Paip1 Indicated Poor Prognosis in Cervical Cancer and Promoted Cervical Carcinogenesis

PURPOSE: This study was aimed to investigate the role of poly(A)-binding protein-interacting protein 1 (Paip1) in cervical carcinogenesis. Materials and Methods: The expression of Paip1 in normal cervical epithelial tissues and cervical cancer (CC) tissues were detected by immunohistochemistry. In vivo and in vitro assays were performed to validate effect of Paip1 on CC progression. RESULTS: Paip1 was found to be up-regulated in CC, which was linked with shorter survival. Knockdown of Paip1 inhibited cell growth, induced apoptosis and cell cycle arrest in CC cells, whereas its overexpression reversed these effects. The in vivo tumor model confirmed the pro-tumor role of Paip1 in CC growth. CONCLUSION: Altogether, the investigation demonstrated the clinical significance of Paip1 expression, which prompted that the up-regulated of Paip1 can presumably be a potential prognostic and progression marker for CC.ope

Reuse of imputed data in microarray analysis increases imputation efficiency

BACKGROUND: The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked. RESULTS: We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments. Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets. CONCLUSIONS: Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data.ope

Sample size estimation using nomogram in dental research

The appropriate sample size calculation in dental research is important to achieve the study purpose at the first step in study design. However, it cannot be easy to calculate sample size using standard formulas, because the several factors must be considered for calculation. This study introduced the graphic method for sample size calculation, which is called nomogram. The purpose of this study is to show the effectiveness of the nomogram using examples, expecting the researchers can easily use nomogram for sample size determination.ope

