RT Journal Article
SR Electronic
T1 Skeletal Muscle Remodeling in Immobilized Patients: Determined Using a Parameter Estimation Histomorphometric Approach
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.06.17.157438
DO 10.1101/2020.06.17.157438
A1 Brent Formosa
A1 Asiri Liyanaarachchi
A1 Samantha Silvers
A1 Domenico L. Gatti
A1 Lars Larsson
A1 Suzan Arslanturk
A1 Bhanu P. Jena
YR 2020
UL http://biorxiv.org/content/early/2020/06/18/2020.06.17.157438.abstract
AB Skeletal muscle biopsy commonly used for light microscopic, electron microscopic and biochemical and transcriptional evaluation remains the gold standard for establishing the etiology of a myopathy. While most myopathies exhibit one or more phenotypes, early stages or several metabolic myopathies often exhibit normal muscle morphology, making diagnosis difficult. In such cases where standard staining techniques fail to offer definitive diagnostic information, a combination of expensive and time-consuming electron microscopy and biochemical testing is required to provide definitive diagnosis. As a step toward overcoming these limitations in diagnostic pathology of skeletal muscle tissue, here we report the application of parameter estimation machine learning approaches on immunofluorescent images of human skeletal muscle tissue acquired using fluorescent microscopy. The machine learning morphometric approach enables the recognition of fine cellular changes in skeletal muscle tissue, allowing determination of skeletal muscle remodeling as a consequence of immobilization.Competing Interest StatementThe authors have declared no competing interest.