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| Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Artículo científico |
| Lenguaje: | en |
| Publicado: |
One health (Amsterdam, Netherlands)
2025
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| Acceso en línea: | https://pubmed.ncbi.nlm.nih.gov/40894953/ |
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- Real-time malaria detection in the Amazon rainforest via drone-collected eDNA and portable qPCR. Ip, Yin Cheong Aden Montemartini, Luca Chang, Jia Jin Marc Desiderato, Andrea Franco-Sierra, Nicolás D Geckeler, Christian Herrera, Mailyn Adriana Gonzalez Gregorini, Michele Jucker, Meret Kirchgeorg, Steffen Lüthi, Martina Mächler, Elvira Thostrup, Frederik Bendix Murari, Guglielmo Mura, Marina Pulido-Santacruz, Paola Sangermano, Florencia Schindler, Tobias Melvad, Claus Mintchev, Stefano Deiner, Kristy Zoonotic malaria risk at human-wildlife-environment interfaces requires surveillance that integrates signals from reservoirs, vectors and the environment. We coupled a drone-based environmental DNA (eDNA) canopy swabbing approach with portable quantitative PCR (qPCR) to detect DNA in situ during a 24-h field exercise in the Amazon rainforest. Drone-lowered sterile swabs into the canopy, which were then extracted and subjected to a multiplex pan- assay targeting five human-infecting species (limit of detection 0.2 parasites μL). Of 12 samples (10 canopy swabs, 2 field blanks; 13 total runs including repeats), one canopy swab amplified in duplicate (Ct = 28.7 and 29.23), while positive controls amplified as expected (Ct = 30.82 and 31.11) and all other environmental samples and blanks were negative. Passive acoustics confirmed co-occurring howler monkeys ( spp.), a known reservoir, whereas mosquitoes were not recovered from concurrently deployed insect canopy traps. The end-to-end workflow, from drone deployment to qPCR diagnostic readout, averaged 1.5 h per assay, without requiring cold-chain logistics. This proof-of-concept demonstrates that intracellular parasite DNA can be recovered from canopy surfaces and read out in real-time, providing upstream, landscape-level intelligence to guide targeted vector surveillance in remote settings. Our approach operationalizes One Health by integrating environmental, wildlife, and vector signals within a single technological platform, representing a paradigm shift from reactive, sector-specific surveillance to proactive, integrated pathogen intelligence across the human-animal-environment interface.