Inhibition of host extracellular matrix destructive enzyme production and activity by a high‐molecular‐weight cranberry fraction
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Periodontal diseases are a group of inflammatory disorders that are initiated by specific gram-negative bacteria and lead to connective tissue destruction. Proteolytic enzymes, including matrix metalloproteinases (MMPs) and elastase, produced by resident and inflammatory cells in response to periodontopathogens and their products, play a major role in gingival tissue destruction. The aim of this study was to investigate the effect of a high-molecular-weight fraction prepared from cranberry juice concentrate on MMP-3, MMP-9 and elastase activities, as well as on MMP production by human cells stimulated with lipopolysaccharide of Actinobacillus actinomycetemcomitans. MATERIAL AND METHODS: MMP-3 and MMP-9 production by gingival fibroblasts and macrophages treated with the cranberry fraction and then stimulated with lipopolysaccharide was measured by enzyme-linked immunosorbent assay. MMP-3, MMP-9 and elastase activities in the presence of the cranberry fraction were evaluated using colorimetric or fluorogenic substrates. The changes in expression and phosphorylation state of fibroblast intracellular signaling proteins induced by A. actinomycetemcomitans lipopolysaccharide and the cranberry fraction were characterized by antibody microarrays. RESULTS: The lipopolysaccharide-induced MMP-3 and MMP-9 responses of fibroblasts and macrophages were inhibited in a dose-dependent manner by the cranberry fraction. This fraction was found to inhibit fibroblast intracellular signaling proteins, a phenomenon that may lead to a down-regulation of activating protein-1 activity. MMP-3, MMP-9 and elastase activities were also efficiently inhibited by the cranberry fraction, even when it was used at low concentrations. CONCLUSION: These results suggest that cranberry compounds offer promising perspectives for the development of novel host-modulating strategies for an adjunctive 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".