Effect of Citrus aurantium L. Essential Oil on Streptococcus mutans Growth, Biofilm Formation and Virulent Genes Expression
Bibliographic record
Abstract
In an oral cavity, dental caries, periodontal disease, and endodontic lesions are caused by well-known bacterial and fungal pathogens. Essential oils (EOs) have demonstrated antimicrobial activity suggesting their use for oral hygiene. The goal of this study was to evaluate the interaction of bitter orange flower (Citrus aurantium L.) essential oil with cariogenic bacteria Streptococcus mutans and human gingival epithelial cells. After extraction, the chemical composition of the essential oil was analyzed by gas chromatography, and its antimicrobial activity was evaluated against the growth and the expression of virulent genes in S. mutans. Finally, the effects of this essential oil on human gingival epithelial cell adhesion and growth were assessed using cell adhesion and proliferation assays. We showed that the major constituents of the tested essential oil were limonene, linalool, and β-ocimene. The essential oil reduced the growth of S. mutans, and decreased expression of comC, comD, comE, gtfB, gtfC, and gbpB genes. It should, however, be noted that essential oil at high concentration was toxic to gingival epithelial cells. Overall, this study suggests that C. aurantium L. essential oil could be used to prevent/control oral infections.
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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".