• Health Policy and Management
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• v.17 no.4
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• pp.82-98
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• 2007

This study aimed to analyze the factors that influence the job satisfaction of nurses involved in medical insurance reviews. The study involved a self-administered questionnaire survey which was conducted with to 297 nurses who were in charge of medical insurance reviews between April 10 and April 28, 2000. The average job satisfaction of the subjects was 3.04. The sub-items of job satisfaction were noted to be high for 'professional status'(3.79) and low for wage (2.46). The job satisfaction of subjects showed statistically significant differences with regard to education, career, and volume of service(p<0.05). The average job stress of subjects was 2.57. The sub-items of job stress included problems pertaining to human relationships problem(2.84), conflicts with doctors at work (2.79), and the burden of excessive workloads(2.79), in that order. Multiple regression analysis demonstrated that job satisfaction was significantly low when the job stress was higher. It also showed that the job satisfaction was significantly high as there was more frequency of judgements and higher education. These results suggest that the job stress of nurses involved in medical insurance reviews has a profound impact on their job satisfaction. Therefore, the efforts should be made to reduce their job stress. It may also be beneficial to reinforce the training with the doctors and nurses in order to improve their communication skills. Disseminating more information about insurance standards may also be considered.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.451-457
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• 2001

This paper describes automated methods for the detection of lung nodules and their volume calculation on CT scans. Gray-level threshold methods were used to segment the thorax from the background and then the lung parenchymes from the thoracic wall and mediastinum. A scanning-ball algorithm was applied to more accurately delineate the lung boundaries, thereby incorporating peripheral nodules contiguous to pleural surface within the segmented lung parenchymes. The lesions which have the high gray value were extracted from the segmented lung parenchymes. The selected lesions include nodules, blood vessels and partial volume effects. The discriminating features such as size, solid-shape, average, standard deviation and correlation coefficient of selected lesions were used to distinguish true nodules from pseudo-lesions. Volume and circularity calculation were performed for each identified nodules. The identified nodules were sorted in descending order of the volume. These method were applied to 621 image slices of 19 cases. The sensitivity was 95% and there was no false-positive result.

• Journal of Ginseng Research
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• v.35 no.1
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• pp.86-91
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• 2011

Ginsenoside (G)-F1 is an enzymatic metabolite generated from G-Rg1. Although this metabolite has been reported to suppress platelet aggregation and to reduce gap junction-mediated intercellular communication, the modulatory activity of G-F1 on the functional role of skin-derived cells has not yet been elucidated. In this study, we evaluated the regulatory role of G-F1 on the cellular responses of B16 melanoma cells. G-F1 strongly suppressed the proliferation of B16 cells up to 60% at 200 ${\mu}g/mL$, while only diminishing the viability of HEK293 cells up to 30%. Furthermore, G-F1 remarkably induced morphological change and clustering of B16 melanoma cells. The melanin production of B16 cells was also significantly blocked by G-F1 up to 70%. Interestingly, intracellular signaling events involved in cell proliferation, migration, and morphological change were up-regulated at 1 h incubation but down-regulated at 12 h. Therefore, our results suggest that G-F1 can be applied as a novel anti-skin cancer drug with anti-proliferative and anti-migration features.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1924-1929
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• 2007

Nowadays, a lot of related data obtained from these research could be given a new present meaning to accomplish the original purpose of the whole research as a human project. The method of tumor classification based on microarray could contribute to being accurate tumor classification by finding differently expressing gene pattern statistically according to a tumor type. Therefore, the process to select a closely related informative gene with a particular tumor classification to classify tumor using present microarray technology with effect is essential. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer, constructed accurate tumor classification model by extracting informative gene list through normalization separately and then did performance estimation by analyzing and comparing each of the experiment results. Result classifying Multi-Perceptron classifier for selected genes using Pearson correlation coefficient represented the accuracy of 95.6%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1243-1248
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• 2008

As development in technology of bioinformatics recently mates it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. In this thesis, we used CDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer. It analyzed and compared performance of each of the experiment result using existing DT, NB, SVM and multi-perceptron neural network classifier combined the similar scale combination method after constructing class classification model by extracting significant gene list with a similar scale combination method proposed in this paper through normalization. Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) represented the accuracy of 98.84%, which show that it improve classification performance than case to experiment using other classifier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.10
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• pp.1992-1998
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• 2007

