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Journal articles on the topic 'Blood cell counting'

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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 features, both from microsystem technology itself (low consumptions of chemicals, better analytical performances, low dead volumes in multifunctional interconnected networks, parallel high-throughput processing, low-cost mass production) and from new project solutions: self-aligning illumination allows to use compact external sources (i.e, LEDs) and requires no delicate optics. Different working set-ups (ranging from series with fixed control volume to parallel differential) can be achieved by adding only few external components. It is finally possible to combine electrical and optical measurements, oriented to multi-feature classification of cell sub-populations.

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2

Schmidt, R. M. "Automated differential blood cell counting systems." Clinical & Laboratory Haematology 1, no. 2 (June 28, 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, Ureshnie Govender, Holger Becker, Pieter Roux, and Kevin Land. "Microfluidic Cartridges for Automated, Point-of-Care Blood Cell Counting." SLAS TECHNOLOGY: Translating Life Sciences Innovation 22, no. 2 (November 19, 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 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.

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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 (September 1, 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 (April 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 (February 18, 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 (November 28, 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 demonstrate its quantification of the expected increase in the hematopoietic stem cell (HSC) fraction of CD34+-selected preparations of human-mobilized peripheral blood cells, an approved treatment product routinely used for HSC transplantation therapies. Previously, we also used the KSC counting technology to define new mathematical algorithms with the potential for rapid determination of TSC-specific fractions without the need for serial culture. A second important HSC transplantation treatment, CD34+-selected umbilical cord blood (UCB) cells, was used to investigate this prediction. We show that, with an input of only simple population doubling time (PDT) data, the KSC counting-derived “Rabbit algorithms” can be used to rapidly determine the specific HSC fraction of CD34+-selected UCB cell preparations with a high degree of statistical confidence. The algorithms define the stem cell fraction half-life (SCFHL), a new parameter that projects stem cell numbers during expansion culture. These findings further validate KSC counting’s potential to meet the long-standing unmet need for a method to determine stem cell-specific dosage in stem cell medicine.

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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 (December 9, 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 of RBCs effectively.

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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 (January 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 (April 24, 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 (January 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 (June 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 (October 20, 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 protocol, 25 µL anti-CD4 conjugated magnetic nanoparticles (NP-CD4, BD Bioscience) were optimized for separating CD4+ T cells from 50 µL of blood in PBS using a DynamagTM-2 magnet, followed by the introduction of 10 µL separated cells into a CountessTM chamber slide for automated counting of CD4+ T cells. To evaluate the reliability of the developed method, 48 blood samples with CD4+ T cell concentrations ranging from 105 to 980 cells/µL were analyzed using both MACS-Countess and FACS. Compared with FACS, MACS-Countess had a mean bias of 3.5% with a limit of agreement (LoA) ranging from −36.4% to 43.3%, which is close to the reliability of the commercial product, PIMA analyzer (Alere), reported previously (mean bias 0.2%; LoA ranging from −42% to 42%, FACS as reference). Further, the MACS-Countess system requires very simple instruments, including only a magnet and an automated cell counter, which are affordable for almost every lab located in a limited resource region.

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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 (October 31, 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 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.

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16

Brownrigg, Emma, Debbie Carr, Michael C. Copeman, Kim Creighton, Catherine Demasi, Julie Domanski, Susan Harrison, et al. "Twenty Units of Blood and Counting." Blood 112, no. 11 (November 16, 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 >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 levels of transfused patients. Thirdly, to review the use of iron chelation in these patients and document potential reasons for non-chelation including co-morbidities and concomitant medications. Method: Medical records for outpatients transfused during the 12-week index period (12 consecutive weeks from Human Research and Ethics Commitee/institution approval) were reviewed in this retrospective multicentre audit. Data were captured using a piloted data collection form. Non-parametric statistical test have been used. Results: To date, 237 patients, aged 0–95 have been reviewed from 10/20 centres. Common underlying conditions necessitating transfusion were: MDS(35%), chemotherapy-induced anaemia(21%) and thalassaemia(14%). The medical record of 26% patients indicated that transfusions had also been given elsewhere. In total, 119 had received >20units, 58(49%) had been prescribed chelation therapy. Fourteen patients who had received >20units had no documented serum ferritin. Patients receiving iron chelation were younger (median 42 cf.73 years, p=0.0004), had received more transfusions(median 60 cf.18, p<0.0001) and more units(median 187 cf.37, p<0.0001). There was no difference in number of concurrent medical conditions(p=0.73) or concomitant medications(p=0.14). Life expectancy was documented for 6 patients only, 3 were chelated. Conclusion: There is variability in the monitoring and treatment of iron overload in Australian hospitals, with not all at-risk patients receiving chelation. The development and implementation of universal tracking tools for transfusion nurses and patients may be one means of improving identification of at-risk patients.

