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December 1, 2022

Automated preclinical detection of mechanical pain hypersensitivity and analgesia

Zihe Zhang, David P. Roberson, Masakazu Kotoda, Bruno Boivin, James P. Bohnslav, Rafael González-Cano, David A. Yarmolinsky, Bruna Lenfers Turnes, Nivanthika K. Wimalasena, Shay Q. Neufeld, Lee B. Barrett, Nara L.M. Quintaño, Victor Fattori, Daniel G. Taub, Alexander B. Wiltschko, Nick A. Andrews, Christopher D. Harvey, Sandeep Robert Datta, Clifford J. Woolf (2022)

Automated preclinical detection of mechanical pain hypersensitivity and analgesia

Pain, 163 (2022) 2326–2336

The lack of sensitive and robust behavioral assessments of pain in preclinical models has been a major limitation for both pain research and the development of novel analgesics. Here, we demonstrate a novel data acquisition and analysis platform that provides automated, quantitative, and objective measures of naturalistic rodent behavior in an observer-independent and unbiased fashion. The technology records freely behaving mice, in the dark, over extended periods for continuous acquisition of 2 parallel video data streams: (1) near-infrared frustrated total internal reflection for detecting the degree, force, and timing of surface contact and (2) simultaneous ongoing video graphing of whole-body pose. Using machine vision and machine learning, we automatically extract and quantify behavioral features from these data to reveal moment-by-moment changes that capture the internal pain state of rodents in multiple pain models. We show that these voluntary pain-related behaviors are reversible by analgesics and that analgesia can be automatically and objectively differentiated from sedation. Finally, we used this approach to generate a paw luminance ratio measure that is sensitive in capturing dynamic mechanical hypersensitivity over a period and scalable for high- throughput preclinical analgesic efficacy assessment.

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HISTORY SHOWS AGAIN AND AGAIN HOW NATURE POINTS OUT THE FOLLY OF MEN – “GODZILLA,” BLUE OYSTER CULT

Sandeep Robert Datta, MD, Ph.D Department of Neurobiology Harvard Medical School