Metabolic effects of glycerol supplementation and aerobic physical training on Wistar rats
Bibliographic record
Abstract
We evaluated the effects of oral glycerol supplementation on trained rats fed a normal diet. Wistar rats were distributed among 6 groups in a completely randomized 2 × 3 factorial design. The animals were subjected to 6 weeks of aerobic training. In the last 4 weeks, the animals' diet was supplemented with saline, glucose, or glycerol. Data were subjected to one-way analysis of variance (ANOVA) followed by a Student-Newmann-Keuls test, with values for P < 0.05 considered statistically significant. The change in body mass was lower in the trained groups, and their food and water consumption were higher. Glycerol supplementation resulted in an increase in the levels of triacylglycerol (TAG) and total cholesterol, as well as in the area and diameter of adipocytes. When associated with training, these parameters were similar to those of other trained groups. Levels of low-density lipoprotein + very-low-density lipoprotein cholesterol decreased in the trained animals that received glycerol compared with the non-trained ones. Glycerol consumption caused a reduction in food intake and increased the villous:crypt (V:C) ratio. No changes in glycemia, high density lipoproteins, or density of adipocytes were observed. Supplementation with glycerol together with aerobic physical training promoted beneficial metabolic effects. However, in non-trained rats glycerol increased the diameter and area of adipocytes, as well as the levels of TAG and total cholesterol.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".