Schwann cell delivery via a novel 3D collagen matrix conduit improves outcomes in critical length nerve gap repairs
Bibliographic record
Abstract
OBJECTIVE: The current clinical standard of harvesting a nerve autograft for repair of long-segment peripheral nerve injuries (PNIs) is associated with many potential complications. Guidance channels offer an alternative therapy. The authors investigate whether autologous Schwann cells (SCs) implanted within a novel collagen-glycosaminoglycan conduit will improve axonal regeneration in a long-segment PNI model. METHODS: Novel NeuraGen 3D collagen matrix conduits were implanted with autologous SCs to investigate axonal regeneration across a critical size defect (13 mm) in male Fischer rat sciatic nerve. Reversed sciatic nerve autografts served as positive controls, and conduits filled with serum only as negative controls. Electrophysiological assessments were made in vivo. Animals were killed at 4 or 16 weeks postinjury, muscle weights were measured, and grafts underwent immunohistochemical and morphometric analysis. RESULTS: SC survival was confirmed by the presence of green fluorescent protein-labeled SCs within regenerated fibers. Regeneration and elongation of myelinated axons in all segments of the graft were significantly enhanced at 16 weeks in the SC-filled conduits compared to the conduit alone and were statistically similar to those of the autograft. Nerves repaired with SC-filled conduits exhibited onset latencies and nerve conduction amplitudes similar to those of the contralateral controls and autograft (p < 0.05). Adding SCs to the conduit also significantly reduced muscle atrophy compared to conduit alone (p < 0.0001). CONCLUSIONS: Repair of long-segment PNI of rat sciatic nerve is significantly enhanced by SC-filled NeuraGen 3D conduits. Improvements in the total number of myelinated axons, axon diameter, and myelin thickness throughout SC-filled conduits allow for significant recovery in nerve conduction and a decrease in muscle atrophy.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".