• Current Optics and Photonics
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• v.1 no.2
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• pp.132-137
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• 2017

To investigate the usefulness of optical coherence tomography (OCT) for imaging lymphedema, we directly compared it to other histological methods in a mouse model of lymphedema. We performed detailed imaging of the lymphedema lesion on a mouse tail. We imaged the mouse tail in vivo with OCT and created histopathological samples. We constructed a spectrometer-based OCT system using a fiber-optic Michelson interferometer. The light was directed to 50:50 couplers that split the light into reference and sample arms. Backscattered light from a reference mirror and the sample produced an interference fringe. An OCT image of the lymphedema model revealed an inflammatory reaction of the skin that was accompanied by edema, leading to an increase in the light attenuation in the dermal and subcutaneous layers. Similar to OCT image findings, histological biopsy showed an inflammatory response that involved edema, increased neutrophils in epidermis and subdermis, and lymphatic microvascular dilatation. Furthermore, the lymphedema model showed an increase in thickness of the dermis in both diagnostic studies. In the mouse tail model of lymphedema, OCT imaging showed very similar results to other histological examinations. OCT provides a quick and useful diagnostic imaging technique for lymphedema and is a valuable addition or complement to other noninvasive imaging tools.

• Journal of Environmental Health Sciences
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• v.27 no.3
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• pp.35-42
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• 2001

The characteristics of water quality variation were predicted by stochastic model in Chungju dam, north Chungcheong province of south Korea, Monthly time series data of water quality from 1989 to 2001;temperature, BOD, COD and SS, were obtained from environmental yearbook and internet homepage of ministry of environment. Development of model was carried out with Box-Jenkins method, which includes model identification, estimation and diagnostic checking. ACF and PACF were used to model identification. AIC and BIC were used to model estimation. Seosonal multiplicative ARIMA(1, 0, 1)(1, 1, 0)$_{12}$ model was appropriate to explain stochastic characteristics of temperature. BOD model was ARMa(2, 2, 1), COD was seasonal multiplicative ARIMA(2. 0. 1)(1. 0, 1)$_{12}$, and SS was ARIMA(1, 0, 2) respectively. The simulated water quality data showed a good fitness to the observed data, as a result of model verification.ion.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2
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• pp.78-86
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• 2005

This study considers the magnitude of the harmonic components radiated from the ultrasonic contrast agents (UCA) activated by a typical diagnostic ultrasound. The nonlinear dynamic response of UCA to a 2 MHz diagnostic ultrasound pulse was predicted using Gilmore Model. The elastic property of the shell membrane of the UCA was ignored in the numerical model. Simulation was carried out for the UCA varying from 1 - 9 $\mu$m in its initial radius and the driving diagnostic ultrasound whose mechanical index (MI) ranges from 0.125 to 8. The powers of the sub. ultra and second harmonics of the acoustic signal from the UCA activated were compared with that of the fundamental component. The results show that. if the UCA is bigger than its resonant size (2 $\mu$m in radius for the present case) the sub harmonic power was much bigger than the fundamental. In particular, the 2nd harmonic component currently used as an imaging parameter for the harmonic imaging, was predicted to be lower in power than both the sub and the ultra harmonic component. This study indicates that, for obtaining harmonic imaging with UCA, the sub or ultra harmonics could be taken as imaging parameters better than the 2nd harmonic component.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.641-648
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• 2019

Objective: The purpose of this study was to investigate whether three-dimensional (3D) magnetic resonance imaging could improve diagnostic accuracy for suspected posterior ligamentous complex (PLC) disruption. Materials and Methods: We used 20 freshly harvested goat spine samples with 60 segments and intact surrounding soft tissue. The animals were aged 1-1.5 years and consisted of 8 males and 12 females, which were sexually mature but had not reached adult weights. We created a paraspinal contusion model by percutaneously injecting 10 mL saline into each side of the interspinous ligament (ISL). All segments underwent T2-weighted sagittal and coronal short inversion time inversion recovery (STIR) scans as well as coronal and sagittal 3D proton density-weighted spectrally selective inversion recovery (3D-PDW-SPIR) scans acquired at 1.5T. Following scanning, some ISLs were cut and then the segments were rescanned using the same magnetic resonance (MR) techniques. Two radiologists independently assessed the MR images, and the reliability of ISL tear interpretation was assessed using the kappa coefficient. The chi-square test was used to compare the diagnostic accuracy of images obtained using the different MR techniques. Results: The interobserver reliability for detecting ISL disruption was high for all imaging techniques (0.776-0.949). The sensitivity, specificity, and diagnostic accuracy of the coronal 3D-PDW-SPIR technique for detecting ISL tears were 100, 96.9, and 97.9%, respectively, which were significantly higher than those of the sagittal STIR (p = 0.000), coronal STIR (p = 0.000), and sagittal 3D-PDW-SPIR (p = 0.001) techniques. Conclusion: Compared to other MR methods, coronal 3D-PDW-SPIR provides a more accurate diagnosis of ISL disruption. Adding coronal 3D-PDW-SPIR to a routine MR protocol may help to identify PLC disruptions in cases with nearby contusion.

