Results 141 to 150 of about 247,506 (355)
Age-specific incidence rates for motor neuron disease. [PDF]
L. T. Kurland, Donald W. Mulder
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ABSTRACT Objective To estimate the risk of epilepsy associated with stroke in a community‐based cohort, with consideration of stroke type, number, and severity. Methods Data from 15,100 Atherosclerosis Risk in Communities (ARIC) Study participants without stroke at baseline (1987–1989) were analyzed through 12/31/2022.
Jiping Zhou+11 more
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Targeting RAGE with Nanobodies for Molecular Imaging of Cancers and Alzheimer's Disease
RAGE‐specific nanobodies were isolated via phage display and characterized by ELISA, cell ELISA, and SPR. In vivo imaging in renal carcinoma and Alzheimer's disease mouse models demonstrated that NbF8, the highest‐affinity clone, selectively targeted RAGE‐overexpressing tumors and brain tissues, highlighting its potential as a molecular imaging agent ...
Guangfeng Liang+13 more
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Extracellular vesicles (EVs) play a dual role in diagnostics and therapeutics, offering innovative solutions for treating cancer, cardiovascular, neurodegenerative, and orthopedic diseases. This review highlights EVs’ potential to revolutionize personalized medicine through specific applications in disease detection and treatment.
Farbod Ebrahimi+4 more
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The environment in childhood and risk of motor neuron disease. [PDF]
C N Martyn, Clive Osmond
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A detailed workflow for recombinant GALC production and characterization is presented to support enzyme replacement therapy for Krabbe disease. In vitro assays demonstrate that physiological GALC doses restore enzymatic activity and autophagic flux without affecting cell viability, whereas higher doses impair autophagy and reduce viability.
Ambra Del Grosso+5 more
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AMYOTROPHIC LATERAL SCLEROSIS AND OTHER MOTOR NEURON DISEASES. Advances in Neurology Series Volume 56. 1991. Edited by P. Rowland. Lewis Published by Raven Press, New York. 591 pages. $149 Cdn. approx. [PDF]
Arthur J. Hudson
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Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani+4 more
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