Postconditioning fails to prevent radial artery endothelial dysfunction induced by ischemia and reperfusion: evidence from a human in vivo study
Bibliographic record
Abstract
Animal studies have shown that, as compared with unrestricted reperfusion, exposure to brief periods of controlled ischemia (postconditioning) at the end of a prolonged ischemia reduces the extent of tissue damage. We set out to test whether postconditioning can prevent endothelial dysfunction induced by ischemia and reperfusion in a human in vivo model. Ten healthy young non-smoking volunteers were enrolled in this cross-over, controlled, investigator-blinded study. Subjects were exposed to 15 min of forearm ischemia followed by either unrestricted reperfusion or postconditioning (3 periods of 20 s of ischemia separated by 10 s of reperfusion). Endothelium-dependent flow-mediated dilation (FMD) was measured at the level of the radial artery before and after ischemia (with or without postconditioning). Forearm ischemia blunted FMD in both study visits (unrestricted reperfusion visit: before ischemia, 7.7% +/- 1.3%; after ischemia, 2.5% +/- 1.4%; and postconditioning visit: before, 7.3% +/- 1.2%; after, 2.6 +/- 1.6%; P < 0.05 for both, P = not significant (NS) between visits). In contrast with data from animal studies, postconditioning (20 s ischemia-- 10 s reperfusion repeated 3 times) does not limit post-ischemic endothelial dysfunction in this human in vivo model. Further human studies are necessary to evaluate other reperfusion protocols in an attempt to limit post-ischemic tissue damage.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".