Extramedullary Chitosan Channels Promote Survival of Transplanted Neural Stem and Progenitor Cells and Create a Tissue Bridge After Complete Spinal Cord Transection
Bibliographic record
Abstract
Transplantation of neural stem and progenitor cells (NSPCs) is a promising strategy for repair after spinal cord injury. However, the epicenter of the severely damaged spinal cord is a hostile environment that results in poor survival of the transplanted NSPCs. We examined implantation of extramedullary chitosan channels seeded with NSPCs derived from transgenic green fluorescent protein (GFP) rats after spinal cord transection (SCT). At 14 weeks, we assessed the survival, maturation, and functional results using NSPCs harvested from the brain (brain group) or spinal cord (SC group) and seeded into chitosan channels implanted between the cord stumps after complete SCT. Control SCT animals had empty chitosan channels or no channels implanted. Channels seeded with brain or spinal cord-derived NSPCs showed a tissue bridge, although the bridges were thicker in the brain group. Both cell types showed long-term survival, but the number of surviving cells in the brain group was approximately five times as great as in the SC group. In both the brain and SC groups at 14 weeks after transplantation, many host axons were present in the center of the bridge in association with the transplanted cells. At 14 weeks astrocytic and oligodendrocytic differentiation in the channels was 24.8% and 17.3%, respectively, in the brain group, and 31.8% and 9.7%, respectively, in the SC group. The channels caused minimal tissue reaction in the adjacent spinal cord. There was no improvement in locomotor function. Thus, implantation of chitosan channels seeded with NSPCs after SCT created a tissue bridge containing many surviving transplanted cells and host axons, although there was no functional improvement.
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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".