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.
Objective To investigate the association between parkin gene S/N167 polymorphism and the risk for Parkinson’s Disease (PD) using the methods of meta-analysis. Method References were retrieved through the computerized Medline, Cochrane Library and CBM search from 1998 to 2003. Similar search strategies were applied to each of these databases. The unpublished data of our study were also included.Studies eligible for this meta-analysis should meet the following inclusion criterias: ① presentation of original data and a cross-sectional design. ② PD as the outcome of interest. ③ an odds ratio (or enough information to calculate it) reported to quantify the association between the frequencies of genotypes and alleles of parkin gene S/N167 polymorphism and the risk for PD. All analyses were conducted with ’Review Manager’ Version 4.2 software. Results A total of 1 239 PD patients and 1 168 control studies were studied. The combined data statistics revealed the frequencies of the genotypes and alleles were higher, but showed no statistically difference, for the total PD group from that ofthe control group (Z=1.57, P=0.12). After stratification according to eastern or western origin, the frequencies of G/A+A/A genotype and a allele of eastern origin were significantly higher [test for overall effect: P=0.01, OR=1.41, 95%CI= (1.08 to1.83); P=0.01, OR=1.25, 95%CI= (1.08 to1.44), respectively] in the PD group than that in the control group. After including our unpublished data, the results remained constant, and the trend was much more pronounced. Conversely, there was no difference [test for overall effect: P=0.08, OR=0.55, 95%CI= (0.30 to1.02); P=0.08, OR=0.55, 95%CI= (0.28 to1.08)] in the frequencies of allele and genotype of western origin between the PD patients and the controls. Conclusions The meta-analysis suggests that the parkin gene S/N167 polymorphism might be a genetic risk factor for PD of eastern origin, but not a definite risk for PD of western origin.
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.
1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline (Sal) is a kind of catechol isoquinoline compound, which mainly exists in mammalian brain and performs a variety of biological functions. Through in vivo metabolism, Sal can be transformed into endogenous neurotoxins and can participate the occurrence of Parkinson’s disease (PD). This has attracted widespread concern of researchers. Recently, many research works have shown that Sal may lead to alcohol addiction and regulate hormone release of the neuroendocrine system, which indicated that it is a potential regulator of dopaminergic neurons. In this paper, we discuss the neural functions of Sal on the above aspects, and wish to provide some theoretical supports for further research on its mechanism.
PTEN-induced putative kinase 1 (PINK1), a Parkinson's disease (PD)-related protein, has two isoforms, the mitochondria-localized full-length isoform PINK1FL and the cytoplasm-localized short isoform PINK1-cyto. Studies have suggested that PINK1FL can selectively accumulate at the surface of damaged mitochondria and cooperate with another Parkinson's Disease-related protein PARKIN to trigger the degradation of MIRO1, a mitochondria trafficking regulator. The functions of PINK1-cyto are, however, not yet clear. To investigate the functions of PINK1-cyto, we expressed different proteins in cultured HEK293 cells by transfecting it with different plasmids, and detected the protein levels by Western blot after expressing for 24 h. We found that in cultured HEK293 cells, PINK1-cyto could also cooperate with PARKIN degrade MIRO1 in the presence of CK2β, and the regulatory subunit of Casein Kinase Ⅱ. Interestingly, this function of CK2β was not dependent on CK2α, the catalytic subunit of Casein Kinase II. We also found that CK2β could promote the direct interaction between PINK1-cyto and MIRO1 by immunocoprecipitation analysis. This result suggested that in addition to CK2α, CK2β could also form a kinase complex with PINK1-cyto with important physiological functions.
Parkinson’s disease patients have early vocal cord damage, and their voiceprint characteristics differ significantly from those of healthy individuals, which can be used to identify Parkinson's disease. However, the samples of the voiceprint dataset of Parkinson's disease patients are insufficient, so this paper proposes a double self-attention deep convolutional generative adversarial network model for sample enhancement to generate high-resolution spectrograms, based on which deep learning is used to recognize Parkinson’s disease. This model improves the texture clarity of samples by increasing network depth and combining gradient penalty and spectral normalization techniques, and a family of pure convolutional neural networks (ConvNeXt) classification network based on Transfer learning is constructed to extract voiceprint features and classify them, which improves the accuracy of Parkinson’s disease recognition. The validation experiments of the effectiveness of this paper’s algorithm are carried out on the Parkinson’s disease speech dataset. Compared with the pre-sample enhancement, the clarity of the samples generated by the proposed model in this paper as well as the Fréchet inception distance (FID) are improved, and the network model in this paper is able to achieve an accuracy of 98.8%. The results of this paper show that the Parkinson’s disease recognition algorithm based on double self-attention deep convolutional generative adversarial network sample enhancement can accurately distinguish between healthy individuals and Parkinson’s disease patients, which helps to solve the problem of insufficient samples for early recognition of voiceprint data in Parkinson’s disease. In summary, the method effectively improves the classification accuracy of small-sample Parkinson's disease speech dataset and provides an effective solution idea for early Parkinson's disease speech diagnosis.
ObjectiveTo summarize and evaluate the quality of methodology, report and evidence of the systematic reviews and meta-analyses (SRs/MAs) of acupuncture and moxibustion interventions for Parkinson's disease. MethodsEight databases including CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, Cochrane Library and Web of Science were searched from inception to May 1, 2023. The quality of methodology, report and evidence involved in these studies were evaluated by AMSTAR 2, PRISMA and GRADE tool. ResultsA total of 28 SRs/MAs were included, and the findings of included studies showed that acupuncture and moxibustion had a clinical advantage for Parkinson's disease. The methodological quality of all studies was extremely low. Thirteen reports were relatively complete, 14 reports had certain flaws, and 1 report had relatively serious flaws. And of the 126 reports for seven outcomes, 1 was graded as high, 12 as moderate, 57 as low, and 56 as critically low. ConclusionThe current evidence shows that acupuncture and moxibustion have a certain clinical effect for Parkinson's disease, but the methodological quality and evidence quality of related SRs/MAs are low, and the standardization still needs to be improved. The efficacy of acupuncture and moxibustion in Parkinson's disease still needs to be verified by high-quality clinical studies in the future.