Maternal Autoimmune Thyroid Disease and the Fetal Immune System
Bibliographic record
Abstract
OBJECTIVE: Several studies indicate that in utero exposure to maternal autoimmune diseases and transplacental passage of autoantibodies affect the risk of autoimmunity in the offspring, e. g., maternally derived GAD65 autoantibody correlates with decreased risk of type 1 diabetes, whereas thyroid peroxidase autoantibody (TPOAb) positivity at birth is associated with increased incidence of autoimmune thyroid disease later in life. The aim of this study was to identify immunological changes in children born to mothers with thyroid autoimmunity that may be related to in utero exposure to autoantibodies. DESIGN AND METHOD: Open label prospective analysis of cord blood lymphocytes and serum cytokines by Flow Cytometry in children born to mothers with autoimmune thyroiditis (AIT) (n=31) and to healthy mothers (n=76) and titers of thyroid autoantibodies were determined in cord blood and in maternal peripheral blood at delivery. RESULTS: We found an increase (almost 30%) in the frequency of cord blood natural killer (NK) cells (p=0.0016) and a minor increase in the subset of T cells expressing NK markers (p=0.028), in children born to AIT mothers. There were no detectable differences in the phenotype or frequency of cord blood memory/activated T cells, including CD4 (+)CD25 (+) T cells, between the 2 groups. The levels of pro-inflammatory cytokines TNF-α, IL-10, IL-12p70, IFN-γ and IL-1β were significantly decreased in offspring of AIT mothers as compared to healthy controls. CONCLUSIONS: Maternal thyroid autoimmunity and transplacental passage of autoantibodies against thyroid antigens may affect the generation or expansion of cells with NK activity and the secretion of inflammatory 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".