Cross-talk between human glial cells and B cells help propagation of CNS-compartmentalized in progressive MS
Bibliographic record
Abstract
Abstract B cell depleting therapies efficiently decrease new multiple sclerosis (MS) relapses. Previously, we demonstrated that MS patients harbor abnormally higher proportions of pro-inflammatory effector B cells (Beff), producing high IL-6. TNF and GM-CSF levels compared to controls. B cells are fostered within the MS central nervous system (CNS). Persistence of B cells was reported within lesions and meningeal aggregates, which are adjacent to subpial cortical injury, involving neuronal loss and microglia/macrophage activation, and known to be associated with progressive MS. However, how distinct B cells persist and potentially propagate CNS-local inflammation potentially contributing to disease progression (unmet need) remains unsolved. First, we demonstrated that supernatant from astrocytes supported HC and MS B cell survival (including CD27+ B cells), while M2c-polarized human microglia appeared cytotoxic to B cells. Activated astrocytes increased CD86 expression on B cell surface. Similar results are obtained when B cells were exposed to supernatants of M1-polarized microglia. Furthermore, B cells previously exposed to activated astrocytes increased T cell proliferation. In turn, Beff but not Breg supernatants down-regulate IL-10 production by microglia/macrophage, and substantially enhance pro-inflammatory cytokines (IL-12, TNF & IL-6), independent of phenotypic changes of microglia/macrophage. These results indicate a potential bi-directional interaction between disease-relevant human B cell subsets and astrocytes CNS resident microglia and infiltrating myeloid cells, which may influence the propagation of MS-CNS compartmentalized inflammation associated with disease progression.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".