<i>Age-related susceptibility to grey matter demyelination and neurodegeneration is associated with meningeal neutrophil accumulation in an animal model of MS</i>
Bibliographic record
Abstract
Abstract People living with multiple sclerosis (MS) experience episodic central nervous system (CNS) white matter lesions instigated by autoreactive T cells. With age, MS patients show evidence of grey matter demyelination and experience devastating non-remitting symptomology. What drives progression is unclear and has been hampered by the lack of suitable animal models. Here we show that passive experimental autoimmune encephalomyelitis (EAE) induced by an adoptive transfer of young Th17 cells induces a non-remitting clinical phenotype that is associated with persistent leptomeningeal inflammation and cortical pathology in old, but not young SJL/J mice. While the quantity and quality of T cells did not differ in the brains of old vs young EAE mice, an increase in neutrophils and a decrease in B cells was observed in the brains of old mice. Neutrophils were also found in the leptomeninges of a subset of progressive MS patient brains that showed evidence of leptomeningeal inflammation and subpial cortical demyelination. Taken together, our data show that while Th17 cells initiate CNS inflammation, subsequent clinical symptoms and grey matter pathology are dictated by age and associated with other immune cells such as neutrophils.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".