Meso-scale multi-material fabrication of a Synthetic ECM Mimic for In vivo-like Peripheral Nerve Regeneration
Bibliographic record
Abstract
Abstract A growing focus and continuing challenge for biological sciences is creating representative in vitro environments to study and influence cell behavior. Here, we describe the synthetic recreation of the highly ordered extracellular matrix (ECM) of the peripheral nervous system (PNS) in terms of structure and scale, providing a versatile 3D culturing platform that achieves some of the highest in vitro neurite growth rates so far reported. By combining electrospinning technology with a unique multi-material processing sequence that harnesses intrinsic material properties, a hydrogel construct is realized that incorporates oriented 6 μm-diameter microchannels decorated with topographical nanofibers. We show that this mimics the native PNS ECM architecture and promotes extensive growth from primary neurons; through controlled variation in design, we show that the open lumens of the microchannels directing rapid axon invasion of the hydrogel while the nanofibers provide essential cues for cell adhesion and topographical guidance. Furthermore, these microstructural and nanofibrillar elements enabled a typically bioinert hydrogel (PEGDA) to achieve similar neurite extension when compared to a biocompatible collagen hydrogel, with PEGDA-based devices approaching neurite growth rates similar to what is observed in vivo . Through the accessible fabrication approach developed here, multi-material scaffolds were designed with cell-relevant architectures ranging from meso-to nanoscale and shown to support nerve growth to mimic PNS regeneration, with potential for regenerative medicine and neural engineering applications.
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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".