Influence of mechanical properties of alginate-based substrates on the performance of Schwann cells in culture
Bibliographic record
Abstract
In tissue engineering, artificial tissue scaffolds containing living cells have been studied for tissue repair and regeneration. Notably, the performance of these encapsulated-in-scaffolds cells in terms of cell viability, proliferation, and expression of function during and after the scaffold fabrication process, has not been well documented because of the influence of mechanical, chemical, and physical properties of the scaffold substrate materials. This paper presents our study on the influence of mechanical properties of alginate-based substrates on the performance of Schwann cells, which are the major glial cells of peripheral nervous system. Given the fact that alginate polysaccharide hydrogel has poor cell adhesion properties, in this study, we examined several types of cell-adhesion supplements and found that alginate covalently modified with RGD peptide provided improved cell proliferation and adhesion. We prepared alginate-based substrates for cell culture using varying alginate concentrations for altering their mechanical properties, which were confirmed by compression testing. Then, we examined the viability, proliferation, morphology, and expression of the extracellular matrix protein laminin of Schwann cells that were seeded on the surface of alginate-based substrates (or 2D culture) or encapsulated within alginate-based substrates (3D cultures), and correlated the examined cell performance to the alginate concentration (or mechanical properties) of hydrogel substrates. Our findings suggest that covalent attachment of RGD peptide can improve the success of Schwann cell encapsulation within alginate-based scaffolds, and provide guidance for regulating the mechanical properties of alginate-based scaffolds containing Schwann cells for applications in peripheral nervous system regeneration and repair.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".