Effect of Soaking and Fermentation of Wheat Bran on Weight Gain, Accumulative Food Intake and Food Efficiency Ratio in Rats
Bibliographic record
Abstract
This study was conducted to investigate the effect of different processing treatments in terms of soaking and fermentation of wheat bran on weight gain, accumulative food intake and food efficiency ratio in Sprague-Dawley rats. The experimental diets included casein diet (zero-bran), untreated bran diet, soaked bran diet, fermented bran diet, “soaked and fermented” bran diet and Arabic bread diet. Each group of rats (6/group) was fed one of the six prepared diets for 6 weeks. There was no significant difference (p > 0.05) in the body weight gain among all rat groups, although the rats fed soaked diet tended to have the highest weight gain in comparison with other groups. There was no significant difference in FER among all groups, although, there was a difference between groups in the accumulative food intake. Accumulative food intake (AFI) of the rat group fed soaked bran based-diet was the highest (685.6 ± 17.3 gm) among all groups whereas AFI of the rat group fed “soaked and fermented” bran-based diet was the lowest (550.0 ± 19.1 gm). Rat group fed “soaked and fermented” bran-based diet had significantly lower levels (p < 0.05) of AFI than those fed untreated bran diet, soaked bran diet and white bread diet (550.0 ± 19.1, 663.4 ± 16.6, 685.6 ± 17.3 and 629.8 ± 28.4 gm respectively). Accordingly, the AFI of the rat group fed soaked bran diet was significantly higher (p < 0.05) than those fed casein and fermented diet (685.6 ± 17.3, 598.4 ± 9.2 and 605.8 ± 25.6 gm respectively). It is concluded that preparation of wheat bran foods by soaking or/and fermentation improve some physiological characteristics of insoluble fibers, including the body weight changes, accumulative food intake and food efficiency ratio for 6 weeks.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".