肌酸
尿
树莓皮
肌酐
检测点注意事项
注意事项
检出限
生物医学工程
色谱法
白蛋白
化学
材料科学
计算机科学
医学
内科学
物联网
病理
嵌入式系统
作者
Szu-Jui Chen,Chin‐Chung Tseng,Kuan-Hsun Huang,Yu‐Chi Chang,Lung‐Ming Fu
出处
期刊:Biosensors
[MDPI AG]
日期:2022-07-07
卷期号:12 (7): 496-496
被引量:16
摘要
A novel assay platform consisting of a microfluidic sliding double-track paper-based chip and a hand-held Raspberry Pi detection system is proposed for determining the albumin-to-creatine ratio (ACR) in human urine. It is a clinically important parameter and can be used for the early detection of related diseases, such as renal insufficiency. In the proposed method, the sliding layer of the microchip is applied and the sample diffuses through two parallel filtration channels to the reaction/detection areas of the microchip to complete the detection reaction, which is a simple method well suited for self-diagnosis of ACR index in human urine. The RGB (red, green, and blue) value intensity signals of the reaction complexes in these two reaction zones are analyzed by a Raspberry Pi computer to derive the ACR value (ALB and CRE concentrations). It is shown that the G + B value intensity signal is linearly related to the ALB and CRE concentrations with the correlation coefficients of R2 = 0.9919 and R2 = 0.9923, respectively. It is additionally shown that the ALB and CRE concentration results determined using the proposed method for 23 urine samples were collected from real suffering chronic kidney disease (CKD) patients are in fine agreement with those acquired operating a traditional high-reliability macroscale method. Overall, for point-of-care (POC) CKD diagnosis and monitoring in clinical applications, the results prove that the proposed method offers a convenient, real time, reliable, and low-spending solution for POC CKD diagnosis.
科研通智能强力驱动
Strongly Powered by AbleSci AI