Modulation of Matrix Metalloproteinase and Cytokine Production by Licorice Isolates Licoricidin and Licorisoflavan A: Potential Therapeutic Approach for Periodontitis
Bibliographic record
Abstract
BACKGROUND: Inflammatory cytokines and matrix metalloproteinases (MMPs) produced by resident and inflammatory cells in response to periodontopathogens play a major role in the tissue destruction observed in periodontitis, which is a disease that affects tooth-supporting structures. In the present study, we investigate the effects of licorice-derived licoricidin (LC) and licorisoflavan A (LIA) on the secretion of various cytokines and MMPs by human monocyte-derived macrophages stimulated with Aggregatibacter actinomycetemcomitans (previously Actinobacillus actinomycetemcomitans) lipopolysaccharide (LPS). METHODS: Macrophages were treated with non-toxic concentrations of LC or LIA before being stimulated with A. actinomycetemcomitans LPS. The secretion of cytokines and MMPs and the activation of nuclear factor-kappa B (NF-κB) p65 and activator protein (AP)-1 were assessed by enzyme-linked immunosorbent assays. RESULTS: LC and LIA inhibited the secretion of interleukin (IL)-6 and chemokine (C-C motif) ligand 5 in a concentration-dependent manner but did not affect the secretion of IL-8 by LPS-stimulated macrophages. LC and LIA also inhibited the secretion of MMP-7, -8, and -9 by macrophages. The suppression of cytokine and MMP secretion by LC and LIA was associated with the reduced activation of NF-κB p65 but not that of AP-1. CONCLUSION: The present study suggests that LC and LIA have potential for the development of novel host-modulating strategies for the treatment of cytokine and/or MMP-mediated disorders such as 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".