CSF levels of glutamine synthetase and GFAP to explore astrocytic damage in seronegative NMOSD
Bibliographic record
Abstract
Objective To explore levels of astrocytopathy in neuromyelitis optica spectrum disorder (NMOSD) by measuring levels of the astrocytic enzyme glutamine synthetase (GS) and glial fibrillary acidic protein (GFAP), an established astrocytic biomarker known to be associated with disease activity in multiple sclerosis. Methods Cerebrospinal fluid concentrations of GS and GFAP were measured by ELISA in patients with NMOSD (n=39, 28 aquaporin-4 (AQP4)-Ab-seropositive, 3 double-Ab-seronegative, 4 myelin oligodendrocyte glycoprotein (MOG)-Ab-seropositive and 4 AQP4-Ab-seronegative with unknown MOG-Ab-serostatus), multiple sclerosis (MS) (n=69), optic neuritis (n=5) and non-neurological controls (n=37). Results GFAP and GS concentrations differed significantly across groups (both p<0.001), showing a similar pattern of elevation in patients with AQP4-Ab-seropositive NMOSD. GS and GFAP were significantly correlated, particularly in patients with AQP4-Ab-seropositive NMOSD (rs=0.70, p<0.001). Interestingly, GFAP levels in some patients with double-Ab-seronegative NMOSD were markedly increased. Conclusions Our data indicate astrocytic injury occurs in some patients with double-Ab-seronegative NMOSD, which hints at the possible existence of yet undiscovered astrocytic autoimmune targets. We hypothesise that elevated GS and GFAP levels could identify those double-Ab-seronegative patients suitable to undergo in-depth autoimmune screening for astrocytic antibodies.
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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 | 0.001 |
| 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 | 0.000 |
| 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".