Goh Brendon, Soares Magalhães Ricardo J, Ciocchetta Silvia, Liu Wenjun, Sikulu-Lord Maggy T
School of the Environment, Faculty of Science, The University of Queensland, Brisbane, Australia.
UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland, Brisbane, Australia.
PLoS One. 2025 Apr 17;20(4):e0321362. doi: 10.1371/journal.pone.0321362. eCollection 2025.
Arbovirus and malaria infections affect more than half of the world's population causing major financial and physical burden. Current diagnostic tools such as microscopy, molecular and serological techniques are technically demanding, costly, or time consuming. Near-infrared spectroscopy has recently been demonstrated as a potential diagnostic tool for malaria and Dengue virus and as a screening tool for disease vectors. However, pathogen specific absorption peaks that allow detection of these infections are yet to be described. In this study, we identified unique visible and near-infrared peaks from existing laboratory strains of four major arboviruses including Barmah Forest virus, Dengue virus, Ross River virus, Sindbis virus and Plasmodium falciparum. Secondly, to determine the diagnostic ability of these peaks, we developed machine learning algorithms using artificial neural networks to differentiate arboviruses from media in which they were grown. Signature peaks for BFV were identified within the visible region at 410, 430, 562 and 588 nm and the near-infrared region at, 946, 958, 1130, 1154 and 1780 nm. DENV related peaks were seen at 410nm within the visible region and 1130 nm within the near-infrared region. Signature peaks for Ross River virus were observed within the visible region at 410 and 430 nm and within the near-infrared region at 1130 and 1780 nm, while Sindbis virus had a prominent peak at 410 nm within the visible region. Peaks at 514, 528, 547, 561, 582, and 595 nm and peaks at 1388, 1432, 1681, 1700, 1721, 1882, 1905, 2245, 2278, 2300 nm were unique for P. falciparum. Near-infrared spectroscopy predictive sensitivity defined as the ability to predict an arbovirus as an infection was 90% (n=20) for Barmah Forest virus, 100% (n=10) for Ross River virus and 97.5% (n=40) for Dengue virus, while infection specificity defined as the ability to predict media as not-infected was 100% (n=10). Our findings indicate that spectral signatures obtained by near-infrared spectroscopy are potential biomarkers for diagnosis of arboviruses and malaria.
虫媒病毒和疟疾感染影响着世界一半以上的人口，造成了巨大的经济和身体负担。当前的诊断工具，如显微镜检查、分子和血清学技术，在技术上要求较高、成本高昂或耗时较长。近红外光谱最近已被证明是一种用于疟疾和登革热病毒的潜在诊断工具，以及一种用于疾病媒介的筛查工具。然而，尚未描述能够检测这些感染的病原体特异性吸收峰。在本研究中，我们从包括巴马森林病毒、登革热病毒、罗斯河病毒、辛德毕斯病毒和恶性疟原虫在内的四种主要虫媒病毒的现有实验室菌株中识别出独特的可见光和近红外峰。其次，为了确定这些峰的诊断能力，我们使用人工神经网络开发了机器学习算法，以区分虫媒病毒与其生长的培养基。巴马森林病毒的特征峰在可见光区域的410、430、562和588nm以及近红外区域的946、958、1130、1154和1780nm处被识别。登革热病毒相关的峰在可见光区域的410nm处以及近红外区域的1130nm处被观察到。罗斯河病毒的特征峰在可见光区域的410和430nm处以及近红外区域的1130和1780nm处被观察到，而辛德毕斯病毒在可见光区域的410nm处有一个突出的峰。514、528、547、561、582和595nm处的峰以及1388、1432、1681、1700、1721、1882、1905、2245、2278、2300nm处的峰是恶性疟原虫特有的。近红外光谱预测敏感性定义为预测虫媒病毒感染的能力，巴马森林病毒为90%（n = 20），罗斯河病毒为100%（n = 10），登革热病毒为97.5%（n = 40），而感染特异性定义为预测培养基未感染的能力为100%（n = 10）。我们的研究结果表明，通过近红外光谱获得的光谱特征是诊断虫媒病毒和疟疾的潜在生物标志物。
