Mencattini Arianna, Mattei Fabrizio, Schiavoni Giovanna, Gerardino Annamaria, Businaro Luca, Di Natale Corrado, Martinelli Eugenio
Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy.
Front Pharmacol. 2019 Feb 26;10:100. doi: 10.3389/fphar.2019.00100. eCollection 2019.
The increasing interest for microfluidic devices in medicine and biology has opened the way to new time-lapse microscopy era where the amount of images and their acquisition time will become crucial. In this optic, new data analysis algorithms have to be developed in order to extract novel features of cell behavior and cell-cell interactions. In this brief article, we emphasize the potential strength of a new paradigm arising in the integration of microfluidic devices (i.e., organ on chip), time-lapse microscopy analysis, and machine learning approaches. Some snapshots of previous case studies in the context of immunotherapy are included as proof of concepts of the proposed strategies while a visionary description concludes the work foreseeing future research and applicative scenarios.
医学和生物学领域对微流控设备的兴趣与日俱增,开启了新的延时显微镜时代,在此时代,图像数量及其采集时间将变得至关重要。从这个角度来看,必须开发新的数据分析算法,以提取细胞行为和细胞间相互作用的新特征。在这篇简短的文章中,我们强调了一种新范式的潜在优势,这种范式产生于微流控设备(即芯片器官)、延时显微镜分析和机器学习方法的整合。免疫治疗背景下先前案例研究的一些快照被纳入,作为所提出策略的概念证明,同时以富有远见的描述结束了这项工作,预见了未来的研究和应用场景。