Diseases of the respiratory system are known to negatively impact the profitability of the pig industry, worldwide. Considering the relatively short lifespan of pigs, lesions can be still evident at slaughter, where they can be usefully recorded and scored. Therefore, the slaughterhouse represents a key check-point to assess the health status of pigs, providing unique and valuable feedback to the farm, as well as an important source of data for epidemiological studies. Although relevant, scoring lesions in slaughtered pigs represents a very time-consuming and costly activity, thus making difficult their systematic recording. The present study has been carried out to train a convolutional neural network-based system to automatically score pleurisy in slaughtered pigs. The automation of such a process would be extremely helpful to enable a systematic examination of all slaughtered livestock. Overall, our data indicate that the proposed system is well able to differentiate half carcasses affected with pleurisy from healthy ones, with an overall accuracy of 85.5%. The system was better able to recognize severely affected half carcasses as compared with those showing less severe lesions. The training of convolutional neural networks to identify and score pneumonia, on the one hand, and the achievement of trials in large capacity slaughterhouses, on the other, represent the natural pursuance of the present study. As a result, convolutional neural network-based technologies could provide a fast and cheap tool to systematically record lesions in slaughtered pigs, thus supplying an enormous amount of useful data to all stakeholders in the pig industry. © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article' s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article'

The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI-based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high-throughput slaughterhouses. The present study aims to develop an AI-based method capable of recognizing and quantifying enzootic pneumonia-like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI-based method proposed herein could properly identify and score enzootic pneumonia-like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.

polyserositis mostly affects 4–8 weeks old piglets and is usually caused by Glaesserella parasuis, and/or Streptococcus suis, and/or Mycoplasma hyorhinis. The present study aimed to investigate the prevalence and etiology of polyserositis in a tricky pig herd. The concurrent effect of vaccination for Glässer’s disease was also assessed. A total of 46 sows and 387 piglets were herein investigated, subdivided into three groups based on their immune status (i.e., vaccination of sows and piglets). All the piglets found spontaneously dead between the 2nd and 16th week of age were recorded and necropsied. Whenever polyserositis was diagnosed, biomolecular investigations were carried out to detect the above-mentioned pathogens. Mycoplasma hyorhinis was detected most frequently (n = 23), often as the only causative agent (n = 15), whereas S. suis was observed in 8 cases (6 as the only pathogen). Moreover, Glaesserella parasuis was demonstrated in 6 piglets, always in combination with Mycoplasma hyorhinis and/or Streptococcus suis. Vaccination did not significantly affect mortality rates. Overall, our data indicate that polyserositis is likely caused by an intricate puzzle of pathogens, even when dealing with a small herd and during a short time span. That makes it challenging to achieve the correct diagnosis and to properly manage this health issue.

The investigation of bacterial microbiota represents a developing research field in veterinary medicine intended to look for correlations between animal health and the balance within bacterial populations. The aim of the present work was to define the bacterial microbiota of the oral cavity of healthy sows, which had not been thoroughly described so far. In total, 22 samples of oral fluid were collected and analyzed by 16S-rRNA gene sequencing. CLC Genomics Workbench 20.0 (QIAGEN Digital Insights, Aarhus, Denmark) was then used to examine the results. The predominant orders were Lactobacillales, Clostridiales, and Corynebacteriales. Lactobacillaceae, Corynebacteriaceae, Moraxellaceae, Aerococcaceae, and Staphylococcaceae were the most represented families. As regards the most abundant genera, Lactobacillus, Corynebacterium, Acinetobacter, Staphylococcus, Rothia, Aerococcus, and Clostridium can be pointed out as the bacterial core microbiota. Sows were also divided into “gestating” and “lactating” groups, and mild differences were found between pregnant and lactating sows. The data herein described represent an original contribution to the knowledge of the porcine bacterial microbiota. Moreover, the choice of sows as experimental animals was strategic for identifying the adult microbial community. These data provide a basis for further studies on the oral bacterial microbiota of pigs.

Background and Aim: Neutrophils represent between 20% and 75% of white blood cells in animals and play a key role in an effective immune response. The generation of reactive oxygen species (ROS) is commonly referred to as an oxidative burst and is crucial under healthy and disease conditions. Interestingly, ROS are emerging as regulators of several neutrophil functions, including their oxidative burst. The present study aimed to investigate the effect of hydrogen peroxide on the oxidative burst of neutrophils, collected from domestic animal species (namely, pig, cattle, and sheep), and exposed to different stimuli. Materials and Methods: A total of 65 slaughtered animals were included in the present study: Twenty-two pigs, 21 cattle, and 22 sheep. Blood samples were collected at bleeding and neutrophils were then purified using ad hoc developed and species-specific protocols. Neutrophils were treated with hydrogen peroxide at micromolar-to-millimolar concentrations, alone, or combined with other stimuli (i.e., opsonized yeasts, and phorbol 12-myristate 13-acetate). The generation of ROS was evaluated using a luminol-derived chemiluminescence (CL) assay. For each animal species, data were aggregated and reported as mean area under curve±standard deviation. Finally, data were statistically analyzed by one-way ANOVA, followed by Tukey's post hoc test. Results: Exposure of bovine and ovine neutrophils to hydrogen peroxide alone resulted in a dose-dependent enhancement of the CL response, which was significantly stronger at its highest concentration and proved particularly prominent in sheep. Opsonized yeasts and phorbol 12-myristate 13-acetate both proved capable of stimulating the generation of ROS in all animal species under study. Hydrogen peroxide negatively modulated the oxidative burst of neutrophils after exposure to those stimuli, observed response patterns varying between pigs and ruminants. Porcine neutrophils, pre-exposed to micromolar concentrations of hydrogen peroxide, showed a decreased CL response only to opsonized yeasts. Conversely, pre-exposure to hydrogen peroxide reduced the CL response of ruminant neutrophils both to yeasts and phorbol 12-myristate 13-acetate, the effect being most prominent at 1 mM concentration. Conclusion: These results indicate that hydrogen peroxide is capable of modulating the oxidative bursts of neutrophils in a species-specific and dose-dependent manner, substantial differences existing between pigs and ruminants. Further investigation is required to fully comprehend such modulation, which is crucial for the proper management of the generation of ROS under healthy and disease conditions.