Journal articles on the topic 'Automated cell counting software'
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González-González, Rogelio, Nelly Molina-Frechero, Ramón G. Carreón-Burciaga, Sandra López-Verdín, Carlos Robles-Bonilla, Vanesa Pereira-Prado, and Ronell Bologna-Molina. "Comparison between Manual and Automated Methods for Ki-67 Immunoexpression Quantification in Ameloblastomas." Analytical Cellular Pathology 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7486989.
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Ameloblastoma is a common and unpredictable odontogenic tumor with high relapse rates. Several studies assessing the proliferative capacity of these neoplasms have been published, mainly using the protein Ki-67. Cell counts must be completed to determine the cell proliferation rate. Multiple methods have been developed for this purpose. The most widely used method is the labeling index, which has undergone changes over time to better facilitate cell counting. Here, we compared manual cell counting methods with automated cell counting (ImmunoRatio) to determine the relative effectiveness of these methods. The results suggest that ImmunoRatio, a free software tool, may be highly advantageous and provide results similar to manual cell counting methods when used with the appropriate calibration. However, ImmunoRatio has flaws that may affect the labeling index results. Therefore, this automated cell counting method must be supplemented with manual cell counting methods.
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Lucian, Zastko, Bereta Martin, Timko Jaroslav, and Belyaev Igor. "Classifier Spot Count Optimization of Automated Fluorescent Slide Scanning System." Acta Medica Martiniana 22, no. 1 (April 1, 2022): 24–33. http://dx.doi.org/10.2478/acm-2022-0004.
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Abstract Purpose: Ionizing radiation induced foci (IRIF) known also as DNA repair foci represent the most sensitive endpoint for assessing DNA double strand breaks (DSB). IRIF are usually visualized and enumerated with the aid of fluorescence microscopy using antibodies to γH2AX and 53BP1. Although several approaches and software packages were developed for the quantification of IRIF, not one of them was commonly accepted and inter-laboratory variability in the outputs was reported. In this study, the sensitization of Metafer software to counting also small appearing IRIF was validated. Materials and Methods: Human lymphocytes were γ-irradiated at a dose of 2 Gy. The cells were fixed at 0.5, 1, 2, and 18 hours post-irradiation, permeabilized and IRIF were immunostained using appropriate antibodies. Cell images were acquired with the automatic Metafer system. Radiation-induced γH2AX and 53BP1 foci were enumerated using either manual counting (JCountPro program) or the Metafer software (after its classifier optimization has been done) and compared. The statistical analysis was performed using One-way ANOVA. Results: The enumeration of 53BP1, γH2AX foci manually by JCountPro did not statistically significantly differ from the automatic one performed with the optimized Metafer classifier. A detailed step-by-step protocol of this successful optimization is described in this study. Conclusions: We concluded that the Metafer software after the optimization was efficient in objectively enumerating IRIF, having a potential for usage in clinics and molecular epidemiology.
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Lewis, Joshua, Conrad Shebelut, Bradley Drumheller, Xuebao Zhang, Nithya Shanmugam, Michel Attieh, Michael Horwath, Anurag Khanna, Geoffrey Smith, and David Gutman. "An Automated Pipeline for Cell Differentials on Whole-Slide Bone Marrow Aspirate Smears." American Journal of Clinical Pathology 158, Supplement_1 (November 1, 2022): S12. http://dx.doi.org/10.1093/ajcp/aqac126.020.
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Abstract Current pathologic diagnosis of benign and neoplastic bone marrow disorders relies in part on the microscopic analysis of bone marrow aspirate (BMA) smears and manual counting of nucleated cell populations to obtain a cell differential. This manual process has significant limitations, including the limited sample of cells analyzed by a conventional 500-cell differential compared to the thousands of nucleated cells present, as well as the inter-observer variability seen between differentials on single samples due to differences in cell selection and classification. To address these shortcomings, we developed an automated computational platform for obtaining cell differentials from scanned whole-slide BMAs at 40x magnification. This pipeline utilizes a sequential process of identifying BMA regions with high proportions of marrow nucleated cells that are ideal for cell counting, detecting individual cells within these optimal regions, and classifying cells into one of 11 types within the differential. Training of convolutional neural network models for region and cell classification, as well as a region-based convolutional neural network for cell detection, involved the generation of an annotated training data set containing 10,948 BMA regions, 28,914 cell boundaries, and 23,609 cell classifications from 73 BMA slides. Among 44 testing BMA slides, an average of 19,209 viable cells per slide were identified and used in automated cell differentials, with a range of 237 to 126,483 cells. In comparing these automated cell differential percentages with corresponding manual differentials, cell type-specific correlation coefficients ranged from 0.913 for blast cells to 0.365 for myelocytes, with an average coefficient of 0.654 among all 11 cell types. A statistically significant concordance was observed among slides with blast percentages less or greater than 20% (p=1.0x10-5) and with plasma cell percentages less or greater than 10% (p=5.9x10-6) between automated and manual differentials, suggesting potential diagnostic utility of this automated pipeline for malignancies such as acute myeloid leukemia and multiple myeloma. Additionally, by simulating the manual counting of 500 cells within localized areas of a BMA slide and iterating over all optimal slide locations, we quantified the inter-observer variability associated with limited sample size in traditional BMA cell counting. Localized differentials exemplify an average variance ranging from 24.1% for erythroid precursors to 1.8% for basophils. Variance in localized differentials of up to 44.8% for blast cells and 36.9% for plasma cells was observed, demonstrating that sample classification based on diagnostic thresholds of cell populations is variable even between different areas within a single slide. Finally, pipeline outputs of region classification, cell detection, cell classification, and localized cell differentials can be visualized using whole-slide image analysis software. By improving cell sampling and reducing inter-observer variability, this automated pipeline has potential to improve the current standard of practice for utilizing BMA smears in the diagnosis of hematologic disorders.
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Scholz, Gregor, Shinta Mariana, Iqbal Syamsu, Agus Budi Dharmawan, Torben Schulze, Kai Mattern, Philipp Hörmann, et al. "Continuous Live-Cell Culture Monitoring by Compact Lensless LED Microscopes." Proceedings 2, no. 13 (December 5, 2018): 877. http://dx.doi.org/10.3390/proceedings2130877.
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A compact lensless microscope comprising a custom-made LED engine and a CMOS imaging sensor has been developed for live-cell culture imaging inside a cell incubator environment. The imaging technique is based on digital inline-holographic microscopy, while the image reconstruction is carried out by angular spectrum approach with a custom written software. The system was tested with various biological samples including immortalized mouse astrocyte cells inside a petri dish. Besides the imaging possibility, the capability of automated cell counting and tracking could be demonstrated. By using image sensors capable of video frame rate, time series of cell movement can be captured.
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Francisco, Jairo Silva, Heleno Pinto de Moraes, and Eliane Pedra Dias. "Evaluation of the Image-Pro Plus 4.5 software for automatic counting of labeled nuclei by PCNA immunohistochemistry." Brazilian Oral Research 18, no. 2 (June 2004): 100–104. http://dx.doi.org/10.1590/s1806-83242004000200002.
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The objective of this study was to create and evaluate a routine (macro) using Image-Pro Plus 4.5 software (Media Cybernetics, Silver Spring, USA) for automatic counting of labeled nuclei by proliferating cell nuclear antigen (PCNA) immunohistochemistry. A total of 154 digital color images were obtained from eleven sections of reticular oral lichen planus stained by PCNA immunohistochemistry. Mean density (gray-level), red density, green density, blue density, area, minor axis, perimeter rate and roundness were parameters used for PCNA labeled nuclei discrimination, followed by their outlined presentation and counting in each image by the macro. Mean density and area thresholds were automatically defined based, respectively, on mean density and mean area of PCNA labeled nuclei in the assessed image. The reference method consisted in visual counting of manually outlined labeled nuclei. Statistical analysis of macro results versus reference countings showed a very significant correlation (r s = 0.964, p < 0.001) for general results and a high level (89.8 ± 3.8%) of correctly counted labeled nuclei. We conclude that the main parameters associated with a high correlation between macro and reference results were mean density (gray-level) and area thresholds based on image profiles; and that Image-Pro Plus 4.5 using a routine with automatic definition of mean density and area thresholds can be considered a valid alternative to visual counting of PCNA labeled nuclei.
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Ziebig, Reinhard, Andreas Lun, and Pranav Sinha. "Leukocyte Counts in Cerebrospinal Fluid with the Automated Hematology Analyzer CellDyn 3500 and the Urine Flow Cytometer UF-100." Clinical Chemistry 46, no. 2 (February 1, 2000): 242–47. http://dx.doi.org/10.1093/clinchem/46.2.242.
