Targeted removal of blood cancer cells from mixed cell populations by cell recognition with matching particle imprints

Abstract: Blood Cancer cells forming a tissue is called lymphoma. Thus, disease decreases the cells to fight against the infection or cancer blood cells. Blood cancer is also categorized in too many types. The two main categories of blood cancer are Acute Lymphocytic Lymphoma and Acute Myeloid Lymphoma. In this project proposes a approach that robotic detects and segments the nucleolus from white blood cells in the microscopic Blood images. Here in this project, we have used the two Machine learning algorithms that are k-means algorithm, Support vector machine algorithm. K-mean algorithm is use for segmentation and clustering. Support vector machine algorithm is used for classification. Keywords: k-means, Support vector machine, Lymphoma, Acute Lymphocytic Lymphoma, Machine Learning

Download Full-text

Analisis dan Strategi Pengendalian Model Matematika Interaksi Sel Kanker Leukemia Mielositik Kronis dan Sel Imunitas

Contemporary Mathematics and Applications (ConMathA) ◽

10.20473/conmatha.v2i2.23853 ◽

2020 ◽

Vol 2 (2) ◽

pp. 89

Author(s):

Nanda Amalia Rahma ◽

Cicik Alfiniyah ◽

Windarto Windarto

Keyword(s):

Equilibrium Point ◽

Cancer Cells ◽

Cancer Cell ◽

Immune Cells ◽

Blood Cells ◽

White Blood Cells ◽

Cell Populations ◽

Asymptotically Stable ◽

Chronic Myelocytic Leukemia ◽

Myelocytic Leukemia

Leukemia is a disease in the classification of cancer in the blood that is characterized by abnormal growth of blood cells in the bone marrow or lymphoid tissue, and generally occurs in leukocytes or white blood cells. White blood cells that look for types of pathogenic diseases that harm the human body and then damage it are the task of the immune system. This thesis analyzes the mathematical model of chronic myelocytic leukemia cancer cell interactions and immune cells to determine the rate of increase in the population of chronic myelocytic leukemia cancer cells to the effect of immune cells. Based on the analysis of the model obtained two equilibrium points namely the equilibrium point of the extinction of chronic myelocytic leukemia cancer cells (E0) and the equilibrium point of the coexistence of chronic myelocytic leukemia cancer cells (E1). The equilibrium point of extinction will be asymptotically stable, whereas the equilibrium point of coexistence tends to be asymptotically stable using phase fields with the help of MATLAB software. Numerical simulation results show that there is an increase in the number of chronic myelocytic leukemia cancer cell populations and a decrease in the number of vulnerable blood cell populations. When immune cells increase in population, chronic myelocytic leukemia in cancer cells decreases in population but is not significant.

Download Full-text

Segmentation of Leukemia Cells Using Clustering

International Journal of Synthetic Emotions ◽

10.4018/ijse.2019070103 ◽

2019 ◽

Vol 10 (2) ◽

pp. 39-48

Author(s):

Eman Mostafa ◽

Heba A. Tag El-Dien

Keyword(s):

Bone Marrow ◽

Cancer Cells ◽

Blood Cells ◽

White Blood Cells ◽

Leukemia Cells ◽

Microscopic Image ◽

Processing Stage ◽

Clustering Techniques ◽

Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

Download Full-text

Leukemia Risk Gene ARID5B is a Crucial Regulator of B-Cell Development

Blood ◽

10.1182/blood-2018-99-113686 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 385-385 ◽

Cited By ~ 1

Author(s):

Charnise Goodings-Harris ◽

Hui Zhang ◽

Xuijie Zhao ◽

Heng Xu ◽

Omar I Abdel-Wahab ◽

...

Keyword(s):

