Surface Analysis of Titanium Biological Modification with Glow Discharge
Bibliographic record
Abstract
BACKGROUND: Glow discharge plasma (GDP) technology has been used to graft various proteins to the titanium surface, including albumin, type I collagen, but without fibronectin. PURPOSE: The aim of this study was to evaluate and analyze the physical properties of fibronectin-grafted titanium surfaces after GDP treatment. MATERIALS AND METHODS: Grade II titanium discs after cleaning and autoclaving were considered as original specimens, thus divided into four groups. The groups were different upon two treatments (GDP only and fibronectin grafting after GDP) and two storage temperature (4°C and 25°C). The implant surface morphology was characterized by scanning electron microscopy (SEM), roughness measurement, and wettability evaluation. The concentration relationship of fibronectin was by fluorescein isothiocyanate (FITC) labeling. RESULTS: SEM images showed that regular planar texture revealed on the surface of GDP-treated group, and irregular-folding protein was found on the fibronectin-grafted discs. Fibronectin-grafted groups had higher hydrophilicity and greater surface roughness than GDP-treated specimens. The storage temperature did not make obvious difference on the surface topography, wettability, and roughness. The number of fibronectin dots on the titanium surface labeling by FITC had positive relationship with the concentration of fibronectin solution used. CONCLUSIONS: Biologically modified titanium surface is more hydrophilic and rougher than GDP-treated ones. GDP treatment combined with fibronectin grafting increased the surface hydrophilicity and surface roughness of titanium discs, which may attribute to the affinity of cell adhesion, migration, proliferation, and differentiation.
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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".