• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.1
• /
• pp.46-51
• /
• 2004

his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.8
• /
• pp.4093-4095
• /
• 2012

Objective: To determine whether pituitary adenomas can be diagnosed by identifying protein biomarkers in the serum. Methods: We compared serum proteins from 65 pituitary adenoma patients and 90 healthy donors using proteomic fingerprint technology combining magnetic beads with matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). Results: A total of 42 M/Z peaks were identified as related to pituitary adenoma (P<0.01). A diagnostic model established based on three biomarkers (3382.0, 4601.9, 9191.2) showed that the sensitivity of diagnosing pituitary adenoma was 90.0% and the specificity was 88.3%. The model was further tested by blind analysis showing that the sensitivity was 88.0% and the specificity was 83.3%. Conclusions: These results suggest that proteomic fingerprint technology can be used to identify pituitary adenoma biomarkers and the model based on three biomarkers (3382.0, 4601.9, 9191.2) provides a powerful and reliable method for diagnosing pituitary adenoma.

• Journal of Microbiology and Biotechnology
• /
• v.31 no.3
• /
• pp.349-357
• /
• 2021

Monoclonal antibodies are widely used as diagnostic reagents and for therapeutic purposes, and their demand is increasing extensively. To produce these proteins in sufficient quantities for commercial use, it is necessary to raise the output by scaling up the production processes. This review describes recent trends in high-density cell culture systems established for monoclonal antibody production that are excellent methods to scale up from the lab-scale cell culture. Among the reactors, hollow fiber bioreactors contribute to a major part of high-density cell culture as they can provide a tremendous amount of surface area in a small volume for cell growth. As an alternative to hollow fiber reactors, a novel disposable bioreactor has been developed, which consists of a polymer-based supermacroporous material, cryogel, as a matrix for cell growth. Packed bed systems and disposable wave bioreactors have also been introduced for high cell density culture. These developments in high-density cell culture systems have led to the monoclonal antibody production in an economically favourable manner and made monoclonal antibodies one of the dominant therapeutic and diagnostic proteins in biopharmaceutical industry.

• Journal of Radiopharmaceuticals and Molecular Probes
• /
• v.9 no.1
• /
• pp.49-55
• /
• 2023

Tumors are encircled by various non-cancerous cell types in the extracellular matrix, including fibroblasts, endothelial cells, immune cells, and cytokines. Fibroblasts are the most critical cells in the tumor stroma and play an important role in tumor development, which has been highlighted in some epithelial cancers. Many studies have shown a tight connection between cancerous cells and fibroblasts in the last decade. Regulatory factors secreted into the tumor environment by special fibroblast cells, cancer-associated fibroblasts (CAFs), play an important role in tumor and vessel development, metastasis, and therapy resistance. This review addresses the development of FAP inhibitors, emphasizing the first, second, and latest generations. First-generation inhibitors exhibit low selectivity and chemical stability, encouraging researchers to develop new scaffolds based on preclinical and clinical data. Second-generation enzymes such as UAMC-1110 demonstrated enhanced FAP binding and better selectivity. Targeted treatment and diagnostic imaging have become possible by further developing radionuclide-labeled fibroblast activation protein inhibitors (FAPIs). Although all three FAPIs (01, 02, and 04) showed excellent preclinical and clinical findings. The final optimization of these FAPI scaffolds resulted in FAPI-46 with the highest tumor-to-background ratio and better binding affinity.

• Journal of the Korean Society of Radiology
• /
• v.3 no.1
• /
• pp.17-21
• /
• 2009

The Recently, large area matrix-addressed image detectors are investigated for X-ray imaging with medical diagnostic and other applications. In this paper, a new flat panel gas detector for diagnostic X-ray imaging is proposed, and its characteristics are investigated. The research of flat panel gas detector is not exist at all. Because of difficulty to inject gas against to atmospheric pressure. So almost gas detector made by chamber shape. We made flat panel sample by display technique. (ex: PDP, Fed, etc.) The experimental measurements, the transparent electrodes, dielectric layer, and the MgO protection layer were formed in front glass. And, the X-ray phosphor layer and address electrodes are formed in the rare glass. The dark current, the x-ray sensitivity and linearity as a function of electric field were measured to investigate the electrical properties. From the results, the stabilized dark current density and the significant x-ray sensitivity were obtained. And the good linearity as a function of exposure dose was showed in wide diagnostic energy range. These results means that the passive matrix-addressed flat panel gas detector can be used for digital x-ray imaging.

