A herbal premix containing <i>Macrotyloma uniflorum</i>, ginger, and whey curtails obesity in rats fed a high-fat diet by a novel mechanism
Bibliographic record
Abstract
The present study designed and evaluated a polyherbal premix comprising Macrotyloma uniflorum, whey protein, Zingiber officinale, and Mentha piperita. Animals were fed a high-fat diet (HFD) for 30 days and were daily administered the premix (1.5 g/kg) in milk (PM) and water (PW), aerobic exercise (AE), premix in milk and water along with AE (PMAE and PWAE), ferulic acid (100 mg/kg), and the reference drug fluoxetine (6 mg/kg). All treatments showed significant reduction in food intake, weight gain, abdominal circumference, and body mass index compared with their initial values. All treatments generated a faster peak of the satiety marker cholecystokinin compared with the HFD group and control groups; PMAE and PWAE exhibited sustained satiety. The HFD-elevated blood glucose levels were significantly attenuated on the 30th day by all treatments when compared with their 15th day and basal values; PMAE exhibited the best results. All treatments significantly attenuated the HFD-elevated serum insulin, homeostasis model assessment of insulin resistance, C-reactive protein, triglycerides, total cholesterol, very-low-density lipoprotein, and low-density lipoprotein levels and significantly restored the HFD-depleted high-density lipoprotein and adiponectin levels. HFD-elevated thiobarbituric acid reactive substances values were attenuated successfully and the HFD-depleted reduced glutathione, superoxide dismutase, and catalase levels were significantly restored by all treatments. The histological findings corroborated the biochemical results. Novelty The polyherbal premix brought about appetite regulation and induction of satiety to control obesity in HFD-fed rats through homeostasis of energy metabolism. The premix along with exercise is a complete way to combat obesity.
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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".