Kidney Ischemia-Reperfusion Elicits Acute Liver Injury and Inflammatory Response
Bibliographic record
Abstract
Ischemia-reperfusion (IR) is a common risk factor that causes acute kidney injury (AKI). AKI is associated with dysfunction of other organs also known as distant organ injury. The liver function is often compromised in patients with AKI and in animal models. However, the underlying mechanisms are not fully understood. Inflammatory response plays an important role in IR-induced tissue injury. Although increased proinflammatory cytokines have been detected in the kidney and the distant organs after renal IR, their original sources remain uncertain. In the present study, we investigated the acute effect of renal IR on hepatic inflammatory cytokine expression and the mechanism involved. Sprague-Dawley rats that were subjected to renal IR (ischemia for 45 min followed by reperfusion for 1 h or 6 h) had increased plasma levels of creatinine, urea, and transaminases, indicating kidney and liver injuries. There was a significant increase in the expression of proinflammatory cytokine mRNA (MCP-1, TNF-α, IL-6) in the kidney and liver in rats with renal IR. This was accompanied by a significant increase in proinflammatory cytokine protein levels in the plasma, kidney, and liver. Activation of a nuclear transcription factor kappa B (NF-κB) was detected in the liver after renal IR. The inflammatory foci and an increased myeloperoxidase (MPO) activity were detected in the liver after renal IR, indicating hepatic inflammatory response and leukocyte infiltration. These results suggest that renal IR can directly activate NF-κB and induce acute production of proinflammatory cytokines in the liver. Renal IR-induced hepatic inflammatory response may contribute to impaired liver function and systemic inflammation.
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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 | 0.001 |
| 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".