Eriodictyol Suppresses Porphyromonas gingivalis-Induced Reactive Oxygen Species Production by Gingival Keratinocytes and the Inflammatory Response of Macrophages
Bibliographic record
Abstract
Porphyromonas gingivalis is a key pathogen of periodontitis, an inflammatory disease that affects the tooth-supporting tissues. The aim of the present study was to investigate the effects of the flavanone eriodictyol on P. gingivalis-induced reactive oxygen species (ROS) production by gingival keratinocytes and the inflammatory response of macrophages. Porphyromonas gingivalis and H2O2 acted synergistically to induce ROS production by keratinocytes. The presence of eriodictyol significantly attenuated ROS production in a dose-dependent manner. We used a macrophage model to show that eriodictyol decreases the secretion of IL-1β, IL-6, IL-8, and TNF-α induced by P. gingivalis. Evidence has been brought that this anti-inflammatory property of eriodictyol may be related to its ability to prevent the activation of the NF-κB signaling pathway by P. gingivalis. This periodontal pathogen was also found to be a potent inducer of matrix metalloproteinase (MMP) production by macrophages, including MMP-2, MMP-8, and MMP-9. Eriodictyol dose-dependently inhibited the production of all three MMPs. Lastly, eriodictyol inhibited the catalytic activity of both MMP-9 and P. gingivalis collagenase. In conclusion, eriodictyol may be a potential therapeutic agent for preventing and/or treating periodontal disease due to its antioxidant, anti-inflammatory, and anti-proteinase properties.
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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".