Reduction of bacterial volatile sulfur compound production by licoricidin and licorisoflavan A from licorice
Bibliographic record
Abstract
Halitosis affects a large proportion of the population and is, in most cases, caused by the production of volatile sulfur compounds (VSCs), particularly methyl mercaptan and hydrogen sulfide, by specific bacterial species colonizing the oral cavity. In this study, a supercritical extract of Chinese licorice (Glycyrrhiza uralensis), and its major isoflavans, licoricidin and licorisoflavan A, were investigated for their effect on growth, VSC production and protease activity of Porphyromonas gingivalis, Prevotella intermedia and Solobacterium moorei, which have been associated with halitosis. The effects of licorice extract, licoricidin, and licorisoflavan A on VSC production in a saliva model were also tested. We first showed that licoricidin and licorisoflavan A, and to a lesser extent the licorice extract, were effective in inhibiting the growth of all three bacterial species, with minimal inhibitory concentrations in the range of 2-80 µg ml(-1). The licorice extract and the two isolates licoricidin and licorisoflavan A, were able to dose-dependently reduce VSC production by P. gingivalis, Prev. intermedia, and S. moorei as well as by a human saliva model. Although the extract and isolates did not inhibit the proteolytic activity of bacteria, they blocked the conversion of cysteine into hydrogen sulfide by Prev. intermedia. Lastly, the deodorizing effects of the licorice extract, licoricidin, and licorisoflavan A were demonstrated, as they can neutralize P. gingivalis-derived VSCs. Licorisoflavan A (10 µg ml(-1)) was found to be the most effective by reducing VSC levels by 50%. Within the limitations of this study, it can be concluded that a licorice supercritical extract and its major isoflavans (licoricidin and licorisoflavan A) represent natural ingredients with a potential for reducing bacterial VSC production and therefore for controlling halitosis.
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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) | 0.001 | 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".