Role of Titanium Surface Topography and Surface Wettability on Focal Adhesion Kinase Mediated Signaling in Fibroblasts
Bibliographic record
Abstract
Changes of titanium surface roughness and surface free energy may influence protein absorption that increases cell differentiation through activation of focal adhesion kinase related pathways. However, the influence of titanium surface roughness and hydrophilicity on fibroblast behavior is not well understood. The aim of this study was to investigate the influence of topography and hydrophilicity on fibroblast attachment, spreading, morphology, intracellular signaling, proliferation, and collagen I mRNA levels. Using a cellular FAK knockout (FAK−/−) model and wild-type (WT) controls, we also investigated the contribution of adhesion in fibroblasts cultured on smooth (PT), sand-blasted, large grit, acid-etched (SLA) and hydrophilic SLA topographies. Loss of FAK did not significantly affect fibroblast attachment to any surface, but SLA and hydrophilic SLA surface attenuated spreading of WT cells significantly more than FAK−/− fibroblasts. Both FAK−/− and WT cells formed numerous focal adhesions on PT surfaces, but significantly less on SLA and hydrophilic SLA surfaces. In WT cells, phosphorylation levels of FAK were lower on SLA and hydrophilic SLA in comparison with PT 24 h post seeding. Labeling of cells with antibodies to cortactin showed that FAK−/−cells contained significantly more cortactin-rich focal adhesion in comparison with WT cells on PT surfaces, but not on SLA or hydrophilic SLA. ERK 1/2 phosphorylation was highest in WT cells on all surfaces which correlated with collagen I expression levels. We conclude that fibroblasts are sensitive to changes in surface roughness and hydrophilicity, with adhesive interactions mediated through FAK, an important modulator of fibroblast response.
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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".