Suppression of experimental autoimmune encephalomyelitis using estrogen receptor-selective ligands
Bibliographic record
Abstract
Estrogens have been shown to modulate disease activity in experimental autoimmune encephalomyelitis (EAE), the mouse model for multiple sclerosis. Consistent with these findings, the severity of disease is reduced in pregnant women with multiple sclerosis when levels of estrogens are high. Estrogens bind to two known estrogen receptors (ER), ERalpha and ERbeta. The relative contribution of these receptors to estrogen-mediated suppression of EAE was explored using ER-selective ligands. The ER antagonist ICI 182 780 reversed the suppressive effects of 17beta-estradiol (E2), demonstrating that the protective effects of E2 on disease are dependent upon ER signaling. Treatment of SJL mice with the ERalpha-selective agonist proteolipid protein (PPT) prior to the induction of disease resulted in suppression of clinical symptoms of disease, whereas treatment with an ERbeta-selective agonist (WAY-202041) had no effect. Treatment of mice with PLP peptide 139-151 (PPT) was also associated with decreased immune responses associated with disease. Consistent with its lack of effect on disease, the ERbeta agonist had minimal effects on immune responses. The use of selective estrogen receptor modulators (SERMs) in this model was also explored, and we show that raloxifene and WAY-138923 were also effective in suppressing disease. These results demonstrate the beneficial effects of estrogen receptor ligands, in particular ERalpha-selective ligands, and may have implications in the development of therapeutic strategies for multiple sclerosis.
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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".