Availability and dose response of phytophenols from a wheat bran rich cereal product in healthy human volunteers
Bibliographic record
Abstract
SCOPE: Phytophenols present in cereals are metabolised to compounds that could be partly responsible for the reduced risk of chronic diseases and all-cause mortality associated with fibre-rich diets. The bioavailability, form and in vivo concentrations of these metabolites require to be established. MATERIALS AND METHODS: Eight healthy volunteers consumed a test meal containing a recommended dose (40 g) and high dose (120 g) of ready-to-eat wheat bran cereal and the systemic and colonic metabolites determined quantitatively by LC-MS. CONCLUSION: Analysis of the systemic metabolomes demonstrated that a wide range of phytophenols were absorbed/excreted (43 metabolites) within 5 h of consumption. These included 16 of the 21 major parent compounds identified in the intervention product and several of these were also found to be significantly increased in the colon. Not all of the metabolites were increased with the higher dose, suggesting some limitation in absorption due to intrinsic factors and/or the food matrix. Many compounds identified (e.g. ferulic acid and major metabolites) exhibit anti-inflammatory activity and impact on redox pathways. The combination of postprandial absorption and delivery to the colon, as well as hepatic recycling of the metabolites at these concentrations, is likely to be beneficial to both systemic and gut health.
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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 | 0.001 |
| 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 | 0.001 | 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".