Autoantibody Signature in Cardiac Arrest
Bibliographic record
Abstract
BACKGROUND: Cardiac arrest is a tragic event that causes 1 death roughly every 90 seconds worldwide. Survivors generally undergo a workup to identify the cause of arrest. However, 5% to 10% of cardiac arrests remain unexplained. Because cardiac arrhythmias underlie most cardiac arrests and increasing evidence strongly supports the involvement of autoantibodies in arrhythmogenesis, a large-panel autoantibody screening was performed in patients with cardiac arrest. METHODS: This is an observational, cross-sectional study of patients from the Montreal Heart Institute hospital cohort, a single-center registry of participants. A peptide microarray was designed to screen for immunoglobulin G targeting epitopes from all known cardiac ion channels with extracellular domains. Plasma samples from 23 patients with unexplained cardiac arrest were compared with those from 22 patients with cardiac arrest cases of ischemic origin and a group of 29 age-, sex-, and body mass index-matched healthy subjects. The false discovery rate, least absolute shrinkage and selection operator logistic regression, and random forest methods were carried out jointly to find significant differential immunoglobulin G responses. RESULTS: =0.002; false discovery rate, 0.007; classification accuracies ≥0.83). Functional studies on human induced pluripotent stem cell-derived cardiomyocytes demonstrated that the anti-L-type voltage-gated calcium channel immunoglobulin G purified from patients with idiopathic cardiac arrest is proarrhythmogenic by reducing the action potential duration through calcium channel inhibition. CONCLUSIONS: The present report addresses the concept of autoimmunity and cardiac arrest. Hitherto unknown autoantibodies targeting extracellular sequences of cardiac ion channels were detected. Moreover, the study identified an autoantibody signature specific to patients with cardiac arrest.
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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) | 0.003 | 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".