Effects of fetal exposure to high-fat diet or maternal hyperglycemia on L-arginine and nitric oxide metabolism in lung
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Alterations in the L-arginine/nitric oxide (NO) metabolism contribute to diseases such as obesity, metabolic syndrome and airway dysfunction. The impact of early-life exposures on the L-arginine/NO metabolism in lung later in life is not well understood. The objective of this work was to study the effects of intrauterine exposures to maternal hyperglycemia and high-fat diet (HFD) on pulmonary L-arginine/NO metabolism in mice. METHODS: We used two murine models of intrauterine exposures to maternal (a) hyperglycemia and (b) HFD to study the effects of these exposures on the L-arginine/NO metabolism in lung in normal chow-fed offspring. RESULTS: Both intrauterine exposures resulted in NO deficiency in the lung of the offspring at 6 weeks of age. However, each of the exposures leading to different metabolic phenotypes caused a distinct alteration in the L-arginine/NO metabolism. Maternal hyperglycemia leading to impaired glucose tolerance but no obesity in the offspring resulted in increased levels of asymmetric dimethylarginine and impairment of NO synthases. Although maternal HFD led to obesity without impairment in glucose tolerance in the offspring, it resulted in increased expression and activity of arginase in the lung of the normal chow-fed offspring. CONCLUSIONS: These data suggest that maternal hyperglycemia and HFD can cause alterations in the pulmonary L-arginine/NO metabolism 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 | 0.000 | 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".