• Molecules and Cells
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• 제41권3호
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• pp.179-187
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• 2018

Proteomic analysis of extracellular vesicles (EVs) from biological fluid is a powerful approach to discover potential biomarkers for human diseases including cancers, as EV secreted to biological fluids are originated from the affected tissue. In order to investigate significant molecules related to the pathogenesis of bladder cancer, EVs were isolated from patient urine which was analyzed by mass spectrometry based proteomics. Comparison of the EV proteome to the whole urine proteome demonstrated an increased number of protein identification in EV. Comparative MS analyses of urinary EV from control subjects and bladder cancer patients identified a total of 1,222 proteins. Statistical analyses provided 56 proteins significantly increased in bladder cancer urine, including proteins for which expression levels varied by cancer stage (P-value < 0.05). While urine represents a valuable, non-invasive specimen for biomarker discovery in urologic cancers, there is a high degree of intra- and inter-individual variability in urine samples. The enrichment of urinary EV demonstrated its capability and applicability of providing a focused identification of biologically relevant proteins in urological diseases.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1247-1247
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• 2001

Constituents of animal biofluids such as milk, blood and urine contain information specifically related to metabolic and health status of the ruminant animals. Some changes in composition of biofluids can be attributed to disease response of the animals. Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and reducing milk quality. The purpose of this study was to investigate potential of NIRS combined with multivariate analysis for cow's mastitis diagnosis based on NIR spectra of milk, blood and urine. A total of 112 bulk milk, urine and blood samples from 4 Holstein cows were analyzed. The milk samples were collected from morning milking. The urine samples were collected before morning milking and stored at -35$^{\circ}C$ until spectral analysis. The blood samples were collected before morning milking using a catheter inserted into the carotid vein. Heparin was added to blood samples to prevent coagulation. All milk samples were analyzed for somatic cell count (SCC). The SCC content in milk was used as indicator of mastitis and as quantitative parameter for respective urine and blood samples collected at same time. NIR spectra of blood and milk samples were obtained by InfraAlyzer 500 spectrophotometer, using a transflectance mode. NIR spectra of urine samples were obtained by NIR System 6500 spectrophotometer, using 1 mm sample thickness. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. SIMCA was implemented to create models of the respective classes based on NIR spectra of milk, blood or urine. For the calibration set of samples, SIMCA models (model for samples from healthy cows and model for samples from mastitic cows), correctly classified from 97.33 to 98.67% of milk samples, from 97.33 to 98.61% of urine samples and from 96.00 to 94.67% of blood samples. From samples in the test set, the percent of correctly classified samples varied from 70.27 to 89.19, depending mainly on spectral data pretreatment. The best results for all data sets were obtained when first derivative spectral data pretreatment was used. The incorrect classified samples were 5 from milk samples,5 and 4 from urine and blood samples, respectively. The analysis of changes in the loading of first PC factor for group of samples from healthy cows and group of samples from mastitic cows showed, that separation between classes was indirect and based on influence of mastitis on the milk, blood and urine components. Results from the present investigation showed that the changes that occur when a cow gets mastitis influence her milk, urine and blood spectra in a specific way. SIMCA allowed extraction of available spectral information from the milk, urine and blood spectra connected with mastitis. The obtained results could be used for development of a new method for mastitis detection.

• 대한한방부인과학회지
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• 제24권1호
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• pp.74-86
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• 2011

Objectives: The purpose of this study was to examine correlation between Heart Rate Variability and urine analysis of women with urinary disturbance. Methods: We studied 34 patients visiting ${\bigcirc}{\bigcirc}$hospital from January 2010 to September 2010. The subjects were categorized in two groups, symptom group (n=11) and no symptom group(n=23). We studied the difference of Heart Rate Variability and urine analysis between two groups by Student T-test and correlation between Heart Rate Variability and urine analysis by Pearson's correlation coefficient test using SPSS for windows (version 17.0). Results: Occult blood of symptom group was significantly higher than no symptom group. SDNN, TP and HF of symptom group was significantly lower than no symptom group. Occult blood and SDNN, occult blood and RMS-SD, occult blood and HF significantly showed negative correlation coefficient. pH and TP, pH and LF significantly showed positive correlation coefficient. Conclusion: The results suggest that urinary disturbance can be related to decreased activity of autonomic nervous system. Also urine from women with urinary disturbance tend to show higher occult blood.

