Thymic stromal lymphopoietin deficiency attenuates experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
In the present study we examined the role of thymic stromal lymphopoietin (TSLP) in experimental autoimmune encephalomyelitis (EAE). Here, we report that TSLP knock-out (KO) mice display a delayed onset of disease and an attenuated form of EAE. This delayed onset was accompanied by a reduced number of encephalitogenic T helper type 1 (Th1) cells in the central nervous system (CNS) of TSLP KO mice. In addition, CD4(+) and CD8(+) T cells from CNS of TSLP KO mice show a reduced activation status in comparison to wild-type mice. It is noteworthy that we could also show that lymph node cells from TSLP KO mice expanded less efficiently and that interleukin (IL)-6-, interferon (IFN)-γ and tumour necrosis factor (TNF)-α levels were reduced. Furthermore, CD3(+) T cells isolated in the preclinical phase from myelin oligodendrocyte glycoprotein peptide 35-55 (MOG(35-55))-immunized TSLP KO mice showed a reduced response after secondary exposure to MOG(35-55), indicating that differentiation of naive T cells into MOG(35-55)-specific effector and memory T cells was impaired in KO mice. The addition of recombinant TSLP enhanced T cell proliferation during MOG(35-55) restimulation, showing that T cells also respond directly to TSLP. In summary, these data demonstrate that expression of, and immune activation by, TSLP contributes significantly to the immunopathology of EAE.
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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 | 0.000 |
| 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".