Over-expression of calpastatin attenuates myocardial injury following myocardial infarction by inhibiting endoplasmic reticulum stress
Bibliographic record
Abstract
Background: Ischemic heart injury activates calpains and endoplasmic reticulum (ER) stress in cardiomyocytes. This study investigated whether over-expression of calpastatin, an endogenous calpain inhibitor, protects the heart against myocardial infarction (MI) by inhibiting ER stress. Methods: Mice over-expressing calpastatin (Tg-CAST) and littermate wild type (WT) mice were divided into four groups: WT-sham, Tg-CAST-sham, WT-MI, and Tg-CAST-MI, respectively. WT-sham and Tg-CAST-sham mice showed similar cardiac function at baseline. MI for 7 days impaired cardiac function in WT-MI mice, which was ameliorated in Tg-CAST-MI mice. Results: Tg-CAST-MI mice exhibited significantly decreased diameter of the left ventricular cavity, scar area, and cardiac cell death compared to WT-MI mice. WT-MI mice had higher cardiac expression of C/EBP homologous protein (CHOP) and BIP, indicators of ER stress, compared to WT-sham mice, indicative of MI-induced ER stress. This increase was abolished in Tg-CAST-MI hearts. Furthermore, administration of tauroursodeoxycholic acid, an inhibitor of ER stress, reduced MI-induced expression of CHOP and BIP, scar area, and myocardial dysfunction. In an in vitro model of oxidative stress, H2O2 stimulation of H9c2 cardiomyoblasts induced calpain activation, CHOP expression, and cell death, all of which were prevented by the calpain inhibitor PD150606, as well as CHOP silencing. Conclusions: Over-expression of calpastatin ameliorates MI-induced myocardial injury in mice. These protective effects of calpastatin are partially achieved through suppression of the ER stress/CHOP 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) | 0.001 | 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".