Effect of exercise training on preeclampsia superimposed on chronic hypertension in a mouse model
Bibliographic record
Abstract
Preeclampsia is among the leading causes of perinatal mortality and morbidity, affecting 2-7% of pregnancies. Its incidence increases to 10-25% in already hypertensive women. To date, no treatment, aside from delivery, is known. Interestingly, several studies have reported that exercise training (ExT) can reduce preeclampsia prevalence although the available studies are considered insufficient. Therefore, the aim of this study is to determine the impact of ExT when practiced before and during gestation on pregnancy outcome in a mouse model of preeclampsia superimposed on chronic hypertension (SPE). To do so, mice overexpressing both human angiotensinogen and renin (R+A+) were used because they are hypertensive at baseline and they develop many hallmark features of SPE. Mice were trained by placing them in a cage with access to a running wheel 4 weeks before and during gestation. ExT in this study prevented the rise in blood pressure at term observed in the sedentary transgenic mothers. This may be realized through an increased activity of the angiotensin-(1-7) axis in the aorta. In addition, ExT prevented the increase in albumin/creatinine ratio. Moreover, placental alterations were prevented with training in transgenic mice, leading to improvements in placental and fetal development. Placental mRNA and circulating levels of sFlt-1 were normalized with training. Additionally, the increase in angiotensin II type I receptor and the decrease in Mas receptor protein were reversed with training. ExT appears to prevent many SPE-like features that develop in this animal model and may be of use in the prevention of preeclampsia in women.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".