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Abstract Background: The counting of leukocytes and erythrocytes in cerebrospinal fluid (CSF) is still performed microscopically, e.g., using a chamber in most laboratories. This requires sufficient practical experience, is time-consuming, and may constitute a problem in emergency diagnostics. Specific automated systems for CSF cell counting are not available at present. Methods: We tested the hematology analyzer CellDyn 3500 (CD) and the urine flow cytometer UF-100 (UF), which are not designed for CSF analysis. We studied >104 samples with both analyzers, and the counts obtained were compared with the reference method (Fuchs-Rosenthal chamber). Results: Good linearity in the medically relevant range of 15 × 106 to 1000 × 106 leukocytes/L and a high degree of within-run accuracy were seen for both analyzers. Cell counting on the UF was excellent, especially when low cell counts were encountered (CV, 4.9% compared with 28% observed for the CD). Method comparison showed that identical results could be detected for a majority of the count pairs. For a few samples, there was a discrepancy between the results from the analyzers and the counting chamber. In most cases, these were CSF samples containing a high proportion of lymphocytes. For these samples, the CD result led to a false-positive high leukocyte count, and on the UF these cells were not allocated to the leukocyte population, thus leading to false-negative counts. Conclusions: Both analyzers should not be used for CSF cell counting in all cases at present. However, once the technical and software problems have been solved, routine use of the two analyzers for CSF analysis should be seriously contemplated.
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Primrose, John N., Siân A. Pugh, Gareth Thomas, Matthew Ellis, Karwan Moutasim, and David Mant. "Intratumoural immune signature to identify patients with primary colorectal cancer who do not require follow-up after resection: an observational study." Health Technology Assessment 25, no. 2 (January 2021): 1–32. http://dx.doi.org/10.3310/hta25020.
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Background Following surgical and adjuvant treatment of primary colorectal cancer, many patients are routinely followed up with axial imaging (most commonly computerised tomography imaging) and blood carcinoembryonic antigen (a tumour marker) testing. Because fewer than one-fifth of patients will relapse, a large number of patients are followed up unnecessarily. Objectives To determine whether or not the intratumoural immune signature could identify a cohort of patients with a relapse rate so low that follow-up is unnecessary. Design An observational study based on a secondary tissue collection of the tumours from participants in the FACS (Follow-up After Colorectal Cancer Surgery) trial. Setting and participants Formalin-fixed paraffin-embedded tumour tissue was obtained from 550 out of 1202 participants in the FACS trial. Tissue microarrays were constructed and stained for cluster of differentiation (CD)3+ and CD45RO+ T lymphocytes as well as standard haematoxylin and eosin staining, with a view to manual and, subsequently, automated cell counting. Results The tissue microarrays were satisfactorily stained for the two immune markers. Manual cell counting proved possible on the arrays, but manually counting the number of cores for the entire study was found to not be feasible; therefore, an attempt was made to use automatic cell counting. Although it is clear that this approach is workable, there were both hardware and software problems; therefore, reliable data could not be obtained within the time frame of the study. Limitations The main limitations were the inability to use machine counting because of problems with both hardware and software, and the loss of critical scientific staff. Findings from this research indicate that this approach will be able to count intratumoural immune cells in the long term, but whether or not the original aim of the project proved possible is not known. Conclusions The project was not successful in its aim because of the failure to achieve a reliable counting system. Future work Further work is needed to perfect immune cell machine counting and then complete the objectives of this study that are still relevant. Trial registration Current Controlled Trials ISRCTN41458548. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 2. See the NIHR Journals Library website for further project information.
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Primrose, John N., Siân A. Pugh, Gareth Thomas, Matthew Ellis, Karwan Moutasim, and David Mant. "Intratumoural immune signature to identify patients with primary colorectal cancer who do not require follow-up after resection: an observational study." Health Technology Assessment 25, no. 2 (January 2021): 1–32. http://dx.doi.org/10.3310/hta25020.
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Background Following surgical and adjuvant treatment of primary colorectal cancer, many patients are routinely followed up with axial imaging (most commonly computerised tomography imaging) and blood carcinoembryonic antigen (a tumour marker) testing. Because fewer than one-fifth of patients will relapse, a large number of patients are followed up unnecessarily. Objectives To determine whether or not the intratumoural immune signature could identify a cohort of patients with a relapse rate so low that follow-up is unnecessary. Design An observational study based on a secondary tissue collection of the tumours from participants in the FACS (Follow-up After Colorectal Cancer Surgery) trial. Setting and participants Formalin-fixed paraffin-embedded tumour tissue was obtained from 550 out of 1202 participants in the FACS trial. Tissue microarrays were constructed and stained for cluster of differentiation (CD)3+ and CD45RO+ T lymphocytes as well as standard haematoxylin and eosin staining, with a view to manual and, subsequently, automated cell counting. Results The tissue microarrays were satisfactorily stained for the two immune markers. Manual cell counting proved possible on the arrays, but manually counting the number of cores for the entire study was found to not be feasible; therefore, an attempt was made to use automatic cell counting. Although it is clear that this approach is workable, there were both hardware and software problems; therefore, reliable data could not be obtained within the time frame of the study. Limitations The main limitations were the inability to use machine counting because of problems with both hardware and software, and the loss of critical scientific staff. Findings from this research indicate that this approach will be able to count intratumoural immune cells in the long term, but whether or not the original aim of the project proved possible is not known. Conclusions The project was not successful in its aim because of the failure to achieve a reliable counting system. Future work Further work is needed to perfect immune cell machine counting and then complete the objectives of this study that are still relevant. Trial registration Current Controlled Trials ISRCTN41458548. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 2. See the NIHR Journals Library website for further project information.
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Dzulkifli, Fahmi Akmal, Mohd Yusoff Mashor, and Hasnan Jaafar. "A Computer-Aided Diagnosis (CAD) System for Automatic Counting of Ki67 Cells in Meningioma." International Journal of Software Engineering and Computer Systems 8, no. 2 (July 1, 2022): 10–24. http://dx.doi.org/10.15282/ijsecs.8.2.2022.2.0099.
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Meningioma is a type of primary brain tumour where this tumour arises in the three thin layers of tissues, called meninges. Tumour grading is usually used to describe tumour cells' characteristics and behaviours and how they look under a microscope. There were many techniques used for determining the grade of the tumour. Ki67 was the most common proliferation marker used to measure cell proliferation activity. Currently, pathologists used the manual counting technique to count the Ki67 cells before determining tumour grading. However, this technique was time-consuming, tiring and the counting results are often not accurate. Besides that, manual counting has poor reproducibility and discordant between counting values’ among the pathologist. Therefore, this study aimed to develop a Computer-Aided Design (CAD) software that automatically counts the Ki67 cells for determining tumour grading. The purpose of developing this software is to alleviate pathologists’ workload associated with counting Ki67 cells and scoring the Ki67 index. The CAD software was developed through seven stages. Based on Pearson Correlation Coefficient results, there was a good positive correlation between the proposed technique with the manual counting technique in counting positive and negative Ki67 cells with a correlation of 0.99 and 0.72 respectively. The proposed CAD system also showed promising results in computing the Ki67 labeling index with a low percentage absolute error of 1.85%.
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Pernthaler, Jakob, Annelie Pernthaler, and Rudolf Amann. "Automated Enumeration of Groups of Marine Picoplankton after Fluorescence In Situ Hybridization." Applied and Environmental Microbiology 69, no. 5 (May 2003): 2631–37. http://dx.doi.org/10.1128/aem.69.5.2631-2637.2003.
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ABSTRACT We describe here an automated system for the counting of multiple samples of double-stained microbial cells on sections of membrane filters. The application integrates an epifluorescence microscope equipped with motorized z-axis drive, shutters, and filter wheels with a scanning stage, a digital camera, and image analysis software. The relative abundances of specific microbial taxa are quantified in samples of marine picoplankton, as detected by fluorescence in situ hybridization (FISH) and catalyzed reporter deposition. Pairs of microscopic images are automatically acquired from numerous positions at two wavelengths, and microbial cells with both general DNA and FISH staining are counted after object edge detection and signal-to-background ratio thresholding. Microscopic fields that are inappropriate for cell counting are automatically excluded prior to measurements. Two nested walk paths guide the device across a series of triangular preparations until a user-defined number of total cells has been analyzed per sample. A backup autofocusing routine at incident light allows automated refocusing between individual samples and can reestablish the focal plane after fatal focusing errors at epifluorescence illumination. The system was calibrated to produce relative abundances of FISH-stained cells in North Sea samples that were comparable to results obtained by manual evaluation. Up to 28 preparations could be analyzed within 4 h without operator interference. The device was subsequently applied for the counting of different microbial populations in incubation series of North Sea waters. Automated digital microscopy greatly facilitates the processing of numerous FISH-stained samples and might thus open new perspectives for bacterioplankton population ecology.
