Inflammatory Response to a Titanium Surface with Potential Bioactive Properties: An In Vitro Study
Bibliographic record
Abstract
BACKGROUND: The current hard tissue implants research aims to accelerate bone healing by designing surfaces that are bioactive. However, the role of the inflammatory response to these surfaces is so far incompletely described. PURPOSE: The aim of the study was to evaluate early inflammatory response in vitro to a potentially bioactive surface--an anodized surface with Mg ions incorporated (anodized/Mg)--and to compare it to a turned, a blasted, and an anodized surface. MATERIALS AND METHODS: An interferometer was used for topographical characterizations. The disks were incubated with human mononuclear cells. Adherent cells were investigated with respect to number of cells, viability, differentiation, and cytokine production with and without lipopolysaccharide stimulation after 24 and 72 hours. RESULTS: The number of adhered mononuclear cells differed significantly between the different modified surfaces, with the highest number on the anodized surface. However, there were no significant differences in cytokine production and differentiation between the different modified surfaces. The amount of anti-inflammatory mediator interleukin-10 remained over time, while the number of cells and pro-inflammatory cytokine tumor necrosis factor-alpha decreased. The cells were viable on all surfaces, respectively. CONCLUSION: The anodized surfaces with and without Mg ions showed an increased cell adherence, however, otherwise an inflammatory response similar to the turned and blasted surfaces. Furthermore, the potentially bioactive anodized/Mg surface showed a similar response to the TiUnite-like anodized surface despite the former having a surface roughness of a smoother character.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".