Reduction in IL-33 expression exaggerates ischaemia/reperfusion-induced myocardial injury in mice with diabetes mellitus
Bibliographic record
Abstract
AIMS: The underlying mechanism(s) of vulnerability of the diabetic myocardium to ischaemia/reperfusion (I/R)-induced injury is not fully understood. Interleukin-33 (IL-33) has been reported showing the beneficial effect to the myocardium on I/R injury. The aims of this study were to test whether diabetes mellitus (DM) affects myocardial levels of IL-33 and to examine whether reduction in IL-33 is responsible for exaggerated I/R injury in the diabetic myocardium. METHODS AND RESULTS: DM hearts were challenged with I/R in vivo, whereas while isolated cardiomyocytes in vitro were conditioned with high glucose (HG) followed by an anoxia/reoxygenation (A/R) challenge. Myocardial levels of IL-33 were decreased in mice with DM which was associated with increased protein kinase C βII (PKCβII) activation. Exogenous IL-33 prevented the DM-induced PKCβII activation and attenuated I/R injuries (myocardial infarction size and apoptosis). HG-conditioned myocytes incurred exaggerated apoptosis when compared with naïve myocytes after A/R which was attenuated by IL-33. HG activated PKCβII in cardiomyocytes, which was further enhanced by A/R. IL-33 prevented the PKCβII activation in myocytes with HG or HG and A/R. Inhibition of PKCβII prevented the beneficial effect of IL-33. Finally, IL-33 up-regulated diacylglycerol kinase zeta (DGK-zeta) in cardiomyocytes and reversed the down-regulation of myocardial DGK-zeta in mice with DM. CONCLUSION: Our results indicate that decreased levels of IL-33 are responsible for the increased sensitivity of the myocardium to I/R in DM. Reduction in IL-33 results in a chronic activation of PKCβII. I/R further enhances PKCβII activation in the diabetic myocardium which results in exaggeration of myocardial injury.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".