• Korean Journal of Radiology
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• v.21 no.6
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• pp.707-716
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• 2020

Objective: To evaluate pharmacokinetic variables from contrast-enhancing lesions (CELs) and non-enhancing T2 high signal intensity lesions (NE-T2HSILs) on dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging for predicting progression-free survival (PFS) in glioblastoma (GBM) patients. Materials and Methods: Sixty-four GBM patients who had undergone preoperative DCE MR imaging and received standard treatment were retrospectively included. We analyzed the pharmacokinetic variables of the volume transfer constant (Ktrans) and volume fraction of extravascular extracellular space within the CEL and NE-T2HSIL of the entire tumor. Univariate and multivariate Cox regression analyses were performed using preoperative clinical characteristics, pharmacokinetic variables of DCE MR imaging, and postoperative molecular biomarkers to predict PFS. Results: The increased mean Ktrans of the CEL, increased 95th percentile Ktrans of the CELs, and absence of methylated O⁶-methylguanine-DNA methyltransferase promoter were relevant adverse variables for PFS in the univariate analysis (p = 0.041, p = 0.032, and p = 0.083, respectively). The Kaplan-Meier survival curves demonstrated that PFS was significantly shorter in patients with a mean Ktrans of the CEL > 0.068 and 95th percentile Ktrans of the CEL > 0.223 (log-rank p = 0.038 and p = 0.041, respectively). However, only mean Ktrans of the CEL was significantly associated with PFS (p = 0.024; hazard ratio, 553.08; 95% confidence interval, 2.27-134756.74) in the multivariate Cox proportional hazard analysis. None of the pharmacokinetic variables from NE-T2HSILs were significantly related to PFS. Conclusion: Among the pharmacokinetic variables extracted from CELs and NE-T2HSILs on preoperative DCE MR imaging, the mean Ktrans of CELs exhibits potential as a useful imaging predictor of PFS in GBM patients.

• Journal of Dental Rehabilitation and Applied Science
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• v.24 no.1
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• pp.1-17
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• 2008

The purpose of this study were to report the reasons for removal of fixed prostheses and survival rate of abutment teeth. A total 192 fixed prostheses removed at Chosun university Dental Hospital and 308 abutment were investigated. The most frequent reason of removal was periodontal problem(30.7%), followed by caries(29.7%), then periapical problem(18.8%) and defective margins(14%). In metal ceramic crown, periodontal problem was the most frequent reason of removal. In complete cast crown, caries was the most frequent reason of removal. Periapical and periodontal problem was the most frequent reason people under forties and over fifties, respectively. Of the 308 abutment teeth, vital and non-vital teeth were 135(43.8%) and 173(56.2%), respectively. Of 135 vital abutment teeth, 39(28.9%) were extracted and of 173 non-vital abutment teeth, 85(49.1%) were extracted. Each risk factor for removal of fixed prostheses and extraction of abutment should be evaluated more clearly for prediction of prognosis of final prostheses and abutment teeth.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Korean Journal of Radiology
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• v.22 no.8
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• pp.1369-1378
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• 2021

Objective: Few attempts have been made to investigate the prognostic value of dynamic contrast-enhanced (DCE) MRI or dynamic susceptibility contrast (DSC) MRI of non-enhancing, T2-high-signal-intensity (T2-HSI) lesions of glioblastoma multiforme (GBM) in newly diagnosed patients. This study aimed to investigate the prognostic values of DCE MRI and DSC MRI parameters from non-enhancing, T2-HSI lesions of GBM. Materials and Methods: A total of 76 patients with GBM who underwent preoperative DCE MRI and DSC MRI and standard treatment were retrospectively included. Six months after surgery, the patients were categorized into early progression (n = 15) and non-early progression (n = 61) groups. We extracted and analyzed the permeability and perfusion parameters of both modalities for the non-enhancing, T2-HSI lesions of the tumors. The optimal percentiles of the respective parameters obtained from cumulative histograms were determined using receiver operating characteristic (ROC) curve and univariable Cox regression analyses. The results were compared using multivariable Cox proportional hazards regression analysis of progression-free survival. Results: The 95th percentile value (PV) of Ktrans, mean Ktrans, and median Ve were significant predictors of early progression as identified by the ROC curve analysis (area under the ROC curve [AUC] = 0.704, p = 0.005; AUC = 0.684, p = 0.021; and AUC = 0.670, p = 0.0325, respectively). Univariable Cox regression analysis of the above three parametric values showed that the 95th PV of Ktrans and the mean Ktrans were significant predictors of early progression (hazard ratio [HR] = 1.06, p = 0.009; HR = 1.25, p = 0.017, respectively). Multivariable Cox regression analysis, which also incorporated clinical parameters, revealed that the 95th PV of Ktrans was the sole significant independent predictor of early progression (HR = 1.062, p < 0.009). Conclusion: The 95th PV of Ktrans from the non-enhancing, T2-HSI lesions of GBM is a potential prognostic marker for disease progression.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1863-1870
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• 2013

