Dual Action of Myricetin on Porphyromonas gingivalis and the Inflammatory Response of Host Cells: A Promising Therapeutic Molecule for Periodontal Diseases
Bibliographic record
Abstract
Periodontitis that affects the underlying structures of the periodontium, including the alveolar bone, is a multifactorial disease, whose etiology involves interactions between specific bacterial species of the subgingival biofilm and the host immune components. In the present study, we investigated the effects of myricetin, a flavonol largely distributed in fruits and vegetables, on growth and virulence properties of Porphyromonas gingivalis as well as on the P. gingivalis-induced inflammatory response in host cells. Minimal inhibitory concentration values of myricetin against P. gingivalis were in the range of 62.5 to 125 μg/ml. The iron-chelating activity of myricetin may contribute to the antibacterial activity of this flavonol. Myricetin was found to attenuate the virulence of P. gingivalis by reducing the expression of genes coding for important virulence factors, including proteinases (rgpA, rgpB, and kgp) and adhesins (fimA, hagA, and hagB). Myricetin dose-dependently prevented NF-κB activation in a monocyte model. Moreover, it inhibited the secretion of IL-6, IL-8 and MMP-3 by P. gingivalis-stimulated gingival fibroblasts. In conclusion, our study brought clear evidence that the flavonol myricetin exhibits a dual action on the periodontopathogenic bacterium P. gingivalis and the inflammatory response of host cells. Therefore, myricetin holds promise as a therapeutic agent for the treatment/prevention 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".