Remote conditioning the heart overview: translatability and mechanism
Bibliographic record
Abstract
Conditioning the heart to resist predictable and unpredictable ischaemia-reperfusion (IR) injury is one of the fastest growing areas of bench to bedside research within cardiology. Basic science has provided important insights into signalling pathways and protective mechanisms in the heart, and a growing number of clinical studies have, with important exceptions, shown the potential applicability and beneficial effect of various mechanical conditioning strategies achieved by intermittent short-lasting-induced ischaemia of the heart itself or a remote tissue. Remote ischaemic conditioning (RIC) in particular has been utilized in a number of clinical settings with promising results. However, while many novel 'downstream' mechanisms of RIC have been discovered, translation to pharmacological conditioning has not yet been convincingly demonstrated in clinical studies. One explanation for this apparent failure may be that most pharmacological approaches mimic a single instrument in a complex orchestra activated by mechanical conditioning. Recent studies, however, provide important insights into upstream events occurring in RIC, which may allow for development of drugs activating more complex systems of biological organ protection. With this review, we will systematically examine the first generation of pharmacological cardioprotection studies and then provide a summary of the recent discoveries in basic science that could illuminate the path towards more advanced approaches in the next generation of pharmacological agents that may work by reproducing the diverse effects of RIC, thereby providing protection against IR injury.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".