Regenerating corticospinal fibers in the Marmoset (<i>Callitrix jacchus</i>) after spinal cord lesion and treatment with the anti‐Nogo‐A antibody IN‐1
Bibliographic record
Abstract
Neutralizing the myelin-associated growth inhibitor Nogo-A in adult spinal cord-injured rats can promote regeneration of injured and compensatory sprouting of uninjured axons. Nogo-A is present in humans, making its neutralization a possible novel treatment option for paraplegic patients. In this study we examined the effects of an extensively used anti-Nogo-A antibody (mAb IN-1) on the regenerative capabilities of lesioned corticospinal tract (CST) axons in a primate, the Marmoset monkey. Unilateral thoracic lesions of the CST were performed in six adult Marmosets, followed by the application of mAb IN-1 into the cerebrospinal fluid circulation by a graft of hybridoma cells. A unilateral injection of biotin dextran amine into the motor cortex was performed to analyse sprouting and regeneration of the lesioned axons. In the control antibody-treated animal CST fibers stopped rostral to the lesion site and often showed retraction bulbs. In contrast, in four out of five mAb IN-1-treated animals fine labeled neurites had grown into, through and around the lesion site. Thus, this study provides first anatomical evidence that in primates, the neutralization of the myelin-associated inhibitor Nogo-A results in increased regenerative sprouting and growth of lesioned spinal cord axons.
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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) | 0.000 | 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 | 0.001 | 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".