11 research outputs found
Comparison of HLA antibody identification methods for the selection of platelet products for HLA ‐mediated platelet refractory patients
In an ineffective transfusion context, solid‐phase immunoassays using the Luminex platform for the detection and characterization of HLA antibodies are currently used to select HLA‐compatible platelet products. A new HLA antibody identification method, the HISTO SPOT® HLA AB test (BAG Health care GmbH, Lich, Germany), based on the detection of antibodies directed against a recombinant single antigen (SA) by colored spots detected by HISTO MATCH HLA AB module software, runs fully automated on the MR.SPOT®. The aim of this study was to compare the ability of the HISTO SPOT HLA AB and C1qScreen™ (C1q SAB) assays with that of the Labscreen single antigen class I (OL SAB) assay to detect anti‐HLA class I antibodies in 56 serum samples from 54 platelet refractory acute myeloid leukemia patients who received HLA mismatch platelet concentrates at a single oncohematology center. In total, 1414 class I specificities, 433 HLA‐A and 981 HLA‐B, were detected by the OL SAB test. The mean fluorescence intensity (MFI) was >5000 for 874 antigens and 5000, respectively, but did not identify 34% and 44% of OL SAB‐detected antigens, highlighting the lower sensitivity of these techniques. Interestingly, the donor‐specific antibodies (DSAs) identified by the HISTO SPOT® HLA AB and C1q SAB assays reacted against HLA mismatch platelet concentrates with the same specificity (86%) and positive predictive (77%) value as in the OL SAB test when the MFI threshold was >2000 for DSA detection. Although the HISTO SPOT® HLA AB test is less sensitive than the OL SAB test, this test could be used for the selection of HLA‐compatible platelet products
A case of platelet transfusion refractoriness in a CD36 ‐negative patient with acute myeloid leukemia: Diagnostic and therapeutic management
International audienc
ROC representation for the diagnosis of cancer in the whole cohort, including the tumor and non-tumor tissue samples.
<p>ROC representation for the diagnosis of cancer in the whole cohort, including the tumor and non-tumor tissue samples.</p
Flow chart for classification of samples and for MSP class allocation.
<p>Flow chart for classification of samples and for MSP class allocation.</p
A comparison of the population characteristics between the origins of the cancerous and non-cancerous resected tumors, mean (± standard deviation), median [limits] or number of subjects.
<p>A comparison of the population characteristics between the origins of the cancerous and non-cancerous resected tumors, mean (± standard deviation), median [limits] or number of subjects.</p
Diagnostic performance of cancer in the definitive database.
<p>Diagnostic performance of cancer in the definitive database.</p
Summary of the 3 evaluation steps of our classification strategy.
<p>Summary of the 3 evaluation steps of our classification strategy.</p
Comparison of the two ROC analyses for the subgroup of tumoral samples, to determine the usefulness of increasing the database.
<p>Comparison of the two ROC analyses for the subgroup of tumoral samples, to determine the usefulness of increasing the database.</p
Global design for developing a database after definitive diagnosis was obtained.
<p>Global design for developing a database after definitive diagnosis was obtained.</p