L‐Selectin‐deficient SJL and C57BL/6 mice are not resistant to experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
L-selectin has been suggested to play a role in the pathogenesis of experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis. Here we demonstrate that L-selectin(-/-) SJL mice are susceptible to proteolipid protein (PLP)-induced EAE because the compromised antigen-specific T cell proliferation in peripheral lymph nodes is fully compensated by the T cell response raised in their spleen. Transfer of PLP-specific T cells into syngeneic recipients induced EAE independent of the presence or absence of L-selectin on PLP-specific T cells or in the recipient. Leukocyte infiltration into the central nervous system parenchyma was detectable independent of the mode of disease induction and the presence or absence of L-selectin. In addition, we found L-selectin(-/-) C57BL/6 mice to be susceptible to myelin oligodendrocyte glycoprotein-induced EAE. Taken together, we demonstrate that in SJL and C57BL/6 mice L-selectin is not required for EAE pathogenesis. The apparent discrepancy of our present observation to previous findings, demonstrating a role of L-selectin in EAE pathogenesis in C57BL/6 mice or myelin-basic protein (MBP)-specific TCR-transgenic B10.PL mice, may be attributed to background genes rather than L-selectin and to a unique role of L-selectin in EAE pathogenesis in MBP-TCR-transgenic mice.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".