Insights Into the Role and Potential of Schwann Cells for Peripheral Nerve Repair From Studies of Development and Injury
Bibliographic record
Abstract
Peripheral nerve injuries arising from trauma or disease can lead to sensory and motor deficits and neuropathic pain. Despite the purported ability of the peripheral nerve to self-repair, lifelong disability is common. New molecular and cellular insights have begun to reveal why the peripheral nerve has limited repair capacity. The peripheral nerve is primarily comprised of axons and Schwann cells, the supporting glial cells that produce myelin to facilitate the rapid conduction of electrical impulses. Schwann cells are required for successful nerve regeneration; they partially "de-differentiate" in response to injury, re-initiating the expression of developmental genes that support nerve repair. However, Schwann cell dysfunction, which occurs in chronic nerve injury, disease, and aging, limits their capacity to support endogenous repair, worsening patient outcomes. Cell replacement-based therapeutic approaches using exogenous Schwann cells could be curative, but not all Schwann cells have a "repair" phenotype, defined as the ability to promote axonal growth, maintain a proliferative phenotype, and remyelinate axons. Two cell replacement strategies are being championed for peripheral nerve repair: prospective isolation of "repair" Schwann cells for autologous cell transplants, which is hampered by supply challenges, and directed differentiation of pluripotent stem cells or lineage conversion of accessible somatic cells to induced Schwann cells, with the potential of "unlimited" supply. All approaches require a solid understanding of the molecular mechanisms guiding Schwann cell development and the repair phenotype, which we review herein. Together these studies provide essential context for current efforts to design glial cell-based therapies for peripheral nerve regeneration.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".