석탄소비량과 경제성장지표 간의 장기적 균형관계에 대한 국제비교 연구

학위논문 (석사)-- 서울대학교 대학원 : 에너지시스템공학부, 2016. 2. 허은녕.본 논문에서는 국가별 석탄소비량과 경제성장간의 장기적 균형관계에 대하여 두 가지 가설을 세우고, 이를 총 31개 국가의 자료를 대상으로 실증분석하고 그 결과를 비교하였다. Jinke et al. (2008) 및 Wolde-Rufael (2010) 등 기존의 국제비교 연구에서는 주로 경제규모나 석탄소비량을 기준으로 비교하였으나 대부분 유의미한 이유를 도출하지 못하였다. 이에, 본 논문은 두 변수간의 장기적 균형관계와 인과관계를 결정하는 요인이 국제적 환경규제와 국가별 석탄발전량 변화라는 두 가지 가설을 세우고 이를 통하여 두 변수간의 장기적 균형관계를 확인하고, 인과관계가 국가별로 상이하게 나타나는 이유를 분석하는데 연구의 목표를 두었다. 연구에 사용된 자료는 총 31개 국가의 1980년부터 2012년까지의 연간자료를 이용하였다. 석탄소비량 자료는 미국에너지정보국 (EIA)의 자료를 이용하였고, 경제성장 지표로는 국제통화기금 (IMF)이 발행하는 실질 GDP자료를 이용하였다. 장기적 균형관계 및 인과관계 분석에 사용한 분석모형은 VAR모형과 VECM을 채택하여 그 결과를 종합적으로 판단하였다. 분석결과 경제성장이 석탄소비를 증가시키는 단방향 인과관계가 존재하는 국가가 6개, 반대 방향으로 단방향 인과관계를 나타낸 국가가 8개로 나타났다. 또한, 브라질, 폴란드, 남아공 등 3개국은 두 변수가 상호 인과하는 양방향 인과관계를 갖는 것으로 나타났으며 나머지 14개 국가에서는 두 변수 간에 특별한 인과관계가 나타나지 않았다. 국가별로 상이한 인과관계의 원인을 분석하기 위한 국제비교분석은 다음의 두 가지 가설을 기반으로 진행되었다. 첫 번째 가설은 국제적 환경규제의 유무가 인과관계에 영향을 줄 것이라고 설정하였다. 대상인 국제적 환경규제로는 기후변화협약을 선정하였으며, 비교결과 국제적 환경규제가 있는 12개 국가 중 10개 국가에서 두 변수 간의 인과관계가 존재하지 않는다는 것을 확인할 수 있었고, 환경규제가 없는 19개 국가 중 15개 국가가 두 변수 간의 인과관계가 존재한다는 것을 알 수 있었다. 즉, 기후변화협약과 같은 국제적 환경규제의 유무가 국가별로 두 변수 간의 인과관계 유무에 상당한 영향을 주고 있음을 확인하였다. 두 번째 가설은 각 국가별 발전부문에서 석탄화력발전이 차지하는 비중의 변화가 인과관계에 영향을 줄 것이라고 설정하였다. 이 비교를 통해 석탄화력발전의 평균변화율이 음수이며 변동률이 작은 5개 국가 중 4개 국가에서 석탄소비가 경제성장을 인과하는 방향의 인과관계가 존재한다는 것을 알 수 있었다. 또, 평균변화율이 음수이며, 변동률이 큰 국가들은 두 변수 간에 인과관계가 존재하지 않는 것을 확인하였다. 반대로, 평균변화율이 양수이며 변동률이 작은 6개 국가 중 4개 국가에서 석탄소비의 증가가 경제성장을 야기하는 방향으로 인과관계가 존재한다는 것을 알 수 있었다. 하지만, 평균변화율이 양수이며 변동률이 큰 6개 국가들은 다양한 인과관계 결과가 혼재되어 있었다. 즉, 해당 그룹은 특별한 경향성을 찾는데 실패하였다. 이 비교를 통해 각 국가의 석탄화력 등 전원구성의 변화가 인과관계의 방향과 크기에 영향을 주었을 가능성이 크다는 것을 확인하였다. 본 논문의 국제비교연구 결과는 석탄사용량과 경제성장간의 장기적인 관계에 대한 연구에서 환경규제와 전원구성의 변화 등 본 논문이 제시한 두 가지 새로운 가설의 중요성과 당위성을 확인하였다. 본 논문의 결과는 각 국가별로 석탄사용과 관련된 정책을 수립할 때 참고할 수 있는 기초자료로 사용이 가능할 것이다.제 1 장 서 론 1 제 1 절 연구의 배경 1 제 2 절 연구의 목표 5 제 3 절 논문의 구성 7 제 2 장 이론적 배경 9 제 1 절 석탄의 특징 9 제 2 절 세계 석탄시장 현황 12 1. 석탄의 소비 및 생산 12 2. 석탄 수출입 13 3. 주요석탄시장 및 가격 15 제 3 절 선행연구 18 1. 2개 이상의 국가를 대상으로 한 선행연구 18 2. 단일국가를 대상으로 한 선행연구 26 제 4절 연구방법론 32 1. 단위근 검정(Unit root test) 방법 32 2. 공적분 검정(Cointegration test) 방법 35 3. Granger 인과관계 검정 방법 37 제 3 장 실증분석 40 제 1 절 분석자료 40 제 2 절 분석결과 43 1. 단위근검정(Unit root test) 결과 43 2. 공적분 검정(Cointegration test) 결과 50 3. Granger 인과관계 검정 결과 55 제 4 장 분석결과 국제비교 64 제 1 절 환경규제와 인과관계 비교 64 제 2 절 석탄화력발전의 비중과 인과관계 비교 70 제 5 장 결과 요약 및 한계점 78 제 1 절 결과 요약 78 제 2 절 연구의 한계점 80 참고문헌 81 Appendix 85 Abstract 122Maste

Statistical methods for accessing agreement between repeated measurements in dental research

The comparison of the repeated measurements is often needed to see whether they agree sufficiently, when a measurement is repeated under identical conditions by different raters. Such investigations are often analyzed inappropriately, by using correlation coefficient. The purpose of this study is to introduce statistical methods for accessing the agreement of the repeated measurements, which include Bland-Altman plot, intra class correlation, Passing-Bablok regression and Cohen's kappa coefficient, and to show how to execute them using examples.ope

Outlier detection in dental research

In clinical dental research, errors occur in spite of careful study design and conduct. Data cleaning procedures intend to identify and correct these errors or at least to minimize their influence on study. Outlier is the one of these errors. Outlier detection is the first step in data analysis process which has a serious effect in the field of dental research. Hence, this paper aims to introduce the methods to detect the outliers and to examine their influences in statistical data analysis.ope

Analysis of the Relationship Between Systemic Health Status and Periodontal Disease in Korean Adults - Survey study of the Fifth Korea National Health and Nutrition Examination -

