Accelerated Osteogenic Differentiation and Bone Formation on Zirconia with Surface Grooves Created with Fiber Laser Irradiation
Bibliographic record
Abstract
BACKGROUND: Modification of the surface topography of biomaterials is a critical factor for the proliferation and differentiation of osteoblasts. Elucidating the biological response to surface roughening is necessary for clinical application of zirconia implants. PURPOSE: To investigate the effects of fiber laser-induced zirconia surface roughening on cultured osteoblast-like cell morphology, proliferation, differentiation, and calcification, and on in vivo bone formation. MATERIALS AND METHODS: Sixty-six machine-surfaced yttria-tetragonal zirconia polycrystal plates (S-Zr) and 16 machine-surfaced implants were used as controls. We prepared 66 rough plates (R-Zr) and 16 rough implants by surface treatment using a fiber laser. RESULTS: MC3T3-E1 cells spread well in all directions on S-Zr, whereas elongated cells with poorly organized actin stress fibers were observed on R-Zr. Cell proliferation was significantly greater on R-Zr than on S-Zr. The Runx2 mRNA level increased time dependently in osteogenic culture condition. Alkaline phosphatase activity and osteocalcin mRNA levels were higher on R-Zr compared with S-Zr. Alizarin red S staining revealed greater calcification on R-Zr than on S-Zr. Laser treatment of zirconia implant bodies placed in rat tibiae increased the bone-implant contact ratio and removal torque considerably. CONCLUSIONS: Our results suggest that fiber laser irradiation produces adequate surface roughening of zirconia ceramics to support 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".