Meningeal B Cell Clusters Correlate with Submeningeal Pathology in a Natural Model of Multiple Sclerosis
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is an idiopathic demyelinating disease in which meningeal inflammation correlates with accelerated disease progression. The study of meningeal inflammation in MS has been limited because of constrained access to MS brain/spinal cord specimens and the lack of experimental models recapitulating progressive MS. Unlike induced models, a spontaneously occurring model would offer a unique opportunity to understand MS immunopathogenesis and provide a compelling framework for translational research. We propose granulomatous meningoencephalomyelitis (GME) as a natural model to study neuropathological aspects of MS. GME is an idiopathic, progressive neuroinflammatory disease of young dogs with a female bias. In the GME cases examined in this study, the meninges displayed focal and disseminated leptomeningeal enhancement on magnetic resonance imaging, which correlated with heavy leptomeningeal lymphocytic infiltration. These leptomeningeal infiltrates resembled tertiary lymphoid organs containing large B cell clusters that included few proliferating Ki67+ cells, plasma cells, follicular dendritic/reticular cells, and germinal center B cell–like cells. These B cell collections were confined in a specialized network of collagen fibers associated with the expression of the lympho-organogenic chemokines CXCL13 and CCL21. Although neuroparenchymal perivascular infiltrates contained B cells, they lacked the immune signature of aggregates in the meningeal compartment. Finally, meningeal B cell accumulation correlated significantly with cortical demyelination reflecting neuropathological similarities to MS. Hence, during chronic neuroinflammation, the meningeal microenvironment sustains B cell accumulation that is accompanied by underlying neuroparenchymal injury, indicating GME as a novel, naturally occurring model to study compartmentalized neuroinflammation and the associated pathology thought to contribute to progressive MS.
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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 | 0.000 | 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".