Delayed administration of the human anti-RGMa monoclonal antibody elezanumab promotes functional recovery including spontaneous voiding after spinal cord injury in rats
Bibliographic record
Abstract
Spinal cord injury (SCI) often results in permanent functional loss due to a series of degenerative events including cell death, axonal damage, and the upregulation of inhibitory proteins that impede regeneration. Repulsive Guidance Molecule A (RGMa) is a potent inhibitor of axonal growth that is rapidly upregulated following injury in both the rodent and human central nervous system (CNS). Previously, we showed that monoclonal antibodies that specifically block inhibitory RGMa signaling promote neuroprotective and regenerative effects when administered acutely in a clinically relevant rat model of thoracic SCI. However, it is unknown whether systemic administration of RGMa blocking antibodies are effective for SCI after delayed administration. Here, we administered elezanumab, a human monoclonal antibody targeting RGMa, intravenously either acutely or at 3 h or 24 h following thoracic clip impact-compression SCI. Rats treated with elezanumab acutely and at 3 h post-injury showed improvements in overground locomotion and fine motor function and gait. Rats treated 24 h post-SCI trended towards better recovery demonstrating significantly greater stride length and swing speed. Treated rats also showed greater tissue preservation with reduced lesion areas. As seen with acute treatment, delayed administration of elezanumab at 3 h post-SCI also increased perilesional neuronal sparing and serotonergic and corticospinal axonal plasticity. In addition, all elezanumab treated rats showed earlier spontaneous voiding ability and less post-trauma bladder wall hypertrophy. Together, our data demonstrate the therapeutic efficacy of delayed systemic administration of elezanumab in a rat model of SCI, and uncovers a new role for RGMa inhibition in bladder 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".