Promoting axonal regeneration in the central nervous system by enhancing the cell body response to axotomy
Bibliographic record
Abstract
Neurons projecting into the peripheral nervous system (PNS) regenerate their axons after injury, in contrast to those confined to the central nervous system (CNS). Both neuronal and nonneuronal factors contribute to the lack of CNS regeneration. In this review we concentrate on the differential gene expression response to axotomy in PNS vs. CNS neurons. In general CNS neurons fail to up-regulate or sustain the expression of regeneration-associated proteins (RAGs), including trophic factors and their receptors. The presumed lack of trophic support of axotomized CNS neurons provided the rationale for the exogenous application of trophic factors, either to the lesion site or to the cell bodies. Here, we review our data on the application of trophic factors to rubrospinal and corticospinal neurons. Cell body treatment of axotomized rubrospinal neurons with brain-derived neurotrophic factor (BDNF) reversed atrophy, increased GAP-43 and Talpha-1 tubulin mRNA expression, and promoted axonal regeneration into peripheral nerve grafts. Importantly, BDNF cell body treatment was still effective in the chronic setting, i.e., when initiated 1 year after injury, but BDNF had no effect when applied to the chronic spinal cord injury site. The ability to promote regeneration in chronically injured neurons will hopefully contribute to the development of treatment strategies for chronic spinal injuries.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".