Siponimod therapy implicates Th17 cells in a preclinical model of subpial cortical injury
Bibliographic record
Abstract
Subpial demyelination is a specific hallmark of multiple sclerosis and a correlate of disease progression. Although the mechanism(s) that mediate pathogenesis in the subpial compartment remain unclear, it has been speculated that inflammation in the overlying meninges may be associated with subpial injury. Here we show that adoptive transfer of proteolipid protein-primed Th17 cells into SJL/J recipient mice induces subpial demyelination associated with microglial/macrophage activation, disruption of the glial limitans, and evidence of an oxidative stress response. This pathology was topologically associated with foci of immune cells in the meninges and occurred in the absence of measurable anti-myelin oligodendrocyte glycoprotein IgM or IgG antibodies. To test the role of brain-infiltrating leukocytes on subpial injury, we modulated sphingosine 1-phosphate (S1P) receptor1,5 activity with BAF312 (siponimod) treatment. Administration of BAF312, even after adoptively transferred T cells had entered the brain, significantly ameliorated clinical experimental autoimmune encephalomyelitis and diminished subpial pathology, concomitant with a selective reduction in the capacity of transferred T cells to make Th17 cytokines. We conclude that sustained subpial cortical injury is associated with the capacity for brain-resident T cells to produce Th17 cytokines, and this pathological process occurs in an S1P receptor1,5-dependent manner.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".