Endothelial nitric oxide synthase is a critical factor in experimental liver fibrosis
Bibliographic record
Abstract
Reduced expression of endothelial nitric oxide synthase (eNOS) in chronic liver disease can reduce hepatic perfusion and accelerate fibrosis. The relationship between eNOS expression and liver fibrogenesis remains unclear. We investigated whether L-arginine attenuated chronic liver fibrosis through eNOS expression. Chronic liver injury was induced by administration of carbon tetrachloride (CCl(4)) to mice for 8 weeks. 5-Methylisothiourea hemisulphate (SMT), an iNOS inhibitor, or L-arginine, a NOS substrate were injected subcutaneously. CCl(4)-induced hepatotoxicity, oxidative stress and accumulation of collagen were detected in the liver. The expression levels of inducible NOS (iNOS) and nuclear factor kappa-B (NF-kappaB) activity in the liver after CCl(4) treatment were increased but eNOS expression and activator protein-1 (AP-1) activity were decreased. Both SMT and L-arginine effectively reduced CCl(4) induced oxidative stress and collagen formation, but L-arginine showed a significantly greater suppression of collagen formation, iNOS expression and NF-kappaB activity. L-arginine also restored the level of eNOS and AP-1 activity. L-arginine was more effective than SMT in suppressing liver fibrosis. L-arginine might improve NO production which facilitates hepatic blood flow and thus retards liver fibrogenesis. Our results showed that the reduced eNOS expression in CCl(4)-treated mice was reversed by L-arginine. Furthermore, L-arginine also reversed the reduced AP-1 activity, an eNOS promoter.
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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".