In recent decades advances in radiation imaging and radiation therapy have led to a dramatic increase in the number of people exposed to radiation. Consequently, there is a clear need for personalized biodosimetry diagnostics in order to monitor the dose of radiation received and adapt it to each patient depending on their sensitivity to radiation exposure (Hall and Brenner 2008 Hall , E. J. , Brenner , D. J. ( 2008 ). Cancer risks from diagnostic radiology . British Journal of Radiology , 81 : 362 – 378 .[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). Similarly, after a large-scale radiological event such as a dirty bomb attack, there will be a major need to assess, within a few days, the radiation doses received by tens of thousands of individuals. Current high-throughput devices can handle only a few hundred individuals per day. Hence, there is a great need for a very fast, self-contained, noninvasive biodosimetric device based on a rapid blood test.

This article presents a case study where regression methods and designed experiments are used to arrive at the optimal settings for various factors that impact the kinetics in a biodosimetric device. We use ridge regression to initially identify a set of potentially important variables in the mixing process, which is one of the critical subsystems of the device. This was followed by a series of designed experiments to arrive at the optimal setting of the significant microfluidic cartridge and piezoelectric disk (PZT; Sadler and Zenhausern 2006 Sadler , D. , Zenhausern , F. (2006). Piezoelectric mixing method. United States Patent 6,986,601. [Google Scholar]; Lee et al. 2005 Lee , S. Y. , Ko , B. , Yang , W. ( 2005 ). Theoretical modeling, experiments and optimization of piezoelectric multimorph . Smart Materials and Structures , 14 : 1343 – 1352 . [Google Scholar]) related factors. This statistical approach has been utilized to study the microfluidic mixing to mix water and dye mixtures of 70 μL volume. The outcome of the statistical design, experimentation, and analysis was then exploited for optimizing the design, fabrication, and assembly of microfluidic devices. As a result of the experiments performed, the system was fine-tuned and the mixing time was reduced from 5.5 to 2 minutes.

近几十年来，放射成像和放射治疗的进步导致暴露于辐射的人数急剧增加。因此，显然需要个性化的生物剂量学诊断，以监测接受的辐射剂量并根据每个患者对辐射暴露的敏感性使其适应（Hall 和 Brenner 2008 霍尔，EJ ， 布伦纳，DJ （2008 年）。来自诊断放射学的癌症风险。英国放射学杂志，81：362 – 378。[Crossref]、[PubMed]、[Web of Science®]、 [Google Scholar]）。同样，在发生脏弹袭击等大规模放射事件后，将非常需要在几天内评估数万人接受的辐射剂量。当前的高通量设备每天只能处理几百个人。因此，非常需要一种基于快速血液测试的非常快速、独立、无创的生物剂量测定装置。

本文介绍了一个案例研究，其中使用回归方法和设计实验来为影响生物剂量测定装置动力学的各种因素找到最佳设置。我们使用岭回归来初步确定混合过程中一组潜在的重要变量，这是设备的关键子系统之一。随后进行了一系列设计实验，以达到显着微流体盒和压电盘的最佳设置（PZT；Sadler 和 Zenhausern 2006 萨德勒，D. ， Zenhausern，F. （2006 年）。压电混合法。美国专利 6,986,601。 [谷歌学术] ; 李等人。2005年 Lee, SY , Ko, B. , Yang, W. ( 2005 )。压电多压电晶片的理论建模、实验和优化。智能材料和结构，14：1343 – 1352。 [Google Scholar] ) 相关因素。这种统计方法已被用于研究微流体混合，以混合 70 μL 体积的水和染料混合物。然后利用统计设计、实验和分析的结果来优化微流体装置的设计、制造和组装。根据所进行的实验，系统进行了微调，混合时间从 5.5 分钟减少到 2 分钟。