• Journal of Gastric Cancer
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• v.23 no.2
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• pp.289-302
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• 2023

Purpose: Liver metastasis (LM) is reported in approximately 40% of patients with advanced/metastatic gastric/gastroesophageal junction adenocarcinoma (metastatic esophagogastric adenocarcinoma; mGEA) and is associated with a worse prognosis. This post-hoc analysis from the RAINBOW trial reported the efficacy, safety, and biomarker outcomes of ramucirumab and paclitaxel combination treatment (RAM+PAC) in patients with (LM+) and without (LM-) LM at baseline. Materials and Methods: Patients (n=665) were randomly assigned on a 1:1 basis to receive either RAM+PAC (LM+: 150, LM-: 180) or placebo and paclitaxel (PL+PAC) (LM+: 138, LM-: 197). The overall survival (OS) and progression-free survival (PFS) were evaluated using stratified Kaplan-Meier and Cox regression models. The correlation of dichotomized biomarkers (VEGF-C, D; VEGFR-1,2) with efficacy in the LM+ versus LM- subgroups was analyzed using the Cox regression model with reported interaction P-values. Results: The presence of LM was associated with earlier progression than those without LM, particularly in patients receiving PL+PAC (hazard ratio [HR], 1.68). RAM+PAC treatment improved OS and PFS irrespective of LM status but showed greater improvement in LM+ than that in LM- (OS HR, 0.71 [LM+] vs. 0.88 [LM-]; PFS HR, 0.47 [LM+] vs. 0.76 [LM-]). Treatment-emergent adverse events were similar between patients with and without LM. No predictive relationship was observed between biomarker levels (VEGF-C, D; VEGFR-1,2) and efficacy outcome (OS, PFS) (all interaction P-values >0.05). Conclusions: RAM provided a significant benefit, irrespective of LM status; however, its effect was numerically stronger in patients with LM. Therefore, RAM+PAC is a clinically meaningful therapeutic option for patients with mGEA and LM.

• The Korean Journal of Pain
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• v.36 no.2
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• pp.184-194
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• 2023

Background: Degenerative lumbar spondylolisthesis (DLS) is frequently associated with lumbar spinal stenosis (LSS) and conservative treatments such as epidural steroid injection do not have long-term benefits in LSS patients with DLS. This study evaluated the effectiveness of percutaneous epidural neuroplasty using a balloon catheter in patients with LSS and DLS. Methods: Patients' sex, age, body mass index, diabetes, hypertension, stenosis grading, pain duration, location, pain intensity, and medications were retrieved from electronic medical records. At 1, 3, and 6 months following the procedure, data on pain severity, medication usage, and physical functional status were analyzed. A generalized estimating equations model was used at the six-month follow-up. Patients were divided into those with DLS (the spondylolisthesis group) and those without DLS (the no spondylolisthesis group) to evaluate whether the effects of percutaneous epidural neuroplasty using a balloon catheter were different. Results: A total of 826 patients were included (spondylolisthesis: 433 patients, 52.4%; no spondylolisthesis: 393 patients, 47.6%). Age, body mass index, hypertension, pain location, and stenosis grading were statistically different between the two groups. The generalized estimating equations analyses with unadjusted and adjusted estimation revealed a significant improvement in the estimated mean numerical rating scale of pain intensities compared to that at baseline in both groups (P < 0.001). Any adverse events that occurred were minor and temporary. Conclusions: Percutaneous epidural neuroplasty using a balloon catheter may be an alternative treatment option for patients with chronic LSS, regardless of accompanying DLS, who have had failed conservative management.

