Chengyu Zhang,Weifan Dang,Jingjing Zhang,Cong Wang,Peng Zhong,Zhaoxin Wang,Yufan Yang,Yuefei Wang,Xiaohui Yan
The abnormal uric acid (UA) level in urine can serve as warning signals of many diseases, such as gout and metabolic cardiovascular diseases. The current methods for detecting UA face limitations of instrument dependence and the requirement for non-invasiveness, making it challenging to fulfill the need for home-based application. In this study, we designed an aptasensor that combined UA-specific transcriptional regulation and a fluorescent RNA aptamer for convenient urinary UA testing. The concentration of UA can be translated into the intensity of fluorescent signals. The aptasensor showed higher sensitivity and more robust anti-interference performance. UA levels in the urine of different volunteers could be accurately tested using this method. In addition, a paper-based aptasensor for UA self-testing was manufactured, in which the urinary UA levels could be determined using a smartphone-based colorimetric approach. This work not only demonstrates a new approach for the design of disease-associated aptasensor, but also offers promising ideas for home-based detection of UA.
Haiqin Li,Yueyue Li,Xiaoliang Zhang,Xiaojun Ren,Xiaochun Li
As a non-invasive monitoring method, urine analysis is one of the “three routines” of medical examination, which is of great significance for the early diagnosis, therapeutic effect judgment and long-term fine monitoring of chronic diseases such as metabolic system diseases and kidney diseases. It is imperative to develop a home urine routine detection system. In this paper, Smart Urine Analyzer is innovatively developed by combining smartphones with array urine dipsticks, and equipped with the unique design of adjustable bracket suitable for camera position of different smartphones. The optimal colorimetric value was selected for analysis based on chromogenic response characteristics combined with digital image colorimetry, and the breakthrough from qualitative semi-quantification to precise quantification of 9 kinds of urine biomarkers was completed. And a smartphone App for automated image recognition and processing was developed, which was applied for at-home routine healthcare. 100 clinical urine samples were used to evaluate the reliability of the system. Among them, the ROC curve showed a high consistency between Smart Urine Analyzer and the Dirui H-800 urine analyzer in hospital, which further verified the practicability. As a result, the user-friendly Smart Urine Analyzer can share health data and condition with doctors in real time, which provides a new scheme and means for the home detection of urine biomarkers, and has great practical application value as a potential home medical service assistant.
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