Beneficial effects of <i>N</i>-acetylcysteine on hepatic oxidative stress in streptozotocin-induced diabetic rats
Bibliographic record
Abstract
Diabetes is one of the leading diseases worldwide and, thus, finding new therapeutic alternatives is essential. The development of non-alcoholic fatty liver disease is a notable diabetic complication. Therefore, antioxidant therapy became a leading topic in the world of diabetes research. The objective of this present study was to evaluate the effects of antioxidant N-acetylcysteine (NAC) administration on serum biochemical parameters and oxidative stress parameters in hepatic tissue of the diabetic rats. Thirty-two animals were divided in 4 groups (n = 8): G1, normal rats; G2, normal rats + NAC; G3, diabetic rats; and G4, diabetic rats + NAC. Diabetes was induced in diabetic groups through streptozotocin. NAC administration was effective in improving hyperglycemia and hypoinsulinemia, as well as reducing serum alanine-aminotransferase and urea, hepatic triglycerides accumulation, and oxidative stress biomarkers in the diabetic liver, as well as improving the activity of hepatic antioxidant enzymes. This effect was likely due to NAC's ability of restoring intracellular glutathione, an important compound for the antioxidant defense, as well as due to NAC's direct antioxidant properties. Thus, NAC administration was useful for reducing hepatic oxidative stress and decreased the deposit of triacylglycerols, minimizing diabetic hepatic damage, making it a promising therapeutic adjuvant in the future.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".