• Journal of Korean Academy of Nursing
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• v.49 no.6
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• pp.655-676
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• 2019

Purpose: The purpose of this study was to investigate the effects of aromatherapy on sleep quality. Methods: This is a systematic review of randomized controlled trial studies (PROSPERO registration number CRD42017064519). In this study, the PICO were adults and the elderly, aromatherapy intervention, a comparative intervention with the control and placebo oil groups, and sleep. The selected articles were in English, Korean, and Chinese. Results: The results of the meta-analysis showed that the effect sizes of the experimental group were 1.03 (n=763, SMD=1.03, 95% CI 0.66 to 1.39) (Z=5.47, p<.001). In the aromatherapy intervention group, the effect size of sleep was statistically significant (Q_B=9.39, df=2, p=.009), with a difference of 0.77 for inhalation, 1.12 for oral intake and 2.05 for massage. A post-analysis showed that the effect of massage on sleep was significantly greater than the inhalation method. The regression coefficient of the intervention period, B=0.01 (Z=1.43, p=.154), also showed that the longer the intervention period, the larger the effect size; however, it was not statistically significant. Conclusion: A total of 23 literature analyses showed that aromatherapy is effective in improving quality of sleep, and the massage method is more effective in improving quality of sleep than the inhalation method. A meta-ANOVA showed that the aromatherapy intervention affected the high heterogeneity of the effect size. Thus, future research with stricter control in methods and experimental procedures is necessary.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.908-918
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• 2020

Objective: To categorize the radiological patterns of recurrence after bevacizumab treatment and to derive the pooled proportions of patients with recurrent malignant glioma showing the different radiological patterns. Materials and Methods: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed to identify studies reporting radiological recurrence patterns in patients with recurrent malignant glioma after bevacizumab treatment failure until April 10, 2019. The pooled proportions according to radiological recurrence patterns (geographically local versus non-local recurrence) and predominant tumor portions (enhancing tumor versus non-enhancing tumor) after bevacizumab treatment were calculated. Subgroup and meta-regression analyses were also performed. Results: The systematic review and meta-analysis included 17 articles. The pooled proportions were 38.3% (95% confidence interval [CI], 30.6-46.1%) for a geographical radiologic pattern of non-local recurrence and 34.2% (95% CI, 27.3-41.5%) for a non-enhancing tumor-predominant recurrence pattern. In the subgroup analysis, the pooled proportion of non-local recurrence in the patients treated with bevacizumab only was slightly higher than that in patients treated with the combination with cytotoxic chemotherapy (34.9% [95% CI, 22.8-49.4%] versus 22.5% [95% CI, 9.5-44.6%]). Conclusion: A substantial proportion of high-grade glioma patients show non-local or non-enhancing radiologic patterns of recurrence after bevacizumab treatment, which may provide insight into surrogate endpoints for treatment failure in clinical trials of recurrent high-grade glioma.

• The Journal of Korean Medicine
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• v.42 no.4
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• pp.222-237
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• 2021

Objectives: The purpose of this study was to investigate the trend in the research on cirrhotic ascites using t herbal medicine. Methods: This review was conducted using six electronic databases(NDSL, KMBASE, Koreantk, KISS, KISTI, KoreaMed) with no restriction in year. The search term was 'liver cirrhosis', 'ascites', or 'cirrhotic ascites', 'herbal medicine', 'traditional Chinese medicine', and 'randomized clinical trial', and there was no restriction in year. The searched studies were analyzed according to the type of research. Results: After scanning the titles and abstracts, 13 articles were ultimately included. Of the outcome measures of 13 studies, effective rate, liver function test, and ascites regression time were included in the meta-analysis, which showed that the effective rate of herbal medicine-supportive treatment combination therapy was 1.27 times higher than that of supportive treatment alone, and the difference was statistically significant. Conclusions: We analyzed the trends of cirrhotic ascites treatment in herbal medicine through this review. It is necessary to conduct further studies, such as well-designed clinical trials based on the results from experimental research.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2082-2093
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• 2021

