Abnormal antibodies to self-carbohydrates in SARS-CoV-2-infected patients
Bibliographic record
Abstract
Abstract Our immune system is critical for preventing and treating SARS-CoV-2 infections, but aberrant immune responses can have deleterious effects. While antibodies to glycans could recognize the virus and influence the clinical outcome, little is known about their roles. Using a carbohydrate antigen microarray, we profiled serum antibodies in healthy control subjects and COVID-19 patients from two separate cohorts. COVID-19 patients had numerous autoantibodies to self-glycans, including antiganglioside antibodies that can cause neurological disorders. Additionally, nearly all antiglycan IgM signals were lower in COVID-19 patients, indicating a global dysregulation of this class of antibodies. Autoantibodies to certain N-linked glycans correlated with more severe disease, as did low levels of antibodies to the Forssman antigen and ovalbumin. Collectively, this study indicates that expanded testing for antiglycan antibodies could be beneficial for clinical analysis of COVID-19 patients and illustrates the importance of including host and viral carbohydrate antigens when studying immune responses to viruses.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".