RGMa inhibition with human monoclonal antibodies promotes regeneration, plasticity and repair, and attenuates neuropathic pain after spinal cord injury
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) causes a cascade of degenerative events including cell death, axonal damage, and the upregulation of inhibitory molecules which prevent regeneration and limit recovery. Repulsive guidance molecule A (RGMa) is a potent neurite growth inhibitor in the central nervous system, exerting its repulsive activity by binding the Neogenin receptor. Here, we show for the first time that inhibitory RGMa is markedly upregulated in multiple cell types after clinically relevant impact-compression SCI in rats, and importantly, also in the injured human spinal cord. To neutralize inhibitory RGMa, clinically relevant human monoclonal antibodies were systemically administered after acute SCI, and were detected in serum, cerebrospinal fluid, and in the injured tissue. Rats treated with RGMa blocking antibodies showed significantly improved recovery of motor function and gait. Furthermore, RGMa blocking antibodies promoted neuronal survival, and enhanced the plasticity of descending serotonergic pathways and corticospinal tract axonal regeneration. RGMa antibody also attenuated neuropathic pain responses, which was associated with fewer activated microglia and reduced CGRP expression in the dorsal horn caudal to the lesion. These results show the therapeutic potential of the first human RGMa antibody for SCI and uncovers a new role for the RGMa/Neogenin pathway on neuropathic pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".