DHA supplementation during prenatal ethanol exposure alters the expression of fetal rat liver genes involved in oxidative stress regulation
Bibliographic record
Abstract
Prenatal ethanol (EtOH) exposure is known to induce adverse effects on fetal brain development. Docosahexaenoic acid (DHA) has been shown to alleviate these effects by up-regulating antioxidant mechanisms in the brain. The liver is the first organ to receive enriched blood after placental transport. Therefore, it could be negatively affected by EtOH, but no studies have assessed the effects of DHA on fetal liver. This study examined the effects of maternal DHA intake on DHA status and gene expression of key enzymes of the glutathione antioxidant system in the fetal liver after prenatal EtOH exposure. Pregnant Sprague–Dawley dams were intubated with EtOH for the first 10 days of pregnancy, while being fed a control or DHA-supplemented diet. Fetal livers were collected at gestational day 20, and free fatty acids and phospholipid profile, as well as glutathione reductase (GR) and glutathione peroxidase-1 (GPx1) gene expressions, were assessed. Prenatal EtOH exposure increased fetal liver weight, whereas maternal DHA supplementation decreased fetal liver weight. DHA supplementation increased fetal liver free fatty acid and phospholipid DHA independently of EtOH. GR and GPx1 messenger RNA (mRNA) expressions were significantly increased and decreased, respectively, in the EtOH-exposed group compared with all other groups. Providing DHA normalized GR and GPx1 mRNA expression to control levels. This study shows that maternal DHA supplementation alters the expression of fetal liver genes involved in the glutathione antioxidative system during prenatal EtOH exposure. The fetal liver may play an important role in mitigating the signs and symptoms of fetal alcohol spectrum disorders in affected offspring.
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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 | 0.000 |
| 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".