Multichannel bridges and NSC synergize to enhance axon regeneration, myelination, synaptic reconnection, and recovery after SCI
Bibliographic record
Abstract
Regeneration in the injured spinal cord is limited by physical and chemical barriers. Acute implantation of a multichannel poly(lactide-co-glycolide) (PLG) bridge mechanically stabilizes the injury, modulates inflammation, and provides a permissive environment for rapid cellularization and robust axonal regrowth through this otherwise inhibitory milieu. However, without additional intervention, regenerated axons remain largely unmyelinated (<10%), limiting functional repair. While transplanted human neural stem cells (hNSC) myelinate axons after spinal cord injury (SCI), hNSC fate is highly influenced by the SCI inflammatory microenvironment, also limiting functional repair. Accordingly, we investigated the combination of PLG scaffold bridges with hNSC to improve histological and functional outcome after SCI. In vitro, hNSC culture on a PLG scaffold increased oligodendroglial lineage selection after inflammatory challenge. In vivo, acute PLG bridge implantation followed by chronic hNSC transplantation demonstrated a robust capacity of donor human cells to migrate into PLG bridge channels along regenerating axons and integrate into the host spinal cord as myelinating oligodendrocytes and synaptically integrated neurons. Axons that regenerated through the PLG bridge formed synaptic circuits that connected the ipsilateral forelimb muscle to contralateral motor cortex. hNSC transplantation significantly enhanced the total number of regenerating and myelinated axons identified within the PLG bridge. Finally, the combination of acute bridge implantation and hNSC transplantation exhibited robust improvement in locomotor recovery. These data identify a successful strategy to enhance neurorepair through a temporally layered approach using acute bridge implantation and chronic cell transplantation to spare tissue, promote regeneration, and maximize the function of new axonal connections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".