Disease modifying treatment of spinal cord injury with directly reprogrammed neural precursor cells in non-human primates
Bibliographic record
Abstract
BACKGROUND: The development of regenerative therapy for human spinal cord injury (SCI) is dramatically restricted by two main challenges: the need for a safe source of functionally active and reproducible neural stem cells and the need of adequate animal models for preclinical testing. Direct reprogramming of somatic cells into neuronal and glial precursors might be a promising solution to the first challenge. The use of non-human primates for preclinical studies exploring new treatment paradigms in SCI results in data with more translational relevance to human SCI. AIM: To investigate the safety and efficacy of intraspinal transplantation of directly reprogrammed neural precursor cells (drNPCs). METHODS: = 3) was injected identically with the equivalent volume of vehicle. RESULTS: Follow-up for 12 wk revealed that animals in the drNPC group demonstrated a significant recovery of the paralyzed hindlimb as well as recovery of somatosensory evoked potential and motor evoked potential of injured pathways. Magnetic resonance diffusion tensor imaging data confirmed the intraspinal transplantation of drNPCs did not adversely affect the morphology of the central nervous system or cerebrospinal fluid circulation. Subsequent immunohistochemical analysis showed that drNPCs maintained SOX2 expression characteristic of multipotency in the transplanted spinal cord for at least 12 wk, migrating to areas of axon growth cones. CONCLUSION: Our data demonstrated that drNPC transplantation was safe and contributed to improvement of spinal cord function after acute SCI, based on neurological status assessment and neurophysiological recovery within 12 wk after transplantation. The functional improvement described was not associated with neuronal differentiation of the allogeneic drNPCs. Instead, directed drNPCs migration to the areas of active growth cone formation may provide exosome and paracrine trophic support, thereby further supporting the regeneration processes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".