The purpose of this study is when the cases will be found, used as a basic data for clinical severity prediction, and research on suicide prevention. By classifying the group of survival and death about the patients who visit the Emergency Medical Center by attempt suicide by drug addiction, identifying the condition when visiting and results of the treatment after visiting. From June 2009 to May 2011, last two years data that among the drug abusers who visited the Emergency Medical Center in C-University Hospital in Gwang-Ju, only suicidal patients, except with unintentional accidents were collected. The findings, among the drug addiction patients who high age, lower level of education and living alone were the mortality rate was higher. And if who drunk the agricultural chemicals, the convalescence was not good. If the causes of suicide were economic problems and depression, the mortality rate was higher. And when visit hospital, if the consciousness was stupor and semi-coma/coma, the convalescence was not good. As grasp the risk for suicide patients of drug addiction, help on the Prediction of clinical severity, also stamp the appropriate drug education with psychological support is more important on them.

• ALGAE
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• v.37 no.1
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• pp.49-62
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• 2022

Water temperature affects plankton survival and growth. The dinoflagellate Shimiella gracilenta survives using the plastids of ingested prey, indicating kleptoplastidy. However, studies on the effects of water temperature on kleptoplastidic dinoflagellates are lacking. We explored the growth and ingestion rates of S. gracilenta as a function of water temperature. Furthermore, using data on its spatiotemporal distribution in Korean coastal waters during 2015-2018, we predicted its distribution under elevated temperature conditions of +2, +4, and +6℃. Growth rates of S. gracilenta with and without Teleaulax amphioxeia prey as well as ingestion rates were significantly affected by water temperature. Growth rates of S. gracilenta with and without prey were positive or zero at 5-25℃ but were negative at ≥30℃. The maximum growth rate of S. gracilenta with T. amphioxeia was 0.85 d^-1, achieved at 25℃, and 0.21 d^-1 at 20℃ without prey. The ingestion rate of S. gracilenta on T. amphioxeia at 25℃ (0.05 ng C predator^-1 d^-1) was greater than that at 20℃ (0.04 ng C predator^-1 d^-1). Thus, feeding may shift the optimal temperature for the maximum growth rate of S. gracilenta from 20 to 25℃. In spring and winter, the distributions of S. gracilenta under elevated temperature conditions were predicted not to differ from those during 2015-2018. However, S. gracilenta was predicted not to survive at some additional stations under elevated temperature conditions of +2, +4, and +6℃ in summer or under elevated temperature conditions of +6℃ in autumn. Therefore, global warming may affect the distribution of S. gracilenta.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.4
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• pp.957-970
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• 2016

