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Here we focus on the problem of prioritising variants with respect to the observed disease phenotype
Our results identify a pretreatment tumor microenvironment that predicts response to immune checkpoint blockade, which can be therapeutically attained
Computer vision technology is advancing rare disease diagnosis to address unmet needs of the more than 300 million individuals affected globally; one in three rare diseases have a known facial phenotype. 3D face model reconstruction is a key driver of these advances.
Infants with KMT2A-rearranged B-cell acute lymphoblastic leukemia (ALL) have a dismal prognosis. Survival outcomes have remained static in recent decades despite treatment intensification and novel therapies are urgently required.
People living with rare diseases (PLWRD) still face huge unmet needs, in part due to the fact that care systems are not sufficiently aligned with their needs and healthcare workforce (HWF) along their care pathways lacks competencies to efficiently tackle rare disease-specific challenges. Level of rare disease knowledge and awareness among the current and future HWF is insufficient.
In clinical genetics, establishing an accurate nosology requires analysis of variations in both aetiology and the resulting phenotypes. At the phenotypic level, recognising typical facial gestalts has long supported clinical and molecular diagnosis; however, the objective analysis of facial phenotypic variation remains underdeveloped.
Cornelia de Lange Syndrome (CdLS) is a rare, dominantly inherited multisystem developmental disorder characterized by highly variable manifestations of growth and developmental delays, upper limb involvement, hypertrichosis, cardiac, gastrointestinal, craniofacial, and other systemic features.
To describe trends, age-specific patterns, and factors influencing hospitalizations for 5 rare craniofacial anomalies.
Advances in screening and diagnostics have changed the way in which we identify and diagnose congenital anomalies.
While chemotherapy remains the first-line treatment for many cancers, it is still unclear what distinguishes responders from non-responders. Here, we characterize the chemotherapy-responsive tumor microenvironment in mice, using RNA sequencing on tumors before and after cyclophosphamide, and compare the gene expression profiles of responders with progressors.