Preeclampsia may influence offspring neuroanatomy and cognitive function: a role for placental growth factor†
Bibliographic record
Abstract
Preeclampsia (PE) is a common pregnancy complication affecting 3-5% of women. Preeclampsia is diagnosed clinically as new-onset hypertension with associated end organ damage after 20 weeks of gestation. Despite being diagnosed as a maternal syndrome, fetal experience of PE is a developmental insult with lifelong cognitive consequences. These cognitive alterations are associated with distorted neuroanatomy and cerebrovasculature, including a higher risk of stroke. The pathophysiology of a PE pregnancy is complex, with many factors potentially able to affect fetal development. Deficient pro-angiogenic factor expression is one aspect that may impair fetal vascularization, alter brain structure, and affect future cognition. Of the pro-angiogenic growth factors, placental growth factor (PGF) is strongly linked to PE. Concentrations of PGF are inappropriately low in maternal blood both before and during a PE gestation. Fetal concentrations of PGF appear to mirror maternal circulating concentrations. Using Pgf-/- mice that may model effects of PE on offspring, we demonstrated altered central nervous system vascularization, neuroanatomy, and behavior. Overall, we propose that development of the fetal brain is impaired in PE, making the offspring of preeclamptic pregnancies a unique cohort with greater risk of altered cognition and cerebrovasculature. These individuals may benefit from early interventions, either pharmacological or environmental. The early neonatal period may be a promising window for intervention while the developing brain retains plasticity.
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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 | 0.001 |
| 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 | 0.001 |
| 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".