GDF15-GFRAL signaling drives weight loss and lipid metabolism in mouse model of amyotrophic lateral sclerosis.

Weight loss is a common early sign in amyotrophic lateral sclerosis (ALS) patients and negatively correlates with survival. In different cancers and metabolic disorders, high levels of serum growth differentiation factor 15 (GDF15) contribute to a decrease of food intake and body weight, acting through GDNF family receptor alpha-like (GFRAL). Here we report that GDF15 is highly expressed in the peripheral blood of ALS patients and in the hSOD1 mouse model and that GFRAL is upregulated in the brainstem of hSOD1 mice.

Vigabatrin-associated brain magnetic resonance imaging abnormalities and clinical symptoms in infants with tuberous sclerosis complex.

Objective: Previous retrospective studies have reported vigabatrin-associated brain abnormalities on magnetic resonance imaging (VABAM), although clinical impact is unknown. We evaluated the association between vigabatrin and predefined brain magnetic resonance imaging (MRI) changes in a large homogenous tuberous sclerosis complex (TSC) cohort and assessed to what extent VABAM-related symptoms were reported in TSC infants.

Methods: The Dutch TSC Registry and the EPISTOP cohort provided retrospective and prospective data from 80 TSC patients treated with vigabatrin (VGB) before the age of 2 years and 23 TSC patients without VGB.

mTORC1 restricts TFE3 activity by auto-regulating its presence on lysosomes.

To stimulate cell growth, the protein kinase complex mTORC1 requires intracellular amino acids for activation. Amino-acid sufficiency is relayed to mTORC1 by Rag GTPases on lysosomes, where growth factor signaling enhances mTORC1 activity via the GTPase Rheb. In the absence of amino acids, GATOR1 inactivates the Rags, resulting in lysosomal detachment and inactivation of mTORC1.

Big data research is everyone's research-Making epilepsy data science accessible to the global community: Report of the ILAE big data commission.

Epilepsy care generates multiple sources of high-dimensional data, including clinical, imaging, electroencephalographic, genomic, and neuropsychological information, that are collected routinely to establish the diagnosis and guide management. Thanks to high-performance computing, sophisticated graphics processing units, and advanced analytics, we are now on the cusp of being able to use these data to significantly improve individualized care for people with epilepsy. Despite this, many clinicians, health care providers, and people with epilepsy are apprehensive about implementing Big Data and accompanying technologies such as artificial intelligence (AI).