Lithium Levels in White Blood Cells and their Relationship to Observed Side Effects

1983 ◽  
Vol 143 (1) ◽  
pp. 36-39 ◽  
Author(s):  
Yong Lock Ong
Keyword(s):  
Side Effects ◽  
Blood Cell ◽  
White Blood Cell ◽  
Red Blood Cell ◽  
Strong Correlation ◽  
Blood Cells ◽  
Side Effect ◽  
White Blood Cells ◽  
Lithium Therapy ◽  
Hand Tremor

SummaryA strong correlation was obtained between white blood cell (WBC) lithium concentrations and the severity of observed side effects in a group of 40 patients receiving prophylactic lithium therapy. However, there was no significant correlation between these levels and the specific side effect of hand tremor, although WBC concentrations were higher in patients with greater tremor. These results contrasted with those for plasma and red blood cell (RBC) lithium concentrations, which showed no relationship to side effects. This suggests that WBC lithium concentrations may be a more sensitive index of side effects than conventional plasma estimations.

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.

scholarly journals PO-283 Correlation Analysis between Blood Cell Viability, Five Elements and Competition Performance of Elite Men’s Rowers

2018 ◽  
Vol 1 (5) ◽  
Author(s):  
Junbei Bai
Keyword(s):  
Blood Cell ◽  
Red Blood Cells ◽  
Aerobic Capacity ◽  
Red Blood Cell ◽  
Iron Content ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Activity ◽  
Ordinary People ◽  
Five Elements

Objective To observe the national elite male rowers blood, red blood cell activity and serum copper, zinc, calcium, magnesium and iron content of the five elements, and compared with the ordinary people. Aimed to investigate the between athletes, athletes and ordinary differences between the two sets of indicators and to explore the impact of element contents in red blood cell activity and five factors. Trying to bring two sets of indicators and specific combining ability, used in training on the monitoring function, and for the future to provide some references for further study. Methods It was included 22 athletes and 22 ordinary men, as the research object, in the collection of blood, measuring red blood cell activity in the blood content of the five elements, simultaneous measurement of physical indicators , will be doing all the data at the differences between the two groups compared to the group to do correlation analysis. The recent record of 2000m, 6000m rowing Dynamometer test results, and red blood cell activity associated with the five elements of content analysis. Results 1. Athletes indicators related to aerobic exercise were significantly higher than ordinary people. The white blood cells of athletes group were average.It shows that athletes have high aerobic capacity, while white blood cells are more stable than normal people. The members of the national rowing men's iron, magnesium content was significantly higher than ordinary group, the iron content is higher than the normal reference value; blood calcium levels were significantly lower than ordinary people, and lower than the normal reference value. The total number of red blood cells and the number of living cells was very significant positive correlation in two groups subjects; Red blood cell activity and red blood cell diameter is proportional, and red blood cell roundness in inverse proportion to the relationship; from this experiment a special ability to see red blood cell activity and there is no correlation. In both groups, hemoglobin was positively correlated with iron content, while iron was positively correlated with copper content. Conclusions 1. Increasing the number and volume of red blood cells can effectively increase the activity of red blood cells; red blood cell activity has no correlation with specific ability, and can not be used as an indicator to determine specific ability. The content of iron and magnesium in rowers is higher than that in ordinary people, which indicates that the adjustment of aerobic capacity and nerve control is very effective. The lower calcium content indicates that the injury caused by calcium loss should be prevented and the urgency of calcium supplementation should be emphasized. In training, we should pay attention to increasing hemoglobin content and aerobic capacity by supplementing iron. We can further consider the effect of supplementing copper to promote iron supplementation.

scholarly journals Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

2019 ◽  
Vol 9 (2) ◽  
pp. 173
Author(s):  
Apri Nur Liyantoko ◽  
Ika Candradewi ◽  
Agus Harjoko
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Back Propagation ◽  
Cell Types ◽  
Diagnostic Process ◽  
The Subject ◽  
Processing Techniques

 Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and  multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

White Blood Cells Classification with Deep Convolutional Neural Networks

2018 ◽  
Vol 32 (09) ◽  
pp. 1857006 ◽  
Author(s):  
Ming Jiang ◽  
Liu Cheng ◽  
Feiwei Qin ◽  
Lian Du ◽  
Min Zhang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Network Architecture ◽  
White Blood Cells ◽  
Activation Function ◽  
Training Set ◽  
Deep Convolutional Neural Networks ◽  
Cell Image ◽  
Attending Physician

The necessary step in the diagnosis of leukemia by the attending physician is to classify the white blood cells in the bone marrow, which requires the attending physician to have a wealth of clinical experience. Now the deep learning is very suitable for the study of image recognition classification, and the effect is not good enough to directly use some famous convolution neural network (CNN) models, such as AlexNet model, GoogleNet model, and VGGFace model. In this paper, we construct a new CNN model called WBCNet model that can fully extract features of the microscopic white blood cell image by combining batch normalization algorithm, residual convolution architecture, and improved activation function. WBCNet model has 33 layers of network architecture, whose speed has greatly been improved compared with the traditional CNN model in training period, and it can quickly identify the category of white blood cell images. The accuracy rate is 77.65% for Top-1 and 98.65% for Top-5 on the training set, while 83% for Top-1 on the test set. This study can help doctors diagnose leukemia, and reduce misdiagnosis rate.

