Weight and lipid‐lowering effects and safety of a herbal formulation in a rat model of obesity
Bibliographic record
Abstract
Both overweight and obesity increase the risk of developing several serious chronic diseases, as well as lipid‐related conditions, such as hypercholesterolemia. Therefore, the present study was conducted to investigate the effect of a proprietary herbal formulation on body weight, food intake, plasma lipids and liver enzymes, and body fat content and distribution in a rat model of obesity. In this study, we used obese‐resistant (OR) and obeseprone (OP) rats, which were housed individually in cages. All rats were fed a high‐fat (60% energy from fat) diet for 9 weeks. The OR rats were used as a normal control group and one group of OP rats was used as an obesity control. An additional three OP groups were challenged with different dose levels of the herbal formulation. The results demonstrated that the formulation was able to lower body weight, absolute body fat, and triacylglycerides. The results also revealed that the product reduced food intake. There are a growing number of available weight loss products on the marketplace but many exhibit adverse side effects. These side effects can be assessed by measuring the concentration of liver enzymes in the serum. In the present study, the formulation administered did not exhibit any liver toxicity at any of the dose levels. In conclusion, the formulation appears to warrant further examination for possible development as a natural product for weight control and lipid management. Grant Funding Source : CIHR
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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 | 0.001 | 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".