Impact of B cell-depletion on plasma cells in a pre-clinical model of meningeal inflammation and subpial demyelination
Bibliographic record
Abstract
Abstract Anti-CD20 B cell depletion therapy has provided evidence for the pathogenic roles of B cells in multiple sclerosis (MS). Terminally differentiated plasma cells (PCs) are spared by anti-CD20, and our lab has previously shown that during experimental autoimmune encephalomyelitis (EAE), IgA+ PCs mediate protection against neuroinflammation. One hypothesis is that in addition to depleting pro-inflammatory B cells, anti-CD20 also removes the major consumers of BAFF (i.e. B cells) thus promoting a favourable niche for regulatory PCs and may represent an additional mechanism by which anti-CD20 therapy confers protection. Adoptive transfer EAE of PLP-primed Th17 cells into SJL/J mice leads to ascending paralysis and meningeal inflammation which parallels brain pathology seen in human MS. Mice were prophylactically treated with anti-CD20 (10mg/kg) or isotype control antibodies which we re-administered throughout the course of the experiment and monitored daily for clinical symptoms. We find that anti-CD20 treatment effectively depleted B cells (but not T cells) in the periphery; within the central nervous system, meningeal immune clusters formed in both anti-CD20 and isotype control treated mice, with the former being devoid of B220+ B cells. Compared to control treated mice, anti-CD20 treated mice showed limited subpial demyelination and pathology adjacent to B cell-less meningeal immune clusters, and is accompanied by a reduction in clinical score. In the serum, anti-CD20 treatment elevated levels of BAFF and IgA, which inversely correlated with disease severity. We thus propose that anti-CD20 exerts its protective effects by providing a favourable niche for suppressive IgA+ PCs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".