Effects of an environmental chemical mixture on early-stage embryo development: In vitro evidence from human embryonic stem cells
Bibliographic record
Abstract
Environmental chemicals are known risk factors for adverse pregnancy outcomes. However, the effects of environmentally relevant concentrations of chemical mixtures are less studied. This study investigated the effects of a mixture containing 23 chemicals reported in blood samples of Nunavik pregnant women, termed the Nunavik Chemical Mixture (NCM), on embryo development using human embryonic stem cells (hESCs). hESCs were exposed to 0-100X of NCM (X is the sum of geometric mean concentrations of NCM's components) for 24 h and 6 d Cell viability, apoptosis, stress response, cell cycle, cytoskeleton, autophagy, and expression of lineage marker genes/proteins were measured after exposures. NCM decreased cell viability and adhesion, induced apoptosis, disrupted the cell cycle, and altered the expression of cytoskeleton, autophagy proteins, and lineage marker genes/proteins in a dose-dependent manner. These results suggested that NCM affected embryo development, leading to potential adverse pregnancy outcomes if it occurs in vivo. Moreover, the effects caused by NCM were different from those caused by the same doses of MeHg alone that we previously found, indicating potential interactions among components within the mixture. Our results highlight the importance of considering the potential combined effects of chemical mixtures when assessing health risks in populations exposed to various environmental chemicals.
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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".