Haploinsufficiency of Endothelial Nitric Oxide Synthase Mitigates Beneficial Effects of Maternal Exercise on Fetal Heart Development During Pregestational Diabetes
Bibliographic record
Abstract
Background Pregestational diabetes (PGD) increases congenital heart defect (CHD) risk over 5‐fold. Maternal exercise enhances eNOS (endothelial nitric oxide synthase) activity, benefiting embryos, though its causal role remains unclear. This study investigated the role of eNOS in maternal exercise‐mediated protection of fetal heart development in a PGD mouse model. Methods PGD was induced in eNOS +/− or wild‐type female mice via streptozotocin before breeding with wild‐type or eNOS +/− males. Pregnant females had access to a running wheel for voluntary exercise or remained sedentary. Fetuses were collected at embryonic day 18.5 for genotyping and CHD assessment. Embryonic day 12.5 hearts were analyzed for proliferation, apoptosis, oxidative stress, and eNOS protein levels. Results Maternal exercise normalized litter size and mortality rates in offspring of diabetic eNOS +/− females but did not reduce CHD incidence in offspring of wild‐type or eNOS +/− females with PGD. CHDs included septal defects, double outlet right ventricle, and valve defects. Exercise increased coronary artery density but not capillary density. Proliferation deficits at embryonic day 12.5 were restored by exercise, yet oxidative stress remained elevated. Maternal exercise in eNOS +/− dams during PGD did not significantly change eNOS protein or phosphorylation levels in both eNOS +/+ and eNOS +/− fetal hearts. Offspring genotype did not affect CHD incidence, cell proliferation, apoptosis, or oxidative stress. Conclusions Maternal exercise does not prevent CHDs in PGD offspring of eNOS +/− mice. Its ability to mitigate PGD‐induced oxidative stress is eNOS dependent and essential for improving heart morphology.
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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) | 0.000 | 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) | 0.001 | 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".