Distinct circulating autoantibodies are associated with COVID-19 hospitalization and SARS-CoV-2 neutralization activity
Bibliographic record
Abstract
SARS-CoV-2 infection disrupts the host's immune system, altering autoimmune responses. This study investigated host autoreactivities in SARS-CoV-2 infections, their association with severe COVID-19, and the neutralizing antibody response. Circulating autoantibodies were detected in convalescent serum samples from unvaccinated SARS-CoV-2-infected patients. Clustering, correlation analysis, principal component analysis, and neural network modeling were used to explore the relationship between autoantibodies, hospitalization, and SARS-CoV-2 neutralization. The presence of one autoantibody correlated with the detection of multiple others. Anti-IFNα antibodies were associated with elevated levels of anti-ENAs (extractable-nuclear antigen) but not with clinical outcome. COVID-19 hospitalization was significantly associated with the collective expression of autoantibodies targeting three ENAs: SSA/Ro52, Jo-1, and RNP. In contrast, autoantibodies targeting RNP/Sm, PCNA, Scl-70, and PL-12 were strongly associated with SARS-CoV-2 neutralization. In summary, this study has identified self-antigens targeted in hospitalized COVID-19 patients and highlights a novel association between the autoantibody response and the antiviral humoral response.
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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 | 0.000 | 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) | 0.001 | 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".