Preconditioning prevents alterations in cardiac SR gene expression due to ischemia-reperfusion
Bibliographic record
Abstract
We have previously shown that ischemic preconditioning (IP) improves cardiac performance and sarcoplasmic reticulum (SR) function in hearts subjected to ischemia-reperfusion (I/R). In this study, we examined the effect of IP on I/R-induced changes in gene expression for SR proteins such as the Ca(2+) release channel, Ca(2+) pump ATPase, phospholamban, and calsequestrin in the isolated rat heart. Normal isolated rat hearts exposed to three brief cycles of IP (5-min ischemia and 5-min reperfusion) exhibited a significant decrease in the transcript levels of SR genes. Nonpreconditioned I/R hearts when subjected to 30-min ischemia and 30-min reperfusion showed a marked decrease in mRNA levels for the SR proteins compared with normal hearts; this decrease was attenuated by preconditioning. Although hearts subjected to Ca(2+) paradox (CP) have been shown to exhibit intracellular Ca(2+) overload and SR dysfunction like those in I/R hearts, virtually nothing is known regarding the effect of CP on cardiac SR gene expression. Accordingly, CP (5-min Ca(2+)-free perfusion and 30-min reperfusion with normal medium) was observed to produce dramatic changes in SR gene expression, and the heart failed to contract; these alterations were attenuated by IP. Our results show that 1) both I/R and CP depress SR gene expression in the normal heart, 2) IP attenuates I/R- and CP-induced depression in cardiac function and SR gene expression, and 3) intracellular Ca(2+) overload may play a role in depressing SR gene expression in both I/R and CP hearts.
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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) | 0.001 | 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".