Highly Active Cranberry’s Polyphenolic Fraction: New Advances in Processing and Clinical Applications
Bibliographic record
Abstract
Cranberry is a fruit originally from New England and currently growing throughout the east and northeast parts of the USA and Canada. The supplementation of cranberry extracts as nutraceuticals showed to contribute to the prevention of urinary tract infections, and most likely it may help to prevent cardiovascular and gastroenteric diseases, as highlighted by several clinical trials. However, aiming to validate the efficacy and safety of clinical applications as long-term randomized clinical trials (RCTs), further investigations of the mechanisms of action are required. In addition, a real challenge for next years is the standardization of cranberry's polyphenolic fractions. In this context, the optimization of the extraction process and downstream processing represent a key point for a reliable active principle for the formulation of a food supplement. For this reason, new non-conventional extraction methods have been developed to improve the quality of the extracts and reduce the overall costs. The aim of this survey is to describe both technologies and processes for highly active cranberry extracts as well as the effects observed in clinical studies and the respective tolerability notes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".