Enhanced proliferation and growth of human stem cells on the surface of HVOF-sprayed Nano TiO2-HA coatings
Bibliographic record
Abstract
Biomedical thermal spray coatings produced via high velocity oxy-fuel (HVOF) from nanostructured titania (n-TiO2) and 10wt% hydroxyapatite (HA) (n-TiO2-10wt%HA) powders have been engineered as possible future alternatives to HA coatings deposited via air plasma spray (APS). This approach was chosen due to (i) the stability of TiO2 in the human body (i.e., no dissolution) and (ii) bond strength values on Ti-6Al4V substrates more than two times higher than those of APS HA coatings. To explore the bioperformance of these novel materials and coatings, human mesenchymal stem cells (hMSCs) were cultured from 1 to 21 days on the surface of HVOF-sprayed n-TiO2 and n-TiO2-10wt%HA coatings. APS HA coatings and uncoated Ti-6Al-4V substrates were employed as controls. The active profiles of the hMSCs were evaluated for (i) cell proliferation by Alamar Bleu assay, (ii) biochemical analysis of alkaline phosphatase (ALP) activity, (iii) cytoskeleton organization (fluorescent/confocal microscopy) and (iv) cell/substrate interaction via scanning electron microscopy (SEM). Cell proliferation and biochemical analysis indicated that the hMSCs cultured on nTiO2-10wt%HA coatings exhibited similar or superior levels of bioactivity to hMSC cultured on APS HA. The cytoskeleton organization demonstrated a higher degree of cell proliferation and attachment on the HVOF-sprayed nTiO2-10wt%HA coatings. These results are considered promising for engineering improved performance and increased longevity in the next generation of thermally sprayed biomedical coatings.
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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".