Changes of Blood Leukocyte and Blood Glucose in Patients with Acute Craniocerebral Trauma and Nursing Effect

2021 ◽  
Vol 7 (5) ◽  
pp. 1670-1680
Author(s):  
Junyi Yang
Keyword(s):  
Blood Glucose ◽  
Brain Injury ◽  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Craniocerebral Trauma ◽  
Control Group ◽  
Craniocerebral Injury ◽  
Significant Difference

Acute craniocerebral injury is one of the most common injury types, and its mortality rate ranks first among all kinds of injuries. The changes of white blood cells and blood glucose play an important role in the treatment and nursing of patients. The purpose of this study is to analyze the changes of white blood cells and blood glucose in patients with acute craniocerebral trauma and nursing effect. In this study, 65 patients with craniocerebral injury admitted to the Department of Neurosurgery of our hospital from January 2017 to September 2019 were selected as the research objects. After admission, appropriate nutritional treatment was given according to the condition of patients. Blood samples were collected on fasting in the morning of 2, 8 and 15 days after admission, while those in the control group were collected by fasting vein for detection of white blood cells, blood glucose, blood lipid, blood uric acid and other indicators. There was no significant difference in the number of neutrophils in each group (P < 0.05, P < 0.05, P < 0.05, P < 0.05, P < 0.05). After acute traumatic brain injury, the white blood cells and blood glucose of patients increased, and the more severe the degree of brain injury, the higher the white blood cell count and blood glucose. This study will contribute to the analysis of white blood cell and blood glucose changes and nursing care of patients with craniocerebral injury.

scholarly journals Effect of Fluticasone and Salmeterol on Tracheal Responsiveness to Ovalbumin and Lung Inflammation, Administrated during and after Sensitization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zahra Gholamnezhad ◽  
Mohammad Hossain Boskabady ◽  
Mohammad Reza Khazdair ◽  
Mahmoud Hosseini ◽  
Mahdi Abbasnejad
Keyword(s):  
Fluticasone Propionate ◽  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
Lung Inflammation ◽  
White Blood Cells ◽  
Control Group ◽  
Treatment Groups ◽  
Group B ◽  
Differential White Blood Cell

The effect of duration of administration of fluticasone propionate and salmeterol on tracheal responsiveness to ovalbumin and total and differential white blood cell in sensitized guinea pig was examined. Six groups of guinea pigs (n=7) were sensitized to ovalbumin. Three groups of them were subjected to inhaled fluticasone propionate and salmeterol, one group during sensitization (A), one group after that (for 18 days, B), and the other one during sensitization but with 18 days delay before measurements (C). Three other groups were treated with placebo in the same manner. The tracheal responsiveness to ovalbumin and total and differential white blood cells of three placebo groups were significantly higher than those of control group (P<0.001for all cases). Tracheal responsiveness to ovalbumin and total and differential white blood cell in treated groups with fluticasone propionate and salmeterol were significantly decreased compared to those of placebo groups (nonsignificant toP<0.001). The improvement in all variables in treatment groups A and C were more pronounced than group B. The results showed that fluticasone propionate and salmeterol had a prevention effect on tracheal hyperresponsiveness to ovalbumin and lung inflammation which was more pronounced when administered during than after sensitization.

scholarly journals Early White Blood Cell Dynamics after Traumatic Brain Injury: Effects on the Cerebral Microcirculation

1997 ◽  
Vol 17 (11) ◽  
pp. 1210-1220 ◽  
Author(s):  
Roger Härtl ◽  
Max B. Medary ◽  
Maximilian Ruge ◽  
Karl E. Arfors ◽  
Jam Ghajar
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Intracranial Pressure ◽  
Inflammatory Response ◽  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cerebral Microcirculation ◽  
Fluid Percussion Injury

