Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Borgmann, DM; Mayr, S; Polin, H; Schaller, S; Dorfer, V; Obritzberger, L; Endmayr, T; Gabriel, C; Winkler, SM; Jacak, J.
Single Molecule Fluorescence Microscopy and Machine Learning for Rhesus D Antigen Classification.
Sci Rep. 2016; 6(10):32317-32317
Doi: 10.1038/srep32317
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- Co-Autor*innen der Med Uni Graz
- GABRIEL Christian
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- Abstract:
- In transfusion medicine, the identification of the Rhesus D type is important to prevent anti-D immunisation in Rhesus D negative recipients. In particular, the detection of the very low expressed DEL phenotype is crucial and hence constitutes the bottleneck of standard immunohaematology. The current method of choice, adsorption-elution, does not provide unambiguous results. We have developed a complementary method of high sensitivity that allows reliable identification of D antigen expression. Here, we present a workflow composed of high-resolution fluorescence microscopy, image processing, and machine learning that - for the first time - enables the identification of even small amounts of D antigen on the cellular level. The high sensitivity of our technique captures the full range of D antigen expression (including D+, weak D, DEL, D-), allows automated population analyses, and results in classification test accuracies of up to 96%, even for very low expressed phenotypes.
- Find related publications in this database (using NLM MeSH Indexing)
- Erythrocytes - metabolism
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- Rho(D) Immune Globulin - metabolism
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