Bone Marrow ◽

Sudden Death ◽

Mouse Model ◽

B Cell ◽

Blood Cells ◽

Association Studies ◽

White Blood Cells ◽

Cell Populations ◽

Spleen Volume ◽

Marrow Cellularity

Abstract Acute lymphoblastic leukemia (ALL) is the most common cancer in children. Genome wide association studies by us and others have identified germline genetic variants at the ARID5B locus that strongly influence susceptibility to ALL (Nat Genet 2009, J Clin Oncol 2012). Despite compelling results from these genetic association studies, the molecular mechanisms by which ARID5B contributes to ALL pathogenesis remains largely unknown. ARID5B is a member of the ARID transcription family characterized by a conservative AT rich binding domain, but the physiological functions of ARID5B are poorly understood. Therefore we sought to develop mouse models to comprehensively characterize the roles of ARID5B in normal and malignant hematopoiesis. To this end, we first established a Vav-1 specific Arid5b overexpression (AOE) mouse model. Mice were designed with a tetO element knocked in before the start codon of Arid5b to create an Arid5b tetO mouse. Arid5b tetO mice were then crossed with Vav-1-tTA mice and, overexpression of Arid5b confirmed in the hematopoietic system of progenies with the desired genotype. At 6-8 weeks old complete blood counts (CBC) analysis revealed that AOE mice had a large decrease in white blood cells (WBCs) and a slight reduction in red blood cells (RBCs) and hemoglobin (Hb). We also noted a significant reduction in the bone marrow cellularity of our Arid5b overexpressing mice. To further characterize our AOE mouse model we used flow cytometry to quantify the proportions of mature as well as various stem and progenitor cell populations during hematopoiesis. We found that AOE mice showed an increase in the MegE biased MPP2 (LSK flt3−/CD150+/CD48+) population and a decrease in the lymphoid biased MPP4 (LSK (Flt3+/CD150−/CD48+/−) population. Overexpression of Arid5b resulted in a significant reduction in the proportion of all bone marrow B cell populations (Hardy Fraction A-F) (Figure 1A) and B220+ cells in the spleen. In vitro methylcellulose colony forming assays further revealed a loss of functional pre B lymphoid progenitor in AOE bone marrow. To determine if the hematopoietic defects seen in younger AOE mice was due to a delay in hematopoiesis we aged AOE mice. We found that as we aged AOE mice there was still a reduction in bone marrow cellularity and all bone marrow B cell populations. Surprisingly, as mice were aged we found that AOE mice were prone to sudden death and displayed a dramatic reduction in overall survival when compared to wildtype littermates with a mean survival age of 8 months (Figure 1B). At the time of death, we found that AOE mice presented with severely enlarged spleens. To predict the death of AOE mice we performed ultrasounds to track spleen volume. Based on ultrasounds performed on AOE mice and wildtype littermates, AOE mice spleens become larger than those of their littermates as early as 6 months old, however spleen volume did not predict sudden death. In aged AOE mice, while the spleens were grossly enlarged and contained larger amounts of red pulp and increased Ter119+ cells, there was still a reduction in the proportion of B cells and no increase in the proportions of other white blood cells (Figure 1D). Older AOE mice exhibited anemia, hypergammaglobulinemia, and splenomegaly. By 30 weeks, defects in B cell proportions were seen in the lymph nodes of AOE mice via immunohistochemistry (Figure 1C). Compared to wildtype littermates, AOE mice displayed reduced white blood cells, red blood cells, and platelets (Figure 1D). The anemia was associated with higher reticulocyte numbers and increased serum erythropoietin concentration. The life span of erythrocytes from AOE mice was less than that of wildtype littermates. Together, these results indicated that Arid5b plays an important role in B lymphopoiesis and erythropoiesis. Disclosures No relevant conflicts of interest to declare.

Download Full-text

Diagnosis, Response Assessment and Treatment Outcomes in Acute Myeloid Leukemia (AML)

10.36811/ojrmi.2021.110036 ◽

2021 ◽

pp. 665-705

Author(s):

Alireza Heidari ◽

Ricardo Gobato ◽

Abhijit Mitra

Keyword(s):

Bone Marrow ◽

Cancer Cells ◽

Blood Cells ◽

Prolonged Exposure ◽

Genetic Diseases ◽

White Blood Cells ◽

Response Assessment ◽

Severe Bleeding ◽

Proliferating Cells ◽

Assessment And Treatment

Leukemia occurs when a person's entire bone marrow tissue space is occupied by cancer cells or blasts that are young, dysfunctional, undifferentiated, and proliferating cells. In this situation, there is no space left for the bone marrow to be able to produce normal blood cells such as platelets, red and white blood cells. These patients suffer from severe bleeding due to decreased platelets or due to a decrease in white blood cells, which are often diagnosed with dangerous infections that cause death in these patients. The exact cause of leukemias is not yet known, but a number of factors have been identified that play a role in the development of these cancers, including high doses of radiation or atomic radiation, prolonged exposure to certain chemicals, and some Mentioned viruses and some genetic diseases such as Down syndrome or underlying diseases. Keywords: Cancer; Cells; Tissues; Tumors; Prevention; Prognosis; Diagnosis; Imaging; Screening, Treatment; Management

Download Full-text

Circulating tumour cells: a broad perspective

Journal of The Royal Society Interface ◽

10.1098/rsif.2020.0065 ◽

2020 ◽

Vol 17 (168) ◽

pp. 20200065 ◽

Cited By ~ 1

Author(s):

Victor Akpe ◽

Tak H. Kim ◽

Christopher L. Brown ◽

Ian E. Cock

Keyword(s):

Cancer Cells ◽

Blood Cells ◽

White Blood Cells ◽

Tumour Cells ◽

Detection Methods ◽

Circulating Tumour Cells ◽

Intrinsic Properties ◽

Broad Perspective ◽

Cellular Invasion ◽

Rare Cells

Circulating tumour cells (CTCs) have recently been identified as valuable biomarkers for diagnostic and prognostic evaluations, as well for monitoring therapeutic responses to treatments. CTCs are rare cells which may be present as one CTC surrounded by approximately 1 million white blood cells and 1 billion red blood cells per millilitre of peripheral blood. Despite the various challenges in CTC detection, considerable progress in detection methods have been documented in recent times, particularly for methodologies incorporating nanomaterial-based platforms and/or integrated microfluidics. Herein, we summarize the importance of CTCs as biological markers for tumour detection, highlight their mechanism of cellular invasion and discuss the various challenges associated with CTC research, including vulnerability, heterogeneity, phenotypicity and size differences. In addition, we describe nanomaterial agents used for electrochemistry and surface plasmon resonance applications, which have recently been used to selectively capture cancer cells and amplify signals for CTC detection. The intrinsic properties of nanomaterials have also recently been exploited to achieve photothermal destruction of cancer cells. This review describes recent advancements and future perspectives in the CTC field.

