Residual serum fibrinogen as a universal biomarker for all serotypes of Myasthenia gravis
Bibliographic record
Abstract
Myasthenia Gravis (MG) is an autoimmune disease associated with severe neuromuscular weakness. Diagnostic confirmation of MG is typically delayed and secured in about 85% and 50% of patients with generalized and ocular MG, respectively with serum antibodies. We have identified a sensitive and specific diagnostic biomarker for various MG serotypes with quantitative proteomics. Serum proteomes of 18 individuals (MG patients, healthy controls (HC), Rheumatoid Arthritis (RA) were quantified in a pilot study and occurrence of high residual fibrinogen was validated by immunoblotting and further investigated by targeted mass spectrometry on the sera of 79 individuals (31 MG of various serotypes, 30 HC, 18 RA). Initial proteomic analysis identified high residual fibrinogen in MG patient sera which was then validated by antibody-based testing. Subsequently, a blinded study of independent samples showed 100% differentiation of MG patients from controls. A final serological quantification of 14 surrogate peptides derived from α-, β-, and γ-subunits of fibrinogen in 79 individuals revealed fibrinogen to be highly specific and 100% sensitive for MG (p < 0.00001), with a remarkable average higher abundance of > 1000-fold over control groups. Our unanticipated discovery of high levels of residual serum fibrinogen in all MG patients can secure rapid bedside diagnosis of MG.
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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 | 0.001 | 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) | 0.000 | 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".