Results 121 to 130 of about 53,436 (301)
Predicting Loss of Ambulation in Limb Girdle Muscular Dystrophy R9
ABSTRACT Background Limb girdle muscular dystrophy type R9 (LGMDR9) results from biallelic variants in FKRP. There is limited data to predict loss of ambulation (LOA) among those with LGMDR9. Methods Participants in an ongoing dystroglycanopathy natural history study (NCT00313677) with FKRP variants who had achieved ambulation and were more than 3 ...
Chandra L. Miller +6 more
wiley +1 more source
Existence theory for a third-order ordinary differential (equation, inclusion) with non-separated multi-point and nonlocal Stieltjes boundary conditions [PDF]
Mona Alsulami
openalex +1 more source
ABSTRACT Objective To investigate the value of constructing models based on habitat radiomics and pathomics for predicting the risk of progression in high‐grade gliomas. Methods This study conducted a retrospective analysis of preoperative magnetic resonance (MR) images and pathological sections from 72 patients diagnosed with high‐grade gliomas (52 ...
Yuchen Zhu +14 more
wiley +1 more source
Effects of Biological Sex and Age on Cerebrospinal Fluid Markers—A Retrospective Observational Study
ABSTRACT Objective Cerebrospinal fluid (CSF) analysis is a key diagnostic tool for neurological diseases. To date, only a few studies have investigated in larger cohorts the effect of age and biological sex on diagnostic markers extracted from CSF. Methods For this retrospective observational study, 4163 CSF findings (2012–2020) were evaluated.
Isabel‐Sophie Hafer +3 more
wiley +1 more source
Neural ordinary differential equations with irregular and noisy data. [PDF]
Goyal P, Benner P.
europepmc +1 more source
Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease
ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito +14 more
wiley +1 more source
Transcriptomic forecasting with neural ordinary differential equations. [PDF]
Erbe R, Stein-O'Brien G, Fertig EJ.
europepmc +1 more source
On flows of Neural Ordinary Differential Equations that are solutions of universal dynamical systems [PDF]
Argimiro Arratia +2 more
openalex +1 more source

