Hyperglycemia Exacerbates Burn-Induced Liver Inflammation via Noncanonical Nuclear Factor-κB Pathway Activation
Bibliographic record
Abstract
Hyperglycemia and inflammation are hallmarks of burn injury. In this study, we used a rat model of hyperglycemia and burn injury to investigate the effects of hyperglycemia on inflammatory responses in the liver. Hyperglycemia was induced in male Sprague-Dawley rats with streptozotocin (STZ) (35-40 mg/kg), followed by a 60% third-degree scald burn injury. Cytokine levels (by multiplex, in cytosolic liver extracts), hormones (by enzyme-linked immunosorbent assay [ELISA], in serum), nuclear factor (NF)-κB protein deoxyribonucleic acid (DNA) binding (by ELISA, in nuclear liver extracts) and liver functional panel (using VetScan, in serum) were measured at different time points up to 7 d after burn injury. Blood glucose significantly increased after burn injury in both groups with different temporal patterns. Hyperglycemic rats were capable of endogenous insulin secretion, which was enhanced significantly versus controls 12 h after burn injury. DNA binding data of liver nuclear extracts showed a robust and significant activation of the noncanonical NF-κB pathway in the hyperglycemic versus control burn animals, including increased NF-κB-inducing kinase expression (p < 0.05). Liver acute-phase proteins and cytokine expression were increased, whereas secretion of constitutive proteins was decreased after burn injury in hyperglycemic versus control animals (p < 0.05). These results indicate that burn injury to the skin rapidly activated canonical and noncanonical NF-κB pathways in the liver. Robust activation of the NF-κB noncanonical pathway was associated with increased expression of inflammatory markers and acute-phase proteins, and impaired glucose metabolism. Hyperglycemia is detrimental to burn outcome by augmenting inflammation mediated by hepatic noncanonical NF-κB pathway activation.
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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 | 0.000 |
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".