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Journal articles on the topic 'Blood cell counting'

Author: Grafiati

Published: 6 September 2023

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1

Piacentini, Niccolò, Danilo Demarchi, Pierluigi Civera, and Marco Knaflitz. "Microsystems for Blood Cell Counting." Advances in Science and Technology 57 (September 2008): 55–60. http://dx.doi.org/10.4028/www.scientific.net/ast.57.55.

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This paper presents two biomedical microsystems for blood cell counting, designed and built through MultiMEMS Multi-Project Wafer (MPW) service and the microBUILDER European project. Dies mm in size, made of a micromachined glass-silicon-glass triple stack, host two new kinds of multiple micro-counters, suitable to investigate the feasibility of blood cell differential analysis by means of Coulter principle in a monolithic lab-on-a-chip, which integrates a microfluidic network, sensing metal electrodes and light-guiding structures. Within these devices, impedance method gains some innovative f

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2

Schmidt, R. M. "Automated differential blood cell counting systems." Clinical & Laboratory Haematology 1, no. 2 (2008): 149–50. http://dx.doi.org/10.1111/j.1365-2257.1979.tb00463.x.

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3

Smith, Suzanne, Phophi Madzivhandila, René Sewart, et al. "Microfluidic Cartridges for Automated, Point-of-Care Blood Cell Counting." SLAS TECHNOLOGY: Translating Life Sciences Innovation 22, no. 2 (2016): 176–85. http://dx.doi.org/10.1177/2211068216677820.

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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

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4

Lewis, S. M., J. M. England, and F. Kubota. "Coincidence correction in red blood cell counting." Physics in Medicine and Biology 34, no. 9 (1989): 1239–46. http://dx.doi.org/10.1088/0031-9155/34/9/009.

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5

Walberg, James. "White blood cell counting techniques in birds." Seminars in Avian and Exotic Pet Medicine 10, no. 2 (2001): 72–76. http://dx.doi.org/10.1053/saep.2001.22051.

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6

Fatichah, Chastine, Diana Purwitasari, Victor Hariadi, and Faried Effendy. "OVERLAPPING WHITE BLOOD CELL SEGMENTATION AND COUNTING ON MICROSCOPIC BLOOD CELL IMAGES." International Journal on Smart Sensing and Intelligent Systems 7, no. 3 (2014): 1271–86. http://dx.doi.org/10.21307/ijssis-2017-705.

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7

Chaturvedi, Shruti. "Counting the cost of caplacizumab." Blood 137, no. 7 (2021): 871–72. http://dx.doi.org/10.1182/blood.2020009250.

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8

James L, Sherley, Daley Michael P, and Dutton Renly A. "Validation of Kinetic Stem Cell (KSC) counting algorithms for rapid quantification of human hematopoietic stem cells." Journal of Stem Cell Therapy and Transplantation 6, no. 1 (2022): 029–37. http://dx.doi.org/10.29328/journal.jsctt.1001028.

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Specific quantification of therapeutic tissue stem cells (TSCs) is a major challenge. We recently described a computational simulation method for accurate and specific counting of TSCs. The method quantifies TSCs based on their unique asymmetric cell kinetics, which is rate-limiting for TSCs’ production of transiently-amplifying lineage-committed cells and terminally arrested cells during serial cell culture. Because of this basis, the new method is called kinetic stem cell (KSC) counting. Here, we report further validations of the specificity and clinical utility of KSC counting. First, we de

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9

H. Al-khafaji, Kawther, and Athraa H. Al-khafaji. "Diagnoses of Blood Disorder in Different Animal Species Depending on Counting Methods in Blood Cell Images." International Journal of Engineering & Technology 7, no. 4.36 (2018): 660. http://dx.doi.org/10.14419/ijet.v7i4.36.24218.

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Counting of red blood cells (RBCs) in microscope blood cell images, can give the pathologists valuable information regarding various hematological disorders, like anemia, leukemia,....etc. in several animal species, in this paper, an automated vision system has been developed which is capable of counting of red blood cells, in blood samples by applying different algorithms, based on red blood cellshape, the difference in the red blood cell shape of animal species make it difficult to use a one algorithm, therefore, for each animal species used specific algorithm which was capable of counting o

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10

MATSUNO, K., and 美恵森本. "Peripheral Blood Cell Counting by Automated Hematology Analyzer." JAPANES JOURNAL OF MEDICAL INSTRUMENTATION 69, no. 1 (1999): 25–29. http://dx.doi.org/10.4286/ikakikaigaku.69.1_25.

