<scp>B</scp> cells set trends: Lessons from multiple sclerosis
Bibliographic record
Abstract
Abstract Until relatively recently, B cells were viewed as relatively passive recipients of T cell help, serving the primary normal function of protective antibody production. Similarly, their role in immune‐mediated diseases, including multiple sclerosis (MS), was traditionally ascribed to production of pathogenic autoantibodies. However, new insights gained from both animal models and in humans, including studies of selective B cell targeting in patients, have shed light on non‐antibody‐mediated functions of B cells as immune regulators in both health and disease. Here, we consider the significance of the recent success of B cell depletion in patients with MS. We submit that it is no longer a question of whether B cells contribute to MS, but how B cells do so. In this review, we consider concepts of the different antibody‐dependent and ‐independent biological roles that B cells might play in MS pathophysiology. Important data from the commonly used animal model of MS, experimental autoimmune encephalomyelitis (EAE), continues to contribute to our understanding of the molecular cascades involved in peripheral immune regulation and in immune‐neural interactions that might be relevant to inflammatory events of multiple sclerosis. We focus this review on results from human‐based studies, occasionally drawing on observations from animal models to highlight specific principles.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".