Ageing promotes non-remitting experimental autoimmune encephalomyelitis with persistent meningeal inflammation and subpial demyelination in A/T SJL/J mice
Bibliographic record
Abstract
Abstract Current therapies for multiple sclerosis (MS) reduce the frequency of relapses but fail to prevent transition from relapse-remitting into progressive MS. Subpial demyelination, meningeal inflammation and neurodegeneration are hallmarks of progressive MS, however there is no single animal model that mimics all these features. Adoptive transfer of proteolipid protein (PLP)-primed Th17 cells into young (~12 weeks old) SJL/J recipient mice induces experimental autoimmune encephalomyelitis (EAE) characterized by subpial demyelination associated with meningeal tertiary lymphoid tissues (TLTs), however young mice develop remitting, non-progressive disease. Since age is a risk factor for transitioning to progressive MS, we hypothesized that adoptive transfer of PLP-primed Th17 cells into old (~12 months old) SJL/J recipient mice would promote a progressive phenotype. Indeed, transfer of cells into aged recipient mice resulted in a non-remitting EAE compared to young recipients despite a similar ability to produce T cell derived cytokines in the CNS at peak disease. However, old mice showed persistent meningeal inflammation as evidenced by more B/T cell rich TLTs in the meninges compared to young mice, and developed increased CNS pathology (subpial demyelination, microglia/macrophage activation, glial limitans disruption, axonal injury, loss of synapses), whereas young EAE mice had recovered most of the pathology seen at peak of disease by day 25 post-transfer. In summary, our model provides an opportunity to assess how subpial demyelination associated with meningeal inflammation impacts the transition from relapse-remitting to progressive MS in a single animal model.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 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".