Effects of titanium oxide surface properties on bone‐forming and soft tissue‐forming cells
Bibliographic record
Abstract
Abstract Background Previous studies have concluded that certain titanium oxide (TiO2) surface properties promote bone‐forming cell attachment. However, no comprehensive studies have investigated the effects of TiO2 surface and film morphology on hard and soft tissues. Purpose The aim of this study is to understand the effects of TiO2 morphology on the proliferation and differentiation of murine preosteoblasts (MC3T3‐E1) and proliferation of human gingival fibroblasts (HGF‐1) using in vitro experiments. Materials and Methods Samples were fabricated with several TiO2 thickness and crystalline structure to mimic various dental implant surfaces. in vitro analysis was performed for 1, 3, and 7 days on these samples to assess the viability of MC3T3‐E1 and HGF‐1 cells in contact with the modified oxide surfaces. Results Results showed that HGF‐1 cells exhibited no significant difference in viability on modified oxide surfaces versus a titanium control across experiments. MC3T3‐E1 cells exhibited a significantly higher viability for the modified oxide surface in 1 day experiments, but not in 3 or 7 day experiments. Alkaline phosphatase expression in MC3T3‐E1 was not significantly different on modified oxide surfaces versus the control across all experiments. A slight positive trend in viability was observed for cells in contact with rougher modified oxide surfaces versus a titanium control in both cell types. Conclusions These observations suggest that crystallinity and thickness do not affect the long‐term viability of hard or soft tissue cells when compared to a cpTi surface. Therefore, treatments like anodization on implant components may not directly affect the attachment of hard or soft tissue cells in vivo.
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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".