This study diagnosed the status of marine environmental impact assessment(MEIA) for project near the habitat of marine protected seagrass species such as Zostera caespitosa, Zostera asiatica, Phyllospadix iwatensis. For the preparation of a marine environmental impact statement, different monitoring parameters are used without any specific guideline for the assessment of current status. And also, both tools and techniques for MEIA are needed to improve for implementing. The monitoring plans and parameters are not considered well with the accuracy of the environmental predictions and effectiveness of any applicable mitigation measures. This study suggested the reasonable standard of the MEIA for the conservation of the marine protected seagrass species which have the habitat located near affected area. The inshore seagrasses need to be monitored including shoot count based on the "No Net Loss of Seagrass" as part of the monitoring parameters to assess the status of marine environment of environmental impact statement. In a process of effect prediction, we suggested a concentration of 10 mg/L suspended solids which added by the new developmental project near seagrasses habitat, referring to study of overseas case. But a further study for an appropriate standard is necessary effectively. In a mitigating process, priority needs to be considered in order of avoidance, minimization, reduction, compensation. In a post-monitoring process, it is necessary to monitor the seagrass species abundance to identify the variation of b/a (before and after) project. And in a case of implementing transplantation, survival rate need to be included to determine a success of project.

• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.227-233
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• 2017

Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.

• Journal of Microbiology and Biotechnology
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• v.31 no.10
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• pp.1343-1349
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• 2021

Cockroaches live in places where various pathogens exist and thus are more likely to use antimicrobial compounds to defend against pathogen intrusions. We previously performed an in silico analysis of the Periplaneta americana transcriptome and detected periplanetasin-5 using an in silico antimicrobial peptide prediction method. In this study, we investigated whether periplanetasin-5 has anticancer activity against the human leukemia cell line K562. Cell growth and survival of K562 cells treated with periplanetasin-5 were decreased in a dose-dependent manner. By using flow cytometric analysis, acridine orange/ethidium bromide (AO/EB) staining and DNA fragmentation, we found that periplanetasin-5 induced apoptotic and necrotic cell death in leukemia cells. In addition, these events were associated with increased levels of the pro-apoptotic proteins Fas and cytochrome c and reduced levels of the anti-apoptotic protein Bcl-2. Periplanetasin-5 induces the cleavage of pro-caspase-9, pro-caspase-8, pro-caspase-3, and poly (ADP-ribose) polymerase (PARP). The above data suggest that periplanetasin-5 induces apoptosis via both the intrinsic and extrinsic pathways. Moreover, caspase-related apoptosis was further confirmed by using the caspase inhibitor carbobenzoxy-valyl-alanyl-aspartyl-[O-methyl]-fluoromethylketone (Z-VAD-FMK), which reversed the periplanetasin-5-induced reduction in cell viability. In conclusion, periplanetasin-5 caused apoptosis in leukemia cells, suggesting its potential utility as an anticancer therapeutic agent.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.8
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• pp.4113-4117
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• 2012

Breast cancer is a prevalent heterogeneous malignant disease. Gene expression profiling by DNA microarray can classify breast tumors into five different molecular subtypes: luminal A, luminal B, HER-2, basal and normal-like which have differing prognosis. Recently it has been shown that immunohistochemistry (IHC) markers including estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her2), can divide tumors to main subtypes: luminal A (ER+; PR+/-; HER-2-), luminal B (ER+;PR+/-; HER-2+), basal-like (ER-;PR-;HER2-) and Her2+ (ER-; PR-; HER-2+). Some subtypes such as basal-like subtype have been characterized by poor prognosis and reduced overall survival. Due to the importance of the ER signaling pathway in mammary cell proliferation; it appears that epigenetic changes in the $ER{\alpha}$ gene as a central component of this pathway, may contribute to prognostic prediction. Thus this study aimed to clarify the correlation of different IHC-based subtypes of breast tumors with $ER{\alpha}$ methylation in Iranian breast cancer patients. For this purpose one hundred fresh breast tumors obtained by surgical resection underwent DNA extraction for assessment of their ER methylation status by methylation specific PCR (MSP). These tumors were classified into main subtypes according to IHC markers and data were collected on pathological features of the patients. $ER{\alpha}$ methylation was found in 25 of 28 (89.3%) basal tumors, 21 of 24 (87.5%) Her2+ tumors, 18 of 34 (52.9%) luminal A tumors and 7 of 14 (50%) luminal B tumors. A strong correlation was found between $ER{\alpha}$ methylation and poor prognosis tumor subtypes (basal and Her2+) in patients (P<0.001). Our findings show that $ER{\alpha}$ methylation is correlated with poor prognosis subtypes of breast tumors in Iranian patients and may play an important role in pathogenesis of the more aggressive breast tumors.