Differential effects of maternal diets on birth outcomes and metabolic parameters in rats after ethanol consumption during pregnancy
Bibliographic record
Abstract
Maternal nutrition status plays an important role in the development of fetal alcohol spectrum disorders (FASD), but its direct evidence is lacking. This study compared a standard chow with a semi-purified energy-dense (E-dense) diet on birth and metabolic outcomes in rats after ethanol (EtOH) consumption during pregnancy. Pregnant Sprague-Dawley rats were randomised into four groups: chow (n 6), chow + EtOH (20 %, v/v) (n 7), E-dense (n 6) and E-dense + EtOH (n 8). Birth outcomes including litter size, body and organ weights were collected. Metabolic parameters were measured in dams and pups at postnatal day (PD) 7. Maternal EtOH consumption decreased body weights (P < 0·0001) and litter sizes (P < 0·05) in chow-fed dams. At PD7, pups born to dams fed the E-dense diet had higher body (P < 0·002) and liver weights (P < 0·0001). These pups also had higher plasma total cholesterol (P < 0·0001), TAG (P < 0·003) and alanine aminotransferase (P < 0·03) compared with those from chow-fed dams. Dams fed the E-dense diet had higher plasma total (P < 0·0001) and HDL-cholesterol (P < 0·0001) and lower glucose (P < 0·0001). EtOH increased total cholesterol (P < 0·03) and glucose (P < 0·05) only in dams fed the E-dense diet. Maternal exposure to the E-dense diet attenuated prenatal EtOH-induced weight loss and produced different metabolic outcomes in both dams and pups. While the long-lasting effects of these outcomes are unknown, this study highlights the importance of maternal diet quality for maternal health and infant growth and suggests that maternal nutrition intervention may be a potential target for alleviating FASD.
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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) | 0.001 | 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".