Leukopenia with Neutropenia Associated with Teicoplanin Therapy

DICP ◽  
1989 ◽  
Vol 23 (1) ◽  
pp. 45-47 ◽  
Author(s):  
Albano Del Favero ◽  
Lucio Patoia ◽  
Giampaolo Bucaneve ◽  
Luciano Biscarini ◽  
Francesco Menichetti
Keyword(s):  
Case Report ◽  
Blood Cell ◽  
Preliminary Data ◽  
Blood Cells ◽  
Side Effect ◽  
White Blood Cells ◽  
Bacterial Endocarditis ◽  
Glycopeptide Antibiotic ◽  
Subacute Bacterial Endocarditis ◽  
First Case

The first case report of leukopenia with neutropenia due to the new glycopeptide antibiotic teicoplanin is described. The side effect occurred in a 73-year-old man hospitalized because of subacute bacterial endocarditis caused by Streptococcus faecalis. Leukopenia with neutropenia (white blood cells 2000/mm3, neutrophils 46%) developed after 20 days of teicoplanin therapy. After stopping teicoplanin white blood cell and neutrophil counts reverted to normal, but dropped again on rechallenge. A review of 1500 records of patients treated with teicoplanin alone or in combination with other drugs was also performed. Five cases were found in which leukopenia was possibly (four cases) or probably (one case) related to teicoplanin therapy. From these preliminary data the incidence of leukopenia related to teicoplanin seems to be low.

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 Subacute bacterial endocarditis complicated with rupture of an intracranial mycotic aneurysm: A case report

1987 ◽  
Vol 76 (3) ◽  
pp. 394-397
Author(s):  
Satoshi MONNO ◽  
Eiji TSUGANE ◽  
Haruhiko IMAI ◽  
Ken-ichi FURUKAWA ◽  
Shigeyuki KUMAZAWA ◽  
...  
Keyword(s):  
Case Report ◽  
Mycotic Aneurysm ◽  
Bacterial Endocarditis ◽  
Subacute Bacterial Endocarditis

scholarly journals Blood Cell Count Using Deep Learning Semantic Segmentation

2019 ◽  
Author(s):  
Thanh Tran ◽  
Lam Binh Minh ◽  
Suk-Hwan Lee ◽  
Ki-Ryong Kwon
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Cell Count ◽  
Blood Cells ◽  
Blood Smear ◽  
White Blood Cells ◽  
Semantic Segmentation ◽  
Peripheral Blood Smear ◽  
Cell Counting ◽  
Blood Cell Count

Clinically, knowing the number of red blood cells (RBCs) and white blood cells (WBCs) helps doctors to make the better decision on accurate diagnosis of numerous diseases. The manual cell counting is a very time-consuming and expensive process, and it depends on the experience of specialists. Therefore, a completely automatic method supporting cell counting is a viable solution for clinical laboratories. This paper proposes a novel blood cell counting procedure to address this challenge. The proposed method adopts SegNet - a deep learning semantic segmentation to simultaneously segment RBCs and WBCs. The global accuracy of the segmentation of WBCs, RBCs, and the background of peripheral blood smear images obtains 89% when segment WBCs and RBCs from the background of blood smear images. Moreover, an effective solution to separate grouped or overlapping cells and cell count is presented using Euclidean distance transform, local maxima, and connected component labeling. The counting result of the proposed procedure achieves an accuracy of 93.3% for red blood cell count using dataset 1 and 97.38% for white blood cell count using dataset 2.

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 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.

ALLIUM SATIVUM ESSENTIAL OIL (ASEO);

2017 ◽  
Vol 24 (04) ◽  
pp. 612-616
Author(s):  
Faisal Irshad ◽  
Hina Mawani ◽  
Sana Naz
Keyword(s):  
Essential Oil ◽  
Blood Cell ◽  
Blood Cells ◽  
Allium Sativum ◽  
White Blood Cells ◽  
Albino Rats ◽  
Serum Triglycerides ◽  
Cell Counts ◽  
Blood Cell Counts ◽  
Animal House

Objectives: To determine the effects of Allium sativum essential oil (ASEO)phytotherapy on serum triglycerides, total cholesterol, HDLc, LDLc and blood cell counts inalbino rat model. Study design: Experimental study. Setting and Duration: Animal House,Sindh Agriculture University and Isra University Hyderabad from May 2014 to January 2015.Materials and Methods: 60 albino rats were divided into four groups. Controls were givenPlacebo. Experimental rat groups were given ASEO 100 mg/kg, 200 mg/kg and 300 mg/kgorally for 30 days. Cardiac puncture was performed for blood sampling. Research variableswere analyzed on Statistix 10.0 (USA). Results: Blood lipids showed significant reduction invarious blood lipid fractions. Serum LDLc exhibited with a concomitant rise in serum HDLc (p=0.0001) in high ASEO treated rats. Red blood cells, white blood cells and platelet showedsignificant improvement ASEO fed rats (p=0.001). Conclusion: Allium sativum essential oil(ASEO) phytotherapy showed a rise in HDLc and a reduction in LDLc, triglycerides and totalcholesterol with improvement in red blood cell counts.

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.

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