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11

Talstad, Ingebrigt. "ANALYSIS OF ERRORS IN ELECTRONIC BLOOD CELL COUNTING." Acta Medica Scandinavica 190, no. 1-6 (2009): 1–5. http://dx.doi.org/10.1111/j.0954-6820.1971.tb07386.x.

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12

Mack, Samantha, and Ralph R. Vassallo. "Component residual white blood cell counting made easy?" Transfusion 60, no. 1 (2020): 4–6. http://dx.doi.org/10.1111/trf.15642.

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13

BRIGGS, C. "Quality counts: new parameters in blood cell counting." International Journal of Laboratory Hematology 31, no. 3 (2009): 277–97. http://dx.doi.org/10.1111/j.1751-553x.2009.01160.x.

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14

Vo, Ngoc Duc, Anh Thi Van Nguyen, Hoi Thi Le, Nam Hoang Nguyen, and Huong Thi Thu Pham. "A Simple Approach for Counting CD4+ T Cells Based on a Combination of Magnetic Activated Cell Sorting and Automated Cell Counting Methods." Applied Sciences 11, no. 21 (2021): 9786. http://dx.doi.org/10.3390/app11219786.

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Frequent tests for CD4+ T cell counting are important for the treatment of patients with immune deficiency; however, the routinely used fluorescence-activated cell-sorting (FACS) gold standard is costly and the equipment is only available in central hospitals. In this study, we developed an alternative simple approach (shortly named as the MACS-Countess system) for CD4+ T cell counting by coupling magnetic activated cell sorting (MACS) to separate CD4+ T cells from blood, followed by counting the separated cells using CountessTM, an automated cell-counting system. Using the cell counting proto

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15

Ahmed, Tanweer, Asad Mahmood, Nasir Uddin, Helen Mary Robert, Muhammad Ashraf, and Usman Tahir Swati. "PERFORMANCE EVALUATION OF NUCLEATED RED BLOOD CELL (NRBC) COUNT USING A FULLY AUTOMATED HAEMATOLOGY ANALYZER VERSUS MANUAL COUNTING." PAFMJ 71, no. 5 (2021): 1806–10. http://dx.doi.org/10.51253/pafmj.v71i5.5706.

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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 a

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16

Brownrigg, Emma, Debbie Carr, Michael C. Copeman, et al. "Twenty Units of Blood and Counting." Blood 112, no. 11 (2008): 4667. http://dx.doi.org/10.1182/blood.v112.11.4667.4667.

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Abstract Iron overload is a condition seen in patients who have received multiple packed red cell transfusions. Generally, patients who have received &gt;20 transfused units will be at risk. Currently in Australia there is no universal method of tracking the number of transfusions a patient has received, and a cumulative figure requires manual calculation. Therefore, identification of at-risk patients is not straightforward. Aim: Firstly, to review the primary diagnosis of transfused patients in haematology day units. Secondly, to quantify number of transfusions received and serum ferritin

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17

Neerukattu Indrani and Chiraparapu Srinivasa Rao. "White Blood Cell Image Classification Using Deep Learning." September 2021 7, no. 09 (2021): 7–12. http://dx.doi.org/10.46501/ijmtst0709002.

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The microscopic inspection of blood smears provides diagnostic information concerning patients’ health status. For example, the presence of infections, leukemia, and some particular kinds of cancers can be diagnosed based on the results of the classification and the count of white blood cells. The traditional method for the differential blood count is performed by experienced operators. They use a microscope and count the percentage of the occurrence of each type of cell counted within an area of interest in smears. Obviously, this manual counting process is very tedious and slow. In addition,

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18

Maitra, Mausumi, Rahul Kumar Gupta, and Manali Mukherjee. "Detection and Counting of Red Blood Cells in Blood Cell Images using Hough Transform." International Journal of Computer Applications 53, no. 16 (2012): 13–17. http://dx.doi.org/10.5120/8505-2274.

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19

Drałus, Grzegorz, Damian Mazur, and Anna Czmil. "Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet." Entropy 23, no. 11 (2021): 1522. http://dx.doi.org/10.3390/e23111522.

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A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes a

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20

M S, Soumiya. "Blood Cell Counting using Image Processing Techniques: A Review." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (2020): 1047–49. http://dx.doi.org/10.22214/ijraset.2020.30406.

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21

Hendra, T. J., and J. S. Yudkin. "Whole Blood Platelet Aggregation Based on Cell Counting Procedures." Platelets 1, no. 2 (1990): 57–66. http://dx.doi.org/10.3109/09537109009005464.

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22

Shapiro, Howard M., Francis F. Mandy, Paul Sandstrom, and Tobias F. Rinke de Wit. "Dried blood spot technology for CD4+ T-cell counting." Lancet 363, no. 9403 (2004): 164. http://dx.doi.org/10.1016/s0140-6736(03)15270-1.

