Malaria is still the number one killer of children under the age of five. In 2021 alone, malaria killed 600,000 people – and over 200 million were infected with the disease (1). Malaria can, of course, be treated, but there are barriers to diagnosis. For example, blood tests offer rapid and relatively cheap detection, but some people fear needles and few people like them, which can prevent asymptomatic patients from coming forward for screening. Looking to fill the diagnostic gap for quick, non-invasive, and scalable tools, researchers have developed a smart-phone operated, near-infrared mass spectrometry (NIRS)-based technique to detect the presence of the parasites responsible (2).
Participants presenting with malaria symptoms in São Gabriel da Cachoeir, Brazil, were subjected to a 5–10 second scan with the portable NIRS device to collect diagnostic spectral signatures indicating the presence of Plasmodium vivax or Plasmodium falciparum in the ears of patients.
Though spectra were also collected from the arms and fingers of malaria positive and negative individuals, spectra from the ear revealed the most distinct bands. “Our method can screen thousands of people in a day and can successfully identify asymptomatic patients. This way, their treatment can be facilitated, and community transmission will be reduced,” says lead author Maggy Lord, School of Biological Sciences, The University of Queensland, Australia.
“[NIRS] is reagent-free, non-invasive, and instantaneous – therefore, it can be used to screen people without the need for drawing blood, specialized skills, or sample processing procedures,” adds Lord.
Given that COVID-19 limited the study’s population size, the authors are aiming to conduct similar work on a larger scale – and in more areas where malaria transmission occurs – in a bid to develop even more robust predictive models.
Looking further ahead, Lord hopes the new technique will help guide the World Health Organization’s epidemiologic approach for malaria control.
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References
- World Health Organization (2022). Available at: https://bit.ly/2UfzJ5E
- GA Garcia et al., PNAS Nexus (2022). PMID: 36712329.
