Evaluating the anti-<i>Candida</i> effects of selenium nanoparticles impregnated in acrylic resins: An in vitro study
Bibliographic record
Abstract
Background. Candida albicans in the oral cavity causes denture-induced stomatitis, but current treatments have side effects and contribute to drug resistance. Selenium nanoparticles (SeNPs) show promise as an antimicrobial agent, but their effectiveness against C. albicans is unknown and warrants further research. Methods. Acrylic resins containing different concentrations of SeNPs (0.2, 2, and 10 g/mL) were formulated and evaluated against C. albicans isolates. The minimum inhibitory concentration (MIC) of SeNPs was determined, and a fungal biofilm was developed on acrylic samples. The quantity of biofilm was assessed using scanning electron microscopy (SEM) and optical density (OD) at 570 nm after staining with crystal violet. Statistical analysis was performed using STATA software, with Kruskal-Wallis and Mann-Whitney tests to establish significance (P<0.05). Results. The MIC of SeNPs was 25%. The OD in the group with 10% SeNPs was 0.477 and 0.547 in the group with 0.2%. Kruskal-Wallis test results showed that at least two groups among those studied had significant differences (P=0.0273). In pairwise comparisons, the differences between all the groups were statistically significant (P=0.049). SEM analysis confirmed the destruction of C. albicans cell walls, leading to reduced colonization, with the 10% group showing the highest efficacy. Conclusion. The study demonstrated that SeNPs are effective against C. albicans colonization when combined with acrylic resin. Specifically, SeNPs exhibited enhanced antifungal properties at a concentration of 10%. These findings confirm that SeNPs are a promising alternative to traditional antifungal agents for treating oral candidiasis and denture-induced stomatitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".