• Journal of Advanced Navigation Technology
• /
• v.18 no.6
• /
• pp.582-587
• /
• 2014

This study have researched on feasibility of bioelectrical impedance analysis (BIA, which is simple useful evaluation tool for predictive factor of cardiovascular disease) to patients who have to travel along the sea for a long-period time and have difficulty in visiting medical institutions. We studied on the basis of total cholesterol value, which is nowadays widely used tool for predictive factor of cardiovascular disease, and also studied its association with BIA value via statistical analysis. Our result showed correlation with fat thickness of individual sites, and especially, fat thickness of left thigh showed high relation with total cholesterol value. This result shows that people who are in travel of long-period of time at sea are feasible of using BIA to evaluate changes of left thigh fat thickness as predictive factor for cardiovascular disease. Due to lack of advanced researches further studies should be done. And based on special circumstances in sea, more studies should be done to validity concerning this circumstances and accuracy of this evaluation tool.

• Journal of Korean Society for Quality Management
• /
• v.48 no.1
• /
• pp.171-186
• /
• 2020

Purpose: The purpose of this study is conducting of predictive models that considered multicollinearity of independent variables in order to carry out more efficient and reliable predictions about differential pressure in seawater reverse osmosis. Methods: The main variables of each RO system are extracted through factor analysis. Common variables are derived through comparison of RO system # 1 and RO system # 2. In order to carry out the prediction modeling about the differential pressure, which is the target variable, we constructed the prediction model reflecting the regression analysis, the artificial neural network, and the support vector machine in R package, and figured out the superiority of the model by comparing RMSE. Results: The number of factors extracted from factor analysis of RO system #1 and RO system #2 is same. And the value of variability(% Var) increased as step proceeds according to the analysis procedure. As a result of deriving the average RMSE of the models, the overall prediction of the SVM was superior to the other models. Conclusion: This study is meaningful in that it has been conducting a demonstration study of considering the multicollinearity of independent variables. Before establishing a predictive model for a target variable, it would be more accurate predictive model if the relevant variables are derived and reflected.

• Journal of Trauma and Injury
• /
• v.31 no.3
• /
• pp.166-173
• /
• 2018

Purpose: Many traumatic patients die from sepsis and multiple organ failure. Early recognition of post-traumatic sepsis in traumatic patients will help improve the prognosis. Recently, procalcitonin (PCT), macrophage migration inhibitory factor (MIF), and lactic acid have emerged as predictive factors. Our study aims to explore the significance of PCT, MIF and lactic acid as a predictor of posttraumatic-sepsis in trauma patients. Methods: This study was conducted on prospective observational study patients who visited an emergency medical center in a university hospital from March 2014 to February 2016. We measured the white blood cells, c-reactive protein (CRP), lactic acid, PCT, and MIF with serum taken from the patient's blood within 1 hour of the occurrence of the trauma. The definition of post-traumatic sepsis was defined as being part of systemic inflammation response syndrome criteria with infections within a week. Results: A total of 132 patients were analyzed, wherein 74 patients were included in the low injury severity score (ISS) group (ISS <15) and 58 patients were included in the high ISS group (ISS ${\geq}15$). The mean PCT, MIF, and lactic acid levels were higher in the high ISS group (p<0.05). Meanwhile, 38 patients were included in the early sepsis group and 94 patients were included in the non-sepsis group. The mean MIF levels were higher in the sepsis group than the non-sepsis group (p<0.05) and there were no significant differences in the initial CRP, lactic acid, and PCT levels in these two groups. Conclusions: MIF may be considered as a predictive factor for sepsis in trauma patients.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.8
• /
• pp.3443-3446
• /
• 2014

The aim of this study was to determine predictive factors for neutropenia after docetaxel-based systemic chemotherapy in patients with castration-resistant prostate cancer (CRPC). The study included 40 Korean CRPC patients who were treated with several cycles of docetaxel plus prednisolone from May 2005 to May 2012. Patients were evaluated for neutropenia risk factors and for the incidence of neutropenia. In this study, nine out of forty patients (22.5%) developed neutropenia during the first cycle of docetaxel-based systemic chemotherapy. Four experienced grade 2, three grade 3, and one grade 4 neutropenia. Multivariate analysis showed that pretreatment white blood cell (WBC) count (p=0.042), pretreatment neutrophil count (p=0.015), pretreatment serum creatinine level (p=0.027), and pretreatment serum albumin level (p=0.017) were significant predictive factors for neutropenia. In conclusion, pretreatment WBC counts, neutrophil counts, serum creatinine levels, and serum albumin levels proved to be significant independent risk factors for the development of neutropenia induced by docetaxel-based systemic chemotherapy in patients with CRPC.

• Journal of Sasang Constitutional Medicine
• /
• v.32 no.2
• /
• pp.10-21
• /
• 2020

Objectives Genome-wide association studies(GWAS) is a useful method to identify genetic associations for various phenotypes. The purpose of this study was to develop predictive models for Sasang constitution types using genetic factors. Methods The genotypes of the 1,999 subjects was performed using Axiom Precision Medicine Research Array (PMRA) by Life Technologies. All participants were prescribed Sasang Constitution-specific herbal remedies for the treatment, and showed improvement of original symptoms as confirmed by Korean medicine doctor. The genotypes were imputed by using the IMPUTE program. Association analysis was conducted using a logistic regression model to discover Single Nucleotide Polymorphism (SNP), adjusting for age, sex, and BMI. Results & Conclusions We developed models to predict Korean medicine constitution types using identified genectic factors and sex, age, BMI using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN). Each maximum Area Under the Curve (AUC) of Teaeum, Soeum, Soyang is 0.894, 0.868, 0.767, respectively. Each AUC of the models increased by 6~17% more than that of models except for genetic factors. By developing the predictive models, we confirmed usefulness of genetic factors related with types. It demonstrates a mechanism for more accurate prediction through genetic factors related with type.

