University of Michigan Health System
3 articles published in JoVE
The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals Shelby R. Svientek1, Justin P. Wisely1, Amir Dehdashtian1, Jarred V. Bratley1, Paul S. Cederna1,2, Stephen W. P. Kemp1,2 1Department of Surgery, Section of Plastic Surgery, The University of Michigan Health System, 2Department of Biomedical Engineering, The University of Michigan, Ann Arbor This manuscript provides an innovative method for developing a biologic peripheral nerve interface termed the Muscle Cuff Regenerative Peripheral Nerve Interface (MC-RPNI). This surgical construct can amplify its associated peripheral nerve's motor efferent signals to facilitate accurate detection of motor intent and the potential control of exoskeleton devices.
Isolation, Fixation, and Immunofluorescence Imaging of Mouse Adrenal Glands Isabella Finco1, Gary D. Hammer1,2,3,4,5 1Department of Internal Medicine (Division of Metabolism, Endocrinology, and Diabetes), University of Michigan Health System, 2Department of Cell and Developmental Biology, University of Michigan Health System, 3Department of Molecular and Integrative Physiology, University of Michigan Health System, 4Endocrine Oncology Program, University of Michigan Health System, 5Comprehensive Cancer Center, University of Michigan Health System Here we present a method to isolate adrenal glands from mice, fix the tissues, section them, and perform immunofluorescence staining.
Determining The Electromyographic Fatigue Threshold Following a Single Visit Exercise Test Sujay S. Galen1, Darren R. Guffey2, Jared W. Coburn3, Moh H. Malek1 1Physical Therapy Program and Integrative Physiology of Exercise Laboratory, Department of Health Care Sciences, Wayne State University, 2Sports Medicine and Physical Therapy, MEDSPORT, University of Michigan Health System, 3Department of Kinesiology, California State University, Fullerton This protocol describes the electromyographic fatigue threshold which demarcates between nonfatiguing and fatiguing exercise workloads. This information could be used to develop a more individualized training program.