Interactions between Schwann cells and macrophages in injury and inherited demyelinating disease
Bibliographic record
Abstract
In this article we first discuss the factors that regulate macrophage recruitment, activation, and myelin phagocytosis during Wallerian degeneration and some of the factors involved in the termination of inflammation at the end of the period of Wallerian degeneration after peripheral nerve injuries. In particular, we deal with the early events that trigger chemokine and cytokine expression; the role of phospholipase A(2) in initiating the breakdown of compact myelin, and chemokine, cytokine expression; and the role of MCP-1, MIP-1alpha, and IL-1beta in macrophage recruitment and myelin phagocytosis. We also discuss how inflammation may be switched off and the recently identified role of the Nogo receptor on activated macrophages in the clearance of these cells from the injured nerve. In the second half of the article we focus on the role of certain Schwann cell borne cytokines and chemokines, such as M-CSF and MCP-1 as well as intracellular signaling that regulate their expression in animal models of inherited demyelinating disease. Additionally, we present the preservation of sensory nerves fibers from macrophage attack in these animal models as a challenging paradigm for the development of putative treatment approaches. Finally, we also discuss the similarities and differences in these Schwann cell-macrophage responses in injury-induced Wallerian degeneration and inherited demyelinating diseases. Knowledge of the molecular mechanisms underlying Schwann cell-macrophage interaction under pathological conditions is an important prerequisite to develop effective treatment strategies for various peripheral nerve disorders.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".