p38 and ERK1/2 MAPKs mediate the interplay of TNF-α and IL-10 in regulating oxidative stress and cardiac myocyte apoptosis
Bibliographic record
Abstract
It is known that TNF-alpha increases the production of ROS and decreases antioxidant enzymes, resulting in an increase in oxidative stress. IL-10 appears to modulate these effects. The present study investigated the role of p38 and ERK1/2 MAPKs in mediating the interplay of TNF-alpha and IL-10 in regulating oxidative stress and cardiac myocyte apoptosis in Sprague-Dawley male rats. Isolated adult cardiac myocytes were exposed to TNF-alpha (10 ng/ml), IL-10 (10 ng/ml), and IL-10 + TNF-alpha (ratio 1) for 4 h. H(2)O(2) (100 microM) as a positive control and the antioxidant Trolox (20 micromol/l) were used to confirm the involvement of oxidative stress. H(2)O(2) treatment increased oxidative stress and apoptosis; TNF-alpha mimicked these effects. Exposure to TNF-alpha significantly increased ROS production, caused cell injury, and increased the number of apoptotic cells and Bax-to-Bcl-xl ratio. This change was associated with an increase in the phospho-p38 MAPK-to-total p38 MAPK ratio and a decrease in the phospho-ERK1/2-to-total ERK1/2 ratio. IL-10 treatment by itself had no effect on these parameters, but it prevented the above-listed changes caused by TNF-alpha. The antioxidant Trolox modulated TNF-alpha-induced changes in Bax/Bcl-xl, cell injury, and MAPKs. Preexposure of cells to the p38 MAPK inhibitor SB-203580 prevented TNF-alpha-induced changes. Inhibition of the ERK pathway with PD-98059 attenuated the protective role of IL-10 against TNF-alpha-induced apoptosis. This study provides evidence in support of the essential role of p38 and ERK1/2 MAPKs in the interactive role of TNF-alpha and IL-10 in cardiac myocyte apoptosis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".