• Journal of Microbiology and Biotechnology
• /
• 제28권11호
• /
• pp.1928-1936
• /
• 2018

Recently, human infections caused by severe fever with thrombocytopenia syndrome virus (SFTSV), which can lead to fatality, have dramatically increased in East Asia. With the unavailability of vaccines or antiviral drugs to prevent and/or treat SFTSV infection, early rapid diagnosis is critical for prevention and control of the disease. Here, we report the development of a simple, rapid and sensitive portable detection method for SFTSV infection applying reverse transcription-loop mediated isothermal amplification (RT-LAMP) combined with one-pot colorimetric visualization and electro-free reaction platform. This method utilizes a pocket warmer to facilitate diagnosis in a resource-limited setting. Specific primers were designed to target the highly-conserved region of L gene of SFTSV. The detection limit of the RT-LAMP assay was approximately $10^0$ viral genome copies from three different SFTSV strains. This assay exhibited comparable sensitivity to qRT-PCR and 10-fold more sensitivity than conventional RT-PCR, with a rapid detection time of 30 to 60 minutes. The RT-LAMP assay using SFTSV clinical specimens has demonstrated a similar detection rate to qRT-PCR and a higher detection rate compared to conventional RT-PCR. Moreover, there was no observed cross-reactive amplification of other human infectious viruses including Japanese Encephalitis Virus (JEV), Dengue, Enterovirus, Zika, Influenza and Middle East Respiratory Syndrome Coronavirus (MERS-CoV). This highly sensitive, electro- and equipment-free rapid colorimetric visualization method is feasible for resource-limited SFTSV field diagnosis.

• 한국가시화정보학회지
• /
• 제17권3호
• /
• pp.19-23
• /
• 2019

The purpose of this research is to develop a facile measurement system for colorimetric analysis for zinc powder and size analyzer for zeolite particles in order to reduce the process time for their characteristic analysis. We present facile smartphone-based analysis methods to measure and estimate the size of zinc power by using colorimeteric method and the size of zeolite particles by using ImageJ program which is an open-source program. The results show a possibility of our methods to replace the previous professional analysis processes with them.

• 한국수산과학회지
• /
• 제56권6호
• /
• pp.923-929
• /
• 2023

This study investigated the optical characterization of water types based on Jerlov's classification, employing the CIE colorimetric system. Digital visualization techniques were applied to articulate watercolor manifestations intuitively. The L^* luminance parameter exhibited a discernible reduction from optical water type I III and from type 1 to 9, registering a range between 66 and 84. Analysis of color attributes in each optical water type revealed that in the transition from type I to III, the color a^* values spanned from -7.43 to -8.32, while color b^* values ranged from -2.97 to -3.33. a^* values for optical water types 1 to 9 varied between -6.28 and -10.50, with corresponding b^* values ranging from -2.51 to -4.20. Consequently, optical water type I, IA, IB, II, and III were discretely categorized by independent color values, as were optical water types 1, 3, 5, 7, and 9. The digitized representation of watercolor in this inquiry facilitated comprehensive information asso,o;atopm. The study highlights limitations in Jerlov's classification for representing watercolors in different ocean conditions. It emphasized the need to collect color data from various marine areas and formulate a novel color standard or method for comparing colors.

• 한국가시화정보학회지
• /
• 제22권2호
• /
• pp.27-34
• /
• 2024

In this study, we propose a novel approach for rapid and accurate pathogen detection by integrating Polydiacetylene (PDA) hydrogel sensors with advanced deep learning algorithms and visualization techniques. PDA hydrogel sensors exhibit a color transition in the presence of pathogens, enabling straightforward and quick pathogen detection. We developed a reliable pathogen detection system that combines deep neural network algorithms with color quantification technology for image-based analysis. This image-based system retains the ease of pathogen detection offered by PDA sensors while deriving quantified color standards to overcome the limitations of human visual assessment, enhancing reliability. This advancement contributes to public health and the development and application of pathogen detection technology.