Electrospun biomaterial scaffolds with varied topographies for neuronal differentiation of human‐induced pluripotent stem cells
Bibliographic record
Abstract
In this study, we investigated the effect of micro and nanoscale scaffold topography on promoting neuronal differentiation of human induced pluripotent stem cells (iPSCs) and directing the resulting neuronal outgrowth in an organized manner. We used melt electrospinning to fabricate poly (ε-caprolactone) (PCL) scaffolds with loop mesh and biaxial aligned microscale topographies. Biaxial aligned microscale scaffolds were further functionalized with retinoic acid releasing PCL nanofibers using solution electrospinning. These scaffolds were then seeded with neural progenitors derived from human iPSCs. We found that smaller diameter loop mesh scaffolds (43.7 ± 3.9 µm) induced higher expression of the neural markers Nestin and Pax6 compared to thicker diameter loop mesh scaffolds (85 ± 4 µm). The loop mesh and biaxial aligned scaffolds guided the neurite outgrowth of human iPSCs along the topographical features with the maximum neurite length of these cells being longer on the biaxial aligned scaffolds. Finally, our novel bimodal scaffolds also supported the neuronal differentiation of human iPSCs as they presented both physical and chemical cues to these cells, encouraging their differentiation. These results give insight into how physical and chemical cues can be used to engineer neural tissue.
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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".