Grape Seed Extract Suppresses Lipopolysaccharide‐Induced Matrix Metalloproteinase (MMP) Secretion by Macrophages and Inhibits Human MMP‐1 and −9 Activities
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinases (MMPs) produced by resident and inflammatory cells in response to Gram-negative periodontopathogens play a major role in the tissue destruction observed during periodontitis, a disease that affects tooth-supporting structures. In this study, we investigated the effect of grape seed extract (GSE) on MMP secretion by human monocyte-derived macrophages stimulated with Aggregatibacter actinomycetemcomitans (previously Actinobacillus actinomycetemcomitans) lipopolysaccharide (LPS) and on the activity of human recombinant MMP-1 and -9. METHODS: Macrophages were treated with various concentrations of GSE prior to being stimulated with A. actinomycetemcomitans LPS. The secretion of MMPs and activation of nuclear factor-kappa B (NF-kappaB) p65 and activator protein-1 (AP-1) were assessed by enzyme-linked immunosorbent assay (ELISA). The effect of GSE on the catalytic activity of human recombinant MMP-1 and -9 was tested using fluorogenic assays. RESULTS: GSE inhibited the secretion of MMP-1, -3, -7, -8, -9, and -13 by LPS-stimulated macrophages in a concentration-dependent manner. The suppression of MMP secretion was associated with inhibition of NF-kappaB p65 and AP-1 activation. Also, GSE dose-dependently inhibited the activity of MMP-1 and -9. CONCLUSION: The present study suggests that GSE may be potentially used in the development of novel host-modulating strategies for the treatment of MMP-mediated disorders such as periodontitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".