Cranberry proanthocyanidins act in synergy with licochalcone A to reduce Porphyromonas gingivalis growth and virulence properties, and to suppress cytokine secretion by macrophages
Bibliographic record
Abstract
AIMS: Periodontitis is an inflammatory disease of polymicrobial origin that affects the tooth-supporting tissues. With the spread of antibiotic resistance among pathogenic bacteria, alternative strategies are required to better control infectious diseases such as periodontitis. The aim of our study was to investigate whether two natural compounds, A-type cranberry proanthocyanidins (AC-PACs) and licochalcone A, act in synergy against Porphyromonas gingivalis and the host inflammatory response of a macrophage model. METHODS AND RESULTS: Using a checkerboard microtitre test, AC-PACs and licochalcone A were found to act in synergy to inhibit P. gingivalis growth and biofilm formation. Fluorescein isothiocyanate-labelled P. gingivalis adhesion to oral epithelial cells was also inhibited by a combination of the two natural compounds in a synergistic manner. Fluorometric assays showed that although AC-PACs and licochalcone A reduced both MMP-9 and P. gingivalis collagenase activities, no synergy was obtained with a combination of the compounds. Lastly, AC-PACs and licochalcone A also acted in synergy to reduce the lipopolysaccharide (LPS)-induced secretion of the pro-inflammatory mediators IL-1β, TNF-α, IL-6 and IL-8 in a macrophage model. CONCLUSIONS: A-type cranberry proanthocyanidins and licochalcone A, natural compounds from cranberry and licorice, respectively, act in synergy on both P. gingivalis and the host immune response, the two principal etiological factors of periodontitis. SIGNIFICANCE AND IMPACT OF THE STUDY: The combined use of AC-PACs and licochalcone A may be a potential novel therapeutic strategy for the treatment and prevention of 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 | 0.000 |
| 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".