Background Automated and accurate assessment for postural abnormalities is necessary to monitor the clinical progress of Parkinson’s disease (PD). The combination of depth camera and machine learning makes this purpose possible. Methods Kinect was used to collect the postural images from 70 PD patients. The collected images were processed to extract three-dimensional body joints, which were then converted to two-dimensional body joints to obtain eight quantified coronal and sagittal features (F1-F8) of the trunk. The decision tree classifier was carried out over a data set established by the collected features and the corresponding doctors’ MDS-UPDRS-III 3.13 (the 13th item of the third part of Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale) scores. An objective function was implanted to further improve the human–machine consistency. Results The automated grading of postural abnormalities for PD patients was realized with only six selected features. The intraclass correlation coefficient (ICC) between the machine’s and doctors’ score was 0.940 (95%CI, 0.905–0.962), meaning the machine was highly consistent with the doctors’ judgement. Besides, the decision tree classifier performed outstandingly, reaching 90.0% of accuracy, 95.7% of specificity and 89.1% of sensitivity in rating postural severity. Conclusions We developed an intelligent evaluation system to provide accurate and automated assessment of trunk postural abnormalities in PD patients. This study demonstrates the practicability of our proposed method in the clinical scenario to help making the medical decision about PD.
T lymphocytes are involved in the pathogenesis of Parkinson’s disease (PD), while the heterogeneity of T-cell subpopulations remains elusive. In this study, we analyzed up to 22 subpopulations of T lymphocytes in 115 PD patients and 60 matched healthy controls (HC) using flow cytometry. We found that PD patients exhibited decreased naïve CD8+ T cells (CD3+ CD8+ CD45RA+ CD45RO−) and increased late-differentiated CD4+ T cells (CD3+ CD4+ CD28− CD27−), compared to HC, which were not affected by anti-parkinsonism medication administration. The proportion of naïve CD8+ T cells in PD patients was positively correlated with their severity of autonomic dysfunction and psychiatric complications, but negatively associated with the severity of rapid eye movement and sleep behavior disorder. The proportion of late-differentiated CD4+ T cells was negatively correlated with the onset age of the disease. We further developed individualized PD risk prediction models with high reliability and accuracy on the base of the T lymphocyte subpopulations. These data suggest that peripheral cellular immunity is disturbed in PD patients, and changes in CD8+ T cells and late-differentiated CD4+ T cells are representative and significant. Therefore, we recommend naïve CD8 + and late-differentiated CD4+ T cells as candidates for multicentric clinical study and pathomechanism study of PD.
Objective To quantify bradykinesia in Parkinson's disease (PD) with a Kinect depth camera-based motion analysis system and to compare PD and healthy control (HC) subjects. Methods Fifty PD patients and twenty-five HCs were recruited. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) was used to evaluate the motor symptoms of PD. Kinematic features of five bradykinesia-related motor tasks were collected using Kinect depth camera. Then, kinematic features were correlated with the clinical scales and compared between groups. Results Significant correlations were found between kinematic features and clinical scales ( P < 0.05). Compared with HCs, PD patients exhibited a significant decrease in the frequency of finger tapping ( P < 0.001), hand movement ( P < 0.001), hand pronation-supination movements ( P = 0.005), and leg agility ( P = 0.003). Meanwhile, PD patients had a significant decrease in the speed of hand movements ( P = 0.003) and toe tapping ( P < 0.001) compared with HCs. Several kinematic features exhibited potential diagnostic value in distinguishing PD from HCs with area under the curve (AUC) ranging from 0.684–0.894 ( P < 0.05). Furthermore, the combination of motor tasks exhibited the best diagnostic value with the highest AUC of 0.955 (95% CI = 0.913–0.997, P < 0.001). Conclusion The Kinect-based motion analysis system can be applied to evaluate bradykinesia in PD. Kinematic features can be used to differentiate PD patients from HCs and combining kinematic features from different motor tasks can significantly improve the diagnostic value.
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