Perronno Paul, Claudinon Julie, Senin Carmen, Elçin-Guinot Serap, Wolter Lena, Makshakova Olga N, Dumas Norbert, Klockenbring Dimitri, Lam-Weil Joseph, Noblet Vincent, Steltenkamp Siegfried, Römer Winfried, Madec Morgan
ICube Laboratory, UMR 7357 (CNRS/University of Strasbourg), 67400, Illkirch-Graffenstaden, France.
Faculty of Biology, University of Freiburg, 79104, Freiburg, Germany.
Sci Rep. 2025 May 13;15(1):16535. doi: 10.1038/s41598-025-95069-9.
Detection of pathogens is a major concern in many fields like medicine, pharmaceuticals, or agri-food. Most conventional detection methods require skilled staff and specific laboratory equipment for sample collection and analysis or are specific to a given pathogen. Thus, they cannot be easily integrated into a portable device. In addition, the time-to-response, including the sample collection, possible transport to the measurement equipment, and analysis, is often quite long, making real-time screening of a large number of samples impossible. This paper presents a new approach that better fulfills industry needs in terms of integrated real-time wide screening of a large number of samples. It combines optical imaging, object detection and tracking, and machine-learning-based classification. Three of the most common bacteria are selected for this study. For all of them, living bacteria are distinguished from inert and inorganic objects (1 μm latex beads) based on their trajectory, with a high degree of confidence. Discrimination between living and dead bacteria of the same species is also achieved. Finally, the method successfully detects abnormal concentrations of a given bacterium compared to a standard baseline solution. Although there is still room for improvement, these results provide a proof of concept for this technology, which has strong application potential in infection spread prevention.
病原体检测是医学、制药或农业食品等许多领域的主要关注点。大多数传统检测方法需要专业人员和特定实验室设备进行样本采集和分析，或者仅针对特定病原体。因此，它们不易集成到便携式设备中。此外，从样本采集、可能的运输到测量设备以及分析的响应时间通常很长，使得对大量样本进行实时筛查变得不可能。本文提出了一种新方法，在对大量样本进行集成实时广泛筛查方面能更好地满足行业需求。它结合了光学成像、目标检测与跟踪以及基于机器学习的分类。本研究选择了三种最常见的细菌。对于所有这些细菌，基于其轨迹，能以高度的置信度将活细菌与惰性和无机物体（1微米乳胶珠）区分开来。同一种细菌的活细菌和死细菌之间也能实现区分。最后，该方法成功检测出与标准基线溶液相比给定细菌的异常浓度。尽管仍有改进空间，但这些结果为该技术提供了概念验证，该技术在感染传播预防方面具有强大的应用潜力。
