Maternal diets affected ceramides and fatty acids in brain regions of neonatal rats with prenatal ethanol exposure
Bibliographic record
Abstract
Objectives: Ceramide (Cer), known as apoptotic markers, increases with prenatal ethanol (EtOH) exposure, resulting in neuroapoptosis. Whether maternal nutrition can impact Cer concentrations in brain, via altering plasma and brain fatty acid compositions have not been examined. This study compared a standard chow with a formulated semi-purified energy dense (E-dense) diet on fatty acid composition, Cer concentrations, and apoptosis in plasma and brain regions (cortex, cerebellum, and hippocampus) of pups exposed to EtOH during gestation. Methods: Pregnant Sprague–Dawley rats were randomized into four groups: chow (n = 6), chow + EtOH (20% v/v) (n = 7), E-dense (n = 6), and E-dense + EtOH (n = 8). At postnatal day 7, representing the peak brain growth spurt in rats, lipids, and apoptosis were analyzed by gas chromatography and a fluorometric caspase-3 assay kit, respectively. Results: Maternal E-dense diet increased total fatty acid concentrations (p < 0.0001), including docosahexaenoic acid (DHA) (p < 0.0001) in plasma, whereas DHA concentrations were decreased in the cerebellum (p < 0.03) of pups than those from chow-fed dams. EtOH-induced Cer elevations in the hippocampus of pups born to dams fed chow were reduced by an E-dense diet (p < 0.02). No significant effects of maternal diet quality and EtOH were observed on caspase-3 activity. No significant correlations existed between plasma/brain fatty acids and Cer concentrations. Discussions: Maternal diet quality affected fatty acid compositions and Cer concentrations of pups with prenatal EtOH exposure, differently. Maternal nutrition has the potential to prevent or alleviate some of the adverse effects of prenatal EtOH exposure.
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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) | 0.001 | 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) | 0.002 | 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".