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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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Zhou, Zhi, Marie Noëlle Pons, Lutgarde Raskin, and Julie L. Zilles. "Automated Image Analysis for Quantitative Fluorescence In Situ Hybridization with Environmental Samples." Applied and Environmental Microbiology 73, no. 9 (March 9, 2007): 2956–62. http://dx.doi.org/10.1128/aem.02954-06.
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ABSTRACT When fluorescence in situ hybridization (FISH) analyses are performed with complex environmental samples, difficulties related to the presence of microbial cell aggregates and nonuniform background fluorescence are often encountered. The objective of this study was to develop a robust and automated quantitative FISH method for complex environmental samples, such as manure and soil. The method and duration of sample dispersion were optimized to reduce the interference of cell aggregates. An automated image analysis program that detects cells from 4′,6′-diamidino-2-phenylindole (DAPI) micrographs and extracts the maximum and mean fluorescence intensities for each cell from corresponding FISH images was developed with the software Visilog. Intensity thresholds were not consistent even for duplicate analyses, so alternative ways of classifying signals were investigated. In the resulting method, the intensity data were divided into clusters using fuzzy c-means clustering, and the resulting clusters were classified as target (positive) or nontarget (negative). A manual quality control confirmed this classification. With this method, 50.4, 72.1, and 64.9% of the cells in two swine manure samples and one soil sample, respectively, were positive as determined with a 16S rRNA-targeted bacterial probe (S-D-Bact-0338-a-A-18). Manual counting resulted in corresponding values of 52.3, 70.6, and 61.5%, respectively. In two swine manure samples and one soil sample 21.6, 12.3, and 2.5% of the cells were positive with an archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in corresponding values of 22.4, 14.0, and 2.9%, respectively. This automated method should facilitate quantitative analysis of FISH images for a variety of complex environmental samples.
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Lewis, Joshua, Xuebao Zhang, Nithya Shanmugam, Bradley Drumheller, Conrad Shebelut, Geoffrey Smith, Lee Cooper, and David Jaye. "Machine Learning-Based Automated Selection of Regions for Analysis on Bone Marrow Aspirate Smears." American Journal of Clinical Pathology 156, Supplement_1 (October 1, 2021): S1—S2. http://dx.doi.org/10.1093/ajcp/aqab189.001.
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Abstract Manual microscopic examination of bone marrow aspirate (BMA) smears and counting of cell populations remains the standard of practice for accurate assessment of benign and neoplastic bone marrow disorders. While automated cell classification software using machine learning models has been developed and applied to BMAs, current systems nonetheless require manual identification of optimal regions within the slide that are rich in marrow hematopoietic cells. To address this issue, we have developed a machine learning-based platform for automated identification of optimal regions in whole-slide images of BMA smears. A training dataset was developed by manual annotation of 53 BMA slides across biopsy diagnoses including unremarkable trilineal hematopoiesis, acute leukemia, and plasma cell neoplasms, as well as across differences in total cellularity represented by a spectrum of marrow nucleated cell content and white blood cell counts. 10,537 regions among these 53 slides were manually annotated as either “optimal” (regions near aspirate particles with high proportions of marrow nucleated cells), “particle” (aspirate particles), or “hemodilute” (blood-rich regions with high proportions of red blood cells). Training of a neural network-based classifier on 10x magnification slides with region cropping and image augmentation resulted in a classifier with substantial accuracy on new testing-set BMA slides (one-vs-rest AUROC > 0.999 across 10 training/testing splits for all 3 region classes), with very few particle and hemodilute regions being classified as optimal (particle: 0.83%, hemodilute: 0.39%). Additionally, this classifier accurately classifies BMA regions on slides from hematological disorders not represented in the training data, including Burkitt lymphoma (AUROC > 0.999 across region classes), chronic myeloid leukemia (AUROC > 0.999 across region classes), and diffuse large B-cell lymphoma (AUROC = 1 across region classes), demonstrating the broad applicability of our approach. To assess the performance of our classifier on whole-slide images, tiles from 10x magnification slides were manually annotated by three participants with notable concordance (Krippendorff’s alpha = 0.424); substantial agreement was found between manual annotations and model predictions within whole-slide images (optimal AUROC = 0.958, particle AUROC = 1.0, hemodilute AUROC = 0.947). Based on these promising results, this machine learning-based region classification model is being connected to a previously-developed bone marrow cell classifier to fully automate differential cell counting in whole-slide images. The development of this novel automated pipeline has potential to streamline the diagnostic process for hematological disorders while enhancing accuracy and replicability, as well as decreasing diagnostic turnaround time for improving patient care.
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Rodenacker, Karsten, Michaela Aubele, Peter Hutzler, and P. S. Umesh Adiga. "Groping for Quantitative Digital 3-D Image Analysis: An Approach to Quantitative Fluorescence In Situ Hybridization in Thick Tissue Sections of Prostate Carcinoma." Analytical Cellular Pathology 15, no. 1 (1997): 19–29. http://dx.doi.org/10.1155/1997/790963.
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In molecular pathology numerical chromosome aberrations have been found to be decisive for the prognosis of malignancy in tumours. The existence of such aberrations can be detected by interphase fluorescence in situ hybridization (FISH). The gain or loss of certain base sequences in the desoxyribonucleic acid (DNA) can be estimated by counting the number of FISH signalsper cell nucleus. The quantitative evaluation of such events is a necessary condition for a prospective use in diagnostic pathology. To avoid occlusions of signals, the cell nucleus has to be analyzed in three dimensions. Confocal laser scanning microscopy is the means to obtain series of optical thin sections from fluorescence stained or marked material to fulfill the conditions mentioned above. A graphical user interface (GUI) to a software package for display, inspection, count and (semi‐)automatic analysis of 3‐D images for pathologists is outlined including the underlying methods of 3‐D image interaction and segmentation developed. The preparative methods are briefly described. Main emphasis is given to the methodical questions of computer‐aided analysis of large 3‐D image data sets for pathologists. Several automated analysis steps can be performed for segmentation and succeeding quantification. However tumour material is in contrast to isolated or cultured cells even for visual inspection, a difficult material. For the present a fully automated digital image analysis of 3‐D data is not in sight. A semi‐automatic segmentation method is thus presented here.
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Ye, Guochang, and Mehmet Kaya. "Automated Cell Foreground–Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data." Bioengineering 9, no. 2 (February 18, 2022): 81. http://dx.doi.org/10.3390/bioengineering9020081.
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Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated cell segmentation method involving morphological operations to automatically achieve cell segmentation for phase-contrast microscopes. Manual/visual counting of cell segmentation serves as the control group (156 images as ground truth) to evaluate the proposed method’s performance. The proposed technology’s adaptive performance is assessed at varying conditions, including artificial blurriness, illumination, and image size. Compared to the Trainable Weka Segmentation method, the Empirical Gradient Threshold method, and the ilastik segmentation software, the proposed method achieved better segmentation accuracy (dice coefficient: 90.07, IoU: 82.16%, and 6.51% as the average relative error on measuring cell area). The proposed method also has good reliability, even under unfavored imaging conditions at which manual labeling or human intervention is inefficient. Additionally, similar degrees of segmentation accuracy were confirmed when the ground truth data and the generated data from the proposed method were applied individually to train modified U-Net models (16848 images). These results demonstrated good accuracy and high practicality of the proposed cell segmentation method with phase-contrast microscopy image data.
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Salgado, Mayra, Nelson Gonzalez, Leonard Medrano, Jeffrey Rawson, Keiko Omori, Meirigeng Qi, Ismail Al-Abdullah, Fouad Kandeel, Yoko Mullen, and Hirotake Komatsu. "Semi-Automated Assessment of Human Islet Viability Predicts Transplantation Outcomes in a Diabetic Mouse Model." Cell Transplantation 29 (January 1, 2020): 096368972091944. http://dx.doi.org/10.1177/0963689720919444.
