Pollination is one of the most essential economic services, and approximately 87.5
Listeria monocytogenes (L. monocytogenes) is a significant foodborne pathogen, posing a threat to public health. This study investigated the prevalence and genomic diversity of L. monocytogenes in 466 wild animals sampled across Central and Southern Italy (2017-2023), including species such as wild boar, red fox, and wolf, to assess their role as reservoirs and potential links to the food chain. Overall, 22.5% of the animals tested positive, and 118 L. monocytogenes strains were isolated, predominantly from wild boar (n=46), red fox (n=20), and Italian wolf (n=15). Whole Genome Sequencing (WGS) analysis revealed high genomic diversity, classifying the strains into 27 Clonal Complexes (CCs) and 31 Sequence Types (STs). Both hypervirulent clones (e.g., CC1, CC6, CC207) and hypovirulent clones (e.g., CC9, CC19), known for their persistence, were identified, with wild boars harboring a majority of the hypervirulent isolates. All strains carried key virulence genes, and accessory virulence factors, particularly LIPI-3, were detected in hypervirulent strains. Persistence factors, such as the Stress Survival Islet 1 (SSI-1) and genes for metal/disinfectant resistance (cadA, qacA), were also detected, particularly in wild boar isolates. Crucially, core-genome MLST (cgMLST) analysis demonstrated direct genomic links between the wildlife isolates and the Italian National Reference Laboratory database. Multiple clusters were identified, connecting strains from wild animals (wild boars, foxes, wolves) with those from meat products, fresh salads, and food processing environments. A persistent CC9 cluster, circulating in the meat chain for seven years, was strongly correlated with wild boar isolates, underscoring the role of wildlife as a reservoir that continuously introduces both high-virulence and highly persistent strains into the food production system. These findings emphasize the necessity of integrating wildlife surveillance into public health strategies to mitigate the risk of zoonotic transmission, particularly through game meat consumption and handling.
Bovine tuberculosis (bTB), caused by Mycobacterium bovis, is one of the most important zoonotic diseases of veterinary and public health concern, with significant economic, productive, and sanitary implications. In the Campania region, characterized by a high density of cattle and buffalo farms and a complex diversity of production systems, understanding the risk factors that influence the persistence and spread of the disease is essential for designing effective control and eradication strategies. This retrospective longitudinal observational cohort study analyzed a seven-year period (2017-2023) with the aim of identifying the main structural, management, and environmental variables associated with the occurrence of bTB outbreaks in cattle and buffalo herds across Campania. Data were integrated from national information systems (SANAN, SIMAN, and BDN) and analyzed using Fisher's exact test, Wilcoxon test, and univariate and multivariate logistic regression models. The covariates considered included herd size, animal movements, previous history of infection, territorial characteristics (farm density, outbreak proximity, presence of pasture), and the presence of other domestic species. In the buffalo cohort (n = 892), 13% of farms reported at least one positive case. Independent risk factors identified were previous infection (OR = 3.14; 95% CI: 1.61-5.92), introduction of animals from outside the region (OR = 0.59; 95% CI: 0.19-0.85), and the number of outbreaks within a 2 km radius (OR = 1.19; 95% CI: 1.07-1.33). In the cattle cohort (n = 4,467), the prevalence of positivity was 3%. Significant predictors included previous infection (OR = 8.98; 95% CI: 4.18-17.76), presence of pasture within 2 km (OR = 2.65; 95% CI: 1.62-4.19), animal movements (OR = 1.91; 95% CI: 1.11-3.57), and the number of outbreaks within a 2 km buffer (OR = 1.43; 95% CI: 1.28-1.58). The findings highlight the crucial role of herd-level (size and movements), historical (previous infection), and spatial (outbreak proximity, pasture presence) factors in the transmission dynamics of bTB. The integration of epidemiological and spatial analytical approaches provides a comprehensive understanding of the regional risk landscape, offering valuable insights for improving biosecurity, surveillance, and control strategies aimed at reducing the burden of bovine tuberculosis in both cattle and buffalo populations.
Noroviruses are a leading cause of acute gastroenteritis worldwide, and their rapid and reliable detection remains a significant challenge for current diagnostic technologies. In this work, we present a plasmonic metasurface platform based on Y-shaped nanocavity arrays designed for label-free viral detection using surface-enhanced Raman spectroscopy (SERS). The nanostructured metasurface was fabricated using electron-beam lithography and engineered to support plasmonic resonances near the 785 nm excitation wavelength, enabling strong electromagnetic field localization and Raman signal enhancement. SERS measurements were performed on human norovirus (HNoV) and murine norovirus (MNV), producing distinct vibrational fingerprints associated with viral capsid biomolecules. Despite the structural similarity between both viruses, multivariate statistical analysis based on principal component analysis (PCA) enabled clear discrimination between their spectral signatures, with the first two principal components explaining more than 98% of the total variance captured. These results demonstrate that plasmonic metasurfaces combined with SERS fingerprinting and statistical analysis provide a powerful strategy for the label-free identification of closely related viral pathogens. The proposed platform highlights the potential of nanophotonic biosensors for rapid pathogen detection in biomedical diagnostics and environmental monitoring.
Abstract This data paper presents a curated, georeferenced dataset of the frequencies of the two main target site mutations (V1016G and F1534C) associated with resistance to pyrethroid insecticides in Aedes albopictus in Italy. Populations were collected in 102 out 107 Italian provinces between 2023 and 2025. Specimens were sampled by members of the Mosquito Insecticide Resistance Italian Network (MosqIRIT) as part of RN2 activities within the INF-ACT project. Genotyping was performed on 3,517 individuals by specific allele-specific PCR assays. Each record includes metadata on sampling site, administrative location, developmental stage, collection method, and mutation-specific genotype frequencies. To support spatial analysis modelling effort, the dataset integrates geographic, eco-climatic, and demographic data. This resource will support mosquito control programs, pyrethroid resistance monitoring and managing, as well as ecological modelling, and is compliant with the FAIR data program.
