• Journal of Life Science
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• v.34 no.5
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• pp.356-364
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• 2024

Lymph nodes (LNs) are crucial sites where immune responses are initiated to combat invading pathogens in the body. LNs are organized into distinctive compartments by stromal cells. Stromal cell subsets constitute special niches supporting the trafficking, activation, differentiation, and crosstalk of immune cells in LNs. Fibroblastic reticular cells (FRC) are a type of stromal cell that form the three-dimensional structure networks of the T cell-rich zones in LNs, providing guidance paths for immigrating T lymphocytes. FRCs imprint immune responses by supporting LN architecture, recruiting immune cells, coordinating immune cell crosstalk, and presenting antigens. During inflammation, FRCs exert both spatial and molecular regulation on immune cells through their topological and secretory responses, thereby steering immune responses. Here, we propose a model in which FRCs regulate immune responses through a three-part scheme: setting up, supporting, or suppressing immune responses. FRCs engage in bidirectional interactions that enhance T cell biological efficiency. In addition, FRCs have profound effects on the innate immune response through phagocytosis. Thus, FRCs in LNs act as gatekeepers of immune responses. Overall, this study aims to highlight the emerging roles of FRCs in controlling both innate and adaptive immunity. This collaborative feedback loop mediated by FRCs may help maintain tissue function during inflammatory responses.

• Korean Journal of Radiology
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• v.24 no.5
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• pp.384-394
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• 2023

Objective: Mammographic density is an independent risk factor for breast cancer that can change after neoadjuvant chemotherapy (NCT). This study aimed to evaluate percent changes in volumetric breast density (ΔVbd%) before and after NCT measured automatically and determine its value as a predictive marker of pathological response to NCT. Materials and Methods: A total of 357 patients with breast cancer treated between January 2014 and December 2016 were included. An automated volumetric breast density (Vbd) measurement method was used to calculate Vbd on mammography before and after NCT. Patients were divided into three groups according to ΔVbd%, calculated as follows: Vbd (post-NCT - pre-NCT)/pre-NCT Vbd × 100 (%). The stable, decreased, and increased groups were defined as -20% ≤ ΔVbd% ≤ 20%, ΔVbd% < -20%, and ΔVbd% > 20%, respectively. Pathological complete response (pCR) was considered to be achieved after NCT if there was no evidence of invasive carcinoma in the breast or metastatic tumors in the axillary and regional lymph nodes on surgical pathology. The association between ΔVbd% grouping and pCR was analyzed using univariable and multivariable logistic regression analyses. Results: The interval between the pre-NCT and post-NCT mammograms ranged from 79 to 250 days (median, 170 days). In the multivariable analysis, ΔVbd% grouping (odds ratio for pCR of 0.420 [95% confidence interval, 0.195-0.905; P = 0.027] for the decreased group compared with the stable group), N stage at diagnosis, histologic grade, and breast cancer subtype were significantly associated with pCR. This tendency was more evident in the luminal B-like and triple-negative subtypes. Conclusion: ΔVbd% was associated with pCR in breast cancer after NCT, with the decreased group showing a lower rate of pCR than the stable group. Automated measurement of ΔVbd% may help predict the NCT response and prognosis in breast cancer.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.101-123
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• 2013

Recently due to the advancements of science and information technology, the socio-economic business areas are changing from the industrial economy to a knowledge economy. Furthermore, companies need to do creation of new value through continuous innovation, development of core competencies and technologies, and technological convergence. Therefore, the identification of major trends in technology research and the interdisciplinary knowledge-based prediction of integrated technologies and promising techniques are required for firms to gain and sustain competitive advantage and future growth engines. The aim of this paper is to understand the recent research trend in management of technology (MOT) and to foresee promising technologies with deep knowledge for both technology and business. Furthermore, this study intends to give a clear way to find new technical value for constant innovation and to capture core technology and technology convergence. Bibliometrics is a metrical analysis to understand literature's characteristics. Traditional bibliometrics has its limitation not to understand relationship between trend in technology management and technology itself, since it focuses on quantitative indices such as quotation frequency. To overcome this issue, the network focused bibliometrics has been used instead of traditional one. The network focused bibliometrics mainly uses "Co-citation" and "Co-word" analysis. In this study, a keywords network analysis, one of social network analysis, is performed to analyze recent research trend in MOT. For the analysis, we collected keywords from research papers published in international journals related MOT between 2002 and 2011, constructed a keyword network, and then conducted the keywords network analysis. Over the past 40 years, the studies in social network have attempted to understand the social interactions through the network structure represented by connection patterns. In other words, social network analysis has been used to explain the structures and behaviors of various social formations such as teams, organizations, and industries. In general, the social network analysis uses data as a form of matrix. In our context, the matrix depicts the relations between rows as papers and columns as keywords, where the relations are represented as binary. Even though there are no direct relations between papers who have been published, the relations between papers can be derived artificially as in the paper-keyword matrix, in which each cell has 1 for including or 0 for not including. For example, a keywords network can be configured in a way to connect the papers which have included one or more same keywords. After constructing a keywords network, we analyzed frequency of keywords, structural characteristics of keywords network, preferential attachment and growth of new keywords, component, and centrality. The results of this study are as follows. First, a paper has 4.574 keywords on the average. 90% of keywords were used three or less times for past 10 years and about 75% of keywords appeared only one time. Second, the keyword network in MOT is a small world network and a scale free network in which a small number of keywords have a tendency to become a monopoly. Third, the gap between the rich (with more edges) and the poor (with fewer edges) in the network is getting bigger as time goes on. Fourth, most of newly entering keywords become poor nodes within about 2~3 years. Finally, keywords with high degree centrality, betweenness centrality, and closeness centrality are "Innovation," "R&D," "Patent," "Forecast," "Technology transfer," "Technology," and "SME". The results of analysis will help researchers identify major trends in MOT research and then seek a new research topic. We hope that the result of the analysis will help researchers of MOT identify major trends in technology research, and utilize as useful reference information when they seek consilience with other fields of study and select a new research topic.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Radiation Oncology Journal
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• v.24 no.2
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• pp.110-115
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• 2006

