ObjectiveTo study the effect of rotenone on rat substantia nigra dopamine (DA) in the nervous system and oxidative stress parameters (malondialdehyde and glutathione), the influence of rotenone on DA neurons toxic effect and its pathogenesis. MethodsThis study applied back subcutaneous injection of rotenone in rats [1.0 mg/(kg·d)], and used immunocytochemistry technique to detect changes in the expression of tyrosine kinase (TH) in 10 rats of the control group and 10 rats of the experimental group. Spectrophotometry was used to detect the change of oxidative stress parameters in rats (malondialdehyde and glutathione). ResultsDA neurons in rats had various degrees of damage. The TH immune response strength of rats in the substantia nigra and striatum decreased significantly. The number of immune response nigra TH positive neurons was significantly less in the experimental group than in the control group (P< 0.01). Spectrophotometer method was used to detect the midbrain nigra of glutathione, which was significantly less in the experimental group than in the control group (P<0.01). Malondialdehyde in the experimental group was significantly higher (P<0.01). ConclusionRotenone has obvious neurotoxicity, and can lead to the damage of DA neurons and obvious oxidative stress injury in rats, which provides an experimental basis for the pathogenesis of Parkinson's disease, and at the same time provides new targets for the treatment.
Pathological neural activity in subthalamic nucleus (STN) is closely related to the symptoms of Parkinson's disease. Local field potentials (LFPs) recordings from subthalamic nucleus show that power spectral peaks exist at tremor, double tremor and tripble tremor frequencies, respectively. The interaction between these components in the multi-frequency tremor may be related to the generation of tremor. To study the linear and nonlinear relationship between those components, we analyzed STN LFPs from 9 Parkinson's disease patients using time frequency, cross correlation, Granger casuality and bi-spectral analysis. Results of the time-frequency analysis and cross-frequency correlation analysis demonstrated that the power density of those components significantly decreased as the alleviation of tremor and cross-correlation (0.18~0.50) exists during tremor period. Granger causality of the time-variant amplitude showed stronger contribution from tremor to double tremor components, and contributions from both tremor and double tremor components to triple tremor component. Quadratic phase couplings among these three components were detected by the bispectral approaches. The linear and nonlinear relationships existed among the multi-components and certainly confirmed that the dependence cross those frequencies and neurological mechanism of tremor involved complicate neural processes.
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 observe the changes of optic disc structure and retinal nerve fiber layer thickness (RNFL) in patients with different degrees of Parkinson's disease (PD).MethodsThirty eyes of 30 patients with primary PD and 20 eyes of 20 healthy subjects (control group) in Xuanwu Hospital of Capital Medical University from October 2016 to October 2017 were enrolled in this study. The patients were divided into mild to moderate PD group (15 eyes of 15 patients) and severe PD group (15 eyes of 15 patients). All the patients underwent OCT examination. The optic disc area, cup area, C/D area ratio, rim volume, disc volume, cup volume, rim area, C/D area, linear C/D, vertical C/D, the thickness of average RNFL, superior, inferior, temporal upper (TU), superior temporal (ST), superior nasal (SN), nasal upper (NU), nasal lower (NL), inferior nasal (IN), inferior temporal (IT), temporal lower (TL) quadrant RNFL thickness. Analysis of variance was performed for comparison among three groups. Minimum significant difference t test was performed for comparison between two groups.ResultsOptic disc structure parameters: there was no significant difference in the area of optic disc between the three groups (F=1.226, P＞0.05). The other optic disc parameters were significantly different in the three groups (F=5.221, 5.586, 6.302, 5.926, 5.319, 5.404, 5.861, 6.603; P＜0.05). The cup area, cup volume, C/D area, linear C/D, vertical C/D of the mild to moderate PD group and severe PD group were higher than that of the control group (P＜0.05). The cup area, cup volume, C/D area, linear C/D, vertical C/D of the severe PD group were higher than those of mild to moderate PD group (P＜0.05), the rim area, rim volume and disc volume of the severe PD group were smaller than that of mild to moderate PD group (P＜0.05). The thickness of RNFL: there was no significant difference between the three groups of ST, SN, NU and NL (F=3.586, 2.852, 2.961, 2.404; P＞0.05). The average thickness of RNFL, TU, IN, IT and TL in patients of the mild to moderate PD group and severe PD group were less than that in the control group (P＜0.05). The thickness of the average RNFL, TU, IN, IT and TL in patients of the severe PD group were less than that in the mild to moderate PD group (P＜0.05). With the increase of PD severity, the RNFL of TL and TU thinned most significantly.ConclusionsWith the increase of the severity of PD, the optic disc structure and RNFL thickness changes obviously, showing reduced optic disc area and volume, enlarged cup area and volume significantly enlarged C/D ratio. The average RNFL thickness of PD patients is significantly thinner than that of the controls, and it is the most obvious in the TU and TL quadrant.