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In clinical and experimental human pancreatic islet transplantations, establishing pretransplant assessments that accurately predict transplantation outcomes is crucial. Conventional in vitro viability assessment that relies on manual counting of viable islets is a routine pretransplant assessment. However, this method does not correlate with transplantation outcomes; to improve the method, we recently introduced a semi-automated method using imaging software to objectively determine area-based viability. The goal of the present study was to correlate semi-automated viability assessment with posttransplantation outcomes of human islet transplantations in diabetic immunodeficient mice, the gold standard for in vivo functional assessment of isolated human islets. We collected data from 61 human islet isolations and 188 subsequent in vivo mouse transplantations. We assessed islet viability by fluorescein diacetate and propidium iodide staining using both the conventional and semi-automated method. Transplantations of 1,200 islet equivalents under the kidney capsule were performed in streptozotocin-induced diabetic immunodeficient mice. Among the pretransplant variables, including donor factors and post-isolation assessments, viability measured using the semi-automated method demonstrated a strong influence on in vivo islet transplantation outcomes in multivariate analysis. We calculated an optimized cutoff value (96.1%) for viability measured using the semi-automated method and showed a significant difference in diabetes reversal rate for islets with viability above this cutoff (77% reversal) vs. below this cutoff (49% reversal). We performed a detailed analysis to show that both the objective measurement and the improved area-based scoring system, which distinguished between small and large islets, were key features of the semi-automated method that allowed for precise evaluation of viability. Taken together, our results suggest that semi-automated viability assessment offers a promising alternative pretransplant assessment over conventional manual assessment to predict human islet transplantation outcomes.
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Ascari, Lorenzo, Cristina Novara, Virginia Dusio, Ludovica Oddi, and Consolata Siniscalco. "Quantitative methods in microscopy to assess pollen viability in different plant taxa." Plant Reproduction 33, no. 3-4 (October 29, 2020): 205–19. http://dx.doi.org/10.1007/s00497-020-00398-6.
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AbstractHigh-quality pollen is a prerequisite for plant reproductive success. Pollen viability and sterility can be routinely assessed using common stains and manual microscope examination, but with low overall statistical power. Current automated methods are primarily directed towards the analysis of pollen sterility, and high throughput solutions for both pollen viability and sterility evaluation are needed that will be consistent with emerging biotechnological strategies for crop improvement. Our goal is to refine established labelling procedures for pollen, based on the combination of fluorescein (FDA) and propidium iodide (PI), and to develop automated solutions for accurately assessing pollen grain images and classifying them for quality. We used open-source software programs (CellProfiler, CellProfiler Analyst, Fiji and R) for analysis of images collected from 10 pollen taxa labelled using FDA/PI. After correcting for image background noise, pollen grain images were examined for quality employing thresholding and segmentation. Supervised and unsupervised classification of per-object features was employed for the identification of viable, dead and sterile pollen. The combination of FDA and PI dyes was able to differentiate between viable, dead and sterile pollen in all the analysed taxa. Automated image analysis and classification significantly increased the statistical power of the pollen viability assay, identifying more than 75,000 pollen grains with high accuracy (R2 = 0.99) when compared to classical manual counting. Overall, we provide a comprehensive set of methodologies as baseline for the automated assessment of pollen viability using fluorescence microscopy, which can be combined with manual and mechanized imaging systems in fundamental and applied research on plant biology. We also supply the complete set of pollen images (the FDA/PI pollen dataset) to the scientific community for future research.
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Hudson, John L., Dennis R. Levin, and Barry J. Hoffer. "A 16-Channel Automated Rotometer System for Reliable Measurement of Turning Behavior in 6-Hydroxydopamine Lesioned and Transplanted Rats." Cell Transplantation 2, no. 6 (November 1993): 507–14. http://dx.doi.org/10.1177/096368979300200608.
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Unilateral 6-hydroxydopamine lesions of the nigrostriatal pathway in rats result in a massive dopamine (DA) denervation of the ipsilateral striatum. Such animals have proven extremely useful as a model for the study of Parkinson's disease, an idiopathic neurodegenerative disorder of humans. Extensive unilateral DA disruption leaves the rat relatively normal in motor behavior; however, the extent of the lesion can be documented by drug-induced rotational behavior. When given an injection of a dopamine agonist, such as apomorphine or d-amphetamine, a lesioned animal will manifest rotational behavior; the number of turns correlates with the degree of unilateral denervation. In order to identify, for various studies, large numbers of animals with specific levels of denervation, the necessity of an automated and reliable rotational counting system (rotometer) becomes obvious. We have developed such a device that allows up to 16 rats to be tested concurrently with one inexpensive computer. This system is more reliable than, and certainly preferable to, more tedious methods such as videotaping and subsequent manual analysis or various other mechanical systems. Plexiglass, formed into large bowls, serve as the rotometer chambers. We have designed a simple, inexpensive, and accurate counting head that can be manufactured from readily available parts and that is very sturdy and reliable. This, together with a thoracic harness, completes the rotometer assembly. The rotational data, from up to 16 separate channels, is collected by a single-chip microprocessor and sent on a serial line to an IBM-type or Macintosh host computer. There, it is graphically displayed on line and subsequently saved to disk with a novel acquisition program. Files generated are in code readable by most spreadsheet software currently available. Therefore, rotational data can be imported to a number of different spreadsheets and macros used for analysis. In summary, the multiple-channel automation for monitoring turning behavior in rats, described here, is a simple, inexpensive and effective system for accurate and rapid data acquisition and analysis.
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Bogdanov, Anita, Valeria Endrész, Szabolcs Urbán, Ildikó Lantos, Judit Deák, Katalin Burián, Kamil Önder, Ferhan Ayaydin, Péter Balázs, and Dezső P. Virok. "Application of DNA Chip Scanning Technology for Automatic Detection of Chlamydia trachomatis and Chlamydia pneumoniae Inclusions." Antimicrobial Agents and Chemotherapy 58, no. 1 (November 4, 2013): 405–13. http://dx.doi.org/10.1128/aac.01400-13.
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ABSTRACTChlamydiae are obligate intracellular bacteria that propagate in the inclusion, a specific niche inside the host cell. The standard method for counting chlamydiae is immunofluorescent staining and manual counting of chlamydial inclusions. High- or medium-throughput estimation of the reduction in chlamydial inclusions should be the basis of testing antichlamydial compounds and other drugs that positively or negatively influence chlamydial growth, yet low-throughput manual counting is the common approach. To overcome the time-consuming and subjective manual counting, we developed an automatic inclusion-counting system based on a commercially available DNA chip scanner. Fluorescently labeled inclusions are detected by the scanner, and the image is processed by ChlamyCount, a custom plug-in of the ImageJ software environment. ChlamyCount was able to measure the inclusion counts over a 1-log-unit dynamic range with a high correlation to the theoretical counts. ChlamyCount was capable of accurately determining the MICs of the novel antimicrobial compound PCC00213 and the already known antichlamydial antibiotics moxifloxacin and tetracycline. ChlamyCount was also able to measure the chlamydial growth-altering effect of drugs that influence host-bacterium interaction, such as gamma interferon, DEAE-dextran, and cycloheximide. ChlamyCount is an easily adaptable system for testing antichlamydial antimicrobials and other compounds that influenceChlamydia-host interactions.
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Reppe, Sjur, Catherine Joan Jackson, Håkon Ringstad, Kim Alexander Tønseth, Hege Bakke, Jon Roger Eidet, and Tor Paaske Utheim. "High Throughput Screening of Additives Using Factorial Design to Promote Survival of Stored Cultured Epithelial Sheets." Stem Cells International 2018 (November 18, 2018): 1–9. http://dx.doi.org/10.1155/2018/6545876.
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There is a need to optimize storage conditions to preserve cell characteristics during transport of cultured cell sheets from specialized culture units to distant hospitals. In this study, we aimed to explore a method to identify additives that diminish the decrease in the viability of stored undifferentiated epidermal cells using multifactorial design and an automated screening procedure. The cultured cells were stored for 7–11 days at 12°C in media supplemented with various additives. Effects were evaluated by calcein staining of live cells as well as morphology. Twenty-six additives were tested using (1) a two-level factorial design in which 10 additives were added or omitted in 64 different combinations and (2) a mixture design with 5 additives at 5 different concentrations in a total of 64 different mixtures. Automated microscopy and cell counting with Fiji enabled efficient processing of data. Significant regression models were identified by Design-Expert software. A calculated maximum increase of live cells to 37 ± 6% was achieved upon storage of cell sheets for 11 days in the presence of 6% glycerol. The beneficial effect of glycerol was shown for epidermal cell sheets from three different donors in two different storage media and with two different factorial designs. We have thus developed a high throughput screening system enabling robust assessment of live cells and identified glycerol as a beneficial additive that has a positive effect on epidermal cell sheet upon storage at 12°C. We believe this method could be of use in other cell culture optimization strategies where a large number of conditions are compared for their effect on cell viability or other quantifiable dependent variables.
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Farhan, Ali, Ferry Saputra, Michael Edbert Suryanto, Fahad Humayun, Roi Martin B. Pajimna, Ross D. Vasquez, Marri Jmelou M. Roldan, et al. "OpenBloodFlow: A User-Friendly OpenCV-Based Software Package for Blood Flow Velocity and Blood Cell Count Measurement for Fish Embryos." Biology 11, no. 10 (October 8, 2022): 1471. http://dx.doi.org/10.3390/biology11101471.
