halassemia is a Greek word that taken from two words, Thalassa means Sea and Emia means blood, thus called Mediterranean anemia or Cooley's anemia, anemia in Persian. Thalassemia is a congenital hemolytic disease that inherited according to Mendel's laws. The first an American scientist named Dr. Cooley defined it's to other in1925. In this synthesis and defective Hb produce. In erythroid precursors additional chains don't pair together, this synthesis that this leads unbalanced Hb chain damage and lyse cells (1). If beta chain is defective, called beta thalassemia and if alpha chain is defective, called alpha thalassemia. Beta-thalassemia syndromes are a group of hereditary blood disorders characterized by reduced or absent beta-globin chains expression that T

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.

: Diagnosis of factor XIII (FXIII) deficiency (FXIIID) as a rare bleeding disorder is a challenge worldwide. Thus, in the present study, we used different methods including two molecular methods for detection of FXIIID. This study was conducted on individuals suspected to FXIIID. All individuals were checked by two routinely used methods of clot solubility test in Iran and two other clot solubility tests as well as FXIII activity and antigen assays. Molecular analysis was performed by PCR-restriction fragment length polymorphism (PCR-RFLP) and tetra-primer amplification refractory mutation system (T-ARMS)-PCR for only FXIIID mutation in southeast Iran (p.Trp187Arg), previously associated with severe FXIIID. Out of 151 individuals, 26 had abnormal clot solubility test with all four methods. PCR-RFLP revealed that 27 patients were homozygotes for p.Trp187Arg, whereas 12 were heterozygotes. Molecular analysis revealed that in routinely used clot solubility combinations, two homozygotes (∼8%) were missed, whereas in two other combinations, one patient (∼4%) was missed. One false positive result was observed in routinely used methods, whereas further combinations don't have false positive. T-ARMS-PCR had three discrepancies with PCR-RFLP and sequencing confirmed that the results of T-ARMS-PCR were false. FXIII antigen assay diagnosed all homozygotes, whereas in FXIII activity assay, two homozygotes had higher than 5% FXIII activity that inconsistent with severe deficiency. It seems that clot solubility test is not enough sensitive and specific and molecular analysis is the most reliable method for detection of FXIIID in areas such Iran with one or few specific mutations.

Factor XIII (FXIII) deficiency is an extremely rare bleeding disorder with an approximately 12-times higher than the rest of the world. The International Society for Thrombosis and Hemostasis (ISTH) suggested a standard algorithm for precise diagnosis and classification of FXIII deficiency (FXIIID). However, due to lack of investment in proper equipment and procedures in Iran, almost no part of this algorithm can be used to diagnose Iranian patients. Thus, this study proposes a guideline for accurate molecular and laboratory diagnosis of FXIIID based on the available tools. Because this study suggests a simple and reliable algorithm for early diagnosis, it can therefore, reduce the rates of morbidity and mortality of FXIIID patients with this condition.

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.