Optical Temperature Control Unit and Convolutional Neural Network for Colorimetric Detection of Loop-Mediated Isothermal Amplification on a Lab-On-A-Disc Platform
| Autor: | Dayeseul Lim, Moo-Jung Seo, Jae Chern Yoo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Materials science
Loop-mediated isothermal amplification convolutional neural network 02 engineering and technology engineering.material lcsh:Chemical technology 01 natural sciences Biochemistry Convolutional neural network Temperature measurement Article Analytical Chemistry Body Temperature Coating Lab-On-A-Chip Devices Humans lcsh:TP1-1185 lab-on-a-disc Graphite Electrical and Electronic Engineering Instrumentation Temperature control business.industry 010401 analytical chemistry technology industry and agriculture Temperature hydroxy naphthol blue 021001 nanoscience & nanotechnology Atomic and Molecular Physics and Optics eye diseases 0104 chemical sciences point-of-care testing Heating system engineering Optoelectronics Colorimetry Naked eye Neural Networks Computer 0210 nano-technology business loop-mediated isothermal amplification |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 19, Iss 14, p 3207 (2019) Sensors Volume 19 Issue 14 |
| ISSN: | 1424-8220 |
| Popis: | Lab-on-a-disc (LOD) has emerged as a promising candidate for a point-of-care testing (POCT) device because it can effectively integrate complex fluid manipulation steps using multiple layers of polymeric substrates. However, it is still highly challenging to design and fabricate temperature measurement and heating system in non-contact with the surface of LOD, which is a prerequisite to successful realization of DNA amplification especially with a rotatable disc. This study presents a Lab-on-a-disc (LOD)-based automatic loop-mediated isothermal amplification (LAMP) system, where a thermochromic coating (< ~420 µ m) was used to distantly measure the chamber&rsquo s temperature and a micro graphite film was integrated into the chamber to remotely absorb laser beam with super high efficiency. We used a deep learning network to more consistently analyze the product of LAMP than we could with the naked eye. Consequently, both temperature heating and measurement were carried out without a physical contact with the surface of LOD. The experimental results show that the proposed approach, which no previous work has attempted, was highly effective in realizing LAMP in LOD. |
| Databáze: | OpenAIRE |
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