Potentiation of the bioavailability of blueberry phenolic compounds by co-ingested grape phenolic compounds in mice, revealed by targeted metabolomic profiling in plasma and feces
Bibliographic record
Abstract
The low bioavailability of dietary phenolic compounds, resulting from poor absorption and high rates of metabolism and excretion, is a concern as it can limit their potential beneficial effects on health. Targeted metabolomic profiling in plasma and feces of mice supplemented for 15 days with a blueberry extract, a grape extract or their combination revealed significantly increased plasma concentrations (3-5 fold) of blueberry phenolic metabolites in the presence of a co-ingested grape extract, associated with an equivalent decrease in their appearance in feces. Additionally, the repeated daily administration of the blueberry-grape combination significantly increased plasma phenolic concentrations (2-3-fold) compared to animals receiving only a single acute dose, with no such increase being observed with individual extracts. These findings highlight a positive interaction between blueberry and grape constituents, in which the grape extract enhanced the absorption of blueberry phenolic compounds. This study provides for the first time in vivo evidence of such an interaction occurring between co-ingested phenolic compounds from fruit extracts leading to their improved bioavailability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".