• 한국정보전자통신기술학회논문지
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• 제2권2호
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• pp.9-14
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• 2009

본 연구에서는 유헬스용 요분석기를 개발하기 위한 선행 연구로서 요분석 스트립의 색변화를 측정할 수 있는 정색반응 전자회로를 개발하였다. 정색반응 시스템은 컴퓨터, 정색반응 전자회로, 트레이장치, 센서조합체 및 소프트웨어로 구성하였다. 요분석 스트립의 색 변화를 측정하기 위하여 칼라센서가 사용되기 때문에, 표준 색상지를 이용하여 칼라센서의 측정값과 RGB값 사이의 선형방정식을 수립하였다. 빨간색(R)의 회귀방정식은 $Red=0.2414{\times}x$(센서 값) - 3.0042($R^2=0.9801$)로 나타났고, 녹색(G)의 회귀방정식은 $Green=0.2857{\times}x$(센서 값) - 6.4251($R^2=0.9868$)로 나타났고, 파란색(B)의 회귀방정식은 $Blue=0.2114{\times}x$(센서 값) - 6.2743($R^2=0.9837$)으로 나타났으므로 표준색상지와 칼라센서는 높은 상관관계가 있는 것을 알 수가 있었다. 정색반응 시스템을 검증하기 위하여 요 성분 중 적혈구, 빌리루빈, 우로빌리노겐, 케톤, 단백질의 5가지 성분에 대하여 각기 다른 농도로 표준시약을 제조하여 정색반응을 측정하였다. 각 시약의 농도에 따른 칼라센서의 정색반응 결과가 통계적으로 타당한 결과를 보였고 유헬스용 요분석기 개발에 적용할 수 있을 것으로 사료된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2492-2494
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• 2003

Urine analysis is one of the most important medical examination in the hospital. Not only the data for the ingredients of urine through chemical analysis, but also the data related to fluid dynamics, e.g., peak flow rate, average flow rate, may provide some useful information about patient's state of health. Therefore, we develop the portable system to measure and analyse fluid volume/flow rate in this study. This system can store and print the measured data during the pre-specified time interval, and provide some meaningful data related with fluid dynamics. We explain the method and the technical stuff to implement the system, and show the result.

• 한국광학회:학술대회논문집
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• 한국광학회 2002년도 제13회 정기총회 및 2002년도 동계학술발표회
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• pp.238-239
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• 2002

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.145-149
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• 1997

This paper describes a methodology for the development of models of discrete event system. The methodology is based on transformation of continuous state space into discrete one to homomorphically represent dynamics of continuous processes in discrete events. This paper proposes a formal structure which can coupled discrete event system models within a framework. The structure employs the discrete event specification formalism for the discrete event system models. The proposed formal structure has been applied to develop a discrete event specification model for the complex spectral density analysis of strip for urin analyzer system. For this, spectral density data of strip is partitioned into a set of Phases based on events identified through urine spectrophotometry. For each phase, a continuous system of the continuous model for the urine spectral density analysis has been simulated by programmed C++. To validate this model, first develop the discrets event specification model, then simulate the model in the DEVSIM++ environment. It has the similar simulation results for the data obtained from the continuous system simulation. The comparison shows that the discrete event specification model represents dynamics of the urine spectral density at each phase.

