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.

We were able to observe optimal results in our patients by using closed reduction and suture anchors without opening the fracture site, thus allowing physiological processes in union without complications of complete union, while also preventing additional costs such as removing the device.

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.

Aim: Menstrual blood derived stem cells (MenSCs) are unique stem cells that have been isolated and identified recently. The special traits of MenSCs can be related to the cell signaling pathways. In this study, in order to find out the role of Wnt signaling on MenSCs proliferation, we evaluated ß-catenin expression as a key participant in Wnt signaling pathway in response to Lithium chloride (LiCl). Methods: MenSCs were isolated from healthy women by combining gradient density centrifugation with plastic adherence. After characterization of the isolated cells, cell proliferation of MenSCs in presence of 10-15 mM LiCl was evaluated by MTT assay. ß-catenin expression of the treated cells was examined using immunofluorescence technique. Results: Flow cytometric analysis revealed that both mesenchymal and embryonic stem cell markers are expressed on menstrual blood stem cells. MTT value decreased depending on the LiCl concentration. The proliferation of MenSCs cultivated in culture media containing 15mM LiCl was approximately two fold less than those grown without LiCl (p<0.01). Moreover, nuclear accumulation of ß-catenin protein in cells treated by LiCl was greater than cells without LiCl. Conclusion: The MenSCs are stem cell populations with high proliferation ability and unique immunophenotyping properties. Our results demonstrated that Wnt signaling pathway regulates MenSCs proliferation via trans-localization of activated-ß-catenin protein.