Nanosurfaces modulate the mechanism of peri-implant endosseous healing by regulating neovascular morphogenesis
Bibliographic record
Abstract
Nanosurfaces have improved clinical osseointegration by increasing bone/implant contact. Neovascularization is considered an essential prerequisite to osteogenesis, but no previous reports to our knowledge have examined the effect of surface topography on the spatio-temporal pattern of neovascularization during peri-implant healing. We have developed a cranial window model to study peri-implant healing intravitally over clinically relevant time scales as a function of implant topography. Quantitative intravital confocal imaging reveals that changing the topography (but not chemical composition) of an implant profoundly affects the pattern of peri-implant neovascularization. New vessels develop proximal to the implant and the vascular network matures sooner in the presence of an implant nanosurface. Accelerated angiogenesis can lead to earlier osseointegration through the delivery of osteogenic precursors to, and direct formation of bone on, the implant surface. This study highlights a critical aspect of peri-implant healing, but also informs the biological rationale for the surface design of putative endosseous implant materials.
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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".