Department of Chemical Engineering, Faculty of Science and Engineering, School of Engineering and Applied Science, Swansea University Fabian Way, Swansea, SA1 8EN, United Kingdom.
Department of Biomedical Engineering, Faculty of Science and Engineering, School of Engineering and Applied Science, Swansea University Fabian Way, Swansea, SA1 8EN, United Kingdom.
Anal Chem. 2022 Mar 1;94(8):3617-3628. doi: 10.1021/acs.analchem.1c05208. Epub 2022 Feb 15.
Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non-Newtonian properties to the biofluids. The concentration of such protein macromolecules in biofluids is regarded as an important biomarker for the diagnosis of several health conditions, including cardiovascular disorders, joint quality, and Alzheimer's. Existing technologies for the measurements of macromolecules in biofluids are limited; they require a long turnaround time, or require complex protocols, thus calling for alternative, more suitable, methodologies aimed at such measurements. According to the well-established relations for polymer solutions, the concentration of macromolecules in solutions can also be derived via measurement of rheological properties such as shear-viscosity and the longest relaxation time. We here introduce a microfluidic rheometer for rapid simultaneous measurement of shear viscosity and longest relaxation time of non-Newtonian solutions at different temperatures. At variance with previous technologies, our microfluidic rheometer provides a very short turnaround time of around 2 min or less thanks to the implementation of a machine-learning algorithm. We validated our platform on several aqueous solutions of poly(ethylene oxide). We also performed measurements on hyaluronic acid solutions in the clinical range for joint grade assessment. We observed monotonic behavior with the concentration for both rheological properties, thus speculating on their use as potential , i.e., rheological biomarkers, across several disease states.
生物流体（如滑液、血浆和唾液）中含有多种蛋白质，这些蛋白质使生物流体呈现出非牛顿特性。生物流体中此类蛋白质大分子的浓度被认为是诊断多种健康状况（包括心血管疾病、关节质量和阿尔茨海默病）的重要生物标志物。目前用于测量生物流体中大分子的技术存在局限性；它们需要较长的周转时间，或者需要复杂的方案，因此需要替代的、更合适的方法来进行此类测量。根据聚合物溶液的既定关系，通过测量剪切粘度和最长松弛时间等流变性质，也可以推导出溶液中大分子的浓度。我们在这里引入了一种用于快速同时测量非牛顿溶液在不同温度下的剪切粘度和最长松弛时间的微流控流变仪。与以前的技术不同，我们的微流控流变仪由于实现了机器学习算法，因此具有非常短的周转时间，约为 2 分钟或更短。我们在几种聚（氧化乙烯）水溶液上验证了我们的平台。我们还在用于关节分级评估的临床范围内对透明质酸溶液进行了测量。我们观察到两种流变性质都与浓度呈单调关系，因此推测它们可作为几种疾病状态下的潜在（即流变学）生物标志物。
