Human Spinal Oligodendrogenic Neural Progenitor Cells Promote Functional Recovery After Spinal Cord Injury by Axonal Remyelination and Tissue Sparing
Bibliographic record
Abstract
Cell transplantation therapy utilizing neural precursor cells (NPCs) is a conceptually attractive strategy for traumatic spinal cord injury (SCI) to replace lost cells, remyelinate denuded host axons and promote tissue sparing. However, the number of mature oligodendrocytes that differentiate from typical NPCs remains limited. Herein, we describe a novel approach to bias the differentiation of directly reprogrammed human NPCs (drNPCs) toward a more oligodendrogenic fate (oNPCs) while preserving their tripotency. The oNPCs derived from different lines of human NPCs showed similar characteristics in vitro. To assess the in vivo efficacy of this approach, we used oNPCs derived from drNPCs and transplanted them into a SCI model in immunodeficient Rowett Nude (RNU) rats. The transplanted cells showed significant migration along the rostrocaudal axis and proportionally greater differentiation into oligodendrocytes. These cells promoted perilesional tissue sparing and axonal remyelination, which resulted in recovery of motor function. Moreover, after transplantation of the oNPCs into intact spinal cords of immunodeficient NOD/SCID mice, we detected no evidence of tumor formation even after 5 months of observation. Thus, biasing drNPC differentiation along an oligodendroglial lineage represents a promising approach to promote tissue sparing, axonal remyelination, and neural repair after traumatic SCI. Stem Cells Translational Medicine 2018;7:806-818.
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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".