly show that bioactive compounds of grapes can modulate the metabolic markers of white and brown adipose tissues in a photoperiod and depot-dependent manner, partly affecting EE when consumed out of season.
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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 | 0.001 |
| 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 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".