Objective: We conducted a systematic review and meta-analysis of the tissue adequacy and complication rates of percutaneous transthoracic needle biopsy (PTNB) for molecular analysis in patients with non-small cell lung cancer (NSCLC). Materials and Methods: We performed a literature search of the OVID-MEDLINE and Embase databases to identify original studies on the tissue adequacy and complication rates of PTNB for molecular analysis in patients with NSCLC published between January 2005 and January 2020. Inverse variance and random-effects models were used to evaluate and acquire meta-analytic estimates of the outcomes. To explore heterogeneity across the studies, univariable and multivariable metaregression analyses were performed. Results: A total of 21 studies with 2232 biopsies (initial biopsy, 8 studies; rebiopsy after therapy, 13 studies) were included. The pooled rates of tissue adequacy and complications were 89.3% (95% confidence interval [CI]: 85.6%-92.6%; I² = 0.81) and 17.3% (95% CI: 12.1%-23.1%; I² = 0.89), respectively. These rates were 93.5% and 22.2% for the initial biopsies and 86.2% and 16.8% for the rebiopsies, respectively. Severe complications, including pneumothorax requiring chest tube placement and massive hemoptysis, occurred in 0.7% of the cases (95% CI: 0%-2.2%; I² = 0.67). Multivariable meta-regression analysis showed that the tissue adequacy rate was not significantly lower in studies on rebiopsies (p = 0.058). The complication rate was significantly higher in studies that preferentially included older adults (p = 0.001). Conclusion: PTNB demonstrated an average tissue adequacy rate of 89.3% for molecular analysis in patients with NSCLC, with a complication rate of 17.3%. PTNB is a generally safe and effective diagnostic procedure for obtaining tissue samples for molecular analysis in NSCLC. Rebiopsy may be performed actively with an acceptable risk of complications if clinically required.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.379-403
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• 2012

In recent years, the need for optimal design of structures under time-history loading aroused great attention in researchers. The main problem in this field is the extremely high computational demand of time-history analyses, which may convert the solution algorithm to an illogical one. In this paper, a new framework is developed to solve the size optimization problem of steel truss structures subjected to ground motions. In order to solve this problem, the covariance matrix adaptation evolution strategy algorithm is employed for the optimization procedure, while a generalized regression neural network is utilized as a meta-model for fitness approximation. Moreover, the computational cost of time-history analysis is decreased through a wavelet analysis. Capability and efficiency of the proposed framework is investigated via two design examples, comprising of a tower truss and a footbridge truss.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6743-6749
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• 2013

Molecular epidemiological studies have shown that gene polymorphisms of estrogen receptor alpha gene (ESR-${\alpha}$) are associated with breast cancer risk. However, previous results from many molecular studies have been inconsistent. In this study, we examined two polymorphisms (PvuII and XbaI RFLPs) of the ESR-${\alpha}$ gene in 542 breast cancer cases and 1,016 controls from China. Associations between the polymorphisms and breast cancer risk were calculated with an unconditional logistic regression model. Linkage disequilibrium and haplotypes were analyzed with the SHEsis software. In addition, we also performed a systematic meta-analysis of 24 published studies evaluating the association. No significant associations were found between the PvuII polymorphism and breast cancer risk. However, a significantly decreased risk of breast cancer was observed among carriers of the XbaI 'G' allele (age-adjusted OR = 0.80; 95% CI = 0.66- 0.97) compared with carriers of the 'A' allele. Haplotype analysis showed significantly decreased cancer risk for carriers of the 'CG' haplotype (OR = 0.79; 95% CI = 0.66- 0.96). In the systematic meta-analysis, the XbaI 'G' allele was associated with an overall significantly decreased risk of breast cancer (OR = 0.90, 95% CI = 0.82- 1.00). In addition, the PvuII 'C' allele showed a 0.96- fold decreased disease risk (95% CI = 0.92- 0.99). In subgroup analysis, an association between the PvuII 'C' and XbaI 'G' alleles and breast cancer risk was significant in Asians ('C' vs. 'T': OR = 0.93, 95% CI = 0.85- 1.00; 'G' vs. 'A': OR = 0.82, 95% CI = 0.68- 0.98), but not in Euro-Americans. Thus, our results provide evidence that ESR-${\alpha}$ polymorphisms are associated with susceptibility to breast cancer. These associations may largely depend on population characteristics and geographic location.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.684-694
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• 2020

