Passive exercise and fetal spinal cord transplant both help to restore motoneuronal properties after spinal cord transection in rats
Bibliographic record
Abstract
Spinal cord transection influences the properties of motoneurons and muscles below the lesion, but the effects of interventions that conserve muscle mass of the paralyzed limbs on these motoneuronal changes are unknown. We examined the electrophysiological properties of rat lumbar motoneurons following spinal cord transection, and the effects of two interventions shown previously to significantly attenuate the associated hindlimb muscle atrophy. Adult rats receiving a complete thoracic spinal cord transection (T-10) were divided into three groups receiving: (1) no further treatment; (2) passive cycling exercise for 5 days/week; or (3) acute transplantation of fetal spinal cord tissue. Intracellular recording of motoneurons was carried out 4-5 weeks following transection. Transection led to a significant change in the rhythmic firing patterns of motoneurons in response to injected currents, as well as a decrease in the resting membrane potential and spike trigger level. Transplants of fetal tissue and cycling exercise each attenuated these changes, the latter having a stronger effect on maintenance of motoneuron properties, coinciding with the reported maintenance of structural and biochemical features of hindlimb muscles. The mechanisms by which these distinct treatments affect motoneuron properties remain to be uncovered, but these changes in motoneuron excitability are consistent with influences on ion conductances at or near the initial segment. The results may support a therapeutic role for passive limb manipulation and transplant of stem cells in slowing the deleterious responses of motoneurons to spinal cord injury, such that they remain more viable for subsequent alternative strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".