Supplementation with a cranberry extract favors the establishment of butyrogenic guilds in the human fermentation SHIME system
Bibliographic record
Abstract
Background: Proanthocyanidins (PAC) and oligosaccharides from cranberry exhibit multiple bioactive health properties and persist intact in the colon post-ingestion. They display a complex bidirectional interaction with the microbiome, which varies based on both time and specific regions of the gut; the nature of this interaction remains inadequately understood. Therefore, we aimed to investigate the impact of cranberry extract on gut microbiota ecology and function. Methods: We studied the effect of a cranberry extract on six healthy participants over a two-week supplementation period using the ex vivo artificial fermentation system TWIN-M-SHIME to replicate luminal and mucosal niches of the ascending and transverse colon. Results: Our findings revealed a significant influence of cranberry extract supplementation on the gut microbiota ecology under ex vivo conditions, leading to a considerable change in bacterial metabolism. Specifically, Bifidobacterium adolescentis (B. adolescentis ) flourished in the mucus of the ascending colon, accompanied by a reduced adhesion of Proteobacteria . The overall bacterial metabolism shifted from acetate to propionate and, notably, butyrate production following PAC supplementation. Although there were variations in microbiota modulation among the six donors, the butyrogenic effect induced by the supplementation remained consistent across all individuals. This metabolic shift was associated with a rise in the relative abundance of several short-chain fatty acid (SCFA)-producing bacterial genera and the formation of a consortium of key butyrogenic bacteria in the mucus of the transverse colon. Conclusions: These observations suggest that cranberry extract supplementation has the potential to modulate the gut microbiota in a manner that may promote overall 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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".