A machine learning model for prediction of sarcopenia in patients with Parkinson's Disease.

OBJECTIVE: Patients with Parkinson's disease (PD) have an increased risk of sarcopenia which is expected to negatively affect gait, leading to poor clinical outcomes including falls. In this study, we investigated the gait patterns of patients with PD with and without sarcopenia (sarcopenia and non-sarcopenia groups, respectively) using an app-derived program and explored if gait parameters could be utilized to predict sarcopenia based on machine learning. METHODS: Clinical and sarcopenia profiles were collected from patients with PD at Hoehn and Yahr (HY) stage ≤ 2. Sarcopenia was defined based on the updated criteria of the Asian Working Group for Sarcopenia. The gait patterns of the patients with and without sarcopenia were recorded and analyzed using a smartphone application. The random forest model was applied to predict sarcopenia in patients with PD. RESULTS: Data from 38 patients with PD were obtained, among which 9 (23.7%) were with sarcopenia. Clinical parameters were comparable between the sarcopenia and non-sarcopenia groups. Among various clinical and gait parameters, the average range of motion of the hip joint showed the highest association with sarcopenia. Based on the random forest algorithm, the combined difference in knee and ankle angles from standing still before walking to the maximum angle during walking (Kneeankle_diff), the difference between the angle when standing still before walking and the maximum angle during walking for the ankle (Ankle_dif), and the min angle of the hip joint (Hip_min) were the top three features that best predict sarcopenia. The accuracy of this model was 0.949. CONCLUSIONS: Using smartphone app and machine learning technique, our study revealed gait parameters that are associated with sarcopenia and that help predict sarcopenia in PD. Our study showed potential application of advanced technology in clinical research.

Photoreaction of adsorbed diiodomethane: halide effects of a series of neutral palladium(ii) coordination cages.

A series of Pd6L4-type neutral coordination cages, [Pd6X12L4] (X(-) = Cl(-) and Br(-)), are constructed via self-assembly of (COD)PdCl2 and K2PdBr4 with C3-symmetric N,N',N''-tris(2-pyridinylmethyl)-1,3,5-benzenetricarboxamide (L), respectively. The iodide analogue [Pd6I12L4] is smoothly synthesized from [Pd6Br12L4] in the presence of CH2I2 under mild conditions. The replacement of bromide to iodide in the nanocage system represents a landmark achievement in synthetic-methodology development. The CH2I2 molecules are adsorbed in the order [Pd6I12L4] > [Pd6Br12L4] > [Pd6Cl12L4] and in the "like-attracts-like" pattern, presumably owing to the van der Waals force. Irradiation of [Pd6I12L4]·3.5CH2I2 with 1-methylcyclohexene in chloroform at 350 nm preferentially affords the cyclopropanation product.