Survival and regeneration of rubrospinal neurons 1 year after spinal cord injury
Bibliographic record
Abstract
Scientific interest to find a treatment for spinal cord injuries has led to the development of numerous experimental strategies to promote axonal regeneration across the spinal cord injury site. Although these strategies have been developed in acute injury paradigms and hold promise for individuals with spinal cord injuries in the future, little is known about their applicability for the vast majority of paralyzed individuals whose injury occurred long ago and who are considered to have a chronic injury. Some studies have shown that the effectiveness of these approaches diminishes dramatically within weeks after injury. Here we investigated the regenerative capacity of rat rubrospinal neurons whose axons were cut in the cervical spinal cord 1 year before. Contrary to earlier reports, we found that rubrospinal neurons do not die after axotomy but, rather, they undergo massive atrophy that can be reversed by applying brain-derived neurotrophic factor to the cell bodies in the midbrain. This administration of neurotrophic factor to the cell body resulted in increased expression of growth-associated protein-43 and Talpha1 tubulin, genes thought to be related to axonal regeneration. This treatment promoted the regeneration of these chronically injured rubrospinal axons into peripheral nerve transplants engrafted at the spinal cord injury site. This outcome is a demonstration of the regenerative capacity of spinal cord projection neurons a full year after axotomy.
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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".