<scp>CD</scp>21<sup>−/low</sup> B cells: A Snapshot of a Unique B Cell Subset in Health and Disease
Bibliographic record
Abstract
B cells represent one of the cellular components of the immune system that protects the individual from invading pathogens. In response to the invader, these cells differentiate into plasma cells and produce large amounts of antibodies that bind to and eliminate the pathogen. A hallmark of autoimmune diseases is the production of autoantibodies i.e. antibodies that recognize self. Those that are considered pathogenic can damage tissues and organs, either by direct binding or when deposited as immune complexes. For decades, B cells have been considered to play a major role in autoimmune diseases by antibody production. However, as pathogenic autoantibodies appear to derive mainly from T cell dependent responses, T cells have been the focus for many years. The successful treatment of patients with autoimmune diseases with either B cell depletion therapy (rituximab) or inhibition of B cell survival (belimumab), suggested that not only the autoantibodies but also other B cell features are important. This has caused a surge of interest in B cells and their biology resulting in the identification of various subsets e.g. regulatory B cells, several memory B cell subsets etc. Also, in other conditions such as chronic viral infections and primary immunodeficiency, several B cell subsets with unique characteristics have been identified. In this review, we will discuss one of these subsets, a subset that is expanded in conditions characterized by chronic immune stimulation. This B cell subset lacks, or expresses low, surface levels of the complement receptor 2 (CD21) and has therefore been termed CD21(-/low) B cells.
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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 | 0.000 |
| 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) | 0.003 | 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".