• Journal of Korean Academy of Nursing
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• v.52 no.1
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• pp.24-35
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• 2022

Purpose: This study investigated the incidence and risk factors of cataract in people with diabetes mellitus (DM) using data from Ansan cohort of the Korean Genome and Epidemiology Study (KoGES). Methods: Data from a total of 329 patients with type 2 DM without cataract who participated in Ansan cohort of the KoGES from baseline survey (2001-2002) to fifth follow-up visit (2011-2012) were examined. The characteristics of the subjects were analyzed with frequency and percentage, and mean and standard deviation. Cataract incidence was measured as incidence proportion (%). For risk factors of cataract, hazard ratio (HR) and 95% confidence interval (CI) were obtained using the Cox proportional hazard model. Results: The cataract incidence over a 10-year follow-up period was 19.1% (15.1 in males and 25.8 in females), and mean age at the incidence of cataract was 63.48 years (61.58 years in males and 65.31 years in females). Age (HR=1.09, 95% CI=1.05-1.13) and HbA1c (HR=1.21, 95% CI=1.07-1.37) or the duration of DM (HR=1.05, 95% CI=1.00-1.09) were found to be independently associated with cataract development. Conclusion: Cataract development in people with DM is common, and its likelihood increases with age, HbA1c, and the duration of DM. Considering negative effect of cataract on their quality of life and economic burden, nurses should identify people with DM at a higher risk of cataract development, and plan individual eye examination programs to detect cataract development as early as possible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• The Journal of Bigdata
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• v.7 no.1
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• pp.75-87
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• 2022

This study investigates a dynamic flexible flow shop scheduling problem under uncertain rework operations for an automobile pipe production line. We propose a weighted dispatching rule (WDR) based on the multiple dispatching rules to minimize the weighted sum of average flowtime and tardiness. The set of weights in WDR should be carefully determined because it significantly affects the performance measures. We build a discrete-event simulation model and propose a genetic algorithm to optimize the set of weights considering complex and variant operations. The simulation experiments demonstrate that WDR outperforms the baseline dispatching rules in average flowtime and tardiness.

• Journal of Korean Biological Nursing Science
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• v.25 no.1
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• pp.73-85
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• 2023

Purpose: This study focused on identifying the interaction effects of genetic and lifestyle-environmental factors on the development of type 2 diabetes mellitus (T2D). Methods: Study subjects were selected from the Korean Genome and Epidemiology Study (KoGES) from 2001 to 2014. Data on genetic variations, anthropometric measurements, biochemical data, and seven lifestyle factors (diet, physical activity, alcohol drinking, smoking, sleep, depression, and stress) were obtained from 4,836 Koreans aged between 40 and 59 years, including those with T2D at baseline (n = 1,209), newly developed T2D (n= 1,298) and verified controls (n = 3,538). The genetic risk score (GRS) was calculated by using 11 single-nucleotide polymorphisms (SNPs) related to T2D development and the second quartile was used as the reference category. A Cox proportional hazards regression model was used to evaluate the associations of GRS and lifestyle factors with T2D risk, controlling for covariates. Results: Multivariate regression analysis revealed that GRS was the strongest risk factor for T2D, and body mass index (BMI), smoking, drinking, and spicy food preference also increased the risk. Lifestyle/environmental factors that showed significant interactions with GRS were BMI, current smoking, current drinking, fatty food preference, and spicy food preference. Conclusions: Interactions between genetic factors and lifestyle/environmental factors were associated with an increased risk of T2D. The results will be useful to provide a new perspective on genetic profiling for the earlier detection of T2D risk and clues for personalized interventions, which might be more effective prevention strategies or therapies in individuals with a genetic predisposition to T2D.

• Journal of Environmental Science International
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• v.32 no.9
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• pp.613-626
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• 2023

This study investigates the impact of increased adoption of eco-friendly vehicles on ozone (O₃) concentrations in South Korea, utilizing the community multiscale air quality (CMAQ) model. In the summer of 2017 (June-August), we conducted two experiments: a BASE experiment, representing baseline emissions, and an R_30 experiment, involving a 30% emission reduction due to eco-friendly vehicles. The contrast between these experiments reveals that, while most air pollutants decreased with reduced vehicle emissions, O₃ concentrations surprisingly increased (up to 2.1 parts per billion) across South Korea. A further examination of O₃ concentration changes was conducted by analyzing daytime and nighttime variations as well as wind direction. During the daytime, O₃ concentrations notably rose near metropolitan areas due to reduced O₃ titration (O₃ + NO → O₂ + NO₂) resulting from emission reductions. At nighttime, O₃ concentrations exhibited a greater increase, attributed to the transport of daytime-generated O₃ to rural regions. Notably, the impact of reduced emissions in metropolitan areas on O₃ concentrations in downwind areas varied depending on the prevailing wind direction. These findings highlight that the promotion of eco-friendly vehicles, though effective in lowering certain air pollutants, might not directly influence O₃ concentrations. This study underscores the need to comprehensively understand the complicated chemistry of O₃ to develop effective strategies for air quality management.

