Remote Ischemic Per-Conditioning
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Remote ischemic preconditioning is a phenomenon by which a short period of sublethal ischemia to an organ protects against subsequent ischemia in another organ. We have recently demonstrated that remote ischemic conditioning by transient hind limb ischemia delivered during ischemia and before reperfusion can provide potent cardioprotection, a phenomenon we termed per-conditioning. This study evaluated whether remote ischemic per-conditioning may provide neuroprotection in a clinically relevant rat model of acute ischemic stroke. METHODS: Remote ischemic conditioning by transient limb ischemia was used in a rat transient middle cerebral artery occlusion model of acute stroke. A total of 39 P60 rats were randomly allocated to receive preconditioning, per-conditioning, or sham conditioning. Cerebral ischemia was maintained for 120 minutes followed by reperfusion. The resulting infarct size at 24 hours was quantified using computerized image analysis of 2-3-5-triphenyl tetrazolium chloride-stained brain sections. RESULTS: Compared with control, both pre- and per-conditioning significantly reduced brain infarct size with the more clinically relevant per-conditioning stimulus being superior to preconditioning. CONCLUSIONS: Remote per-conditioning by transient limb ischemia is a facile, clinically relevant stimulus that provides potent neuroprotection in a model of regional brain ischemia-reperfusion injury. Further studies are required to better understand the mechanisms and biology of this response before translation to randomized controlled trials of remote per-conditioning for acute ischemic stroke.
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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) | 0.001 | 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) | 0.014 | 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".