Peripheral Nerve Matrix Hydrogel Promotes Recovery after Nerve Transection and Repair
Bibliographic record
Abstract
BACKGROUND: Nerve transection is the most common form of peripheral nerve injury. Treatment of peripheral nerve injury has primarily focused on stabilization and mechanical cues to guide extension of the regenerating growth cone across the site of transection. The authors investigated the effects of a peripheral nerve matrix (PNM) hydrogel on recovery after nerve transection. METHODS: The authors used rodent models to determine the effect of PNM on axon extension, electrophysiologic nerve conduction, force generation, and neuromuscular junction formation after nerve transection and repair. The authors complemented this work with in vivo and in vitro fluorescence-activated cell sorting and immunohistochemistry approaches to determine the effects of PNM on critical cell populations early after repair. RESULTS: Extension of axons from the proximal stump and overall green fluorescent protein-positive axon volume within the regenerative bridge were increased in the presence of PNM compared with an empty conduit ( P < 0.005) 21 days after repair. PNM increased electrophysiologic conduction (compound muscle action potential amplitude) across the repair site ( P < 0.05) and neuromuscular junction formation ( P = 0.04) 56 days after repair. PNM produced a shift in macrophage phenotype in vitro and in vivo ( P < 0.05) and promoted regeneration in a murine model used to characterize the early immune response to PNM ( P < 0.05). CONCLUSION: PNM, delivered by subepineural injection, promoted recovery after nerve transection with immediate repair, supporting a beneficial macrophage response, axon extension, and downstream remodeling using a range of clinically relevant outcome measures. CLINICAL RELEVANCE STATEMENT: This article describes an approach for subepineural injection at the site of nerve coaptation to modulate the response to injury and improve outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".