The Effect of Surface Processing of Titanium Implants on the Behavior of Human Osteoblast-Like Saos-2 Cells
Bibliographic record
Abstract
BACKGROUND: The surface qualities of dental implants appear to modulate osteoblasts' growth and differentiation, affecting bone healing. During manufacturing of implants, the surface quality is affected by industrial processes. PURPOSE: To examine the effect of manufacturing procedures on the growth and differentiation of human osteoblast-like cells, Saos-2. MATERIALS AND METHODS: Saos-2 cells were cultured on titanium (Ti) disks. Cell growth was examined using the XTT assay, and cell differentiation was tested by alkaline phosphatase (ALP) activity and osteocalcin secretion. The following variables were examined: roughening of the surface by sandblasting and acid-etching, aging of the acid used for etching, fluoride modification of the surface, and the type of the packaging material. RESULTS: An inverse relationship was noted between Saos-2 growth and ALP activity on the tested surfaces. Roughening of the surface tended to decrease cell proliferation and to increase differentiation. Immersion of up to 200 cycles in acid decreased proliferation and increased differentiation. Cells grown on fluoride-modified surfaces exhibited more ALP activity as compared to the unmodified surfaces. No difference was noted between the three packaging materials tested. CONCLUSIONS: The data suggests that industrial processes may affect the behavior of osteoblast-like cells around titanium implants and should be monitored carefully by bioassays.
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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 | 0.001 |
| 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".