G
Geoffrey Brown
Researcher at Indiana University
Publications - 111
Citations - 6252
Geoffrey Brown is an academic researcher from Indiana University. The author has contributed to research in topics: Antigen & Medicine. The author has an hindex of 32, co-authored 98 publications receiving 6157 citations. Previous affiliations of Geoffrey Brown include Hewlett-Packard & University College London.
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Journal ArticleDOI
Production of monoclonal antibodies to group A erythrocytes, HLA and other human cell surface antigens-new tools for genetic analysis.
Colin J. Barnstable,Walter F. Bodmer,Geoffrey Brown,G. Galfre,C. Milstein,Alan F. Williams,Andreas Ziegler +6 more
TL;DR: The experiments established the usefulness of the bybrid myeloma technique in preparing monospecific antibodies against human cell surface antigens and highlights the possibilities not only of obtaining reagents for somatic cell genetics, but also of obtaining mouse antibodies detecting human antigenic polymorphisms.
Journal ArticleDOI
Transferrin receptors in human tissues: their distribution and possible clinical relevance.
TL;DR: The receptor was widely distributed in carcinomas, sarcomas and in samples from cases of Hodgkin's disease, suggesting that malignancy-associated expression of the receptor may play a role in the anaemia of advanced malignancies by competing with the bone marrow for serum iron.
Journal ArticleDOI
Antisera to acute lymphoblastic leukemia cells.
TL;DR: It is concluded that antisera to ALL may define an antigen which may be restricted in expression to a large subgroup of ALL cases, and which offers considerable diagnostic and prognostic potential.
Journal Article
Purification of Human T and B Lymphocytes
Mel Greaves,Geoffrey Brown +1 more
TL;DR: The application of various markers are reported to determine the efficacy of separation techniques aimed at providing a simple and reproducible protocol for the preparation of effectively pure human T and B lymphocytes for functional studies.
Proceedings ArticleDOI
Lx: a technology platform for customizable VLIW embedded processing
TL;DR: The experiments described in the paper show that specialization for an application domain is effective, yielding large gains in price/performance ratio and how scaling machine resources scales performance, although not uniformly across all applications.