Acute liver failure after amanitin poisoning: a porcine model to detect prognostic markers for liver regeneration
- PMID: 26202414
- DOI: 10.1007/s12072-013-9491-7
Acute liver failure after amanitin poisoning: a porcine model to detect prognostic markers for liver regeneration
Abstract
Purpose: Over 90 % of fatal mushroom poisoning occurs after ingestion of amanitin-containing species. This study aimed to investigate markers indicating spontaneous liver regeneration in a porcine acute liver failure (ALF) model after α-amanitin intoxication.
Methods: German landrace pigs received either 0.15 mg/kg (n = 5) α-amanitin intravenously or 0.35 mg/kg (n = 5) intraportally. Pigs were invasively monitored and kept under general anesthesia throughout the experiment. Laboratory parameters were analyzed every 8 h.
Results: ALF occurred in all animals (10/10) 41 ± 3 h after intoxication. All pigs receiving 0.35 mg/kg α-amanitin and one pig receiving 0.15 mg/kg α-amanitin died 57 ± 16 h after the primary onset of ALF. Four pigs of the 0.15 mg/kg intoxication group recovered spontaneously from ALF after 56 ± 6 h. Starting at 32 h after intoxication, significantly higher values of albumin and total plasma protein could be measured in surviving animals (p < 0.05). A significant temporary increase in the tumor necrosis factor alpha (TNF-α) plasma concentration was detected 40-80 h after intoxication in recovering animals (p < 0.05).
Conclusions: This porcine model represents a novel tool to analyse multiple aspects of liver regeneration following α-amanitin poisoning to allow early discrimination between a fatal course and survivors. Decreased albumin and total plasma protein concentrations in the early intoxication phase indicated a lethal outcome, while an increase in the TNF-α plasma concentration was identified as the earliest prognostic plasma marker detecting liver regeneration a long time before liver function was biochemically and clinically impaired.
Keywords: Acute liver failure; Amanitin poisoning; Liver regeneration; Porcine model; Prognostic factors.
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