Innovative biomaterials for the treatment of periodontal disease
Bibliographic record
Abstract
Periodontitis is a multifactorial disease that involves the destruction of hard and soft tissues surrounding the tooth. Routine periodontal treatment includes mechanical debridement (surgical and non-surgical) and the systemic administration of antibiotics. In contrast, severe and chronic periodontitis involves aggressive tissue destruction and bone resorption, and the damage is usually irreversible. In these severe cases, bone grafts, the delivery of growth hormones, and guided tissue regeneration can all be used to stimulate periodontal regeneration. However, these approaches do not result in consistent and predictable treatment outcomes. As a result, advanced biomaterials have evolved as an adjunctive approach to improve clinical performance. These novel biomaterials are designed to either prolong the release of antibacterial agents or osteogenic molecules, or to act as immunomodulators to promote healing. The first half of this review briefly summarizes the key immune cells and their underlying cellular pathways implicated in periodontitis. Advanced biomaterials designed to promote periodontal regeneration will be highlighted in the second half. Finally, the limitations of the current experimental design and the challenges of translational science will be discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".