Biocompatible <scp>Nanotube‐Strontium</scp>/polydopamine‐arginine–glycine–aspartic acid coating on <scp>Ti6Al4V</scp> enhances osteogenic properties for biomedical applications
Bibliographic record
Abstract
Abstract Titanium (Ti) alloys, particularly Ti6Al4V, are the most commonly used biomedical implant material. Ti alloys are biologically inert, so there have been continuous efforts to improve their osteogenic properties and clinical performance. Since TiO2 nanotubes (NT) appear to be excellent drug platforms, and strontium reportedly enhances osteogenesis, we constructed a TiO2 nanotube coating on the surface of Ti6Al4V and immersed it in Sr (OH)2 solution in order to incorporate Sr into TiO2 nanotubes (NT‐Sr). The results of field emission scanning electron microscope and X‐ray diffraction analysis verified the fabrication of NT‐Sr. We next added polydopamine (PDA) and cyclo‐ (arginine–glycine–aspartic acid–phenylalanine–cysteine) [c(RGDfC)] peptides to further promote biocompatibility of the implant. Fourier transform infrared spectroscopy and X‐ray photoelectron spectroscopy confirmed the existence of PDA and c(RGDfC). Mesenchymal stem cells (MSCs) were planted on Ti, NT, NT‐Sr, NT‐Sr/PDA, and NT‐Sr/PDA‐RGD surfaces. The adhesion and differentiation of MSCs on different surfaces were evaluated. The mRNA expression of alkaline phosphatase, runt‐related transcription factor 2 (Runx2) and type I collagen (Col I) of different groups were also tested. Finally, we observed that the NT‐Sr/PDA‐RGD group showed significantly better performance than other groups in terms of the differentiation and osteogenesis‐related gene expression of MSCs. Thus, the NT‐Sr/PDA‐RGD complex may be an important modification strategy for Ti, as it shows excellent osteogenic potential.
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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".