Nanoscale implant anchorage aided by cement line deposition into titanium dioxide nanotubes
Bibliographic record
Abstract
The success of titanium dental implants relies on osseointegration, the load-bearing connection between bone tissue and the device that, in contact osteogenesis, comprises the deposition of bony cement line matrix onto the implant surface. Titanium dioxide nanotubes (NTs) are considered a promising surface for improved osseointegration, yet the mechanisms of cement line integration with such features remains elusive. Herein, we illustrate cement line deposition into NTs on the surface of titanium implants with two underlaying microstructures: a machined surface or a blasted/acid etched surface placed in the tibiae of Wistar rats. After retrieval, scanning electron microscopy of tissue reflected from the implant surface indicated minimal incursion of the cement line matrix into the NTs. To investigate this further, focused ion beam was utilized to prepare cross-sectional samples that could be characterized using scanning transmission electron microscopy. The cement line matrix covered NTs regardless of underlying microstructure, which was further confirmed by elemental analysis. In some instances, cement line infiltration into the NTs was noted, which reveals a mechanism of nanoscale anchorage. This study is the first to demonstrate cement line deposition into titanium NTs, suggesting nano-anchorage as a mechanism for the success of the NT modified surfaces in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".