Objective: The purpose was to review the diagnostic performance of the length of tumor capsular contact (LCC) on magnetic resonance imaging (MRI) for detecting prostate cancer extraprostatic extension (EPE). Materials and Methods: PubMed and EMBASE databases were searched up to March 24, 2019. We included diagnostic accuracy studies that evaluated LCC on MRI for EPE detection using radical prostatectomy specimen histopathology as the reference standard. Quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled and graphically presented using hierarchical summary receiver operating characteristic (HSROC) plots. Meta-regression and subgroup analyses were conducted to explore heterogeneity. Results: Thirteen articles with 2136 patients were included. Study quality was generally good. Summary sensitivity and specificity were 0.79 (95% confidence interval [CI] 0.73-0.83) and 0.67 (95% CI 0.60-0.74), respectively. Area under the HSROC was 0.81 (95% CI 0.77-0.84). Substantial heterogeneity was present among the included studies according to Cochran's Q-test (p < 0.01) and Higgins I² (62% and 86% for sensitivity and specificity, respectively). In terms of heterogeneity, measurement method (curvilinear vs. linear), prevalence of Gleason score ≥ 7, MRI readers' experience, and endorectal coils were significant factors (p ≤ 0.01), whereas method to determine the LCC threshold, cutoff value, magnet strength, and publication year were not (p = 0.14-0.93). Diagnostic test accuracy estimates were comparable across all assessed MRI sequences. Conclusion: Greater LCC on MRI is associated with a higher probability of prostate cancer EPE. Due to heterogeneity among the studies, further investigation is needed to establish the optimal cutoff value for each clinical setting.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.4
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• pp.1431-1434
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• 2015

Background: Breast cancer is one of the most common cancers among women worldwide and the HER2 receptor plays an important role in its development and progression. This systematic review aimed to summarize the role of HER2 in brain metastasis in patients with breast cancer. Materials and Methods: We conducted a literature search by advanced search in title field using the Scopus, Pubmed, and Google scholar databases until the end of June 2014. With metastasis, metastatic, HER2, brain, and breast cancer, as terms of search we selected 31 articles, which were reviewed by two independent and blinded expert reviewers. The studies were first selected according to their titles and abstracts. Quality of the studies were then assessed using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) protocol for observational studies and CONSORT(Consolidation of Standards for Reporting Trials) protocol for clinical trials. For statistical analyses, we used STATA, version 11.0 software. Forest and funnel diagrams were drawn and for heterogeneity, index was also considered. Also we used meta regression analysis. Results: Finally, we reviewed 10 studies. The prevalence of brain metastasis in HER2-positive breast cancer patients was 24.9%. There was publication bias in the reviewed studies. Meta regression analysis showed that follow up time had no significant effect (p=0.396) on the prevalence of brain metastasis. Conclusions: The results showed a high prevalence of brain metastasis in HER2 positive breast cancer patients.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.527-535
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• 2015

With results from previous Korean studies on forest thinning, we conducted a meta-analysis on the effect of thinning on diameter at breast height (DBH) growth and carbon (C) stocks (tree, litter layer, coarse woody debris (CWD), and soils) in Korean forests. Thinning increased the DBH growth and the C stocks in soils by 39.2% and 12.8%, respectively, while it decreased the C stocks in tree by 30.9%. In contrast, thinning had no significant effect on the C stocks in litter layer and CWD. The DBH growth and the C stocks in tree showed significant correlations with thinning intensity and recovery time. The C stocks in litter layer correlated with recovery time while those in CWD and soils did not show significant correlation neither with thinning intensity nor with recovery time. Regression models of the DBH growth and the C stocks in tree were developed to quantify the effect of thinning intensity and recovery time. An integration of the regression model of the tree C stock into forest carbon models is expected to be essential to quantify the effect of thinning on the C stocks in litter layer, CWD, and soils. We also suggested expansion of study species, long-term and frequent monitoring, and investigation on understory vegetation in order to elucidate changes in Korean forests following thinning practices.