Parkinson's Disease最新文献

Alexithymia, characterized by difficulty in recognizing and verbalizing emotions, is reported to be more prevalent in subjects with Parkinson's disease (PD) than in the general population. Although it is one of the nonmotor symptoms of PD, alexithymia is often overlooked in clinical practice. The aim of this systematic review is to investigate the prevalence of alexithymia in PD, assess its impact on quality of life, and explore the rehabilitation approaches for alexithymia. Research articles, selected from PubMed, Scopus, and Web of Science, were limited to those published in English from 2013 to 2023. The search terms combined were "Alexithymia," "Parkinson's disease,", and "Quality of life." Current literature review indicates that alexithymia is commonly assessed using the Toronto Alexithymia Scale (TAS-20), and it is associated with deficits in visuospatial and executive functions. Presently, rehabilitation interventions for alexithymia are scarce, and their effectiveness remains controversial. Future research should focus on developing comprehensive assessments and rehabilitation strategies for emotional processing, considering its significant impact on the quality of life of both patients and caregivers.

Alzheimer's disease is a chronic clinical condition that is predominantly seen in age groups above 60 years. The early detection of the disease through image classification aids in effective diagnosis and suitable treatment. The magnetic resonance imaging (MRI) data on Alzheimer's disease have been collected from Kaggle which is a freely available data source. These datasets are divided into training and validation sets. The present study focuses on training MRI datasets using TinyNet architecture that suits small-scale image classification problems by overcoming the disadvantages of large convolutional neural networks. The architecture is designed such that convergence time is reduced and overall generalization is improved. Though the number of parameters used in this architecture is lesser than the existing networks, still this network can provide better results. Training MRI datasets achieved an accuracy of 98% with the method used with a 2% error rate and 80% for the validation MRI datasets with a 20% error rate. Furthermore, to validate the model-supporting data collected from Kaggle and other open-source platforms, a comparative analysis is performed to substantiate TinyNet's applicability and is projected in the discussion section. Transfer learning techniques are employed to infer the differences and to improve the model's efficiency. Furthermore, experiments are included for fine-tuning attempts at the TinyNet architecture to assess how the nuances in convolutional neural networks have an impact on its performance.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by alterations in motor capacity resulting from a decrease in the neurotransmitter dopamine due to the selective death of dopaminergic neurons of the nigrostriatal pathway. Unfortunately, conventional pharmacological treatments fail to halt disease progression; therefore, new therapeutic strategies are needed, and currently, some are being investigated. The endocannabinoid system (ECS), highly expressed in the basal ganglia (BG) circuit, undergoes alterations in response to dopaminergic depletion, potentially contributing to motor symptoms and the etiopathogenesis of PD. Substantial evidence supports the neuroprotective role of the ECS through various mechanisms, including anti-inflammatory, antioxidative, and antiapoptotic effects. Therefore, the ECS emerges as a promising target for PD treatment. This review provides a comprehensive summary of current clinical and preclinical evidence concerning ECS alterations in PD, along with potential pharmacological targets that may exert the protection of dopaminergic neurons.

Objectives: The aim of this study was to compare the Sniffin' Sticks 12-identification test (SIT-12), China-modified version of the SIT-12 test (Ch-SIT-12) and brief smell identification test for Chinese (B-SITC) in Chinese population of Parkinson's disease (PD) and multiple system atrophy (MSA).

Methods: 36 patients with PD and 7 patients with MSA were enrolled in this study. Three olfactory testing methods (SIT-12, Ch-SIT-12, and B-SITC) were used to test the olfactory function in all participants. Furthermore, demographic and clinical data were collected.

Results: There was no significant difference between three olfactory tests in patients with PD (B-SITC vs. SIT-12: P=0.508; Ch-SIT-12 vs. B-SITC: P=0.146; and SIT-12 vs. Ch-SIT-12: P=0.375). Tremor-dominant (TD) subtypes have better olfactory function than akinetic-rigid dominant (ARD) subtypes when using Ch-SIT-12 (77.8% vs. 29.6%, P=0.019) or B-SITC (55.6% vs. 14.8%, P=0.026). There was a statistical difference between the PD and MSA using Ch-SIT-12 to test the olfactory function (P=0.046).

