Noninvasive approach to mend the broken heart: Is “remote conditioning” a promising strategy for application in humans?
Bibliographic record
Abstract
Currently, there are no satisfactory interventions to protect the heart against the detrimental effects of ischemia-reperfusion injury. Although ischemic preconditioning (PC) is the most powerful form of intrinsic cardioprotection, its application in humans is limited to planned interventions, due to its short duration and technical requirements. However, many organs/tissues are capable of producing "remote" PC (RPC) when subjected to brief bouts of ischemia-reperfusion. RPC was first described in the heart where brief ischemia in one territory led to protection in other area. Later on, RPC started to be used in patients with acute myocardial infarction, albeit with ambiguous results. It is hypothesized that the connection between the signal triggered in remote organ and protection induced in the heart can be mediated by humoral and neural pathways, as well as via systemic response to short sublethal ischemia. However, although RPC has a potentially important clinical role, our understanding of the mechanistic pathways linking the local stimulus to the remote organ remains incomplete. Nevertheless, RPC appears as a cost-effective and easily performed intervention. Elucidation of protective mechanisms activated in the remote organ may have therapeutic and diagnostic implications in the management of myocardial ischemia and lead to development of pharmacological RPC mimetics.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".