ArticlePNAS nexus2025
Long-term scratching analysis of mice using machine learning.
Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Deep behavioral phenotyping reveals novel features in a mouse model of metabolic dysfunction-associated steatohepatitis.Scientific reports · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pruritus is a bothersome symptom that can occur at any time and is caused by a variety of physical and mental disorders. There is an urgent need to elucidate the detailed pathophysiology using animal models. Using image analysis and machine learning, we analyzed the scratching behavior of BALB/c mouse under both light and dark conditions and objectively quantified persistency, strength, and amount of pruritus. A 24-h analysis in naive mice revealed that mice scratched themselves more frequently and longer in the light period than in the dark period, and that scratching behavior in the light period was hard to extinguish. Administration of dinitrofluorobenzene (DNFB) induced persistent and biphasic scratching behavior while the vehicle only induced transient one. Of interest, DNFB-induced scratching behavior occurred even when the mice were resting. These features are revealed for the first time, highlighting the usefulness of automated long-term scratching behavior analysis.
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Registered trials
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