Respiratory syncytial virus-specific immunoglobulin G (IgG) concentrations associate with environmental and genetic factors: the Factors Influencing Pediatric Asthma Study
Bibliographic record
Abstract
Abstract Exposure to respiratory syncytial virus (RSV) during childhood is nearly ubiquitous by age two, and infants who develop severe RSV bronchiolitis are more likely to develop asthma later in life. In the Factors Influencing Pediatric Asthma (FIPA) study including 319 children from a Northern Plains American Indian community, we found 73% of children to have high concentrations of RSV-specific IgG (>40 IU/mL). High concentration of RSV-specific IgG was associated with increased exposure to second-hand tobacco smoke (p=7.5×10 −4 ), larger household size (p=4.0×10 −3 ), and lower levels of total serum IgE (p=5.1×10 −3 ). Parents of children with asthma more often reported an RSV diagnosis and/or hospitalization due to RSV, and children with asthma had lower concentrations of RSV IgG as compared to those without asthma among RSV-exposed individuals (mean 117 IU/mL vs. 154, p=7.1×10 −4 ). However, lower RSV IgG was surprisingly exclusive to children with asthma recruited during the winter months when RSV is thought to circulate more broadly. Multivariate regression indicated the strongest predictors of RSV-specific IgG concentration included asthma status (p=0.040 ) , per cent eosinophils (p=0.035), and an asthma x RSV season interaction (p=3.7×10 −3 ). Among candidate genes, we identified a genetic association between an intronic variant in IFNL4 and RSV-specific IgG concentration whereby the minor allele (A) was associated with higher concentration (rs12979860, p=4.3×10 −3 ). Overall our findings suggest there are seasonal differences in immunological response to RSV infection in asthma cases vs. controls, and identify both environmental and genetic contributions that warrant further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".