Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning.

BACKGROUND: It is not clear how specific gait measures reflect disease severity across the disease spectrum in Parkinson's disease (PD). OBJECTIVE: To identify the gait and mobility measures that are most sensitive and reflective of PD motor stages and determine the optimal sensor location in each disease stage. METHODS: Cross-sectional wearable-sensor records were collected in 332 patients with PD (Hoehn and Yahr scale I-III) and 100 age-matched healthy controls. Sensors were adhered to the participant's lower back, bilateral ankles, and wrists. Study participants walked in a ~15-meter corridor for 1 minute under two walking conditions: (1) preferred, usual walking speed and (2) walking while engaging in a cognitive task (dual-task). A subgroup (n = 303, 67% PD) also performed the Timed Up and Go test. Multiple machine-learning feature selection and classification algorithms were applied to discriminate between controls and PD and between the different PD severity stages. RESULTS: High discriminatory values were found between motor disease stages with mean sensitivity in the range 72%-83%, specificity 69%-80%, and area under the curve (AUC) 0.76-0.90. Measures from upper-limb sensors best discriminated controls from early PD, turning measures obtained from the trunk sensor were prominent in mid-stage PD, and stride timing and regularity were discriminative in more advanced stages. CONCLUSIONS: Applying machine-learning to multiple, wearable-derived features reveals that different measures of gait and mobility are associated with and discriminate distinct stages of PD. These disparate feature sets can augment the objective monitoring of disease progression and may be useful for cohort selection and power analyses in clinical trials of PD. © 2021 International Parkinson and Movement Disorder Society.

Effects of accumulation of lipid droplets on load transfer between and within adipocytes.

Adipogenesis, a process of cell proliferation followed by the accumulation of lipid droplets (LDs), is accompanied by morphological changes in adipocytes, leading to a gradual rise in the structural stiffness of these cells. The increase in cellular structural stiffness can potentially influence the localized deformations of adjacent adipocytes in weight-bearing fat tissues, which, based on previous work, may accelerate intracytoplasmatic lipid production to form even larger and more tightly packed intracellular LDs. This process is based on mechanotransduction phenomena which are hypothesized (again, following empirical studies), to play a critical role in "en mass" adipocyte hypertrophy, and hence are important to characterize through computational modeling. Accordingly, we examined here how maturing adipocytes may affect localized loads acting on adjacent immature cells, using a set of finite element models of adipocytes embedded in an extracellular matrix. The peak strain energy density at the plasma membrane (PM) of the adipocytes, when constructs were externally loaded, was found to depend on the levels of lipid accumulation in the neighboring cells if the external compressive and shear deformations were large enough ([Formula: see text] and [Formula: see text], respectively). The mechanosignaling transduces through the PM and could therefore affect intracellular pathways to produce more lipid contents. Our results support the theory of deformation-induced differentiation in adipocytes. The findings are thus relevant in the context of a sedentary lifestyle, in which sustained deformations of weight-bearing adipose tissues may activate a positive feedback loop that promotes the "en mass" differentiation of cells, which subsequently increases the total mass of living fat tissues.