Maternal exposure to polyethylene micro- and nanoplastics impairs umbilical blood flow but not fetal growth in pregnant mice
Bibliographic record
Abstract
Abstract While microplastics have been recently detected in human blood and the placenta, their impact on human health is not well understood. Using a mouse model of environmental exposure during pregnancy, our group has previously reported that exposure to polystyrene micro- and nanoplastics throughout gestation results in fetal growth restriction. While polystyrene is environmentally relevant, polyethylene is the most widely produced plastic and amongst the most commonly detected microplastic in drinking water and human blood. In this study, we investigated the effect of maternal exposure to polyethylene micro- and nanoplastics on fetal growth and placental function. Healthy, pregnant CD-1 dams were divided into three groups: 10 6 ng/L of 740–4990 nm polyethylene with surfactant in drinking water ( n = 12), surfactant alone in drinking water ( n = 12) or regular filtered drinking water ( n = 11). At embryonic day 17.5, high-frequency ultrasound was used to investigate the placental and fetal hemodynamic responses following exposure. While maternal exposure to polyethylene did not impact fetal growth, there was a significant effect on placental function with a 43% increase in umbilical artery blood flow in the polyethylene group compared to controls (p < 0.01). These results suggest polyethylene has the potential to cause adverse pregnancy outcomes through abnormal placental function.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".