Increasing clinical and experimental evidence suggests that traumatic brain injury (TBI) elicits an acute inflammatory response. In the present study we investigated whether white blood cells (WBC) are activated in the cerebral microcirculation early after TBI and whether WBC accumulation affects the posttraumatic cerebrovascular response. Twenty-four anesthetized rabbits had chronic cranial windows implanted 3 weeks before experimentation. Animals were divided into four experimental groups and were studied for 7 hours (groups I, IIa, and III) or 2 hours (group IIb). Intravital fluorescence videomicroscopy was used to visualize WBC (rhodamine 6G, intravenously), pial vessel diameters, and blood–brain barrier (BBB) integrity (Na+-fluorescein) at 6 hours (groups I, IIa, and III) or 1 hour (group IIb) after TBI. Group I (n = 5) consisted of sham-operated animals. Groups IIa (n = 7) and IIb (n = 5) received fluid-percussion injury at 1 hour. Group III (n = 7) received fluid-percussion injury and 1 mg/kg anti–adhesion monoclonal antibody (MoAb) “IB4” 5 minutes before injury. Venular WBC sticking, intracranial pressure (ICP), and arterial vessel diameters increased significantly for 6 hours after trauma. IB4 reduced WBC margination and prevented vasodilation. Intracranial pressure was not reduced by treatment with IB4. Blood–brain barrier damage occurred at 1 hour but not at 6 hours after TBI and was independent of WBC activation. This first report using intravital videomicroscopy to study the inflammatory response after TBI reveals upregulated interaction between WBC and cerebral endothelium that can be manipulated pharmacologically. White blood cell activation is associated with pial arteriolar vasodilation. White blood cells do not induce BBB breakdown less than 6 hours after TBI and do not contribute to posttraumatic ICP elevation. The role of WBC more than 6 hours after TBI should be investigated further.

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 Effects of dietary Mannan oligosaccharide (MOS) on grown, survival rate, intestinal morphology and blood cell count of the golden trevally fish (Gnathanodon specious)

2019 ◽  
Vol 19 (4A) ◽  
pp. 241-250
Author(s):  
Dang Tran Tu Tram ◽  
Nguyen Thi Nguyet Hue ◽  
Ho Son Lam ◽  
Nguyen Truong Tan Tai ◽  
Dao Thi Hong Ngoc
Keyword(s):  
Survival Rate ◽  
Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Intestinal Morphology ◽  
The Other ◽  
Control Group ◽  
Blood Cell Count ◽  
Mannan Oligosaccharide ◽  
Erythrocyte Density

The golden trevally fishes (Gnathanodon specious) (2.19 ± 0.23 g) were cultured in glass tanks with density of 20 fishes/tank and they were fed supplemental diets of different MOS concentrations (0; 0.2; 0.4 and 0.6%) for 90 days. Collected data included growth rate, survival rate and some hematological characteristics of this fish. The results demonstrated that MOS supplementation did not affect growth performance, erythrocyte density and blood cell size, however the survival rate was significantly increased. On the other hand, the total number of white blood cells (BC) on the 60th day in the fish fed with MOS supplements (5.78–6.96 × 104TB/mm3) was higher than that in the control group (only 5.43 × 104TB/mm3) with the largest total leukocytes (6.96 ± 0.50 × 104TB /mm3) at 0.2% MOS (p < 0.05).

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.

scholarly journals Detection of Benzo[a]Pyrene Diol Epoxide-DNA Adducts in White Blood Cells of Asphalt Plant Workers in Syria

2021 ◽  
Author(s):  
Razan Zohairee
Keyword(s):  
Dna Adducts ◽  
Blood Cells ◽  
White Blood Cells ◽  
Control Group ◽  
Study Group ◽  
Carcinogenic Potential ◽  
Potential Biomarker ◽  
Significant Difference ◽  
Polycyclic Aromatic ◽  
Peripheral White Blood Cells

Benzo[a]pyrene (B[a]P) is a major polycyclic aromatic hydrocarbon (PAH), it can bind the DNA to produce DNA-adducts, which has major carcinogenic potential. Enzyme-linked immunosorbet assay (ELISA) is the method used to detect these DNA adducts of B[a] P diolepoxide (BPDE) within the living cells.The aim of this study is to evaluate exposure to bitumen fumes, and to B[a]P in asphalt plant workers by measuring the BPDE-DNA adducts in their peripheral white blood cells (WBC), which are considered biological markers for exposure risk assessment.In this study, Hemostatic blood (CBC, AST) were measured, and the levels of BPDE-DNA adducts were measured in DNA samples of WBC obtained from asphalt plant workers in Syria and compared to those measured from a control group. The measurement was performed using BPDE-DNA Adducts ELISA.kit. The sample size was determined to be 50 with 25 asphalt plant workers and 25 healthy volunteers with no occupational exposure to PAHs.The results showed some diseases associated with exposure to asphalt fumes among the workers in the study group and a statistically significant difference in the values of (CBC; WBC, leukocytes, HCT, MCHC and AST) between the study group and the control group. BPDE-DNA adducts were detected in WBC of 11 asphalt plant workers with concentrations ranging between 0 and 2.75 ng/ml and only one individual in the control group with concentration of 0.75 ng/ml. These results indicate significant positive relationship between exposure to the bitumen fumes and formation of BPDE-DNA adducts.BPDE-DNA adducts is potential biomarker for PAHs exposure and likely helpful indicator of PAH-induced DNA damage and possibly carcinogenesis.

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.

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