The remote ischemic preconditioning stimulus modifies inflammatory gene expression in humans
Bibliographic record
Abstract
Remote ischemic preconditioning (IPC) reduces tissue injury caused by ischemia-reperfusion (IR) in distant organs. We tested the hypothesis that remote IPC (rIPC) modifies inflammatory gene transcription in humans. Using a microarray method, we demonstrated that a simple model of brief forearm ischemia suppresses proinflammatory gene expression in circulating leukocytes. Genes encoding key proteins involved in cytokine synthesis, leukocyte chemotaxis, adhesion and migration, exocytosis, innate immunity signaling pathways, and apoptosis were all suppressed within 15 min (early phase IPC) and more so after 24 h (second window IPC). Changes in leukocyte CD11b expression measured by flow cytometry mirrored this pattern, with there being a significant (P = 0.01) reduction at 24 h. The results of this study show that the rIPC stimulus modifies leukocyte inflammatory gene expression. This effect may contribute to the protective effect of IPC against IR injury and may have broader implications in other inflammatory processes. This is the first study of human gene expression following rIPC stimulus. rIPC stimulus suppressed proinflammatory gene transcription in human leukocytes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".