Madukwe David Uche Promise, Mike-Ogburia Moore Ikechi, Nduka Nonso, Nzeobi Japhet
Department of Mechanical Engineering, University of Colorado-Boulder, USA.
Department of Medical Laboratory Science, Rivers State University, Nigeria.
Crit Rev Biomed Eng. 2023;51(1):41-58. doi: 10.1615/CritRevBiomedEng.2023047211.
The COVID-19 pandemic, emerging/re-emerging infections as well as other non-communicable chronic diseases, highlight the necessity of smart microfluidic point-of-care diagnostic (POC) devices and systems in developing nations as risk factors for infections, severe disease manifestations and poor clinical outcomes are highly represented in these countries. These POC devices are also becoming vital as analytical procedures executable outside of conventional laboratory settings are seen as the future of healthcare delivery. Microfluidics have grown into a revolutionary system to miniaturize chemical and biological experimentation, including disease detection and diagnosis utilizing μPads/paper-based microfluidic devices, polymer-based microfluidic devices and 3-dimensional printed microfluidic devices. Through the development of droplet digital PCR, single-cell RNA sequencing, and next-generation sequencing, microfluidics in their analogous forms have been the leading contributor to the technical advancements in medicine. Microfluidics and machine-learning-based algorithms complement each other with the possibility of scientific exploration, induced by the framework's robustness, as preliminary studies have documented significant achievements in biomedicine, such as sorting, microencapsulation, and automated detection. Despite these milestones and potential applications, the complexity of microfluidic system design, fabrication, and operation has prevented widespread adoption. As previous studies focused on microfluidic devices that can handle molecular diagnostic procedures, researchers must integrate these components with other microsystem processes like data acquisition, data processing, power supply, fluid control, and sample pretreatment to overcome the barriers to smart microfluidic commercialization.
新冠疫情、新出现/再次出现的感染以及其他非传染性慢性病，凸显了发展中国家对智能微流控即时诊断（POC）设备和系统的需求，因为这些国家感染、严重疾病表现和不良临床结果的风险因素极为突出。这些POC设备也变得至关重要，因为在传统实验室环境之外可执行的分析程序被视为医疗服务的未来。微流控技术已发展成为一种革命性的系统，可将化学和生物实验小型化，包括利用μPads/纸基微流控设备、聚合物基微流控设备和三维打印微流控设备进行疾病检测和诊断。通过液滴数字PCR、单细胞RNA测序和下一代测序的发展，类似形式的微流控技术一直是医学技术进步的主要贡献者。微流控技术和基于机器学习的算法相互补充，由于该框架的稳健性而具有科学探索的可能性，因为初步研究已在生物医学领域取得了重大成就，如分选、微囊化和自动检测。尽管取得了这些里程碑式的进展和潜在应用，但微流控系统设计、制造和操作的复杂性阻碍了其广泛应用。由于先前的研究集中在能够处理分子诊断程序的微流控设备上，研究人员必须将这些组件与其他微系统过程（如数据采集、数据处理、电源供应、流体控制和样品预处理）集成，以克服智能微流控商业化的障碍。
