Literature DB >> 17016037

Flow cytometry as a diagnostic tool for hereditary spherocytosis.

Gudrun Stoya1, Bernd Gruhn, Heinz Vogelsang, Eckehard Baumann, Werner Linss.   

Abstract

Flow cytometric analysis of eosin-5'-maleimide-labeled red blood cells has been proposed as a new method of identifying hereditary spherocytosis (HS). The aim of the present study was to analyze sensitivity and specificity of this method. Red blood cells from patients with HS (n = 58) revealed significantly lower mean channel fluorescence values than red blood cells from normal subjects (n = 110), unaffected HS family members (n = 8), and patients with other anemias (n = 44). Taking a mean channel fluorescence of 400.0 units as the threshold value identified by logistic regression, sensitivity and specificity of the test for HS were 96.6 and 99.1%, respectively. Flow cytometric analysis is a valuable screening test for the diagnosis of HS. Copyright 2006 S. Karger AG, Basel.

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Year:  2006        PMID: 17016037     DOI: 10.1159/000094679

Source DB:  PubMed          Journal:  Acta Haematol        ISSN: 0001-5792            Impact factor:   2.195


  5 in total

Review 1.  Hereditary red cell membrane defects: diagnostic and clinical aspects.

Authors:  Wilma Barcellini; Paola Bianchi; Elisa Fermo; Francesca G Imperiali; Anna P Marcello; Cristina Vercellati; Anna Zaninoni; Alberto Zanella
Journal:  Blood Transfus       Date:  2011-01-13       Impact factor: 3.443

2.  Diagnostic power of laboratory tests for hereditary spherocytosis: a comparison study in 150 patients grouped according to molecular and clinical characteristics.

Authors:  Paola Bianchi; Elisa Fermo; Cristina Vercellati; Anna P Marcello; Laura Porretti; Agostino Cortelezzi; Wilma Barcellini; Alberto Zanella
Journal:  Haematologica       Date:  2011-11-04       Impact factor: 9.941

Review 3.  Old and new insights into the diagnosis of hereditary spherocytosis.

Authors:  Olga Ciepiela
Journal:  Ann Transl Med       Date:  2018-09

4.  Yippee like 4 (Ypel4) is essential for normal mouse red blood cell membrane integrity.

Authors:  Alexander Mattebo; Taha Sen; Maria Jassinskaja; Kristýna Pimková; Isabel Prieto González-Albo; Abdul Ghani Alattar; Ramprasad Ramakrishnan; Stefan Lang; Marcus Järås; Jenny Hansson; Shamit Soneji; Sofie Singbrant; Emile van den Akker; Johan Flygare
Journal:  Sci Rep       Date:  2021-08-05       Impact factor: 4.379

Review 5.  Application of Machine Learning for Cytometry Data.

Authors:  Zicheng Hu; Sanchita Bhattacharya; Atul J Butte
Journal:  Front Immunol       Date:  2022-01-03       Impact factor: 7.561

  5 in total

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