Unleashing Intrinsic Growth Pathways in Regenerating Peripheral Neurons
Bibliographic record
Abstract
Common mechanisms of peripheral axon regeneration are recruited following diverse forms of damage to peripheral nerve axons. Whether the injury is traumatic or disease related neuropathy, reconnection of axons to their targets is required to restore function. Supporting peripheral axon regrowth, while not yet available in clinics, might be accomplished from several directions focusing on one or more of the complex stages of regrowth. Direct axon support, with follow on participation of supporting Schwann cells is one approach, emphasized in this review. However alternative approaches might include direct support of Schwann cells that instruct axons to regrow, manipulation of the inflammatory milieu to prevent ongoing bystander axon damage, or use of inflammatory cytokines as growth factors. Axons may be supported by a growing list of growth factors, extending well beyond the classical neurotrophin family. The understanding of growth factor roles continues to expand but their impact experimentally and in humans has faced serious limitations. The downstream signaling pathways that impact neuron growth have been exploited less frequently in regeneration models and rarely in human work, despite their promise and potency. Here we review the major regenerative signaling cascades that are known to influence adult peripheral axon regeneration. Within these pathways there are major checkpoints or roadblocks that normally check unwanted growth, but are an impediment to robust growth after injury. Several molecular roadblocks, overlapping with tumour suppressor systems in oncology, operate at the level of the perikarya. They have impacts on overall neuron plasticity and growth. A second approach targets proteins that largely operate at growth cones. Addressing both sites might offer synergistic benefits to regrowing neurons. This review emphasizes intrinsic aspects of adult peripheral axon regeneration, emphasizing several molecular barriers to regrowth that have been studied in our laboratory.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".