64 research outputs found

    PEDIA: prioritization of exome data by image analysis.

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    PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

    Framework and baseline examination of the German National Cohort (NAKO)

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    The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19–74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2–3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4–5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-022-00890-5

    Identifying source regions for airborne particles in East Antarctica, Dronning Maud Land, using backward trajectory modelling

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    Atmospheric composition plays an important role in present and near-future climate change. Airborne particles can serve as cloud condensation and ice nuclei and have therefore a strong influence on cloud formation and thus also on precipitation. This is in particular of interest in Antarctica, since precipitation is the only source of mass gain to the Antarctic ice sheet, which is expected to become the dominant contributor to global sea level rise in the 21st century. A detailed insight into the transport pathways and distribution of airborne particles is therefore essential. At the Belgian Antarctic research station Princess Elisabeth in Dronning Maud Land, East Antarctica, aerosol particles and their characteristics are measured. Atmospheric particles have been collected on filters during the last three austral summers for organic and inorganic chemical analysis by high-volume sampling. In addition, the atmospheric particle number concentration, size distribution and optical particle properties have been measured since 2010. The geographical source regions of airborne particles in Dronning Maud Land remain however to a large extent unknown. In this work, we investigate the climatology of the particle properties with respect to their source regions. To that end, we use the FLEXTRA model to calculate 10-day 3D backward trajectories over the past 10 years. We apply a non-hierarchical cluster method to identify and classify the dominant source regions

    The Family First Prevention Services Act: A New Era of Child Welfare Reform

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    Passed by Congress in early 2018, the Family First Prevention Services Act (Family First)1 is a major step forward in federal child welfare reform and has the potential to transform the way states provide services to children who are abused or neglected. This installment of Law and the Public’s Health offers an overview of the major provisions of Family First. As states and counties respond to the new incentives and restrictions imposed by the statute, collaboration among researchers, advocates, and policy makers will be needed to ensure that the promise of these reforms translates into meaningful change for children and families. Quality evaluations of new preventive services and changes to institutional care will be important, particularly in the early years of these reforms
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