Spondyloenchondrodysplasia Due to Mutations in ACP5: A Comprehensive Survey

Purpose: Spondyloenchondrodysplasia is a rare immuno-osseous dysplasia caused by biallelic mutations in ACP5. We aimed to provide a survey of the skeletal, neurological and immune manifestations of this disease in a cohort of molecularly confirmed cases. Methods: We compiled clinical, genetic and serological data from a total of 26 patients from 18 pedigrees, all with biallelic ACP5 mutations. Results: We observed a variability in skeletal, neurological and immune phenotypes, which was sometimes marked even between affected siblings. In total, 22 of 26 patients manifested autoimmune disease, most frequently autoimmune thrombocytopenia and systemic lupus erythematosus. Four patients were considered to demonstrate no clinical autoimmune disease, although two were positive for autoantibodies. In the majority of patients tested we detected upregulated expression of interferon-stimulated genes (ISGs), in keeping with the autoimmune phenotype and the likely immune-regulatory function of the deficient protein tartrate resistant acid phosphatase (TRAP). Two mutation positive patients did not demonstrate an upregulation of ISGs, including one patient with significant autoimmune disease controlled by immunosuppressive therapy. Conclusions: Our data expand the known phenotype of SPENCD. We propose that the OMIM differentiation between spondyloenchondrodysplasia and spondyloenchondrodysplasia with immune dysregulation is no longer appropriate, since the molecular evidence that we provide suggests that these phenotypes represent a continuum of the same disorder. In addition, the absence of an interferon signature following immunomodulatory treatments in a patient with significant autoimmune disease may indicate a therapeutic response important for the immune manifestations of spondyloenchondrodysplasia

Etude rétrospective française des anomalies vasculaires osseuses agressives de l'enfant 1988-2009

MONTPELLIER-BU Médecine UPM (341722108) / SudocMONTPELLIER-BU Médecine (341722104) / SudocSudocFranceF

Hyperspectral compressive microscopy based on structured light sheet and deep convolutional neural network

International audienceWe describe a compressive hyperspectral microscope based on a structured light sheet and a deep convolutional neural network. The setup extends the concept of the computational hyperspectral microscope introduced in Ref. 1. First, we significantly improve the quality of the structured light sheet using a digital micro-mirror device to generate the light patterns directly. Second, to reduce the acquisition time, only fewer light patterns are acquired. The resulting inverse problem is solved using a deep neural network that includes traditional Tikhonov regularization

Hyperspectral compressive microscopy based on structured light sheet and deep convolutional neural network

International audienceWe describe a compressive hyperspectral microscope based on a structured light sheet and a deep convolutional neural network. The setup extends the concept of the computational hyperspectral microscope introduced in Ref. 1. First, we significantly improve the quality of the structured light sheet using a digital micro-mirror device to generate the light patterns directly. Second, to reduce the acquisition time, only fewer light patterns are acquired. The resulting inverse problem is solved using a deep neural network that includes traditional Tikhonov regularization

Microscopie compressive hyperspectrale par feuillet de lumière structurée

International audienc

Microscopie compressive hyperspectrale par feuillet de lumière structurée

International audienc

Microscopie compressive hyperspectrale par feuillet de lumière structurée

International audienc

Hyperspectral compressive microscopy based on structured light sheet and deep convolutional neural network

International audienceWe describe a compressive hyperspectral microscope based on a structured light sheet and a deep convolutional neural network. The setup extends the concept of the computational hyperspectral microscope introduced in Ref. 1. First, we significantly improve the quality of the structured light sheet using a digital micro-mirror device to generate the light patterns directly. Second, to reduce the acquisition time, only fewer light patterns are acquired. The resulting inverse problem is solved using a deep neural network that includes traditional Tikhonov regularization

Microscopie compressive hyperspectrale par feuillet de lumière structurée

International audienc