Poly(dopamine)-Assisted Immobilization of Arg-Gly-Asp Peptides, Hydroxyapatite, and Bone Morphogenic Protein-2 on Titanium to Improve the Osteogenesis of Bone Marrow Stem Cells
Bibliographic record
Abstract
Osteointegration of titanium implants in bone defects is clinically important for long-term performance of orthopaedic implants. In this work, we developed a facile and effective "one-pot" deposition method based on dopamine polymerization for the development of cell-adhesive, osteoconductive, and osteoinductive titanium implants. Arg-Gly-Asp (RGD)-conjugated polymers, hydroxyapatite (HAp) nanoparticles, and bone morphogenic protein-2 (BMP-2) were mixed with an alkaline dopamine solution, and then, titanium substrates were immersed in the mixture for an hour. During poly(dopamine) coating, the three types of bioactive substances were immobilized on the titanium surfaces. Our results indicate that RGD conjugation enhanced the adhesion of human bone marrow stem cell line, while HAp incorporation facilitated cellular osteodifferentiation. The immobilization of BMP-2 induced the osteogenesis of the stem cells, indicated by reverse-transcriptase polymerase chain reaction (RT-PCR) analysis. The mineralization on the deposited substrates was also enhanced greatly. This functionalized layer on titanium substrate promoted mesenchymal stem cell to osteoblast and improved osteogenic differentiation and mineralization. In conclusion, the surface modification method shows a great potential for enhancement of osteointegration of orthopaedic and dental implants.
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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) | 0.000 | 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 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".