The Wellcome Foundation Lecture, 1986 - The molecular regulators of normal and leukaemic blood cells

1987 ◽  
Vol 231 (1264) ◽  
pp. 289-312 ◽  
Keyword(s):  
Blood Cell ◽  
Cell Viability ◽  
Blood Cells ◽  
External Source ◽  
Cell Development ◽  
The Body ◽  
Normal Blood ◽  
Genetic Changes ◽  
Leukaemic Cells ◽  
Differentiation Inducers

The development of a cell-culture system for the cloning and clonal differentiation of different types of blood cell has made it possible to identify: (i), the proteins that regulate growth and differentiation of different cell lineages in normal and leukaemic blood cells; (ii), the molecular basis of normal and abnormal control of cell development in blood-forming tisue; and (iii), how to suppress malignancy in leukaemic cells. By using myeloid blood cells as a model system, it has been shown that normal blood cells require different proteins to induce cell viability and multiplication (growth-inducers) and differentiation (differentiation- inducers), that there is a hierarchy of growth-inducers which act at various stages of cell development, and that a growth-inducer can switch on production of a differentiation-inducer. Gene cloning has established a multigene family for these proteins. Identification of these proteins and their interaction has shown how growth and differentiation are regulated in normal development and demonstrated the mechanisms that uncouple growth and differentiation so as to produce malignant cells. Normal cells require an external source of growth-inducing protein for cell viability and multiplication. Cells can become leukaemic by genetically changing this normal requirement for growth without blocking response to normal differentiation-inducers. The mature cells induced by adding these normal protein-inducers are then no longer malignant. Other genetic changes which inhibit differentiation by the normal blood-cell regulatory proteins can occur in the evolution of leukaemia. But even these leukaemic cells may still be induced to differentiate by other compounds that can induce differentiation by alternative pathways. The differentiation of leukaemic to mature cells, which stops the cells from multiplying, results in the suppression of malignancy by bypassing genetic changes that produce the malignant phenotype. The activity of blood-cell growth-and differentiation-inducing proteins has been shown in culture and in the body. They can, therefore, be clinically useful to correct defects in the development of normal and leukaemic blood cells.

scholarly journals The Roles of SNF2/SWI2 Nucleosome Remodeling Enzymes in Blood Cell Differentiation and Leukemia

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Punit Prasad ◽  
Andreas Lennartsson ◽  
Karl Ekwall
Keyword(s):  
Blood Cell ◽  
Chromatin Remodeling ◽  
Large Scale ◽  
Cell Development ◽  
Normal Blood ◽  
Aberrant Expression ◽  
Atpase Subunit ◽  
Hematopoietic Stem ◽  
Nucleosome Remodeling ◽  
Genes Encoding

Here, we review the role of sucrose nonfermenting (SNF2) family enzymes in blood cell development. The SNF2 family comprises helicase-like ATPases, originally discovered in yeast, that can remodel chromatin by changing chromatin structure and composition. The human genome encodes 30 different SNF2 enzymes. SNF2 family enzymes are often part of multisubunit chromatin remodeling complexes (CRCs), which consist of noncatalytic/auxiliary subunit along with the ATPase subunit. However, blood cells express a limited set of SNF2 ATPases that are necessary to maintain the pool of hematopoietic stem cells (HSCs) and drive normal blood cell development and differentiation. The composition of CRCs can be altered by the association of specific auxiliary subunits. Several auxiliary CRC subunits have specific functions in hematopoiesis. Aberrant expressions of SNF2 ATPases and/or auxiliary CRC subunit(s) are often observed in hematological malignancies. Using large-scale data from the International Cancer Genome Consortium (ICGC) we observed frequent mutations in genes encoding SNF2 helicase-like enzymes and auxiliary CRC subunits in leukemia. Hence, orderly function of SNF2 family enzymes is crucial for the execution of normal blood cell developmental program, and defects in chromatin remodeling caused by mutations or aberrant expression of these proteins may contribute to leukemogenesis.

scholarly journals Potential new drug for leukaemia

2014 ◽  
pp. 164-167
Author(s):  
Eileen Russell
Keyword(s):  
Cell Growth ◽  
Blood Cells ◽  
White Blood Cells ◽  
Vital Role ◽  
The Body ◽  
Red Cells ◽  
Normal Cells ◽  
Blood Cancer ◽  
Different Types ◽  
Leukaemic Cells

