Anti-microbial effect of Piper betle crude extract and essential oil incorporated into short-term soft lining material
Bibliographic record
Abstract
Introduction: Using soft lining material to treat denture stomatitis for an extended period of time may result in oral bacterial and fungal accumulations.Several additives, including natural products, have been identified as antimicrobial agents.Objectives: The aim of the present study was to evaluate the anti-microbial activity of Piper betle crude extract (PBC) and Piper betle essential oil (PBO) incorporated into soft liner with different concentrations against Candida albicans and Streptococcus mutans.Cytotoxicity of various additives applied into soft lining material was also investigated.Material and methods: Anti-microbial activity of PBC and PBO were assessed using agar disc diffusion method.Agar well diffusion method was also applied to evaluate inhibitory effect of PBC and PBO incorporated into soft lining material.Cell viability of human gingival fibroblast (HGF) cell line after exposing to soft lining materials with PBC was investigated.Results: With the same minimum inhibitory concentration (MIC) of 2.5% w/w, PBC showed anti-fungal and anti-bacterial activities against C. albicans and S. mutans.However, PBO had a 2.5% v/v MIC against C. albicans, but a 10% v/v MIC against S. mutans.Accordingly, GC soft liner with PBC had MICs of 5% w/w against C. albicans and 10% w/w against S. mutans, whereas GC soft liner with PBO had MICs of 20% v/v and 60% v/v against C. albicans and S. mutans, respectively.Statistical analysis revealed that there was no significant difference in cell viability among GC soft liner, with 5% and 10% w/w PBC and GC soft liner without additive (p > 0.05).Conclusions: Both PBC and PBO showed anti-bacterial and anti-fungal activity; however, the 10% w/w PBC incorporated into GC soft liner demonstrated the optimal concentration to serve as an anti-microbial agent against both C. albicans and S. mutans, with no toxicity to HGF cells.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".