Taurine alleviates kidney injury in a thioacetamide rat model by mediating Nrf2/HO-1, NQO-1, and MAPK/NF-κB signaling pathways
Bibliographic record
Abstract
This study investigated the molecular mechanisms by which taurine exerts its reno-protective effects in thioacetamide (TAA) – induced kidney injury in rats. Rats received taurine (100 mg/kg daily, intraperitoneally) either from day 1 of TAA injection (250 mg/kg twice weekly for 6 weeks) or after 6 weeks of TAA administration. Taurine treatment, either concomitant or later as a therapy, restored kidney functions, reduced blood urea nitrogen (BUN), creatinine, and malondialdehyde (MDA), increased renal levels of superoxide dismutase (SOD), and reversed the increase of kidney injury molecule-1 (KIM-1) and neutrophil gelatinase–associated lipocalin (NGAL) caused by TAA. Taurine treatment also led to a significant rise in nuclear factor erythroid 2–related factor 2 (Nrf2), hemoxygenase-1 (HO-1), and NADPH quinone oxidoreductase-1 (NQO-1) levels, with significant suppression of extracellular signal-regulated kinase (ERK) 1/2, nuclear factor kappa B (NF-κB), and tumor necrosis factor α (TNF-α) gene expressions, and interleukin-18 (IL-18) and TNF-α protein levels compared with those in TAA kidney-injured rats. Taurine exhibited reno-protective potential in TAA-induced kidney injury through its antioxidant and anti-inflammatory effects. Taurine antioxidant activity is accredited for its effect on Nrf-2 induction and subsequent activation of HO-1 and NQO-1. In addition, taurine exerts its anti-inflammatory effect via regulating NF-κB transcription and subsequent production of pro-inflammatory mediators via mitogen-activated protein kinase (MAPK) signaling regulation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".