Results 181 to 190 of about 34,230,496 (248)

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components. [PDF]

PLoS One
Ahmad A, Rose EJ, Roy MS, Valachovic E.
europepmc   +1 more source

Spatial and Volumetric Characteristics of Glioblastoma: Associations With Clinical Presentation and Survival

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective We aim to comprehensively analyze how regional tumor and edema characteristics are associated with clinical presentations and survival outcomes in a large cohort of glioblastoma patients. Methods Patients with IDH‐wildtype glioblastoma who received brain MRI from 2010 to 2023 were included.
Daniel J. Zhou, Marc Jaskir, Julian Gal, Yombe Fonkeu, Christos Davatzikos, MacLean P. Nasrallah, Jose Garcia, Rubo Xing, Stephen J. Bagley, Arati Desai, Richard E. Phillips, Steven Tobochnik, Colin A. Ellis, James J. Gugger, Nishant Sinha, Joel M. Stein, Kathryn A. Davis   +16 more
wiley   +1 more source

Explainable artificial intelligence reveals divergent learning in pharmacophore-based hierarchical pooling graph neural networks. [PDF]

Sci Rep
Urrutia MJT, Mastropietro A, Bajorath J.
europepmc   +1 more source

MOGAD Is the Most Common Cause of Isolated Optic Neuritis in Children

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objectives The study aimed to characterize the clinical features, etiologies, and outcomes of isolated, first‐time pediatric ON in the post‐MOG‐IgG era. Methods This was a single‐center retrospective cohort study at Texas Children's Hospital of patients diagnosed with first‐time ON between 2018–2024, with follow‐up data collected through 2025.
Chaitanya Aduru, Akansha Chandrasekar, Kyla Blasingame, Jonathan Rosen, Jesse M. Levine, Karla Salazar, Madhuri Chilakapati, Rod Foroozan, Victoria Hardwick, Jonathan M. Yarimi, Nikita Shukla, Timothy E. Lotze, Kristen S. Fisher, Alexander J. Sandweiss   +13 more
wiley   +1 more source

Cross-dataset benchmarking of machine learning models for marine and atmospheric environmental prediction. [PDF]

PLoS One
Zhou X, Zhang H, Du T, Yuan Q, Wang H.
europepmc   +1 more source

Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig, Nadav Amir, Maya Schiller, Roni Loebenstein, Johnatan Nissan, Marina Boxer, Shany Guly Gofrit, Gal Goshen, Sándor Beniczky, Nicola Maggio   +9 more
wiley   +1 more source

Physics-informed hybrid GR4J-XGBoost model for streamflow prediction: integrating conceptual states, SHAP interpretability, and uncertainty analysis. [PDF]

Sci Rep
Kisi O, Heddam S, Parmar KS, Külls C, Zounemat-Kermani M.   +4 more
europepmc   +1 more source

Prominent Movement Disorders in RNU2‐2‐Related Spliceosomopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Pediatric movement disorders often overlap with neurodevelopmental diseases, suggesting shared molecular mechanisms. Variants in small nuclear RNA (snRNA) genes encoding spliceosome components have recently been associated with neurodevelopmental disorders, termed “RNUopathies.” We analyzed genome sequencing data from 14 patients with ...
Magdalena Krygier, Ugo Sorrentino, Matias Wagner, Marta Zawadzka, Anna Lemska, Maria Mazurkiewicz‐Bełdzińska, Michael Zech   +6 more
wiley   +1 more source

Physics-informed machine learning for lead-free dual-absorber perovskites: unveiling the synergy of Cs₂NaIrCl₆/Cs₂AgBi_0.75Sb_0.25Br₆. [PDF]

RSC Adv
Mia MM, Harun-Or-Rashid M, Bhattarai S, Badi N, Al-Atawi FH, Irfan A, Rahman MF.   +6 more
europepmc   +1 more source

Electroencephalographic Normalization as a Biomarker of Clinical Recovery in Down Syndrome Regression Disorder

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Down syndrome regression disorder is a syndrome characterized by subacute loss of cognitive, behavioral, and functional abilities in individuals with Down syndrome. Electroencephalography abnormalities are frequently observed during evaluation, but it remains unclear whether these findings represent a dynamic marker of disease ...
Jonathan D. Santoro, Mackenzie Silverman, Maeve C. Lucas, Mariam M. Yousuf, Samuel T. Otey, Stella V. Gray, Brittany Jordan, Madeline D. Kahan, Latanya D. Agurs, Michelle Van Hirtum Das, Deborah Holder, Devin King, Eileen A. Quinn, Ryan Kammeyer, Michael S. Rafii   +14 more
wiley   +1 more source