A hacked kitchen scale for quantification of grip strength in rodents
Jan HomolakiD, Davor ViragiD, Ivan KodvanjiD, Ivica MatakiD, Ana Babić PerhočiD, Ana KnezovićiD, Jelena Osmanović BarilariD, Vladimir TrkuljaiD, Melita Šalković‐PetrišićiD
Abstract
Assessment of neuromuscular function is critical for understanding pathophysiological changes related to motor system dysfunction in many rodent disease models. Among methods used for quantification of grip performance in rodents, gauge-based grip strength meters provide the most reliable results, however, such instruments are unaffordable by many laboratories. Here we demonstrate how to build a rodent grip strength apparatus from scratch using a digital kitchen scale, an empty cage, and a microcontroller, with both hardware and software being completely open-source to enable maximal modularity and flexibility of the instrument in concordance with the principles of open-source bioinstrumentation. Furthermore, we test the griPASTA system for assessment of increased muscular rigidity in the proof-of-concept experiment in the rat model of Parkinsons disease induced by intrastriatal administration of 6-hydroxydopamine (6-OHDA). Finally, the importance of bioinstrumental customization is demonstrated by utilizing griPASTA for assessment of trial speed from initial-to-maximal deflection time segments and controlling for its potential confounding effects on the grip strength. Significance StatementNeuromuscular function analyzed by grip strength performance tests is an integral part of motor system neuroscience and neurotoxicology. Strain gauge-based grip strength meters provide the most reliable results, however, commercial solutions are unaffordable by many. Consequently, cheap semi-quantitative tests are often used at the expense of precision and reliability. We propose griPASTA - a simple and robust open-source grip strength platform made from an ordinary kitchen scale. griPASTA could improve the quality and reproducibility of grip strength experiments by enabling researchers to obtain quantitative grip strength data in high resolution using a highly customizable platform.
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