Functional Consequences of Neuromyelitis Optica-IgG Astrocyte Interactions on Blood-Brain Barrier Permeability and Granulocyte Recruitment
Bibliographic record
Abstract
Autoantibody neuromyelitis optica-IgG (NMO-IgG) recognizing aquaporin-4 (AQP4) is implicated as playing a central role in the physiopathology of NMO. The aim of this in vitro-based study was to characterize functional consequences of interaction between NMO-IgG and cells of the neurovascular unit (astrocytes and brain endothelium) that would provide insight into recognized features of NMO, namely altered blood-brain barrier (BBB) permeability and granulocyte recruitment. We used sera from NMO and longitudinally extensive transverse myelitis cases shown to bind in a characteristic perivascular pattern to primate cerebellar slices. Using flow cytometry, we found that sera from NMO-IgG-positive patients reacted with CNS-derived human fetal astrocytes, whereas sera from multiple sclerosis patients did not. We demonstrated that NMO-IgG binding to astrocytes alters aquaporin-4 polarized expression and increases permeability of a human BBB endothelium/astrocyte barrier. We further demonstrated that NMO-IgG binding to human fetal astrocytes can result in NK cell degranulation, astrocyte killing by Ab-dependent cellular cytotoxicity and complement-dependent granulocyte attraction through the BBB model. Our study highlights important functional roles for NMO-IgG that could account for pathological lesions and BBB dysfunction observed in NMO.
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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) | 0.001 | 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".