B-cell subsets: cellular interactions and relevance in multiple sclerosis
Bibliographic record
Abstract
There has been longstanding interest in how B-cell responses may contribute to the pathology of neurological diseases. Traditionally, the premise has been that any such contribution relates to the potential of B cells to produce autopathogenic antibodies. Targeting autoantibodies continues to be an important therapeutic approach, particularly in disorders where the role of antibodies is relatively well established, such as in certain inflammatory disorders of peripheral nerves or the neuromuscular junction. In other conditions, such as multiple sclerosis, the role of circulating antibodies targeting the CNS has been less clear, although pathologic studies continue to implicate CNS-reactive antibodies, as well as B cells within the CNS compartment. Recently, new insights into fundamental properties of B cells have suggested that these cells may contribute in an antibody-independent fashion, both to normal immune responses, and in the context of immune mediated diseases. Here, we will consider the potential roles of antibody-dependent as well as antibody-independent B-cell involvement in multiple sclerosis. The topic is of particular interest at a time that B-cell-directed therapies are being evaluated for this and other autoimmune diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".