Laboratório de Mosquitos Transmissores de Hematozoários, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Rio de Janeiro, Brazil.
School of Biological Sciences, University of Queensland, Brisbane, QLD 4072, Australia.
Viruses. 2022 Dec 20;15(1):11. doi: 10.3390/v15010011.
The transmission of dengue (DENV) and Zika (ZIKV) has been continuously increasing worldwide. An efficient arbovirus surveillance system is critical to designing early-warning systems to increase preparedness of future outbreaks in endemic countries. The Near Infrared Spectroscopy (NIRS) is a promising high throughput technique to detect arbovirus infection in with remarkable advantages such as cost and time effectiveness, reagent-free, and non-invasive nature over existing molecular tools for similar purposes, enabling timely decision making through rapid detection of potential disease. Our aim was to determine whether NIRS can differentiate females infected with either ZIKV or DENV single infection, and those coinfected with ZIKV/DENV from uninfected ones. Using 200 females reared and infected in laboratory conditions, the training model differentiated mosquitoes into the four treatments with 100% accuracy. DENV-, ZIKV-, and ZIKV/DENV-coinfected mosquitoes that were used to validate the model could be correctly classified into their actual infection group with a predictive accuracy of 100%, 84%, and 80%, respectively. When compared with mosquitoes from the uninfected group, the three infected groups were predicted as belonging to the infected group with 100%, 97%, and 100% accuracy for DENV-infected, ZIKV-infected, and the co-infected group, respectively. Preliminary lab-based results are encouraging and indicate that NIRS should be tested in field settings to evaluate its potential role to monitor natural infection in field-caught mosquitoes.
登革热(DENV)和寨卡(ZIKV)的传播在全球范围内持续增加。建立高效的虫媒病毒监测系统对于设计预警系统至关重要,可以提高流行国家对未来疫情的防范能力。近红外光谱(NIRS)是一种很有前途的高通量技术,可以检测到感染的虫媒病毒,具有成本效益高、时间效率高、试剂免费和非侵入性等优点,优于现有用于类似目的的分子工具,通过快速检测潜在疾病,从而能够及时做出决策。我们的目的是确定 NIRS 是否可以区分感染了单一 ZIKV 或 DENV 的雌性蚊子,以及感染了 ZIKV/DENV 的雌性蚊子与未感染的蚊子。使用在实验室条件下饲养和感染的 200 只雌性蚊子,训练模型可以 100%准确地将蚊子分为四个处理组。用于验证模型的 DENV-、ZIKV-和 ZIKV/DENV-感染的蚊子可以分别以 100%、84%和 80%的预测准确率正确分类到其实际感染组。与未感染组的蚊子相比,三个感染组被预测为属于感染组,DENV 感染组、ZIKV 感染组和混合感染组的预测准确率分别为 100%、97%和 100%。初步的实验室结果令人鼓舞,表明应该在野外环境中测试 NIRS,以评估其在监测野外捕获的蚊子自然感染方面的潜在作用。