Low Environmental Contamination by Lead in Pregnant Women: Effect on Calcium Transfer in Human Placental Syncytiotrophoblasts
Bibliographic record
Abstract
There is an extensive literature on the neurotoxic effects of lead (Pb) on the developing fetus; however, little is known about the mechanisms of action at low levels. Heavy metals are known to affect calcium (Ca2+) homeostasis through perturbation of Ca2+ channels and pumps and interference with protein kinase C (PKC) and Ca2+ binding protein (CaBP). During pregnancy, placental Ca2+ exchange is one of the most important mechanisms for fetal survival. This ion is an essential element for healthy fetal growth and development. The aim of the present study was to determine the influence of low maternal blood Pb levels on Ca2+ levels in serum and placenta and placental Ca2+ transfer. Blood samples (maternal and cord) and placental tissue were obtained at birth from 30 women residing in southwest Quebec. Total Ca2+ and Pb levels were measured in maternal and umbilical cord samples and placental tissue at term. The placentas were taken for trophoblast cell isolation and Ca2+ incorporation kinetic experiments. Data showed that Ca2+ in maternal blood did not influence Ca2+ uptake by syncytiotrophoblast. However, although maternal and cord blood Pb levels were low, maternal blood Pb concentration was significantly linked to a decrease in Ca2+ uptake by syncytiotrophoblast. This suggests that exposure to very levels of Pb significantly modifies Ca2+ transfer in syncytiotrophoblasts.
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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 | 0.000 | 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".