At present the parkinsonian rigidity assessment depends on subjective judgment of neurologists according to their experience. This study presents a parkinsonian rigidity quantification system based on the electromechanical driving device and mechanical impedance measurement method. The quantification system applies the electromechanical driving device to perform the rigidity clinical assessment tasks (flexion-extension movements) in Parkinson’s disease (PD) patients, which captures their motion and biomechanical information synchronously. Qualified rigidity features were obtained through statistical analysis method such as least-squares parameter estimation. By comparing the judgments from both the parkinsonian rigidity quantification system and neurologists, correlation analysis was performed to find the optimal quantitative feature. Clinical experiments showed that the mechanical impedance has the best correlation (Pearson correlation coefficient r = 0.872, P < 0.001) with the clinical unified Parkinson’s disease rating scale (UPDRS) rigidity score. Results confirmed that this measurement system is capable of quantifying parkinsonian rigidity with advantages of simple operation and effective assessment. In addition, the mechanical impedance can be adopted to help doctors to diagnose and monitor parkinsonian rigidity objectively and accurately.
Parkinson’s disease is a common chronic progressive neurodegenerative disease, and its main pathological change is the degeneration and loss of dopaminergic neurons in substantia nigra striatum. Vitamin D receptors are widely distributed in neurons and glial cells, and the normal function of substantia nigra striatum system depends on the level of vitamin D and the normal expression of vitamin D receptors. In recent years, from basic to clinical research, there are some differences in the conclusion of the correlation of vitamin D and its receptor gene polymorphism with Parkinson’s disease. This paper aims to review the research on the correlation of vitamin D and vitamin D receptor gene polymorphism with Parkinson’s disease, and discuss the future research direction in this field.
Parkinson's disease (PD) diagnosis based on speech data has been proved to be an effective way in recent years. There are still some problems on preprocessing samples, ensemble learning, and so on. The problems can further cause misleading of classifiers, unsatisfactory classification accuracy and stability. This paper proposed a new diagnosis algorithm of PD by combining multi-edit sample selection method and random forest. At the end of it, this paper presents a group of experiments carried out with the newest public datasets. Experimental results showed that this proposed algorithm realized the classification of the samples and the subjects of PD. Furthermore, it achieved average classification accuracy of 100% and obtained improvement of up to 29.44% compared to those provided by the subjects. This paper proposes a new speech diagnosis algorithm for PD based on instance selection; and the method algorithm has a higher and more stable classification accuracy, compared with the other algorithms.
People with Parkinson’s disease (PD) exhibit multi-system damaged. Medication mainly targets impairments related to dopaminergic lesions. Moreover, in later stages of the disease, medication becomes less effective. Rehabilitation therapy is believed that it can improve multiple functional disorders, including myotonia, bradykinesia, and postural gait abnormalities. It not only reduces the severity of non-motor symptoms and improves the quality of life in PD patients, but also delays the development of PD and improves the activity of daily life of patients. This article summarizes the progress of rehabilitation assessment and the therapy of PD.
ObjectiveTo conduct a bioinformatics analysis of gene expression profiles in frontal lobe of patients with Parkinson disease (PD), in order to explore the potential mechanism related to depression in PD.MethodsAll the bioinformatics data before March 20th 2019 were acquired from Gene Expression Omnibus (GEO) database, using " Parkinson disease” as the key word. The species was limited to human (Homo sapiens), and the detective method was limited to expression profiling by array. ImgGEO (Integrative Gene Expression Meta-Analysis from GEO database), DAVID (the Database for Annotation, Visualization and Integrated Discovery), STRING and Cytoscape 3.6.1 software were utilized for data analysis.ResultsTotally, 45 samples (24 PD cases and 21 healthy controls) were obtained from 2 datasets. We identified 236 differentially expressed genes (DEGs) in the post-mortem frontal lobe between PD cases and healthy controls, in which 146 genes were up-regulated and 90 genes were down-regulated. Based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis, the DEGs were mainly enriched in the structures of postsynaptic membrane, cell membrane component, postsynaptic membrane dense area, and myelin sheath, and were involved in the occurrence of PD, depression, and other diseases. These genes were involved in the biological processes of dopaminergic, glutamate-nergic, GABA-nergic synapses, and some other synapses, as well as several signaling pathways (e.g. mitogen- activated protein kinase signal pathway, p53 signal pathway, and Wnt signal pathway), which were associated with PD and depression pathogenesis. Besides, we found that NFKBIA, NRXN1, and RPL35A were the Hub proteins.ConclusionsGene expression in frontal lobe of patients with PD is associated with the pathogenesis of PD. This study provides a theoretical basis for understanding the mechanism of PD occurrence and progression, as well as the potential mechanism of depression in PD.