Neural Precursor Cell Transplantation Enhances Functional Recovery and Reduces Astrogliosis in Bilateral Compressive/Contusive Cervical Spinal Cord Injury
Bibliographic record
Abstract
Spinal cord injury has a significant societal and personal impact. Although the majority of injuries involve the cervical spinal cord, few studies of cell transplantation have used clinically relevant models of cervical spinal cord injury, limiting translation into clinical trials. Given this knowledge gap, we sought to examine the effects of neural stem/precursor cell (NPC) transplants in a rodent model of bilateral cervical contusion-compression spinal cord injury. Bilateral C6-level clip contusion-compression injuries were performed in rats, which were then blindly randomized at 2 weeks after injury into groups receiving adult brain-derived NPCs, vehicle, or sham operation. Long-term survival of NPCs was evident at 10 weeks after transplant. Cell grafts were localized rostrocaudally surrounding the lesion, throughout white and gray matter. Graft-derived cells were found within regions of gliotic scar and motor tracts and deposited myelin around endogenous axons. The majority of NPCs developed an oligodendroglial phenotype with greater neuronal profiles in rostral grafts. Following NPC transplantation, white matter was significantly increased compared with control. Astrogliosis and glial scar deposition, measured by GFAP-positive and chondroitin sulfate proteoglycan-positive volume, was significantly reduced. Forelimb grip strength, fine motor control during locomotion, and axonal conduction (by in vivo electrophysiology) was greater in cell-treated animals compared with vehicle controls. Transplantation of NPCs in the bilaterally injured cervical spinal cord results in significantly improved spinal cord tissue and forelimb function, warranting further study in preclinical cervical models to improve this treatment paradigm for clinical translation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".