Faculty Opinions recommendation of Eliciting inflammation enables successful rehabilitative training in chronic spinal cord injury.
Bibliographic record
Abstract
Rehabilitative training is one of the most successful therapies to promote motor recovery after spinal cord injury, especially when applied early after injury.Polytrauma and management of other medical complications in the acute post-injury setting often preclude or complicate early rehabilitation.Therefore, interventions that reopen a window of opportunity for effective motor training after chronic injury would have significant therapeutic value.Here, we tested whether this could be achieved in rats with chronic (8 weeks) dorsolateral quadrant sections of the cervical spinal cord (C4) by inducing mild neuroinflammation.We found that systemic injection of a low dose of lipopolysaccharide improved the efficacy of rehabilitative training on forelimb function, as assessed using a single pellet reaching and grasping task.This enhanced recovery was found to be dependent on the training intensity, where a high-intensity paradigm induced the biggest improvements.Importantly, in contrast to training alone, the combination of systemic lipopolysaccharide and high-intensity training restored original function (reparative plasticity) rather than enhancing new motor strategies (compensatory plasticity).Accordingly, electrophysiological and tract-tracing studies demonstrated a recovery in the cortical drive to the affected forelimb muscles and a restructuration of the corticospinal innervation of the cervical spinal cord.Thus, we propose that techniques that can elicit mild neuroinflammation may be used to enhance the efficacy of rehabilitative training after chronic spinal cord injury.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.373 | 0.233 |
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".