Project description:Case series of children and adolescents undergoing growth hormone stimulation testing for investigation of short stature. The aim of this study was to identify whether a machine learning approach utilising gene expression data could predict which short children would test positive for GHD and which would not.
Project description:Microglia are important immune cells in the central nervous system (CNS). Dysfunctions of gene-deficient microglia contribute to the development and progression of multiple CNS diseases. Microglia replacement by nonself cells has been proposed to treat microglia associated disorders. However, most of attempts are failed due to low replacement efficiencies, such as with the traditional bone marrow transplantation approach. In this study, we develop three efficient strategies for microglia replacement: microglia replacement by bone marrow transplantation (mrBMT), microglia replacement by peripheral blood (mrPB) and microglia replacement by microglia transplantation (mrMT). mrBMT and mrPB allow microglia-like cells to efficiently replace resident microglia in the whole CNS. On the other hand, mrMT achieves microglia replacement in brain regions of interest. In summary, the present study offers effective tactics for microglia replacement with diverse application scenarios, which potentially opens up a window on treating microglia-associated CNS disorders.