Objective: The aim of the study is to map the shared genetic component and relationships between thyroid and reproductive health traits to improve the understanding of the interplay between those domains.
Design: A large-scale genetic analysis of thyroid traits (hyper- and hypothyroidism, and thyroid-stimulating hormone levels) was conducted in up to 743 088 individuals of European ancestry from various cohorts.
Methods: We evaluated genetic associations using genome-wide association study (GWAS) meta-analysis, GWAS Catalog lookup, gene prioritization, mouse phenotype lookup, and genetic correlation analysis.
Background: Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed.
View Article and Find Full Text PDFObjective: The aim of this study was to evaluate the prevalence of venous thromboembolism (VTE) in patients included in the European Registry on Cushing's syndrome (ERCUSYN), compare their clinical characteristics with those who did not develop VTE and identify risk factors for VTE.
Design: A retrospective observational cohort study.
Methods: Data extraction from the registry was taken on February, 7, 2022.
Heritable renal cancer syndromes (RCS) are associated with numerous chromosomal alterations including inactivating mutations in von Hippel-Lindau (VHL) gene. Here we identify a novel aspect of the phenotype in VHL-deficient human renal cells. We call it reductive stress as it is characterised by increased NADH/NAD ratio that is associated with impaired cellular respiration, impaired CAC activity, upregulation of reductive carboxylation of glutamine and accumulation of lipid droplets in VHL-deficient cells.
View Article and Find Full Text PDFIn this study, a novel method for automatic microaneurysm detection in color fundus images is presented. The proposed method is based on three main steps: (1) image breakdown to smaller image patches, (2) inference to segmentation models, and (3) reconstruction of the predicted segmentation map from output patches. The proposed segmentation method is based on an ensemble of three individual deep networks, such as U-Net, ResNet34-UNet and UNet++.
View Article and Find Full Text PDF