Molecular Basis of Cardioprotection by Erythropoietin
Bibliographic record
Abstract
Erythropoietin (EPO), a glycoprotein essential for red blood cell production acts on several non-erythropoietic tissues. The EPO receptor (EPOR) is expressed in a variety of cell types including neurons, endothelial cells, and cardiomyocytes. Recently, a number of reports have indicated that EPO preserves heart function in models of cardiac ischemia-reperfusion (I/R) injury. A diverse range of cellular/physiological processes is modulated by EPO and are thought to play a role in the preservation of heart function. In vivo, reductions in infarct size, apoptosis, oxidative stress, and inflammation have been reported. More recently, increases in angiogenesis and reductions in arrhythmias have been implicated in the cardioprotective effects of EPO. In vitro, EPO reduces apoptosis, oxidative stress, and inflammation. These cardioprotective effects appear to be mediated by a receptor interaction that is distinct from that responsible for EPO ’ s erythropoietic effects. Downstream of receptor interactions, the activation of phosphatidylinositol-3 kinase (PI3-kinase) and Akt appear to mediate many of EPOs cardioprotective effects. However, there is emerging evidence for Akt-independent mechanisms of cardioprotection including the inhibition of glycogen synthase kinase 3β, as well as the activation of potassium channels, protein kinase C, and protein kinases such as ERK1/2. This review focuses on the effects of EPO in the heart and the molecular mechanisms by which EPO achieves its cardioprotective effects. Keywords: Erythropoietin, cardioprotection, myocardial infarction, ischemia and reperfusion, apoptosis, nitric oxide, signal transduction, angiogenesis
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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) | 0.001 | 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".