Mix of Allegedly Functional Components Improves Metabolic Syndrome Risk Factors
Bibliographic record
Abstract
Background: Food is one of the main factors that diminish the risk of development of chronic diseases in humans. In view of this fact, increasing attention has focused on the production and consumption of foods that help reduce this risk. Besides, metabolic syndrome risk factors are increasing in almost all populations of the world. Therefore, the objective of this work was to evaluate the effects of a mixture of ingredients containing textured soy protein, wheat bran, oats, black sesame seeds, white sesame seeds, brown linseed, granola and brown sugar on the biochemical profile, body weight and intestinal motility of Wistar rats. Methods: The experiment involved male rats, which were divided into two groups: one that ingested the mix and the other a control group. Results: After 40 days of treatment, it was found that body weight, total cholesterol, triglycerides, and glucose levels were reduced and HDL-c increased. Intestinal transit time was also improved. Conclusion: It was concluded that the use of this mix had beneficial effects on the metabolic profile contributing to the prevention of metabolic syndrome risk factors and improved intestinal transit of Wistar rats. J Endocrinol Metab. 2015;5(4):238-244 doi: http://dx.doi.org/10.14740/jem292w
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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) | 0.001 | 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) | 0.003 | 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".