Repeated Remote Ischemic Postconditioning Protects Against Adverse Left Ventricular Remodeling and Improves Survival in a Rat Model of Myocardial Infarction
Bibliographic record
Abstract
RATIONALE: Remote ischemic conditioning induced by repeated episodes of transient limb ischemia is a clinically applicable method for protecting the heart against injury at the time of reperfusion. OBJECTIVE: To assess the effect of chronic, repeated, remote conditioning on infarct size and long-term remodeling after myocardial infarction. METHODS AND RESULTS: Rats with ischemia/reperfusion injury received different protocols of remote limb conditioning. While a single early episode of remote ischemic conditioning during coronary occlusion (perconditioning) resulted in a decrease in infarct size on both day 4 and day 28, when it was repeated (postconditioning) intermittently (every 3 days) and intensively (every day), it was not associated with a further decrease in infarct size. However, the protection against adverse remodeling offered by a single episode of limb perconditioning was further enhanced by repeated remote postconditioning therapy in a dose-dependent manner. In separate experiments there was a dose-dependent improvement in survival at 84 days by Kaplan-Meier analysis. CONCLUSIONS: Whereas a single early episode of remote perconditioning reduces infarct size, repeated remote postconditioning further reduces adverse LV remodeling and improves survival in a dose-dependent fashion. These data may have clinical implications for the treatment of patients with evolving myocardial infarction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".