Blockade of KATP Channels Reduces Endothelial Hyperpolarization and Leukocyte Recruitment upon Reperfusion After Hypoxia
Bibliographic record
Abstract
Ischemia/reperfusion injury in renal transplantation leads to slow or initial nonfunction, and predisposes to acute and chronic rejection. In fact, severe ischemia reperfusion injury can significantly reduce graft survival, even with modern immunosuppressive agents. One of the mechanisms by which ischemia/reperfusion causes injury is activation of endothelial cells resulting in inflammation. Although several therapies can be used to prevent leukocyte recruitment to ischemic vessels (e.g. antiadhesion molecule antibodies), there have been no clinical treatments reported that can prevent initial immediate neutrophil recruitment upon reperfusion. Using intravital microscopy, we describe abrogation of immediate neutrophil recruitment to ischemic microvessels by the K(ATP) antagonist glibenclamide (Glyburide). Further, we show that glibenclamide can reduce leukocyte recruitment in vitro under physiologic flow conditions. ATP-regulated potassium channels (K(ATP)) are important in the control of cell membrane polarization. Here we describe profound hyperpolarization of endothelial cells during hypoxia, and the reduction of this hyperpolarization using glibenclamide. These findings suggest that control of endothelial membrane potential during ischemia may be an important therapeutic tool in avoiding ischemia/reperfusion injury, and therefore, enhancing transplant long-term function.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".