• Korean Journal of Optics and Photonics
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• v.24 no.2
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• pp.86-91
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• 2013

In this study, we have investigated the correlation of far-field detection sensitivity of surface plasmon resonance (SPR) biosensors with optical signatures associated with the near-field overlap of biomolecules. The results confirm a direct relation between the far-field and near-field parameters, particularly for optical signatures defined in terms of lateral electric field components that are tangential to the interface and thus continuous across the interface. The overall correlation between near-field optical signatures and far-field resonance shift exceeded 97%. The results can be highly useful to evaluate detection sensitivity of SPR biosensors that take advantage of complex structures for localization of surface waves.

• Annals of Laboratory Medicine
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• v.39 no.1
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• pp.50-57
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• 2019

Background: The Automated Fluorescent Immunoassay System (AFIAS) rotavirus assay (Boditech Med Inc., Chuncheon, Korea) is a new rapid antigen test for rotavirus detection. We evaluated the performance of this assay for detecting rotaviruses and their specific genotypes in clinical stool samples. Methods: AFIAS rotavirus assay was performed in 103 rotavirus-positive and 103 rotavirus-negative stool samples (confirmed by both PCR and ELISA), and its results were compared with those of PCR, ELISA, and immunochromatographic assay (ICA). We evaluated diagnostic sensitivity/specificity, the detectability of rotavirus subtypes, lower limit of detection (LLOD), reproducibility, cross-reactivity, and interference of AFIAS rotavirus assay. Results: Based on PCR and ELISA results, diagnostic sensitivity and specificity of the AFIAS rotavirus assay were both 99.0%. LLOD results showed that the AFIAS assay had sensitivity similar to or greater than ICA and ELISA. High reproducibility was confirmed, and no cross-reactivity or interference was detected. This assay could detect genotypes G1P[8], G2P[4], G3P[8], G4P[6], G4P[8], G8P[4], G8P[8], G9P[4], and G9P[8]. Conclusions: The AFIAS rotavirus assay showed high reproducibility, sensitivity, and specificity as well as excellent agreement with ELISA, PCR, and ICA. It detected the most common as well as unusual genotypes of rotavirus prevalent in Korea. It could be a useful onsite assay for rapid, convenient, and cost-effective detection of rotavirus infection.

• Journal of radiological science and technology
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• v.44 no.5
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• pp.459-464
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• 2021

The detector performance is important role in acquiring the gamma rays from patients. Among parameters of detector performances, there is density, which relates to respond to gamma rays. Therefore, we confirm the detection efficiency according to various detector materials based on the density parameter using GATE (geant4 application for emission tomography) simulation tool. The NaI (density: 3.67 g/cm³), CZT (Cadimium Zinc Telluride) (density: 5.80 g/cm³), CdTe (Cadmium Telluride) (5.85 g/cm³), and GAGG (Gadoinium Aluminum Gallium Garnet) (density g/cm³) were used as detector materials. In addition, the point source and quadrant bar phantom, which is modeled for 0.5, 1.0, 1.5, and 2.0 mm thicknesses, were modeled to confirm the quatitative analysis using sensitivity (cps/MBq) and the full width at half maximum (FWHM, mm) at the 2.0 mm bar thickness containing visual evaluation. Based on the results, the sensitivity for NaI, CZT, CdTe, and GAGG detector materials were 0.12, 0.15, 0.16, and 0.18 cps/MBq. In addition, the FWHM for quadrant bar phantom in the 2.0 mm bar thickness is 3.72, 3.69, 3.70, and 3.73 mm for NaI, CZT, CdTe, and GAGG materials, respectively. Compared with performance of detector materials according to density, the high density can improve detection efficiency in terms of sensitivity and mean count. Among these detector materials, the GAGG material is efficient for detection of gamma rays.

• Korean Journal of Radiology
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• v.24 no.2
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• pp.155-165
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• 2023

Objective: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists' diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model. Materials and Methods: This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expert-determined standards as the reference standard, and the results were compared using the t test with Bonferroni correction. Results: The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expert-determined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all p-values < 0.05). DLAD assistance diminished the overestimation of sensitivity but exaggerated the underestimation of specificity (all p-values < 0.001). The DLAD model improved sensitivity and specificity to a greater extent when using the clinical gold standard than when using the expert-determined standards (all p-values < 0.001), except for sensitivity with the LCA model (p = 0.094). Conclusion: The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.

• Parasites, Hosts and Diseases
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• v.47 no.2
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• pp.153-157
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• 2009

Diagnosis of hydatidosis is based on immunodiagnostic methods along with radiological and ultrasound examinations. The objectives of the present study were to develop a specific and simple antigen-based ELISA method for diagnosis of hydatidosis and compare it with antibody detection method. The subjects in this study included 89 patients in the following groups: surgically confirmed hydatidosis patients (35 cases), control with other parasitic diseases (29 cases), and healthy controls (25 cases). Hyperimmune serum was raised against hydatid cyst fluid in rabbits. Anti-hydatid cyst IgG was purified by affinity chromatography using protein A column and labeled with horseradish peroxidase. Collected sera were assessed for hydatid cyst antigens and antibody by ELISA. Circulating hydatid antigen was found in 9 out of 35 patients with surgically confirmed hydatidosis. A sensitivity of 25.7% and a specificity of 98.0% were calculated for the antigen detection assay. Antibody detection by indirect ELISA, using antigen B, showed that 94.2% of patients (33 cases) have anti-hydatid cyst antibodies in their serum while cross reaction was noted in a few of non-hydatidosis patients. A sensitivity of 94.2% and specificity of 81.6% were found for the antibody detection assay. Findings of this study indicated that antibody detection assay is a sensitive approach for diagnosis of hydatid cyst while antigen detection assay might be a useful approach for assessment of the efficacy of treatment especially after removal of the cyst.

