Bioactive glass-collagen/poly (glycolic acid) scaffold nanoparticles exhibit improved biological properties and enhance osteogenic lineage differentiation of mesenchymal stem cells
Bibliographic record
Abstract
Today's using tissue engineering and suitable scaffolds have got attention to increase healing of non-union bone fractures. In this study, we aimed to prepare and characterize scaffolds with functional and mechanical properties suitable for bone regeneration. Porous scaffolds containing collagen-poly glycolic acid (PGA) blends and various quantities of bioactive glass (BG) 45S5 were fabricated. Scaffolds with different compositions (BG/collagen-PGA ratios (w/w): 0/100; 40/60; 70/30) were characterized for their morphological properties, bioactivity, and mechanical behavior. Then, biocompatibility and osteogenic differentiation potential of the scaffolds were analyzed by seeding mesenchymal stem cells (MSCs). Scaffolds made with collagen-PGA combined with the BG (45S5) were found to have interconnected pores (average pore diameter size 75-115 µm) depending on the percentage of the BG added. Simulated body fluid (SBF) soaking experiments indicated the stability of scaffolds in SBF regardless of their compositions, while the scaffolds retained their highly interconnected structure. The elastic moduli, cell viability, osteogenic differentiation of the BG/collagen-PGA 40/60 and 70/30 scaffolds were superior to the original BG/collagen-PGA (0/100). These results suggest that BG incorporation enhanced the physical stability of our collagen-PGA scaffold previously reported. This new scaffold composition provides a promising platform to be used as a non-toxic scaffold for bone regeneration and tissue engineering.
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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 | 0.000 | 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 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".