• Journal of Power Electronics
• /
• v.15 no.1
• /
• pp.202-215
• /
• 2015

This paper investigates the stability and performance of model predictive controlled active-front-end (AFE) rectifiers for energy storage systems, which has been increasingly applied in power distribution sectors and in renewable energy sources to ensure an uninterruptable power supply. The model predictive control (MPC) algorithm utilizes the discrete behavior of power converters to determine appropriate switching states by defining a cost function. The stability of the MPC algorithm is analyzed with the discrete z-domain response and the nonlinear simulation model. The results confirms that the control method of the active-front-end (AFE) rectifier is stable, and that is operates with an infinite gain margin and a very fast dynamic response. Moreover, the performance of the MPC controlled AFE rectifier is verified with a 3.0 kW experimental system. This shows that the MPC controlled AFE rectifier operates with a unity power factor, an acceptable THD (4.0 %) level for the input current and a very low DC voltage ripple. Finally, an efficiency comparison is performed between the MPC and the VOC-based PWM controllers for AFE rectifiers. This comparison demonstrates the effectiveness of the MPC controller.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
• /
• v.35 no.5
• /
• pp.287-293
• /
• 2009

Cell survival is the result of a balance between programmed cell death and cellular proliferation. Cell membrane receptors and their associated signal transducing proteins control these processes. Of the numerous receptors and signaling proteins, epidermal growth factor receptor (EGFR) is one of the most important receptors involved in signaling pathways implicated in the proliferation and survival of cancer cells. EGFR is often highly expressed in human tumors including oral squamous cell carcinomas, and there is increasing evidence that high expression of EGFR is correlated with poor clinical outcome of common human cancers. Therefore, we examined the antiproliferative activity of gefitinib, epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKI), in head and neck cancer cell lines. SCC-9, KB cells were cultured and growth inhibition activity of gefitinib was measured with MTT assay. To study influence of gefitinib in cell cycle, we performed cell cycle analysis with flow cytometry. Western blot was done to elucidate the expression of EGFR in cell lines and phosphorylation of EGFR and downstream kinase protein, Erk and Akt. Significant growth inhibition was observed in SCC-9 cells in contrast with KB cells. Also, flow cytometric analysis showed G1 phase arrest only in SCC-9 cells. In Western blot analysis for investigation of EGFR expression and downstream molecule phosphorylation, gefitinib suppressed phosphorylation of EGFR and downstream protein kinase Erk, Akt in SCC-9. However, in EGFR positive KB cells, weak expression of active form of Erk and Akt and no inhibitory activity of phosphorylation in Erk and Akt was observed. The antiproliferative activity of gefitinib was not correlated with EGFR expression and some possibility of phosphorylation of Erk and Akt as a predictive factor of gefitinib response was emerged. Further investigations on more reliable predictive factor indicating gefitinib response are awaited to be useful gefitinib treatment in head and neck cancer patients.

• Journal of Biomedical Engineering Research
• /
• v.27 no.2
• /
• pp.47-52
• /
• 2006

The purpose of this study was to predict successful defibrillation in ventricular fibrillation using parameters extracted by wave analysis method and neuro-fuzzy. Total 15 dogs were tested for predicting successful defibrillation. Feature parameters were extracted for return of spontaneous circulation (ROSC) and non-ROSC by wave analysis method, and these parameters are an irregularity factor, spectral moments, mean power of level-crossing spectrum, and mean of alpha-significant value. Additionally, two parameters by analyzing method of frequency were extracted into a mean of power spectrum and a mean frequency. Then extracted parameters were analyzed in which parameters result to have high performance of discriminating ROSC and non-ROSC by a statistical method of t-test. The average of sensitivity and specificity were 62.5% and 75.0%, respectively. The average of positive predictive factor and negative predictive factor were 61.2% and 75.8%, respectively.

• Journal of Environmental Health Sciences
• /
• v.50 no.2
• /
• pp.113-124
• /
• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.24 no.2
• /
• pp.277-285
• /
• 2000

The purpose of this study was to quantify the relationship between the sense of touch and mechanical properties of man-made leather. The first was to develop the five conversion equations which convert mechanical properties of man-made leather into five factor scores, which express five factors of the sense of touch(surface property, stretchiness, thickness & weight, thermal property(warmth & coolness), and moisture property(sticky & clingy)). The second was to develop the conversion equation which converts five factor scores into score of the sense of touch. Five factor scores were predicted by the following mechanical properties; surface property factor by log2HB and (log2HB)2, stretchiness factor by logEM, thickness & weight factor by logT, log2HB, logW, thermal property factor by logT, logHB, logSMd, and moisture property factor by logMMD, RC, RC2, (logEM)2, RT2. Subsequently, these five factor scores were converted into score of the sense of touch. The predictive abilities of the developed equations were satisfied.