Effects of Non-Pharmacological Treatments on Quality of Life in Parkinson's Disease: A Review

Liposome-Derived Nanosystems for the Treatment of Behavioral and Neurodegenerative Diseases: The Promise of Niosomes, Transfersomes, and Ethosomes for Increased Brain Drug Bioavailability

Psychiatric and neurodegenerative disorders are amongst the most prevalent and debilitating diseases, but current treatments either have low success rates, greatly due to the low permeability of the blood-brain barrier, and/or are connected to severe side effects. Hence, new strategies are extremely important, and here is where liposome-derived nanosystems come in. Niosomes, transfersomes, and ethosomes are nanometric vesicular structures that allow drug encapsulation, protecting them from degradation, and increasing their solubility, permeability, brain targeting, and bioavailability. This review highlighted the great potential of these nanosystems for the treatment of Alzheimer's disease, Parkinson's disease, schizophrenia, bipolar disorder, anxiety, and depression. Studies regarding the encapsulation of synthetic and natural-derived molecules in these systems, for intravenous, oral, transdermal, or intranasal administration, have led to an increased brain bioavailability when compared to conventional pharmaceutical forms. Moreover, the developed formulations proved to have neuroprotective, anti-inflammatory, and antioxidant effects, including brain neurotransmitter level restoration and brain oxidative status improvement, and improved locomotor activity or enhancement of recognition and working memories in animal models. Hence, albeit being relatively new technologies, niosomes, transfersomes, and ethosomes have already proven to increase the brain bioavailability of psychoactive drugs, leading to increased effectiveness and decreased side effects, showing promise as future therapeutics.

Functionality and Quality of Life with Parkinson's Disease after Use of a Dynamic Upper Limb Orthosis: A Pilot Study

Parkinson's disease (PD) is a chronic, neurodegenerative movement disorder, whose symptoms have a negative impact on quality of life and functionality. Although its main treatment is pharmacological, non-pharmacological aids such as the dynamic elastomeric fabric orthosis (DEFO) merit an evaluation. Our objective is to assess the DEFO in upper limb (UL) functional mobility and in the quality of life of PD patients. A total of 40 patients with PD participated in a randomized controlled crossover study, and were assigned to a control group (CG) and to an experimental group (EG). Both groups used the DEFO for two months, the experimental group the first two months of the study and the control group the last two. Motor variables were measured in the ON and OFF states at the baseline assessment and at two months. Differences from the baseline assessment were observed in some motor items of the Kinesia assessment, such as rest tremor, amplitude, rhythm or alternating movements in the ON and OFF states with and without orthosis. No differences were found in the unified Parkinson's disease rating scale (UPDRS) or the PD quality-of-life questionnaire. The DEFO improves some motor aspects of the UL in PD patients but this does not translate to the amelioration of the standard of functional and quality-of-life scales.

Towards Real-time Prediction of Freezing of Gait in Patients with Parkinsons Disease: A Novel Deep One-class Classifier

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinsons disease. FoG impairs walking and is associated with increased fall risk. On-demand external cueing systems can detect FoG and provide stimuli to help individuals overcome freezing. Predicting FoG before onset enables preemptive cueing and may prevent FoG. However, detection and prediction remain challenging. If FoG data are not available for an individual, patient-independent models have been used to detect FoG, which have shown great sensitivity and poor specificity, or vice versa. In this study, we introduce a Deep Gait Anomaly Detector (DGAD) using a transfer learning-based approach to improve FoG detection accuracy. We also evaluate the effect of data augmentation and additional pre-FoG segments on prediction rate. Seven individuals with PD performed a series of daily walking tasks wearing inertial measurement units on their ankles. The DGAD algorithm demonstrated average sensitivity and specificity of 63.0% and 98.6% (3.2% improvement compared with the highest specificity in the literature). The target models identified 87.4% of FoG onsets, with 21.9% predicted. This study demonstrates our algorithm's potential for accurate identification of FoG and delivery of cues for patients for whom no FoG data is available for model training.

Phase-Dependent Effects of Closed-Loop Tactile Feedback on Gait Stability in Parkinson's Disease

Gait disturbances in people with Parkinson's disease (PD) are a major cause for functional dependence and have recently been shown to be the largest risk factor for falls, institutionalization and death in PD. The use of external cues has been successful at improving gait in people with PD, but the effect of external cues on gait stability is unclear. We examined whether different forms of cueing, open-loop and closed-loop, influenced the local dynamic stability of three critical phases of gait. Forty-three adults with PD completed six, two-minute long walking trials in the following cued conditions: no cue (B), open-loop cueing, fixed auditory cue (OL), closed-loop cueing, tactile feedback delivered to wrist when the ipsilateral foot contacted with the ground (CL). Conditions were performed with and without a cognitive task. Kinematic data were recorded with inertial sensors. Only CL cueing was associated with changes in trunk stability, and these changes were only evident during the weight transfer phase of gait. Both OL and CL caused reductions in overall gait speed, stride length, and an increase in stride time. While CL cueing significantly influenced local dynamic stability during weight transfer, it remains unknown whether these changes are associated with more or less global stability. Future research will explore the clinical implications.

