Maternal high fat diet and acute viral mimic exposure impact placental inflammation, lipid peroxidation and cellular proliferation-to-death ratio across mouse pregnancy
Bibliographic record
Abstract
Abstract Maternal obesity and viral infection induce placental inflammation, but how their co-exposure influence fetoplacental development remains unclear. We hypothesized that maternal high fat (HF) diet and viral infection would independently induce placental inflammation and lipid peroxidation, reduce antioxidant defence, and cellular turnover. Further, HF diet would compromise placental capacity to adapt to infection. Female C57BL/6J mice were fed a control (CON) or 62% HF diet six weeks before and throughout pregnancy and injected with poly(I:C) (viral mimic) or vehicle (VEH) 24h before sacrifice at gestational days (GD) 12.5, 15.5, and 18.5 (n=5–8/group/GD). Placental inflammasome (NLRP3), oxidative stress (4-HNE), antioxidant defence (GPx-4), and cellular proliferation-to-death ratio (Ki-67, Caspase-3) were assessed by immunohistochemistry, and mRNA expression of Tlr3, Irf3, Tlr4, Tirap , and Il-1β were measured by qPCR. Data were analysed by linear mixed models (p≤0.05). At GD12.5, infection was associated with increased Tlr3 mRNA and immunoreactive (ir)-4-HNE, and reduced ir-GPx-4 expression in the placental labyrinth zone (LZ). By GD15.5, HF diet was associated with increased ir-NLRP3 in both LZ and junctional zones (JZ). Exposure to infection alone and co-exposure to HF diet and infection further increased LZ ir-NLRP3. At GD18.5, HF diet was associated with increased Tirap and Il-1β mRNA expression, ir-4-HNE in the JZ and ir-Caspase-3 in the LZ. Maternal HF diet and infection exert distinct effects on the placenta across gestation, suggesting that maternal overnutrition might reduce the placenta’s capacity to handle adverse exposures, which may increase susceptibility to poor fetal 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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".