Veterinaria italiana最新文献

The bovine leukemia virus (BLV) is a pathogen of high importance for the dairy industry. Currently, twelve genotypes have been described worldwide with different pathogenicity and virulence, so it is critical to evaluate the circulating genotypes in each country/region to associate this information with risk situations. The aim of this work was to perform a phylogenetic and mutational analysis of the BLV tax gene in cows that belong to specialized dairies in the Department of Antioquia, Colombia. A conventional PCR for the tax gene was performed on 86 bovine samples. Sanger sequencing was carried out on 22 PCR products with a size of 959 bp. The sequences obtained were aligned and analyzed using the Maximum Likelihood and Bayesian phylogenetic approaches. A predictor was used to analyze the possible impact of amino acid substitution on the Tax structure and function. Although all sequences were found to belong to genotype 1, four of the 22 sequences were grouped into a different subclade G1A. Fifty percent of the samples showed punctual mutations in their amino acids. Mutation S104L was identified as "possibly harmful," while the V146A change found in all subclade G1A samples was identified as "possibly benign." Although further studies are necessary to determine whether there is an effect of these mutations on the development of the disease, this study presents part of the evolution of the virus and the changes at the amino acid level that are occurring in cattle from specialized dairy farms in Antioquia.

Taenia multiceps is found in canids and in its larval stage is known as Coenurus cerebralis causes coenurosis. The disease has a significant impact on the economic value of sheep and goats. The aim of the current study was to identify multiple cysts in the brain of a sheep displaying common symptoms of C. cerebralis and to amplify and sequence analyse the mitochondrial cytochrome oxidase subunit 1 gene of each individual cyst by PCR. The research material used was the head of a sheep exhibiting neurological symptoms. Seven cysts associated with C. cerebralis were detected in the brain upon thorough examination. The mt-CO1 gene was amplified by PCR, and all isolates were sequenced. Sequence alignment revealed the presence of point mutations, and 20 polymorphic sites were identified, of which 7.7% (1/13) were parsimony informative. The isolates demonstrated significant haplotype diversity and low nucleotide diversity. In this study, only one isolate obtained from Turkey belonged to the fourth main haplotype, while the remaining six isolates constituted a distinct and unique single haplotype. This is the first time that haplotypic distinctions have been identified among isolates obtained from a sheep brain that is multiply infected with C. cerebralis.

From 19 to 21 September 2023, the city of Silvi Marina, in the province of Teramo, hosted GeoVet2023, continuing a tradition that, since 2001, has positioned the conference as a global reference for interdisciplinary research at the intersection of geospatial science and veterinary medicine. With the theme "Expanding Boundaries: Interdisciplinary Geospatial Research for the One Health Era", GeoVet2023 gathered experts from diverse fields to address critical challenges, including the impacts of climate change, human activities, and interactions between domestic and wild animals on veterinary and public health, as well as food safety. GeoVet2023 continued the trajectory set by GeoVet2019, which explored how emerging technologies and data-driven approaches in the Big Data era redefined spatial analysis in animal and public health. The 2023 edition expanded these discussions by incorporating practical applications of real-time data science, artificial intelligence, and decision-making tools, along with social network data, citizen science, and advanced spatio-temporal methods to address challenges posed by climate change and the interconnectedness of human, animal, and environmental health. Another key aspect of the conference was the dialogue between scientists and international organizations, pointing out the crucial role of effective communication in bridging research and policymaking. Indeed, in his opening keynote, Marius Gilbert shared lessons from managing the COVID-19 pandemic in Belgium, highlighted the challenges related to public communication and underscoring the need for health science literacy, trust, and structured scientific debate. The scientific program of GeoVet2023 included five keynotes, eight senior presentations, 43 engaging talks, and 50 informative posters, representing 106 research projects in total. The relationships established and the knowledge shared during this conference not only reflect its innovation but also provide a roadmap to guide the progress of interdisciplinary geospatial research and One Health strategies in the years to come. This special issue of Veterinaria Italiana captures the innovation, integration, and practical applications that characterized the GeoVet2023 conference discussions. Presenting 12 selected studies, it showcases the latest development in spatial epidemiology and geospatial tools, providing solutions for pressing issues such as disease surveillance, antimicrobial resistance, and the impacts of environmental changes on health systems. These studies provide concrete examples of how geospatial research improves veterinary and public health within the One Health framework.

Water temperature is a vital parameter impacting the growth and survival of aquatic life. Using satellite-derived infrared data, this study analysed the trend of sea surface temperature (SST) from 2008 to 2022 of the Adriatic coastal waters of Italian regions. The "Mediterranean Sea High Resolution and Ultra High Resolution Sea Surface Temperature Analysis" product collected from the Copernicus Marine Service of European Copernicus programme was used, as a good compromise among spatial accuracy, temporal frequency and coverage. SST were derived in 176 locations, placed in the Adriatic Sea from the southern limit of the lagoon of Venice (Veneto) to Santa Maria di Leuca (LE), at a distance from the coast between 500 m and 5000 m (0.3 - 2.7 nautical miles). Time series analysis was applied to average value of daily SST calculated from the selected spatial locations to identify the additive model components: trend, seasonality and random effects. The trend component was isolated and assessed using a linear regression model to determine its significance and magnitude. A 0.010 °C/year increase in SST was observed. Additionally, marine heatwaves and cold spells were consistently registered throughout the entire observation period, with a north-south gradient in intensity.

