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Record W4414341150 · doi:10.1084/jem.20241255

Dynamic alterations of dural and bone marrow B cells in an animal model of progressive multiple sclerosis

2025· article· en· W4414341150 on OpenAlexafffund
Alexandra Florescu, Michelle Zuo, A. Wang, Kevin Champagne-Jorgensen, Mohammed A. Noor, L Ward, Erwin van Puijenbroek, Christian Klein, Jennifer L. Gommerman

Bibliographic record

VenueThe Journal of Experimental Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchMultiple Sclerosis Society of Canada
KeywordsLeptomeningesBone marrowMultiple sclerosisInflammationParenchymaDura materImmune systemPopulation

Abstract

fetched live from OpenAlex

In multiple sclerosis (MS), the leptomeninges (LM) are populated with immune cell aggregates that correlate with disease progression. The impact of LM inflammation on the adjacent dura is largely unknown. Using a mouse model of MS that induces brain LM inflammation and age-dependent disease progression, we found that encephalitogenic T cells and B220high B cells accumulate substantially in the brain LM and parenchyma of both young and aged mice, while the adjacent dura remains relatively inert. We also observed a population of anti-CD20-resistant B220low B cells in the dura and bone marrow that virtually disappear at disease onset and accumulate in the brain of young mice concomitant with disease remission. In contrast, aged mice show a paucity of brain-resident B220low B cells at the expense of class-switched B220high B cells accompanied by severe, chronic disease. In summary, dynamic changes in the brain, LM, and dural B cells are associated with age-dependent disease severity in an animal model of progressive 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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.063
GPT teacher head0.362
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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