Compensatory changes after spinal cord injury in a remyelination deficient mouse model
Bibliographic record
Abstract
The development of therapeutic strategies to reduce impairments following spinal cord injury (SCI) motivates an active area of research, because there are no effective therapies. One strategy is to address injury-induced demyelination of spared axons by promoting endogenous or exogenous remyelination. However, previously, we showed that new myelin was not necessary to regain hindlimb stepping following moderate thoracic spinal cord contusion in 3-month-old mice. The present analysis investigated two potential mechanisms by which animals can re-establish locomotion in the absence of remyelination: compensation through intact white matter and conduction through spared axons. We induced a severe contusion injury to reduce the spared white matter rim in the remyelination deficient model, with no differences in recovery between remyelination deficient animals and injured littermate controls. We investigated the nodal properties of the axons at the lesion and found that in the remyelination deficient model, axons express the Nav1.2 voltage-gated sodium channel, a sub-type not typically expressed at mature nodes of Ranvier. In a moderate contusion injury, conduction velocities through the lesions of remyelination deficient animals were similar to those in animals with the capacity to remyelinate after injury. Detailed gait analysis and kinematics reveal subtle differences between remyelination deficient animals and remyelination competent controls, but no worse deficits. It is possible that upregulation of Nav1.2 channels may contribute to establishing conduction through the lesion. This conduction could contribute to compensation and regained motor function in mouse models of SCI. Such compensatory mechanism may have implications for interpreting efficacy results for remyelinating interventions in mice and the development of therapies for improving recovery following SCI.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".