Highly Flexible 3D Printed Gelatin-Pluronic F127 Scaffolds Seeded with Schwann Cells toward Nerve Regeneration
Bibliographic record
Abstract
Peripheral nerve injury increasingly affects people around the world, leading to very incapacitating conditions with the loss of motor and sensory functions. Combining biomaterials with glial cells is particularly promising to reconnect injured axons to their original target, as they represent a supportive environment facilitating cell and axonal growth. Neural tissue engineering using biomimetic soft scaffolds often faces challenges related to handling, suturability, and integration into the host tissue. This study aimed to develop a soft and flexible biomimetic scaffold that supports colonization with a high density of glial Schwann cells (SCs). The strategy consists of printing tubular multichannel (500 to 1000 μm channels) nerve guides (NG) (5 × 5 mm) presenting an anisotropic architecture using a high-resolution stereolithography printing process. To this aim, the synthesis of photosensitive methacrylated gelatin (GelMA) inks was optimized and combined with various ratios of dimethacrylated F127 Pluronic. We showed that the physicochemical and mechanical properties of the printed hydrogels can be controlled by polymer concentrations and ratios. Specifically, in a 12:3 GelMA:F127DMA ratio, Pluronic provides enhanced flexibility while maintaining softness similar to nerve tissues. Importantly, gelatin-Pluronic scaffolds better withstand handling than gelatin scaffolds, as demonstrated by a higher strain at break in compression assays. Moreover, strain at break in suturing experiments was more than doubled with GelMA:F127DMA (35%) hydrogels in contrast to fragile and brittle gelatin-only scaffolds (15%). Schwann cells adhere, proliferate, and remain viable over 7 days within the channels demonstrating that these cellularized gelatin-Pluronic nerve guides hold significant promise for nerve regeneration.
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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".