Crosstalk among apoptosis, inflammation, and autophagy in relation to melatonin protective effect against contrast-induced nephropathy in rats
Bibliographic record
Abstract
Contrast medium (CM) is a chemical substance that is used for imaging anatomical boundaries and to explore normal and abnormal physiological findings; the use of CM was associated with kidney injury and acute renal failure. Melatonin (M) possesses antioxidant, anti-inflammatory, and antiapoptotic effects in addition to autophagy modulation. This study aimed to investigate the protective effect of M against contrast-induced nephropathy (CIN) and its impact on the crosstalk between inflammasome, apoptosis, and autophagy in CIN. Male albino rats received M (10, 20, and 40 mg/kg/day, intraperitoneally) for 3 days. One hour after the last administration, rats were subjected to CIN induction (10 mg/kg indomethacin, double doses of l-NAME 10 mg/kg, i.v., and meglumine diatrizoate 60% 6 mL/kg, i.v.). CIN-induced kidney damage was evidenced through elevated kidney function biomarkers and induced renal histopathological changes. Pretreatment with M caused a significant decrease in nephrotoxicity biomarkers and histopathological alterations. Moreover, CIN-induced oxidative stress, NLRP3 inflammasome, and apoptosis were attenuated by M. Furthermore, M modulates autophagy in CIN rats. M inhibits CIN-induced NLRP3-inflammasome activation and apoptosis as well as enhances autophagy.
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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 | 0.001 | 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".