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The transparent appearance of fish embryos provides an excellent assessment feature for observing cardiovascular function in vivo. Previously, methods to conduct vascular function assessment were based on measuring blood-flow velocity using third-party software. In this study, we reported a simple software, free of costs and skills, called OpenBloodFlow, which can measure blood flow velocity and count blood cells in fish embryos for the first time. First, videos captured by high-speed CCD were processed for better image stabilization and contrast. Next, the optical flow of moving objects was extracted from the non-moving background in a frame-by-frame manner. Finally, blood flow velocity was calculated by the Gunner Farneback algorithm in Python. Data validation with zebrafish and medaka embryos in OpenBloodFlow was consistent with our previously published ImageJ-based method. We demonstrated consistent blood flow alterations by either OpenBloodFlow or ImageJ in the dorsal aorta of zebrafish embryos when exposed to either phenylhydrazine or ractopamine. In addition, we validated that OpenBloodFlow was able to conduct precise blood cell counting. In this study, we provide an easy and fully automatic programming for blood flow velocity calculation and blood cell counting that is useful for toxicology and pharmacology studies in fish.
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Bolívar-Marin, Sara, Irene Bosch, and Carlos F. Narváez. "Combination of the Focus-Forming Assay and Digital Automated Imaging Analysis for the Detection of Dengue and Zika Viral Loads in Cultures and Acute Disease." Journal of Tropical Medicine 2022 (July 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/2177183.
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Optimized methods for the detection of flavivirus infections in hyperendemic areas are still needed, especially for working with patient serum as a starting material. The focus-forming assay (FFA) reveals critical aspects of virus-host interactions, as it is a quantitative assay to determine viral loads. Automated image analysis provides evaluations of relative amounts of intracellular viral protein at the single-cell level. Here, we developed an optimized FFA for the detection of infectious Zika virus (ZIKV) and dengue virus (DENV) viral particles in cell cultures and clinical serum samples, respectively. Vero-76 cells were infected with DENV-2 (16681) or ZIKV (PRVA BC59). Using a panel of anti-DENV and anti-ZIKV NS1-specific monoclonal antibodies (mAbs), the primary mAbs, concentration, and the optimal time of infection were determined. To determine whether intracellular accumulation of NS1 improved the efficiency of the FFA, brefeldin A was added to the cultures. Focus formation was identified by conventional optical microscopy combined with CellProfiler™ automated image analysis software. The FFA was used with spike assays for ZIKV and clinical specimens from natural infection by DENV-1 and DENV-2. mAb 7744-644 for ZIKV and mAb 724-323 for DENV used at a concentration of 1 μg/ml and a time of 24 hours postinfection produced the best detection of foci when combining conventional counting and automated digital analysis. Brefeldin A did not improve the assessment of FFUs or their digitally assessed intensity at single-cell level. The FFA showed 95% ZIKV recovery and achieved the detection of circulating DENV-1 and DENV-2 in the plasma of acutely ill patients. The combination of the two techniques optimized the FFA, allowing the study of DENV and ZIKV in culture supernatants and clinical specimens from natural infection in hyperendemic areas.
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Bennke, Christin M., Greta Reintjes, Martha Schattenhofer, Andreas Ellrott, Jörg Wulf, Michael Zeder, and Bernhard M. Fuchs. "Modification of a High-Throughput Automatic Microbial Cell Enumeration System for Shipboard Analyses." Applied and Environmental Microbiology 82, no. 11 (March 25, 2016): 3289–96. http://dx.doi.org/10.1128/aem.03931-15.
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ABSTRACTIn the age of ever-increasing “-omics” studies, the accurate and statistically robust determination of microbial cell numbers within often-complex samples remains a key task in microbial ecology. Microscopic quantification is still the only method to enumerate specific subgroups of microbial clades within complex communities by, for example, fluorescencein situhybridization (FISH). In this study, we improved an existing automatic image acquisition and cell enumeration system and adapted it for usage at high seas on board an oceanographic research ship. The system was evaluated by testing settings such as minimal pixel area and image exposure times ashore under stable laboratory conditions before being brought on board and tested under various wind and wave conditions. The system was robust enough to produce high-quality images even with ship heaves of up to 3 m and pitch and roll angles of up to 6.3°. On board the research ship, on average, 25% of the images acquired from plankton samples on filter membranes could be used for cell enumeration. Automated enumeration was highly correlated with manual counts (r2> 0.9). Even the smallest of microbial cells in the open ocean, members of the alphaproteobacterial SAR11 clade, could be confidently detected and enumerated. The automated image acquisition and cell enumeration system developed here enables an accurate and reproducible determination of microbial cell counts in planktonic samples and allows insight into the abundance and distribution of specific microorganisms already on board within a few hours.IMPORTANCEIn this research article, we report on a new system and software pipeline, which allows for an easy and quick image acquisition and the subsequent enumeration of cells in the acquired images. We put this pipeline through vigorous testing and compared it to manual microscopy counts of microbial cells on membrane filters. Furthermore, we tested this system at sea on board a marine research vessel and counted bacteria on board within a few hours after the retrieval of water samples. The imaging and counting system described here has been successfully applied to a number of laboratory-based studies and allowed the quantification of thousands of samples and FISH preparations (see, e.g., H. Teeling, B. M. Fuchs, D. Becher, C. Klockow, A. Gardebrecht, C. M. Bennke, M. Kassabgy, S. Huang, A. J. Mann, J. Waldmann, M. Weber, A. Klindworth, A. Otto, J. Lange, J. Bernhardt, C. Reinsch, M. Hecker, J. Peplies, F. D. Bockelmann, U. Callies, G. Gerdts, A. Wichels, K. H. Wiltshire, F. O. Glöckner, T. Schweder, and R. Amann, Science 336:608–611, 2012,http://dx.doi.org/10.1126/science.1218344). We adjusted the standard image acquisition software to withstand ship movements. This system will allow for more targeted sampling of the microbial community, leading to a better understanding of the role of microorganisms in the global oceans.
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Zini, Gina, Mariagrazia Garzia, Antonella Di Mario, Elena Rossi, Giuliana Farina, and Giuseppe d’Onofrio. "Automated Quantitative Assessment of Bone Marrow in Normal Samples: Correlation with the Reference Method." Blood 106, no. 11 (November 16, 2005): 4192. http://dx.doi.org/10.1182/blood.v106.11.4192.4192.
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Abstract Bone marrow (BM) analysis is conventionally performed by microscope examination on films of about 0.3ml of aspirated bone marrow fluid, stained with Romanowsky dyes. Until 1996 the simple automated screening of marrow composition was made very difficult by a number of factors, mainly the lack of the erythroblasts quantitation and the fat interference. From 1996 last generation automated hematology analyzers provide accurate and precise erythroblasts counts; moreover same systems have improved their software reducing the problem of fat interference. We have analyzed data from 100 normal BM samples from patients submitted for diagnostic and/or follow up purposes in our Hematology Day Hospital. BM fluid was harvested from the superior posterior iliac crest. The first 0,3–0.5 ml were used for smears, while the next 1–2 ml of BM, collected into K3-EDTA, were analysed with Coulter LH 750, a fully automated hematology analyzer which provides Complete Blood Count, White Blood Count Differential included Nucleated Red Blood Cells (NRBC) and Reticulocytes count. We used the microscope examination conventionally performed on films stained with Romanowsky dyes as reference method. Quantitative detection BM cellularity was obtained by semi quantitative evaluation based on the evaluation of hematopoietic cells in several marrow particles: physiological differences age related were also taken in account. If hematopoietic cells occupy less than 25% or more than 85% the sample is defined respectively hypocellular or hypercellular (none of our sample was as). Differential cell count was usually performed on two different slides counting 500 cells (1000 when hypercellular, but none of our sample was as). We found a strict correlation between microscope semi-quantitative cellularity evaluation and the instrumental cell count as sum of WBC plus NRBC, the Total Nucleted Cell Count (TNCC). The mean value of the TNCC in normal PM samples was 29,48 x109/L with a range 25,9–54,9 x109/L. These results are in good agreement with normal BM cell count reported in the literature using a cytofluorimetric method, which is 34,5 x109/L (SD28.0). The instrumental mean percentage of BM granulocytes corrected for TNNC was 62% (range: 23,5–93,7) versus a mean microscope percentage of 58,42% (range: 40–72). The automated NRBC BM count corrected for TNCC was 11,38% (range: 2,7 – 39,17) versus a microscopic mean value of 28% (range: 9–45). These results, including the slight NRBC underestimation probably due to partial mature cell lysis, are in line with the data of the literature. This study confirms the feasibility of routine automated cell count using a hematology in normal BM fluid samples. Automated methods will support morphologists quickly providing accurate and precise quantitative information such as TNCC and myeloid/erythroid ratio.
