A Phenolic-rich Extract of Cocoa (Theobroma cacao L.) Beans Impairs the Pathogenic Properties of Porphyromonas gingivalis and Attenuates the Activation of Nuclear Factor Kappa B in a Monocyte Model
Bibliographic record
Abstract
Periodontitis, an inflammatory disease that affects tooth-supporting tissues, is the result of a polymicrobial infection involving mainly Gram negative anaerobic bacteria. The aim of the present study was to investigate the effects of a phenolic-rich extract of cocoa (Theobroma cacao L.) beans on the pathogenic properties of Porphyromonas gingivalis, which is well-known as a keystone pathogen in the development of periodontitis. The effect of the cocoa extract on P. gingivalis-induced activation of the nuclear factor kappa B (NF-κB) transcription factor in a monocyte model was also assessed. The cocoa extract, whose major phenolic compound was epicatechin, inhibited the growth, hemolytic activity, proteolytic activities, and adherence properties (basement membrane matrix, erythrocytes) of P. gingivalis in a dose-dependent manner. It also protected the barrier function of a keratinocyte model against the deleterious effects mediated by P. gingivalis, and attenuated reactive oxygen species (ROS) production by oral keratinocytes treated with P. gingivalis. Lastly, the cocoa extract showed an anti-inflammatory property by preventing P. gingivalis-induced NF-κB activation in monocytes. In conclusion, this in vitro study highlighted the potential value of an epicatechin-rich extract of cocoa beans for preventing and/or treating periodontal diseases.
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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".