Raloxifene Modulates Estrogen-mediated B Cell Autoreactivity in NZB/W F1 Mice
Bibliographic record
Abstract
OBJECTIVE: Estrogen has been found to exacerbate disease activity in murine lupus and to induce a lupus-like syndrome in nonspontaneously autoimmune mice. This has led to the consideration that estrogen may be a risk factor for the development of systemic lupus erythematosus (SLE), and selective estrogen receptor modulators (SERM) may serve to ameliorate lupus activity. We evaluated the effects and mechanism of action of the SERM raloxifene in murine lupus. METHODS: Effects of raloxifene on the development of lupus in NZB/W F1 mice were evaluated in the presence and absence of estrogen by assessing the serum DNA reactivity, glomerular IgG deposition and kidney damage, B cell maturation and selection, and activation status of marginal zone and follicular B cells. RESULTS: Compared to estradiol-treated mice, mice treated with estradiol and raloxifene had significantly lower serum anti-DNA antibody levels and less kidney damage. These effects of raloxifene were due, at least in part, to antagonism of the influence of estrogen on DNA-reactive B cells. Raloxifene was found to prevent estrogen-mediated suppression of autoreactive B cell elimination at the T1/T2 selection checkpoint, to reduce estrogen-induced CD40 overexpression on follicular B cells, making them less responsive to T cell costimulation, and to ameliorate estrogen-mediated CD22 downregulation on marginal zone B cells, thereby decreasing their responsiveness to B cell antigen receptor-mediated stimuli. CONCLUSION: Raloxifene suppressed estrogen-mediated effects on the survival, maturation, and activation of autoreactive B cells in NZB/W F1 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".