Development of a rat model for type 2 diabetes mellitus peri‐implantitis: A preliminary study
Bibliographic record
Abstract
OBJECTIVE: To develop an in vivo model to simulate the complex internal environment of diabetic peri-implantitis (T2DM-PI) model for a better understanding of peri-implantitis in type 2 diabetic patients. MATERIALS AND METHODS: Maxillary first molars were extracted in Sprague-Dawley (SD) rats, and customized cone-shaped titanium implants were installed in the extraction sites. Thereafter, implants were uncovered and customized abutments were screwed into implants. A high-fat diet and a low-dose injection of streptozotocin were utilized to induce T2DM. Finally, LPS was locally injected in implant sulcus to induce peri-implantitis. RESULTS: In the present study, T2DM-PI model has been successfully established. Imaging analysis revealed that abundant inflammatory cells infiltrated in the soft tissue in T2DM-PI group with concomitant excessive secretion of inflammatory cytokines. Moreover, higher expression of MMP and increased number of osteoclasts led to collagen disintegration and bone resorption in T2DM-PI group. CONCLUSIONS: These results describe a novel rat model which stimulate T2DM-PI in vivo, characterized by overwhelming inflammatory response and bone resorption. This model has a potential to be used for investigation of initiation, progression and interventional therapy of T2DM-PI.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".