A low-fat diet enriched in fish oil increased lipogenesis and fetal outcome of C57BL/6 mice
Bibliographic record
Abstract
Abstract There is clear evidence that nutritional strategy employed during pregnancy has profound influence on the offspring health outcomes. However, the effect of the quality and the quantity of maternal fat intake on maternal metabolic profile during different stages of pregnancy and its impact on pregnancy sustainability is not known. Female C57BL/6 mice (7 weeks old) were fed diets varying in the quantity of fat (5% vs 11%) for two weeks prior to mating and throughout pregnancy. The 5% fat diet was enriched with longer chain omega (n)-3 polyunsaturated fatty acids (PUFA) from fish oil. Maternal plasma and tissues were collected before mating and during pregnancy at days 6.5, 12.5 and 18.5. Plasma lipids, glucose, insulin, progesterone and estradiol levels were measured. Cholesterol efflux capacity of maternal plasma as well as the mRNA expression of placental steroidogenic acute regulatory protein and hepatic lipogenic genes (acetyl-CoA carboxylase-1, fatty acid synthase, diacylglycerol acyltransferase-2 and stearoyl-CoA desaturase-1) was determined. Feto-placental weight and fetuses sustained throughout gestation were recorded. A low-fat maternal diet enriched with n-3 PUFA increased maternal plasma triacylglycerol and the mRNA expression of rate-limiting lipogenic enzymes, along with increasing cholesterol efflux capacity (P < 0.05), likely to meet fetal lipid demand during pregnancy. Furthermore, diet enriched with longer chain n-3 PUFA increased the maternal plasma concentration of progesterone and estradiol during pregnancy (P < 0.05), which coincides with an increase in the number of fetuses sustained till day 18.5. These novel findings may be important when designing dietary strategies to optimize reproductive capability and pregnancy outcomes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".