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http://dx.doi.org/10.1016/j.ajog.2007.05.025 | DOI Listing |
JAMA Neurol
October 2024
Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California.
Importance: The X chromosome has remained enigmatic in Alzheimer disease (AD), yet it makes up 5% of the genome and carries a high proportion of genes expressed in the brain, making it particularly appealing as a potential source of unexplored genetic variation in AD.
Objectives: To perform the first large-scale X chromosome-wide association study (XWAS) of AD.
Design, Setting, And Participants: This was a meta-analysis of genetic association studies in case-control, family-based, population-based, and longitudinal AD-related cohorts from the US Alzheimer's Disease Genetics Consortium, the Alzheimer's Disease Sequencing Project, the UK Biobank, the Finnish health registry, and the US Million Veterans Program.
Immunity
August 2024
Malaria Infection Biology and Immunity Section, Laboratory of Immunogenetics, NIAID, NIH, Rockville, MD, USA; Protozoa Immunology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany. Electronic address:
Many infections, including malaria, are associated with an increase in autoantibodies (AAbs). Prior studies have reported an association between genetic markers of susceptibility to autoimmune disease and resistance to malaria, but the underlying mechanisms are unclear. Here, we performed a longitudinal study of children and adults (n = 602) in Mali and found that high levels of plasma AAbs before the malaria season independently predicted a reduced risk of clinical malaria in children during the ensuing malaria season.
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March 2024
Laboratory for Tumor Immunology, The First Hospital of Jilin University, Changchun, China.
Front Immunol
February 2024
Affiliated Cancer Hospital, Dalian University of Technology, Dalian, China.
Background: Immunohistochemistry (IHC) is a widely used laboratory technique for cancer diagnosis, which selectively binds specific antibodies to target proteins in tissue samples and then makes the bound proteins visible through chemical staining. Deep learning approaches have the potential to be employed in quantifying tumor immune micro-environment (TIME) in digitized IHC histological slides. However, it lacks of publicly available IHC datasets explicitly collected for the in-depth TIME analysis.
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