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23

Jenwitheesuk, Ekachai. "Dried blood spot technology for CD4+ T-cell counting." Lancet 363, no. 9403 (2004): 164. http://dx.doi.org/10.1016/s0140-6736(03)15271-3.

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24

Mwaba, P., S. Cassol, AJ Nunn, C. Chintu, and A. Zumla. "Dried blood spot technology for CD4+ T-cell counting." Lancet 363, no. 9403 (2004): 164–65. http://dx.doi.org/10.1016/s0140-6736(03)15272-5.

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25

Janossy, George. "Dried blood spot technology for CD4+ T-cell counting." Lancet 363, no. 9414 (2004): 1074. http://dx.doi.org/10.1016/s0140-6736(04)15849-2.

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26

Mwaba, Peter. "Dried blood spot technology for CD4+ T-cell counting." Lancet 363, no. 9414 (2004): 1074–75. http://dx.doi.org/10.1016/s0140-6736(04)15850-9.

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27

Cassol, Sharon, and Tanya Welz. "Dried blood spot technology for CD4+ T-cell counting." Lancet 363, no. 9414 (2004): 1075. http://dx.doi.org/10.1016/s0140-6736(04)15851-0.

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28

Wang, Xinhao, Guohong Lin, Guangzhe Cui, Xiangfei Zhou, and Gang Logan Liu. "White blood cell counting on smartphone paper electrochemical sensor." Biosensors and Bioelectronics 90 (April 2017): 549–57. http://dx.doi.org/10.1016/j.bios.2016.10.017.

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29

Sepunaru, Lior, Stanislav V. Sokolov, Jennifer Holter, Neil P. Young, and Richard G. Compton. "Electrochemical Red Blood Cell Counting: One at a Time." Angewandte Chemie International Edition 55, no. 33 (2016): 9768–71. http://dx.doi.org/10.1002/anie.201605310.

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30

Sepunaru, Lior, Stanislav V. Sokolov, Jennifer Holter, Neil P. Young, and Richard G. Compton. "Electrochemical Red Blood Cell Counting: One at a Time." Angewandte Chemie 128, no. 33 (2016): 9920–23. http://dx.doi.org/10.1002/ange.201605310.

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31

Bai, Hua, Xuechun Wang, Yingjian Guan, Qiang Gao, and Zhibo Han. "Blood cell counting based on U-Net++ and YOLOv5." Optoelectronics Letters 19, no. 6 (2023): 370–76. http://dx.doi.org/10.1007/s11801-023-2165-3.

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32

A S M, Waliullah. "FEASIBILITY STUDY ON BLOOD CELL COUNTING USING MOBILE PHONE-BASED PORTABLE MICROSCOPE." International Journal of Clinical and Biomedical Research 4, no. 3 (2018): 76–79. http://dx.doi.org/10.31878/ijcbr.2018.43.16.

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Objectives: To check the feasibility of using the mobile phone-based microscope for blood cell counting from human blood histological sample. Methodology: A feasibility study was performed by imaging blood histology samples with one novel type of microscope “Foldscope” and image compared with a conventional microscope in the laboratory facility. The image acquired from both modalities were processed further and compared and analyzed. Results: Mobile phone-based microscope acquired images were observed and compared with a conventional microscope and found the blood cell counting feasibility and

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33

Maličev, Elvira, Klara Železnik, and Katerina Jazbec. "An evaluation of a volumetric method for the flow cytometric determination of residual leukocytes in blood transfusion units." PLOS ONE 17, no. 12 (2022): e0279244. http://dx.doi.org/10.1371/journal.pone.0279244.

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The removal of leukocytes from blood components helps to prevent or reduce some adverse reactions that occur after blood transfusions. The implementation of the leukodepletion process in the preparation of blood units requires quality control, consisting of a reliable cell counting method to determine residual leukocytes in blood components. The most widely used methodology is a flow cytometric bead-based counting method. To avoid the need for commercial counting beads, we evaluated a volumetric counting method of leukocyte enumeration. A total of 160 specimens of leukodepleted plasma, red cel

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34

Wei, Xudong, Yiping Cao, Guangkai Fu, and Yapin Wang. "A counting method for complex overlapping erythrocytes-based microscopic imaging." Journal of Innovative Optical Health Sciences 08, no. 06 (2015): 1550033. http://dx.doi.org/10.1142/s1793545815500339.

