Combined local ischemic postconditioning and remote perconditioning recapitulate cardioprotective effects of local ischemic preconditioning
Bibliographic record
Abstract
Ischemic postconditioning (PostC) and perconditioning (PerC) provide practical methods for protecting the heart against ischemia-reperfusion (I/R) injury, but their combined effects have not been studied in detail. Using an in vivo rat I/R model, we tested 1) whether additive effects were produced when local PostC was preceded by varying doses of remote PerC, and whether the optimal PostC+PerC regime is additive to local ischemic preconditioning (IPC), and 2) how combined PostC+PerC alters the activity of the reperfusion injury salvage kinase pathway. The optimal combination of PerC and PostC therapy was produced by PerC delivered with four cycles of 5 min of limb ischemia followed by 5-min reperfusion. This resulted in lower infarct size (22.56 +/- 4.45%) compared with rats with PostC alone (29.39 +/- 3.66%) and PerC alone (33.49 +/- 5.81%) and complementary differences in the generation of reactive oxygen species and apoptotic signaling. However, this optimal combination of PostC+PerC resulted in protection similar to local IPC alone (18.8 +/- 2.54%, P = 0.13), and when added to IPC there was no additional protection (19.62 +/- 2.89%, P = 0.675). Akt and ERK1/2 phosphorylation was induced by PostC and PerC and maximally by combined PostC+PerC treatment, and protection was abolished by phosphatidylinositol 3-kinase or ERK1/2 inhibitors. This study shows that neither PostC nor a maximized "dose" of PerC leads to optimal kinase signaling or cardioprotection compared with IPC alone. However, combined PostC+PerC may result in complementary effects on kinase signaling to recapitulate the effects of local IPC. Finally, combined PostC+PerC is not additive to IPC, suggesting that each works via a common 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) | 0.001 | 0.001 |
| 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".