Deep Learning-Based Method for Detecting Parkinson using 1D Convolutional Neural Networks and Improved Jellyfish Algorithms DOI Open Access

Arogia Victor Paul M,

Sharmila Shankar

International journal of electrical and computer engineering systems, Journal Year: 2024, Volume and Issue: 15(6), P. 515 - 522

Published: June 7, 2024

Parkinson's disease (PD) is a common that predominantly impacts the motor scheme of neural central scheme. While primary symptoms overlap with those other conditions, an accurate diagnosis typically relies on extensive neurological, psychiatric, and physical examinations. Consequently, numerous autonomous diagnostic assistance systems, based machine learning (ML) methodologies, have emerged to assist in evaluating patients PD. This work proposes novel deep learning-based classification using voice recordings people into normal, idiopathic Parkinson, familial Parkinson. The improved jellyfish algorithm (IJFA) utilized for hyper-parameter selection (HPS) 1D convolutional network (1D-CNN). proposed technique makes use significant elements 1D-CNN filter-based feature models. Because their strong performance dealing noisy data, algorithms Relief, mRMR, Fisher Score were chosen as top choices. Using just 62 characteristics, combination relief features was able discriminate between people. competence IJFA method determined through specific metrics. attains total accuracy 98.6%, which comparatively better than existing techniques. model produced around 9.5% improvements accuracy, respectively, when compared data obtained without dimensionality reduction.

Language: Английский

GPNMB is a biomarker for lysosomal dysfunction and is secreted via LRRK2-modulated lysosomal exocytosis DOI Creative Commons
Erin C. Bogacki,

G Longmore,

Patrick A. Lewis

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Abstract Genome-wide association studies have identified Glycoprotein Nmb ( GPNMB ) as a risk factor for Parkinson’s Disease. The allele increases transcription and protein levels in the CSF highlighting GPMNB potential biomarker However, lack of knowledge GPNMB’s function mechanism secretion hindered an interpretation secreted levels. In this study, we assessed by macrophages, primary cell type expressing brain. We show that is response to lysosomal stress via exocytosis highlight Disease LRRK2 strong modulator secretion.

Language: Английский

Citations

1
Unlocking the Potential: Semaglutide’s Impact on Alzheimer’s and Parkinson’s Disease in Animal Models DOI Creative Commons
Andreea-Daniela Meca, Ianis Kevyn Stefan Boboc, Liliana Mititelu-Tarţău

et al.

Current Issues in Molecular Biology, Journal Year: 2024, Volume and Issue: 46(6), P. 5929 - 5949

Published: June 13, 2024

Semaglutide (SEM), a glucagon-like peptide-1 receptor agonist, has garnered increasing interest for its potential therapeutic effects in neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s (PD). This review provides comprehensive description of SEM’s mechanism action preclinical studies these debilitating conditions. In animal models AD, SEM proved beneficial on multiple pathological hallmarks the disease. administration been associated with reductions amyloid-beta plaque deposition mitigation neuroinflammation. Moreover, treatment shown to ameliorate behavioral deficits related anxiety social interaction. SEM-treated animals exhibit improvements spatial learning memory retention tasks, evidenced by enhanced performance maze navigation tests novel object recognition assays. Similarly, PD, demonstrated promising neuroprotective through various mechanisms. These include modulation neuroinflammation, enhancement mitochondrial function, promotion neurogenesis. Additionally, improve motor function dopaminergic neuronal loss, offering disease-modifying strategies. Overall, accumulating evidence from suggests that holds promise approach AD PD. Further research is warranted elucidate underlying mechanisms translate findings into clinical applications devastating disorders.

Language: Английский

Citations

4
Is There a Place for Lewy Bodies before and beyond Alpha-Synuclein Accumulation? Provocative Issues in Need of Solid Explanations DOI Open Access
Paola Lenzi, Gloria Lazzeri, Michela Ferrucci

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(7), P. 3929 - 3929

Published: April 1, 2024

In the last two decades, alpha-synuclein (alpha-syn) assumed a prominent role as major component and seeding structure of Lewy bodies (LBs). This concept is driving ongoing research on pathophysiology Parkinson’s disease (PD). line with this, alpha-syn considered to be guilty protein in process, it may targeted through precision medicine modify progression. Therefore, designing specific tools block aggregation spreading represents effort development disease-modifying therapies PD. The present article analyzes concrete evidence about significance within LBs. this effort, some dogmas are challenged. concerns question whether more abundant compared other proteins Again, occurrence non-protein constituents scrutinized. Finally, LBs causing PD questioned. These revisited concepts helpful process validating which proteins, organelles, pathways likely involved damage meso-striatal dopamine neurons brain regions

Language: Английский

Citations

2
No association between genetically predicted vitamin D levels and Parkinson’s disease DOI Creative Commons

Zihao Wang,

Huan Xia,

Yunfa Ding

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(11), P. e0313631 - e0313631

Published: Nov. 15, 2024

Background Parkinson’s disease (PD) is a neurodegenerative disorder, primarily characterized by motor impairments. Vitamin D has several regulatory functions in nerve cell survival and gene expression via its receptors. Although research shown that vitamin deficiency prevalent among PD patients, the causal link to risk remains unclear. This study aims investigate relationship between using bidirectional two-sample Mendelian randomization (MR) analysis method. Methods applied MR explore PD. We selected statistically significant single nucleotide polymorphisms (SNPs) related 25-hydroxyvitamin (25(OH)D) as instrumental variables (IVs), ensuring no association with known confounders. The used GWAS data from over 1.2 million Europeans across four major published datasets, elucidating genetic correlation levels Results identified 148 SNPs associated 25(OH)D. After adjustment for confounding-related SNPs, 131 remained analysis. Data three cohorts revealed 25(OH)D IVW method ( P cohort1 = 0.365, cohort2 0.525, cohort3 0.117). reverse indicated insufficient evidence of causing decreased 0.776). Conclusion first use results indicate are not significantly causally at level. Therefore, future studies should exercise caution when investigating risk. While direct exists PD, this does preclude potential biomarker diagnosis. Furthermore, larger-scale longitudinal necessary evaluate diagnostic predictive value

Language: Английский

Citations

2
Deep Learning-Based Method for Detecting Parkinson using 1D Convolutional Neural Networks and Improved Jellyfish Algorithms DOI Open Access

Arogia Victor Paul M,

Sharmila Shankar

International journal of electrical and computer engineering systems, Journal Year: 2024, Volume and Issue: 15(6), P. 515 - 522

Published: June 7, 2024

Parkinson's disease (PD) is a common that predominantly impacts the motor scheme of neural central scheme. While primary symptoms overlap with those other conditions, an accurate diagnosis typically relies on extensive neurological, psychiatric, and physical examinations. Consequently, numerous autonomous diagnostic assistance systems, based machine learning (ML) methodologies, have emerged to assist in evaluating patients PD. This work proposes novel deep learning-based classification using voice recordings people into normal, idiopathic Parkinson, familial Parkinson. The improved jellyfish algorithm (IJFA) utilized for hyper-parameter selection (HPS) 1D convolutional network (1D-CNN). proposed technique makes use significant elements 1D-CNN filter-based feature models. Because their strong performance dealing noisy data, algorithms Relief, mRMR, Fisher Score were chosen as top choices. Using just 62 characteristics, combination relief features was able discriminate between people. competence IJFA method determined through specific metrics. attains total accuracy 98.6%, which comparatively better than existing techniques. model produced around 9.5% improvements accuracy, respectively, when compared data obtained without dimensionality reduction.

Language: Английский

Citations

0