Potential Effect of Mucilage of Calophyllum inophyllum Nuts on Some Metabolic Syndrome Features Associated with a High-fat/High-sucrose Diet in Wistar Rats
Bibliographic record
Abstract
Metabolic syndrome (MetS) is a multi-faceted condition involving dyslipidemia, hyperglycemia, and overweight. The present work investigates the effect of mucilage of Calophyllum inophyllum nuts on body weight, plasma glucose, and plasma lipids in an animal model. Firstly, male Wistar rats were fed a high-fat/high sucrose (HFS) diet made of standard laboratory chow enriched with sucrose and egg yolk (caloric content: fats: 33.20%; sucrose: 21.30%) for 33 days. Thereafter, they were divided into groups of five animals, each group receiving one of the following by oral route daily for 14 days: distilled water (HFS-Ctrl), mucilage at doses of 250 or 500 mg/kg body weight (HFS-Ci250 and HFS-Ci500 respectively). The body weight of rats was measured at 3-day intervals. Fasting plasma glucose, oral glucose tolerance, and plasma lipid profile were assessed at the end of the study and cardiovascular risk indices were calculated. Mucilage treatment caused a significant decrease in body weight in groups HFS-Ci250 (-8.56%, p<0.05) and HFS-Ci500 (-14.27%, p<0.05) in comparison with the HFS-Ctrl group. HFS-fed rats treated with mucilage had an improved oral glucose tolerance with total incremental plasma glucose significantly lower than that of the HFS-Ctrl group. Mucilage-treated rats had significantly lower plasma total cholesterol, LDL-cholesterol, and triglycerides as well as higher HDL-cholesterol (+96.29%, p<0.05) which led to lower values of cardiovascular risk indices. Our results suggest that mucilage obtained from Calophyllum inophyllum nuts may find applications in the management of human metabolic syndrome by addressing its features such as obesity, hyperglycemia, and dyslipidemias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".