Molecular Targets to Promote Central Nervous System Regeneration
Bibliographic record
Abstract
Trauma in the adult mammalian central nervous system (CNS) results in devastating clinical consequences due to the failure of injured axons to spontaneously regenerate. This regenerative failure can be attributed to both a lack of positive cues and to the presence of inhibitory cues that actively prevent regeneration. Substantial progress has been made in elucidating the molecular identity of negative cues present at the CNS injury site following injury. In the past several years, multiple myelin-associated inhibitors including Nogo, Myelin-associated glycoprotein and Oligodendrocyte-myelin glycoprotein have been characterized. Furthermore a neuronal receptor complex and several intracellular substrates leading to outgrowth inhibition have been identified. Rapid progress has also been made in identifying the role of neurotrophins and other positive cues in promoting axonal regrowth. The most recent advances in our understanding of positive stimuli for axon regeneration come from transplantation studies at the CNS lesion site. A number of artificial substrates, tissues, and cells including fetal cells, neural stem cells, Schwann cells and olfactory-ensheathing cells have been tested in animal models of CNS injury. Based on our expanded knowledge of inhibitory influences and on the positive characteristics of various transplants, a number of interventions have been tested to promote recovery in models of CNS trauma. These advances represent the first steps in developing a viable therapy to promote axon regeneration following CNS trauma.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".