Effect of gingival fibroblasts and ultrasound on dogs′ root resorption during orthodontic treatment
Bibliographic record
Abstract
OBJECTIVES: To investigate the effect of using osteogenic induced gingival fibroblasts (OIGFs) and low intensity pulsed ultrasound (LIPUS) on root resorption lacunae volume and cementum thickness in beagle dogs that received orthodontic tooth movement. MATERIALS AND METHODS: Seven beagle dogs were used, from which gingival cells (GCs) were obtained and were induced osteogenically to produce OIGFs. Each third and fourth premolar was randomly assigned to one of the five groups, namely, LIPUS, OIGFs, bone morphogenetic protein-2 (BMP-2), OIGFs + LIPUS, and control. All groups received 4 weeks of bodily tooth movement, then LIPUS-treated groups received LIPUS for 20 min/day for 4 weeks, and OIGFs groups received an injection of OIGFs near the root apex. Microcomputed tomography analysis was used to calculate root resorption lacunae volume and histomorphometric analysis was performed to measure the cementum thickness of each root at 3 root levels on compression and tension sides. RESULTS: > 0.05) in fourth premolars near the apex. Furthermore, BMP2 increased cementum thickness at the coronal third at the compression side. CONCLUSION: OIGFs, LIPUS, and BMP-2 can be potential treatments for orthodontically induced root resorption, however, improvements in experimental design and treatment parameters are required to further investigate these repair modalities.
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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) | 0.001 | 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".