Functional Biomaterials for Local Control of Orthodontic Tooth Movement
Bibliographic record
Abstract
Orthodontic tooth movement (OTM) occurs with the application of a controlled mechanical force and results in coordinated tissue resorption and formation in the surrounding bone and periodontal ligament. The turnover processes of the periodontal and bone tissue are associated with specific signaling factors, such as Receptor Activator of Nuclear factor Kappa-β Ligand (RANKL), osteoprotegerin, runt-related transcription factor 2 (RUNX2), etc., which can be regulated by different biomaterials, promoting or inhibiting bone remodeling during OTM. Different bone substitutes or bone regeneration materials have also been applied to repair alveolar bone defects followed by orthodontic treatment. Those bioengineered bone graft materials also change the local environment that may or may not affect OTM. This article aims to review functional biomaterials that were applied locally to accelerate OTM for a shorter duration of orthodontic treatment or impede OTM for retention purposes, as well as various alveolar bone graft materials which may affect OTM. This review article summarizes various types of biomaterials that can be locally applied to affect the process of OTM, along with their potential mechanisms of action and side effects. The functionalization of biomaterials can improve the solubility or intake of biomolecules, leading to better outcomes in terms of increasing or decreasing the speed of OTM. The ideal timing for initiating OTM is generally considered to be 8 weeks post-grafting. However, more evidence is needed from human studies to fully understand the effects of these biomaterials, including any potential adverse effects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".