599,972 research outputs found

    PENGARUH PEMBERIAN MELATONIN TERHADAP JUMLAH LEUKOSIT PADA TIKUS WISTAR MODEL SEPSIS

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    Background : Melatonin was a free radical frequently used as an antioxidant. Melatonin played a role in increasing immune response, and supporting cytoprotective process. On some animal models, melatonin has been identified to be able to resist bacteria infections, viruses, and parasites through some mechanisms such as immunomodulation or antioxidant activities. Melatonin could decrease the level of inflammation cytokine, oxidative stress, and mitochondria dysfunction. Melatonin was one of medicine developed as a sepsis therapy. Objective : this research was to find out the melatonin influence on the amount of white blood cells of a wistar rat sepsis model and to obtain the information that melatonin could decrease the number of white blood cells. Methods : this research was an experimental research with a randomized control group using pre and post test. The samples were 12 male wistar rats with certain criteria divided into 2 groups. The first group was given an intraperitoneal injection of lipopolysaccharide (LPS) and was not given melatonin as control group. The second group was given an intraperitoneal injection of lipopolysaccharide (LPS) and was given melatonin by oral sonde as treatment group. After a week, in the eighth day, the blood of each rat was taken from the retro-orbital blood vessel. The statistical test used paired t-test, independent t-test, and Mann Whitney test. Results : In the independent test, the average score of the amount of white blood cells from control group was higher than the experimental group. In the paired t-test, the control group underwent a significant change (p<0,05) compared to experimental group which showed a meaningless result. In the Mann Whitney test, the result of pre-post 1 and post 2 from the control group got a significant increase while the result of pre LPS – post 1 and post 2 from the experimental group got a significant decrease (p<0,05). Conclusion : The melatonin treatment did not cause a significant decrease of the amount of white blood cells. Keywords : Sepsis,The amount of white blood cells, Melatonin, Lipopolysaccharide

    Automatic Leukemia Cell Counting using Iterative Distance Transform for Convex Sets

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    The calculation of white blood cells on the acute leukemia microscopic images is one of the stages in the diagnosis of Leukemia disease. The main constraint on calculating the number of white blood cells is the precision in the area of overlapping white blood cells. The research on the calculation of the number of white blood cells overlapping generally based on geometry. However, there was still a calculation error due to over segment or under segment. This paper proposed an Iterative Distance Transform for Convex Sets (IDTCS) method to determine the markers and calculate the number of overlapping white blood cells. Determination of marker was performed on every cell both in single and overlapping white blood cell area. In this study, there were tree stages: segmentation of white blood cells, marker detection and white blood cell count, and contour estimation of every white blood cell. The used data testing was microscopic acute leukemia image data of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). Based on the test results, Iterative Distance Transform for Convex Sets IDTCS method performs better than Distance Transform (DT) and Ultimate Erosion for Convex Sets (UECS) method

    Analysis of platelet-rich plasma extraction variations in platelet and blood components between 4 common commercial kits

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    Background: Platelet-rich plasma (PRP) has been extensively used as a treatment in tissue healing in tendinopathy, muscle injury, and osteoarthritis. However, there is variation in methods of extraction, and this produces different types of PRP. Purpose: To determine the composition of PRP obtained from 4 commercial separation kits, which would allow assessment of current classification systems used in cross-study comparisons. Study Design: Controlled laboratory study. Methods: Three normal adults each donated 181 mL of whole blood, some of which served as a control and the remainder of which was processed through 4 PRP separation kits: GPS III (Biomet Biologics), Smart-Prep2 (Harvest Terumo), Magellan (Arteriocyte Medical Systems), and ACP (Device Technologies). The resultant PRP was tested for platelet count, red blood cell count, and white blood cell count, including differential in a commercial pathology laboratory. Glucose and pH measurements were obtained from a blood gas autoanalyzer machine. Results: Three kits taking samples from the “buffy coat layer” were found to have greater concentrations of platelets (3-6 times baseline), while 1 kit taking samples from plasma was found to have platelet concentrations of only 1.5 times baseline. The same 3 kits produced an increased concentration of white blood cells (3-6 times baseline); these consisted of neutrophils, leukocytes, and monocytes. This represents high concentrations of platelets and white blood cells. A small drop in pH was thought to relate to the citrate used in the sample preparation. Interestingly, an unexpected increase in glucose concentrations, with 3 to 6 times greater than baseline levels, was found in all samples. Conclusion:This study reveals the variation of blood components, including platelets, red blood cells, leukocytes, pH, and glucose in PRP extractions. The high concentrations of cells are important, as the white blood cell count in PRP samples has frequently been ignored, being considered insignificant. The lack of standardization of PRP preparation for clinical use has contributed at least in part to the varying clinical efficacy in PRP use. Clinical Relevance: The variation of platelet and other blood component concentrations between commercial PRP kits may affect clinical treatment outcomes. There is a need for standardization of PRP for clinical use

    An Investigation of Image and Video Classification Algorithm for White Blood Cells Detection in Real Time View

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    Medical industry is struggling in finding the cure for many types of disease especially cancer. It is known that white blood cell is used to protect the body against bacteria and diseases. Nowadays, many ways in separating white blood cells in human body were introduced for example; centrifugation. In this report, the author is using a new approach in separating the white blood cells to help with the immune system of human body. The new approach used in separating it by using image classification algorithm to separate the white blood cells from the blood capillaries and it will be done live from a video. By separating the white blood cells, we can study the behaviour of the immune system since white blood cells is responsible for immune system in human body

    Synergistic effect of zero-G and radiation on white blood cells. An experiment for the Gemini III manned space flight Annual report, period ending 30 Jun. 1965

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    Synergistic effect of weightlessness and radiation on white blood cells during Gemini 3 missio

    Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

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    In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells

    Unraveling Glucocorticoid Resistance In MLLrearranged Infant Acute Lymphoblastic Leukemia

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    __Abstract__ In the Netherlands, approximately 650 children aged between 0 and 18 years are diagnosed with cancer every year, including ~120 patients suffering from leukemia. Leukemia (Greek for leukos - white, and haima for blood) is a type of cancer characterized by an abnormal increase of immature (non-functional) white blood cells in the bone marrow. As a result, the production of all healthy, functional blood cells is impaired, leading to anemia (loss of functional red blood cells), infections (loss of functional white blood cells) and (internal) bleeding (loss of functional platelets), and eventually to leukemic infiltration of other tissues such as liver, spleen, skin and in some instances even in the central nervous system. Depending on the rate of disease progression, leukemia is classified into “acute” (rapidly developing) or “chronic” (slowly developing). Acute leukemias are usually characterized by uncontrolled proliferation of highly immature (leukemic) white blood cells, whereas chronic leukemias more often involve the malignant transformation of more differentiated white blood cells. Leukemia can further be classified into lymphoid (B-cell or T-cell leukemias) or non-lymphoid (myeloid) types of leukemia, depending on the type of white blood cell that was subjected to leukemic transformation
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