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17

Neerukattu Indrani and Chiraparapu Srinivasa Rao. "White Blood Cell Image Classification Using Deep Learning." September 2021 7, no. 09 (September 27, 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, the cell classification and counting accuracy may depend on the capabilities and experiences of the operators. Therefore, the necessity of an automated differential counting system becomes inevitable. In this paper, CNN models are used. In order to achieve good performance from deep learning methods, the network needs to be trained with large amounts of data during the training phase. We take the images of the white blood cells for the training phase and train our model on them. With this method we achieved good accuracy than traditional methods. And we can generate the results within the seconds also.

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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 (September 25, 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 (November 16, 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 and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.

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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 (July 31, 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 (January 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 (January 2004): 164. http://dx.doi.org/10.1016/s0140-6736(03)15270-1.

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Jenwitheesuk, Ekachai. "Dried blood spot technology for CD4+ T-cell counting." Lancet 363, no. 9403 (January 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 (January 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 (March 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 (March 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 (March 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 (June 29, 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 (June 29, 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 (June 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 (July 31, 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 morphology analysis of the blood histology sample were significantly similar as of conventional light microscope images. Conclusion: By comparing the image from both microscopes, it could be stated that this method is feasible for human blood histopathological sample investigations like blood cell counting and morphology analysis especially in the low resource area or in case of any emergency situations.

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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 (December 19, 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 cell and platelet units, as well as 58 samples of commercially available controls containing different concentration levels of leukocytes, were included in the study. The conventional quality control method using the bead-based counting method performed with the FACSCalibur flow cytometer was compared to the bead-based counting method and the volumetric counting method performed with the MACSQuant 10 flow cytometer. Our results show that the MACSQuant bead-based method, as well as the volumetric MACSQuant method, meet the sensitivity requirements of residual leukocyte enumeration when compared to the gold standard, bead-based FACSCalibur method. We conclude that the volumetric method can be a substitute for the bead-based counting of residual leukocytes in a variety of blood components.

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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 (October 27, 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 implemented on the complex overlapping clusters in a microscopic smear image. Experimental results show that the proposed method is of higher counting accuracy and it performs much better than most counting algorithms existed in the situation of three or more RBCs overlapping complexly into a group. The average total erythrocytes counting accuracy of the proposed method reaches 92.9%.

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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 (January 15, 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 identify DC. A normal range was determined from samples drawn from 103 healthy adults. The mean percentage of DC present in blood mononuclear cells (MNC) was 0.42%, and the mean absolute DC count was 10 × 106 DC/L blood. The normal ranges for DC (mean ± 1.96 standard deviation [SD]) were 0.15% to 0.70% MNC or 3 to 17 × 106 DC/L blood. This method has applications for monitoring attempts to mobilize DC into the blood to facilitate their collection for immunotherapeutic purposes and for counting blood DC in other patients. In preliminary studies, we have found a statistically significant decrease in the blood DC counts in individuals at the time of blood stem cell harvest and in patients with acute illnesses, including allogeneic bone marrow transplant (BMT) recipients with acute graft-versus-host disease (aGVHD).

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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 (January 15, 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 DC. A normal range was determined from samples drawn from 103 healthy adults. The mean percentage of DC present in blood mononuclear cells (MNC) was 0.42%, and the mean absolute DC count was 10 × 106 DC/L blood. The normal ranges for DC (mean ± 1.96 standard deviation [SD]) were 0.15% to 0.70% MNC or 3 to 17 × 106 DC/L blood. This method has applications for monitoring attempts to mobilize DC into the blood to facilitate their collection for immunotherapeutic purposes and for counting blood DC in other patients. In preliminary studies, we have found a statistically significant decrease in the blood DC counts in individuals at the time of blood stem cell harvest and in patients with acute illnesses, including allogeneic bone marrow transplant (BMT) recipients with acute graft-versus-host disease (aGVHD).

