Periodontal Regeneration Using Strontium-Loaded Mesoporous Bioactive Glass Scaffolds in Osteoporotic Rats
Bibliographic record
Abstract
Recent studies demonstrate that the rate of periodontal breakdown significantly increased in patients compromised from both periodontal disease and osteoporosis. One pharmacological agent used for their treatment is strontium renalate due to its simultaneous ability to increase bone formation and halt bone resorption. The aim of the present study was to achieve periodontal regeneration of strontium-incorporated mesoporous bioactive glass (Sr-MBG) scaffolds in an osteoporotic animal model carried out by bilateral ovariectomy (OVX). 15 female Wistar rats were randomly assigned to three groups: control unfilled periodontal defects, 2) MBG alone and 3) Sr-MBG scaffolds. 10 weeks after OVX, bilateral fenestration defects were created at the buccal aspect of the first mandibular molar and assessed by micro-CT and histomorphometric analysis after 28 days. Periodontal fenestration defects treated with Sr-MBG scaffolds showed greater new bone formation (46.67%) when compared to MBG scaffolds (39.33%) and control unfilled samples (17.50%). The number of TRAP-positive osteoclasts was also significantly reduced in defects receiving Sr-MBG scaffolds. The results from the present study suggest that Sr-MBG scaffolds may provide greater periondontal regeneration. Clinical studies are required to fully characterize the possible beneficial effect of Sr-releasing scaffolds for patients suffering from a combination of both periodontal disease and osteoporosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".