The Ultrastructural Relationship Between Osteocytes and Dental Implants Following Osseointegration
Bibliographic record
Abstract
BACKGROUND: Osteocytes, the most abundant cells in bone, have multiple functions, including acting as mechanosensors and regulating mineralization. It is clear that osteocytes influence bone remodeling by controlling the differentiation and activity of osteoblasts and osteoclasts. Determining the relationship between titanium implants and osteocytes may therefore benefit our understanding of the process of osseointegration. PURPOSE: The aim of this study was to visualize the ultrastructural relationship between osteocytes and the titanium implant surface following osseointegration in vivo. MATERIALS AND METHODS: Titanium implants were placed in the maxillary molar regions of eight female Sprague Dawley rats, 3 months old. The animals were sacrificed 8 weeks after implantation, and undecalcified tissue sections were prepared. Resin-cast samples were subsequently acid-etched with 37% phosphoric acid prior to examination using scanning electron microscopy. RESULTS: Compared with mature bone, where the osteocytes were arranged in an ordered fashion, the osteocytes appeared less organized in the newly formed bone around the titanium implant. Further, a layer of mineralization with few organic components was observed on the implant surface. This study shows for the first time that osteocytes and their dendrites are directly connected with the implant surface. CONCLUSIONS: This study shows the direct anchorage of osteocytes via dendritic processes to a titanium implant surface in vivo. This suggests an important regulatory role for osteocytes and their lacunar-canalicular network in maintaining long-term osseointegration.
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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) | 0.000 | 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) | 0.001 | 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".