Biocompatible combinations of nisin and licorice polyphenols exert synergistic bactericidal effects against Enterococcus faecalis and inhibit NF-κB activation in monocytes
Bibliographic record
Abstract
Enterococcus faecalis is one of the bacterial species most frequently isolated from persistent endodontic and apical periodontal infections. The aim of the present study was to evaluate the synergistic antibacterial effects of nisin and selected licorice polyphenols (glabridin, licoricidin, licochalcone A) against planktonic and biofilm-embedded E. faecalis cells. The biocompatibility and anti-inflammatory properties of the nisin/licorice polyphenol combinations were also investigated. The lantibiotic bacteriocin (nisin), the two isoflavonoids (glabridin, licoricidin), and the chalcone (licochalcone A) efficiently inhibited the growth of E. faecalis, with MICs ranging from 6.25 to 25 µg/mL. Combining nisin with each licorice polyphenol individually resulted in a significant synergistic antibacterial effect. Following a 30-min contact, nisin in combination with either glabridin, licoricidin, or licochalcone A caused significant biofilm killing. The nisin/licorice polyphenol combinations had no cytotoxic effects (oral epithelial cells, gingival fibroblasts, and stem cells of the apical papilla), with the exception of nisin/glabridin, when used at their MICs. Lastly, we showed that nisin/glabridin, nisin/licoricidin, and nisin/licochalcone A inhibit NF-κB activation induced by E. faecalis in a monocyte model, suggesting that these combinations possess anti-inflammatory properties. The present study provides evidence that combinations of nisin and glabridin, licoricidin, or licochalcone A show promise as root canal disinfection agents.
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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".