Maternal blood concentrations of toxic metal(loid)s and trace elements from preconception to pregnancy and transplacental passage to fetuses
Bibliographic record
Abstract
Intrauterine exposure to heavy metals may adversely affect the developing fetus and health later in life, while certain trace elements may be protective. There is limited data on their dynamic fluctuation in circulating concentration of women from preconception to pregnancy and the degree of transplacental passage to fetus. Such information is critically needed for an optimal design of research studies and intervention strategies. In the present study, we profiled the longitudinal patterns and trajectories of metal(loid)s and trace elements from preconception to late pregnancy and in newborns. We measured whole blood metal(loid)s in women at preconception, 16, 24 and 32 weeks of gestation and in cord blood in 100 mother-newborn pairs. Our data showed that the mean concentrations of mercury (Hg), lead (Pb), rubidium (Rb), manganese (Mn), and iron (Fe) were lower during early-, mid-, and late-pregnancy than at preconception. Copper (Cu), and calcium (Ca) concentrations increased after pregnancy (Cu 798 versus 1353, 1488, and 1464 μg/L). Concentrations at preconception were correlated with those during pregnancy for all examined metal(loid)s. Maternal Hg, Pb, and Se concentrations at late-pregnancy were correlated with those in newborn cord blood in various degrees (correlation coefficients: Hg 0.66, Pb 0.29, Se 0.39). The estimated placental transfer ratio for toxic metal(loid)s ranging from 1.68 (Hg) to 0.18 (Cd). Two trajectory groups were identified for Hg, Pb, Cd, Se concentrations. Hg concentrations may be correlated with maternal education levels. The study is the first to present longitudinal circulating concentration trajectories of toxic metal(loid)s and trace elements from preconception to pregnancy stages. A high degree of transplacental passage was observed in toxic metals Pb and Hg which may pose hazards to the developing fetus.
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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 | 0.001 | 0.001 |
| 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".