Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany.
Signalling Research Centres BIOSS and CIBSS, University of Freiburg, 79104 Freiburg, Germany.
Biosensors (Basel). 2020 Dec 23;11(1):2. doi: 10.3390/bios11010002.
The detection of pathogens is a major public health issue. Every year, thousands of people die because of nosocomial infections. It is therefore important to be able to detect possible outbreaks as early as possible, especially in the hospital environment. Various pathogen detection techniques have already been demonstrated. However, most of them require expensive and specific equipment, and/or complex protocols, which, most of the time, involve biochemical reaction and labelling steps. In this paper, a new method that combines microscopic imaging and machine learning is described. The main benefits of this approach are to be low-cost, label-free and easy to integrate in any suitable medical device, such as hand hygiene dispensers. The suitability of this pathogen detection method is validated using four bacteria, both in PBS (Phosphate Buffered Saline) and in isopropanol. In particular, we demonstrated an efficient pathogenic detection that is sensible to changes in the composition of a mixture of pathogens, even in alcohol-based solutions.
病原体检测是一个主要的公共卫生问题。每年,都有数千人因医院感染而死亡。因此,能够尽早发现可能的疫情非常重要,尤其是在医院环境中。已经有许多病原体检测技术得到了证明。然而,其中大多数需要昂贵且特定的设备和/或复杂的方案,这些方案大多数时候涉及生化反应和标记步骤。在本文中,描述了一种将显微镜成像和机器学习相结合的新方法。这种方法的主要优点是成本低、无需标记,并且易于集成到任何合适的医疗设备中,如手部卫生分配器。使用四种细菌在 PBS(磷酸盐缓冲液)和异丙醇中对这种病原体检测方法的适用性进行了验证。特别地,我们展示了一种高效的病原体检测方法,即使在基于酒精的溶液中,对病原体混合物组成变化也具有敏感性。