Association between SARS-CoV-2 infection and anti-apolipoprotein A-1 antibody in children
Bibliographic record
Abstract
Background and aims: Autoantibodies against apolipoprotein A-1 (AAA1) are elicited by SARS-CoV-2 infection and predict COVID-19 symptoms persistence at one year in adults, but whether this applies to children is unknown. We studied the association of SARS-CoV-2 exposure with AAA1 prevalence in children and the association of AAA1 seropositivity with symptom persistence. Methods: Anti-SARS-CoV-2 and AAA1 serologies were examined in 1031 participants aged 6 months to 17 years old from the prospective SEROCOV-KIDS cohort and recruited between 12.2021 and 02.2022. Four SARS-CoV-2 serology-based groups were defined: "Infected-unvaccinated (I+/V-)", "Uninfected-vaccinated (I-/V+)", "Infected-Vaccinated (I+/V+)", and "Naïve (I-/V-)". Reported outcomes were collected using online questionnaires. Associations with study endpoints were assessed using logistic regression. Results: Overall, seropositivity rates for anti-RBD, anti-N, and AAA1 were 71% (736/1031), 55% (568/1031), and 5.8% (60/1031), respectively. AAA1 showed an inverse association with age but not with any other characteristics. The I+/V- group displayed higher median AAA1 levels and seropositivity (7.9%) compared to the other groups (p ≤ 0.011), translating into a 2-fold increased AAA1 seroconversion risk (Odds ratio [OR]: 2.11, [95% Confidence Interval (CI)]: 1.22-3.65; p=0.008), unchanged after adjustment for age and sex. AAA1 seropositivity was independently associated with a 2-fold odds of symptoms persistence at ≥ 4 weeks (p ≤ 0.03) in the entire dataset and infected individuals, but not ≥ 12 weeks. Conclusions: Despite the limitations of the study (cross-sectional design, patient-related outcomes using validated questionnaires), the results indicate that SARS-CoV-2 infection could elicit an AAA1 response in children, which could be independently associated with short-time symptoms persistence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".