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Red blood cell (RBC) counting is a standard medical test that can help diagnose various conditions and diseases. Manual counting of blood cells is highly tedious and time consuming. However, new methods for counting blood cells are customary employing both electronic and computer-assisted techniques. Image segmentation is a classical task in most image processing applications which can be used to count blood cells in a microscopic image. In this research work, an approach for erythrocytes counting is proposed. We employed a classification before counting and a new segmentation idea was impleme

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35

Fearnley, D. B., L. F. Whyte, S. A. Carnoutsos, A. H. Cook, and D. N. J. Hart. "Monitoring Human Blood Dendritic Cell Numbers in Normal Individuals and in Stem Cell Transplantation." Blood 93, no. 2 (1999): 728–36. http://dx.doi.org/10.1182/blood.v93.2.728.

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Abstract Dendritic cells (DC) originate from a bone marrow (BM) precursor and circulate via the blood to most body tissues where they fulfill a role in antigen surveillance. Little is known about DC numbers in disease, although the reported increase in tissue DC turnover due to inflammatory stimuli suggests that blood DC numbers may be altered in some clinical situations. The lack of a defined method for counting DC has limited patient studies. We therefore developed a method suitable for routine monitoring of blood DC numbers, using the CMRF44 monoclonal antibody (MoAb) and flow cytometry to

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36

Fearnley, D. B., L. F. Whyte, S. A. Carnoutsos, A. H. Cook, and D. N. J. Hart. "Monitoring Human Blood Dendritic Cell Numbers in Normal Individuals and in Stem Cell Transplantation." Blood 93, no. 2 (1999): 728–36. http://dx.doi.org/10.1182/blood.v93.2.728.402k03_728_736.

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Dendritic cells (DC) originate from a bone marrow (BM) precursor and circulate via the blood to most body tissues where they fulfill a role in antigen surveillance. Little is known about DC numbers in disease, although the reported increase in tissue DC turnover due to inflammatory stimuli suggests that blood DC numbers may be altered in some clinical situations. The lack of a defined method for counting DC has limited patient studies. We therefore developed a method suitable for routine monitoring of blood DC numbers, using the CMRF44 monoclonal antibody (MoAb) and flow cytometry to identify

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37

Lombarts, A. J. P. F., A. L. Koevoet, and B. Leijnse. "Basic Principles and Problems of Haemocytometry." Annals of Clinical Biochemistry: International Journal of Laboratory Medicine 23, no. 4 (1986): 390–404. http://dx.doi.org/10.1177/000456328602300404.

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After some brief remarks on counting chambers, references to the ICSH-recommended haemoglobin-determination are given. The microhaematocrit of normal blood is advocated as a potential routine calibration method. Comments are given on discrepancies between centrifugal and flow haemocytometry haematocrits of abnormal and artificial bloods. Flow haemocytometry instruments are classified into analogue and digital instruments or into electrical and optical instruments. Their hydrodynamic properties are discussed. The principles and problems of electrical and optical cell counting and sizing are dea

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38

Baron, Udo, Jeannette Werner, Konstantin Schildknecht, et al. "Epigenetic immune cell counting in human blood samples for immunodiagnostics." Science Translational Medicine 10, no. 452 (2018): eaan3508. http://dx.doi.org/10.1126/scitranslmed.aan3508.

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Immune cell profiles provide valuable diagnostic information for hematologic and immunologic diseases. Although it is the most widely applied analytical approach, flow cytometry is limited to liquid blood. Moreover, either analysis must be performed with fresh samples or cell integrity needs to be guaranteed during storage and transport. We developed epigenetic real-time quantitative polymerase chain reaction (qPCR) assays for analysis of human leukocyte subpopulations. After method establishment, whole blood from 25 healthy donors and 97 HIV+ patients as well as dried spots from 250 healthy n

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39

Noda, Y., M. Hanafusa, A. Yamamoto, et al. "Integrated blood cell counting device using a hydrophobic surface treatment." Sensors and Actuators B: Chemical 171-172 (August 2012): 1321–26. http://dx.doi.org/10.1016/j.snb.2012.06.047.

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40

钟, 天. "Research on Blood Cell Recognitionand Counting Based on ImprovedYOLO v7." Advances in Applied Mathematics 12, no. 03 (2023): 1083–89. http://dx.doi.org/10.12677/aam.2023.123110.

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41

Lee, Shin-Jye, Pei-Yun Chen, and Jeng-Wei Lin. "Complete Blood Cell Detection and Counting Based on Deep Neural Networks." Applied Sciences 12, no. 16 (2022): 8140. http://dx.doi.org/10.3390/app12168140.

