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Bovine adenovirus 7 (BAdV-7) is an unclassified member of the genus Atadenovirus with a worldwide distribution and has been reported to induce clinical disease of varying severity in infected cattle, ranging from asymptomatic infections to severe enteric or respiratory disease. In this study, we used next-generation sequencing to obtain the first complete genome sequence of a European strain of BadV-7, from pooled spleen and liver tissue obtained from a deceased newborn Limousin calf. Histopathological analysis and electron microscopy showing systemic lesions in multiple organs with intranuclear amphophilic inclusions observed in endothelial cells in multiple peripheral tissues. Virus isolation was readily achieved from tissue homogenate using bovine esophagus cells (KOP-R), a strategy which should facilitate future in vitro or in vivo BAdV-7 studies. Phylogenetic analysis of available genome sequences of BAdV-7 showed that the newly identified strain groups most closely with a recent BAdV-7 strain, SD18-74, from the USA, confirming that this newly identified strain is a member of the Atadenovirus genus. The fiber gene was found to be highly conserved within BAdV-7 strains but was highly divergent in comparison to Ovine adenovirus 7 (OAdV-7) (39.56% aa sequence identity). Furthermore, we report a variable region of multiple tandem repeats between the coding regions of E4.1 and RH5 genes. In summary, the presented pathological and molecular characterization of this case suggests that further research into the worldwide molecular epidemiology and disease burden of BAdV-7 is warranted., (© 2022. The Author(s).)

The extraordinary olfactory sense of canines combined with the possibility to learn by operant conditioning enables dogs for their use in medical detection in a wide range of applications. Research on the ability of medical detection dogs for the identification of individuals with infectious or non-infectious diseases has been promising, but compared to the well-established and-accepted use of sniffer dogs by the police, army and customs for substances such as money, explosives or drugs, the deployment of medical detection dogs is still in its infancy. There are several factors to be considered for standardisation prior to deployment of canine scent detection dogs. Individual odours in disease consist of different volatile organic molecules that differ in magnitude, volatility and concentration. Olfaction can be influenced by various parameters like genetics, environmental conditions, age, hydration, nutrition, microbiome, conditioning, training, management factors, diseases and pharmaceuticals. This review discusses current knowledge on the function and importance of canines' olfaction and evaluates its limitations and the potential role of the dog as a biomedical detector for infectious and non-infectious diseases., (© 2021. The Author(s).)

Background: The main strategy to contain the current SARS-CoV-2 pandemic remains to implement a comprehensive testing, tracing and quarantining strategy until vaccination of the population is adequate. Scent dogs could support current testing strategies., Methods: Ten dogs were trained for 8 days to detect SARS-CoV-2 infections in beta-propiolactone inactivated saliva samples. The subsequent cognitive transfer performance for the recognition of non-inactivated samples were tested on three different body fluids (saliva, urine, and sweat) in a randomised, double-blind controlled study., Results: Dogs were tested on a total of 5242 randomised sample presentations. Dogs detected non-inactivated saliva samples with a diagnostic sensitivity of 84% (95% CI: 62.5-94.44%) and specificity of 95% (95% CI: 93.4-96%). In a subsequent experiment to compare the scent recognition between the three non-inactivated body fluids, diagnostic sensitivity and specificity were 95% (95% CI: 66.67-100%) and 98% (95% CI: 94.87-100%) for urine, 91% (95% CI: 71.43-100%) and 94% (95% CI: 90.91-97.78%) for sweat, 82% (95% CI: 64.29-95.24%), and 96% (95% CI: 94.95-98.9%) for saliva respectively., Conclusions: The scent cognitive transfer performance between inactivated and non-inactivated samples as well as between different sample materials indicates that global, specific SARS-CoV-2-associated volatile compounds are released across different body secretions, independently from the patient's symptoms. All tested body fluids appear to be similarly suited for reliable detection of SARS-CoV-2 infected individuals., (© 2021. The Author(s).)

Background: As the COVID-19 pandemic continues to spread, early, ideally real-time, identification of SARS-CoV-2 infected individuals is pivotal in interrupting infection chains. Volatile organic compounds produced during respiratory infections can cause specific scent imprints, which can be detected by trained dogs with a high rate of precision., Methods: Eight detection dogs were trained for 1 week to detect saliva or tracheobronchial secretions of SARS-CoV-2 infected patients in a randomised, double-blinded and controlled study., Results: The dogs were able to discriminate between samples of infected (positive) and non-infected (negative) individuals with average diagnostic sensitivity of 82.63% (95% confidence interval [CI]: 82.02-83.24%) and specificity of 96.35% (95% CI: 96.31-96.39%). During the presentation of 1012 randomised samples, the dogs achieved an overall average detection rate of 94% (±3.4%) with 157 correct indications of positive, 792 correct rejections of negative, 33 incorrect indications of negative or incorrect rejections of 30 positive sample presentations., Conclusions: These preliminary findings indicate that trained detection dogs can identify respiratory secretion samples from hospitalised and clinically diseased SARS-CoV-2 infected individuals by discriminating between samples from SARS-CoV-2 infected patients and negative controls. This data may form the basis for the reliable screening method of SARS-CoV-2 infected people.

Harbour porpoises (Phocoena phocoena) in the North Sea live in an environment heavily impacted by humans, the consequences of which are a concern for their health. Autopsies carried out on stranded harbour porpoises provide an opportunity to assess health problems in this species. We performed 61 autopsies on live-stranded harbour porpoises, which died following admission to a rehabilitation centre between 2003 and 2016. The animals had stranded on the Dutch (n = 52) and adjacent coasts of Belgium (n = 2) and Germany (n = 7). We assigned probable causes for stranding based on clinical and pathological criteria. Cause of stranding was associated in the majority of cases with pathologies in multiple organs (n = 29) compared to animals with pathologies in a single organ (n = 18). Our results show that the three most probable causes of stranding were pneumonia (n = 35), separation of calves from their mother (n = 10), and aspergillosis (n = 9). Pneumonia as a consequence of pulmonary nematode infection occurred in 19 animals. Pneumonia was significantly associated with infection with Pseudalius inflexus, Halocercus sp., and Torynurus convolutus but not with Stenurus minor infection. Half of the bacterial pneumonias (6/12) could not be associated with nematode infection. Conclusions from this study are that aspergillosis is an important probable cause for stranding, while parasitic infection is not a necessary prerequisite for bacterial pneumonia, and approximately half of the animals (29/61) probably stranded due to multiple causes. An important implication of the observed high prevalence of aspergillosis is that these harbour porpoises suffered from reduced immunocompetence.

Background: Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results., Results: We demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research., Conclusions: Network meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.