Effects of Carbonate Apatite and Bone Mixture on Bone and Soft Tissue Integration in a Rat Implant Model
Bibliographic record
Abstract
ABSTRACT Background Achieving stable bone regeneration and soft tissue integration is critical for the success of dental implants, especially in patients with alveolar bone defects. Carbonate apatite (CO3Ap), a synthetic bone substitute, has emerged as a promising alternative due to its excellent osteoconductivity and biocompatibility. However, CO3Ap lacks osteoinductive capacity, which limits its effectiveness in promoting bone regeneration on its own. Purpose This study aimed to evaluate the effects of a CO3Ap–autogenous bone (AB) mixture on bone regeneration and soft tissue integration in a rat maxillary implant model. Materials and Methods Sixty rats underwent extraction of their maxillary molars, followed by titanium implant placement. The extraction sockets were filled with three different materials: CO3Ap, AB, or a CO3Ap–AB mixture. In vivo bone tissue and soft tissue evaluations were performed at specified time points. Additionally, in vitro experiments were conducted to assess the osteogenic differentiation of mesenchymal stem cells when exposed to the CO3Ap–AB mixture. Results In vivo experiments showed that the CO3Ap–AB mixture significantly enhanced bone volume and maintained high bone mineral density compared to CO3Ap and AB alone. Furthermore, the mixture promoted longer epithelial attachment, suggesting its potential for long‐term soft tissue stabilization. In vitro, the CO3Ap–AB mixture effectively promoted osteogenic differentiation of mesenchymal stem cells. Conclusions The combination of CO3Ap and AB exhibited a synergistic effect, enhancing early bone regeneration, osseointegration, and soft tissue sealing, which are crucial for implant stability. The CO3Ap–AB mixture shows great potential as a clinically effective bone substitute for dental implant treatment in patients with compromised bone conditions.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".