The role of microenvironment in the regenerative potential of neural stem cells after spinal cord injury
Bibliographic record
Abstract
Spinal cord injury (SCI) results in permanent loss of myelin forming oligodendrocytes that significantly contribute to white matter degeneration. Despite the existence of multipotent neural precursor cells (NPCs) inside the spinal cord, replacement of oligodendrocytes is severely limited after SCI. Interestingly, NPCs mainly contribute to oligodendrocyte turnover in the normal spinal cord; however, in SCI activated NPCs predominantly differentiate into astrocytes and contribute to scar formation. To date, we have only a poor understanding of the extracellular events that modulate NPCs in their post‐SCI niche. After SCI, local microenvironment undergoes profound modifications that limit the regenerative capacities of NPCs. Optimizing the SCI microenvironment is therefore critical not only to promote NPCs cell replacement, but also to attenuate the otherwise non‐constructive effects of NPCs activation after SCI. We have recently identified several mechanisms that influence the behaviour of NPCs after SCI. Our data suggest a role for the matrix molecules chondroitin sulfate proteoglycans (CSPGs) in limiting the activation and differentiation of NPCs after SCI. We also show that the inadequate oligodendrocyte cell replacement in SCI may be attributed to the impaired expression of neureglulin‐1/ErbB signalling in the spinal cord. This talk will discuss our recent research findings in these areas
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".