Activation of NF-κB is a critical element in the antiapoptotic effect of anesthetic preconditioning
Bibliographic record
Abstract
Anesthetic preconditioning (APC), defined as brief exposure to inhalational anesthetics before cardiac ischemia-reperfusion (I/R), limits injury in both animal models and in humans. APC can result in the production of reactive oxygen species (ROS), and prior work has shown that APC can modify activation of NF-kappaB during I/R, with consequent reduction in the expression of inflammatory mediators. However, the role of NF-kappaB activation before I/R is unknown. Therefore, these experiments tested the hypothesis that APC-induced ROS results in activation of NF-kappaB before I/R, with consequent increased expression of antiapoptotic proteins such as Bcl-2 and decreased apoptosis. Experiments utilized an established perfused heart rat model of sevoflurane APC and I/R. The role of NF-kappaB was defined by a novel method of transient inhibition of the regulatory kinase IKK using the reversible inhibitor SC-514. In addition to functional measures of left ventricular developed and end-diastolic pressure, phosphorylation of IkappaBalpha and activation of NF-kappaB were measured along with cytosolic protein content of Bcl-2, release of cytochrome c, and degradation of caspase-3. APC resulted in ROS-dependent phosphorylation of IkappaBalpha and activation of NF-kappaB before I/R. APC also increased the expression of Bcl-2 before I/R. In addition to functional protection following I/R, APC resulted in lower release of cytochrome c and caspase-3 degradation. These protective effects of APC were abolished by transient inhibition of IkappaBalpha phosphorylation and NF-kappaB activation by SC-514 followed by washout. ROS-dependent activation of NF-kappaB by APC before I/R is a critical element in the protective effect of APC. APC reduces apoptosis and functional impairment by increasing Bcl-2 expression before I/R. Interventions that increase NF-kappaB activation before I/R should protect hearts from I/R injury.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".