The short-term consumption of a moderately high-fat diet alters nitric oxide bioavailability in lean female Zucker rats
Bibliographic record
Abstract
To determine whether short-term consumption of a moderately high-fat diet (MHFD) affects nitric oxide (NO) production, the concentration of stable NO metabolites (NOx) in urine and plasma of rats fed a MHFD (15.6 %g fat) or control diet (4.5 %g fat) was measured weekly for 4 weeks. Plasma and urine NOx levels were significantly depressed in the MHFD group by week 1 and remained so for the duration of the study. Decreased NO bioavailability may result from a decrease in NO production or the scavenging of NO by reactive oxygen species (ROS). Because endothelial NOS (eNOS) is the major contributor to NO production and circulating levels of NOx, eNOS expression was measured in several tissues. At week 1, there was a MHFD-associated decrease in eNOS expression in the liver. Subsequently, eNOS expression declined in the heart and kidney medulla of MHFD-fed rats at weeks 3 and 4, respectively. The expression of eNOS in the kidney cortex and adipose tissue did not change. These results suggest that a MHFD alters eNOS expression in a time-dependent and tissue-specific manner. In the liver, NOS activity and tissue levels of NOx and nitrotyrosine were measured. Nitrotyrosine levels were used as an indirect measure of the NO scavenged by ROS. There was a decrease in NOS activity, suggesting that the low levels of hepatic NOx were due, in part, to a decrease in NO production. In addition, there was a dramatic increase in nitrotyrosine formation, suggesting that the decline in hepatic NOx was also due to an increased interaction of NO with ROS. Tyrosine nitration commonly has detrimental effects on proteins. The decrease in NO and increase in protein nitration could potentially have adverse effects on tissue function.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".