TiO<sub>2</sub>-enriched polymeric powder coatings support human mesenchymal cell spreading and osteogenic differentiation
Bibliographic record
Abstract
Novel polymeric powder coatings (PPC) were prepared by ultrafine powder coating technology and shown to support human mesenchymal cell attachment and growth. PPC surfaces enriched with nano-TiO(2) (nTiO(2)) showed enhanced cellular responses, and were compared to commercially pure titanium (cpTi). After cell attachment and growth, osteogenic differentiation and bone matrix formation ensures osseointegration for implantable biomaterials. Therefore, the objective of this study was to determine if mesenchymal cells grown on PPC could undergo osteogenic differentiation by inducing Runx2 and bone matrix proteins, and then initiate mineralization. Atomic force microscopy revealed intricate three-dimensional micro-topographies, and the measures of nano-roughness and porosity were similar for all PPC surfaces. Scanning electron microscopy showed that the cells attached and spread out over all of the surfaces. After 1 week in osteogenic media, RT-PCR analysis showed the induction of Runx2, the up-regulation of type I collagen, and the initial detection of alkaline phosphatase and bone sialoprotein. After 4 weeks, Alizarin Red staining showed mineral deposition. However, cell spreading and osteogenic differentiation were significantly (P < 0.05) higher on the cpTi controls than on the PPC surfaces. Furthermore, spreading and differentiation were consistently higher on the titanium-enriched PPC-2, -3 and -4 than on the titanium-free PPC-1. Therefore, despite the presence of complex micro-topographies and nano-features, titanium-enrichment enhanced the cellular response, and pure titanium still provided the best substrate. These findings confirm the cytocompatibility of these novel polymeric coatings and suggest that titanium-enrichment and nTiO(2) additives may enhance their performance.
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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".