We are making great progress in enhancing malaria screening tests for large population surveys ~ Malaria screening in large populations requires a method that is both low-cost and highly sensitive so that we can pick up even those infections with very very low parasite densities. This is particularly helpful for elimination of the disease. In this new study led by my colleague, Issa Mshani (@IssaMshani1), we show that using mid-infrared spectroscopy with AI can quickly and accurately detect malaria, even at very low levels and in people with anemia. This method simply involves scanning dried blood spots with a desktop mid-infrared scanner and analysing the spectra using pre-trained AI classifiers, making it readily adaptable to field conditions in low-resource settings. It is a simple, cost-effective approach that could significantly improve malaria detection and help in the fight to eliminate the disease, especially in low-resource areas ~ Mshani et al 2024. Screening of malaria infections in human blood samples with varying parasite densities and anaemic conditions using AI-Powered mid-infrared spectroscopy link.springer.com/article/10… @ifakarahealth, @UofGlasgow, @VectorsGlasgow,@IssaMshani1 @Baldini_Fra @SimonAB @magonji @manuepeter, @DoreenSiria @SophiaMwinyi

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes ~ Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning to swiftly and accurately detect P. falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. The technique is fast, scanning 60–100 mosquitoes per hour, and cost-efficient, requiring no biochemical reactions and therefore no reagents. Given its previously proven capability in monitoring key entomological indicators like mosquito age, human blood index, and identities of vector species, we conclude that MIRS-ML could constitute a low-cost multi-functional toolkit for monitoring malaria risk and evaluating interventions. Work led by @manuepeter_ Mwanga et al 2024 nature.com/articles/s41598-0… @ifakarahealth @UofGSBOHVM @Baldini_Fra @SimonAB @IssaMshani1