Ticks attaching to ear canals of humans and animals are the cause of otoacariasis, common in rural areas of Nepal. The plant Clerodendrum viscosum is used in multiple indigenous systems of medicine by ethnic communities in the Indo-Nepali-Malaysian region. Visiting the Chitwan National Park, we learned that in indigenous medicine, flower extract of C. viscosum is utilized to treat digestive disorders and extracts from leaves as tick repellent to prevent ticks from invading or to remove them from the ear canal. The objective of our study was to provide support to indigenous medicine by characterizing the in vivo effect of leave extracts on ticks under laboratory conditions and its phytochemical composition. We collected plant parts of C. viscosum (leaves and flowers) and mango (Mangifera indica) leaves at the Chitwan National Park, previously associated with repellent activity to characterize their effect on Ixodes ricinus ticks by in vivo bioassays. A Q-ToF high-resolution analysis (HPLC-ESI-QToF) was conducted to elucidate phenolic compounds with potential repellent activity. Clerodendrum viscosum and M. indica leaf extracts had the highest tick repellent efficacy (%E = 80-100%) with significant differences when compared to C. viscosum flowers extracts (%E = 20-60%) and phosphate-buffered saline. Phytochemicals with tick repellent function as caffeic acid, fumaric acid and p-coumaric acid glucoside were identified in C. viscosum leaf extracts by HPLC-ESI-QToF, but not in non-repellent flower extracts. These results support the Nepali indigenous medicine application of C. viscosum leaf extracts to repel ticks. Additional research is needed for the development of natural and green repellent formulations to reduce the risks associated with ticks resistant to acaricides.
Host-virus associations have co-evolved under ecological and evolutionary selection pressures that shape cross-species transmission and spillover to humans. Observed virus-host associations provide relevant context for newly discovered wildlife viruses to assess knowledge gaps in host-range and estimate pathways for potential human infection. Using models to predict virus-host networks, we predicted the likelihood of humans as hosts for 513 newly discovered viruses detected by large-scale wildlife surveillance at high-risk animal-human interfaces in Africa, Asia, and Latin America. Predictions indicated that novel coronaviruses are likely to infect a greater number of host species than viruses from other families. Our models further characterize novel viruses through prioritization scores and directly inform surveillance targets to identify host ranges for newly discovered viruses.