Regulation of neuroinflammation by B cells and plasma cells
Bibliographic record
Abstract
Abstract The remarkable success of anti‐CD20 B cell depletion therapies in reducing the burden of multiple sclerosis (MS) disease has prompted significant interest in how B cells contribute to neuroinflammation. Most focus has been on identifying pathogenic CD20+ B cells. However, an increasing number of studies have also identified regulatory functions of B lineage cells, particularly the production of IL‐10, as being associated with disease remission in anti‐CD20–treated MS patients. Moreover, IL‐10–producing B cells have been linked to the attenuation of inflammation in experimental autoimmune encephalomyelitis (EAE), the animal model of MS. In addition to IL‐10–producing B cells, antibody‐producing plasma cells (PCs) have also been implicated in suppressing neuroinflammation. This review will examine regulatory roles for B cells and PCs in MS and EAE. In addition, we speculate on the involvement of regulatory PCs and the cytokine BAFF in the context of anti‐CD20 treatment. Lastly, we explore how the microbiota could influence anti‐inflammatory B cell behavior. A better understanding of the contributions of different B cell subsets to the regulation of neuroinflammation, and factors that impact the development, maintenance, and migration of such subsets, will be important for rationalizing next‐generation B cell–directed therapies for the treatment of MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".