The aim of this study was to investigate to what extent PD affects the ability to walk, respond to balance perturbations in a single training session, and produce acute short-term effects to improve compensatory reactions and control of unperturbed walking stability. Understanding the mechanism of compensation and neuroplasticity to unexpected step perturbation training during walking and static stance can inform treatment of PD by helping to design effective training regimens that remediate fall risk. Current rehabilitation therapies are inadequate at reducing falls in people with Parkinson's disease (PD). While pharmacologic and surgical treatments have proved largely ineffective in treating postural instability and gait dysfunction in people with PD, studies have demonstrated that therapy specifically focusing on posture, gait, and balance may significantly improve these factors and reduce falls. The primary goal of this study was to assess the effectiveness of a novel and promising intervention therapy (protective step training - i.e., PST) to improve balance and reduce falls in people with PD. A secondary goal was to understand the effects of PST on proactive and reactive feedback responses during stance and gait tasks. Multiple-baseline, repeated measures analyses were performed on the multitude of proactive and reactive performance measures to assess the effects of PST on gait and postural stability parameters. In general, the results indicate that participants with PD were able to use experiences with perturbation training to integrate and adapt feedforward and feedback behaviors to reduce falls. The ability of the participants with PD to adapt to changes in task demands suggests that individuals with PD could benefit from the protective step training to facilitate balance control during rehabilitation.
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http://dx.doi.org/10.3389/fneur.2023.1211441 | DOI Listing |
Protein Sci
January 2025
Department of Neuroscience, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
Human succinic semialdehyde dehydrogenase is a mitochondrial enzyme fundamental in the neurotransmitter γ-aminobutyric acid catabolism. It catalyzes the NAD-dependent oxidative degradation of its derivative, succinic semialdehyde, to succinic acid. Mutations in its gene lead to an inherited neurometabolic rare disease, succinic semialdehyde dehydrogenase deficiency, characterized by mental and developmental delay.
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December 2024
School of Electronic and Nanoscale Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
In the era of the Internet of Things (IoT), the transmission of medical reports in the form of scan images for collaborative diagnosis is vital for any telemedicine network. In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods.
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December 2024
Naval Special Medical Center, Naval Medical University, Shanghai, 200433, China.
Superoxide dismutase (SOD) plays important roles in the balance of oxidation and antioxidation in body mostly by scavenging superoxide anion free radicals (O). Previously, we reported a novel Cu/Zn SOD from jellyfish Cyanea capillata, named CcSOD1, which exhibited excellent SOD activity and high stability. TAT peptide is a common type of cell penetrating peptides (CPPs) that efficiently deliver extracellular biomacromolecules into cytoplasm.
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December 2024
Department of Civil, Construction and Environmental Engineering (Dept 2470), North Dakota State University, PO Box 6050, Fargo, ND, 58108-6050, USA.
A precise streamflow forecast is crucial in hydrology for flood alerts, water quantity and quality management, and disaster preparedness. Machine learning (ML) techniques are commonly employed for hydrological prediction; however, they still face certain drawbacks, such as the need to optimize the appropriate predictors, the ability of the models to generalize across different time horizons, and the analysis of high-dimensional time series. This research aims to address these specific drawbacks by developing a novel framework for streamflow forecasting.
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December 2024
Department of Materials, Imperial College London, London, SW7 2AZ, UK.
Topological Insulators (TIs) are promising platforms for Quantum Technology due to their topologically protected surface states (TSS). Plasmonic excitations in TIs are especially interesting both as a method of characterisation for TI heterostructures, and as potential routes to couple optical and spin signals in low-loss devices. Since the electrical properties of the TI surface are critical, tuning TI surfaces is a vital step in developing TI structures that can be applied in real world plasmonic devices.
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