Hypoalbuminemia, Low Base Excess Values, and Tachypnea Predict 28-Day Mortality in Severe Sepsis and Septic Shock Patients in the Emergency Department

PURPOSE: The objective of this study was to develop a new nomogram that can predict 28-day mortality in severe sepsis and/or septic shock patients using a combination of several biomarkers that are inexpensive and readily available in most emergency departments, with and without scoring systems. MATERIALS AND METHODS: We enrolled 561 patients who were admitted to an emergency department (ED) and received early goal-directed therapy for severe sepsis or septic shock. We collected demographic data, initial vital signs, and laboratory data sampled at the time of ED admission. Patients were randomly assigned to a training set or validation set. For the training set, we generated models using independent variables associated with 28-day mortality by multivariate analysis, and developed a new nomogram for the prediction of 28-day mortality. Thereafter, the diagnostic accuracy of the nomogram was tested using the validation set. RESULTS: The prediction model that included albumin, base excess, and respiratory rate demonstrated the largest area under the receiver operating characteristic curve (AUC) value of 0.8173 [95% confidence interval (CI), 0.7605-0.8741]. The logistic analysis revealed that a conventional scoring system was not associated with 28-day mortality. In the validation set, the discrimination of a newly developed nomogram was also good, with an AUC value of 0.7537 (95% CI, 0.6563-0.8512). CONCLUSION: Our new nomogram is valuable in predicting the 28-day mortality of patients with severe sepsis and/or septic shock in the emergency department. Moreover, our readily available nomogram is superior to conventional scoring systems in predicting mortality.ope

p21WAF1CIP1 expression correlates with calcium-induced differentiation in normal human oral keratinocytes 칼슘으로 분화유도된 사람 구강각화세포에서 p21WAF1CIP1 발현 변화에 관한 연구

Thesis (master`s)--서울대학교 대학원 :치의학과 구강생화학전공,1997.Maste

기술 하이프의 속성 비교: 데이터 소비자, 생산자, 유통자

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2018. 8. 박용태.The goal of this study is to add analytical value to the hype cycle theory through empirical evidence drawn from 70 technologies by emphasizing the perspectives of the components influencing the hype cycle: producer, consumer and distributer, and to ultimately propose a novel procedure capable to predict technology trends in the future. Conventional hype cycle researches tended to draw conclusions based on data from one or two emerging technologies. If their objective was to further develop the hype cycle theory, their study would severely lack in sample size. Also, they focused mainly on comprehending and forecasting specific technology trends by use of bibliometric analysis. Alternately, this study targets to expound on the idiosyncrasies of the ii hype cycle theory based on empirical evidence obtained from a large sample size with bibliometric methods and analyzing its content with structural topic modeling (STM), text mining algorithms to summarize documents into a number of topics and significant keywords associated with the topics. In addition, this study will utilize three social actors that are responsible for understanding the different facets of science and technology. The results of this analysis will be able to confirm or deny the observations made by previous studies and consequently enhance the forecasting capabilities of the hype cycle model. The proposed technological hype analysis consists of the following five steps: (1) construct a database by collecting and preprocessing web documents of patent data, search traffic data and article data of 70 technologies from selected websites, (2) plot the three data metrics from step 1 on the y-axis and time on the x-axis in order to see if these metrics produce any patterns useful for analysis, (3) run STM on the content of the articles and patents to analyze the pattern of technological hype. (4) record any generalized patternsand (5) propose various potential technological forecasting methods. Based on combined quantitative and qualitative analysis of three indicators, the analysis stage of this study can be summarized by the following three broad observations: (1) The distributer (article data) graphs peak first, the consumer graphs (search traffic data) peak second and the producer (patent data) graphs peak last. (2) The article data, search traffic data and patent data all depict distinct characteristics and patterns. (3) Comparing old and new technologies, the time lapse of an innovative technology disseminating from article to search traffic becomes shorter. iii Once the recorded observations of a hype cycles components and its corresponding indicators are verified by data from numerous technologies and industries, it will become possible to obtain general conclusions and develop a potential technology forecasting method. For example, R&D managers will be able to use this studys data on hype indicators to measure the current visibility of a technology and also to estimate the future visibility. With this study, managers and investors will be able to make systematic decisions regarding emerging technologies much more effectively than they did in the past, with reduced amount of time, labor, and thus the total costs.Chapter 1 Introduction 1 Chapter 2 Theoretical background 4 2.1 Hype cycle model 4 2.2 Social actors of STS, producer, consumer and distributer 7 2.3 Bibliometric analysis of hype cycle 10 2.4 Text-mining 15 Chapter 3 Proposed procedure 17 3.1 Proposed procedure 17 3.2 Constructing database 18 3.2.1 Selection of technology 19 3.2.2 Web crawling 22 3.3 Bibliometric analyis 24 3.4 Structural topic modeling 25 Chapter 4 Analysis and its intrepretation 28 4.1 Analysis 28 4.1.1 observation 1 28 4.1.2 observation 2 30 4.1.3 observation 3 34 4.2 Potential methods of analyzing technolgoy trend 37 Chapter 5 Conclusion and future research 41 5.1 Conclusion 41 5.2 Limitation and Future work 42 Appendix 44 Bibliography 50 국문초록 55 감사의 글 58Maste