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1.
Clin Lab ; 70(5)2024 May 01.
Article in English | MEDLINE | ID: mdl-38747914

ABSTRACT

BACKGROUND: Nucleic acid amplification testing is the gold standard for SARS-CoV-2 diagnostics, although it may produce a certain number of false positive results. There has not been much published about the characteristics of false positive results. In this study, based on retesting, specimens that initially tested positive for SARS-CoV-2 were classified as true or false positive groups to characterize the distribution of cycle threshold (CT) values for N1 and N2 targets and number of targets detected for each group. METHODS: Specimens that were positive for N-gene on retesting and accompanied with S-gene were identified as true positives (true positive based on retesting, rTP), while specimens that retested negative were classified as false positives (false positive based on retesting, rFP). RESULTS: Of the specimens retested, 85/127 (66.9%) were rFP, 16/47 (34.0%) specimens with both N1 and N2 targets initially detected were rFP, and the CT values for each target was higher in rFP than in rTP. ROC curve analysis showed that optimal cutoff values of CT to differentiate between rTP and rFP were 34.8 for N1 and 33.0 for N2. With the optimal cutoff values of CT for each target, out of the 24 specimens that were positive for both N1 and N2 targets and classified as rTP, 23 (95.8%) were correctly identified as true positives. rFP specimens had a single N1 target in 52/61 (85.2%) and a single N2 target in 17/19 (89.5%). Notably, no true positive results were obtained from any specimens with only N2 target detected. CONCLUSIONS: These results suggest that retesting should be performed for positive results with a CT value greater than optimal cutoff value for each target or with a single N1 target amplified, considering the possibility of a false positive. This may provide guidance on indications to perform retesting to minimize the number of false positives.


Subject(s)
COVID-19 Nucleic Acid Testing , COVID-19 , SARS-CoV-2 , Humans , False Positive Reactions , SARS-CoV-2/genetics , COVID-19/diagnosis , COVID-19/virology , COVID-19 Nucleic Acid Testing/methods , COVID-19 Nucleic Acid Testing/standards , ROC Curve , Spike Glycoprotein, Coronavirus/genetics , Sensitivity and Specificity , Coronavirus Nucleocapsid Proteins/genetics , RNA, Viral/genetics , RNA, Viral/analysis
2.
Int J Hematol ; 94(2): 169-177, 2011 Aug.
Article in English | MEDLINE | ID: mdl-21732036

ABSTRACT

We evaluated the diagnostic utility of peripheral blood neutrophil distribution patterns obtained using the CELL-DYN SAPPHIRE hematology analyzer in patients with myelodysplastic syndrome (MDS). Peripheral blood was obtained from 467 individuals including 32 patients with MDS, and the respective neutrophil distribution patterns were observed using two light scatters [7-degree complexity (7D) and 90-degree lobularity (90D)]. These scattering intensities are shown as median (median neutrophil distribution: MND) and coefficient of variation (neutrophil distribution width: NDW). Generally, MDS patients showed lower 7D MND, higher 7D NDW, lower 90D MND and higher 90D NDW than other comparable groups. Whereas 90D parameters were more diagnostically efficient than 7D ones in patients with MDS. The sensitivity and specificity of 90D MND for MDS patients became 78.1 and 78.9%, respectively (cut-off value = 14,514). 90D NDW was most diagnostically effective with 87.5% sensitivity and 91.0% specificity (cut-off value = 21.2%). Both 90D parameters showed no evident correlation with the degree of either leukocytopenia or peripheral blood dysgranulopoiesis. In conclusion, neutrophil distribution parameters, especially 90D NDW, appear to provide convenient and objective markers for the screening of patients with MDS in routine laboratory hematology.


Subject(s)
Hematologic Tests/instrumentation , Myelodysplastic Syndromes/diagnosis , Neutrophils/pathology , Adult , Aged , Aged, 80 and over , Biomarkers/blood , Female , Humans , Light , Male , Middle Aged , Scattering, Radiation , Sensitivity and Specificity , Statistical Distributions
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