• Bulletin of the Korean Chemical Society
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• v.27 no.9
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• pp.1346-1352
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• 2006

We describe an effective method of microchip electrophoresis (ME) based on single strand conformation poly-morphism (SSCP) analysis to rapidly detect the point mutation, Leu72Met, in a human obesity gene. The 207-bp dsDNA in the Leu72Met region, an estimate of the child obesity DNA mutant, was amplified by polymerase chain reaction (PCR) and submitted to a conventional glass microchip analysis with a sieving matrix of 1.75% poly(vinylpyrrolidone) (Mr 1 300 000), 1.0% poly(ethyleneoxide) (Mr 600 000) and 5.0% w/w glycerol. When combined with base stacking (BS) with hydroxide ions, the SSCP-ME provided rapid analysis as well as sensitive detection. The detection sensitivity was effectively enhanced in the OH- concentration range of 0.01-0.025 M NaOH. The sensitivity and speed of this ME-based SSCP methodology for the rapid detection of Leu72Met point mutations makes this an attractive method for diagnosing childhood obesity in a clinical diagnostic laboratory.

• Smart Structures and Systems
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• v.15 no.2
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• pp.315-334
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• 2015

A main goal of this study is to propose a damage detection technique to detect and localize damages of a top-tensioned riser. In this paper, the top-tensioned finite element (FE) model is considered as an analytical model of the riser, and a vibration-based damage detection method is proposed. The present method consists of a FE model updating and damage index method. In order to accomplish the goal of this study, first, a sensitivity-based FE model updating method using natural frequencies and zero frequencies is introduced. Second, natural frequencies and zero frequencies of the axial mode on the top-tensioned riser are estimated by eigenvalue analysis. Finally, the locations and severities of the damages are estimated from the damage index method. Three numerical examples are considered to verify the performance of the proposed method.

• Journal of Microbiology and Biotechnology
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• v.27 no.4
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• pp.808-815
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• 2017

We demonstrated the quantitative detection of a toxin-producing Microcystis aeruginosa (M. aeruginosa) strain with the laboratory protocol of the NanoGene assay. The NanoGene assay was selected because its laboratory protocol is in the process of being transplanted into a portable system. The mcyD gene of M. aeruginosa was targeted and, as expected, its corresponding fluorescence signal was linearly proportional to the mcyD gene copy number. The sensitivity of the NanoGene assay for this purpose was validated using both dsDNA mcyD gene amplicons and genomic DNAs (gDNA). The limit of detection was determined to be 38 mcyD gene copies per reaction and 9 algal cells/ml water. The specificity of the assay was also demonstrated by the addition of gDNA extracted from environmental algae into the hybridization reaction. Detection of M. aeruginosa was performed in the environmental samples with environmentally relevant sensitivity (${\sim}10^5$ algal cells/ml) and specificity. As expected, M. aeruginosa were not detected in nonspecific environmental algal gDNA over the range of $2{\times}10^0$ to $2{\times}10^7$ algal cells/ml.

• Mass Spectrometry Letters
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• v.4 no.1
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• pp.18-20
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• 2013

Headspace solid phase micro-extraction gas chromatography/flame ionization detection (HS-SPME-GC/FID) method was compared with headspace gas chromatography/mass selective detection (HS-GC/MS). Organic solvent-spiked urine as well as urine samples from workspace was analyzed under optimal condition of each method. Detection limit of each compound by HS-SPME-GC/FID was $3.4-9.5{\mu}g/L$, which enabled trace analysis of organic solvents in urine. Linear range of each organic solvent was $10-400{\mu}g/L$, with fair correlation coefficient between 0.992 and 0.999. The detection sensitivity was 4 times better than HS-GC/MS in selected ion monitoring (SIM) mode. Accuracy and precision was confirmed using commercial reference material, with accuracy around 90% and precision less than 4.6% of coefficient of variance. Among 48 urine samples from workplace, toluene was detected from 45 samples in the range of $20-324{\mu}g/L$, but no other solvents were found. As a method for trace analysis, SPME HS GC/FID showed high sensitivity for biological monitoring of organic solvent in urine.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.420-427
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• 2011

The population of elderly people increases rapidly as our society moves towards the aged one. Healthcare for the elderly becomes an important issue and falling down is one of the critical problems although not well recognized. In this study, a fall detection system was developed using a 3-axis accelerometer. Analyzing fall patterns, we took into account the degree of impact, posture angle, the repetitions of similar movements and the activities after a potential fall and proposed an algorithm of fall detection. Information of the fall sensor was sent to a remote healthcare server through the wireless networks of Zigbee and WLAN. Our system was designed to monitor multiples users. 12 persons participated in experiment and each one performed 24 different movements. Our proposed algorithm was compared with other reported ones. Our method produced the excellent results having a sensitivity of 96.4 % and a specificity of 100 % whereas other methods had a sensitivity range between 87.5 % and 94.8 % and a specificity range between 63.5 % and 83.3 %.