Farm fragmentation refers to spatial disaggregation of farms into smaller, often highly separated parcels of land. This can create a number of problems; administrative, economic, environmental and epidemiological. Ireland has a high proportion of fragmented farms, although this an issue not unique to Ireland. From a epidemiological perspective, where a farm is heavily fragmented, there is uncertainty in assigning a location to where livestock have spent time on that farm. We explore techniques to quantify the extent and regional variation in fragmentation and the between-fragment distances of fragmented farms in Ireland with the aim of reducing this uncertainty. The findings, which have made available as an online resource, allow for more precision in spatial analyses of bovine populations and help enhance surveillance and field epidemiology.

Ticks represent a reservoir of zoonotic pathogens, and their numbers are increasing largely in wildlife. This work is aimed at producing maps of suitable habitats for ticks in Aosta Valley, Italy based on multitemporal EO data and veterinary datasets (tick species and distribution in wild hosts). EO data were processed in Google Earth Engine considering the following inputs: A) Growing Degree Ticks (GDT), B) NDVI from MOD09GA, C) NDVI entropy, D) distance from water bodies, E) topography, F) rainfalls from CHIRPS as monthly composites along the 2020, 2021 and 2022 years. Ticks were collected from hunted, injured, and found-dead wild animals ( Sus scrofa, Capreolus capreolus, Rupicapra rupicapra, Cervus elaphus); they were labeled at species level using taxonomic keys. Between September 2020 and December 2022, a total of 90 ticks were collected from 89 wild animals. Ixodes ricinus was the most prevalent tick species, followed by Dermacentor marginatus and Dermacentor spp. Molecular analyses demonstrated the presence of Anaplasma spp., B. burgdorferi sensu lato and Rickettsia spp. pathogens in infected ticks. To assess human population potential exposure to tick Meta® population dataset was used. In conclusion this study shows the potentialities of Remote sensing improving the technological transfer to the veterinarian sector.

During epidemics, pandemics, or animal disease outbreaks, the large-scale disposal of carcasses presents greater environmental and biosecurity challenges. In Europe, disposal through a rendering plant is the preferred option, but the on-site carcasses burial may be authorised due to logistical and economic advantages. This study utilised a comprehensive GIS-based approach and focuses on the challenges and strategies for large-scale carcass disposal, particularly in the context of avian influenza outbreaks in the Lazio Region of Italy. Integrating data from official geospatial sources regarding presence of environmental restrictions and regulations, factors affecting susceptibility to groundwater contamination, factors affecting soil stability over time, potential burial sites were identified. The resulting map provides decision-makers with valuable information for prompt and efficient response during disease outbreaks. The study underscores the importance of a multidisciplinary approach involving veterinarians, epidemiologists, GIS experts, and geologists. Further research and international consensus are essential to standardize the selection of geographic variables/layers to use in similar projects. This study significantly contributes to the preparedness of environment, health and animal/human interface events.

Documented freedom from disease is paramount for international free trade of animals and animal products. This study describes a scenario tree analysis to estimate the probability of freedom from Enzootic bovine leukosis (EBL) in Italy and Slovenia using information gathered via the data collection tool developed in the COST action project SOUND-control. Data on EBL control programmes (CPs) from 2018 to 2021 were used to build the models. Since animals are only sampled on the farm, one surveillance system component (SSC) was considered. The posterior probability of freedom (PostPfree) was estimated in time steps of one year, from 2018 to 2021.  After each year, the calculated from the previous year, combined with the probability of introduction, was used as a prior probability for the next year.  The herd level design prevalence was set to 0.2% in accordance with the Council Directive 64/432/EEC and the within herd design prevalence was set to 15%.  As Slovenia implemented a risk-based surveillance, targeting the herds importing cattle, in its model the design herd prevalence was combined with an average adjusted risk to calculate the effective probability of a herd importing cattle being infected.  The models were run for 10,000 iterations.  Over the study period the mean estimates were: i) for Italy both the surveillance system sensitivity ( SSe) and PostPFree 100%, with no differences between simulations and years, ii) for Slovenia the SSe was 50.5% while the PostPFree was 81.6%.

Disease maps are integral to spatial epidemiology and public health. The map appearance and analysis of corresponding data may both depend on a map projection used to transform the 3-dimensional world onto a 2-dimensional surface. Map projections necessarily introduce bias - an issue that has not received full attention in the literature. This study aims to demonstrate the impact map projections can have on spatial analysis and disease maps for public health. Case studies applied varying map projections, including the Lambert, Mercator and Robinson projections, to Israel, North Carolina and Southern Ontario as study areas. The effect of projections on various measures, estimates, tests and models was assessed. When the map projection was changed: (i) a distance in Israel increased by 30%; (ii) for Southern Ontario an areal size increased by almost 95%; Moran's I test switched from significant to not; and (iii) a single disease cluster in North Carolina converted into three distinct clusters. Visual bias in disease mapping is unavoidable and should be recognized. Disease maps and spatial analytical inferences, including disease clusters should be reported with their geographic projection. Using geographic coordinates can prevent analytical bias.

One health is based on an interdisciplinary collaboration across professions using a common language. Geographic epidemiology is the study of spatial patterns of population health in a study area. Such spatial patterns (trend, cluster and clustering) require clear definition to be meaningful in science communication. However, the term "disease cluster" has been defined in the literature in various and rather different ways. When geographic epidemiology is unable to make sense of its own concepts it is questionable how respective research results can benefit one health. The goal of this study was to clarify the disease cluster concept. Examples of disease cluster definitions from the literature were used for illustration. The epidemiological triangle of causation (agent, host and environment) was used to conceptualize geographic epidemiological data analysis. The term disease cluster was distinguished from related concepts (clustering, high-risk area, hot spot and outbreak) additionally the semantics and statistical meaning of expectation and prediction were reviewed to further identify the cluster concept as a statistical outlier. The new paradigm of the geographic epidemiological trillium is proposed here and embedded within the spatial generalized linear mixed model to clarify concepts of spatial patterns and guide epidemiological research and teaching.