Intrinsic determinants of differential axonal regeneration by adult mammalian central nervous system neurons
Bibliographic record
Abstract
Traumatic injuries to the spinal cord cannot be repaired and have devastating and protracted effects on the lives of many thousands of people, mainly because axons in the injured CNS, unlike those in peripheral nerves, do not normally regenerate. Consequently, one of the most important goals of neurobiology is to understand the causes of the failure of axonal regeneration in the CNS so that treatments for the injured spinal cord can be developed. For many years, however, it was widely believed that most CNS neurons were intrinsically unable to regrow their axons following injury. This view was dramatically changed, and the study of CNS regeneration was reinvigorated by the experiments of Aguayo and colleagues in Montreal, and Berry in London, with peripheral nerve grafts implanted into the CNS (eg Benfey and Aguayo, 1982) or attached to severed optic nerves (eg Berry et al., 1986), which showed that injured CNS axons could regenerate under some circumstances. Aguayo’s experiments were not entirely novel; it has been known for a century that peripheral nerve grafts in the brain and spinal cord are invaded by axons, but the interpretation of such results was confused by the difficulty of identifying the source of the invading axons (which could have been from sensory or autonomic neurons with cell bodies outside the CNS). The considerable impact of the early studies by Aguayo’s group was, therefore, partly the result of their careful exploitation of recently developed axonal tracing technology which allowed the categorical demonstration that neurons inside the CNS had regenerated their axons. Nerve grafting experiments added considerable weight to the hypothesis that CNS neurons are capable of axonal regeneration, but that the environment surrounding the injured axons is normally non-permissive or inadequate for axonal growth.
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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 | 0.001 |
| 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".