Bioceramic micro-fillers reinforce antibiofilm and remineralization properties of clear aligner attachment materials
Bibliographic record
Abstract
Introduction: Clear aligners, while offering a more hygienic alternative to fixed appliances, are still associated with challenges including plaque accumulation and enamel demineralization. The aim of the present study was to investigate the antibiofilm and remineralization effectiveness of innovative flowable composite attachments containing bioceramic micro-fillers. Methods: Four experimental attachments were formulated and bonded to human enamel specimens: 3M Filtek Supreme flowable composite (Filtek SF) + 10% bioactive glass 45S5 (BAG), Filtek SF + 30% BAG, Filtek SF + 10% Bredigite (BRT), Filtek SF + 30% BRT. Plaque biofilms were grown on the bonded enamel using a standardized protocol and the biofilm-killing effect was assessed by confocal laser scanning microscopy and scanning electron microscopy. Vickers microhardness was measured to evaluate the remineralization effect of the attachments containing bioceramic fillers after acid challenge. Shear bond test was performed to assess the bonding strength. Results: Attachments with bioceramic fillers significantly inhibited plaque biofilm growth in 3 weeks on enamel, contributing over 20% bacterial cell killing in 10% filler groups and over 30% killing in 30% filler groups. All four experimental groups demonstrated significantly higher microhardness values than the control group without fillers on the attachment side. The shear bonding strength was not compromised in the attachments with micro-fillers. Discussion: Proper incorporation of bioceramic micro-fillers in attachments provides an innovative approach for clear aligner therapy with reinforced antibiofilm and remineralization effects without weakening shear bonding strength.
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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 | 0.000 | 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) | 0.000 | 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".