IL-33 Attenuates Anoxia/Reoxygenation-Induced Cardiomyocyte Apoptosis by Inhibition of PKCβ/JNK Pathway
Bibliographic record
Abstract
BACKGROUND: Interleukin-33 (IL-33) is a new member of the IL-1 cytokine family. The objectives of present study are to assess whether IL-33 can protect cardiomyocytes from anoxia/reoxygenation (A/R)-induced injury and the mechanism involved in the protection. METHODS: Cardiomyocytes derived from either wild type or JNK1(-/-) mice were challenged with an A/R with or without IL-33. Myocyte apoptosis was assessed by measuring caspase 3 activity, fragmented DNA and TUNEL staining. In addition, cardiomyocyte oxidative stress was assessed by measuring DHR123 oxidation; PKCβII and JNK phosphorylation were assessed with Western blot. RESULTS: Challenge of cardiomyocytes with an A/R resulted in cardiomyocyte oxidative stress, PKCβII and JNK phosphorylation, and myocyte apoptosis. Treatment of the cardiomyocytes with IL-33 attenuated the A/R-induced myocyte oxidative stress, prevented PKCβII and JNK phosphorylation and attenuated the A/R-induced myocyte apoptosis. The protective effect of the IL-33 did not show in cardiac myocytes with siRNA specific to PKCβII or myocytes deficient in JNK1. Inhibition of PKCβII prevented the A/R-induced JNK phosphorylation, but inhibition of JNK1 showed no effect on A/R-induced PKCβII phosphorylation. CONCLUSIONS: Our results indicate that IL-33 prevents the A/R-induced myocyte apoptosis through inhibition of PKCβ/JNK pathway.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".