Kontos Evangelos, Samimi Aria, Hakze-van der Honing Renate W, Priem Jan, Avarguès-Weber Aurore, Haverkamp Alexander, Dicke Marcel, Gonzales Jose L, van der Poel Wim H M
InsectSense, Plus Ultra-II Building, Bronland, 10, 6708 WH, Wageningen, The Netherlands.
Laboratory of Entomology, P.O. Box 16, 6700 AA, Wageningen, The Netherlands.
Biol Open. 2022 Apr 15;11(4). doi: 10.1242/bio.059111. Epub 2022 May 3.
The COVID-19 pandemic has illustrated the need for the development of fast and reliable testing methods for novel, zoonotic, viral diseases in both humans and animals. Pathologies lead to detectable changes in the volatile organic compound (VOC) profile of animals, which can be monitored, thus allowing the development of a rapid VOC-based test. In the current study, we successfully trained honeybees (Apis mellifera) to identify SARS-CoV-2 infected minks (Neovison vison) thanks to Pavlovian conditioning protocols. The bees can be quickly conditioned to respond specifically to infected mink's odours and could therefore be part of a wider SARS-CoV-2 diagnostic system. We tested two different training protocols to evaluate their performance in terms of learning rate, accuracy and memory retention. We designed a non-invasive rapid test in which multiple bees are tested in parallel on the same samples. This provided reliable results regarding a subject's health status. Using the data from the training experiments, we simulated a diagnostic evaluation trial to predict the potential efficacy of our diagnostic test, which yielded a diagnostic sensitivity of 92% and specificity of 86%. We suggest that a honeybee-based diagnostics can offer a reliable and rapid test that provides a readily available, low-input addition to the currently available testing methods. A honeybee-based diagnostic test might be particularly relevant for remote and developing communities that lack the resources and infrastructure required for mainstream testing methods.
新冠疫情表明，需要开发针对人类和动物新型人畜共患病毒性疾病的快速可靠检测方法。疾病会导致动物挥发性有机化合物（VOC）谱发生可检测的变化，对此可进行监测，从而开发基于VOC的快速检测方法。在本研究中，我们通过巴甫洛夫条件反射实验方案，成功训练蜜蜂（西方蜜蜂）识别感染了新冠病毒的水貂（美国短毛水貂）。蜜蜂能够快速形成条件反射，对感染水貂的气味做出特异性反应，因此可成为更广泛的新冠病毒诊断系统的一部分。我们测试了两种不同的训练方案，以评估它们在学习速度、准确性和记忆保持方面的表现。我们设计了一种非侵入性快速检测方法，让多只蜜蜂同时对相同样本进行检测。这为受试者的健康状况提供了可靠结果。利用训练实验的数据，我们模拟了诊断评估试验，以预测我们诊断测试可能的效果，结果显示诊断敏感性为92%，特异性为86%。我们认为，基于蜜蜂的诊断方法可以提供一种可靠且快速的检测，为现有检测方法提供一种易于获得、低投入的补充。基于蜜蜂的诊断测试对于缺乏主流检测方法所需资源和基础设施的偏远及发展中社区可能尤为重要。
