Predictors of interleukin‐33 and thymic stromal lymphopoietin levels in cord blood
Bibliographic record
Abstract
BACKGROUND: The fetal immune system is a critical window of development. The epithelial cell-derived cytokines, thymic stromal lymphopoietin (TSLP), and interleukin-33 (IL-33) have received attention for their role in allergic responses but not been studied during this critical window. The objectives were to assess correlations among IL-33, TSLP, and IgE in umbilical cord blood samples and identify prenatal predictors of these biomarkers. METHODS: This study utilized data and banked cord blood collected in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a trans-Canada cohort study of 2001 pregnant women. Our analytic sample comprised the 1254 women with a singleton, term birth with a cord blood sample. Spearman correlation coefficients (SCC) and logistic regression models were used to examine associations between biomarkers and identify potential predictors of elevated biomarker levels. RESULTS: Thymic stromal lymphopoietin and IL-33 were more strongly correlated with each other (SCC = 0.75, p < 0.0001) than with IgE (IL-33 SCC = 0.14, TSLP SCC = 0.21). Maternal allergy, heavy street traffic, and elevated birth weight were significantly associated with jointly elevated TSLP and IL-33 levels, whereas maternal age and female infant sex were inversely associated with elevated IgE. CONCLUSIONS: In this population of Canadian women and infants, TSLP and IL-33 were detectable in cord blood, more strongly correlated with each other than with IgE, and associated with maternal characteristics indicative of inflammatory responses. This study motivates investigation into the value of cord blood IL-33 and TSLP levels as childhood allergy predictors and raises interesting questions regarding in utero coordinated regulation of these cytokines.
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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 | 0.004 |
| 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 | 0.001 |
| 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".