Reduction in Regulatory T Cells in Early Pregnancy Causes Uterine Artery Dysfunction in Mice
Bibliographic record
Abstract
Preeclampsia, fetal growth restriction, and miscarriage remain important causes of maternal and perinatal morbidity and mortality. These complications are associated with reduced numbers of a specialized T lymphocyte subset called regulatory T cells (Treg cells) in the maternal circulation, decidua, and placenta. Treg cells suppress inflammation and prevent maternal immunity toward the fetus, which expresses foreign paternal alloantigens. Treg cells are demonstrated to contribute to vascular homeostasis, but whether Treg cells influence the vascular adaptations essential for a healthy pregnancy is unknown. Thus, using a mouse model of Treg-cell depletion, we investigated the hypothesis that depletion of Treg cells would cause increased inflammation and aberrant uterine artery function. Here, we show that Treg-cell depletion resulted in increased embryo resorption and increased production of proinflammatory cytokines. Mean arterial pressure exhibited greater modulation by NO in Treg cell-deficient mice because the L-N G -nitroarginine methyl ester–induced increase in mean arterial pressure was 46% greater compared with Treg cell-replete mice. Uterine artery function, which is essential for the supply of nutrients to the placenta and fetus, demonstrated dysregulated hemodynamics after Treg-cell depletion. This was evidenced by increased uterine artery resistance and pulsatility indices and enhanced conversion of bET-1 (big endothelin-1) to the active and potent vasoconstrictor, ET-1 (endothelin-1). These data demonstrate an essential role for Treg cells in modulating uterine artery function during pregnancy and implicate Treg-cell control of maternal vascular function as a key mechanism underlying normal fetal and placental development.
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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) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".