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Complete blood cell (CBC) counting has played a vital role in general medical examination. Common approaches, such as traditional manual counting and automated analyzers, were heavily influenced by the operation of medical professionals. In recent years, computer-aided object detection using deep learning algorithms has been successfully applied in many different visual tasks. In this paper, we propose a deep neural network-based architecture to accurately detect and count blood cells on blood smear images. A public BCCD (Blood Cell Count and Detection) dataset is used for the performance eval

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42

Fatonah, Nenden Siti, Handayani Tjandrasa, and Chastine Fatichah. "Automatic Leukemia Cell Counting using Iterative Distance Transform for Convex Sets." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1731. http://dx.doi.org/10.11591/ijece.v8i3.pp1731-1740.

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The calculation of white blood cells on the acute leukemia microscopic images is one of the stages in the diagnosis of Leukemia disease. The main constraint on calculating the number of white blood cells is the precision in the area of overlapping white blood cells. The research on the calculation of the number of white blood cells overlapping generally based on geometry. However, there was still a calculation error due to over segment or under segment. This paper proposed an Iterative Distance Transform for Convex Sets (IDTCS) method to determine the markers and calculate the number of overla

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43

Jahangir, Alam S. M., Guo Qing Hu, and Ling Ke Yu. "Simulation of Red Particles in Blood Cell." Applied Mechanics and Materials 477-478 (December 2013): 330–34. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.330.

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Red blood cell (RBC) particle detection and counting with characteristics in blood cell systems has been done by computer simulation. A simulation region, including plasma, red blood cells (RBCs) and platelets, was modeled by an assembly of discrete particles. The proposed method has detected the red particle from blood cell systems through different simulations of MATLAB and GAMBIT & FLUENT. After the detection, the number of red particles in a sampled cell has been counted and the characteristics about the red particles for analyzing the Birth-Death growth of each red particle have been

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44

Ngoma, Alain, Shunnichi Saito, Hitoshi Ohto, et al. "CD34+ Cell Enumeration by Flow Cytometry: A Comparison of Systems and Methodologies." Archives of Pathology & Laboratory Medicine 135, no. 7 (2011): 909–14. http://dx.doi.org/10.5858/2010-0119-0ar.1.

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Abstract Context.—An increasing number of medical centers can collect bone marrow, peripheral blood, or umbilical cord stem cells. Pathology laboratories should accommodate this trend, but investment in additional equipment may be impractical. Objectives.—To compare CD34+ cell counting results by using 2 widely available flow cytometry systems, with and without the use of a separate hematology analyzer (ie, single-platform versus dual-platform methodologies). Design.—Whole blood and peripheral blood stem cell (PBSC) samples were analyzed from 13 healthy allogeneic PBSC donors and 46 autologous

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45

Ghosh, Pramit, Debotosh Bhattacharjee, and Mita Nasipuri. "Blood smear analyzer for white blood cell counting: A hybrid microscopic image analyzing technique." Applied Soft Computing 46 (September 2016): 629–38. http://dx.doi.org/10.1016/j.asoc.2015.12.038.

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46

Tawana, Kiran, and Jude Fitzgibbon. "Inherited DDX41 mutations: 11 genes and counting." Blood 127, no. 8 (2016): 960–61. http://dx.doi.org/10.1182/blood-2016-01-690909.

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47

Ariëns, Robert A. S. "Counting 1 fibrin molecule at a time." Blood 121, no. 8 (2013): 1251–52. http://dx.doi.org/10.1182/blood-2013-01-474635.

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48

Al-Gayem, Qais, Hussain F. Jaafar, and Saad S. Hreshee. "Self-diagnostic approach for cell counting biosensor." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 2 (2021): 688. http://dx.doi.org/10.11591/ijeecs.v22.i2.pp688-698.

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<span id="docs-internal-guid-66699b2e-7fff-5c7e-3931-5de920908f42"><span>In this research, a test monitoring strategy for an array of biosensors is proposed. The principle idea of this diagnostic technique is to measure and compare the impedance of each sensor in the array to achieve fully controlled online health monitoring technique at the system level. The work includes implementation of the diagnostic system, system architecture for analogue part, and SNR analysis. The technique has been applied on a cell coulter counting biochip where the design and fabrication of this sensing

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49

Rooney, Cliona M. "Counting EBV and T cells to predict PTLD." Blood 101, no. 11 (2003): 4227–28. http://dx.doi.org/10.1182/blood-2003-03-0998.

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50

Yang, Ye, Zhenxi Zhang, Xinhui Yang, Joon Hock Yeo, LiJun Jiang, and Dazong Jiang. "Blood cell counting and classification by nonflowing laser light scattering method." Journal of Biomedical Optics 9, no. 5 (2004): 995. http://dx.doi.org/10.1117/1.1782572.

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