Impact of Cranberries on Gut Microbiota and Cardiometabolic Health: Proceedings of the Cranberry Health Research Conference 2015
Bibliographic record
Abstract
Recent advances in cranberry research have expanded the evidence for the role of this Vaccinium berry fruit in modulating gut microbiota function and cardiometabolic risk factors. The A-type structure of cranberry proanthocyanidins seems to be responsible for much of this fruit's efficacy as a natural antimicrobial. Cranberry proanthocyanidins interfere with colonization of the gut by extraintestinal pathogenic Escherichia coli in vitro and attenuate gut barrier dysfunction caused by dietary insults in vivo. Furthermore, new studies indicate synergy between these proanthocyanidins, other cranberry components such as isoprenoids and xyloglucans, and gut microbiota. Together, cranberry constituents and their bioactive catabolites have been found to contribute to mechanisms affecting bacterial adhesion, coaggregation, and biofilm formation that may underlie potential clinical benefits on gastrointestinal and urinary tract infections, as well as on systemic anti-inflammatory actions mediated via the gut microbiome. A limited but growing body of evidence from randomized clinical trials reveals favorable effects of cranberry consumption on measures of cardiometabolic health, including serum lipid profiles, blood pressure, endothelial function, glucoregulation, and a variety of biomarkers of inflammation and oxidative stress. These results warrant further research, particularly studies dedicated to the elucidation of dose-response relations, pharmacokinetic/metabolomics profiles, and relevant biomarkers of action with the use of fully characterized cranberry products. Freeze-dried whole cranberry powder and a matched placebo were recently made available to investigators to facilitate such work, including interlaboratory comparability.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".