Central nervous system macrophages in progressive multiple sclerosis: relationship to neurodegeneration and therapeutics
Bibliographic record
Abstract
There are over 15 disease-modifying drugs that have been approved over the last 20 years for the treatment of relapsing-remitting multiple sclerosis (MS), but there are limited treatment options available for progressive MS. The development of new drugs for the treatment of progressive MS remains challenging as the pathophysiology of progressive MS is poorly understood.The progressive phase of MS is dominated by neurodegeneration and a heightened innate immune response with trapped immune cells behind a closed blood-brain barrier in the central nervous system. Here we review microglia and border-associated macrophages, which include perivascular, meningeal, and choroid plexus macrophages, during the progressive phase of MS. These cells are vital and are largely the basis to define lesion types in MS. We will review the evidence that reactive microglia and macrophages upregulate pro-inflammatory genes and downregulate homeostatic genes, that may promote neurodegeneration in progressive MS. We will also review the factors that regulate microglia and macrophage function during progressive MS, as well as potential toxic functions of these cells. Disease-modifying drugs that solely target microglia and macrophage in progressive MS are lacking. The recent treatment successes for progressive MS include include B-cell depletion therapies and sphingosine-1-phosphate receptor modulators. We will describe several therapies being evaluated as a potential treatment option for progressive MS, such as immunomodulatory therapies that can target myeloid cells or as a potential neuroprotective agent.
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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 | 0.000 |
| 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 | 0.002 |
| 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".