Identification of visible and near-infrared signature peaks for arboviruses and Plasmodium
Arbovirus and malaria infections affect more than half of the worlds 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 arbovirus and as a screening tool for disease vectors. However, pathogen specific infrared peaks that allow detection of these infections are yet to be described. In this study, we identified unique NIRS peaks from existing laboratory strains of four major arboviruses including Barmah Forest virus (BFV), Dengue virus (DENV), Ross River virus (RRV), Sindbis virus (SINV) and Plasmodium falciparum. Secondly, to determine the diagnostic ability of these peaks, we developed machine learning algorithms using Artificial Neural network (ANN) to differentiate arboviruses from media in which they are grown. Signature peaks for BFV were identified within the visible region at 410, 430, 562 and 588nm and the NIR 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 NIR region. Signature peaks for RRV were observed within the visible region at 410 and 430 nm and within the NIR region at 1130 and 1780 nm, while SINV had a prominent peak at 410 nm within the visible region. Peaks at 514, 528, 547, 561, 582, and 595nm and peaks at 1388, 1432, 1681, 1700, 1721, 1882, 1905, 2245, 2278, 2300 nm were unique for P. falciparum. NIRS predictive sensitivity defined as the ability to predict an arbovirus as an infection was 90% (n = 20) for BFV, 100% (n =10) for RRV and 97.5% (n= 40) for DENV, 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 NIRS are potential biomarkers for diagnosis arboviruses and malaria. Author summaryMore than half of the world is at risk of contracting either malaria or arboviral infections. In resource limited settings, timely detection of these infections and the ability to screen thousands of people in a day using affordable tools is key to preventing their spread and unprecedented outbreaks. This emphasizes the need to develop portable, rapid, and easy to use diagnostic and surveillance tools. The near-infrared spectroscopy technique has recently been shown to be a potential tool for the diagnosis and surveillance of both malaria and arboviral infections. However, signature spectral biomarkers for these infections remain to be described. This study has identified several spectral biomarkers for DEN, RRV, BVF, SIN arboviruses and P. falciparum parasites. These biomarkers will assist the future assessment of this technique as a diagnostic and or surveillance tool for these infections in the field.