Is Intrinsic Cardioprotection a Laboratory Phenomenon or a Clinically Relevant Tool to Salvage the Failing Heart?
Bibliographic record
Abstract
Cardiovascular diseases, especially ischemic heart disease, as a leading cause of heart failure (HF) and mortality, will not reduce over the coming decades despite the progress in pharmacotherapy, interventional cardiology, and surgery. Although patients surviving acute myocardial infarction live longer, alteration of heart function will later lead to HF. Its rising incidence represents a danger, especially among the elderly, with data showing more unfavorable results among females than among males. Experiments revealed an infarct-sparing effect of ischemic "preconditioning" (IPC) as the most robust form of innate cardioprotection based on the heart's adaptation to moderate stress, increasing its resistance to severe insults. However, translation to clinical practice is limited by technical requirements and limited time. Novel forms of adaptive interventions, such as "remote" IPC, have already been applied in patients, albeit with different effectiveness. Cardiac ischemic tolerance can also be increased by other noninvasive approaches, such as adaptation to hypoxia- or exercise-induced preconditioning. Although their molecular mechanisms are not yet fully understood, some noninvasive modalities appear to be promising novel strategies for fighting HF through targeting its numerous mechanisms. In this review, we will discuss the molecular mechanisms of heart injury and repair, as well as interventions that have potential to be used in the treatment of patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".