Antiviral Inflammation during Early Pregnancy Reduces Placental and Fetal Growth Trajectories
Bibliographic record
Abstract
Abstract Many viruses are detrimental to pregnancy and negatively affect fetal growth and development. What is not well understood is how virus-induced inflammation impacts fetal–placental growth and developmental trajectories, particularly when inflammation occurs in early pregnancy during nascent placental and embryo development. To address this issue, we simulated a systemic virus exposure in early pregnant rats (gestational day 8.5) by administering the viral dsRNA mimic polyinosinic:polycytidylic acid (PolyI:C). Maternal exposure to PolyI:C induced a potent antiviral response and hypoxia in the early pregnant uterus, containing the primordial placenta and embryo. Maternal PolyI:C exposure was associated with decreased expression of the maternally imprinted genes Mest, Sfrp2, and Dlk1, which encode proteins critical for placental growth. Exposure of pregnant dams to PolyI:C during early pregnancy reduced fetal growth trajectories throughout gestation, concomitant with smaller placentas, and altered placental structure at midgestation. No detectable changes in placental hemodynamics were observed, as determined by ultrasound biomicroscopy. An antiviral response was not evident in rat trophoblast stem (TS) cells following exposure to PolyI:C, or to certain PolyI:C-induced cytokines including IL-6. However, TS cells expressed high levels of type I IFNR subunits (Ifnar1 and Ifnar2) and responded to IFN-⍺ by increasing expression of IFN-stimulated genes and decreasing expression of genes associated with the TS stem state, including Mest. IFN-⍺ also impaired the differentiation capacity of TS cells. These results suggest that an antiviral inflammatory response in the conceptus during early pregnancy impacts TS cell developmental potential and causes latent placental development and reduced fetal growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".