Role of 7-chloro-4-(phenylselanyl) quinoline in the treatment of oxaliplatin-induced hepatic toxicity in mice
Bibliographic record
Abstract
There is an increasing incidence of hepatotoxicity induced by oxaliplatin (OXA); therefore, researchers’ attention has been drawn to therapeutic alternatives that may decrease OXA-induced hepatotoxicity. Studies indicate that oxidative stress plays a major role in OXA-induced liver injury. As several pharmacological effects of 7-chloro-4-(phenylselanyl) quinole (4-PSQ) involve its antioxidant action, the hypothesis that this organoselenium compound could be promising for the treatment or prevention of hepatotoxicity induced by treatment with OXA was investigated. To test this hypothesis, male Swiss mice received OXA (10 mg·kg –1 ) on days 0 and 2, followed by oral administration of 4-PSQ (1 mg·kg –1 ) on days 2 to 14. 4-PSQ reduced the plasma aspartate, and alanine aminotransferase activity increased by exposure to OXA. The histopathological examination of the liver showed that 4-PSQ markedly improved OXA-induced hepatic injury. In addition, treatment with 4-PSQ reduced the oxidation of lipids and proteins (thiobarbituric acid reactive species levels and protein carbonyl content) and attenuated the increase of hepatic catalase and glutathione peroxidase activity caused by OXA. The inhibition of hepatic δ-aminolevulinic dehydratase activity induced by OXA was reverted by 4-PSQ. In conclusion, results indicate that 4-PSQ may be a good therapeutic strategy for attenuating OXA-induced liver damage.
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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) | 0.001 | 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".