It is important to obtain conn cytodiagnosis to classify background, cytoplasm, and nucleus from the diagnostic image. This study mose an algorithm that detects and classifies carcinoma cells of the uterine cervix in Pap smear using features of cervical cancer. It applies Median filter and Gaussian filter to get noise-removed nucleus area and also applies Kapur method in binarization of the resultant image. We apply 8-directional contour tracking algorithm and stretching technique to identify and revise clustered cells that often hinder to obtain correct analysis. The resulted nucleus area has distinguishable features such as cell size, integration rate, and directional coefficient from normal cells so that we can detect and classify carcinoma cells successfully. The experiment results show that the performance of the algorithm is competitive with human expert.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.24-34
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• 2019

In this research, a practical deep learning framework to differentiate the lesions and nodules in breast acquired with ultrasound imaging has been proposed. 7408 ultrasound breast images of 5151 patient cases were collected. All cases were biopsy proven and lesions were semi-automatically segmented. To compensate for the shift caused in the segmentation, the boundaries of each lesion were drawn using Fully Convolutional Networks(FCN) segmentation method based on the radiologist's specified point. The data set consists of 4254 benign and 3154 malignant lesions. In 7408 ultrasound breast images, the number of training images is 6579, and the number of test images is 829. The margin between the boundary of each lesion and the boundary of the image itself varied for training image augmentation. The training images were augmented by varying the margin between the boundary of each lesion and the boundary of the image itself. The images were processed through histogram equalization, image cropping, and margin augmentation. The networks trained on the data with augmentation and the data without augmentation all had AUC over 0.95. The network exhibited about 90% accuracy, 0.86 sensitivity and 0.95 specificity. Although the proposed framework still requires to point to the location of the target ROI with the help of radiologists, the result of the suggested framework showed promising results. It supports human radiologist to give successful performance and helps to create a fluent diagnostic workflow that meets the fundamental purpose of CADx.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1144-1149
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• 2020

The magnetic valve of the breast tissue expander generates imaging artifacts during MRI examination, so MRI examination is limited. To evaluate the effect of imaging artifacts on the diagnosis area for patients with breast tissue expander who need MRI examination. Imaging artifacts were measured using self-made phantoms and actual clinical conditions. Imaging artifacts were measured differently depending on the environment of 1.5 Tesla and 3.0 Tesla, and the effects of imaging artifacts were less in the C-spine and L-spine tests. If MRI due to breast cancer metastasis is absolutely necessary, head & neck examination and L-spine can be examined mainly at 1.5 Tesla, but some sequences may cause distortion due to image artifacts. In terms of safety, MRI scans of patients with breast tissue expanders can be performed conditionally at 1.5T, avoiding 3.0T.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1132-1137
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• 2020

In order to reduce unnecessary exposure doses generated when mammography is performed using a mammography device, a shielding ratio analysis was performed when a self-made shielding body made of bismuth was applied to the breast opposite to the imaging site. In order to determine the scattering dose of uncompressed breasts during CC and MLO tests when the right and left are compressed, the experiment is divided into when bismuth is not shielded (Not used: NU group) and when shielded (Used: U group). Proceeded. The average dose of the NU group was 9.568μSv, and the average dose of the U group was 1.038μSv. The average measured dose before and after the use of the bismuth shield was reduced by 89.15%. The use of a bismuth shield for mammography can shield scattered radiation and keep exposure to radiation to a minimum.

• BMB Reports
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• v.54 no.3
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• pp.164-169
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• 2021

Neuronal growth regulator 1 (NEGR1) is a GPI-anchored membrane protein that is involved in neural cell adhesion and communication. Multiple genome wide association studies have found that NEGR1 is a generic risk factor for multiple human diseases, including obesity, autism, and depression. Recently, we reported that Negr1-/- mice showed a highly increased fat mass and affective behavior. In the present study, we identified Na/K-ATPase, beta1-subunit (ATP1B1) as an NEGR1 binding partner by yeast two-hybrid screening. NEGR1 and ATP1B1 were found to form a relatively stable complex in cells, at least partially co-localizing in membrane lipid rafts. We found that NEGR1 binds with ATP1B1 at its C-terminus, away from the binding site for the alpha subunit, and may contribute to intercellular interactions. Collectively, we report ATP1B1 as a novel NEGR1-interacting protein, which may help deciphering molecular networks underlying NEGR1-associated human diseases.