Folic acid supplementation attenuates high fat diet induced hepatic oxidative stress via regulation of NADPH oxidase
Bibliographic record
Abstract
Diets high in saturated fat and cholesterol facilitate weight gain, a predisposing factor that contributes to the onset of obesity and metabolic disorders. Hepatic oxidative stress is commonly reported in various animal models of obesity and has been associated with enhanced expression of NADPH oxidase. We have previously reported several antioxidant mechanisms through which folic acid confers protection during hyperhomocysteinemia-induced oxidative stress. The objective of the present study was to investigate whether folic acid supplementation ameliorates high-fat diet induced oxidative stress in the liver, and to identify the underlying mechanisms. Male C57BL/6J mice were fed a control diet, a high-fat diet, or a high-fat diet supplemented with folic acid for 12 weeks. A high-fat diet led to increased body mass, hepatic lipid peroxidation, and liver injury. There was a significant increase in hepatic NADPH oxidase activity, which was associated with enhanced expression of several NADPH-oxidase subunits. Folic acid supplementation had a protective effect against high-fat diet induced hepatic oxidative stress and liver injury. Further analysis revealed that the antioxidant effect of folic acid was attributed, in part, to transcriptional regulation of NADPH oxidase. These results suggested that folic acid supplementation may be hepatoprotective from liver injury associated with a high-fat diet.
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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".