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Moerland, Elna, Rens L. H. P. M. van Hezik, Toine C. J. M. van der Aa, Mike W. P. M. van Beek, and Adriaan J. C. van den Brule. "Detection of HER2 Amplification in Breast Carcinomas: Comparison of Multiplex Ligation-Dependent Probe Amplification (MLPA) and Fluorescence In Situ Hybridization (FISH) combined with Automated Spot Counting." Analytical Cellular Pathology 28, no. 4 (January 1, 2006): 151–59. http://dx.doi.org/10.1155/2006/741586.
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In this study the detection of HER2 gene amplification was evaluated using Fluorescence In Situ Hybridization (FISH; PathVysion) in comparison with Multiplex Ligation-dependent Probe Amplification (MLPA), a PCR based technique. These two methods were evaluated on a series of 46 formalin fixed paraffin embedded breast carcinomas, previously tested for protein overexpression by HercepTest (grouped into Hercep 1+, 2+ and 3+). HER2 gene amplification (ratio ≥ 2.0) by FISH was found in 9/10, 10/30 and 0/6 in IHC 3+, 2+ and 1+/0 cases, respectively. Digitalized automated spot counting performed with recently developed CW4000 CytoFISH software was 100% concordant with manual FISH scoring. Using MLPA 18/46 samples showed a clear HER2 amplification. Comparing MLPA and IHC showed the same results as for FISH and IHC. All but one FISH positive cases (18/19) were confirmed by MLPA for the presence of the gene amplification. The overall concordance of detection of Her2 gene amplification by FISH and MLPA was 98% (45/46). Furthermore, both the level of amplification and equivocal results correlated well between both methods. In conclusion, MLPA is a reliable and reproducible technique and can be used as an either alternative or additional test to determine HER2 status in breast carcinomas.
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Goldis, Adrian, Ramona Goldis, Marioara Cornianu, Norina Basa, Daniela Lazar, Amadeus Dobrescu, and Fulger Lazar. "Immunohistochemical Study of Cell Proliferation in Hepatocellular Carcinoma." Revista de Chimie 70, no. 6 (July 15, 2019): 2198–203. http://dx.doi.org/10.37358/rc.19.6.7304.
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Hepatocellular carcinoma (HCC), the most common primary liver cancer, with a poor prognosis, is the fifth most common cancer in men and the seventh in women. The study was made on 32 surgically removed liver carcinomas. In order to compare results, we included a group of non-tumor lesions obtained by liver biopsy. Assessment of the proliferative activity of the studied liver lesions was made using immunohistochemical stains with the monoclonal Ki-67 antibody, clone MIB-1 ready-to-use (DAKO Cytomation CA, USA), in the LSAB-HRP work system. To appreciate the proliferation index of Ki-67 (PI Ki-67), we used the semi-automated method of counting the nuclei on digital images. The statistical analysis was performed using SPSS software, Version 20.0 (IBM SPSS Statistics) and Microsoft Office Excel 2007. Mean value of Ki-67 index was 0.4 � 0.2 in normal liver, 3.52 � 0.2 in non-tumor liver lesions and 13.4�7.7 in HCC (p [ 0.001). In chronic hepatitis, PI Ki-67 varied between 2.5 and 5.8 %, with a mean value of 5.2% in portal chronic hepatitis and 5.5% in active chronic hepatitis with cirrhotic evolution. In HCC, the values of Ki-67 index were between 0.7% and 52%, with a mean of PI Ki-67 of 13.43�7.7. 66.6% of HCC associated with hepatitis B virus infection and those developped from a cirrhotic lesion had a high Ki-67 score (p = 0.1). The results we obtained showed: a low Ki-67 score in patients with well-differentiated HCC (G1) (p[0.001), with or without capsule infiltration (p = 0.003); high PI Ki-67 in 33.33% of HCC detected in the right hepatic lobe and those extended bilaterally at the moment of diagnosis (p = 0.142) and a significant relationship between high Ki-67 score and vascular invasion (p [ 0.001), the presence of intrahepatic metastasis being correlated with a high proliferative rate (p [ 0.001). Differences between the proliferation rate of HCC and non-tumor liver lesions (p [ 0.001) show that the uncontrolled division of tumor cells can play an important role in the developpment of HCC.
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Fertrin, Kleber Yotsumoto, Elza Miyuki Kimura, Fernanda Madureira de Oliveira Araujo, Maria de Fatima Pereira Gilberti, Sara T. O. Saad, and Fernando F. Costa. "Hypersegmented Neutrophil Percentage Using Automated Digital Cell Morphology: A Simple Laboratory Parameter to Monitor Hydroxyurea Therapy in Sickle Cell Disease Patients." Blood 126, no. 23 (December 3, 2015): 2188. http://dx.doi.org/10.1182/blood.v126.23.2188.2188.
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Abstract Fetal hemoglobin (HbF) induction with hydroxyurea (HU) remains the only specific pharmacological treatment for sickle cell disease (SCD) patients. Compliance is key to achieve high HbF levels, but assessing patient adherence to long-term treatment is difficult. Since patient interviews are often unreliable, most hematologists rely on mean corpuscular volume (MCV) and HbF levels to monitor compliance, but red blood cell indices change slowly. Hypersegmented neutrophils (HN) have been for long recognized in patients taking HU, and automated digital cell morphology platforms allow routine peripheral blood smears to be conveniently photographed and stored for analysis. We have designed a protocol to determine the percentage of circulating HN and have studied how this parameter compares with others commonly used in clinical practice during HU therapy, such as HbF, MCV, and reticulocyte count. We collected blood samples from 38 patients with SCD in steady state, receiving an average dose of 20.5mg/kg/day of HU (range 10.2-33.3) at our Hematology outpatient clinic. Complete blood counts and HN percentage determinations were performed on a Sysmex XE-5000/Cellavision DM96 equipment. HbF levels were determined by HPLC (Bio-Rad). Statistical analysis was performed using GraphPad Prism 6.0 software. Standard protocol for image acquisition initially recorded 100 cells per smear and HN count was performed independently by two experienced laboratory personnel using the same image database, by counting cells with 5 or more nuclear segments. To increase precision due to the relatively low numbers of neutrophils in some samples, analysis was subsequently improved by acquiring 300 images per smear aiming to picture 100 neutrophils. Mean hemoglobin level in our population was 8.6±1.3g/dL (mean±SD), MCV 103.8±14.2fL, reticulocyte count 222,950±129,090/uL, and HbF was 13.1±7.8% (range 2.1-30.9%). HN percentage as determined with 100 images per smear displayed only borderline correlation with HbF levels (P =0.094), but acquisition of 300 images per smear yielded over 90 neutrophil images in 35/38 samples. Average HN percentage was 14,5% (range 2,0-45,0%) and correlated positively with HbF (r=0.4172, P =0.009) and MCV (r=0.4301, P =0.007). As expected, HbF also correlated with MCV (r=0.5777, P =0.0001) and reticulocyte count (r=-0.489, P =0.003). Despite our limited number of patients, ROC curve analysis showed that HN percentage had an area under curve of 0.7241 to detect patients with HbF>20% (P =0.047). Patients with more than 6 segments per neutrophil were also more likely to have higher HbF. Average daily HU dose did not correlate with HbF or any of the parameters analyzed. Lack of correlation between dose and HbF supports that medication adherence may indeed be suboptimal in the study population, but that may also be caused by individual differences in HU metabolism. While MCV still displayed the strongest correlation with HbF in our study, HN percentage performed similarly, with higher counts associated with higher HbF. Neutrophils have a much shorter half-life than erythrocytes, with an estimated half-life of hours rather than the 20 days calculated for sickle red blood cells. Therefore, digital cell morphology analysis enables clinical laboratories to determine HN counts that can change more quickly than HbF, MCV, or reticulocyte counts during HU therapy. Monitoring the number of HN may allow more timely assessment of compliance in patients starting HU or in those in need for HU in combination with sporadic blood transfusions, since neutrophil hypersegmentation should not be affected by changes in red blood cell mass. Further studies should investigate HN percentage as a potential surrogate marker of response to HU and of patient compliance. Financial support: FAPESP Disclosures No relevant conflicts of interest to declare.
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Salib, Christian, Swati Bhardwaj, Shafinaz Hussein, Siraj El Jamal, Bruce Petersen, Eileen Scigliano, Francine R. Dembitzer, and Julie Teruya-Feldstein. "Digital AI in Hematology - Integration of the Scopio Labs x100 Scanner with Newly Implemented AI Capabilities into Routine Clinical Workflow." Blood 138, Supplement 1 (November 5, 2021): 4932. http://dx.doi.org/10.1182/blood-2021-148821.
