Cranberry Research Progress: A Systematic Review of Chemical Composition, Pharmacological Mechanisms, Clinical Applications, and Nutritional Significance
Bibliographic record
Abstract
in the family Ericaceae, often used as a functional food. Cranberries are primarily distributed in the northern United States-including Massachusetts, Wisconsin, and Maine-as well as in Quebec, Canada; the state of Columbia; Chile in South America; and northeastern Europe. They are also found in China's Greater Khingan Range and Fuyuan City, Jiamusi, Heilongjiang Province. The plants thrive in cool environments and exhibit considerable adaptability to soil conditions, preferring acidic soils. Cranberries are rich in a variety of biologically active components, such as polyphenols (proanthocyanidins, chlorogenic acid, flavonols, anthocyanins, caffeic acid, etc.), triterpenoids, and other nutrients. Studies have shown that the chemical components extracted from cranberry fruit have pharmacological effects such as antioxidant, anti-inflammatory, anti-cancer, and urinary tract infection prevention and treatment, and are commonly used clinically in the treatment of cardiovascular diseases, the prevention of urinary tract infections, blood pressure lowering, and the fight against Helicobacter pylori, among other clinical diseases. Cranberries also play a huge role in daily nutrition, and they are named for their richness in a variety of mineral elements, trace elements and vitamins. This work uses information from Pubmed, Web of Science, Scopus, CNKI (China National Knowledge Infrastructure), and related papers. In this paper, a comprehensive review of the phytochemical composition, pharmacological mechanism of action, clinical application value and nutritional significance of cranberry was conducted in recent years to provide references for the further extraction of chemical components in cranberry and rational clinical application, which can help to guide people to rationalize their diets and promote the formation of healthy diets.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".