Cytokine-Defined B Cell Responses as Therapeutic Targets in Multiple Sclerosis
Bibliographic record
Abstract
Important antibody-independent pathogenic roles of B cells are emerging in autoimmune diseases, including multiple sclerosis (MS). The contrasting results of different treatments targeting B cells in patients (in spite of predictions of therapeutic benefits from animal models) call for a better understanding of the multiple roles that distinct human B cell responses likely play in MS. In recent years, both murine and human B cells have been identified with distinct functional properties related to their expression of particular cytokines. These have included regulatory (Breg) B cells (secreting interleukin (IL)-10 or IL-35) and pro-inflammatory B cells (secreting tumor necrosis factor α, LTα, IL-6, and granulocyte macrophage colony-stimulating factor). Better understanding of human cytokine-defined B cell responses is necessary in both health and diseases, such as MS. Investigation of their surface phenotype, distinct functions, and the mechanisms of regulation (both cell intrinsic and cell extrinsic) may help develop effective treatments that are more selective and safe. In this review, we focus on mechanisms by which cytokine-defined B cells contribute to the peripheral immune cascades that are thought to underlie MS relapses, and the impact of B cell-directed therapies on these mechanisms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".