Effect Of Using Gingival Stem Cells And Therapeutic Ultrasound On Periodontal Ligament During Orthodontic Treatment In Beagle Dogs.
Bibliographic record
Abstract
Previous studies have shown that low intensity pulsed ultrasound (LIPUS) can prevent orthodontically induced teeth root resorption (OITRR) in human. Also, stem cells have been used to treat different types of bone defects. Severe OITRR is still untreatable problem in orthodontics. The aim of this study was to evaluate the effect of local injection of osteogenically induced gingival stem cells (OIGSCs) and LIPUS on periodontal ligament (PDL) during tooth movement that induces OITRR in beagle dogs. We hypothesized that local injection of OIGSCs and LIPUS can enhance PDL metabolism and hence enhances the reparative effect of OITRR. Seven adolescent beagle dogs were used and their third and fourth premolars were moved orthodontically. Gingival stem cells were isolated, characterized by flowcytometry and were differentiated into osteoblast -like cells using osteogenic medium to produce osteogenically induced gingival cells (OIGCs). OIGSCs were then re-injected into the alveolar bone in the proximity of the roots of the orthodontically moved teeth. Premolars were randomly divided into five groups. 1) Negative control (OITRR only); 2) Local injection of BMP; 3) OIGSCs; 4) LIPUS and 5) LIPUS +OIGSCs. In LIPUS groups, premolars were treated for four weeks. Animals were then euthanized and tissue blocks were processed for histological and histomorphometric analysis. Cementum thickness, PDL thickness and PDL cell number were counted using Metamorph software. Measurements were made at three levels of the tested roots. Level 1 is the coronal level 9towards the crown); level 2 (middle level of the root) and level 3 (apical, towards the apex of the roots). Variables were compared between groups by Wilcoxon Signed Ranks Test using SPSS statistical package. Results showed that LIPUS, BMP2 and LIPUS+OIGCs significantly increased cementum thickness compared to control group in the apical area of the teeth roots (P<0.05). There was statistically significant increases in PDL thickness and PDL cell count in all groups compared to control group (P<0.05). The combined LIPUS+OIGCs showed the highest cell count between al the groups. Conclusion: LIPUS + OIGCs showed the highest PDL regeneration potential and may be used in clinical cases with severe OITRR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".