• Asian Pacific Journal of Cancer Prevention
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• v.13 no.1
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• pp.199-203
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• 2012

Purpose: To evaluate the prognostic value of serum CYFRA21-1, CEA and hemoglobin levels regarding long-term survival of patients with esophageal squamous cell carcinoma (ESCC) treated with concurrent chemoradiotherapy (CRT). Methods: Age, gender, Karnofsky Performance Status (KPS), tumor location, tumor length, T stage, N stage and serum hemoglobin, and CYFRA21-1 and CEA levels before concurrent CRT were retrospectively investigated and related to outcome in 113 patients receiving 5-fluorouracil and cisplatin combined with radiotherapy for ESCC. The Kaplan-Meier method was used to analyze prognosis, the log-rank to compare groups, the Cox proportional hazards model for multivariate analysis, and ROC curve analysis for assessment of predictive performance of biologic markers. Results: The median survival time was 20.1 months and the 1-, 2-, 3-, 5- year overall survival rates were 66.4%, 43.4%, 31.9% and 15.0%, respectively. Univariate analysis showed that factors associated with prognosis were KPS, tumor length, T-stage, N-stage, hemoglobin, CYFRA21-1 and CEA level. Multivariate analysis showed T-stage, N-stage, hemoglobin, CYFRA21-1 and CEA level were independent predictors of prognosis. By ROC curve, CYFRA21-1 and hemoglobin showed better predictive performance for OS than CEA (AUC= 0.791, 0.704, 0.545; P=0.000, 0.000, 0.409). Conclusions: Of all clinicopathological and molecular factors, T stage, N stage, hemoglobin, CYFRA21-1 and CEA level were independent predictors of prognosis for patients with ESCC treated with concurrent CRT. Among biomarkers, CYFRA21-1 and hemoglobin may have a better predictive potential than CEA for long-term outcomes.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.6
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• pp.19-30
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• 2016

The projection of climate-related range shift is critical information for conservation planning of Korean fir (Abies koreana E. H. Wilson). We first modeled the distribution of Korean fir under current climate condition using five single-model species distribution models (SDMs) and the pre-evaluation weighted ensemble method and then predicted the distributions under future climate conditions projected with HadGEM2-AO under four $CO_2$ emission scenarios, the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. We also investigated the predictive uncertainty stemming from five individual algorithms and four $CO_2$ emission scenarios for better interpretation of SDM projections. Five individual algorithms were Generalized linear model (GLM), Generalized additive model (GAM), Multivariate adaptive regression splines (MARS), Generalized boosted model (GBM) and Random forest (RF). The results showed high variations of model performances among individual SDMs and the wide range of diverging predictions of future distributions of Korean fir in response to RCPs. The ensemble model presented the highest predictive accuracy (TSS = 0.97, AUC = 0.99) and predicted that the climate habitat suitability of Korean fir would increase under climate changes. Accordingly, the fir distribution could expand under future climate conditions. Increasing precipitation may account for increases in the distribution of Korean fir. Increasing precipitation compensates the negative effects of increasing temperature. However, the future distribution of Korean fir is also affected by other ecological processes, such as interactions with co-existing species, adaptation and dispersal limitation, and other environmental factors, such as extreme weather events and land-use changes. Therefore, we need further ecological research and to develop mechanistic and process-based distribution models for improving the predictive accuracy.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2705-2709
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• 2012

Objective: To evaluate the predictive value of glutathione S-transferase (GST) gene polymorphisms for the prognosis of osteosarcoma patients receiving chemotherapy. Methods: A total of 159 patients were included in our study between January 2005 and December 2007., with follow-up until January 2012. Genotyping was based upon the duplex polymerase-chain-reaction with the PCR-CTPP method. Results: At the time of diagnosis, 15.4% of the patients presented with metastasis, while 22.3% developed metastasis during follow-up. At the time of final analysis on January 2012, the median follow-up was 45.5 months. Patients with null GSTM1 and GSTT1 had a higher event free survival rate than non-null genotype, but no significant association was found between the two genotypes and prognosis of osteosarcoma. Individuals with GSTP1 Val/Val genotype tended to live shorter than with the IIe/IIe genotype, and we found a significantly higher risk of death from osteosarcoma (adjusted HR=2.35, 95% CI=1.13-4.85). Conclusion: The GSTP1 gene polymorphism may have an important role in the prognosis of osteosarcoma patients with chemotherapy. Further analyses with larger samples and more genes encoding metabolizing and DNA repair enzymes are warranted.

