Remote Ischemic Preconditioning of the Recipient Reduces Myocardial Ischemia-Reperfusion Injury of the Denervated Donor Heart via a Katp Channel-Dependent Mechanism
Bibliographic record
Abstract
BACKGROUND: We assess whether remote ischemic preconditioning (rIPC) of the recipient can modify ischemia-reperfusion (IR) injury in the donor heart following orthotopic heart transplantation from brain dead donors and to examine potential mechanisms of protection. METHODS: Sixteen pigs weighing from 26 to 34.2 (mean 29.2) kg, randomized to control group (n=5), ischemic preconditioning (rIPC) group (n=6), and to receive rIPC with prior glibenclamide administration (Glib + rIPC) group (n=5) underwent orthotopic heart transplantation with the support of hypothermic (32 degrees C) cardiopulmonary bypass (CPB). The hearts were harvested from donor animal rendered brain dead by balloon compression via a craniotomy. Preconditioning of the recipients was induced by four 5-min cycles of lower limb ischemia. Myocardial infarction (MI) was induced following heart transplantation by 30 min of left anterior descending (LAD) artery occlusion following by 2 hr of regional reperfusion. The extent of myocardial infarction was assessed by triphenyltetrazolium (TTC) staining. RESULTS: Preconditioning of the recipient reduced the mass of MI (6.75+/-6.3 g in rIPC vs. 18.1+/-5.8 g in control, P=0.01), MI to area at risk (ARR) mass ratio by 57% (15.6%+/-15.2% vs. 36.3%+/-13.4%, P=0.04). The protective effect of preconditioning was abolished by pretreatment with glibenclamide. CONCLUSIONS: Remote ischemic preconditioning of the recipient, decreases ischemia-reperfusion injury in the brain dead donor heart following orthotopic heart transplantation via a Katp channel-dependent mechanism. This study suggests that a circulating effector persists after the rIPC stimulus is applied, and excludes an ongoing afferent neurogenic mechanism of cardioprotection.
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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) | 0.000 | 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) | 0.003 | 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".