Human genetic and immunological determinants of SARS-CoV-2 infection and multisystem inflammatory syndrome in children
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces pneumonia and acute respiratory failure in coronavirus disease 2019 (COVID-19) patients with inborn errors of immunity to type I interferon (IFN-I). The impact of SARS-CoV-2 infection varies widely, ranging from mild respiratory symptoms to life-threatening illness and organ failure, with a higher incidence in men than in women. Approximately 3-5% of critical COVID-19 patients under 60 and a smaller percentage of elderly patients exhibit genetic defects in IFN-I production, including X-chromosome-linked TLR7 and autosomal TLR3 deficiencies. Around 15-20% of cases over 70 years old, and a smaller percentage of younger patients, present with preexisting autoantibodies neutralizing type I interferons. Additionally, innate errors affecting the control of the response to type I interferon have been associated with pediatric multisystem inflammatory syndrome (MIS-C). Several studies have described rare errors of immunity, such as XIAP deficiency, CYBB, SOCS1, OAS1/2, and RNASEL, as underlying factors in MIS-C susceptibility. However, further investigations in expanded patient cohorts are needed to validate these findings and pave the way for new genetic approaches to MIS-C. This review aims to present recent evidence from the scientific literature on genetic and immunological abnormalities predisposing individuals to critical SARS-CoV-2 infection through IFN-I. We will also discuss multisystem inflammatory syndrome in children (MIS-C). Understanding the immunological mechanisms and pathogenesis of severe COVID-19 may inform personalized patient care and population protection strategies against future serious viral infections.
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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 | 0.000 |
| 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 | 0.000 | 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".