Zinc oxide nanoparticles synthesized using Hyssopus Officinalis L. Extract Induced oxidative stress and changes the expression of key genes involved in inflammatory and antioxidant Systems
Bibliographic record
Abstract
BACKGROUND: Recent advances in the synthesis of bioactive nanoparticles resulted in the discovery and introduction of new bioactive nanoparticles to the pharmaceutical industry. In this regard, this research is aimed to synthesize the zinc oxide nanoparticles (ZnO-NPs) using Hyssopus officinalis L. extract and to evaluate the safety of nanoparticles using Balb/C mice. METHODS: Forty male mice were divided into four groups and received 0, 50, 100, and 200 mg/kg of ZnO-NPs for thirty days. At the end of the experiment, blood sugar, creatinine, aspartate aminotransferase (A.S.T.), and alanine aminotransferase (A.L.T.) were determined. Furthermore, histopathological and oxidative stress biomarker analyses in liver and kidney tissues were performed. The changes in the major inflammatory- and antioxidant-related genes were determined. RESULTS: The results showed that blood sugar and creatinine reduced significantly (P < 0.05) when 50, 100, and 200 mg/kg ZnO-NPs were supplemented to the diet. The serum ALT and AST and lipid peroxidation in the liver and kidney tissues were increased significantly (p < 0.05) when 50, 100, and 200 mg/kg ZnO-NPs were supplemented to the diet. Supplementation of ZnO-NPs suppressed the expression of antioxidant-related genes (SOD and CAT) and up-regulated the inflammatory biomarkers (iNOS and TNF- α). The concentration of 200 mg/Kg nanoparticles indicated cellular degeneration and necrosis in the liver and kidney tissues. CONCLUSIONS: Overall, it can be concluded that supplementation of ZnO-NPs synthesized using Hyssopus Officinalis L. extract in this study at 50 mg/kg or higher concentrations might be toxic to the mice.
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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".