ABSTRACT
The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an F1 score of 98.84%.
ABSTRACT
Red blood cell (RBC) dataset was obtained from four thalassemia peripheral blood smears and a healthy peripheral blood smear. The dataset contains 7108 images of individual red blood cells for nine cell types. The first process is image acquisition, which is the process of retrieving microscopic image data from peripheral blood smears through a Olympus CX21 microscope using an Optilab advance plus camera. Laboratory assistants helped obtain ideal erythrocyte images. We provide peripheral blood smear from four thalassemia patients in the ThalassemiaPBS dataset. After image acquisition, the image is resized from 4100 × 3075 pixels to 800 × 600 pixels to reduce the computing load in the next stage. We extracted the green color component (green channel) of the RGB image and used it in the next process. We chose the green channel because it is not affected by variations in color and brightness. Furthermore, the segmentation stage is carried out to obtain an object in the form of a single red blood cell. After that, the object can be classified according to the type of red blood cell. This dataset can become an opportunity for international researchers to develop the classification method for red blood cells.
ABSTRACT
Transient pancytopenia due to reactive bone marrow suppression often occurs in hemophagocytic lymphohistiocytosis (HLH), a syndrome resulting from excessive immune activation following a severe infection. We reported two cases with pancytopenia and disseminated histoplasmosis accompanied by HLH, initially suspected to be blood malignancies. Our first case documented the relevance between the improvement of pancytopenia and the clearance of Histoplasma capsulatum in serial bone marrow aspirations. The second case showed immense Histoplasma engulfment by the macrophage in relation to a severe clinical condition, followed by improvement of clinical symptoms in accordance with the recovery of pancytopenia. These two cases highlighted the importance of comprehensive and critical analysis for cases with concurrent pancytopenia and severe infection, since it may be that the pancytopenia underlies the severe infection or vice versa.
ABSTRACT
BACKGROUND: iron deficiency in pulmonary tuberculosis (TB) patients may weaken their immune system, causing difficulty in overcoming the infection. Accurate diagnosis of iron deficiency anemia (IDA) in pulmonary TB patients is essential. In order to prove the iron deficient state, diagnosis should focus on inflammatory factors, which could highly affect the outcome of iron status, such as measurement of serum ferritin (SF). Soluble Transferrin Receptor (sTfR) is the best parameter to diagnose iron deficiency in the inflammatory condition. This study aimed to understand the role of sTfR to identify IDA in TB patients. METHODS: cross-sectional study were applied to 3 study groups: anemic pulmonary TB (68 subjects), IDA (7 subjects), and non-anemic pulmonary TB (15 subjects). The test averages and correlations between sTfR, SF, and other hematological parameters were measured and analyzed. RESULTS: significant differences of sTfR were found in the anemic TB group compared to the IDA group and also between the IDA and non-anemic TB groups (p<0.0001). However, there was no significant difference (p>0.05) between TB anemic and non-anemic groups. We also found no significant difference between the TB anemic sub-group with normal levels of sTfR compared with the non-anemic group. There was no significant difference of sTfR levels between sub-group of increasing sTfR and group IDA (p>0.05). However, there was strong correlation between sTfR and SF in the IDA group (r=-0.89; p=0.007). CONCLUSION: the findings suggest verifying the sTfR amount in anemic patients with pulmonary TB suffering from inflammation, so that the iron deficiency could be more accurately identified and properly treated.
