IgA autoantibodies target pulmonary surfactant in patients with severe COVID-19
Bibliographic record
Abstract
ABSTRACT Complications affecting the lung are hallmarks of severe coronavirus disease 2019 (COVID-19). While there is evidence for autoimmunity in severe COVID-19, the exact mechanisms remain unknown. Here, we established a prospective observational cohort to study lung specific autoantibodies (auto-Abs). Incubation of plasma from severe COVID-19 patients with healthy human lung tissue revealed the presence of IgA antibodies binding to surfactant-producing pneumocytes. Enzyme-linked immunosorbent assays (ELISA) and protein pull-downs using porcine surfactant confirmed the presence of auto-Abs binding to surfactant proteins in severe COVID-19 patients. Mass spectrometry and ELISAs with recombinant proteins identified IgA auto-Abs that target human surfactant proteins B and C. In line with these findings, lungs of deceased COVID-19 patients showed reduced pulmonary surfactant. Our data suggest that IgA-driven autoimmunity against surfactant may result in disease progression of COVID-19.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".