Combined Metabolic and Viral Insults in Pregnancy Disrupt Specific Placental Nutrient Transporter Systems in Mice
Bibliographic record
Abstract
Abstract The placenta adapts to support nutrient transport to meet the demands of the growing fetus. Maternal obesity and viral infection are associated with adverse fetoplacental development. How co-exposure affects placental morphology and nutrient transport across gestation remains unclear. We hypothesise that co-exposure would have a greater impact on placental morphology and nutrient transporter expression than either of the exposures alone. C57BL/6J mice were fed a control (CON) or high-fat (HF) diet before and throughout pregnancy and exposed to poly(I:C) or vehicle at gestational days (GD) 12.5, 15.5, or 18.5. Placental morphology, folate and fatty acid transporter mRNA expression and localisation were evaluated by haematoxylin and eosin staining, immunohistochemistry (reported as % immunoreactivity (ir)-area stained), and qPCR, respectively. At GD12.5, HF diet associated with reduced junctional zone (JZ) and labyrinth zone (LZ) area and increased maternal blood space, followed by reduced fetal blood space at GD15.5 and increased JZ and LZ area size by GD18.5. HF diet and viral infection independently associated with increased placental Fat/Cd36 mRNA expression at GD12.5. However, HF diet and infection associated with increased ir-FR-α while HF diet alone decreased ir-PCFT at GD12.5. By GD15.5, HF diet associated with reduced placental ir-FR-α but increased ir-PCFT. At GD18.5, infection associated with reduced Rfc1 mRNA expression. HF diet and infection associated with greater prevalence of SGA and LGA fetuses, particularly at GD18.5. Obesity and infection independently and synergistically disrupted placental morphology and nutrient transporter expression in a zone- and gestational age-specific manner, with consequences for fetal growth across gestation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".