Effects of the Licorice Isoflavans Licoricidin and Glabridin on the Growth, Adherence Properties, and Acid Production of Streptococcus mutans, and Assessment of Their Biocompatibility
Bibliographic record
Abstract
Pharmacological studies have linked a number of human health benefits with licorice due to its anticancer, anti-inflammatory, anti-oxidant, and antimicrobial properties. The aim of this study was to investigate the effects of licoricidin and glabridin, two major licorice isoflavans, on growth and virulence properties (biofilm formation, acid production, dextran production, adherence) of the cariogenic bacterium Streptococcus mutans. Moreover, the biocompatibility of these licorice compounds was assessed in an in vitro model of oral keratinocytes. We used a broth microdilution assay to show that licoricidin and glabridin exhibit a marked antibacterial activity against S. mutans. Glabridin and, to a lesser extent, licoricidin reduced the biofilm viability of S. mutans. In addition, glabridin decreased the production of dextran by S. mutans. The two licorice isoflavans attenuated the adherence of S. mutans to a saliva-coated hydroxylapatite surface, and reduced acid production from glucose. Lastly, depending on the concentrations tested, the two licorice isoflavans showed no or low toxicity toward oral keratinocytes. Within the limitations of this study, our data suggest that licoricidin and glabridin may be promising agents for controlling dental caries.
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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".