Results 71 to 80 of about 464,452 (200)
Multidimensional Profiling of MRI‐Negative Temporal Lobe Epilepsy Uncovers Distinct Phenotypes
ABSTRACT Objective Although hippocampal sclerosis (TLE‐HS) represents the most frequent cause of temporal lobe epilepsy (TLE), up to 30% of patients show no lesion on visual MRI inspection (TLE‐MRIneg). These cases pose diagnostic and therapeutic challenges and are underrepresented in surgical series.
Alice Ballerini +28 more
wiley +1 more source
SPG4 and Dementia: Expanding the Clinical Spectrum
ABSTRACT Objective Hereditary spastic paraplegia (HSP) is a group of disorders characterized by progressive spasticity and lower limb weakness, with mutations in SPG4/SPAST being the most common cause. Detailed studies and clinical and molecular comparisons across different populations are missing.
Emanuele Panza +19 more
wiley +1 more source
ABSTRACT Objective Stereoelectroencephalography‐guided radiofrequency thermocoagulation (SEEG‐RFTC) has emerged as a safe and effective minimally invasive treatment for children with drug‐resistant focal epilepsy. Although evidence from real‐world studies remains limited, numerous pediatric cases have demonstrated promising outcomes. This retrospective
Weitao Chen +7 more
wiley +1 more source
Objective Body mass index (BMI), glomerular filtration rate (GFR), and pretreatment urate levels have been reported to influence the urate‐lowering response to allopurinol. We investigated whether the fractional excretion of uric acid (FEUA) also modulates this response and relates to oxypurinol concentrations.
Pascal Richette +13 more
wiley +1 more source
Objective The purpose was to evaluate a biomarker score consisting of MUC5B rs35705950 promoter variant, plasma matrix metalloproteinase‐7 (MMP‐7), and serum anti–malondialdehyde‐acetaldehyde (anti‐MAA) antibody for rheumatoid arthritis (RA)–associated interstitial lung disease (ILD) risk stratification.
Kelsey Coziahr +16 more
wiley +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
PREdicting LNP In Vivo Efficacy (PRELIVE) framework enables the prediction of lipid nanoparticle (LNPs) organ‐specific delivery through dual modeling approaches. Composition‐based models using formulation parameters and protein corona‐based models using biological fingerprints both achieve high predictive accuracy across multiple organs.
Belal I. Hanafy +3 more
wiley +1 more source
CORE: Cholesterol Altered Lipid Nanoparticles for Splenic Expression of mRNA Payloads
In this paper researchers introduce CORE LNPs, a new class of lipid nanoparticles engineered to redirect mRNA expression away from the liver and into the spleen, a key immune organ. By combining chemical design with computational tools, they created cholesterol analogs that enable precise spleen‐targeted expression, providing greater applications for ...
Eshan A. Narasipura +4 more
wiley +1 more source
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu +3 more
wiley +1 more source
A rigorous and efficient asymptotic test for power-law cross-correlation [PDF]
Podobnik and Stanley recently proposed a novel framework, Detrended Cross-Correlation Analysis, for the analysis of power-law cross-correlation between two time-series, a phenomenon which occurs widely in physical, geophysical, financial and numerous ...
Blythe, Duncan A. J.
core

