Monocytes in central nervous system remyelination
Bibliographic record
Abstract
Remyelination failure with aging and progression of neurodegenerative disorders contributes to axonal dysfunction, highlighting the importance of understanding the mechanisms underpinning this process to develop regenerative therapies. Central nervous system (CNS) macrophages, encompassing both resident microglia and blood monocyte-derived cells, play a crucial role in driving successful remyelination. Although there has been a focus on the critical roles of microglia in remyelination, the specific contribution of monocyte-derived macrophages is still not fully understood. Until recently, the lack of tools enabling distinction between CNS macrophage populations has hindered our understanding of monocyte influence on remyelination. Recent advances have allowed for identification and characterization of monocyte populations in health, aging and in neurodegenerative conditions like multiple sclerosis, indicating heterogeneity of monocyte subsets impacted by both intrinsic and extrinsic factors. Here, we discuss the new tools enabling distinction between macrophage populations and advancements in understanding the importance of monocytes in remyelination, and reflect on the potential for therapeutic targeting of monocytes to promote 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".