Parkinson's Disease

Deep Brain Stimulation Induces Antidepressant Effects by Restoring High-Fidelity Communication in the BNST-NAc Circuit.

Deep brain stimulation (DBS) for treatment-resistant depression (TRD) is challenged by significant individual variability in efficacy and unclear neural circuit mechanisms. To address this, a cross-species, multi-level electrophysiological study was conducted to elucidate the core underlying pathophysiology and reveal the precise therapeutic mechanisms of DBS. Based on the clinical trial (NCT04530...

Interpretable and granular video-based quantification of motor characteristics from the finger-tapping test in Parkinson's disease.

Accurately quantifying motor characteristics in Parkinson's disease is crucial for monitoring disease progression and optimizing treatment strategies. The finger-tapping test is a standard motor assessment. Clinicians visually evaluate a patient's tapping performance and assign an overall severity score based on tapping amplitude, speed, and irregularity. Simultaneous video recording during the st...

Balance biomarker for early differentiation of Parkinson's disease and multiple system atrophy with parkinsonian type.

BACKGROUND: Parkinson's disease (PD) and multiple system atrophy with parkinsonian type (MSA-P) share various motor and nonmotor symptoms, complicatin...

Carbon quantum dots exacerbate Parkinson's disease-like pathology through microglia-mediated neuroinflammatory responses with toxicological implications.

The global burden of Parkinson's disease (PD) is projected to double by 2050, with early-onset cases demonstrating accelerated progression and limited...

Cultivating Clarity: Integrating Zen-Informed Principles Into Contemporary Radiology Practice.

Modern radiology requires sustained attention, rapid decision-making, and emotional resilience amid increasing imaging volumes, diagnostic complexity,...

M[Formula: see text]DGAT: Multi-view multi-scale dynamic graph attention network(GAT) based prediction of Parkinson's disease(PD) progression using whole-blood RNA sequencing data.

With emerging single-cell transcriptomics data, deep learning approaches have enabled the diagnosis of neurodegenerative disorders such as Parkinson's...

Identifying Drugs Associated With Parkinson's Disease Risk Using Machine Learning.

Machine learning (ML)-based methods have been proposed as a potential approach for identifying candidate drugs to be repurposed as disease-modifying t...

How do AI ensemble pipelines treat disorder? A head-to-head comparison on α-synuclein.

Intrinsically disordered proteins (IDPs) play central roles in signalling, regulation, and disease, yet they remain a severe challenge for current art...

Machine Learning-Informed Nano Co-Assembly Inhibits Fibroblast Activation Protein and Improves Drug Delivery in Fibrotic Tissue.

Nanoparticle-based drug delivery faces persistent challenges, including complex fabrication processes and limited lesional accumulation. Here we intro...

A feasibility open-label clinical trial utilizing second-generation artificial intelligence based on the constrained-disorder principle in patients with Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder treated with Levodopa, but long-term use often causes motor complications like "w...

AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.

BACKGROUND: Artificial intelligence (AI)-enabled wearable devices are rapidly emerging in rehabilitation and motor function assessment for patients wi...

PDXNet: An eXplainable Hybrid Attention Convolutional Neural Network for Parkinson's Disease Monitoring and Evaluation.

Parkinson's disease (PD), the second most common neurodegenerative disorder, affects patients and caregivers worldwide. There is a growing need for te...

Machine learning integration of serial blood biomarkers enhances cognitive decline prediction in early Parkinson's disease.

Cognitive decline is a major non-motor complication in early Parkinson's disease (PD), but predicting its progression remains challenging. Using data ...

Self-Supervised joint flow and depth estimation via Multi-Cue uncertainty modeling.

Understanding motion and 3D structure from dynamic scenes is a fundamental computer vision challenge. Unsupervised learning addresses the high cost of...

Multimodal Machine Learning and Deep Learning Approaches for Parkinson's Disease Diagnosis: A Comprehensive Survey.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that profoundly affects patients' quality of life. Early and accurate diagnosis i...

Vocal biomarkers for geriatric health assessment: a scoping review protocol.

INTRODUCTION: Global ageing populations require accessible, non-invasive tools for early detection and monitoring of neurological chronic and neurodeg...

Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including tremor, rigidity, and postural insta...

Machine learning guided discovery of water stable metal-organic frameworks for photocatalytic hydrogen production.

With remarkably tunable porosity and modular chemistry, metal-organic frameworks (MOFs) present a versatile platform for photocatalytic hydrogen (H2) ...

Addressing Gaps in Parkinson's Disease Etiology: The Need for a Polyexposure Score.

Parkinson's disease (PD) risk and progression are influenced by a complex interplay of genetic, environmental, and internal factors. Environmental exp...

Bioelectronic interface for real-time brain biomarker analysis.

The brain is a highly complex organ, exhibiting a highly dynamic chemical environment, playing crucial role in coordinating pathophysiological process...