Purpose: This study was performed to determine the prognostic factors influencing relapse pattern, overall and disease-free survival in patients treated with postoperative radiotherapy for endometrial carcinoma. Materials and Methods: The records of 54 patients with endometrial adenocarcinoma treated postoperative radiotherapy at Pusan National University Hospital between April 1992 and May 2003 were reviewed retrospectively. Median age of the patients was 55 (range $35{\sim}76$). The distribution by surgical FIGO stages were 63.0% for 0Stage I, 14.8% for Stage II, 22.2% for Stage III. All patients received postoperative external radiotherapy up to $41.4{\sim}54Gy$ (median: 50.4 Gy). Additional Intravaginal brachytherapy was app led to 20 patients (37.0% of all). Median follow-up time was 35 months ($5{\sim}115$ months). Significant factors of this study: histologic grade, Iymphovascular space invasion and myometrial invasion depth were scored (GLM score) and analyzed. Survival analysis was peformed using Kaplan-Meier method. The log-rank test was used for univariate analysis and the Cox regression model for multivariate analysis. Results: 5-year overall and disease-free survival rates were 87.7% and 871%, respectively. Prognostic factors related with overall and disease-free survival were histologic grade, Iymphovascular space invasion and myometrial invasion according to the univariate analysis. According to the multivariate analysis, Iymphovascular space invasion was associated with decreased disease-free survival. GLM score was a meaningful factor affecting overall and disease-free survival (p=0.0090, p=0.0073, respectively) and distant recurrence (p=0.0132), which was the sum of points of histologic grade, Iymphovascular space Invasion and myometrial invasion. Total failure rate was 11% with 6 patients. Relapse sites were 2 para-aortic Iymph nodes, 2 lungs, a supraclavicular Iymph node and a vagina. Conclusion: The prognosos in patients with endometrial carcinoma treated by postoperative radiotherapy was closely related with surgical histopathology. If further explorations confirm the system of prognostic factors in endometrial carcinoma, it will help us to predict the progression pattern and to manage.

• Clinical and Experimental Pediatrics
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• v.51 no.11
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• pp.1198-1204
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• 2008

Purpose : The cause of subacute necrotizing lymphadenitis, a rare disease in children, has not been completely clarified. This study was aimed to investigate the disease mechanism by examining clinical, radiologic, and immunohistochemical findings in children diagnosed with subacute necrotizing lymphadenitis after an excisional biopsy. Methods : We examined 19 lymph node tissue specimens from 17 children diagnosed with subacute necrotizing lymphadenitis at Gyeongsang National University Hospital from March, 1998 to July, 2006. A retrospective survey of the medical records was performed. CT findings were analyzed. Immunohistochemical staining was done on tissues obtained by excisional biopsy from all patients. Results : The patient's age ranged from 5 to 19 years (average age :11.8 years). The main symptoms included a neck mass (17/19), pain in the mass (6/17), and fever (12/19). The palpable lymph nodes were mostly cervical in location; the maximum diameter, which was measured radiologically, was less than 3 cm in all 10 cases. The masses were pathologically divided into proliferative, necrotic, and xanthomatous types. With immunohistochemical staining the masses were divided into lesion (L), perilesion (PL), and necrosis (N). The CD8 staining was stronger than the CD4 staining for all regions in three types. The CD4 staining intensity was mainly increased in the perilesion, and CD8 was mainly increased in the lesion. Conclusion : We compared the radiologic findings, clinical symptoms, and pathology to help understand the cause of disease in patients with subacute necrotizing lymphadenitis.