Leukaemia is a cancer of the blood. Cancer is a group of diseases characterized by unregulated cell growth. There are over 200 different types of cancer, each classified by the type of cell that is affected. Blood is composed of red cells, white cells, platelets and plasma. These components are marked in figure 1. White blood cells play a vital role in fighting infection. In leukaemia, there is an unregulated increase in abnormal white blood cells. This explains where the term ‘leukaemia’ originated as it comes from the Greek words “leukos” and “heima,” also meaning “white blood”. These abnormal white blood cells, or leukaemic cells, grow rapidly and crowd out the normal cells that the body requires to function properly. In addition, leukaemic cells can move from the blood to other parts of the body. This movement, known as metastasis, allows the cancer to spread. Although leukaemia develops far less ...

scholarly journals Molecular Rotors as intracellular probes of Red Blood Cell stiffness

Soft Matter ◽  
2021 ◽  
Author(s):  
Alice Briole ◽  
Thomas Podgorski ◽  
Berengere Abou
Keyword(s):  
Blood Cell ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
The Body ◽  
Molecular Rotors ◽  
Cell Stiffness ◽  
Essential Parameter

The deformability of red blood cells is an essential parameter that controls the rheology of blood as well as its circulation in the body. Characterizing the rigidity of the cells...

scholarly journals A review of microscopic analysis of blood cells for disease detection with AI perspective

2021 ◽  
Vol 7 ◽  
pp. e460
Author(s):  
Nilkanth Mukund Deshpande ◽  
Shilpa Gite ◽  
Rajanikanth Aluvalu
Keyword(s):  
Machine Learning ◽  
Blood Cell ◽  
Blood Cells ◽  
Microscopic Analysis ◽  
The Body ◽  
Disease Detection ◽  
Results Of Treatment ◽  
Cell Components ◽  
Critical Activity ◽  
Blood Cell Morphology

Background Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. Methodology A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. Conclusion There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.

Gene Therapy: The New Weapon Against Diseases Until there Difficult to Overcome, Some Current Facts of Gene Therapy and Cases of Sickle Cell Anemia

2020 ◽  
Author(s):  
Carolle Laure Matene Fongang
Keyword(s):  
Gene Therapy ◽  
Blood Cell ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Genetic Disease ◽  
Blood Cells ◽  
The Body ◽  
Sub Saharan Africa ◽  
The World ◽  
Sub Saharan

Sickle cell anemia is an inherited genetic disease that affects the hemoglobin chains of red blood cell hemoglobin, carrying oxygen-less well through the body. It is a rare disease; however, it is the most widespread genetic disease in the world and especially widespread in sub-Saharan Africa. It causes anemia, painful seizures that affect several organs; it is also called sickle cell anemia, this disease results in a deformation of red blood cells in the form of sickle or a crescent moon, which prevents normal circulation in the blood vessels.

scholarly journals An Assessment of Gpu and Cpu based Convolutional Neural Network for Classification of White Blood Cells

2020 ◽  
Vol 9 (7) ◽  
pp. 554-559
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Image Data ◽  
The Body ◽  
Machine Learning Techniques ◽  
Vast Number

White blood cell (Leukocytes) is made up of bone marrow located in the blood and lymph tissue. They are portion of the human body’s immune system, thereby helping the body system to fight against infection and other related diseases. The number of leukocytes in the blood is usually part of a complete blood cell (CBC) test, which may be used to check for conditions such as infection, inflammation, allergies, and leukemia. Automation of variance count of leukocytes offers valuable information to medical pathologist to diagnose and treat of many blood based diseases. Early characterization and classification of blood sample is a major lacuna in the medical field, giving rise to lots of challenges for pathologist to adequately predict blood based disease. Several successful efforts have been made to address the aforementioned challenges with the use of machine learning generally and Convolution Neural Network in particular. However the processor configuration which can result in real time, and accurate classification of the high dimensional pattern is imminent, and a vast number of researchers are not explicit on the system configuration used to obtain the result in their report, which is the crux of this research. In this research,12,500 augment images of blood cells was obtained from the Kaggle Repository online. The leukocytes are contained in the blood smear image and categorized into five major types of their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte. The color, geometric and texture features are used by the pathologists to differentiate the leukocytes. The Simulation was done using python programing language and python libraries including Keras, pandas, sklearn, numpy, scipy and matplot for potting of graphs of results. The simulation was done on both CPU and GPU processor to compare the performance of the processors on CNNs based classification of the data. While CPU has faster clock speed GPU has more cores. Hence the evaluation metrics used which are precision, specificity, sensitivity, training accuracy and validation accuracy revealed that GPU processor outperforms CPU in terms of the stated metrics of comparison. Therefore a high configuration processor (GPU), which handles graphics better is recommended for processing image data that involves the use of machine learning techniques

