Cell phone microscopy enabled low-cost manufacturable colorimetric urine glucose test
Cell phone microscopy enabled low-cost manufacturable colorimetric urine glucose test
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DOI：
10.1007/s10544-023-00682-y
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发表时间：
2023-12-01
影响因子：
2.8
通讯作者：
Javanmard，Mehdi
中科院分区：
文献类型：
--
作者：
Meng，Zhuolun;Raji，Hassan;Javanmard，Mehdi
葡萄糖是一种关键的生物标志物，对监测和诊断一系列医疗状况至关重要，包括低血糖、高血糖和糖尿病，所有这些疾病都可能在个体中引发严重的临床表现。因此，医疗领域对开发快速、具有成本效益和用户友好的诊断工具的需求不断增长。在这篇研究文章中，我们介绍了一种创新的葡萄糖传感器，它依赖于由一次性医用级胶带精心制作的微流体装置。这些设备将葡萄糖尿液分析条安全地贴在显微镜玻片上。微流体通道是通过激光切割复杂地创建的，代表了传统洁净室技术的背离。这种方法简化了生产过程，提高了成本效益，并消除了对专用设备的需求。在目标溶液吸收之后，将一次性装置封闭在3d打印外壳内。通过使用智能手机相机进行随后的比色分析，无缝地促进了图像捕获。我们的研究熟练地证明了葡萄糖传感器准确量化蔗糖溶液中葡萄糖浓度的能力。这是通过采用指数回归模型来实现的，该模型阐明了葡萄糖浓度和平均RGB（红-绿-蓝）值之间的复杂关系。此外，我们的综合分析揭示了传感器性能在不同实例中的最小变化。值得注意的是，这项研究强调了我们的解决方案对各种检测类型和基于智能手机的传感器系统的潜在适应性和多功能性，使其在资源受限的环境和不发达国家的部署特别有希望。葡萄糖浓度与平均RGB值之间建立了强大的相关性，令人印象深刻的r平方值为0.98709，强调了我们在医学领域开创性方法的有效性和可靠性。图形抽象
Glucose serves as a pivotal biomarker crucial for the monitoring and diagnosis of a spectrum of medical conditions, encompassing hypoglycemia, hyperglycemia, and diabetes, all of which may precipitate severe clinical manifestations in individuals. As a result, there is a growing demand within the medical domain for the development of rapid, cost-effective, and user-friendly diagnostic tools. In this research article, we introduce an innovative glucose sensor that relies on microfluidic devices meticulously crafted from disposable, medical-grade tapes. These devices incorporate glucose urine analysis strips securely affixed to microscope glass slides. The microfluidic channels are intricately created through laser cutting, representing a departure from traditional cleanroom techniques. This approach streamlines production processes, enhances cost-efficiency, and obviates the need for specialized equipment. Subsequent to the absorption of the target solution, the disposable device is enclosed within a 3D-printed housing. Image capture is seamlessly facilitated through the use of a smartphone camera for subsequent colorimetric analysis. Our study adeptly demonstrates the glucose sensor’s capability to accurately quantify glucose concentrations within sucrose solutions. This is achieved by employing an exponential regression model, elucidating the intricate relationship between glucose concentrations and average RGB (Red-Green-Blue) values. Furthermore, our comprehensive analysis reveals minimal variation in sensor performance across different instances. Significantly, this study underscores the potential adaptability and versatility of our solution for a wide array of assay types and smartphone-based sensor systems, making it particularly promising for deployment in resource-constrained settings and undeveloped countries. The robust correlation established between glucose concentrations and average RGB values, substantiated by an impressive R-square value of 0.98709, underscores the effectiveness and reliability of our pioneering approach within the medical field.Graphical abstract