Fluoride release and anti-bacterial activityof different bioactive restorative materials:an in vitro comparative study
Bibliographic record
Abstract
Introduction: Anti-bacterial properties of restorative materials through fluoride release enhance their inhibition ability against bacterial development, and reduce recurrent caries incidence.Objectives: The aim of the current in-vitro research was to assess and compare fluoride release and anti-bacterial activity of three distinct bioactive dental restorations, such as resin-modified glass ionomer (RMGI), giomer, and Activa bioactive composite at different time intervals in vitro.Material and methods: 60 disc-shaped specimens were prepared and based on the kind of dental restoration materials, and were grouped into three equivalent major groups (20 specimens each group): RMGI (Fuji II LC), giomer (Beautifil II), and enhanced RMGI (Activa bioactive composite).Every main group was divided into two sub-groups based on the evaluator criteria, such as fluoride release (n = 15) and anti-bacterial action (n = 5).Based on storage duration, every fluoride release sub-group was then sub-divided into 3 equivalent divisions: 24 hours, 1 month, and 3 months (5 specimens each group).An ion-specific electrode was used to quantify fluoride.Anti-bacterial activity was recorded following one day according to sizes of inhibition zone.Results: Fluoride release and anti-bacterial activity were statistically higher in Fuji II followed by Activa, while Beautifil II showed the least fluoride release.The fluoride release amount of all groups was greatest in the first 24 hours, and decreased significantly with time (1 month > 3 months).Conclusions: Given the context of this research, RMGI presents better fluoride release and anti-bacterial activity than Activa and giomer.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".