Remote scoring models of rigidity and postural stability of Parkinson's disease based on indirect motions and a low-cost RGB algorithm.

Background and objectives: The Movement Disorder Society's Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson's disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions. Methods: The red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman's correlation coefficient (rho) were used to evaluate the performance of model. Results: For model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate). Conclusion: Our study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.

Association between serum bilirubin concentration and Parkinson's disease: a meta-analysis.

BACKGROUND: The antioxidant effects of bilirubin in Parkinson's disease (PD) have recently gained much attention from the research community. However, results from these studies have been conflicting. This meta-analysis is conducted to assess the relationship between the serum bilirubin concentration and the risk of PD. METHODS: Two reviewers performed a systematic literature search across five databases (MEDLINE, PubMed, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials). The case-control studies regarding bilirubin levels in PD patients published up to April 2020 were included. These studies were subjected to rigorous scrutiny and data extraction to determine the standard mean difference (SMD) and the 95% confidence interval (CI), which were analyzed using the Stata V.12.0 statistical software. RESULTS: A total of eight studies which included 1463 PD cases and 1490 controls were incorporated into our meta-analysis. SMD analysis showed that there was a higher total bilirubin (TBIL) and direct bilirubin (DBIL) levels in PD patients compared with controls (for TBIL, SMD: 0.300, 95% CI: 0.050-0.549, P = 0.018; for DBIL, SMD: 0.395, 95% CI: 0.102-0.688, P = 0.008). However, no significant relationship was found between the serum indirect bilirubin and PD patients (SMD: -0.223, 95% CI: -0.952-0.505, P = 0.548). A subgroup analysis based on ethnicity indicated that the serum TBIL was higher in PD patients of Caucasian descent in contrast to matched healthy controls (SMD: 0.511, 95% CI: 0.324-0.698, P = 0.000, I2 = 58.0%). CONCLUSION: Higher serum bilirubin levels in PD patients suggest that bilirubin might play a role in the pathogenesis of PD and have the potential to be utilized as a biochemical marker for PD diagnosis and treatment.