Exercise training can attenuate preeclampsia-like features in an animal model
Bibliographic record
Abstract
OBJECTIVE: Exercise training benefits have been widely investigated and used as alternative treatment for different pathological conditions. Since preeclampsia is a severe pregnancy-associated disease for which no treatment is available, our aim was to investigate the protective role of exercise training on pregnancy outcome using a mouse model of the disease. METHODS: We used transgenic female mice overexpressing human angiotensinogen, which develop preeclampsia when mated with human renin-overexpressing males. Females were placed in exercise cages 4 weeks prior to mating, and remained in these throughout gestation. Blood pressure was measured by telemetry, and proteinuria was quantified by ELISA. Placentas were assessed by histology and immunohistochemistry, whereas vascular endothelial growth factor was measured by real-time PCR and immunoblot. Endothelial function was assessed in isolated mesenteric arteries. RESULTS: Conversely to sedentary transgenic females (131.20 ± 4.08 mmHg), trained dam's mean arterial pressure was no longer different from normal mice at the end of gestation (117.5 ± 10.6 vs. 112.3 ± 5.5 mmHg). Proteinuria observed in transgenic dams (3.364 ± 1.62 μg/mg) was absent in trained mice (0.894 ± 0.43 μg/mg). Placental disease and cardiac hypertrophy were also normalized, whereas vascular reactivity was significantly ameliorated. Furthermore, placental vascular endothelial growth factor was normalized in trained transgenic mice. CONCLUSIONS: To our knowledge, we are the first to clearly demonstrate that exercise training both before and during gestation can reduce preeclampsia features in a mouse model. Consequently, women at risk for this disease could benefit from exercise training to protect themselves and their future fetuses.
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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 | 0.000 |
| 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".