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.
Objective To review the progress of perioperative treatments for patients of Parkinson’s disease and hip fractures. Methods The related literature of treatments for patients of Parkinson’s disease and hip fractures were reviewed and analyzed from the aspects such as the perioperative management, selection of operation ways, and prognosis. Results The patients of Parkinson’s disease are more likely to sustain hip fractures because of postural instability and osteoporosis. The perioperative treatments for patients of Parkinson’s disease and hip fractures should be determined by orthopedists, neurologist, anesthesiologist, and physical therapist. There is still controversy about the selection of operation and surgical approach. And the prognosis of patients of Parkinson’s disease and hip fractures are associated with the severity of Parkinson’s disease. Conclusion There are few clinical studies about the patients of Parkinson’s disease and hip fractures. The mid-term and long-term functional outcomes of patients of Parkinson’s disease and hip fractures are unsufficient. And the best treatments of patients of Parkinson’s disease and hip fractures need to be further explored.
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.
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.
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.
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.
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.
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.
ObjectiveThis study aims to analyze the trends in Parkinson’s disease incidence rates among the elderly population in China from 1990 to 2021 and to forecast incidence growth over the next 20 years, providing. MethodsJoinpoint regression and age-period-cohort models were employed to analyze temporal trends in Parkinson’s disease incidence, and the Nordpred model was used to predict case numbers and incidence rates among the elderly in China from 2022 to 2044. ResultsFindings indicated a significant increase in Parkinson’s disease incidence among China’s elderly population from 1990 to 2021, with crude and age-standardized incidence rates rising from 95.37 per 100 000 and 111.05 per 100 000 to 170.52 per 100 000 and 183.91 per 100 000, respectively. Predictions suggested that by 2044, the number of cases will rise to approximately 878 264, with the age-standardized incidence rate reaching 223.4 per 100 000, and men showing significantly higher incidence rates than women. The rapid increase in both cases and incidence rates indicated that Parkinson’s disease will continue to impose a heavy disease burden on China’s elderly population. ConclusionThe burden of Parkinson’s disease in China’s elderly population has grown significantly and is expected to worsen. To address the rising incidence rates effectively, it is recommended to enhance early screening and health education for high-risk groups, improve diagnostic and treatment protocols, and prioritize resource allocation to Parkinson’s disease prevention and care services to reduce future public health burdens.
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.