Pharmacologic therapeutics for cardiac reperfusion injury
Bibliographic record
Abstract
Cardiovascular disease is the leading cause of morbidity and mortality in industrial societies, with myocardial infarction as the primary assassin. Pharmacologic agents, including the myocardial cell membrane receptor agonists adenosine, bradykinin/angiotensin-converting enzyme inhibitors, opioids and erythropoietin or the mixed cell membrane and intracellular agonists, glucose insulin potassium, and volatile anesthetics, either clinically or experimentally reduce the extent of myocardial injury when administered just prior to reperfusion. Agents that specifically target proteins, transcription factors or ion channels, including PKC agonists/antagonists, PPAR, Phosphodiesterase-5 inhibitors, 3-Hydroxy-3-methyl glutaryl coenzyme A reductase and the ATP-dependent potassium channel are also promising. However, no agent has been specifically approved to reduce reperfusion injury clinically. In this review, we will discuss the advantages and limitations of agents to combat reperfusion injury, their market development status and findings reported in both clinical and preclinical studies. The molecular pathways activated by these agents that preserve myocardium from reperfusion injury, which appear to commonly involve glycogen synthase kinase 3beta and mitochondrial permeability transition pore inhibition, are also described.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".