Effect of a Dietary Supplement on Peri-Implant Bone Strength in a Rat Model of Machined Surface Implants
Bibliographic record
Abstract
Efforts to improve the bone-implant interface to accelerate and improve the quality of osseointegration have generally focused on chemically improving the interface by incorporating inorganic phases on or into the titanium oxide layer or physically improving it by increasing the level of roughness. However, some types of rough-surfaced implants, such as those coated with hydroxyapatite (HA), may result in a higher incidence of complications. Once peri-implantitis occurs, the factors promoting bone integration can turn into risk factors, exacerbating inflammation around the implant fixture. Thus, it is extremely important to study approaches for accelerating bone formation around the machined surface dental implants. Peri-implant bone formation may be enhanced by systemic approaches, such as the use of osteoporosis supplements, to promote bone metabolism. The present study aimed to investigate if peri-implant bone mineral density (BMD) was improved after oral synthetic bone mineral (SBM) intake, which facilitates improved secondary stability of the machined surface dental implants and shortens the healing period. Twenty-four 7-week-old female Wistar rats were randomly assigned to receive a standardized diet with or without SBM (diet with SBM group and diet without SBM group, respectively; n = 12 for both). The rats underwent implant surgery at 9 weeks of age under general anesthesia. The main outcome measures BMD, pull-out strength, real-time PCR and Fluorescence microscopy observations of the implant from the femur were compared at 2 and 4 weeks after implantation using the Mann-Whitney U test. At 2 and 4 weeks after implantation, BMD, pull-out strength, real-time PCR and fluorescence microscopy observations were both significantly greater in the diet with SBM group than in the diet without SBM group. This study demonstrated that SBM could be effective in accelerating peri-implant bone formation for machined surface implants during the healing period after implantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".