Potent Anti-Adipogenic Effect of Green Tea and Green Tea Extracts in Chicken
Bibliographic record
Abstract
This study was conducted to examine the effect of green tea on body, adipose tissue, and liver weights and adipose tissue weight to body weight ratio and the adipogenic differentiation and expression of adipogenic transcripts in chicken (Gallus gallus) preadipocytes. In experiment one, chicks were weighed and randomly assigned to a control diet (CTRL) and green tea treatment (GT 1%, w/w) for 38 days. In experiment 2, preadipocytes were isolated from 20 wk old chicken and treated with an adipogenic cocktail (DMIOA) containing 500 nM dexamethasone, 0.5 mM 3-isobutyl-1-methylxanthine, 20 μg/mL insulin, and 300 μM OA, DMIOA+30 μg of extract B, E, H, Mc, T, and W, respectively, for 48 h. Data were analysed using the General Liner Model procedure of the Statistics Analysis System (SAS) Institute version 9.4, and differences were considered significant at P < 0.05. Gene expression was measured using quantitative real-time PCR. GT 1% significantly reduced body (- 9%; P = 0.0447), liver (-20%; P = 0.0206), and abdominal fat weight (- 44%; P = 0.0055) compared with the control (CTRL) group (GT 0%). The abdominal fat/body weight ratio of green tea supplemented group (- 36%; P = 0.0125) was also significantly lower than that of control group. In cell culture study, all green tea extracts inhibited C/EBPα and β mRNA expression compared to DMIOA. DMIOA+B, T, or W reduced mRNA expression of FABP4 by three-fold compared to DMIOA. Although all green tea extracts reduced adipocyte formation, T and W had the strongest anti-adipogenic effects. These results demonstrate that supplementation of green tea could be an effective strategy in the control of obesity in chickens.
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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".