RNA viruses exhibit high mutation rates and strong host adaptive capacity, posing major public health challenges. Although meta-transcriptomic studies have uncovered vast numbers of novel RNA viral sequences, identifying those with spillover risks remains difficult. Current virus host-prediction methods can only predict a narrow set of host labels at coarse taxonomic levels (e.g., kingdom or order), which hampers precise evaluation of cross-species transmission risk and may overlook potential zoonotic hosts. To overcome these limitations, we developed UniVH, a unified virus–host association prediction framework trained on an exceptionally broad spectrum of 90 viral families and 240 host families, enabling robust prediction even for phylogenetically distant or data-scarce hosts. UniVH achieved a host prediction accuracy of 71.2% for novel viruses discovered after 2020, representing a 15.3% improvement over conventional BLASTp-based homology approaches. Feature interpretation revealed that viral structural genes and host immune- and metabolism-related genes contributed most significantly to predictive performance. Model predictions indicated widespread host-range expansion, with 20 mammalian virus families doubling their documented mammalian host ranges and several showing marked increases in viruses with human-infection potential. This unified, interpretable framework represents an important methodological advance for future RNA virus spillover-risk evaluation and emerging virus prioritization.

Ticks transmit diverse pathogens, posing significant public health threats in northeastern China; clarifying human-biting tick species and their carried pathogens is crucial for risk assessment and prevention. This study was designed as a pathogen detection and molecular epidemiological investigation. During the May-August period of each of 2023 and 2024, 232 ticks that had bitten humans were collected from a local sentinel hospital. Ticks were morphologically identified, followed by DNA extraction, semi-nested/nested PCR targeting specific genetic markers, and Sanger sequencing to detect bacterial pathogens. Four tick species were found, with Ixodes persulcatus dominant (87.9%), followed by Dermacentor silvarum (6.9%), Haemaphysalis concinna (3.5%), and Haemaphysalis japonica (1.7%). Seven bacterial pathogens were detected, including Candidatus Rickettsia tarasevichiae (35.3%) and Borreliella garinii (17.2%); 49.6% of ticks were pathogen-positive. The coinfection rate was 13.8%, most commonly Candidatus R. tarasevichiae plus B. garinii, and I. persulcatus carried all pathogens. These findings provide basic data on human-biting tick species and their bacterial pathogen spectrum in the region, serving as a reference for subsequent tick-borne disease research and targeted prevention.