Role of zinc oxide nanoparticles in alleviating hepatic fibrosis and nephrotoxicity induced by thioacetamide in rats
Bibliographic record
Abstract
The present research studied the influence of zinc oxide nanoparticles (ZnO-NPs; 5, 7.5, and 10 mg/kg, i.p.) on the liver and kidney injuries motivated by thioacetamide (TAA; 100 mg/kg, i.p.). Each treatment was carried out 3 times per week for 8 weeks. ZnO-NPs relieved the decrease of hepatic or renal reduced glutathione (GSH), catalase (CAT), and superoxide dismutase (SOD) induced by TAA. Moreover, ZnO-NPs lowered tissue malondialdehyde (MDA, an indicator for lipid peroxidation). TAA treatment led to a significant increase in plasma inflammatory markers (TNF-α, IL-6), liver enzymes (gamma-glutamyltransferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and kidney function parameters (creatinine, urea, uric acid). However, these parameters were reduced after treatment with ZnO-NPs. In addition, the hepatic fibrosis markers, hydroxyproline level, and α-smooth muscle actin immunopositive stain were lowered by ZnO-NPs. The protective effect of ZnO-NPs in respect to biochemical changes was also confirmed by histopathological and immunohistochemistry studies in the liver and kidney sections. Our results suggested that ZnO-NPs may attenuate TAA toxicity via suppression of oxidative stress.
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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) | 0.000 | 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".