ArticleCell reports methods2023
Reproducible and fully automated testing of nocifensive behavior in mice.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Deep Learning-Based Quantification of Spontaneous Pain Behaviours in Mice.European journal of pain (London, England) · 2026Article
- Nociceptor circadian clock genes control excitability and pain perception in mice in a sex- and time-dependent manner.Nature communications · 2026Article
- Article
- Behavioral assessment of pain in rodents: advances from evoked responses to spontaneous states and multimodal approaches.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Remote automated delivery of mechanical stimuli coupled to brain recordings in behaving mice.eLife · 2025Article
- Article
- A machine learning tool with light-based image analysis for automatic classification of 3D pain behaviors.Cell reports methods · 2025Article
- Discordance between preclinical and clinical testing of Na V 1.7-selective inhibitors for pain.Pain · 2025Review
- Encoding of Vibrotactile Stimuli by Mechanoreceptors in Rodent Glabrous Skin.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- From facial expressions to algorithms: a narrative review of animal pain recognition technologies.Frontiers in veterinary science · 2024Review
- An automaton for preclinical pain testing.Cell reports methods · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pain in rodents is often inferred from their withdrawal from noxious stimulation. Threshold stimulus intensity or response latency is used to quantify pain sensitivity. This usually involves applying stimuli by hand and measuring responses by eye, which limits reproducibility and throughput. We describe a device that standardizes and automates pain testing by providing computer-controlled aiming, stimulation, and response measurement. Optogenetic and thermal stimuli are applied using blue and infrared light, respectively. Precise mechanical stimulation is also demonstrated. Reflectance of red light is used to measure paw withdrawal with millisecond precision. We show that consistent stimulus delivery is crucial for resolving stimulus-dependent variations in withdrawal and for testing with sustained stimuli. Moreover, substage video reveals "spontaneous" behaviors for consideration alongside withdrawal metrics to better assess the pain experience. The entire process was automated using machine learning. RAMalgo (reproducible automated multimodal algometry) improves the standardization, comprehensiveness, and throughput of preclinical pain testing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.