Evidence has been retrieved through MEDLINE and Cochrane Libray about the treatment for patients with advanced Parkinson’s disease who suffered from on-off, dyskinesia and depression after chronic use of L-dopa. All of the evidence has been evaluated. Methods of evidence-based treatment were drawn up according to the evidence, clinciams’ experiences and patients’ preferences. All symptoms of the patient have been improved obviously.
For speech detection in Parkinson’s patients, we proposed a method based on time-frequency domain gradient statistics to analyze speech disorders of Parkinson’s patients. In this method, speech signal was first converted to time-frequency domain (time-frequency representation). In the process, the speech signal was divided into frames. Through calculation, each frame was Fourier transformed to obtain the energy spectrum, which was mapped to the image space for visualization. Secondly, deviations values of each energy data on time axis and frequency axis was counted. According to deviations values, the gradient statistical features were used to show the abrupt changes of energy value in different time-domains and frequency-domains. Finally, KNN classifier was applied to classify the extracted gradient statistical features. In this paper, experiments on different speech datasets of Parkinson’s patients showed that the gradient statistical features extracted in this paper had stronger clustering in classification. Compared with the classification results based on traditional features and deep learning features, the gradient statistical features extracted in this paper were better in classification accuracy, specificity and sensitivity. The experimental results show that the gradient statistical features proposed in this paper are feasible in speech classification diagnosis of Parkinson’s patients.
Objective To evaluate the effectiveness of repetitive transcranial magnetic stimulation (rTMS) for treating dysfunction in patients with Parkinson’s disease (PD). Methods We searched the Cochrane Library (Issue 1, 2010), MEDLINE, EMbase, CBMdisc, and CNKI from the date of the database establishment to April 2010. Randomized controlled trials (RCTs) of rTMS for patients with PD were collected. The quality of the included RCTs was critically appraised and data were extracted by two reviewers independently. Meta-analyses were conducted for the eligible RCTs. Results Eight RCTs were included. The pooled results of the first 2 RCTs showed that, there was no significant difference compared with control group about treating PD patients with clinical motor dysfunction by high-frequency rTMS 10 days later (WMD= –4.75, 95%CI –13.73 to 4.23). The pooled analysis of another 3 studies showed that, no significant difference were found about improving symptoms with treatment of low-frequency rTMS for 1 month compared with control group (WDM= –8.51, 95%CI –18.48 to 1.46). The pooled analysis of last 3 studies showed that, patient with treatment of low-frequency rTMS for 3 months, had been significantly improved in clinical symptoms such as neurological, behavior and emotional state, clinical motor function, and activities of daily living (WDM= –5.79, 95%CI –8.44 to –1.13). The frontal or motor cortex rTMS manifested as low frequency (≤1Hz), high intensity (≥90% RMT), multi-frequency (≥3 times) and long time (≥3 months) had a positive effect on the clinical symptoms of patients with PD and also had a long-term effect. Conclusions rTMS can improve clinical symptoms and dysfunction of the patients with PD.
Objective To assess the changes in depression symptoms in patients with Parkinson’s disease (PD) receiving combined treatment of deep brain stimulation (DBS) and antiparkinsonian drug therapy (DT) compared with under DT alone. Methods Related literature was retrieved from electronic databases, including PubMed, Cochrane Library, Embase, China National Knowledge Infrastructure, Wanfang Data, and VIP databases. Stata 14.0 software was used for statistical analysis. Network meta-analysis was performed using frequentist model to compare different interventions with each other. Results Five cohort studies and seven randomized controlled trials (RCTs) were included. The total number of participants was 1241. Assessed by the Beck Depression Inventory (BDI) score as the primary outcome, patients who received DT alone showed worse outcome in depression as compared to those who received subthalamic nucleus (STN)-DBS plus DT [standardized mean difference (SMD)=0.30, 95% confidence interval (CI) (0.01, 0.59), P<0.05], and there was no significant difference between the patients receiving globus pallidus interna (GPi)-DBS plus DT and those receiving STN-DBS plus DT [SMD=–0.12, 95%CI (–0.41, 0.16), P>0.05] or those receiving DT alone [SMD=–0.42, 95%CI (–0.84, 0.00), P>0.05]. Assessed by BDI-Ⅱ as the primary outcome, patients who received DT alone showed worse outcome in depression than those who received STN-DBS plus DT [SMD=0.29, 95%CI (0.05, 0.54), P<0.05]; compared with STN-DBS plus DT and DT alone, GPi-DBS plus DT was associated with better improvement in depression [SMD=–0.26, 95%CI (–0.46, –0.06), P<0.05; SMD=–0.55, 95%CI (–0.88, –0.23), P<0.05]. The ranking results of surface under the cumulative ranking curves showed that DBS plus DT had a better superiority in depression symptoms, and GPi-DBS was better than STN-DBS. Conclusion Compared with DT, STN-DBS plus DT is more likely to improve the depressive symptoms of PD patients, and GPi-DBS may be better than STN-DBS.
Diagnosis of Parkinson’s disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.
Methods for achieving diagnosis of Parkinson’s disease (PD) based on speech data mining have been proven effective in recent years. However, due to factors such as the degree of disease of the data collection subjects and the collection equipment and environment, there are different categories of sample aliasing in the sample space of the acquired data set. Samples in the aliased area are difficult to be identified effectively, which seriously affects the classification accuracy of the algorithm. In order to solve this problem, a partition bagging ensemble learning is proposed in this article, which measures the aliasing degree of the sample by designing the the ratio of sample centroid distance metrics and divides the training set into multiple subsets. And then the method of transfer training of misclassified samples is used to adjust the results of subset partitioning. Finally, the optimized weights of each sub-classifier are used to integrate the test results. The experimental results show that the classification accuracy of the proposed method is significantly improved on two public datasets and the increasement of mean accuracy is up to 25.44%. This method not only effectively improves the classification accuracy of PD speech dataset, but also increases the sample utilization rate, providing a new idea for the diagnosis of PD.
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.
Dysphagia is a common non-motor symptom in Parkinson’s disease (PD), with a high incidence and insidious progression. It can lead to complications such as dehydration, malnutrition, aspiration pneumonia, and even death, seriously affecting the quality of life and prognosis of patients. Therefore, early screening, assessment, and intervention are crucial for improving the quality of life and prognosis of PD patients with dysphagia. This article mainly reviews the risk factors and management strategies of dysphagia in PD, with the aim of providing a reference for healthcare professionals to conduct subsequent evaluations and develop targeted interventions.
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.
The aim of this study is to investigate the protective effect of idebenone (IDE) combined with borneol (BO) against Parkinson’s disease (PD). In this study, wild-type AB zebrafish and transgenic Tg (vmat2: GFP) zebrafish with green fluorescence labeled dopamine neurons were used to establish the PD model with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine hydrochloride (MPTP). Following drug treatment, the behavioral performance and dopamine neuron morphology of zebrafish were evaluated, and regulation of dopamine signaling pathway-related genes was determined using RT-qPCR. The results showed that IDE combined with BO improved the behavioral disorders of zebrafish such as bradykinesia and shortening movement distance, also effectively reversed the damage of MPTP-induced dopaminergic neurons. At the same time, the expression of dopamine synthesis and transportation-related genes was up-regulated, and the normal function of the signal transduction pathway was restored. The combination showed a better therapeutic effect compared to the IDE monotherapy group. This study reveals the protective mechanism of IDE combined with BO on the central nervous system for the first time, which provides an important experimental basis and theoretical reference for clinical combination strategy in PD treatment.