Accurate and early detection of anomalies in peripheral white blood cells plays a crucial role in the evaluation of well-being in individuals and the diagnosis and prognosis of hematologic diseases. For example, some blood disorders and immune system-related diseases are diagnosed by the differential count of white blood cells, which is one of the common laboratory tests. Data is one of the most important ingredients in the development and testing of many commercial and successful automatic or semi-automatic systems. To this end, this study introduces a free access dataset of normal peripheral white blood cells called Raabin-WBC containing about 40,000 images of white blood cells and color spots. For ensuring the validity of the data, a significant number of cells were labeled by two experts. Also, the ground truths of the nuclei and cytoplasm are extracted for 1145 selected cells. To provide the necessary diversity, various smears have been imaged, and two different cameras and two different microscopes were used. We did some preliminary deep learning experiments on Raabin-WBC to demonstrate how the generalization power of machine learning methods, especially deep neural networks, can be affected by the mentioned diversity. Raabin-WBC as a public data in the field of health can be used for the model development and testing in different machine learning tasks including classification, detection, segmentation, and localization.
Accurate and early detection of peripheral white blood cell anomalies plays a crucial role in the evaluation of an individual's well-being. The emergence of new technologies such as artificial intelligence can be very effective in achieving this. In this regard, most of the state-of-the-art methods use deep neural networks. Data can significantly influence the performance and generalization power of machine learning approaches, especially deep neural networks. To that end, we collected a large free available dataset of white blood cells from normal peripheral blood samples called Raabin-WBC. Our dataset contains about 40000 white blood cells and artifacts (color spots). To reassure correct data, a significant number of cells were labeled by two experts, and the ground truth of nucleus and cytoplasm were extracted by experts for some cells (about 1145), as well. To provide the necessary diversity, various smears have been imaged. Hence, two different cameras and two different microscopes were used. The Raabin-WBC dataset can be used for different machine learning tasks such as classification, detection, segmentation, and localization. We also did some primary deep learning experiments on Raabin-WBC, and we showed how the generalization power of machine learning methods, especially deep neural networks, was affected by the mentioned diversity.
Objective: Far beyond hemostasis and thrombosis, significant evidence has indicated the critical role of platelets in atherosclerosis. SDF-1 is among the pro-inflammatory chemokines that are increased in platelets of patients with coronary artery disease (CAD). The goal of the current work is to identify the in vitro effect of platelets from either CAD patients or healthy volunteers on the induction of macrophages and foam cells. Materials and Methods: The expression of SDF-1 on platelet surfaces in CAD patients and healthy volunteers was investigated using flow cytometry. We also evaluated the CXCR4/CXCR7 expression on monocytes from buffy coats of healthy volunteers. The effect of platelets from CAD patients and healthy volunteers on differentiation of monocytes and foam cell formation was evaluated using Oil Red O (ORO) staining. Flow cytometry and real-time PCR were also employed to evaluate surface markers and mRNA expression of genes involved in this process after co-culture of platelets with monocytes. Results: Monocytes in co-culture with platelets acquired a spindleshape appearance and ORO-positive lipid droplets. In addition, platelets could induce CD163 expression, as an important marker of M2 macrophage, and upregulate the mRNA expression of the SRB, CD36, ACAT, LXR-α , and ABCA1 genes in monocytes. Notably, platelets of CAD patients with higher expression of SDF-1, increased the expression of genes encoding SRB and CD36 as compared to platelets of healthy volunteers. Conclusion: Our results indicate that platelets from CAD patients could provoke monocyte differentiation into macrophages with an M2 phenotype, which in turn may participate in an atheroprotective process.
Background: Hematogones are normal B-cell precursor which can be seen in different physiological and pathological conditions. Due to variation in B-cell acute lymphoblastic leukemia (B-ALL) blasts immunophenotyping and interference of hematogones in minimal residual disease (MRD) assessment, precise discrimination of hematogones is very crucial. The purpose of this study was to evaluate the expression pattern of surface markers in hematogones and compare them with lymphoblasts. Material and Methods: In this applied study, flow cytometric analysis was performed using Coulter FC-500 and MXP software in 4-color combination and 6 different tubes. In this study, 85 patients diagnosed with acute lymphoblastic leukemia were evaluated. Out of these patients, 45 were boys and 40 were girls. Patients aged from 1 to 15 years old. In addition, 27 bone marrow samples from other patients aged 4 to 18 years were included in this investigation. These samples had been obtained for other diagnostic purposes, such as immune thrombocytopenic purpura and juvenile idiopathic arthritis. Results: During flow cytometric analysis, hematogones showed expressions of CD19, CD20, CD22, CD10, CD45, CD81, CD123, CD9, CD34 (partial expression), and tdt (partial expression). In these patients, hematgones were negative for CD66c expression. Lymphoblastic cells were positive for CD19, CD20 (in some cases), CD22, CD10, CD45, CD81, CD123, CD58, CD9, CD66c, CD34 (in most cases), and TDT. CD81 mean fluorescence intensity (MFI) in hematogones was higher than that in lymphoblasts. (112.5 (30-251) vs. 17.5 (5-30); P<0.0001) Conclusion: According to findings of this study, it seems that the use of CD81, CD58, CD123, CD66c, CD9, and CD81 MFI in combination with B-Cells associated markers can be very effective in differentiating hematogone from lymphoblast.
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