Cytokine‐producing B cells: a translational view on their roles in human and mouse autoimmune diseases
Bibliographic record
Abstract
B-cell depletion therapy has beneficial effects in autoimmune diseases. This is only partly explained by an elimination of autoantibodies. How does B-cell depletion improve disease? Here, we review preclinical studies showing that B cells can propagate autoimmune disorders through cytokine production. We also highlight clinical observations indicating the relevance of these B-cell functions in human autoimmunity. Abnormalities in B-cell cytokine production have been observed in rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, and systemic lupus erythematosus. In the first two diseases, B-cell depletion erases these abnormalities, and improves disease progression, suggesting a causative role for defective B-cell cytokine expression in disease pathogenesis. However, in the last two disorders, the pathogenic role of B cells and the effect of B-cell depletion on cytokine-producing B cells remain to be clarified. A better characterization of cytokine-expressing human B-cell subsets, and their modulation by B cell-targeted therapies might help understanding both the successes and failures of current B cell-targeted approaches. This may even lead to the development of novel strategies to deplete or amplify selectively pathogenic or protective subsets, respectively, which might be more effective than global depletion of the B-cell compartment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".