Rapidly formed stable and aligned dense collagen gels seeded with Schwann cells support peripheral nerve regeneration
Bibliographic record
Abstract
Abstract Objective. Gel aspiration-ejection (GAE) has recently been developed for the rapid production of dense, anisotropic collagen gel scaffolds with adjustable collagen fibrillar densities. In this study, a GAE system was applied to produce aligned Schwann cells within a type-1 collagen matrix to generate GAE-engineered neural tissues (GAE-EngNT) for potential nerve tissue engineering applications. Approach. The stability and mechanical properties of the constructs were investigated along with the viability, morphology and distribution of Schwann cells. Having established the methodology to construct stable robust Schwann cell-loaded engineered neural tissues using GAE (GAE-EngNTs), the potential of these constructs in supporting and guiding neuronal regeneration, was assessed both in vitro and in vivo. Main results. Dynamic mechanical analysis strain and frequency sweeps revealed that the GAE-EngNT produced via cannula gauge number 16 G (∼1.2 mm diameter) exhibited similar linear viscoelastic behaviors to rat sciatic nerves. The viability and alignment of seeded Schwann cells in GAE-EngNT were maintained over time post GAE, supporting and guiding neuronal growth in vitro with an optimal cell density of 2.0 × 106 cells ml−1. An in vivo test of the GAE-EngNTs implanted within silicone conduits to bridge a 10 mm gap in rat sciatic nerves for 4 weeks revealed that the constructs significantly promoted axonal regeneration and vascularization across the gap, as compared with the empty conduits although less effective regeneration compared with the autograft groups. Significance. Therefore, this is a promising approach for generating anisotropic and robust engineered tissue which can be used with Schwann cells for peripheral nerve 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 | 0.000 |
| 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".