Multifaceted Involvement of Microglia in Gray Matter Pathology in Multiple Sclerosis
Bibliographic record
Abstract
In the inflammatory demyelinating neurodegenerative disease multiple sclerosis (MS), there is increasing interest in gray matter pathology, as neuronal loss and cortical atrophy correlate with disability and disease progression, and MS therapeutics fail to significantly slow or stop neurodegeneration. Microglia, the central nervous system (CNS)-resident macrophages, are extensively involved in white matter MS pathology, but are also implicated in gray matter pathology, similar to other neurodegenerative diseases, for which there is synaptic, axonal, and neuronal degeneration. Microglia display regional heterogeneity within the CNS, which reflects their highly plastic nature and their ability to deliver context-dependent responses tailored to the demands of their microenvironment. Therefore, microglial roles in the MS gray matter in part reflect and in part diverge from those in the white matter. The present review summarizes current knowledge of microglial involvement in gray matter changes in MS, in demyelination, synaptic damage, and neurodegeneration, with evidence implicating microglia in pathology, neuroprotection, and repair. As our understanding of microglial physiology and pathophysiology increases, we describe how we are moving toward potential therapeutic applications in MS, harnessing microglia to protect and regenerate the CNS.
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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 | 0.000 |
| 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 | 0.001 |
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".