Exploring Mechanisms of Human Neutrophils’ Degradative Activities toward Resin-based Restorative Materials
Bibliographic record
Abstract
Restorative dentistry, a $46 billion industry annually in the USA, heavily relies on methacrylate resin-based composites for dental restorations. However, these materials frequently fail prematurely due to secondary caries at restoration margins. Neutrophils, abundant immune cells in the oral cavity, can degrade these materials, potentially compromising the tooth-restoration interface. This study aimed to elucidate the mechanisms underlying neutrophil-mediated degradation of methacrylate resin-based materials. Neutrophils were found to upregulate markers of adhesion and migration when exposed to resin-based materials, as measured in Flow Cytometry analysis. Neutrophils also degraded urethane dimethacrylate (UDMA) monomers, and possibly triethylene glycol dimethacrylate (TEGDMA) monomers as calculated via Ultra-Performance Liquid Chromatography, although evidence of the latter remains inconclusive. Also, neutrophil-derived enzyme, Neutrophil elastase, cleaved methacrylate resin-based materials in a material-dependent manner. Understanding these mechanisms can lead to the development of preventive strategies to improve the integrity and longevity of dental restorations and enhance patient oral health.
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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) | 0.002 | 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".