Human Spinal Oligodendrogenic Neural Progenitor Cells Enhance Pathophysiological Outcomes and Functional Recovery in a Clinically Relevant Cervical Spinal Cord Injury Rat Model
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) results in the loss of neurons, oligodendrocytes, and astrocytes. Present interventions for SCI include decompressive surgery, anti-inflammatory therapies, and rehabilitation programs. Nonetheless, these approaches do not offer regenerative solutions to replace the lost cells, fiber tracts, and circuits. Neural stem/progenitor cell (NPC) transplantation is a promising strategy that aims to encourage regeneration. However, NPC differentiation remains inconsistent, thus, contributing to suboptimal functional recovery. As such, we have previously engineered oligodendrogenically biased NPCs (oNPCs) and demonstrated their efficacy in a thoracic model of SCI. Since the majority of patients with SCI experience cervical injuries, our objective in the current study was to generate human induced pluripotent stem cell-derived oNPCs (hiPSC-oNPCs) and to characterize these cells in vitro and in vivo, utilizing a clinically relevant rodent model of cervical SCI. Following transplantation, the oNPCs engrafted, migrated to the rostral and caudal regions of the lesion, and demonstrated preferential differentiation toward oligodendrocytes. Histopathological evaluations revealed that oNPC transplantation facilitated tissue preservation while diminishing astrogliosis. Moreover, oNPC transplantation fostered remyelination of the spared tissue. Functional analyses indicated improved forelimb grip strength, gait, and locomotor function in the oNPC-transplanted rats. Importantly, oNPC transplantation did not exacerbate neuropathic pain or induce tumor formation. In conclusion, these findings underscore the therapeutic potential of oNPCs in promoting functional recovery and histopathological improvements in cervical SCI. This evidence warrants further investigation to optimize and advance this promising cell-based therapeutic approach.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".