Locally limited inhibition of bone resorption and orthodontic relapse by recombinant osteoprotegerin protein
Bibliographic record
Abstract
OBJECTIVES: To determine minimal dose levels required for local inhibition of orthodontic relapse by recombinant OPG protein (OPG-Fc), while also determining effects of injected OPG-Fc on alveolar bone and long bone. SETTING AND SAMPLE POPULATION: The Department of Orthodontics and Pediatric Dentistry at the University of Michigan. Eighteen male Sprague Dawley rats. MATERIALS & METHODS: Maxillary molars were moved with nickel-titanium springs and then allowed to relapse in Sprague Dawley rats. Upon appliance removal, animals were injected with a single dose of 1.0 mg/kg OPG-Fc, 0.1 mg/kg OPG-Fc, or phosphate-buffered saline (vehicle) just distal to the molar teeth. Tooth movement measurements were made from stone casts, which were scanned and digitally measured. Alveolar tissues were examined by histology. Micro-computed tomography was used to quantify changes in alveolar and femur bone. RESULTS: Local injection of OPG-Fc inhibited molar but not incisor relapse, when compared to vehicle-injected animals. No significant differences in alveolar or femur bone were seen between the three treatment groups after 24 days of relapse. CONCLUSIONS: Our results demonstrate that a single local injection of OPG-Fc effectively inhibits orthodontic relapse, with minimal systemic bone metabolic effects. Our results also show that a single injection of OPG-Fc will influence tooth movement only in teeth close to the injection site. These findings indicate that OPG-Fc has potential as a safe and effective pharmacological means to locally control osteoclasts, for uses such as maintaining anchorage during orthodontic tooth movement and preventing orthodontic relapse in humans.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".