Molecular basis of IL‐10 attenuation of TNF‐α induced NFκB pathway activation and cardiomyocyte apoptosis
Bibliographic record
Abstract
We have reported that tumor necrosis factor‐α (TNF‐α) increases oxidative stress and apoptosis in cardiomyocytes by upregulating p38 mitogen activated protein (MAP) kinase phosphorylation. Interleukin‐10 (IL‐10) blocked these effects of TNF‐α by upregulating ERK 1/2 MAP kinase phosphorylation. However, the precise site of this IL‐10 action is still unknown and this was investigated in the present study. Cardiomyocytes isolated from adult Sprague Dawley rats were exposed to TNF‐α (10ng/ml), IL‐10 (10ng/ml) and IL‐10α (ratio 1) for 4hrs. Hydrogen peroxide (H2O2) and antioxidant trolox were used as positive controls. Exposure to TNF‐α resulted in an increase in the production of reactive oxygen species, number of apoptotic cells, caspase‐3 activation and PARP cleavage. Increased oxidative stress by using H2O2 also caused apoptosis. These changes due to TNF‐α were associated with an increase in IKK and NFκB phosphorylation. IL‐10 by itself had no effect, but it prevented TNF‐α induced changes. Trolox also mitigated TNF‐α induced changes. Pre‐exposure of cells to IKK inhibitor, prevented TNF‐α induced changes and inhibition of ERK 1/2 MAP kinase attenuated protective role of IL‐10. The study shows that IL‐10 prevents TNF‐α‐induced NFκB activation and proapoptotic changes in cardiomyocytes by inhibiting IKK phosphorylation through the activation of ERK 1/2 MAPkinase. (Supported by CIHR).
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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".