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Abstract BACKGROUND Digital pathology and artificial intelligence (AI) are areas of growing interest in pathology. A number of institutes have already integrated digital imaging into routine workflow, relying on AI algorithms for the detection of various cancers and mitotic activity quantification. Despite the use of whole slide imaging (WSI) for tissue evaluation, the field of hematology has lagged behind. While many hospitals rely on limited technologies for automated peripheral blood evaluation (e.g. CellavisionTM), the Scopio LabsTM X100 digital scanner provides high resolution oil-immersion level dynamic images of large scanned areas (https://scopiolabs.com/hematology/). With recent FDA-clearance and newly implemented AI capabilities, the Scopio Labs scanner allows for clear and accurate cytomorphologic characterization and cell quantification for peripheral blood smears (PBS). To this end, we aimed to be one of the few pioneering institutes in the United States to adopt early and implement this technology into our routine workflow as a 'hub and spoke' model for optimized case assessment, data sharing and result reporting across multiple satellite locations within our hospital health system. DESIGN A Scopio x100 digital scanner was deployed at our main hospital site, with an anticipated secondary scanner for installment at a satellite laboratory. PBS flagged for hematopathologist review from two satellite laboratories were scanned, and full-field digitalized slides were evaluated by hematopathologists following AI automated analyses. RESULTS 311 peripheral smears were scanned since April 2021 and representative slides were digitalized at 100x magnification (Figure 1, weblink: https://demo.scopiolabs.com/#/view_scan/9231acaf-f898-4649-950d-a41c26c2baaa) with rapid monolayer, monolayer, full-field, and full-field cytopenia scan options available. The automated AI capabilities classified cells into lineage-specific categories with quantification based on cytomorphologic features (Figure 2). Other AI features include additional cell assignment, cell annotation and comments accessible to all users, finalized report PDF generation, export, upload into our current PowerPath TM software with linkage to the corresponding flow cytometry and bone marrow biopsy reports; and the ability to share digitalized slides with clinicians, laboratory personnel and trainees using uniquely generated weblinks. Images can be used for lectures and tumor boards. Additionally, an 80-case study set for PBS was created for medical students, residents and fellow teaching purposes, including cases displaying acute B-cell lymphoblastic leukemia (B-ALL), acute myelomonocytic leukemia (AMML), hypersegmented neutrophils in COVID-19(+) patients, myelodysplastic syndrome (MDS), atypical lymphocytes, hemoglobinopathies, platelet disorders and various lymphomas. Overall improvements were made to the following areas: CLINICAL WORK/DIAGNOSIS 1. Time-saving due to pre-categorization of cells into lineage-specific groups for pathologist review 2. Minimizes subjectivity in cell counting and cellularity assessment EDUCATION 1. Case-based collection with flow and molecular being maintained here 2. Efficient case retrieval with retained annotations/comments for teaching purposes 3. Wide array of digitalized images for hematology atlas and publications ARCHIVING 1. Collection of reference images (intra/inter departmental) for an array of morphological entities for clinical reference and refined diagnosis (e.g. Bethesda reference images for pap by ASC) 2. Digital catalogue for long-term case follow-up and retrospective review CONCLUSION The Scopio Labs X100 digital system provides an efficient and cost-effective web-based tool to streamline clinical workflow and enhance PBS evaluation. With its recent AI capabilities of cell quantification, lineage-assignment and report-generation, we aim to continue our efforts to fully integrate Scopio Labs into our routine daily clinical workflow for reviewing PBS specimens. CONFLICT OF INTEREST STATEMENT The authors have nothing to disclose with regard to the submitted work Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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Ershov, A. S., I. P. Antropova, E. A. Volokitina, and L. P. Evstigneeva. "Hematological parameters of rheumatoid arthritis patients undergoing total hip replacement." Genij Ortopedii 27, no. 5 (October 2021): 514–20. http://dx.doi.org/10.18019/1028-4427-2021-27-5-514-520.
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(THR). Inflammation in RA is the main factor manifesting anaemia, neutropenia, thrombocytosis and eosinophilia. The changes in blood components are important for the outcomes of major orthopaedic surgery. The purpose was to identify hematological parameters in RA patients undergoing THR and assess the effect on intraoperative blood loss. Material and methods Outcomes of 44 THR patients treated for grade III degenerative coxarthrosis (n = 21, OA group) and RA coxarthrosis (n = 23, RA group) in Ekaterinburg regional hospital № 1 between 2018 and 2019 were reviewed. The patients' age ranged from 41 to 70 years. Clinical, radiological, laboratory examinations, computed tomography and statistical analysis were used for the study. Cell counting was produced with the Sysmex XT-4000i automated hematology system. Statistical analysis was performed using the tools of Statistica software. Nonparametric Mann-Whitney test was used to compare cell counts between the groups. The Spearman Rank correlation was used to analyse the correlation between the the cell counts in the groups. For calculations, a significance level of р < 0.05 was adopted. Results There were no significant differences in the preoperative white blood cell (WBC), neutrophil, lymphocyte, monocyte, eosinophil counts between RA and OA groups. The RA group showed an evident decrease in red blood cell (RBC) count and haemoglobin level as compared to OA group. The RA group demonstrated the higher platelet count with mean platelet volume (MPV) being significantly lower than that in the OA group. WBC count, neutrophils, in particular, was shown to increase with lymphocyte, RBC, platelet count and hemoglobin, plateletcrit levels decreased at 24 hours postoperatively. There were no significant differences in WBC and RBC counts in the groups postoperatively. The differences in the MPV were leveled up in the groups with the platelet count being higher in the RA group as compared to the OA group. Conclusions Hematological parameters of RA patients who had undergone specific preoperative preparation were not shown to be associated with greater blood loss during hip replacement surgery. The leukocyte count leveled up in the preoperative and early postoperative periods can be indicative of the absence of a significant effect of RA on the postoperative inflammation.
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Ingram, Zachary, Hannah Matheney, Emma Wise, Courtney Weatherford, and Amy E. Hulme. "Overlap Intensity: An ImageJ Macro for Analyzing the HIV-1 In Situ Uncoating Assay." Viruses 13, no. 8 (August 13, 2021): 1604. http://dx.doi.org/10.3390/v13081604.
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Capsid uncoating is at the crossroads of early steps in HIV-1 replication. In recent years, the development of novel assays has expanded how HIV-1 uncoating can be studied. In the in situ uncoating assay, dual fluorescently labelled virus allows for the identification of fused viral cores. Antibody staining then detects the amount of capsid associated with each viral core at different times post-infection. Following fixed cell imaging, manual counting can be used to assess the fusion state and capsid signal for each viral core, but this method can introduce bias with increased time of analysis. To address these limitations, we developed the Overlap Intensity macro in ImageJ. This macro automates the detection of viral cores and quantification of overlapping fusion and capsid signals. We demonstrated the high accuracy of the macro by comparing core detection to manual methods. Analysis of an in situ uncoating assay further verified the macro by detecting progressive uncoating as expected. Therefore, this macro improves the accessibility of the in situ uncoating assay by replacing time-consuming manual methods or the need for expensive data analysis software. Beyond the described assay, the Overlap Intensity macro includes adjustable settings for use in other methods requiring quantification of overlapping fluorescent signals.
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Clayton, Joe. "Automated Label-Free Cell Counting." Genetic Engineering & Biotechnology News 37, no. 14 (August 2017): 12–13. http://dx.doi.org/10.1089/gen.37.14.07.
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Barber, Paul R., Borivoj Vojnovic, Jane Kelly, Catherine R. Mayes, Peter Boulton, Michael Woodcock, and Michael C. Joiner. "Automated counting of mammalian cell colonies." Physics in Medicine and Biology 46, no. 1 (November 30, 2000): 63–76. http://dx.doi.org/10.1088/0031-9155/46/1/305.
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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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Marotz, J., C. Lübbert, and W. Eisenbeiß. "Effective object recognition for automated counting of colonies in Petri dishes (automated colony counting)." Computer Methods and Programs in Biomedicine 66, no. 2-3 (September 2001): 183–98. http://dx.doi.org/10.1016/s0169-2607(00)00128-0.
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Hartmann, Michael, Elisabet Gas-Pascual, Andrea Hemmerlin, Michel Rohmer, and Thomas J. Bach. "Development of an image-based screening system for inhibitors of the plastidial MEP pathway and of protein geranylgeranylation." F1000Research 4 (January 16, 2015): 14. http://dx.doi.org/10.12688/f1000research.5923.1.
