Higher chronic stress and less satisfaction with GP communication in migrants with unemployment experience: data from the representative German DEGS1 and the GPCare-1 study.

Background: The impact of unemployment on health is well studied. However, information on associations of unemployment, migration background and general practitioner-patient communication is scarce.

Methods: Data from the representative German Health Interview and Examination Survey for Adults (DEGS1) of individuals in working age (n = 5938) were analysed stratified by unemployment and migration background.

More GP contacts and poorer health of informal caregivers with low socioeconomic status in Germany: results from the population-based DEGS1 and the cross-sectional GPCare-1 study.

Objectives: Informal caregivers are known to have poorer mental health. Risk factors for caregiver burden include low education, female gender, cohabitation with the care recipient and lack of resources. General practitioners (GPs) have an important role in supporting caregivers.

FatSegNet: A fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon MRI.

Purpose: Introduce and validate a novel, fast, and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify visceral and subcutaneous adipose tissue (VAT and SAT) within a consistent, anatomically defined abdominal region on Dixon MRI scans.

Methods: FatSegNet is composed of three stages: (a) Consistent localization of the abdominal region using two 2D-Competitive Dense Fully Convolutional Networks (CDFNet), (b) Segmentation of adipose tissue on three views by independent CDFNets, and (c) View aggregation. FatSegNet is validated by: (1) comparison of segmentation accuracy (sixfold cross-validation), (2) test-retest reliability, (3) generalizability to randomly selected manually re-edited cases, and (4) replication of age and sex effects in the Rhineland Study-a large prospective population cohort.