PAW, a cost-effective and open-source alternative to commercial rodent running wheels.

HardwareX

Department of Cell Biology and Anatomy, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta T2N 4N1, Canada.

Published: March 2024

AI Article Synopsis

  • Current commercial wheel monitoring systems for rodent activity can be very expensive, costing thousands of dollars, which is a barrier for many researchers.
  • Open-source alternatives exist but often lack adaptability for different projects.
  • The PAW system is a new, affordable ($75 CAD), 3D printable Arduino-based wheel logger that is wireless, easy to assemble, and compatible with various rodent species and wheel designs, making it a valuable tool for researchers.

Article Abstract

Voluntary wheel running is a common measure of general activity in many rodent models across neuroscience and physiology. However, current commercial wheel monitoring systems can be cost-prohibitive to many investigators, with many of these systems requiring investments of thousands of dollars. In recent years, several open-source alternatives have been developed, and while these tools are much more cost effective than commercial system, they often lack the flexibility to be applied to a wide variety of projects. Here, we have developed PAW, a 3D Printable Arduino-based Wheel logger. PAW is wireless, fully self-contained, easy to assemble, and all components necessary for its production can be obtained for only $75 CAD. Furthermore, with its compact internal electronics, the 3D printed casing can be easily modified to be used with a wide variety of running wheel designs for a wide variety of rodent species. Data recorded with the PAW system shows circadian patterns of activity which is expected from mice and is consistent with results found in the literature. Altogether, PAW is a flexible, low-cost system that can be beneficial to a broad range of researchers who study rodent models.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10776975PMC
http://dx.doi.org/10.1016/j.ohx.2023.e00499DOI Listing

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