Inflammatory Cascades Driven by Tumor Necrosis Factor-Alpha Play a Major Role in the Progression of Acute Liver Failure and Its Neurological Complications
Bibliographic record
Abstract
BACKGROUND/AIMS: Acute liver failure (ALF) due to ischemic or toxic liver injury is a clinical condition that results from massive loss of hepatocytes and may lead to hepatic encephalopathy (HE), a serious neuropsychiatric complication. Although increased expression of tumor necrosis factor-alpha (TNF-α) in liver, plasma and brain has been observed, conflicting results exist concerning its roles in drug-induced liver injury and on the progression of HE. The present study aimed to investigate the therapeutic value of etanercept, a TNF-α neutralizing molecule, on the progression of liver injury and HE in mice with ALF resulting from azoxymethane (AOM) hepatotoxicity. METHODS/PRINCIPAL FINDINGS: Mice were administered saline or etanercept (10 mg/kg; i.p.) 30 minutes prior to, or up to 6 h after AOM. Etanercept-treated ALF mice were sacrificed in parallel with vehicle-treated comatose ALF mice and controls. AOM induced severe hepatic necrosis, leading to HE, and etanercept administered prior or up to 3 h after AOM significantly delayed the onset of coma stages of HE. Etanercept pretreatment attenuated AOM-induced liver injury, as assessed by histological examination, plasma ammonia and transaminase levels, and by hepatic glutathione content. Peripheral inflammation was significantly reduced by etanercept as shown by decreased plasma IL-6 (4.1-fold; p<0.001) and CD40L levels (3.7-fold; p<0.001) compared to saline-treated ALF mice. Etanercept also decreased IL-6 levels in brain (1.2-fold; p<0.05), attenuated microglial activation (assessed by OX-42 immunoreactivity), and increased brain glutathione concentrations. CONCLUSIONS: These results indicate that systemic sequestration of TNF-α attenuates both peripheral and cerebral inflammation leading to delayed progression of liver disease and HE in mice with ALF due to toxic liver injury. These results suggest that etanercept may provide a novel therapeutic approach for the management of ALF patients awaiting liver transplantation.
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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) | 0.000 | 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".