ObjectiveTo observe the macular retinal thickness and volume in patients with different degrees of Parkinson's disease (PD).MethodsThirty eyes of 30 patients with primary PD and 20 eyes of 20 healthy subjects (control group) in Xuanwu Hospital of Capital Medical University from October 2016 to October 2017 were enrolled in this study. There were 17 males and 13 females, with the mean age of 63.2±6.4 years and disease course of 3.9±2.4 years. The patients were divided into mild to moderate PD group (15 eyes of 15 patients) and severe PD group (15 eyes of 15 patients). The macular area was automatically divided into 3 concentric circles by software, which were foveal area with a diameter of 1 mm (inner ring), middle ring of 1 to 3 mm, and outer ring of 3 to 6 mm. The middle and outer ring were divided into 4 quadrants by 2 radiations, respectively. The changes of retinal thickness and macular volume of the macular center and its surrounding quadrants were analyzed. SPSS 16.0 software was used for statistical analysis. One-way ANOVA were used to analyze all data.ResultsCompared with the control group, the retinal thickness and volume in macular center and each quadrant of the mild to moderate PD group and severe PD group were reduced. Compared with the mild to moderate PD group, the retinal thickness and volume in macular center and each quadrant of the severe PD group were reduced. The differences of retinal thickness and macular volume among 3 groups were significant (F=5.794, 5.221, 5.586, 5.302, 5.926, 5.319, 5.404, 5.261, 5.603; P=0.001, 0.007, 0.003, 0.005, 0.000, 0.004, 0.004, 0.006, 0.002). In inner ring of the mild to moderate PD group and the severe PD group, the retinal thickness and macular volume in the upper and the nasal were the largest, the inferior was followed, and the temporal was the smallest. In outer ring of the mild to moderate PD group and the severe PD group, the retinal thickness and macular volume in the nasal was the largest, the upper was the second, the temporal and the inferior were the smallest.ConclusionsThe retinal thickness and volume of the macular central fovea and its surrounding areas in PD patients are significantly thinner than that in the healthy subjects. And with the increase of the severity of PD, the macular structure changes obviously, showing macular center and its surrounding macular degeneration thin, macular volume reduced.
ObjectiveTo systematically review the efficacy and safety of selective serotonin reuptake inhibitors (SSRIs) in the treatment of Parkinson's disease patients with depression. MethodsThe Cochrane Library (Issue 5, 2014), PubMed, EMbase, CNKI, VIP and WanFang Data databases were searched from inception to May 2014 for randomized controlled trials (RCTs) investigating the efficacy and safety of SSRIs for Parkinson's disease patients with depression. Two reviewers independently screened literature according to the inclusion and exclusion criteria, extracted data, and assessed the methodological quality of included studies. Then meta-analysis was performed using RevMan 5.2 software. ResultsA total of 12 RCTs were included. The results of meta-analysis showed that the efficacy of SSRIs was better than placebo (RR=2.18, 95%CI 1.60 to 2.97, P<0.000 01) and the dropouts rates of SSRIs were higher than placebo (OR=3.02, 95%CI 1.04 to 8.79, P=0.04). However, the incidence rate of adverse events between the SSRIs group and the placebo group was not statistically different. ConclusionCurrent evidence indicates that SSRIs are effective for the Parkinson's disease patients with depression. Because of the limitation of quantity and quality of included studies, large-scale multi-center RCTs are required to confirm these findings.