Automated home-cage-like monitoring for assessing innate behaviors in a murine hangover model
Year: 2025
Type: article
Abstract: The prevalence of alcohol consumption among the younger generation remains alarmingly high. A hangover is a common short-term consequence observed after consuming alcohol. To effectively study alcohol-induced hangovers, reliable and translational animal models, along with appropriate testing methods, are required. While several testing approaches have been used in hangover-induced mice, they often fail to assess innate behaviors comprehensively and are limited by short observation periods. Although existing studies have developed methods to assess hangover-related behaviors in rodents, few have focused on innate behaviors. This study aimed to establish a model for assessing the innate behaviors of hangover-induced mice using automated home-cage-like behavioral monitoring. Mice were intraperitoneally injected with ethanol at doses of 3, 2, or 1 g/kg, followed by behavioral assessments, including exploratory actions and long-term home-cage-like behaviors during both day and night phases. Results showed a significant reduction in mobile behaviors (climbing, locomotion, rearing), speed, and distance traveled, along with increased immobility in both exploratory and long-term home-cage-like assessments. Furthermore, there was a significant decrease in exploratory behaviors and long-term home-cage-like activities, which were linked to hangover symptoms. This study provides a preliminary approach for assessing hangover behaviors in mice using automated behavioral monitoring, ensuring improved animal welfare, optimised timing, and extended assessment durations. Hence, we propose automated home-cage-like behavioral assessment as an exploratory model for evaluating hangover behaviors in mice, which may serve as a useful tool for future research on the therapeutic efficacy of anti-hangover compounds.
Source: Scientific Reports
Institutions: Chulalongkorn University, University of Ruhuna
Language: English
Subfield: Pathology and Forensic Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funder: Chulalongkorn University
Award: G1145120748
OpenAlex ID: W4413105220
