TY - JOUR
T1 - Developing a fluorescent sensing based portable medical open-platform - a case study for albuminuria measurement in chronic kidney disease screening and monitoring
AU - Pham, Anh Tran Tam
AU - Tohl, Damian
AU - Wallace, Angus
AU - Hu, Qi
AU - Li, Jordan
AU - Reynolds, Karen J.
AU - Tang, Youhong
PY - 2022/8
Y1 - 2022/8
N2 - In this work, a portable medical platform is developed to monitor fluorescence assay for in-field and point of care (POC) analysis of albumin levels in urine. The platform can rapidly and efficiently measure the fluorescence intensity yielded from the mixture of recognising reagent and the biomarker. After mixing with the recognising reagent and placing under a certain optical excitation wavelength, the sample containing the target biomarker emits a fluorescent signal, which is captured by a Raspberry Pi Camera and analysed in real time to evaluate the Red Green Blue (RGB) values of the image and give an estimation of the biomarker concentration in the sample. Each biomarker, in testing for fluorescence, requires different excitation wavelengths and sensitivities for detection. The developed platform has been designed with flexibility for customization to suit the different requirements of various fluorescence reactions. In this study, we perform two stages of evaluating the device. In stage 1, we demonstrate the developed platform can detect known concentrations of albumin in artificial urine samples by using different recognising reagents. In stage 2, we confirm the developed platform can detect unknown concentrations of albumin in patients' urine samples and provide comparative accuracy results compared with current laboratory testing results. This study provides proof of concept that the developed device can measure the concentration of different biomarkers if the biomarker detection follows the rules of fluorescence sensing.
AB - In this work, a portable medical platform is developed to monitor fluorescence assay for in-field and point of care (POC) analysis of albumin levels in urine. The platform can rapidly and efficiently measure the fluorescence intensity yielded from the mixture of recognising reagent and the biomarker. After mixing with the recognising reagent and placing under a certain optical excitation wavelength, the sample containing the target biomarker emits a fluorescent signal, which is captured by a Raspberry Pi Camera and analysed in real time to evaluate the Red Green Blue (RGB) values of the image and give an estimation of the biomarker concentration in the sample. Each biomarker, in testing for fluorescence, requires different excitation wavelengths and sensitivities for detection. The developed platform has been designed with flexibility for customization to suit the different requirements of various fluorescence reactions. In this study, we perform two stages of evaluating the device. In stage 1, we demonstrate the developed platform can detect known concentrations of albumin in artificial urine samples by using different recognising reagents. In stage 2, we confirm the developed platform can detect unknown concentrations of albumin in patients' urine samples and provide comparative accuracy results compared with current laboratory testing results. This study provides proof of concept that the developed device can measure the concentration of different biomarkers if the biomarker detection follows the rules of fluorescence sensing.
KW - Albuminuria
KW - Clinic evaluation
KW - Fluorescence monitoring open platform
KW - Image processing RGB
KW - Point of care medical device
UR - http://www.scopus.com/inward/record.url?scp=85132316180&partnerID=8YFLogxK
U2 - 10.1016/j.sbsr.2022.100504
DO - 10.1016/j.sbsr.2022.100504
M3 - Article
AN - SCOPUS:85132316180
SN - 2214-1804
VL - 37
JO - Sensing and Bio-Sensing Research
JF - Sensing and Bio-Sensing Research
M1 - 100504
ER -