Antibody repertoire associated with clinically diverse presentations of pediatric SARS-CoV-2 infection
Bibliographic record
Abstract
Pediatric SARS-CoV-2 infection results in clinical presentations ranging from asymptomatic/mild infection to severe pulmonary COVID-19, to Multisystem Inflammatory Syndrome in Children (MIS-C), characterized by hyperinflammation and multi-organ involvement. While various aspects of antibody responses to pediatric SARS-CoV-2 infection manifestations have been reported, parallel studies of antibody responses to viral and self-antigens are understudied. We tested whether clinical presentations of increasing severity corresponded to different antiviral antibody and autoantibody signatures. Using custom arrays, we found that, relative to uninfected subjects, all SARS-CoV-2 infection manifestations were associated with increased autoantibody production, suggesting pediatric SARS-CoV-2 infection as a risk factor for autoimmune complications. Subtle differences were seen in autoantibody patterns among infection groups, with some autoantibodies more associated with mild manifestations and others with severe ones. When we compared MIS-C and severe COVID-19 subjects, we found differences in IgG (mostly IgG1) abundance but not in Fc-mediated effector functions. Thus, MIS-C may be associated with abnormal antibody function, suggesting that this syndrome, and perhaps other post-acute sequelae of SARS-CoV-2 infection, may be associated with antibody dysfunction. Our study shows that the antibody repertoire varies with clinical presentation of SARS-CoV-2 in children and its analysis may help understand long COVID pathogenesis.
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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) | 0.002 | 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".