Losartan inhibits STAT1 activation and protects human glomerular mesangial cells from angiotensin II induced premature senescence
Bibliographic record
Abstract
Human glomerular mesangial cells (HMCs) have a finite lifespan, and eventually enter irreversible growth arrest known as cellular senescence, which is thought to contribute to kidney ageing and age-related kidney disorders, such as chronic kidney disease. The signal transducer and activator of transcription 1 (STAT1) is a latent transcription factor involved in a variety of signal transduction pathways, including cell proliferation, apoptosis, and differentiation, but whether it could regulate HMC senescence still remains to be explored. In our study, the induction of angiotensin II (Ang II)-accelerated HMC senescence, as judged by increased senescence-associated β-galactosidase (SA-β-gal)-positive staining cells, morphological changes, and G0/G1 cell cycle arrest. STAT1 activity and the expression of p53 and p21(Cip1) were increased after Ang II treatment. STAT1 knockdown using RNA interference significantly inhibited the progression of HMC senescence and decreased the elevated expression of p53 and p21(Cip1). Pretreating HMCs with Ang II receptor blocker losartan also inhibited the progression of HMC senescence and STAT1 activity. Our results indicate that STAT1 is implicated in the mediation of Ang II-induced HMC senescence through p53/ p21(Cip1) pathway, and that losartan could attenuate HMC senescence by regulating STAT1. The antioxidant N-acetyl-L-cysteine reduced ROS production and STAT1 activity induced by Ang II, indicating that Ang II uses ROS as a second messenger to regulate STAT1 activity.
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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".