Download Full-text

Perbandingan Local Binary Pattern untuk Klasifikasi Sel Darah Putih

Jurnal ULTIMATICS ◽

10.31937/ti.v9i2.663 ◽

2018 ◽

Vol 9 (2) ◽

pp. 118-121

Author(s):

Felix Indra Kurniadi

Keyword(s):

Support Vector Machine ◽

Blood Cells ◽

Local Binary Pattern ◽

White Blood Cells ◽

Support Vector ◽

Nearest Neighbour ◽

Rotation Invariant ◽

Blood Cancer ◽

Index Terms ◽

Uniform Local Binary Pattern

In recent year, a lot of researches try to overcome problem in recognition and classify white blood cells to help hematologists diagnose white blood cells disease such blood cancer, leukemia and AIDS. This paper compares several methods Local Binary Pattern such as Local Binary Pattern Uniform, Local Binary Pattern Rotation Invariant and Local Binary Pattern Rotation Invariant Uniform to classify five types of white blood cells using two classifier: Support Vector Machine and K-Nearest Neighbour. Index Terms—LBP, LBP-U, LBP-RI, LBP-RIU, white blood cells

Download Full-text

Discrimination between Breast Cancer Cells and White Blood Cells by Non-Invasive Measurements: Implications for a Novel In Vitro-Based Circulating Tumor Cell Model Using Digital Holographic Cytometry

Applied Sciences ◽

10.3390/app10144854 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4854

Author(s):

Zahra El-Schich ◽

Birgit Janicke ◽

Kersti Alm ◽

Nishtman Dizeyi ◽

Jenny L. Persson ◽

...

Keyword(s):

Breast Cancer ◽

Tumor Cell ◽

Cancer Cells ◽

Cell Lines ◽

Blood Cells ◽

Breast Cancer Cells ◽

Cell Model ◽

White Blood Cells ◽

Circulating Tumor Cell

Breast cancer is the second most common cancer worldwide. Metastasis is the main reason for death in breast cancer, and today, there is a lack of methods to detect and isolate circulating tumor cells (CTCs), mainly due to their heterogeneity and rarity. There are some systems that are designed to detect rare epithelial cancer cells in whole blood based on the most common marker used today, the epithelial cell adhesion molecule (EpCAM). It has been shown that aggressive breast cancer metastases are of non-epithelial origin and are therefore not always detected using EpCAM as a marker. In the present study, we used an in vitro-based circulating tumor cell model comprising a collection of six breast cancer cell lines and white blood cell lines. We used digital holographic cytometry (DHC) to characterize and distinguish between the different cell types by area, volume and thickness. Here, we present significant differences in cell size-related parameters observed when comparing white blood cells and breast cancer cells by using DHC. In conclusion, DHC can be a powerful diagnostic tool for the characterization of CTCs in the blood.

Download Full-text

Biocompatible and label-free separation of cancer cells from cell culture lines from white blood cells in ferrofluids

Lab on a Chip ◽

10.1039/c7lc00327g ◽

2017 ◽

Vol 17 (13) ◽

pp. 2243-2255 ◽

Cited By ~ 22

Author(s):

Wujun Zhao ◽

Rui Cheng ◽

So Hyun Lim ◽

Joshua R. Miller ◽

Weizhong Zhang ◽

...

Keyword(s):

Cell Culture ◽

Cancer Cells ◽

Cell Lines ◽

Blood Cells ◽

White Blood Cells ◽

Label Free ◽

Low Concentration

A biocompatible and label-free method for separation of low-concentration cancer cells from cell lines from white blood cells is developed.

Download Full-text

An efficient technique for identification of leukemia in microscopic blood samples using image processing.

International Journal of Research in Pharmaceutical Sciences ◽

10.26452/ijrps.v10i3.1487 ◽

2019 ◽

Vol 10 (3) ◽

pp. 2409-2416 ◽

Cited By ~ 1

Author(s):

Meghana M.R ◽

Akshatha Prabhu

Keyword(s):

Image Processing ◽

Bone Marrow ◽

Image Segmentation ◽

Blood Cells ◽

White Blood Cells ◽

Efficient Technique ◽

Blood Samples ◽

Blood Cancer ◽

The Way

Leukemia is a blood cancer which features through the ejection of manipulated and strange fabrication of white blood cells which is the way of bone marrow within the blood. The project aims at designing and developing an efficient technique for the detection of luekemia based on image segmentation techniques and nuclei analysis which incorporates the affected percentage and are compared and classified using KNN and SVM. The DNA of youngster cells, for the maximum detail white platelets, subsequently finally ends up harmed here and there. This version from the norm reasons platelets to increase and separate constantly. Sound platelets bypass on inevitably and are supplanted by approach of new cells, which might be brought in bone marrow.

Download Full-text