Real-time Red Blood Cell Counting and Osmolarity Analysis using a Photoacoustic-based Microfluidic System

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Wenxiu Zhao ◽  
Haibo Yu ◽  
Yangdong Wen ◽  
Hao Luo ◽  
Boliang Jia ◽  
...  
Keyword(s):  
Blood Cell ◽  
Physical Properties ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Diagnostic Procedure ◽  
Microfluidic System ◽  
Cell Counting ◽  
Blood Samples ◽  
System P ◽  
Blood Cell Counting

Counting the number of red blood cells (RBCs) in blood samples is a common clinical diagnostic procedure, but conventional methods are unable to provide the size and other physical properties...

scholarly journals PERFORMANCE EVALUATION OF NUCLEATED RED BLOOD CELL (NRBC) COUNT USING A FULLY AUTOMATED HAEMATOLOGY ANALYZER VERSUS MANUAL COUNTING

2021 ◽  
Vol 71 (5) ◽  
pp. 1806-10
Author(s):  
Tanweer Ahmed ◽  
Asad Mahmood ◽  
Nasir Uddin ◽  
Helen Mary Robert ◽  
Muhammad Ashraf ◽  
...  
Keyword(s):  
Blood Cell ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Linear Regression Analysis ◽  
Cell Counting ◽  
Nucleated Red Blood Cells ◽  
Manual Counting ◽  
Blood Cell Counting ◽  
Nucleated Red Blood Cell

Objective: To evaluate the performance of Nucleated RBC (NRBC) Count using a fully automated haematology analyzer versus manual counting. Study Design: Cross-Sectional Study. Place and Duration of Study: Department of Hematology, Armed Forces Institute of Pathology, from Sep 2019-Jun 2020. Methodology: Routine fresh whole blood samples were run on Sysmex XN-3000 automated haematology analyzer and 384 samples with results of ≥0.1% Nucleated red blood cells were included in this study. Manual NRBC counting was carried out twice on Leishman-stained peripheral blood smears from all 384 samples. Comparison between manual and automated nucleated red blood cell counting methods was statistically analyzed through linear regression analysis & coefficient correlation. The degree of agreement between two methods was analyzed through Bland-Altman plot. Finally, concordance between the two methods was also analyzed at 5 different ranges of nucleated red blood cells. Results: Linear regression analysis revealed a (r2) value of 0.97. Regression equation was calculated as XN = 0.76MC ± 1.28, with 95% limits of agreement between ± 40.42% and -24.47%. A mean bias of 7.97% was demonstrated through Bland-Altman plot. Concordance analysis revealed a concordance rate of 93.74% (360/384). Nucleated red blood cell counting between two methods were more concordant when nucleated red blood cell counts were <200%. Conclusion: Nucleated red blood cells counting by XN-3000 automated hematology analyzer is statistically comparable to manual nucleated red blood cell counting. We suggest that automated counting can be adopted in routine hematology laboratory as a replacement of manual NRBC counting.

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

2017 ◽  
Vol 19 (12) ◽  
pp. 124014 ◽  
Author(s):  
Xi Liu ◽  
Mei Zhou ◽  
Song Qiu ◽  
Li Sun ◽  
Hongying Liu ◽  
...  
Keyword(s):  
Blood Cell ◽  
Hyperspectral Imaging ◽  
Red Blood Cell ◽  
Cell Counting ◽  
Counting Method ◽  
Imaging Technology ◽  
Blood Cell Counting

scholarly journals Hematological changes of Anemia`s in Horses of Equestrian club in Baghdad

2012 ◽  
Vol 36 (0A) ◽  
pp. 92-97
Author(s):  
Faisal G. Habasha
Keyword(s):  
Blood Cell ◽  
Sedimentation Rate ◽  
Red Blood Cell ◽  
Packed Cell Volume ◽  
Blood Cells ◽  
White Blood Cells ◽  
Blood Samples ◽  
Erythrocyte Sedimentation ◽  
Blood Smears ◽  
Hematological Changes

This study was conducted to know thehematological changes of anemia in horsesat equestrian club in Baghdad. Blood samples were collected from 151 horses of both sexes(74 male and 77 female) and different agesrandomly. The study includedred blood cells count, white blood cells, hemoglobin, packed cell volume and differential blood smears, togetherwith erythrocyte sedimentation rate readings. The study showed increased white blood cells count mainly neutrophilwith decreased hemoglobinand red blood cell countin addition to erythrocyte sedimentation rate.The blood smears showeddifferent changes of red blood cell.

scholarly journals Microfluidic Cartridges for Automated, Point-of-Care Blood Cell Counting

2016 ◽  
Vol 22 (2) ◽  
pp. 176-185
Author(s):  
Suzanne Smith ◽  
Phophi Madzivhandila ◽  
René Sewart ◽  
Ureshnie Govender ◽  
Holger Becker ◽  
...  
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Red Blood Cell ◽  
Blood Count ◽  
Point Of Care ◽  
Cell Counting ◽  
Full Blood Count ◽  
Diagnostic Tools ◽  
Cell Counts ◽  
Blood Cell Counting

Disposable, low-cost microfluidic cartridges for automated blood cell counting applications are presented in this article. The need for point-of-care medical diagnostic tools is evident, particularly in low-resource and rural settings, and a full blood count is often the first step in patient diagnosis. Total white and red blood cell counts have been implemented toward a full blood count, using microfluidic cartridges with automated sample introduction and processing steps for visual microscopy cell counting to be performed. The functional steps within the microfluidic cartridge as well as the surrounding instrumentation required to control and test the cartridges in an automated fashion are described. The results recorded from 10 white blood cell and 10 red blood cell counting cartridges are presented and compare well with the results obtained from the accepted gold-standard flow cytometry method performed at pathology laboratories. Comparisons were also made using manual methods of blood cell counting using a hemocytometer, as well as a commercially available point-of-care white blood cell counting system. The functionality of the blood cell counting microfluidic cartridges can be extended to platelet counting and potential hemoglobin analysis, toward the implementation of an automated, point-of-care full blood count.

Electrochemical Red Blood Cell Counting: One at a Time

2016 ◽  
Vol 55 (33) ◽  
pp. 9768-9771 ◽  
Author(s):  
Lior Sepunaru ◽  
Stanislav V. Sokolov ◽  
Jennifer Holter ◽  
Neil P. Young ◽  
Richard G. Compton
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Cell Counting ◽  
Blood Cell Counting
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