Chemical-induced heart defects using a transgenic zebrafish model
Bibliographic record
Abstract
Congenital heart defects (CHDs) are common birth defects attributed to genetic and environmental factors, such as pharmaceuticals and chemicals. Identifying modifiable environmental factors and understanding their impact on heart development is crucial for mitigating chemical-induced CHDs. Given the increasing number of chemical agents, efficient high-throughput systems are essential to evaluate their teratogenic potential during cardiovascular development, which is a major concern for chemical safety. In this study, we developed 3 transgenic zebrafish reporter lines, myl7:EGFP, kdrl:mRFP, and gata1:mKate2, which enable real-time visualization of myocardial and endocardial development and cardiac function based on blood flow. These transgenic embryos were used to investigate the teratogenic effects of chemicals well known to induce heart defects in mammals, including humans. Our real-time imaging revealed that the teratogens induced significant malformations in cardiac morphogenesis, including abnormal heart tube formation, incomplete cardiac looping, and reduced heart chamber size. These teratogens also disrupted the expression of cardiac progenitor markers, suggesting impaired cardiac progenitor development. These defects were detected at the early stages (4-48 h post-fertilization), suggesting that the stages of progenitor development to heart looping were most susceptible to teratogen exposure, i.e. the critical period for teratogen-induced heart defects. Functional defects, such as impaired blood flow, were observed using real-time imaging of the gata1-reporter line. This study demonstrates the utilization of transgenic zebrafish embryo models for high-throughput teratogenicity testing, which also allows us to analyze the mechanisms underlying chemical-induced heart defects. Therefore, our zebrafish models would contribute to the identification and reduction of risks associated with CHDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".