Teratogenicity of valproic acid and its constitutional isomer, amide derivative valnoctamide in mice
Bibliographic record
Abstract
OBJECTIVES: The anticonvulsant valproic acid (VPA) has a known teratogenic effect capable of inducing major congenital malformations and developmental disorders. A comparative teratogenicity study of VPA and its analog valnoctamide (VCD), which is a new generation candidate antiepileptic drug, was carried out using Swiss Vancouver (SWV) mice. METHODS: Pregnant SWV dams were treated with either a single intraperitoneal injection of VPA (1.8 and 2.7 mmol/kg), VCD (1.8 and 2.7 mmol/kg), or vehicle on E8:12 (gestational day:hour). The numbers of implantation and resorption, viable and dead fetuses, and the presence of gross fetal visceral and skeletal abnormalities were determined (E18). Real-time Polymerase chain reaction (RT-PCR) arrays were used to analyze the expression of 84 genes related to the processes of neurogenesis and neural stem cell differentiation. RESULTS: Significant decreases in pregnancy weight gain and the number of live fetuses were observed when VPA was administered at the high dose, whereas the percentage of exencephalic fetuses was significantly increased in VPA treated compared with an equivalent VCD dosage group. There was a dose-related increase in visceral defects in the VPA-exposed fetuses. Missing skull bones and fused vertebrae in fetuses occurred at the high dose of VPA. Three genes (Mtap2, Bmp8b, and Stat3) were significantly upregulated and one (Heyl) was downregulated in samples from VPA-treated dams. CONCLUSIONS: The study demonstrates that the teratogenicity of VPA was significantly greater than that of an equimolar dose of VCD. Four genes (Mtap2, Bmp8b, Stat3, and Heyl) represent candidate target genes for the underlying teratogenic mechanism responsible for VPA-induced malformations.
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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 | 0.001 | 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".