The purpose of this study was to synthetically examine the relationship between systemic diseases and periodontal diseases. The data obtained from the Fifth Korea National Health and Nutrition Examination Survey were used. SPSS 18.0 for Windows was applied for statistical analysis. The surveyed data were analyzed by using independent sample t-test for the difference between Body Mass Index and clinical test according to the existence of periodontal disease, and X2 test for the relationship between periodontal disease and systemic disease. Multiple logistic regression analysis was used in order to figure out the influence upon the periodontal disease prevalence among general characteristics and systemic diseases. As results, the values of high density lipoprotein (HDL) and HBA1C were statistically significant, depending on the presence of periodontal disease. As for the relationship between periodontal disease and systemic disease, hypertension (odds ratio 1.362, p<.05), cardiovascular disease (odds ratio 2.118, p<.05), arthritis (odds ratio 1.289, p<.05) and cirrhosis (odds ratio 6.124, p<.05) were statistically significant. According to Multiple logistic regression analysis, gender (odds ratio 1.24, p<.05), alcohol intake (odds ratio 1.25, p<.05), cardiovascular diseases (odds ratio 1.56, p<.05), and liver cirrhosis (odds ratio 1.17, p<.05) were related to the prevalence of periodontal diseases. In conclusion, the systemic diseases such as cardiovascular system, diabetes, and liver diseases revealed to have relationship with periodontal disease. To strengthen oral health education is needed to enhance systemic health as well as oral health. Moreover, basic biological research should be followed to support this surveyed study.ope

Nomogram for Predicting Survival for Oral Squamous Cell Carcinoma

An accurate system for predicting the survival of patients with oral squamous cell carcinoma (OSCC) will be useful for selecting appropriate therapies. A nomogram for predicting survival was constructed from 96 patients with primary OSCC who underwent surgical resection between January 1994 and June 2003 at the Yonsei Dental Hospital in Seoul, Korea. We performed univariate and multivariate Cox regression to identify survival prognostic factors. For the early stage patients group, the nomogram was able to predict the 5 and 10 year survival from OSCC with a concordance index of 0.72. The total point assigned by the nomogram was a significant factor for predicting survival. This nomogram was able to accurately predict the survival after treatment of an individual patient with OSCC and may have practical utility for deciding adjuvant treatment.ope

Possibility of the use of public microarray database for identifying significant genes associated with oral squamous cell carcinoma.

There are lots of studies attempting to identify the expression changes in oral squamous cell carcinoma. Most studies include insufficient samples to apply statistical methods for detecting significant gene sets. This study combined two small microarray datasets from a public database and identified significant genes associated with the progress of oral squamous cell carcinoma. There were different expression scales between the two datasets, even though these datasets were generated under the same platforms - Affymetrix U133A gene chips. We discretized gene expressions of the two datasets by adjusting the differences between the datasets for detecting the more reliable information. From the combination of the two datasets, we detected 51 significant genes that were upregulated in oral squamous cell carcinoma. Most of them were published in previous studies as cancer-related genes. From these selected genes, significant genetic pathways associated with expression changes were identified. By combining several datasets from the public database, sufficient samples can be obtained for detecting reliable information. Most of the selected genes were known as cancer-related genes, including oral squamous cell carcinoma. Several unknown genes can be biologically evaluated in further studies.ope

Identification of combined biomarkers for predicting the risk of osteoporosis using machine learning

Osteoporosis is a severe chronic skeletal disorder that affects older individuals, especially postmenopausal women. However, molecular biomarkers for predicting the risk of osteoporosis are not well characterized. The aim of this study was to identify combined biomarkers for predicting the risk of osteoporosis using machine learning methods. We merged three publicly available gene expression datasets (GSE56815, GSE13850, and GSE2208) to obtain expression data for 6354 unique genes in postmenopausal women (45 with high bone mineral density and 45 with low bone mineral density). All machine learning methods were implemented in R, with the GEOquery and limma packages, for dataset download and differentially expressed gene identification, and a nomogram for predicting the risk of osteoporosis was constructed. We detected 378 significant differentially expressed genes using the limma package, representing 15 major biological pathways. The performance of the predictive models based on combined biomarkers (two or three genes) was superior to that of models based on a single gene. The best predictive gene set among two-gene sets included PLA2G2A and WRAP73. The best predictive gene set among three-gene sets included LPN1, PFDN6, and DOHH. Overall, we demonstrated the advantages of using combined versus single biomarkers for predicting the risk of osteoporosis. Further, the predictive nomogram constructed using combined biomarkers could be used by clinicians to identify high-risk individuals and in the design of efficient clinical trials to reduce the incidence of osteoporosis.ope