Conclusions: Our results indicated that SIT-12, Ch-SIT-12 and B-SITC can be used for the detection of olfactory dysfunction in Chinese population of PD. TD subtypes may have better olfactory function than ARD subtypes. In addition, Ch-SIT-12 may be used to differentiate PD from MSA, but that should be confirmed in a larger population.

Background: Postural instability and gait difficulties (PIGD) are a significant cause of falls, mobility loss, and lower quality of life in Parkinson's disease (PD). The connection between PD progression and diminished strength in the lower limbs has been acknowledged. However, the identification of specific muscle groups linked to PIGD and non-PIGD motor features is still unknown.

Objective: To explore the relationship between the strength of specific lower limb muscle groups, along with muscle mass, and their associations with PIGD, PIGD subtypes, and non-PIGD motor features in PD.

Methods: 95 PD participants underwent detailed motor and non-motor test batteries, including lower limb isometric strength testing and whole-body lean mass assessments. Correlation analysis and univariate and multivariate linear/logistic forward stepwise regression were performed to test associations between PIGD and non-PIGD motor features with normalized value (z-score) of lower limb muscle strength and measures of lean mass.

Results: Multivariate regression analysis, adjusted for age, gender, and levodopa equivalent dose, revealed that hip abductor strength was significantly associated with overall PIGD motor severity ratings (p < 0.001), impaired balance (p < 0.001), and non-PIGD Parkinsonian motor features (p < 0.001). Conversely, hip extensor strength was significantly associated with falls, slow walking, and FoG motor features (p=0.016; p=0.003; p=0.020, respectively).

Conclusion: We found that lower hip abductor strength was associated with PIGD and non-PIGD motor features. The association between non-PIGD motor features may suggest specific vulnerability of the hip abductors as part of a proposed brain-muscle loop hypothesis in PD. Moreover, lower hip extensor strength correlated with falls, slow walking, and FoG.

Fatigue is a common and debilitating symptom affecting a significant proportion of individuals with Parkinson's disease (PD), often overshadowing even motor symptoms in its impact on quality of life. The accurate definition and assessment of mental fatigue in PD is crucial for both clinical management and research, yet it remains a challenge due to the subjective nature of the symptom and the heterogeneity of assessment scales. This systematic review examined the existing measures of self-reported mental fatigue in PD by searching through PubMed, Embase, and Scopus databases using specific keywords from 2001 to 2024. Out of the 4182 articles found, 40 met the inclusion criteria, and 14 different scales were identified to measure self-reported fatigue in PD patients. However, most of these scales lack a consistent definition of fatigue, indicating a need for validated combinations of unidimensional and multidimensional scales to accurately assess mental fatigue in PD. The review found that it is best to use Fatigue Severity Inventory (FSI) and Multidimensional Fatigue Inventory (MdFI) to screen for severity of PD mental fatigue and Neuro-QoL Item Bank v1.0 (Neuro-QoL) to evaluate its impact on patients' lives. Furthermore, multidimensional scales Parkinson's Disease Questionnaire (PDQ) and Functional Assessment of Chronic Illness Therapy-Fatigue Scale (FACIT-F) are frequently coupled with Fatigue Severity Scale (FSS), Parkinson's Fatigue Scale (PFS), and/or Modified Fatigue Impact Scale (MFIS) due to their short length and holistic coverage of variables in patients' quality of life. Combining fatigue scales can be used for screening and scoring methods. The review also recommends validating fatigue scales translation and combining them with biomarkers to improve the accuracy and effectiveness of fatigue assessment in clinical practice. Future research should analyze correlations between fatigue scales, expand language types, and explore the link between fatigue scales and the pathophysiological basis of PD. Our findings underscore the need for a standardized approach to the measurement of fatigue in PD and set the stage for future research to consolidate assessment tools that can reliably guide treatment strategies and improve patient outcomes.