High-Resolution Visualization of the Osteocyte Lacuno-Canalicular Network Juxtaposed to the Surface of Nanotextured Titanium Implants in Human
Bibliographic record
Abstract
Abstract Osseointegration is controlled by a number of cellular mechanisms. Although factors governing bone formation are well-understood, the maintenance of bone at the bone-implant interface is less clear. Of some interest is the role of osteocytes, which via mechanotransduction are believed to be responsible for mechanical loading-based remodelling events in bone. Using a resin cast etching technique, we investigated the osteocyte lacuno-canalicular network adjacent to nanostructured titanium human dental implants after four years in clinical function. Correlative electron microscopy showed nanoscale osteocyte processes extending directly onto the implant surface. Calcium signal mapping via electron energy loss spectroscopy (EELS) showed apatite ingrowth into the nanotextured surface, while the apatite platelet c-axis was oriented approximately parallel to the collagen fibril direction. Furthermore, Z-contrast electron tomography demonstrated that natural bone-osteocyte and engineered bone-implant interfaces are similar in ultrastructural morphology. The present ultrastructural observation of multiple connections between osteocyte canaliculi and the nanotextured surface oxide suggests that osteocytes contribute toward the maintenance of osseointegration in long-term clinical function.
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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".