Piperine-Coated Gold Nanoparticles Alleviate Paraquat-Induced Neurotoxicity in Drosophila melanogaster

Parkinson's disease (PD) is the most common progressive neurodegenerative disease known to impart bradykinesia leading to diverse metabolic complications. Currently, scarcity of effective drug candidates against this long-term devastating disorder poses a big therapeutic challenge. Here, we have synthesized biocompatible, polycrystalline, and uniform piperine-coated gold nanoparticles (AuNPs^piperine) to specifically target paraquat-induced metabolic complications both in Drosophila melanogaster and SH-SY5Y cells. Our experimental evidence clearly revealed that AuNPs^piperine can effectively reverse paraquat-induced lethal effects in both in vitro and in vivo model systems of PD. AuNPs^piperine were found to suppress oxidative stress and mitochondrial dysfunction, leading to inhibition of apoptotic cell death in paraquat-treated flies. AuNPs^piperine were also found to protect SH-SY5Y cells against paraquat-induced toxicity at the cellular level preferably by maintaining mitochondrial membrane potential. Both experimental and computational data point to the possible influence of AuNPs^piperine in regulating the homeostasis of parkin and p53 which may turn out to be the key factors in reducing PD symptoms. The findings of this work may facilitate the development of piperine-based nanoformulations against PD.

Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: Addressing the Class Imbalance Problem

Freezing of gait (FoG) is a common motor symptom in patients with Parkinson's disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. However, predicting FoG before its occurrence enables preemptive cueing and may prevent FoG. Such prediction remains challenging given the relative infrequency of freezing compared to non-freezing events. In this study, we investigated the ability of individual and ensemble classifiers to predict FoG. We also studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance. Eighteen PD patients performed a series of daily walking tasks wearing accelerometers on their ankles, with nine experiencing FoG. The ensemble classifier formed by Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptron using bagging techniques demonstrated highest performance (F1 = 90.7) when synthetic FoG samples were added to the training set and class cost was set as twice that of normal gait. The model identified 97.4% of the events, with 66.7% being predicted. This study demonstrates our algorithm's potential for accurate prediction of gait events and the provision of preventive cueing in spite of limited event frequency.

Prediction of Freezing of Gait in Parkinson's Disease Using Statistical Inference and Lower-Limb Acceleration Data

The freezing of gait (FoG) is a common type of motor dysfunction in advanced Parkinson's disease (PD) associated with falls. Over the last decade, a significant amount of studies has been focused on detecting FoG episodes in clinical and home environments. Yet, there remains a paucity of techniques regarding real-time prediction of FoG before its occurrence. In this paper, a new algorithm was employed to define the best combination of sensor position, axis, sampling window length, and features to predict FoG. We hypothesized that gait deterioration before FoG onsets can be discriminated from normal gait using statistical analysis of features from successive windows of data collected from lower-limb accelerometers. We defined a new performance measure, "predictivity", to compare the number of correctly predicted FoG events among different combinations. We characterized the system performance using data from 10 PD patients, who experienced FoG while performing several walking tasks in a lab environment. The analysis of 120 different combinations revealed that prediction of FoG can be realized by using an individual shank sensor and sample entropy calculated from the horizontal forward axis with window length of 2 s (88.8%, 92.5%, and 89.0% for average predictivity, sensitivity, and specificity, respectively).

Parkinson’s disease: coping and coexistence

Abstract Objective: To know the ways of coping when living with Parkinson’s disease. Method: The methodological framework used was the Grounded Theory. Thirty people diagnosed with Parkinson’s disease registered at the Parkinson Santa Catarina Association, SC, Brazil. participated in the in-depth interview. After the data coding process, five people with the disease validated the findings. Data collection took place between September 2013 to April 2014. Results: The categories that emerged were: Share activities with people with Parkinson’s disease; Have family support; Seek healthy living: activities for self-esteem and quality of life. Discussion: The study highlights the importance of family company in promoting patient stability and self-esteem, where family support helps in coping with the health condition. Conclusion: It was possible to know the ways of coping to live with the disease, especially in sharing experiences with peers; family support, leisure activities, and lifestyle changes; Such characteristics are pertinent to the health care of people with neurodegenerative diseases.