Effects of Wheat Bran and Brown Rice Cereals on the Intestinal Environment and Skin Conditions
Bibliographic record
Abstract
Ingestion of brown rice cereal has been found to improve skin conditions, while the effects of wheat bran cereal were limited (Ide et al., J. Integr. Study Diet. Habits, 2005, in press). The effects of these breakfast cereals on the composition of intestinal flora and the intestinal environment were investigated. Two different types of breakfast cereals, wheat bran cereal that is particularly rich in dietary fiber and brown rice cereal fortified with vitamins and minerals, were consumed twice a day by young female volunteers. The control group ingested their usual diet. Composition of fecal flora, fecal moisture, fecal pH, fecal enzymic activities and concentrations of intestinal putrefactive products and short chain fatty acids as well as skin conditions were analyzed. The populations of Enterobacteriaceae and Streptococcaceae significantly decreased after two weeks of wheat bran cereal consumption. Moisture of feces was significantly decreased and the properties of feces and defecation frequency were improved by wheat bran cereal. Activity of β-glucosidase increased significantly and the concentrations of putrefactive products decreased slightly. The effects of brown rice cereal on composition of intestinal flora and intestinal environment were not obvious. No direct correlation between the effects on the intestinal environment and skin conditions was found. The results suggest that the two different types of breakfast cereals used in the present study have different impacts on the intestinal environment and skin conditions.
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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) | 0.002 | 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".