• Bulletin of the Korean Chemical Society
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• 제34권11호
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• pp.3444-3450
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• 2013

Three phase hollow fiber-liquid phase microextraction (HF-LPME), which is faster, simpler and uses a more environmentally friendly sample-preparation technique, was developed for the analysis of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) in human urine. For the effective simultaneous extraction/concentration of NSAIDs by three phase HF-LPME, parameters (such as extraction organic solvent, pH of donor/acceptor phase, stirring speed, salting-out effect, sample temperature, and extraction time) which influence the extraction efficiency were optimized. NSAIDs were extracted and concentrated from 4 mL of aqueous solution at pH 3 (donor phase) into dihexyl ether immobilized in the wall pores of a porous hollow fiber, and then extracted into the acceptor phase at pH 13 located in the lumen of the hollow fiber. After the extraction, 5 ${\mu}L$ of the acceptor phase was directly injected into the HPLC/UV system. Simultaneous chromatographic separation of seven NSAIDs was achieved on an Eclipse XDB-C18 (4.6 mm i.d. ${\times}$ 150 mm length, 5 ${\mu}m$ particle size) column using isocratic elution with 0.1% formic acid and methanol (30:70) at a HPLC-UV/Vis system. Under optimized conditions (extraction solvent, dihexyl ether; $pH_{donor}$, 3; $pH_{acceptor}$, 13; stirring speed, 1500 rpm; NaCl salt, 10%; sample temperature, $60^{\circ}C$; and extraction time, 45 min), enrichment factors (EF) were between 59 and 260. The limit of detection (LOD) and limit of quantitation (LOQ) in the spiked urine matrix were in the concentration range of 5-15 ng/mL and 15-45 ng/mL, respectively. The relative recovery and precision obtained were between 58 and 136% and below 15.7% RSD, respectively. The calibration curve was linear within the range of 0.015-0.96 ng/mL with the square of the correlation coefficient being more than 0.997. The established method can be used to analyse of NSAIDs of low concentration (ng/mL) in urine.

• 한국응용과학기술학회지
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• 제32권4호
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• pp.724-730
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• 2015

For ex-vivo diabetic control, the voltammetric diagnosis of glucose (GU) was conducted with a modified carbon nanotube paste electrode, using handheld analytical circuits. The optimum analytical conditions were attained within the 0.5-4.0 ug/L working range and at the 0.06 ug/L detection limit, which system was interfaced to the feedback circuits and was applied to human urine for diabetic-patient diagnosis. It can be used for ex-vivo flow control analysis, vascular flow detection and other medicinal assays. The equations of the patients' urine are y=36.65x+12.13 and $R^2=0.987$, those of the healthy person of y= 2.5x+10.9 and $R^2=0.928$ (patients: 118 ug/L; healthy person: 12.34 ug/L).

• Environmental Analysis Health and Toxicology
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• 제29권
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• pp.18.1-18.9
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• 2014

Objectives The purpose of this study was to determine a separation method for each arsenic metabolite in urine by using a high performance liquid chromatography (HPLC)-inductively coupled plasma-mass spectrometer (ICP-MS). Methods Separation of the arsenic metabolites was conducted in urine by using a polymeric anion-exchange (Hamilton PRP X-100, $4.6mm{\times}150mm$, $5{\mu}m$) column on Agilent Technologies 1260 Infinity LC system coupled to Agilent Technologies 7700 series ICP/MS equipment using argon as the plasma gas. Results All five important arsenic metabolites in urine were separated within 16 minutes in the order of arsenobetaine, arsenite, dimethylarsinate, monomethylarsonate and arsenate with detection limits ranging from 0.15 to $0.27{\mu}g/L$ ($40{\mu}L$ injection). We used G-EQUAS No. 52, the German external quality assessment scheme and standard reference material 2669, National Institute of Standard and Technology, to validate our analyses. Conclusions The method for separation of arsenic metabolites in urine was established by using HPLC-ICP-MS. This method contributes to the evaluation of arsenic exposure, health effect assessment and other bio-monitoring studies for arsenic exposure in South Korea.