• Journal of Biomedical Engineering Research
• /
• v.28 no.1
• /
• pp.162-168
• /
• 2007

For the past decade, the full-field digital mammography has been widely used for early diagnosis of breast cancer, and computer aided diagnosis has been developed to assist physicians as a second opinion. In this study, we try to predict the breast cancer using both mediolateral oblique(MLO) view and craniocaudal(CC) view together. A skilled radiologist selected 35 pairs of ROIs from both MLO view and CC view of digital mammogram. We extracted textural features using Spatial Grey Level Dependence matrix from each mammogram and evaluated the generalization performance of the classifier using Support Vector Machine. We compared the multi-view based classifier to single-view based classifier that is built from each mammogram view. The results represent that the multi-view based computer aided diagnosis in digital mammogram could improve the diagnostic performance and have good possibility for clinical use to assist physicians as a second opinion.

• Journal of The Korean Association For Science Education
• /
• v.35 no.2
• /
• pp.267-275
• /
• 2015

This research purports to find out the characteristics of Korean students cognitive attributes and compare it with that of high-achieving countries who took TIMSS 2011 based on the Cognitive Diagnostic Theory. Based on TIMSS 2011 Science framework, nine cognitive attributes were extracted and the researcher analyzed that 216 of the TIMSS 2011 science items require these attributes. This analysis was conducted to come up with a Q-matrix. After producing the Q-matrix, multi-level IRT was used to figure out each countries' characteristics for each of the cognitive attribute. According to the study results, four attributes, such as 'Use Models,' 'Interpret Information,' 'Draw Conclusions,' and 'Evaluate and justify' were easier attributes for Korean middle school students. However, the other five attributes such as 'Recall/Recognize', 'Explain', 'Classify', 'Integrate', 'Hypothesize and Design' were considered as harder attributes compared to other countries. Korean students also considered 'Interpret Information' as the easiest attributes, and 'Explain' as the hardest attributes of all. For Korean students, those attributes considered to be easy were the easiest and hard attributes as the hardest compared to other countries, showing very extreme cases. Therefore, to give students more meaningful learning experience, it is better to use all the attributes altogether rather than use specific attributes while constructing Science curriculum or textbooks.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1996.04a
• /
• pp.505-510
• /
• 1996

Many diseases cause other diseases with strength of influences and time intervals. Prognostic and therapeutic assessments are the important part of clinical medicine as well as diagnostic assessments. In cases where a patient already has manufestations of multiple disorders (complications), progress forecasting and therapy decision by physicians without support tools are very dificult: physicians often say that "Once complications set in, the patient may die". Treating complications are difficult tasks for physicians, because they have to consider all of the complexities, possibilities and interactions between the diseases. The prediction of multiple disorders has many bundles that arise from such time-dependent interrelationships between diseases and nonlinear progress. This paper proposes a model based on time-dependent influences, which appropriately describes the progress of mulitple disorders, and gives some modificaitons for applying this model to medical domains: time-dependent influence matrix manifestation vector, therapy efficacy matrix, S-shaped curve approximation, definitions of which are provided. This research proposes an algorithm for forecasting the state of each disease on the time horizon and for evaluation of therapy alternatives with not toy example, but real patient history of multiple disorders.disorders.

• Journal of the Korea Safety Management & Science
• /
• v.15 no.2
• /
• pp.243-253
• /
• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.307-310
• /
• 2021

In the recent years, studies using Computer-Aided Diagnostics(CAD) have been actively conducted, such as signal and image processing technology using breast ultrasound images, automatic image optimization technology, and automatic detection and classification of breast masses. As computer diagnostic technology is developed, it is expected that early detection of cancer will proceed accurately and quickly, reducing health insurance and test ice for patients, and eliminating anxiety about biopsy. In this paper, a quantitative analysis of tumors was conducted in ultrasound images using a gray level co-occurrence matrix(GLCM) to experiment with the possibility of use for computer assistance diagnosis.