scholarly journals A Neural-Network-Based Approach to White Blood Cell Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mu-Chun Su ◽  
Chun-Yen Cheng ◽  
Pa-Chun Wang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Classification System ◽  
Blood Cells ◽  
White Blood Cells ◽  
Recognition Rate ◽  
Color Space ◽  
Segmentation Algorithm ◽  
Cell Classification ◽  
Morphological Process

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

scholarly journals STUDIES ON THE PHYSIOLOGY OF THE WHITE BLOOD CELL

Blood ◽  
1947 ◽  
Vol 2 (3) ◽  
pp. 235-243 ◽  
Author(s):  
RICHARD WAGNER
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Glycogen Content ◽  
Striated Muscle ◽  
White Blood Cells ◽  
Glycogen Storage ◽  
Different Types ◽  
Blast Cells ◽  
Reducing Substances

Abstract The technic of determining glycogen in isolated white blood cells was applied to the study of the different types of leukemia and of polycythemia, in order to obtain information on the physiology of the white blood cell. From this study it is concluded that the granulated leukocyte is the only carrier of glycogen in whole blood. The "reducing substances" in lymphocytes and blast cells are not considered as true glycogen. The glycogen content of wet white blood cells in the rabbit amounts to about 1 per cent. In the human being a range of from 0.17 to 0.67 per cent was calculated. In disease higher percentages occur, in polycythemia up to 1.64 per cent and in glycogen storage disease up to 3.05 per cent. The glycogen concentration of normal white blood cells is within the same range as that of the striated muscle.

Perturbation of red blood cell flow in small tubes by white blood cells

1989 ◽  
Vol 413 (4) ◽  
pp. 372-377 ◽  
Author(s):  
Thomas Neil Thompson ◽  
Paul L. La Celle ◽  
Giles R. Cokelet
Keyword(s):  
Blood Cell ◽  
Red Blood Cell ◽  
Blood Cells ◽  
White Blood Cells

scholarly journals Hematological changes of Anemia`s in Horses of Equestrian club in Baghdad

2012 ◽  
Vol 36 (0A) ◽  
pp. 92-97
Author(s):  
Faisal G. Habasha
Keyword(s):  
Blood Cell ◽  
Sedimentation Rate ◽  
Red Blood Cell ◽  
Packed Cell Volume ◽  
Blood Cells ◽  
White Blood Cells ◽  
Blood Samples ◽  
Erythrocyte Sedimentation ◽  
Blood Smears ◽  
Hematological Changes

This study was conducted to know thehematological changes of anemia in horsesat equestrian club in Baghdad. Blood samples were collected from 151 horses of both sexes(74 male and 77 female) and different agesrandomly. The study includedred blood cells count, white blood cells, hemoglobin, packed cell volume and differential blood smears, togetherwith erythrocyte sedimentation rate readings. The study showed increased white blood cells count mainly neutrophilwith decreased hemoglobinand red blood cell countin addition to erythrocyte sedimentation rate.The blood smears showeddifferent changes of red blood cell.

scholarly journals Detection and Classification of White Blood Cells through Deep Learning Techniques

2020 ◽  
Vol 16 (15) ◽  
pp. 94
Author(s):  
Samir Abou El-Seoud ◽  
Muaad Hammuda Siala ◽  
Gerard McKee
Keyword(s):  
Neural Network ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Blood Cells ◽  
Human Life ◽  
White Blood Cells ◽  
Learning Techniques ◽  
Normal White Blood Cell ◽  
High Level

Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images.

scholarly journals White blood cells and severe COVID-19: a Mendelian randomization study

2020 ◽  
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 ◽  
White Blood Cells ◽  
Causal Effects ◽  
Blood Cell Count ◽  
Granulocyte Count

AbstractBackgroundThe pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly emerged to seriously threaten public health. We aimed to investigate whether white blood cell traits have potential causal effects on severe COVID-19 using Mendelian randomization (MR).MethodsTo evaluate the causal associations between various white blood cell traits and severe COVID-19, we conducted a two-sample MR analysis with summary statistics from recent large genome-wide association studies.ResultsOur MR results indicated potential causal associations of white blood cell count, myeloid white blood cell count, and granulocyte count with severe COVID-19, with odds ratios (OR) of 0.84 (95% CI: 0.72-0.98), 0.81 (95% CI: 0.70-0.94), and 0.84 (95% CI: 0.71-0.99), respectively. Increasing eosinophil percentage of white blood cells was associated with a higher risk of severe COVID-19 (OR: 1.22, 95% CI: 1.03-1.45).ConclusionsOur results suggest the potential causal effects of lower white blood cell count, lower myeloid white blood cell count, lower granulocyte count, and higher eosinophil percentage of white blood cells on an increased risk of severe COVID-19.

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