Effect of Alcohol and Wnt Signaling Pathway Interactions in Zebrafish (Danio rerio) Tooth Development.
Bibliographic record
Abstract
Alcohol consumption during pregnancy can negatively affect fetal development.Prenatal alcohol exposure (PAE) has been linked to various birth defects, including several dental anomalies commonly observed in individuals with Fetal Alcohol Spectrum Disorder (FASD), such as Decayed, Missing, and Filled Teeth (DMFT), malocclusion, caries, speech impairments, and enamel defects.While distinct developmental defects associated with PAE are welldocumented, the underlying mechanisms leading to such phenotypes in tooth development remain poorly understood.One possible explanation is that alcohol may interfere with key signaling pathways involved in tooth development.In this study, we used zebrafish (Danio rerio) as a model to investigate how alcohol disrupts tooth morphology, histological arrangement, and cellular changes through its interaction with the Wnt signaling pathway.Zebrafish embryos were treated with 1% alcohol, 2mM LiCl (Wnt activator), 10nM WC-59 (Wnt inhibitor), and combinations of alcohol with the Wnt modulators.Whole-mount staining and histological analysis across experimental groups revealed differences in tooth and tooth germ numbers, size, shape, mineralization patterns, and Wnt10a and Wnt10b expression levels.Interestingly, the combined treatment of alcohol and LiCl resulted in phenotypes similar to alcohol exposure alone, while the combination of alcohol and WC-59 acted synergistically to further disrupt tooth development.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".