Publications by authors named "R Oegema"

BCL11B is a Cys2-His2 zinc-finger (C2H2-ZnF) domain-containing, DNA-binding, transcription factor with established roles in the development of various organs and tissues, primarily the immune and nervous systems. BCL11B germline variants have been associated with a variety of developmental syndromes. However, genotype-phenotype correlations along with pathophysiologic mechanisms of selected variants mostly remain elusive.

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Malformations of the brain are common and vary in severity, from negligible to potentially fatal. Their causes have not been fully elucidated. Here, we report pathogenic variants in the core protein-folding machinery TRiC/CCT in individuals with brain malformations, intellectual disability, and seizures.

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The rate of discovery and increased understanding of genetic causes for neurodevelopmental disorders has peaked over the past decade. It is well recognised that some genes show marked variability in neuroradiological phenotypes, and inversely, some radiological phenotypes are associated with several different genetic conditions. However, some readily recognisable brain magnetic resonance imaging (MRI) patterns, especially in the context of corresponding associated clinical findings, should prompt consideration of a pathogenic variant in a specific gene or gene pathway.

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Article Synopsis
  • Brain malformations are diverse abnormalities in brain development that affect neural structure and connectivity, often impacting brain size.
  • Effective prenatal detection of these malformations relies on a solid grasp of embryology and developmental anatomy at different stages of pregnancy.
  • This review aims to simplify the process of identifying and characterizing these structural brain issues by examining various neuroimaging techniques, such as prenatal neurosonography and MRI, and incorporating insights from post-mortem imaging throughout different developmental stages.
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Article Synopsis
  • Dysmorphologists face challenges due to the diverse phenotypic variability of human faces, particularly when using Next-Generation Phenotyping (NGP) tools, which are often trained on limited data.
  • To address this, the GestaltMatcher Database (GMDB) was created, compiling over 10,980 facial images from various global populations, significantly improving the representation of underrepresented ancestries, especially African and Asian patients.
  • The study found that incorporating data from non-European patients enhanced NGP accuracy by over 11% without compromising performance for European patients, highlighting the importance of diverse datasets in identifying genetic disorders.
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