Synergistic ameliorative effects of sesame oil and alpha-lipoic acid against subacute diazinon toxicity in rats: hematological, biochemical, and antioxidant studies
Bibliographic record
Abstract
Diazinon (DZN) is a common organophosphorus insecticide extensively used for agriculture and veterinary purposes. DZN toxicity is not limited to insects; it also induces harmful effects in mammals and birds. Our experiment evaluated the protective and antioxidant potential of sesame oil (SO) and (or) alpha-lipoic acid (ALA) against DZN toxicity in male Wistar albino rats. DZN-treated animals exhibited macrocytic hypochromic anemia and significant increases in serum biochemical parameters related to liver injury, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), γ-glutamyl transferase (γGT), cholesterol, and triglycerides. They also had elevated levels of markers related to cardiac injury, such as lactate dehydrogenase (LDH) and creatine phosphokinase (CPK), and increased biomarkers of renal injury, urea and creatinine. DZN also increased hepatic, renal, and cardiac lipid peroxidation and decreased antioxidant biomarker levels. SO and (or) ALA supplementation ameliorated the deleterious effects of DZN intoxication. Treatment improved hematology and serum parameters, enhanced endogenous antioxidant status, and reduced lipid peroxidation. Importantly, they exerted synergistic hepatoprotective, nephroprotective, and cardioprotective effects. Our findings demonstrate that SO and (or) ALA supplementation can alleviate the toxic effects of DZN via their potent antioxidant and free radical-scavenging activities.
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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 | 0.000 |
| 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".