Results 311 to 320 of about 9,215,739 (339)

Validity and Reliability of Clinical and Patient‐Reported Outcomes in Multisystem Proteinopathy 1

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Valosin‐containing protein (VCP)‐associated multisystem proteinopathy 1 (MSP1) is caused by variants in the VCP gene. MSP1 results in various phenotypes including progressive myopathy, Paget's disease of bone, frontotemporal dementia, amyotrophic lateral sclerosis, and parkinsonism, among others.
Lindsay N. Alfano   +15 more
wiley   +1 more source

Glycosylation Gene Signatures as Prognostic Biomarkers in Glioblastoma

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Glioblastoma (GBM) is an aggressive brain tumor characterized by significant heterogeneity. This study investigates the role of glycosylation‐related genes in GBM subtyping, prognosis, and response to therapy. Methods We analyzed mRNA expression data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression ...
Tong Zhao   +4 more
wiley   +1 more source

Coffee Consumption Is Associated With Later Age‐at‐Onset of Parkinson's Disease

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Observation studies suggest that coffee consumption may lower the risk and delay the age‐at‐onset (AAO) of Parkinson's disease (PD). The aim of this study was to explore the causal relationship and genetic association between coffee consumption and the AAO, risk, and progression of PD. Using Mendelian randomization, we identified a significant
Dariia Kuzovenkova   +3 more
wiley   +1 more source

Pharmacognostic Evaluation and Antioxidant Profiling of Five Varieties of <i>Ribes nigrum</i> Grown in Romania. [PDF]

open access: yesPlants (Basel)
Ștefănescu R   +5 more
europepmc   +1 more source

Precision‐Optimised Post‐Stroke Prognoses

open access: yesAnnals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Current medicine cannot confidently predict who will recover from post‐stroke impairments. Researchers have sought to bridge this gap by treating the post‐stroke prognostic problem as a machine learning problem, reporting prediction error metrics across samples of patients whose outcomes are known.
Thomas M. H. Hope   +4 more
wiley   +1 more source

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