Regenerative Capacity of Macrophages for Remyelination
Bibliographic record
Abstract
White matter injury, consisting of loss of axons, myelin, and oligodendrocytes, is common in many neurological disorders and is believed to underlie several motor and sensory deficits. Remyelination is the process in which the insulative myelin sheath is restored to axons, thereby facilitating recovery from functional loss. Remyelination proceeds with oligodendrocyte precursor cells (OPCs) that differentiate into oligodendrocytes to synthesize the new myelin sheath after demyelination. This process is influenced by several factors, including trophic factors, inhibitory molecules in the lesion microenvironment, age of the subject, as well as the inflammatory response. Currently studied strategies that enhance remyelination consist of pharmacological approaches that directly induce OPC differentiation or using agents to neutralize the inhibitory microenvironment. Another strategy is to harness a reparative inflammatory response. This response, coordinated by central nervous system resident microglia and peripherally-derived infiltrating macrophages, has been shown to be important in the remyelination process. These innate immune cells perform important functions in remyelination, including the proteolysis and phagocytosis of inhibitory molecules present in the lesion microenvironment, the provision of trophic and metabolic factors to OPCs, in addition to iron handling capacity. Additionally, an initial pro-inflammatory phase followed by a regulatory/anti-inflammatory phase has been shown to be important for OPC proliferation and differentiation, respectively. This review will discuss the beneficial roles of macrophages/microglia in remyelination and discuss therapeutic strategies to obtain the optimal regenerative macrophage phenotype for enhanced remyelination.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".