Fingolimod targets cerebral endothelial activation to block leukocyte recruitment in the central nervous system
Bibliographic record
Abstract
Fingolimod (FTY720), an immunomodulator, is approved as an oral treatment for patients with relapsing forms of multiple sclerosis. Its effects are largely attributed to its mechanism of selectively retaining lymphocytes in the lymph nodes to reduce autoreactive T-cell recruitment in the CNS. In this study, we investigated the therapeutic effect of FTY720 on an animal model of CNS inflammation induced by intracerebral ventricle LPS injection. We found that FTY720 treatment significantly prevented LPS-induced neutrophil recruitment in the CNS by inhibiting leukocyte recruitment in cerebral microvessels. Furthermore, FTY720 also inhibited the expressions of adhesion molecules on the cerebral endothelium, but did not affect the expression levels of pro-inflammatory cytokines (TNF-α and IL-6) and chemokines (CXCL1 and CXCL2) in the CNS parenchyma. The inhibition of endothelial activation was accompanied by reduced phosphorylation of signaling molecules, including serine/threonine-specific protein kinase (Akt), STAT6, and nuclear factor-κB. This FTY720-attenuated inhibition of leukocyte recruitment and endothelial activation was reversed by blocking the functions of sphingosine kinase 2 or sphingosine-1-phosphate receptor 1. Our study demonstrated, for the first time, that FTY720 directly inhibits the phosphorylation of multiple signaling molecules in endothelial cells, thereby effectively blocking leukocyte recruitment in the CNS.
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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 | 0.000 | 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".