Characterizing the effects of in utero exposure to valproic acid on murine fetal heart development
Bibliographic record
Abstract
BACKGROUND: Recently, the use of the antiepileptic drug valproic acid (VPA) for the treatment of psychiatric conditions has been on the rise. However, studies have shown that in utero VPA exposure can affect embryonic development, including being associated with congenital heart defects. One proposed mechanism of VPA-initiated teratogenicity is the inhibition of histone deacetylase, which is involved in the regulation of transcription factors that regulate cardiogenesis. Myocyte enhancing factor 2C (Mef2c), a transcription factor involved in the development of cardiac structure and cardiomyocyte differentiation, has been shown to increase in response to in utero VPA exposure, associating with contractile dysfunction and myocardial disorganization. METHODS: To characterize the effects of VPA on murine heart development, pregnant CD-1 mice were dosed with 400 mg/kg of VPA on gestational day (GD) 9. Using high-resolution ultrasound, we examined the effects of VPA on cardiac contractile function on GD 14-18, with fetal hearts being harvested on GD 19 for histological analysis. Lastly, we conducted quantitative real-time polymerase chain reaction to measure the relative Mef2c gene expression in GD 16 murine hearts. RESULTS: We observed structural anomalies at GD 19 in the hearts of VPA-treated mice. Additionally, our results showed alterations in measures of cardiac contractility, with a decrease or increase in cardiac contractile ability in VPA-treated mice depending on the GD and measurement taken. CONCLUSIONS: These results further characterize the effects of VPA on heart development and suggest that alterations in Mef2c gene expression, at least on GD 16, do not mediate VPA-induced cardiotoxicity in CD-1 mice.
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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 | 0.001 |
| 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".