Low maternal vitamin A intake increases the incidence of teratogen induced congenital diaphragmatic hernia in mice
Bibliographic record
Abstract
BACKGROUND: Congenital diaphragmatic hernia (CDH) is a severe birth defect associated with high perinatal mortality and long-term morbidity. The etiology of CDH is poorly understood although abnormal retinoid signaling has been proposed to contribute to abnormal diaphragm development. Existing epidemiological data suggest that inadequate dietary vitamin A intake is a risk factor for developing CDH. METHODS: Using a mouse model of teratogen-induced CDH, the objective of this study was to test the hypothesis that low maternal vitamin A intake contributes to abnormal diaphragm development. To test this hypothesis, we optimized a model of altered maternal dietary vitamin A intake and a teratogenic model of CDH in mice that recapitulates the hallmark features of posterolateral diaphragmatic hernia in humans. RESULTS: Our data uniquely show that low maternal dietary vitamin A intake and marginal vitamin A status increases the incidence of teratogen-induced CDH in mice. CONCLUSION: Low dietary vitamin A intake and marginal vitamin A status lead to an increased incidence of teratogen-induced CDH in mice, highlighting the importance of adequate dietary vitamin A intake and CDH risk. IMPACT: This study describes and validates a mouse model of altered maternal and fetal vitamin A status. This study links existing epidemiological data with a mouse model of teratogen-induced congenital diaphragmatic hernia, highlighting the importance of low maternal vitamin A intake as a risk factor for the development of congenital diaphragmatic hernia. This study supports the Retinoid Hypothesis, which posits that the etiology of congenital diaphragmatic hernia is linked to abnormal retinoid signaling in the developing diaphragm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".