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We have recently established an in vivo visualization system for the geranylgeranylation of proteins in a stably transformed tobacco BY-2 cell line, which involves expressing a dexamethasone-inducible GFP fused to the prenylable, carboxy-terminal basic domain of the rice calmodulin CaM61, which naturally bears a CaaL geranylgeranylation motif (GFP-BD-CVIL). By using pathway-specific inhibitors it was demonstrated that inhibition of the methylerythritol phosphate (MEP) pathway with oxoclomazone and fosmidomycin, as well as inhibition of protein geranylgeranyl transferase type 1 (PGGT-1), shifted the localization of the GFP-BD-CVIL protein from the membrane to the nucleus. In contrast, the inhibition of the mevalonate (MVA) pathway with mevinolin did not affect this localization. Furthermore, complementation assays with pathway-specific intermediates confirmed that the precursors for the cytosolic isoprenylation of this fusion protein are predominantly provided by the MEP pathway. In order to optimize this visualization system from a more qualitative assay to a statistically trustable medium or a high-throughput screening system, we established new conditions that permit culture and analysis in 96-well microtiter plates, followed by fluorescence microscopy. For further refinement, the existing GFP-BD-CVIL cell line was transformed with an estradiol-inducible vector driving the expression of a RFP protein, C-terminally fused to a nuclear localization signal (NLS-RFP). We are thus able to quantify the total number of viable cells versus the number of inhibited cells after various treatments. This approach also includes a semi-automatic counting system, based on the freely available image processing software. As a result, the time of image analysis as well as the risk of user-generated bias is reduced to a minimum. Moreover, there is no cross-induction of gene expression by dexamethasone and estradiol, which is an important prerequisite for this test system.
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Hartmann, Michael, Elisabet Gas-Pascual, Andrea Hemmerlin, Michel Rohmer, and Thomas J. Bach. "Development of an image-based screening system for inhibitors of the plastidial MEP pathway and of protein geranylgeranylation." F1000Research 4 (August 12, 2015): 14. http://dx.doi.org/10.12688/f1000research.5923.2.
Full textAbstract:
In a preceding study we have recently established an in vivo visualization system for the geranylgeranylation of proteins in a stably transformed tobacco BY-2 cell line, which involves expressing a dexamethasone-inducible GFP fused to the prenylable, carboxy-terminal basic domain of the rice calmodulin CaM61, which naturally bears a CaaL geranylgeranylation motif (GFP-BD-CVIL). By using pathway-specific inhibitors it was there demonstrated that inhibition of the methylerythritol phosphate (MEP) pathway with oxoclomazone and fosmidomycin, as well as inhibition of protein geranylgeranyl transferase type 1 (PGGT-1), shifted the localization of the GFP-BD-CVIL protein from the membrane to the nucleus. In contrast, the inhibition of the mevalonate (MVA) pathway with mevinolin did not affect this localization. Furthermore, in this initial study complementation assays with pathway-specific intermediates confirmed that the precursors for the cytosolic isoprenylation of this fusion protein are predominantly provided by the MEP pathway. In order to optimize this visualization system from a more qualitative assay to a statistically trustable medium or a high-throughput screening system, we established now new conditions that permit culture and analysis in 96-well microtiter plates, followed by fluorescence microscopy. For further refinement, the existing GFP-BD-CVIL cell line was transformed with an estradiol-inducible vector driving the expression of a RFP protein, C-terminally fused to a nuclear localization signal (NLS-RFP). We are thus able to quantify the total number of viable cells versus the number of inhibited cells after various treatments. This approach also includes a semi-automatic counting system, based on the freely available image processing software. As a result, the time of image analysis as well as the risk of user-generated bias is reduced to a minimum. Moreover, there is no cross-induction of gene expression by dexamethasone and estradiol, which is an important prerequisite for this test system.
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Kickler, Thomas S. "Clinical Analyzers. Advances in Automated Cell Counting." Analytical Chemistry 71, no. 12 (June 1999): 363–65. http://dx.doi.org/10.1021/a1999911a.
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Branda, John A., and Alexander Kratz. "Effects of Yeast on Automated Cell Counting." American Journal of Clinical Pathology 126, no. 2 (August 2006): 248–54. http://dx.doi.org/10.1309/1g5hb7pak8v0kar7.
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Dehlinger, Dietrich, Lynn Suer, Maher Elsheikh, José Peña, and Pejman Naraghi-Arani. "Dye free automated cell counting and analysis." Biotechnology and Bioengineering 110, no. 3 (January 7, 2013): 838–47. http://dx.doi.org/10.1002/bit.24757.
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Alcaide Martín, María José, Laura Altimira Queral, Laura Sahuquillo Frías, Laura Valiña Amado, Anna Merino, and Luis García de Guadiana-Romualdo. "Automated cell count in body fluids: a review." Advances in Laboratory Medicine / Avances en Medicina de Laboratorio 2, no. 2 (March 15, 2021): 149–61. http://dx.doi.org/10.1515/almed-2021-0011.
Full textAbstract:
Abstract Body fluid cell counting provides valuable information for the diagnosis and treatment of a variety of conditions. Chamber cell count and cellularity analysis by optical microscopy are considered the gold-standard method for cell counting. However, this method has a long turnaround time and limited reproducibility, and requires highly-trained personnel. In the recent decades, specific modes have been developed for the analysis of body fluids. These modes, which perform automated cell counting, are incorporated into hemocytometers and urine analyzers. These innovations have been rapidly incorporated into routine laboratory practice. At present, a variety of analyzers are available that enable automated cell counting for body fluids. Nevertheless, these analyzers have some limitations and can only be operated by highly-qualified laboratory professionals. In this review, we provide an overview of the most relevant automated cell counters currently available for body fluids, the interpretation of the parameters measured by these analyzers, their main analytical features, and the role of optical microscopy as automated cell counters gain ground.
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Waithe, Dominic, Peter Rennert, Gabriel Brostow, and Matthew D. W. Piper. "QuantiFly: Robust Trainable Software for Automated Drosophila Egg Counting." PLOS ONE 10, no. 5 (May 18, 2015): e0127659. http://dx.doi.org/10.1371/journal.pone.0127659.
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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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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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Cadena-Herrera, Daniela, Joshua E. Esparza-De Lara, Nancy D. Ramírez-Ibañez, Carlos A. López-Morales, Néstor O. Pérez, Luis F. Flores-Ortiz, and Emilio Medina-Rivero. "Validation of three viable-cell counting methods: Manual, semi-automated, and automated." Biotechnology Reports 7 (September 2015): 9–16. http://dx.doi.org/10.1016/j.btre.2015.04.004.
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Amorim Siqueira, Alisson, and Paulo Gustavo Serafim de Carvalho. "MicroCount: Free Software For Automated Microorganism Colony Counting By Computer." IEEE Latin America Transactions 15, no. 10 (October 2017): 2006–11. http://dx.doi.org/10.1109/tla.2017.8071248.
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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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Frotscher, B., S. Salignac, M. Muller, V. Latger-Cannard, P. Feugier, and J. F. Lesesve. "Interference of blast cell fragments with automated platelet counting." International Journal of Laboratory Hematology 37, no. 5 (April 28, 2015): 613–19. http://dx.doi.org/10.1111/ijlh.12371.
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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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Phanomchoeng, Gridsada, Chayatorn Kukiattikoon, Suphanut Plengkham, Siwaporn Boonyasuppayakorn, Saran Salakij, Suvit Poomrittigul, and Lunchakorn Wuttisittikulkij. "Machine-learning-based automated quantification machine for virus plaque assay counting." PeerJ Computer Science 8 (March 4, 2022): e878. http://dx.doi.org/10.7717/peerj-cs.878.
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The plaque assay is a standard quantification system in virology for verifying infectious particles. One of the complex steps of plaque assay is the counting of the number of viral plaques in multiwell plates to study and evaluate viruses. Manual counting plaques are time-consuming and subjective. There is a need to reduce the workload in plaque counting and for a machine to read virus plaque assay; thus, herein, we developed a machine-learning (ML)-based automated quantification machine for viral plaque counting. The machine consists of two major systems: hardware for image acquisition and ML-based software for image viral plaque counting. The hardware is relatively simple to set up, affordable, portable, and automatically acquires a single image or multiple images from a multiwell plate for users. For a 96-well plate, the machine could capture and display all images in less than 1 min. The software is implemented by K-mean clustering using ML and unsupervised learning algorithms to help users and reduce the number of setup parameters for counting and is evaluated using 96-well plates of dengue virus. Bland–Altman analysis indicates that more than 95% of the measurement error is in the upper and lower boundaries [±2 standard deviation]. Also, gage repeatability and reproducibility analysis showed that the machine is capable of applications. Moreover, the average correct measurements by the machine are 85.8%. The ML-based automated quantification machine effectively quantifies the number of viral plaques.
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Johnston, Grace. "Automated handheld instrument improves counting precision across multiple cell lines." BioTechniques 48, no. 4 (April 2010): 325–27. http://dx.doi.org/10.2144/000113407.
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