A Biosensor Assay for the Detection of Muscle Specific Tyrosine Kinase (MuSK) Antibodies in Myasthenia Gravis Patients (P2.089)
Bibliographic record
Abstract
OBJECTIVE: To optimize and validate a biosensor (Biacore™) assay for the detection of muscle specific tyrosine kinase (MuSK) autoantibodies in myasthenia gravis (MG) patients. BACKGROUND: MG is an antibody-mediated autoimmune disease of the neuromuscular junction, characterized by skeletal muscle weakness and fatiguability. 85-90% of MG patients have antibodies targeting the nicotinic acetylcholine receptor (AChR), while a smaller proportion has autoantibodies targeting MuSK, an AChR-clustering molecule. Biacore™, a label-free biosensor, utilizes surface plasmon resonance (SPR) to detect biomolecular interactions in real-time. DESIGN/METHODS: MuSK was immobilized onto Biacore™ 3000 sensor chips and diluted sera sequentially injected over the surface. Assay conditions were optimized by studying the binding of 20 healthy control, 10 MuSK antibody-negative, and 10 MuSK antibody-positive sera (reference assay Prof. Angela Vincent, Oxford, UK). We used different MuSK immobilization densities: low (300RU), medium (1200RU), and high (4500RU). An initial screen on a high-density MuSK surface was performed on 120 sera, consisting of 73 AChR antibody-negative, 31 normal healthy control and 16 non-healthy control (multiple sclerosis patients). Intra- and inter-assay variations were determined by calculating % coefficient of variation (%CV). RESULTS: Using a cut-off of mean+3SD of 20 healthy controls on the high density MuSK surface (4500RU), all healthy control and Oxford-tested MuSK-negative samples tested negative, while all 10 Oxford-tested MuSK-positive samples tested positive. Similar results were obtained using the medium density surface (1200RU), however the sensitivity of the assay decreased with a lower surface density (300RU). The assay was found to be highly reproducible, with intra-assay %CVs <10% and inter-assay %CVs <15%. Of the 120 sera screened, 24/73 AChR antibody-negative sera tested positive for anti-MuSK antibodies, while all 16 MS and 31 healthy controls tested negative. CONCLUSIONS: We have developed a rapid Biacore™-based assay for detecting anti-MuSK antibodies, with the assay time for each sample being approximately 7min. It is highly specific, sensitive and reproducible, and is being further validated and developed into a quantitative assay.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".