The Concentration of Organic Acids in Cranberry Juice Modulates the Gut Microbiota in Mice
Bibliographic record
Abstract
A daily consumption of cranberry juice (CJ) is linked to many beneficial health effects due to its richness in polyphenols but could also awake some intestinal discomforts due to its organic acid content and possibly lead to intestinal inflammation. Additionally, the impact of such a juice on the gut microbiota is still unknown. Thus, this study aimed to determine the impacts of a daily consumption of CJ and its successive deacidification on the intestinal inflammation and on the gut microbiota in mice. Four deacidified CJs (DCJs) (deacidification rates of 0, 40, 60, and 80%) were produced by electrodialysis with bipolar membrane (EDBM) and administered to C57BL/6J mice for four weeks, while the diet (CHOW) and the water were ad libitum. Different parameters were measured to determine intestinal inflammation when the gut microbiota was profiled. Treatment with a 0% DCJ did not induce intestinal inflammation but increased the gut microbiota diversity and induced a modulation of its functions in comparison with control (water). The effect of the removal of the organic acid content of CJ on the decrease of intestinal inflammation could not be observed. However, deacidification by EDBM of CJ induced an additional increase, in comparison with a 0% DCJ, in the Lachnospiraceae family which have beneficial effects and functions associated with protection of the intestine: the lower the organic acid content, the more bacteria of the Lachnospiraceae family and functions having a positive impact on the gut microbiota.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".