Nogo‐A antibody improves regeneration and locomotion of spinal cord–injured rats
Bibliographic record
Abstract
Spinal cord trauma leads to loss of motor, sensory and autonomic functions below the lesion. Recovery is very restricted, due in part to neurite growth inhibitory myelin proteins, in particular Nogo-A. Two neutralizing antibodies against Nogo-A were used to study recovery and axonal regeneration after spinal cord lesions. Three months old Lewis rats were tested in sensory-motor tasks (open field locomotion, crossing of ladder rungs and narrow beams, the CatWalk(R) runway, reactions to heat and von Frey hairs). A T-shaped lesion was made at T8, and an intrathecal catheter delivered highly purified anti-Nogo-A monoclonal IgGs or unspecific IgGs for 2 weeks. A better outcome in motor behavior was obtained as early as two weeks after lesion in the animals receiving the Nogo-A antibodies. Withdrawal responses to heat and mechanical stimuli were not different between the groups. Histology showed enhanced regeneration of corticospinal axons in the anti-Nogo-A antibody groups. fMRI revealed significant cortical responses to stimulation of the hindpaw exclusively in anti-Nogo-A animals. These results demonstrate that neutralization of the neurite growth inhibitor Nogo-A by intrathecal antibodies leads to enhanced regeneration and reorganization of the injured CNS, resulting in improved recovery of compromised functions in the absence of dysfunctions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".