A Longitudinal Pilot Study on Cognition and Cerebral Hemodynamics in a Mouse Model of Preeclampsia Superimposed on Hypertension: Looking at Mothers and Their Offspring
Bibliographic record
Abstract
Preeclampsia is a common hypertensive disorder in pregnant women and whose causes and consequences have focused primarily on cardiovascular outcomes on the mother and offspring, often without taking into consideration the possible effects on the brain. One possible cause of preeclampsia has been attributed to alterations in the renin-angiotensin system, which has also been linked to cognitive decline. In this pilot study, we use a transgenic mouse model that chronically overexpresses human angiotensinogen and renin (R+A+ mice) that displayed characteristics of preeclampsia such as proteinuria during gestation. Offspring of these mothers as well as from control mothers were also examined. We were primarily interested in detecting whether cognitive deficits were present in the mothers and offspring in the long term and used a spatial learning and memory task as well as an object recognition task at three timepoints: 3, 8, and 12 months post-partum or post-natal, while measuring blood pressure and performing urine analysis after each timepoint. While we did not find significant deficits in preeclamptic mothers at the later timepoints, we did observe negative consequences in the pups of R+A+ mice that coincided with hemodynamic alterations whereby pups had higher whisker-evoked oxygenated hemoglobin levels and increased cerebral blood flow responses compared to control pups. Our study provides validation of this preeclampsia mouse model for future studies to decipher the underlying mechanisms of long-term cognitive deficits found in offspring.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".