Characterizing the Low-Dose Effects of Methylmercury on the Early Stages of Embryo Development Using Cultured Human Embryonic Stem Cells
Bibliographic record
Abstract
BACKGROUND: Global concerns of methylmercury (MeHg) exposure have been raised, especially on its effects on pregnant women. Recent epidemiological studies have revealed associations between maternal blood/hair MeHg concentrations, adverse pregnancy outcomes, and developmental deficits. However, the underlying mechanisms remain unclear. OBJECTIVES: In this study, we characterized the effects of MeHg exposure on undifferentiated human embryonic stem cells (hESCs) and extrapolated the effects to human embryonic development. METHODS: MeHg for 24 h or 6 d. Cell adherence and colony formation and expansion were examined under the microscope. Cell attachment, viability/proliferation, apoptosis, stress response, cell cycle, autophagy, and expression of cell lineage marker genes and proteins were measured at the end of exposures. RESULTS: ) autophagy inhibition, as shown by a lower LC3BII/LC3BI ratio and accumulation of SQSTM1 (p62). These outcomes were accompanied by higher expressions of self-renewal genes or proteins or both, including OCT4, SOX2, NANOG, and cytokine receptor IL6ST, as well as pluripotency and the cell fate regulator cyclin D1. DISCUSSION: evidence that low doses of MeHg adversely affect hESCs when exposed during a period of time that models embryonic pre-, during, and early postimplantation stages. https://doi.org/10.1289/EHP7349.
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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".