Systemic administration of anti-CD20 indirectly reduces B cells in the inflamed meninges in a chronic model of central nervous system autoimmunity
Bibliographic record
Abstract
Abstract Anti-CD20 B cell depleting therapies have demonstrated that B cells are important drivers of disease progress in Multiple Sclerosis, although the pathogenic mechanisms are not well understood. A population of B cells accumulates in the inflamed meninges in MS and also in some chronic animal models of disease, typically adjacent to demyelinating lesions. The role of these meningeal B cells in disease is not completely understood, nor is their susceptibility to anti-CD20 therapy. Here, we administered anti-CD20 to 2D2 IgHMOG spontaneous experimental autoimmune encephalomyelitis mice in the chronic phase of disease, after the establishment of meningeal B cell clusters. Compared to the circulation, lymph nodes, and spleen, B cell depletion from the CNS was delayed and not evident until 7d post administration of anti-CD20. Further, we did not find evidence that anti-CD20 accessed meningeal B cells directly, but rather that depletion was indirect and the result of ongoing turnover of the meningeal population and elimination of the peripheral pool from which it is sustained. The reduction of B cell numbers in the CNS coincided with less demyelination of the spinal cord white matter and, surprisingly, an increase in the number of T cells in the meninges but not parenchyma. YT is the recipient of an endMS Doctoral Studentships from the Multiple Sclerosis Society of Canada (MSSOC). KAP is the recipient of an endMS Post-Doctoral Fellowship from MSSOC. This study was funded by operating grants from the Canadian Institutes of Health Research and the MSSOC.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".