Parkinson's Disease

Developing and validating a clinically actionable prediction tool for Parkinson disease using explainable machine learning with multidimensional national data: A cross-sectional study.

Parkinson disease (PD) is a rapidly growing neurodegenerative disorder that presents a significant public health challenge in aging societies, particularly in China. Although genetic, clinical, and environmental risk factors have been well-established, the predictive value of multidimensional factors (encompassing socioeconomic determinants, prodromal functional impairments, and lifestyle) remains...

A multimodal biomechanics dataset with synchronized kinematics and internal tissue motions during reaching.

Tissue motions within body segments, such as the relative movements of muscles, fascia, and bone, remain largely unexplored despite their relevance to movement dysfunction, force transmission, and motor skill. Here, we present a time-synchronized multimodal dataset that bridges this gap by capturing both internal tissue dynamics and conventional biomechanical measurements during arm reaching. Thir...

Bioinspired Auxetic Metastructures Enable Biomechanically Adaptive, Machine Learning-Enhanced Self-Powered Sensing with Ultrahigh Efficiency.

Self-powered flexible sensors exhibit revolutionary potential in next-generation wearable technologies owing to their exceptional sensitivity and self...

Financial Burden in Adults With Chronic Illness in Switzerland: A Secondary Analysis of Qualitative Interviews Using Natural Language Processing and Topic Modeling.

BACKGROUND: Chronic illness may cause a financial burden that affects patients, their caregivers, and families. While international research, mostly f...

Heel-tapping-based Parkinson's Disease Progression Monitoring using Smart Insoles.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor symptoms, which complicate both diagnosis and disea...

[Basal ganglia and movement disorders : Part 1: the parkinsonian spectrum-Neuroradiological patterns and diagnostic considerations].

Movement disorders are a frequent yet diagnostically unspecific reason for referral in routine neuroradiological practice. Hypokinetic syndromes with ...

HyCoSwin-PD: An explainable hybrid ConvNeXtV2-Swin transformer framework for Parkinson's disease detection from neuroimaging.

Accurate detection of Parkinson's disease (PD) from structural MRI remains a significant challenge due to the diffuse and heterogeneous nature of PD-r...

Chronological Diagnostic Algorithm Predicting Neuropathology in Parkinsonism.

OBJECTIVE: Pre-mortem diagnosis of parkinsonism is often challenging due to atypical presentations, overlapping syndromes, and co-pathologies. This st...

Dose reduction for synaptic density PET imaging in Parkinson's disease.

OBJECTIVE: Dynamic PET imaging with 11C-UCB-J enables in vivo quantification of synaptic vesicle glycoprotein 2A (SV2A), with prior reports of lower s...

Epilepsy: Modern Treatment Approaches, Clinical Aspects, and Patent Status.

Epilepsy is a chronic neurological disorder causing recurrent seizures. Improved diagnosis and management, including high-resolution imaging, genetic ...

Autophagy-exosome crosstalk in neurodegeneration: Mechanisms and therapeutic opportunities.

Neurodegenerative diseases (NDs), including Alzheimer's, Parkinson's, Huntington's, amyotrophic lateral sclerosis, and multiple sclerosis, share a com...

Bioinspired additive manufacturing material optimization for increased stiffness and improved strain sensing in robotic limbs.

Robots traversing uneven terrain may require precise calculation of limb kinematics (e.g. foot position from joint angles and segment dimensions) and ...

Machine learning -driven discovery of Parkinsonian-like phenotypes in idiopathic REM sleep behavior disorder.

Evaluation of Deep Learning-Based Event Detection for Parameter Estimation During Complex Walking in Parkinson's Disease.

OBJECTIVE: Despite recent advances in wearable technology and its use in quantifying movement, there is still a need for reliable methods of quantifyi...

Creation of a machine learning-based model for early identification of mild cognitive impairment risk in Parkinson's disease: A PD research center-based population study.

BackgroundThis study aimed to use data from the PD-MDCNC database to develop a risk prediction model using machine learning (ML) methods for the early...

Functional customization of peptide linkers in fusion proteins through multimodal deep learning approach.

Peptide linkers are critical modulators of function in fusion proteins, a foundational technology in modern bioengineering. However, the rational cust...

Artificial intelligence and multiomics integration for Parkinson's disease drug development.

Recent advances underscore the potential of integrating multiomics, such as genomics, transcriptomics, proteomics, and metabolomics, and artificial in...

Backbone rigidity of disordered protein linkers from NMR experiments and MD simulations.

Disordered protein linkers are essential for multidomain protein function and engineering, but quantitative methods for their biophysical characteriza...

Comparison of monocular video-based methods for measuring the amplitude of hand tremor.

Accurate assessment of hand tremor is critical to help diagnose and monitor multiple neurological conditions. In routine practice, clinicians subjecti...

The Brain-Age Gap in Pediatric Dystonia: Neuroanatomical Deviations Inform Deep Brain Stimulation Outcomes.

BACKGROUND: Dystonia in children is a heterogeneous condition with variable response to deep brain stimulation (DBS). Brain-age gap, a machine learnin...