Chronic pharmacological preconditioning against ischemia
Bibliographic record
Abstract
Despite decades of research and thousands of experimental publications, acute preconditioning strategies have yet to be implemented in clinical practice. While some have attributed this to a failure of the experimental studies to mimic the clinical environment, others have suggested that acute preconditioning strategies themselves may possess physiological limitations. In particular, there is evidence to suggest a reduced efficacy of acute preconditioning in the aged heart and in disease states, such as diabetes, hypertension, hyperlipidemia, and atherosclerosis. In addition, pharmacologic agent commonly used in clinical practice, such as sulfonylureas and non-steroidal anti-inflammatory agents may interfere with acute preconditioning signaling pathways. Such considerations may preclude the translation of acute preconditioning strategies to the clinical setting. This has led some to shift attention to alternate strategies of cardioprotection, one such strategy being the possibility of generating a prolonged state of cardioprotection. Although preliminary, studies to date have suggested that sustained preconditioning strategies may not be associated with the same drawbacks as acute preconditioning. Further, cardioprotective signaling pathways that elicit the sustained preconditioning response may be distinct from acute signaling pathways, which permit pharmacologic targeting of these pathways in the future. Additionally, sustained preconditioning strategies may be clinically applicable in the setting of acute myocardial infarction, a setting where acute preconditioning strategies are inherently limited. This review will briefly discuss the current data regarding sustained preconditioning strategies, including those in humans, and discuss the goal of future studies in this setting.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".