COMPLEMENT ACTIVATION IN MULTIPLE SCLEROSIS AND NMO SPECTRUM DISORDERS
Bibliographic record
Abstract
MS and NMOSD are inflammatory CNS diseases and early manifestations can be similar creating management problems, since MS drugs may be ineffective and/or worsen NMOSD. MR imaging and AQP4-Abs provide diagnostic information in most NMOSD cases, but a minority remain AQP4-Ab-negative; underlining the need for alternative diagnostic biomarkers. Complement (C) activation is a core pathological feature in both. We have investigated whether plasma C analytes can distinguish MS from NMOSD. Plasma from 53 NMOSD, 49 MS and 69 controls was tested in 2 multiplex assays: the first measuring 5 C activation products and the second comprising 5 C proteins. All activation products were significantly elevated in NMOSD compared to control or MS, particularly in AQP4-Ab-positive samples. Four C proteins (C1inh,C1s,C5,FH) were significantly higher in NMOSD (notably AQP4-Ab-positive) compared to MS or controls, whilst one (C3) was significantly lower. Receiver operating characteristic curves for each comparator identified best distinguishing analytes; a model developed from the most predictive gave an area under the curve of 0.938 for NMOSD versus controls and 0.977 for NMOSD versus MS. These data demonstrate NMOSD is characterised by significant C activation and C3 consumption, and a subset of C analytes could provide a supplementary tool for diagnosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".