Results 171 to 180 of about 752,283 (330)

Feasibility and Tolerability of Performing Portable MRI for Neurological Disorders in an Outpatient Neurology Clinic: A Prospective Cohort

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Accessing brain magnetic resonance imaging (MRI) can be challenging, especially for underserved patients, which may lead to disparities in neurological diagnosis. Method This mixed‐methods study enrolled adults with one of four neurological disorders: mild cognitive impairment or dementia of the Alzheimer type, multiple sclerosis ...
Maya L. Mastick, Nomin Enkhtsetseg, Joseph Sadok, Matheus Mochetti, Annabel Sorby Adams, Jennifer Guo, Cristina Trapaga Hacker, Isabella Gomez Hjerthen, Seungwon Lee, Siddharth Satish, Andrew S. Ham, Justin Hill, Denis Balaban, Kevin Kyle, Rebecca L. Gillani, Marcelo Matiello, Aleksandar Videnovic, Eric C. Klawiter, W. Taylor Kimberly, Farrah J. Mateen   +19 more
wiley   +1 more source

Development and optimization of a morphodiversity model for mountainous areas using supervised classification and artificial neural networks. [PDF]

Sci Rep
Bartuś T.
europepmc   +1 more source

MULTI-CLASS TEXT SUPERVISED CLASSIFICATION ON ROMANIAN FINANCIAL BANKING REVIEWS

, 2019
Irina Raicu, Rãzvan Bologa, Radu Constantinescu   +2 more
openalex   +1 more source

Deep graph learning for semi-supervised classification [PDF]

, 2020
Guangfeng Lin, Xiaobing Kang, Kaiyang Liao, Fan Zhao, Yajun Chen   +4 more
openalex   +1 more source

Structure–Function Decoupling of the Sensorimotor and Default Mode Networks in Black Americans With MS

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano, Giacomo Boffa, Maria Petracca, Marta Ponzano, Nicole Graziano, Claire Wigley, Claire Riley, Jonathan Howard, Pietro Bontempi, Sylvia Klineova, Fred Lublin, Matilde Inglese   +11 more
wiley   +1 more source

Policy Learning for Actively Labeled Sample Selection on Lumbar Semi-supervised Classification. [PDF]

J Imaging Inform Med
Hai J, Chen J, Qiao K, Su Z, Lu H, Yan B.   +5 more
europepmc   +1 more source

Enhancing rare cardiac disease classification with GAN-augmented supervised and self-supervised learning: a case study on Brugada syndrome

B Zanchi, G Monachino, G Conte, F Faraci
openalex   +1 more source

Developmental and Epileptic Encephalopathy due to Biallelic Pathogenic Variants in PIGM

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective PIGM encodes a critical enzyme in the glycosylphosphatidylinositol (GPI)‐anchor biosynthesis pathway. While promoter‐region mutations in PIGM have been associated with a relatively mild phenotype characterized by portal vein thrombosis and absence seizures, recent evidence suggests that coding‐region mutations result in a more severe
Júlia Sala‐Coromina, Anna Marcé‐Grau, Barbara Masotto, Marta Codina, Lamia BenJemaa, Yasmina Elaribi, Mónica Martinez‐Gallo, Roger Colobran, Angel Sanchez‐Montañez, Irene Valenzuela, Yoshiko Murakami, Alfons Macaya   +11 more
wiley   +1 more source

Epilepsy‐Associated Variants of a Single SCN1A Codon Exhibit Divergent Functional Properties

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Pathogenic variants in SCN1A, which encodes the voltage‐gated sodium channel NaV1.1, are associated with multiple epilepsy syndromes exhibiting a range of clinical severity. SCN1A variants are reported in different syndromes, including Dravet syndrome, which is associated with loss‐of‐function, whereas neonatal/infantile‐onset ...
Lanie N. Liebovitz, Christopher H. Thompson, Linda C. Laux, Alfred L. George Jr.   +3 more
wiley   +1 more source

An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation. [PDF]

Sci Rep
Chowa SS, Bhuiyan MRI, Tahosin MS, Karim A, Montaha S, Hassan MM, Shah MA, Azam S.   +7 more
europepmc   +1 more source