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Identification of distinct immune signatures in inclusion body myositis by peripheral blood immunophenotyping using machine learning models

Clin Transl Immunology. 2024-04; 
Emily McLeish, Anuradha Sooda, Nataliya Slater, Kelly Beer, Ian Cooper, Frank L Mastaglia, Merrilee Needham, Jerome D Coudert
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Proteins, Expression, Isolation and Analysis … Cell lysate was mixed with protein A beads (GenScript; L00273) and anti-PRMT6 antibody at 4 C overnight with gentle rotation. The beads were washed three times with ice-cold lysis … Get A Quote

摘要

unassigned: Inclusion body myositis (IBM) is a progressive late-onset muscle disease characterised by preferential weakness of quadriceps femoris and finger flexors, with elusive causes involving immune, degenerative, genetic and age-related factors. Overlapping with normal muscle ageing makes diagnosis and prognosis problematic. unassigned: We characterised peripheral blood leucocytes in 81 IBM patients and 45 healthy controls using flow cytometry. Using a random forest classifier, we identified immune changes in IBM compared to HC. K-means clustering and the random forest one-versus-rest model classified patients into three immunophenotypic clusters. Functional outcome measures including mTUG, 2MWT, IBM-FRS, ... More

關鍵詞

AI, IBM, inflammatory myopathies, machine learning, random forest
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