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 Ti 6 Al 4 V, 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 TiO 2 nanotubes (NT) appear to be excellent drug platforms, and strontium reportedly enhances osteogenesis, we constructed a TiO 2 nanotube coating on the surface of Ti 6 Al 4 V and immersed it in Sr (OH) 2 solution in order to incorporate Sr into TiO 2 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".