• Journal of Periodontal and Implant Science
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• v.48 no.2
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• pp.114-123
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• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• Korean Chemical Engineering Research
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• v.51 no.1
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• pp.87-92
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• 2013

Microalgae have been suggested as a promising feedstock for producing biofuel because of their potential of lipid production. In this study, a first principles ODE model for microalgae growth and neutral lipid synthesis proposed by Surisetty et al. (2010) is investigated for the purpose of maximizing the rate of microalgae growth and the amount of neutral lipid. The model has 6 states and 12 parameters and follows the assumption of Droop model which explains the growth as a two-step phenomenon; the uptake of nutrients is first occurred in the cell, and then use of intra-cellular nutrient to support cells growth. In this study, optimal input design using D-optimality criterion is performed to compute the system input profile and sensitivity analysis is also performed to determine which parameters have a negligible effect on the model predictions. Furthermore, model predictive control based on successive linearization is implemented to maximize the amount of neutral lipid contents.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5371-5375
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• 2014

Background: An accurate assessment of potential lymph node metastasis is important for the appropriate treatment of early gastric cancers. Therefore, this study analyzed predictive factors associated with lymph node metastasis and identified differences between mucosal and submucosal gastric cancers. Materials and Methods: A total of 518 early gastric cancer patients who underwent radical gastrectomy were reviewed in this study. Clinicopathological features were analyzed to identify predictive factors for lymph node metastasis. Results: The rate of lymph node metastasis in early gastric cancer was 15.3% overall, 3.3% for mucosal cancer, and 23.5% for submucosal cancer. Using univariate analysis, risk factors for lymph node metastasis were identified as tumor location, tumor size, depth of tumor invasion, histological type and lymphovascular invasion. Multivariate analysis revealed that tumor size >2 cm, submucosal invasion, undifferentiated tumors and lymphovascular invasion were independent risk factors for lymph node metastasis. When the carcinomas were confined to the mucosal layer, tumor size showed a significant correlation with lymph node metastasis. On the other hand, histological type and lymphovascular invasion were associated with lymph node metastasis in submucosal carcinomas. Conclusions: Tumor size >2 cm, submucosal tumor, undifferentiated tumor and lymphovascular invasion are predictive factors for lymph node metastasis in early gastric cancer. Risk factors are quite different depending on depth of tumor invasion. Endoscopic treatment might be possible in highly selective cases.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.1961-1966
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• 2016

Background: To investigate the prevalence of premature ejaculation (PE) and erectile dysfunction (ED) in a sample of patients with prostate cancer and to determine the utility of the previously suggested cutoffs of the Premature Ejaculation Diagnostic Tool (PEDT) for the diagnosis of PE and that of International Index of Erectile Function (IIEF-5) for ED. Materials and Methods: A total of 1,202 men with prostate cancer were invited from urology clinics at the universities of Iran, Tehran, Qazvin, Ahvaz, Guilan and Tabriz. Clinical characteristics were collected through medical records. PE and ED diagnoses were made by trained urologists. In addition to the clinical diagnoses, PE and ED were measured through self-report using the PEDT and the IIEF-5. Questionnaire cutoff scores were determined using receiver operating characteristic (ROC) curves and confirmed by predictive ability using logistic regression. Results: The prevalence of PE was 63.7% and that of ED was 66.2%. Prevalences of PE decreased and that of ED increased with advanced TNM stages. According to ROC, the suggested cutoff for the PEDT to diagnose a PE was ${\geq}11$ (sensitivity=0.988, 1-specificity=0.084, and predictive ability=0.914) and ${\leq}17$ for the IIEF-5 (sensitivity=0.966, 1-specificity=0.031, and predictive ability=0.967). Conclusions: Prevalence of sexual problems was high in prostate cancer patients in Iran, therefore oncologists should take into account these potential problems when deciding on treatment modalities.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.22 no.2
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• pp.201-210
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• 1996

