Liuwei Dihuang Aqueous Extract Reduces Weight Gain in Obese‐Prone Rats through Multi‐Mechanisms
Bibliographic record
Abstract
Liuwei Dihuang (LWDH), a traditional Chinese herbal formulation, has been demonstrated to reduce weight gain in obese rats; however, the active components and mechanisms are unknown. In the present study, water extract of LWDH was obtained and determined for its effect and mechanism of action on weight gain in male obese‐prone CD rats. The rats were divided into three groups and fed a high‐fat diet (60 kcal% from fat). Two treatment groups received 600 (WE600) or 1200 (WE1200) mg/kg/d LWDH water extract dissolved in water once a day via gavage feeding for 10 wk. The control rats were gavaged with the control vehicle. It was found that WE1200 lowered body weights after 3 weeks of treatment and the effect was retained throughout the remaining study period. WE1200 also lowered visceral fat mass, serum free fatty acids, serum leptin, as well as blood cholesterol and triacylglycerides. Oxygen consumption, carbon dioxide production and fat oxidation were increased in both light and dark periods, while carbon dioxide oxidation increased in the light period in the WE1200 group. Energy expenditure was increased in the WE1200 group in both light and dark periods. Moreover, rats in the WE1200 group had lower levels of serum free fatty acids and leptin levels. Rats in the WE600 had lower serum free fatty acids and leptin levels without showing significant effects on other parameters compared to controls. These results demonstrated that consumption of LWDH water extract suppressed weight gain, visceral fat mass and improved several phenotypes of metabolic syndrome in obese rats through increasing energy expenditure, decreasing energy intake and improving leptin sensitivity. ‐‐‐‐Research was supported by CIHR grant CCI‐92219.
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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) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".