Anti-inflammatory Activity of a High-molecular-weight Cranberry Fraction on Macrophages Stimulated by Lipopolysaccharides from Periodontopathogens
Bibliographic record
Abstract
Periodontitis is a chronic inflammatory disease affecting oral tissues. The continuous, high production of cytokines by host cells triggered by periodontopathogens is thought to be responsible for the destruction of tooth-supporting tissues. Macrophages play a critical role in this host inflammatory response to periodontopathogens. The aim of this study was to investigate the effect of non-dialyzable material prepared from cranberry juice concentrate on the pro-inflammatory cytokine response of macrophages induced by lipopolysaccharides (LPS) from Actinobacillus actinomycetemcomitans, Fusobacterium nucleatum subsp. nucleatum, Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, and Escherichia coli. Interleukin-1 beta (IL-1beta), IL-6, IL-8, tumor necrosis factor alpha (TNF-alpha), and Regulated on Activation Normal T-cell Expressed and Secreted (RANTES) production by macrophages treated with the cranberry fraction prior to stimulation by LPS was evaluated by ELISA. Our results clearly indicate that the cranberry fraction was a potent inhibitor of the pro-inflammatory cytokine and chemokine responses induced by LPS. This suggests that cranberry constituents may offer perspectives for the development of a new therapeutic approach to the prevention and treatment of periodontitis.
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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 | 0.000 |
| 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".