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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 (July 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 dealt with. The importance of the refractive index and of flow-induced cell shape changes for the MCV determinations is stressed. It is argued that MCV and haematocrit values are exaggerated at both low and high values and consequently MCHC is erroneously constant. Various prevailing red cell distribution width (RDW) and platelet distribution width (PDW) definitions bring about considerable confusion. The major features of the counting and sizing of white blood cells and platelets are described.

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Baron, Udo, Jeannette Werner, Konstantin Schildknecht, Janika J. Schulze, Andargaschew Mulu, Uwe-Gerd Liebert, Ulrich Sack, et al. "Epigenetic immune cell counting in human blood samples for immunodiagnostics." Science Translational Medicine 10, no. 452 (August 1, 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 newborns and 24 newborns with primary immunodeficiencies were analyzed. Concordance between flow cytometric and epigenetic data for neutrophils and B, natural killer, CD3+ T, CD8+ T, CD4+ T, and FOXP3+ regulatory T cells was evaluated, demonstrating substantial equivalence between epigenetic qPCR analysis and flow cytometry. Epigenetic qPCR achieves both relative and absolute quantifications. Applied to dried blood spots, epigenetic immune cell quantification was shown to identify newborns suffering from various primary immunodeficiencies. Using epigenetic qPCR not only provides a precise means for immune cell counting in fresh-frozen blood but also extends applicability to dried blood spots. This method could expand the ability for screening immune defects and facilitates diagnostics of unobservantly collected samples, for example, in underdeveloped areas, where logistics are major barriers to screening.

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Noda, Y., M. Hanafusa, A. Yamamoto, M. Ijuin, M. Hori, T. Osumi, T. Suzuki, I. Kanno, and H. Kotera. "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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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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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 (August 14, 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 evaluation of our architecture. It is not uncommon that blood smear images are in low resolution, and blood cells on them are blurry and overlapping. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. With different settings in the proposed architecture, five models are constructed herein. We compare their performance on red blood cells (RBC), white blood cells (WBC), and platelet detection and deeply investigate the factors related to their performance. The experiment results show that our models can recognize blood cells accurately when blood cells are not heavily overlapping.

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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 (June 1, 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 overlapping white blood cells. Determination of marker was performed on every cell both in single and overlapping white blood cell area. In this study, there were tree stages: segmentation of white blood cells, marker detection and white blood cell count, and contour estimation of every white blood cell. The used data testing was microscopic acute leukemia image data of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). Based on the test results, Iterative Distance Transform for Convex Sets IDTCS method performs better than Distance Transform (DT) and Ultimate Erosion for Convex Sets (UECS) method.

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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 found.

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Ngoma, Alain, Shunnichi Saito, Hitoshi Ohto, Kazuhiko Ikeda, Hiroyasu Yasuda, Kinuyo Kawabata, Takahiro Kanno, Atsushi Kikuta, Kazuhiro Mochizuki, and Kenneth E. Nollet. "CD34+ Cell Enumeration by Flow Cytometry: A Comparison of Systems and Methodologies." Archives of Pathology & Laboratory Medicine 135, no. 7 (July 1, 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 PBSC donors with various malignancies. The Cytomics FC500 (Beckman Coulter, Fullerton, California) was compared with the FACSCalibur (BD Biosciences, San Jose, California). Dual-platform CD34+ cell counting incorporated data from a KX-21 hematology analyzer (Sysmex, Kobe, Japan). Results.—Subtle differences in CD34+ cell counting between 2 systems and 2 methods did not achieve statistical significance. Conclusion.—Different systems and methods for CD34+ cell enumeration, properly validated, can support care for patients undergoing transplants and provide meaningful data for multicenter studies or meta-analyses.

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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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Tawana, Kiran, and Jude Fitzgibbon. "Inherited DDX41 mutations: 11 genes and counting." Blood 127, no. 8 (February 25, 2016): 960–61. http://dx.doi.org/10.1182/blood-2016-01-690909.

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Ariëns, Robert A. S. "Counting 1 fibrin molecule at a time." Blood 121, no. 8 (February 21, 2013): 1251–52. http://dx.doi.org/10.1182/blood-2013-01-474635.

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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 (May 1, 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 chip with electrodes make the coulter counter be an effective mean to count and analyses the cells in a blood sample. The experimental results show that the indication factor of the sensing electrodes has increased from 1 to 1.8 gradually depending on the fault level.</span></span>

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Rooney, Cliona M. "Counting EBV and T cells to predict PTLD." Blood 101, no. 11 (June 1, 2003): 4227–28. http://dx.doi.org/10.1182/blood-2003-03-0998.

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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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