Lycopene improves methotrexate-induced functional alterations of the Madin–Darby kidney cells in a concentration-dependent manner
Bibliographic record
Abstract
Lycopene is one of the most potent antioxidants among carotenoids due to its ability to quench singlet oxygen and react with free radicals to reduce DNA damage. Methotrexate is widely used in the treatment of several types of cancers and autoimmune diseases. One of the most common side effects of a high-dose of methotrexate is kidney injury. In this study, we evaluated effects of lycopene on the Madin–Darby canine kidney cells (MDCK) treated with methotrexate through the estimation of their mitochondrial and lysosomal functions ((4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide reduction assay and neutral red uptake assay) and changes in cell oxidative status (determination of advanced oxidized proteins concentrations and reduced glutathione levels) and lysosomal enzymes activity (β-N-acetyl glucosaminidase activity). Results of our study showed that lycopene applied in high concentration caused significant impairment of the MDCK function leading to cell death. Contrarily, in relatively low concentrations lycopene moderately ameliorated methotrexate-induced MDCK cell death estimated by both biochemical and microscopic analyses. It also prevented a significant decline in the MDCK cell lysosomal function estimated by neutral red accumulation ability and activity of the lysosomal enzyme β-N-acetyl glucosaminidase.
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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 | 0.000 | 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".