Hepatic Apoptosis Postburn Is Mediated by C-Jun N-Terminal Kinase 2
Bibliographic record
Abstract
The trauma of a severe burn injury induces a hypermetabolic response that increases morbidity and mortality. Previously, our group showed that insulin resistance after burn injury is associated with endoplasmic reticulum (ER) stress. Evidence suggests that c-Jun N-terminal kinase (JNK) 2 may be involved in ER stress-induced apoptosis. Here, we hypothesized that JNK2 contributes to the apoptotic response after burn injury downstream of ER stress. To test this, we compared JNK2 knockout mice (-/-) with wild-type mice after inducing a 30% total body surface area thermal injury. Animals were killed after 1, 3, and 5 days. Inflammatory cytokines in the blood were measured by multiplex analysis. Hepatic ER stress and insulin signaling were assessed by Western blotting, and insulin resistance was measured by a peritoneal glucose tolerance test. Apoptosis in the liver was quantified by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling staining. Liver function was quantified by aspartate aminotransferase and alanine aminotransferase activity assays. Endoplasmic reticulum stress increased after burn in both JNK2 and wild-type mice, indicating that JNK2 activation is downstream of ER stress. Knockout of JNK2 did not affect serum inflammatory cytokines; however, the increase in interleukin 6 mRNA expression was prevented in the knockouts. Serum insulin did not significantly increase in the JNK2 group. On the other hand, insulin signaling (PI3K/Akt pathway) and glucose tolerance tests did not improve in JNK2. As expected, apoptosis in the liver increased after burn injury in wild-type mice but not in JNK2. Aspartate aminotransferase/alanine aminotransferase activity revealed that liver function recovered more quickly in JNK2. This study indicates that JNK2 is a central mediator of hepatic apoptosis after a severe burn.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".