Titania–hydroxyapatite nanocomposite coatings support human mesenchymal stem cells osteogenic differentiation
Why this work is in the frame
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Bibliographic record
Abstract
In addition to mechanical and chemical stability, the third design goal of the ideal bone-implant coating is the ability to support osteogenic differentiation of mesenchymal stem cells (MSCs). Plasma-sprayed TiO(2)-based bone-implant coatings exhibit excellent long-term mechanical properties, but their applications in bone implants are limited by their bioinertness. We have successfully produced a TiO(2) nanostructured (grain size <50 nm) based coating charged with 10% wt hydroxyapatite (TiO(2)-HA) sprayed by high-velocity oxy-fuel. On Ti64 substrates, the novel TiO(2)-HA coating bond 153× stronger and has a cohesive strength 4× higher than HA coatings. The HA micro- and nano-sized particles covering the TiO(2)-HA coating surface are chemically bound to the TiO(2) coating matrix, producing chemically stable coatings under high mechanical solicitations. In this study, we elucidated the TiO(2)-HA nanocomposite coating surface chemistry, and in vitro osteoinductive potential by culturing human MSCs (hMSCs) in basal and in osteogenic medium (hMSC-ob). We assessed the following hMSCs and hMSC-ob parameters over a 3-week period: (i) proliferation; (ii) cytoskeleton organization and cell-substrate adhesion; (iii) coating-cellular interaction morphology and growth; and (iv) cellular mineralization. The TiO(2) -HA nanocomposite coatings demonstrated 3× higher hydrophilicity than HA coatings, a TiO(2)-nanostructured surface in addition to the chemically bound HA micron- and nano-sized rod to the surface. hMSCs and hMSC-ob demonstrated increased proliferation and osteoblastic differentiation on the nanostructured TiO(2)-HA coatings, suggesting the TiO(2)-HA coatings nanostructure surface properties induce osteogenic differentiation of hMSC and support hMSC-ob osteogenic potential better than our current golden standard HA coating.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it