Roles of nitric oxide, superoxide, and peroxynitrite in myocardial ischemia-reperfusion injury and ischemic preconditioning
Bibliographic record
Abstract
Ischemic heart disease, a major cause of mortality in industrialized countries, is characterized by insufficient blood supply to certain regions of the myocardium which leads to tissue necrosis (infarction). It develops secondary to a variety of disorders such as hypertension, atherosclerosis, dyslipidemia, and diabetes. The treatment of this condition has entered a new era in which mortality can be approximately halved by procedures which allow for the rapid restoration of blood flow (reperfusion), to the ischemie zone of the myocardium. Reperfusion, however, may lead to further complications such as diminished cardiac contractile function (stunning) and arrhythmias. Therefore, development of cardioprotective agents to improve myocardial function, decrease the incidence of arrhythmias, lessen necrotic tissue mass, and delay the onset of necrosis as a result of ischemia-reperfusion is of great clinical importance. The heart was also found to have an inherent ability to adapt to ischemie stress called ischemie preconditioning (PC) [1]. It is a well described adaptive response in which brief exposure of the heart to brief episode(s) of ischemia (PC ischemia) markedly enhances its ability to withstand a subsequent ischemie injury (test ischemia) (see for review see [2]). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".