Renin–angiotensin system inhibition ameliorates CCl<sub>4</sub>-induced liver fibrosis in mice through the inactivation of nuclear transcription factor kappa B
Bibliographic record
Abstract
Therapeutic interventions for liver fibrosis are still limited due to the complicated molecular pathogenesis. Renin–angiotensin system (RAS) seems to contribute to the development of hepatic fibrosis. Therefore, we aimed to examine the effect of RAS inhibition on CCl4-induced liver fibrosis. Mice were treated with silymarin (30 mg·kg−1), perindopril (1 mg·kg−1), fosinopril (2 mg·kg−1), or losartan (10 mg·kg−1). The administration of RAS inhibitors improved liver histology and decreased protein expression of alpha smooth muscle actin (α-SMA) and hepatic content of hydroxyproline. These effects found to be mediated via inactivation of nuclear transcription factor kappa B (NFκB) pathway by the inhibition of NFκB p65 phosphorylation at the Ser536 residue and phosphorylation-induced degradation of nuclear factor kappa-B inhibitor alpha (NFκBia) subsequently inhibited NFκB-induced TNF-α and TGF-β1, leading to lower levels of tissue inhibitor of metalloproteinase-1 (TIMP-1) and vascular endothelial growth factor (VEGF). We concluded that the tissue affinity of the angiotensin converting enzyme inhibitors (ACEIs) has no impact on its antifibrotic activity and that interfering the RAS either through the inhibition of ACE or the blockade of AT1R has the same therapeutic benefit. These results suggest RAS inhibitors as promising candidates for further clinical trials in the management of hepatic fibrosis.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".