Insights Into the Antigenic Repertoire of Unclassified Synaptic Antibodies
Bibliographic record
Abstract
OBJECTIVE: We sought to characterize the sixth most common finding in our neuroimmunological laboratory practice (tissue assay-observed unclassified neural antibodies [UNAs]), combining protein microarray and phage immunoprecipitation sequencing (PhIP-Seq). METHODS: Patient specimens (258; 133 serums; 125 CSF) meeting UNA criteria were profiled; October 2022-September 2023. Top-ranking candidate antigens were validated in silico, by dual-staining confocal microscopy, and ≥ 1 protein-specific assay. Clinical data were reviewed. RESULTS: Among 21 patients, 11 autoantibodies were characterized (serum, 19; CSF, all 9 available). Autoantigens were CACNA1I, 1; CAMK2B, 2; CLIP2, 1; FMN2, 2; MAP1A, 2; MAP2, 5; NECAB1, 1; SNAP91, 3; SRCIN1, 1; SYNJ1, 1; SYT3, 2. Analytical validation was by confocal TIIFA (all), western blot (10/10 available), and cell-based assay (5/5 performed). Clinical accompaniments were: encephalitis, 6; brainstem encephalitis, 2; encephalomyelitis, 2; cerebellar ataxia, 2; longitudinally extensive transverse myelitis (LETM), 2; sensory neuronopathy, 1; peripheral neuropathy, 4, and movement disorders, 2. Inflammatory MRI abnormalities were identified in 5/16 patients (31%) with CNS disorders: T2 signal change (2), LETM (2), leptomeningeal enhancement (1). Seven of 8 (88%) had inflammatory CSF (pleocytosis, 5 [median 25.5 cells, range 7-294]; elevated IgG index/synthesis rate, 4; CSF-exclusive oligoclonal bands, 4). Six had paraneoplastic causation (lung cancer, 2; other, 4); 3 were postinfectious (1 each of COVID-19, HSV-1, and post-Group A streptococcal infection). Of 9 immunotherapy-treated patients, 5 improved. INTERPRETATION: UNAs are partly accounted for by a repertoire of diverse mostly intracellular synaptic antigens. Their characterization is expedited by protein arrays and PhIPSeq. Further individual studies are needed to assess them as disease biomarkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".