Canadian Association of Neuroscience Review: Axonal Regeneration in the Peripheral and Central Nervous Systems – Current Issues and Advances
Bibliographic record
Abstract
Injured nerves regenerate their axons in the peripheral (PNS) but not the central nervous system (CNS). The contrasting capacities have been attributed to the growth permissive Schwann cells in the PNS and the growth inhibitory environment of the oligodendrocytes in the CNS. In the current review, we first contrast the robust regenerative response of injured PNS neurons with the weak response of the CNS neurons, and the capacity of Schwann cells and not the oligodendrocytes to support axonal regeneration. We then consider the factors that limit axonal regeneration in both the PNS and CNS. Limiting factors in the PNS include slow regeneration of axons across the injury site, progressive decline in the regenerative capacity of axotomized neurons (chronic axotomy) and progressive failure of denervated Schwann cells to support axonal regeneration (chronic denervation). In the CNS on the other hand, it is the poor regenerative response of neurons, the inhibitory proteins that are expressed by oligodendrocytes and act via a common receptor on CNS neurons, and the formation of the glial scar that prevent axonal regeneration in the CNS. Strategies to overcome these limitations in the PNS are considered in detail and contrasted with strategies in the CNS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".