3D bioprinting of scaffolds with living Schwann cells for potential nerve tissue engineering applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Three-dimensional bioprinting of biomaterials shows great potential for producing cell-encapsulated scaffolds to repair nerves after injury or disease. For this, preparation of biomaterials and bioprinting itself are critical to create scaffolds with both biological and mechanical properties appropriate for nerve regeneration, yet remain unachievable. This paper presents our study on bioprinting Schwann cell-encapsulated scaffolds using composite hydrogels of alginate, fibrin, hyaluronic acid, and/or RGD peptide, for nerve tissue engineering applications. For the preparation of composite hydrogels, suitable hydrogel combinations were identified and prepared by adjusting the concentration of fibrin based on the morphological spreading of Schwann cells. In bioprinting, the effects of various printing process parameters (including the air pressure for dispensing, dispensing head movement speed, and crosslinking conditions) on printed structures were investigated and, by regulating these parameters, mechanically-stable scaffolds with fully interconnected pores were printed. The performance of Schwann cells within the printed scaffolds were examined in terms of viability, proliferation, orientation, and ability to produce laminin. Our results show that the printed scaffolds can promote the alignment of Schwann cells inside scaffolds and thus provide haptotactic cues to direct the extension of dorsal root ganglion neurites along the printed strands, demonstrating their great potential for applications in the field of nerve tissue engineering.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it