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Abstract
To eliminate malaria, scalable tools that are rapid, affordable, and can detect patients with low parasitemia are required. Non-invasive diagnostic tools that are rapid, reagent-free, and affordable would also provide a justifiable platform for testing malaria in asymptomatic patients. However, non-invasive surveillance techniques for malaria remain a diagnostic gap. Here, we show near-infrared Plasmodium absorption peaks acquired non-invasively through the skin using a miniaturized hand-held near-infrared spectrometer. Using spectra from the ear, these absorption peaks and machine learning techniques enabled non-invasive detection of malaria-infected human subjects with varying parasitemia levels in less than 10 s.
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Details
1 Laboratório de Transmissores de Hematozoários, Instituto Oswaldo Cruz , Fiocruz, Rio de Janeiro, RJ 21040-900 , Brazil
2 School of Biological Sciences, Faculty of Science, The University of Queensland , Brisbane, QLD 4072, , Australia
3 School of Computer Science, Centre for Data Science, Queensland University of Technology , Brisbane, QLD 4000 , Australia
4 Fundação de Vigilância em Saúde , Manaus, AM 69093-018 , Brazil
5 Laboratório de Imunoparasitologia, Instituto Oswaldo Cruz , Fiocruz, Rio de Janeiro, RJ 21040-900 , Brazil