Molecular detection and epidemiological features of picobirnavirus in sheep and goat herds from southern Brazil.

Nathalia Leontino Scherpinski — 2026-10-21

Picobirnaviruses (PBVs) are bisegmented double-stranded RNA viruses that have been detected in faecal samples from diverse hosts, but their epidemiology in small ruminants remains poorly understood. This study investigated PBV occurrence and epidemiological factors associated with detection in 405 animals (355 sheep and 50 goats) from nine herds in southern Brazil. Faecal samples were analysed by polyacrylamide gel electrophoresis (PAGE) and RT-PCR targeting PBV genogroup I (PBV-GI). PBV was detected in all nine herds and by either method in 173/405 (42.7%) animals, including 134/355 (37.7%) sheep and 39/50 (78.0%) goats. Epidemiological analyses were restricted to sheep. After mutual adjustment, faecal consistency was not associated with PBV detection (adjusted odds ratio [aOR] = 0.73, 95% CI: 0.44–1.19; P = 0.207), whereas farming system showed an overall association (P = 0.012), with higher odds in subsistence than extensive systems (aOR = 2.68, 95% CI: 1.39–5.21). Leave-one-herd-out analyses supported the stability of the faecal-consistency result but showed that the farming-system association was sensitive to herd composition. Sequencing of 15 selected RT-PCR amplicons confirmed their identity as PBV, and BLAST analysis revealed heterogeneous closest sequence matches. To our knowledge, this is the first multi-herd epidemiological investigation of PBV in small ruminants in Brazil, demonstrating widespread viral detection and providing new insights into factors associated with PBV detection in sheep.

Deep-learning-based classification of pig carcasses according to sex

Matteo D'Angelo — 2026-10-09

Slaughterhouses provide an opportunity for the automated collection of data from large numbers of animals under standardised conditions. This study investigated the use of deep learning for automated classification of pig carcasses according to sex. A total of 4,219 images of female, entire male and surgically castrated male pigs slaughtered in Italy, Spain and the Netherlands were used. Images were acquired from different perspectives, and the vulva, scrotum or perianal region was annotated according to sex category. A YOLO11m object-detection model was trained and evaluated on an independent test set comprising 672 images from 224 carcasses. Classification performance was assessed both at the single-image level and at the animal level by combining predictions from three views of each carcass. Image-level accuracy was 97.5%, with macro-averaged precision, recall and F1-score of 97.7%, 97.5% and 97.6%, respectively. At the animal level, 219/224 carcasses were correctly classified, corresponding to an accuracy of 97.8%, with macro-averaged precision, recall and F1-score of 97.9%, 98.0% and 97.9%, respectively. All classification errors involved castrated males, particularly their distinction from females, whereas direct confusion between entire males and females did not occur. These results demonstrate the feasibility of deep learning-based sex classification of pig carcasses and support its potential integration into automated slaughterhouse data-collection systems.

Veterinaria Italiana

Molecular detection and epidemiological features of picobirnavirus in sheep and goat herds from southern Brazil.

Deep-learning-based classification of pig carcasses according to sex