Coadministration of epigallocatechin‐3‐gallate (EGCG) and caffeine in low dose ameliorates obesity and nonalcoholic fatty liver disease in obese rats
Bibliographic record
Abstract
Epigallocatechin-3-gallate (EGCG) and caffeine in tea exert anti-obesity effects and induces nonalcoholic fatty liver disease (NAFLD) amelioration. However, previous studies usually performed a high-dose EGCG administration, whereas the insecurity was arisen in recent researches. In this study, we treated obese rats with an elaborate dose-40 mg/kg EGCG, 20 mg/kg caffeine, and the coadministration of them as low dose, which were similar to the daily intake; 160 mg/kg EGCG as high dose, which was the maximum safe dose had touched the contentious edge. The results suggested that the coadministration of EGCG and caffeine exerted more remarkable function on suppressing body weight gain, reducing white adipose tissue weight and decreasing the energy intake than single use. This may be due to the variation in serum lipid profile, oxidative stress, and adipose-derived and inflammatory cytokines. The pathological micrographs showed long-term high-fat diets caused severe NAFLD, but it was ameliorated at different levels by all of the administrations. In summary, low dose of EGCG or caffeine only showed a mild effect of anti-obesity and NAFLD amelioration. The coadministration of them could exert a superior curative effect as well as high dose EGCG but no anxiety regarding safety.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".