A5035971937- Chronic Kidney Disease and Diabetes
- Disaster Response and Management
- Renal Diseases and Glomerulopathies
- Pancreatic function and diabetes
- Cardiovascular Conditions and Treatments
- Genetic Syndromes and Imprinting
- COVID-19 diagnosis using AI
- ATP Synthase and ATPases Research
- COVID-19 and healthcare impacts
- Signaling Pathways in Disease
- Biochemical Acid Research Studies
- Genetic Associations and Epidemiology
- Sarcoidosis and Beryllium Toxicity Research
- Parathyroid Disorders and Treatments
- Artificial Intelligence in Healthcare
- Transplantation: Methods and Outcomes
- Organ and Tissue Transplantation Research
- Amyloidosis: Diagnosis, Treatment, Outcomes
- Viral Infections and Immunology Research
- Data-Driven Disease Surveillance
- Congenital Heart Disease Studies
- Macrophage Migration Inhibitory Factor
- Congenital Diaphragmatic Hernia Studies
- Pericarditis and Cardiac Tamponade
- COVID-19 epidemiological studies
Icahn School of Medicine at Mount Sinai
2020-2024
Abstract COVID-19 has been a significant public health concern for the last four years; however, little is known about mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning predict acute outcomes in hospitalized patients. Using 10-fold cross-validated random forest algorithm, identified set of proteins demonstrated predictive power both discovery validation with 87% 79% accuracy,...
Abstract Background and Aims Diabetic kidney disease (DKD) is a leading cause of end-stage (ESKD), however therapies targeting causal pathways have been limited by heterogeneity. Integrating electronic health record (EHR) data genomics may uncover hidden subphenotypes in DKD. In this study, we use deep learning to identify novel genetic variant ARHGEF18 associated with significantly higher risk DKD ESKD (Figure 1A). We further employed quantitative microscopy techniques biochemical assays...
Abstract Background and Aims COVID-19 has been a significant public health concern for the last three years; however, not much is known about mechanisms that lead to severe kidney outcomes in patients hospitalized with COVID-19. In this multicenter study, we combine isobaric TMT-tagged urinary proteomics machine learning predict patients. Method Urine samples from two medical centers (Mount Sinai Hospital University of Michigan) were used study adherence proper consenting protocols. prepared...
A bstract Kidney disease affects 50% of all diabetic patients; however, prediction progression has been challenging due to inherent heterogeneity. We use deep learning identify novel genetic signatures prognostically associated with outcomes. Using autoencoders and unsupervised clustering electronic health record data on 1,372 kidney patients, we establish two clusters differential prevalence end-stage disease. Exome-wide associations a variant in ARHGEF18, Rho guanine exchange factor...