Two different melatonin treatment regimens prevent an increase in kidney injury marker-1 induced by carbon tetrachloride in rat kidneys
Bibliographic record
Abstract
Acute kidney injury is a frequent disorder that can be mimicked by the application of different nephrotoxic agents, including carbon tetrachloride (CCl4), where kidney injury marker-1 (KIM-1) has been recognized as a highly specific marker. Melatonin is one of the most powerful natural antioxidants and has numerous beneficial properties. We evaluated the nephroprotective potential of 2 melatonin treatment regimens (pre- and post-intoxication) in a CCl4-induced acute kidney injury model based on the standard serum parameters, kidney tissue antioxidative capacity, KIM-1 levels, and kidney tissue morphological changes. The two treatment regimens were found to preserve kidney function, as judged from the evaluated standard serum parameters. Only when administered after the intoxication, melatonin preserved total kidney antioxidant capacity; pre-treatment melatonin only preserved reduced glutathione levels. An increase in tissue KIM-1 level was found to be prevented by both treatment regimens, which correlated with the morphological changes seen in the kidney tissues of animals treated with melatonin and CCl4. The findings of our study are in agreement with the known actions of melatonin in relieving kidney tissue oxidative burden, but also contribute to the understanding of its action by preventing an increase in KIM-1.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".