Interplay of pro‐inflammatory and anti‐inflammatory cytokines in regulating oxidative stress in isolated adult rat cardiac myocytes
Bibliographic record
Abstract
Interleukin‐10 (IL‐10), an anti‐inflammatory cytokine, has been shown to antagonize some of deleterious effects of pro‐inflammatory cytokine, Tumor Necrosis Factor‐α (TNF‐α). We hypothesized that the balance between TNF‐α and IL‐10 is of greater significance in regulating oxidative stress. Methods Myocytes were exposed for 4 hours to different doses of IL‐10 (1–20 ng/ml) or combination of TNF‐α and IL‐10 in different ratios. Cell lysates were analyzed for protein levels and mRNA levels for Copper/zinc superoxide dismutase [CuSOD], Manganese superoxide dismutase [MnSOD], Catalase [Cat] and Glutathione peroxidase [GSHPx]. Oxidative stress was assessed by redox‐sensitive fluorescence dye (H2DCFDA) as well as by the study of lipid peroxidation. Effects of oxidative stress induced by H 2 O 2 on these parameters were also examined for comparison purposes. Results H 2 O 2 treatment significantly decreased protein and mRNA levels of all four antioxidants. IL‐10 treatment alone had no effect on the protein levels of antioxidants but caused a dose dependent increase in the mRNA levels of all antioxidant enzymes with a significant change seen at 20 ng/ml. TNF‐α induced decrease in MnSOD, Cat and GSHPx protein levels were prevented by IL‐10/TNF‐α ratio of 1. Similarly this ratio of 1 had an optimal effect on the mRNA levels for MnSOD and Cat. Combination treatment at a ratio of 1 also significantly decreased lipid peroxidation and intracellular ROS. Conclusion This study suggests that the relative concentration of TNF‐α and IL‐10 is of more physiological importance than either of the cytokines individually. Supported by ICRH
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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".