Effect of polyvinylidene fluoride electrospun fiber orientation on neural stem cell differentiation
Bibliographic record
Abstract
Electrospun polymer piezoelectric fibers can be used in neural tissue engineering (NTE) to mimic the physical, biological, and material properties of the native extracellular matrix. In this work, we have developed scaffolds based on polymer fiber architectures for application in NTE. To study the role of such three-dimensional scaffolds, a rotating drum collector was used for electrospinning poly(vinylidene) fluoride (PVDF) polymer at various rotation speeds. The morphology, orientation, polymorphism, as well as the mechanical behavior of the nonaligned and aligned fiber-based architectures were characterized. We have demonstrated that the jet flow and the electrostatic forces generated by electrospinning of PVDF induced local conformation changes which promote the generation of the β-phase. Fiber anisotropy could be a critical feature for the design of suitable scaffolds for NTEs. We thus assessed the impact of PVDF fiber alignment on the behavior of monkey neural stem cells (NSCs). NSCs were seeded on nonaligned and aligned scaffolds and their morphology, adhesion, and differentiation capacities into the neuronal and glial pathways were studied using microscopic techniques. Significant changes in the growth and differentiation capacities of NSCs into neuronal and glial cells as a function of the fiber alignment were evidenced. These results demonstrate that PVDF scaffolds may serve as instructive scaffolds for NSC survival and differentiation, and may be valuable tools for the development of cell- and scaffold-based strategies for neural repair. © 2016 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 105B: 2376-2393, 2017.
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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".