Predictive tests for the identification of contact sensitizing chemicals have been developed. We measured the sensitization potential with three predictive tests, the in vitro and the in vivo Local Lymph Node Assay(LLNA), ELISA to detect interferon-gamma(IFN-${\gamma}$) from supernatant and flow cytometry to detect change of cell surface proteins, using draining lymph nodes of mice. BALB/c mice were exposed to various chemicals or vehicles on the ears daily for 3 consecutive days in all experiments. With some exceptions of propyl paraben, neomycin sulfate, the in vivo LLNA was able to detect the sensitizing capacity of test chemicals and was more sensitive than the in vitro LLNA for chemicals used in the present study. In another experiment, contact sensitivity was assessed by the ELISA to detect IFN-Υ from the supernatants of the cultured LNCs after sensitization with chemicals. There was a good correlation between the LLNA and the IFN-Υ production for test chemicals. We also examined the change of cell surface proteins on LNCs after sensitization by flow cytometry for some cell adhesion molecules(ICAM-1, E-cadherine, B7 molecule), T cell markers(CD3, CD4, CD8, T$\alpha$$\beta$,T${\gamma}$$\delta$) and B cell markers(LR1, CD45R, I-Ad). The number of ICAM-1 positive cells and B cells in LNCs were increased after sensitization with DNCB, TNCB, isoeugenol and 25%, 50% cinnamic aldehyde compared with that of vehicle as a control. In conclusion, the in vivo LLNA could provide more sensitive screening test for moderate to strong sensitizers and some weak sensitizers including cosmetic raw materials than the in vitro LLNA. The production of IFN-Υ by allergen-activated LNCs might be a values indicators without radioisotopes for the identification of contact allergens. Detection of allergens by testing the increase of ICAM-1 positive cells and B cells in LNCs by flow cytometry might be used as a test method to detect allergens.

• Journal of Korean Society on Water Environment
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• v.39 no.6
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• pp.465-474
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• 2023

The chemical composition and molecular weight characteristics of dissolved organic matter (DOM) exert a profound influence on the efficiency of organic matter removal in water treatment systems, acting as efficiency predictive indicators. This research evaluated the primary chemical and molecular weight properties of DOM derived from diverse sources, including rivers, lakes, and biomasses, and assessed their relationship with the efficiency of coagulation/flocculation treatments. Dissolved organic carbon (DOC) removal efficiency through coagulation/flocculation exhibited significant correlations with DOM's hydrophobic distribution, the ratio of humic-like to protein-like fluorescence, and the molecular weight associated with humic substances (HS). These findings suggest that the DOC removal rate in coagulation/flocculation processes is enhanced by a higher presence of HS in DOM, an increased influence of externally sourced DOM, and more presence of high molecular weight compounds. The results of this study further posit that the efficacy of water treatment processes can be more accurately predicted when considering multiple DOM characteristics rather than relying on a singular trait. Based on major results from this study, a predictive model for DOC removal efficiency by coagulation/flocculation was formulated as: 24.3 - 7.83 × (fluorescence index) + 0.089 × (hydrophilic distribution) + 0.102 × (HS molecular weight). This proposed model, coupled with supplementary monitoring of influent organic matter, has the potential to enhance the design and predictive accuracy for coagulation/flocculation treatments targeting DOC removal in future applications.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.221-228
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• 2024

Predicting insurance claims is a key task for insurance companies to manage risks and maintain financial stability. Accurate insurance claim predictions enable insurers to set appropriate premiums, reduce unexpected losses, and improve the quality of customer service. This study aims to enhance the performance of insurance claim prediction models by applying ensemble learning techniques. The predictive performance of models such as Random Forest, Gradient Boosting Machine (GBM), XGBoost, Stacking, and the proposed Dynamic Weighted Ensemble (DWE) model were compared and analyzed. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). Experimental results showed that the DWE model outperformed others in terms of evaluation metrics, achieving optimal predictive performance by combining the prediction results of Random Forest, XGBoost, LR, and LightGBM. This study demonstrates that ensemble learning techniques are effective in improving the accuracy of insurance claim predictions and suggests the potential utilization of AI-based predictive models in the insurance industry.