Resveratrol attenuates the pathogenic and inflammatory properties of <i>Porphyromonas gingivalis</i>
Bibliographic record
Abstract
Porphyromonas gingivalis has been strongly associated with chronic periodontitis, which affects tooth-supporting tissues. This Gram-negative anaerobic bacterium produces a repertoire of virulence factors that modulate tissue destruction directly or indirectly by the induction of inflammatory processes. The aim of this study was to investigate the effects of resveratrol, a major polyphenol found in grapes and wine, on the growth and virulence properties of P. gingivalis as well as on gingival keratinocyte tight junction integrity and the host inflammatory response. Resveratrol exhibited antibacterial activity that may result from damage to the bacterial cell membrane. Resveratrol also killed a pre-formed P. gingivalis biofilm and reduced bacterial adherence to matrix proteins. In addition, resveratrol had a protective effect on the integrity of the keratinocyte tight junctions by inhibiting its breakdown by P. gingivalis. This may be related to the ability of resveratrol to inhibit the protease activities of P. gingivalis. Lastly, resveratrol reduced P. gingivalis-mediated activation of the NF-κB signaling pathway and attenuated TREM-1 gene expression as well as soluble TREM-1 secretion in monocytes. The effect on NF-κB activation likely results from the ability of resveratrol to act as a PPAR-γ agonist. In summary, the antibacterial, anti-adherence, and antiprotease properties of resveratrol, as well as its ability to protect the gingival keratinocyte barrier and attenuate the inflammatory response in monocytes suggest that it may be a promising novel therapeutic agent for treating periodontal disease.
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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 | 0.001 |
| 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 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".