Schwann cell plasticity‐roles in tissue homeostasis, regeneration, and disease
Bibliographic record
Abstract
How tissues are maintained over a lifetime and repaired following injury are fundamental questions in biology with a disruption to these processes underlying pathologies such as cancer and degenerative disorders. It is becoming increasingly clear that each tissue has a distinct mechanism to maintain homeostasis and respond to injury utilizing different types of stem/progenitor cell populations depending on the insult and/or with a contribution from more differentiated cells that are able to dedifferentiate to aid tissue regeneration. Peripheral nerves are highly quiescent yet show remarkable regenerative capabilities. Remarkably, there is no evidence for a classical stem cell population, rather all cell-types within the nerve are able to proliferate to produce new nerve tissue. Co-ordinating the regeneration of this tissue are Schwann cells (SCs), the main glial cells of the peripheral nervous system. SCs exist in architecturally stable structures that can persist for the lifetime of an animal, however, they are not postmitotic, in that following injury they are reprogrammed at high efficiency to a progenitor-like state, with these cells acting to orchestrate the nerve regeneration process. During nerve regeneration, SCs show little plasticity, maintaining their identity in the repaired tissue. However, once free of the nerve environment they appear to exhibit increased plasticity with reported roles in the repair of other tissues. In this review, we will discuss the mechanisms underlying the homeostasis and regeneration of peripheral nerves and how reprogrammed progenitor-like SCs have broader roles in the repair of other tissues with implications for pathologies such as cancer.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".