Blood Cell Parameters and Predicting Coronary In-Stent Restenosis

Angiology ◽  
2019 ◽  
Vol 70 (8) ◽  
pp. 711-718 ◽  
Author(s):  
Zhichao Wang ◽  
Chi Liu ◽  
Hong Fang
Keyword(s):  
Blood Cell ◽  
Platelet Activation ◽  
Blood Cells ◽  
Inflammatory Marker ◽  
Distribution Width ◽  
Cell Parameters ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Stent Restenosis ◽  
In Stent Restenosis

Major advances in coronary interventional techniques and pharmacotherapy as well as the use of drug-eluting stents (DESs) have considerably reduced the risk of in-stent restenosis (ISR). However, ISR remains a major clinical challenge. Inflammation and platelet activation are important processes that underlie the pathophysiology of ISR. Parameters related to blood cells, entailing both cell count and morphology, are useful markers of the inflammatory response and platelet activation in clinical practice. Recent studies have highlighted several new combined or derived parameters related to blood cells that independently predict ISR after DES implantation. The neutrophil/lymphocyte ratio, an inflammatory marker, is regarded as a predictor of the risk of ISR and the stability of atherosclerotic plaques. The mean platelet volume, a widely used platelet activation parameter, has been shown to be a predictor of the risk of ISR and the efficacy of antiplatelet therapy. Other markers considered include the platelet/lymphocyte ratio, red blood cell distribution width, and platelet distribution width. This review provides an overview of these parameters that may help stratify the risk of coronary angiographic and clinical outcomes related to ISR.

Real-time Red Blood Cell Counting and Osmolarity Analysis using a Photoacoustic-based Microfluidic System

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Wenxiu Zhao ◽  
Haibo Yu ◽  
Yangdong Wen ◽  
Hao Luo ◽  
Boliang Jia ◽  
...  
Keyword(s):  
Blood Cell ◽  
Physical Properties ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Diagnostic Procedure ◽  
Microfluidic System ◽  
Cell Counting ◽  
Blood Samples ◽  
System P ◽  
Blood Cell Counting

Counting the number of red blood cells (RBCs) in blood samples is a common clinical diagnostic procedure, but conventional methods are unable to provide the size and other physical properties...

scholarly journals White Blood Cells and Severe COVID-19: A Mendelian Randomization Study

2021 ◽  
Vol 11 (3) ◽  
pp. 195
Author(s):  
Yitang Sun ◽  
Jingqi Zhou ◽  
Kaixiong Ye
Keyword(s):  
Blood Cell ◽  
Cell Count ◽  
White Blood Cell Count ◽  
White Blood Cell ◽  
Blood Cells ◽  
Mendelian Randomization ◽  
Association Studies ◽  
White Blood Cells ◽  
Genome Wide Association Studies ◽  
Blood Cell Count

Increasing evidence shows that white blood cells are associated with the risk of coronavirus disease 2019 (COVID-19), but the direction and causality of this association are not clear. To evaluate the causal associations between various white blood cell traits and the COVID-19 susceptibility and severity, we conducted two-sample bidirectional Mendelian Randomization (MR) analyses with summary statistics from the largest and most recent genome-wide association studies. Our MR results indicated causal protective effects of higher basophil count, basophil percentage of white blood cells, and myeloid white blood cell count on severe COVID-19, with odds ratios (OR) per standard deviation increment of 0.75 (95% CI: 0.60–0.95), 0.70 (95% CI: 0.54–0.92), and 0.85 (95% CI: 0.73–0.98), respectively. Neither COVID-19 severity nor susceptibility was associated with white blood cell traits in our reverse MR results. Genetically predicted high basophil count, basophil percentage of white blood cells, and myeloid white blood cell count are associated with a lower risk of developing severe COVID-19. Individuals with a lower genetic capacity for basophils are likely at risk, while enhancing the production of basophils may be an effective therapeutic strategy.

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