COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

Background: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment.

Multiplex testing for the screening of lysosomal storage disease in urine: Sulfatides and glycosaminoglycan profiles in 40 cases of sulfatiduria.

Purpose: To describe an efficient and effective multiplex screening strategy for sulfatide degradation disorders and mucolipidosis type II/III (MLII/III) using 3 mL of urine.

Methods: Glycosaminoglycans were analyzed by liquid chromatography-tandem mass spectrometry. Matrix assisted laser desorption/ionization-time of flight tandem mass spectrometry was used to identify free oligosaccharides and identify 22 ceramide trihexosides and 23 sulfatides, which are integrated by 670 calculated ratios.

Diagnosing Lysosomal Storage Disorders: The GM2 Gangliosidoses.

The GM2 gangliosidoses are a group of autosomal recessive lysosomal storage disorders caused by defective β-hexosaminidase. There are three clinical conditions in this group: Tay-Sachs disease (TSD), Sandhoff disease (SD), and hexosaminidase activator deficiency. The three conditions are clinically indistinguishable.