• Journal of Korean Society for Atmospheric Environment
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• v.33 no.1
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• pp.45-53
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• 2017

A high resolution model is proposed for calculating the temperature field of a large city, based upon a Lagrangian particle model. Utilizing the analogy between the heat and mass transport phenomena in turbulent flows, a Lagrangian particle model, originally developed for air pollutant dispersion problems, is adapted for simulating heat transport. In the model conceptual heat particles are released into the atmosphere from the heat sources and move along with the turbulent winds in accordance with the Markov process. The potential temperature assumed to be conserved along with heat particles serves as a tag, so the temperature fields can be deduced from the distribution of particles. The wind fields are constructed from a diagnostic meteorology model incorporating a morphological model designed for building flows. Test run shows the robustness of the modeling system.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4467-4474
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• 2014

Background: We aimed to comprehensively review the evidence for using sputum DNA to detect non-small cell lung cancer (NSCLC). Materials and Methods: We searched PubMed, Science Direct, Web of Science, Chinese Biological Medicine (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang, Vip Databases and Google Scholar from 2003 to 2013. The meta-analysis was carried out using a random-effect model with sensitivity, specificity, diagnostic odd ratios (DOR), summary receiver operating characteristic curves (ROC curves), area under the curve (AUC), and 95% confidence intervals (CI) as effect measurements. Results: There were twenty-two studies meeting the inclusion criteria for the meta-analysis. Combined sensitivity and specificity were 0.62 (95%CI: 0.59-0.65) and 0.73 (95%CI: 0.70-0.75), respectively. The DOR was 10.3 (95%CI: 5.88-18.1) and the AUC was 0.78. Conclusions: The overall accuracy of the test was currently not strong enough for the detection of NSCLC for clinical application. Dscovery and evaluation of additional biomarkers with improved sensitivity and specificity from studies rated high quality deserve further attention.

• Smart Structures and Systems
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• v.22 no.5
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• pp.587-609
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• 2018

There is greater significance in identifying the incipient damages in structures at the time of their initiation as timely rectification of these minor incipient cracks can save huge maintenance cost. However, the change in the global dynamic characteristics of a structure due to these subtle damages are insignificant enough to detect using the majority of the current damage diagnostic techniques. Keeping this in view, we propose a hybrid damage diagnostic technique for detection of minor incipient damages in the structures. In the proposed automated hybrid algorithm, the raw dynamic signatures obtained from the structure are decomposed to uni-modal signals and the dynamic signature are reconstructed by identifying and combining only the uni-modal signals altered by the minor incipient damage. We use these reconstructed signals for damage diagnostics using ARMAX model. Numerical simulation studies are carried out to investigate and evaluate the proposed hybrid damage diagnostic algorithm and their capability in identifying minor/incipient damage with noisy measurements. Finally, experimental studies on a beam are also presented to compliment the numerical simulations in order to demonstrate the practical application of the proposed algorithm.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.179-185
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• 2015

The cost of advanced medical technologies is commonly considered to be a major factor in the overall escalation of expenditures on health. The use of computed tomography (CT) scanning has increased dramatically over the past decade. CT has been rapidly adopted, despite their high cost. The aim of this study is to analysis the increasing factor of the frequency of the CT, using the decision tree model. Finally, we propose the effective policy option of diagnostic imaging technology in Korea.

• Biomolecules & Therapeutics
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• v.21 no.1
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• pp.1-9
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• 2013

Polyamines, putrescine, spermidine and spermine, are ubiquitous in living cells and are essential for eukaryotic cell growth. These polycations interact with negatively charged molecules such as DNA, RNA, acidic proteins and phospholipids and modulate various cellular functions including macromolecular synthesis. Dysregulation of the polyamine pathway leads to pathological conditions including cancer, inflammation, stroke, renal failure and diabetes. Increase in polyamines and polyamine synthesis enzymes is often associated with tumor growth, and urinary and plasma contents of polyamines and their metabolites have been investigated as diagnostic markers for cancers. Of these, diacetylated derivatives of spermidine and spermine are elevated in the urine of cancer patients and present potential markers for early detection. Enhanced catabolism of cellular polyamines by polyamine oxidases (PAO), spermine oxidase (SMO) or acetylpolyamine oxidase (AcPAO), increases cellular oxidative stress and generates hydrogen peroxide and a reactive toxic metabolite, acrolein, which covalently incorporates into lysine residues of cellular proteins. Levels of protein-conjuagated acrolein (PC-Acro) and polyamine oxidizing enzymes were increased in the locus of brain infarction and in plasma in a mouse model of stroke and also in the plasma of stroke patients. When the combined measurements of PC-Acro, interleukin 6 (IL-6), and C-reactive protein (CRP) were evaluated, even silent brain infarction (SBI) was detected with high sensitivity and specificity. Considering that there are no reliable biochemical markers for early stage of stroke, PC-Acro and PAOs present promising markers. Thus the polyamine metabolites in plasma or urine provide useful tools in early diagnosis of cancer and stroke.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.