Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.

Autor: Mwanga EP; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania. emwanga@ihi.or.tz.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK. emwanga@ihi.or.tz., Kweyamba PA; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.; Swiss Tropical and Public Health Institute, Kreuzstrasse 2, 4123, Allschwil, Switzerland.; University of Basel, Petersplatz 1, 4001, Basel, Switzerland., Siria DJ; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK., Mshani IH; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK., Mchola IS; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., Makala FE; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., Seleman G; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., Abbasi S; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., Mwinyi SH; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania., González-Jiménez M; School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK., Waynne K; School of Chemistry, The University of Glasgow, Glasgow, G12 8QQ, UK., Baldini F; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK., Babayan SA; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK., Okumu FO; Environmental Health and Ecological Sciences Department, Ifakara Health Institute, Morogoro, Tanzania. fredros@ihi.or.tz.; School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, G12 8QQ, UK. fredros@ihi.or.tz.; School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. fredros@ihi.or.tz.; School of Life Science and Bioengineering, The Nelson Mandela African Institution of Science and Technology, P. O. Box 447, Arusha, Tanzania. fredros@ihi.or.tz.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 27; Vol. 14 (1), pp. 12100. Date of Electronic Publication: 2024 May 27.
DOI: 10.1038/s41598-024-63082-z
Abstrakt: Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission intensities, computed as a product of human biting rates and prevalence of Plasmodium sporozoites in mosquitoes. Unfortunately, current methods for identifying infectious mosquitoes are laborious, time-consuming, and may require expensive reagents that are not always readily available. Here, we demonstrate the first field-application of mid-infrared spectroscopy and machine learning (MIRS-ML) to swiftly and accurately detect Plasmodium falciparum sporozoites in wild-caught Anopheles funestus, a major Afro-tropical malaria vector, without requiring any laboratory reagents. We collected 7178 female An. funestus from rural Tanzanian households using CDC-light traps, then desiccated and scanned their heads and thoraces using an FT-IR spectrometer. The sporozoite infections were confirmed using enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR), to establish references for training supervised algorithms. The XGBoost model was used to detect sporozoite-infectious specimen, accurately predicting ELISA and PCR outcomes with 92% and 93% accuracies respectively. These findings suggest that MIRS-ML can rapidly detect P. falciparum in field-collected mosquitoes, with potential for enhancing surveillance in malaria-endemic regions. The technique is both 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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