Sphingosine 1-phosphate receptor modulation suppresses pathogenic astrocyte activation and chronic progressive CNS inflammation
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune inflammatory demyelinating disease of the CNS that causes disability in young adults as a result of the irreversible accumulation of neurological deficits. Although there are potent disease-modifying agents for its initial relapsing-remitting phase, these therapies show limited efficacy in secondary progressive MS (SPMS). Thus, there is an unmet clinical need for the identification of disease mechanisms and potential therapeutic approaches for SPMS. Here, we show that the sphingosine 1-phosphate receptor (S1PR) modulator fingolimod (FTY720) ameliorated chronic progressive experimental autoimmune encephalomyelitis in nonobese diabetic mice, an experimental model that resembles several aspects of SPMS, including neurodegeneration and disease progression driven by the innate immune response in the CNS. Indeed, S1PR modulation by FTY720 in murine and human astrocytes suppressed neurodegeneration-promoting mechanisms mediated by astrocytes, microglia, and CNS-infiltrating proinflammatory monocytes. Genome-wide studies showed that FTY720 suppresses transcriptional programs associated with the promotion of disease progression by astrocytes. The study of the molecular mechanisms controlling these transcriptional modules may open new avenues for the development of therapeutic strategies for progressive MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".