• Korean Journal of Materials Research
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• v.34 no.2
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• pp.85-94
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• 2024

The optimization of deacetylation process parameters for producing chitosan from isolated chitin shrimp shell waste was investigated using response surface methodology with central composite design (RSM-CCD). Three independent variables viz, NaOH concentration (X₁), radiation power (X₂), and reaction time (X₃) were examined to determine their respective effects on the degree of deacetylation (DD). The DD of chitosan was also calculated using the baseline approach of the Fourier Transform Infrared (FTIR) spectra of the yields. RSM-CCD analysis showed that the optimal chitosan DD value of 96.45 % was obtained at an optimized condition of 63.41 % (w/v) NaOH concentration, 227.28 W radiation power, and 3.34 min deacetylation reaction. The DD was strongly controlled by NaOH concentration, irradiation power, and reaction duration. The coefficients of correlation were 0.257, 0.680, and 0.390, respectively. Because the procedure used microwave radiation absorption, radiation power had a substantial correlation of 0.600~0.800 compared to the two low variables, which were 0.200~0.400. This independently predicted robust quadratic model interaction has been validated for predicting the DD of chitin.

• Hip & pelvis
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• v.35 no.4
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• pp.217-227
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• 2023

Purpose: Periprosthetic femoral fracture (PFF) is a common complication after total hip arthroplasty, and open reduction and internal fixation (ORIF) is a common surgical treatment. We conducted a meta-analysis to compare the outcomes of ORIF in patients with different fracture patterns (Vancouver B1 and B2). Materials and Methods: We conducted a systematic search of PubMed, Embase, Cochrane Library and KoreaMed from inception to August 2022. We conducted a pair-wise meta-analysis (with a fixed-effects model) on the 10 comparative studies and a proportional meta-analysis on the data from the 39 articles to determine a consensus. The outcomes were the incidence of reoperations that included osteosynthesis, irrigation/debridement and revision arthroplasty. Results: The pair-wise meta-analysis showed similar outcomes between two groups; the risk of reoperation (odds ratio [OR]=0.82, confidence interval [CI] 0.43-1.55, P=0.542), nonunion (OR=0.49; CI 0.22-1.10, P=0.085) and deep infection (OR=1.89, CI 0.48-7.46, P=0.361). In proportion meta-analysis, pooled prevalence of reoperation was 9% (95% CI, 6-12) in B1 and 8% (95% CI, 2-15) in B2 (heterogeneity between two groups (Q), P=0.772). The pooled prevalence of nonunion was same as of 4% in B1 and B2 (Q, P=0.678), and deep infection was 2% (95% CI, 1-3) in B1 and 4% (95% CI, 2-7) in B2 (Q, P=0.130). Conclusion: ORIF is a feasible treatment for B1 and B2 periprosthetic femoral fractures, with acceptable outcomes in terms of, nonunion and infection. The results of this study would help clinicians and provide baseline data for further studies validating PFF.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.33-43
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• 2024

Automated text summarization (ATS) systems rely on language resources as datasets. However, creating these datasets is a complex and labor-intensive task requiring linguists to extensively annotate the data. Consequently, certain public datasets for ATS, particularly in languages such as Thai, are not as readily available as those for the more popular languages. The primary objective of the ATS approach is to condense large volumes of text into shorter summaries, thereby reducing the time required to extract information from extensive textual data. Owing to the challenges involved in preparing language resources, publicly accessible datasets for Thai ATS are relatively scarce compared to those for widely used languages. The goal is to produce concise summaries and accelerate the information extraction process using vast amounts of textual input. This study introduced ThEconSum, an ATS architecture specifically designed for Thai language, using economy-related data. An evaluation of this research revealed the significant remaining tasks and limitations of the Thai language.