Effects of CeO<sub>2</sub> nanoparticles on the <i>HO-1</i>, <i>NQO1</i>, and <i>GCLC</i> expression in the testes of diabetic rats
Bibliographic record
Abstract
nanoparticles (CNPs) as effective ROS scavengers exhibit potent antioxidant activity. In this study the effect of CNPs investigated was on HO-1, NQO1, and GCLC expression in the streptozotocin (STZ)-induced diabetic rats. Twenty-four male Wistar rats were divided into 4 groups: controls did not receive any treatment; diabetic rats received STZ (60 mg/kg daily); CNPs group received CNPs 30 mg/kg daily for 2 weeks; and rats in STZ + CNPs group received CNPs 30 mg/kg daily for 2 weeks following STZ injection. Oxidative stress was evaluated by measurement of total antioxidant capacity (TAC) and total oxidative status (TOS levels). HO-1, NQO1, and GCLC expression was measured using quantitative real-time PCR. Following STZ injection, significant lower levels of TAC and higher levels of TOS were observed. CNPs could alleviate deleterious effects of diabetes through the enhancement of TAC levels and a significant decline in TOS levels. HO-1, NQO1, and GCLC expression in the diabetic rats were lower than controls. HO-1, NQO1, and GCLC was upregulated in the diabetic rats treated with CNPs. There were significant correlations between NQO1 and GCLC, NQO1 and HO-1, and between HO-1 and GCLC expression. Moreover, Nrf2 was associated with NQO1, GCLC, and HO-1 expression. CNPs as Nrf2 upregulator confer protection against oxidative stress in the testes of STZ-induced diabetic rats by upregulating HO-1, GCLC, and NQO1 cytoprotective genes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".