Osteogenic Differentiation Potential of iMSCs on GelMA-BG-MWCNT Nanocomposite Hydrogels
Bibliographic record
Abstract
The ability of bone biomaterials to promote osteogenic differentiation is crucial for the repair and regeneration of osseous tissue. The development of a temporary bone substitute is of major importance in enhancing the growth and differentiation of human-derived stem cells into an osteogenic lineage. In this study, nanocomposite hydrogels composed of gelatin methacryloyl (GelMA), bioactive glass (BG), and multiwall carbon nanotubes (MWCNT) were developed to create a bone biomaterial that mimics the structural and electrically conductive nature of bone that can promote the differentiation of human-derived stem cells. GelMA-BG-MWCNT nanocomposite hydrogels supported mesenchymal stem cells derived from human induced pluripotent stem cells, hereinafter named iMSCs. Cell adhesion was improved upon coating nanocomposite hydrogels with fibronectin and was further enhanced when seeding pre-differentiated iMSCs. Osteogenic differentiation and mature mineralization were promoted in GelMA-BG-MWCNT nanocomposite hydrogels and were most evidently observed in the 70-30-2 hydrogels, which could be due to the stiff topography characteristic from the addition of MWCNT. Overall, the results of this study showed that GelMA-BG-MWCNT nanocomposite hydrogels coated with fibronectin possessed a favorable environment in which pre-differentiated iMSCs could better attach, proliferate, and further mature into an osteogenic lineage, which was crucial for the repair and regeneration of bone.
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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".