Maternal malnutrition impacts placental morphology and transport. An origin for poor offspring growth and vulnerability to disease
Bibliographic record
Abstract
Abstract The placenta promotes fetal growth through nutrient transfer and selective barrier systems. An optimally developed placenta can adapt to changes in the pregnancy environment, buffering the fetus from adverse exposures. We hypothesised that the placenta adapts differently to suboptimal maternal diets, evidenced by changes in placental morphology, developmental markers, and key transport systems. Mice were fed a control diet (CON) during pregnancy, or undernourished (UN) by 30% of control intake from gestational day (GD)5.5-18.5, or fed 60% high fat diet (HF) eight weeks before and during pregnancy. At GD18.5, placental morphometry, development, and transport were assessed. Junctional and labyrinthine areas of UN and HF placentae were smaller than CON by >10%. Fetal blood space area and fetal blood space:fetal weight ratios were reduced in HF vs. CON and UN. Trophoblast giant cell marker Ctsq mRNA expression was lower in UN vs. HF, and expression of glycogen cell markers Cx31.1 and Pcdh12 was lower in HF vs. UN. Efflux transporter Abcb1a mRNA expression was lower in HF vs. UN, and Abcg2 expression was lower in UN vs. HF. mRNA expression of fatty acid binding protein Fabp pm was higher in UN vs. CON and HF. mRNA and protein levels of the lipid transporter FAT/CD36 were lower in UN, and FATP4 protein levels were lower in HF vs. UN. UN placentae appear less mature with aberrant transport. HF placentae adapt to excessive nutrient supply. Understanding placental adaptations to common nutritional adversities may reveal mechanisms underlying the developmental origins of later disease.
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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 | 0.000 |
| 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".