Probucol inhibits JAK2−STAT pathway activation and protects human glomerular mesangial cells from <i>tert</i>-butyl hydroperoxide induced premature senescence
Bibliographic record
Abstract
Human 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 JAK2-STAT pathway plays a pivotal role in transmitting cytokine signals, including cell proliferation, apoptosis, and differentiation, but whether it could regulate HMC senescence still remains to be explored. In our study, tert-butyl hydroperoxide (tBHP)-induced cells accelerated HMC senescence, as judged by increased senescence-associated β-galactosidase stained positive cells, morphological changes, and G0-G1 cell cycle arrest. STAT1 and STAT3 activity were increased in tBHP-induced cells. After tBHP treatment, Bcl-2 protein expression decreased and Bax protein expression increased. Blocking the JAK2-STAT pathway with AG490 and using probucol significantly inhibited the progression of HMC senescence. Bax protein expression decreased, but Bcl-2 protein expression increased after AG490 and probucol treatment. Our results indicated that the JAK2-STAT pathway might mediate tBHP-induced HMC senescence through the Bcl-2-Bax pathway, and that probucol could attenuate HMC senescence by regulating STATs.
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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 | 0.000 |
| 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".