Transplantation of Neural Stem Cells Clonally Derived from Embryonic Stem Cells Promotes Recovery After Murine Spinal Cord Injury
Bibliographic record
Abstract
The pathology of spinal cord injury (SCI) makes it appropriate for cell-based therapies. Treatments using neural stem cells (NSCs) in animal models of SCI have shown positive outcomes, although uncertainty remains regarding the optimal cell source. Pluripotent cell sources such as embryonic stem cells (ESCs) provide a limitless supply of therapeutic cells. NSCs derived using embryoid bodies (EB) from ESCs have shown tumorigenic potential. Clonal neurosphere generation is an alternative method to generate safer and more clinically relevant NSCs without the use of an EB stage for use in cell-based therapies. We generated clonally derived definitive NSCs (dNSCs) from ESC. These cells were transplanted into a mouse thoracic SCI model. Embryonic stem cell-derived definitive neural stem cell (ES-dNSC)-transplanted mice were compared with controls using behavioral measures and histopathological analysis of tissue. In addition, the role of remyelination in injury recovery was investigated using transmission electron microscopy. The SCI group that received ES-dNSC transplantation showed significant improvements in locomotor function compared with controls in open field and gait analysis. The cell treatment group had a significant enhancement of spared neural tissue. Immunohistological assessments showed that dNSCs differentiated primarily to oligodendrocytes. These cells were shown to express myelin basic protein, associate with axons, and support nodal architecture as well as display proper compact, multilayer myelination in electron microscopic analysis. This study provides strong evidence that dNSCs clonally derived from pluripotent cells using the default pathway of neuralization improve motor function after SCI and enhance sparing of neural tissue, while remaining safe and clinically relevant.
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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 | 0.001 |
| 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".