Cannabis is used in the treatment of several human conditions; however, its use is still less explored in veterinary medicine. This systematic review aims to summarize the evidence of efficacy and safety of the use of cannabis for the treatment of animal disease. A literature search was performed for studies published until 16 March 2021 in five databases. Randomized clinical trials (RCTs) that reported the efficacy or safety of cannabis in the treatment of animal disease were included. The RoB 2 Tool was used to assess the risk of bias. A total of 2427 records were identified, of which six studies fully met the eligibility criteria. RCTs were conducted in dogs with osteoarthritis (n = 4), with epilepsy (n = 1), and with behavioral disorders (n = 1). All studies used cannabidiol (CBD) oil in monotherapy or in combination with other drugs. Studies used CBD at 2 or 2.5 mg kg−1 twice daily (n = 4), orally (n = 5), during 4 or 6 weeks (n = 3), and compared CBD with placebo (n = 5). CBD significantly reduced pain and increased activity in dogs with osteoarthritis (n = 3). Moreover, CBD significantly reduced the frequency of seizures in dogs with epilepsy (n = 1) and the aggressive behavior of dogs (n = 1). Although promising results were identified, studies were heterogeneous and presented risks of bias that required caution in the interpretation of findings. Therefore, there was some evidence to support the use of CBD in dogs with osteoarthritis to reduce pain and increased activity, but limited evidence against epilepsy and behavioral problems. In addition, CBD was well tolerated with mild adverse effects. More RCTs with high quality of evidence are needed, including greater numbers of animal subjects, additional species, and clear readout measures to confirm these findings.

The aim of this systematic review was to investigate the prevalence of leptospirosis among stray and sheltered dogs worldwide. Six databases were searched, which resulted in the retrieval of 476 articles. Sixty articles were selected for analysis according to 10 quality criteria. Among the selected papers, 26 papers [43.4% (26/60)] met five of the 10 quality criteria established, 10 papers [16.7% (10/60)] met three criteria, nine papers [15.0% (9/60)] met four criteria, six papers [10.0% (6/60)] met six criteria, four papers [6.7% (4/60)] met eight criteria, and three papers [5.0% (3/60)] met nine of the 10 criteria, whereas two papers [1.7% (1/60)] met two and seven [1.7% (1/60)] criteria. Publications originated mainly from the Americas [45.0% (27/60)] and in the last 16 years (2003–2019) [81.7% (49/60)], and most of the sampled dogs were stray dogs [65.0% (39/60)]. The most commonly used diagnostic test for leptospirosis was the microscopic agglutination test [78.4% (47/60)] followed by polymerase chain reaction [21.7% (13/60)], and the most common serovars were Canicola [71.4% (35/49)], Icterohaemorrhagiae [65.3% (32/49)], Grippotyphosa [40.8% (20/49)], and Pomona [40.8% (20/49)]. In conclusion, our results showed that Leptospira spp. are present in unowned dogs worldwide; however, the low-methodological quality of the recovered cross-sectional studies precluded a meta-analysis.

Livestock welfare assessment helps monitor animal health status to maintain productivity, identify injuries and stress, and avoid deterioration. It has also become an important marketing strategy since it increases consumer pressure for a more humane transformation in animal treatment. Common visual welfare practices by professionals and veterinarians may be subjective and cost-prohibitive, requiring trained personnel. Recent advances in remote sensing, computer vision, and artificial intelligence (AI) have helped developing new and emerging technologies for livestock biometrics to extract key physiological parameters associated with animal welfare. This review discusses the livestock farming digital transformation by describing (i) biometric techniques for health and welfare assessment, (ii) livestock identification for traceability and (iii) machine and deep learning application in livestock to address complex problems. This review also includes a critical assessment of these topics and research done so far, proposing future steps for the deployment of AI models in commercial farms. Most studies focused on model development without applications or deployment for the industry. Furthermore, reported biometric methods, accuracy, and machine learning approaches presented some inconsistencies that hinder validation. Therefore, it is required to develop more efficient, non-contact and reliable methods based on AI to assess livestock health, welfare, and productivity.

Acute interstitial pneumonia (AIP) of cattle has been recognized for many decades. While the pathogenesis and risk factors for this condition in pastured cattle are relatively well characterized, there remains a poor understanding of the disease as it occurs in intensively fed cattle such as in beef feedlots. Specifically, in pastured cattle, AIP results from excessive ruminal production of the pneumotoxicant 3-methylindole (3-MI). In feedlot cattle, the evidence to substantiate the role of 3-MI is comparatively deficient and further investigations into the cause, pathogenesis, and control are sorely needed. This review highlights our current understanding of AIP with a focus on the disease as it occurs in feedlot cattle. Additionally, it illustrates the need for further work in understanding the specific animal factors (e.g. the ruminal microbiome, and the role of concurrent diseases), management factors (e.g. animal stocking and vaccination protocols), and dietary factors (e.g. dietary supplements) that may impact the development of AIP and which are relatively unique to the feedlot setting. All stakeholders in the beef industry stand to benefit from a greater understanding of what remains a pressing yet poorly understood issue in beef production.

Sepsis is a complex clinical syndrome triggered by an inflammatory host response to an infection. It is usually complicated to detect and diagnose, and has severe consequences in human and veterinary health, especially when treatment is not started early. Therefore, efforts to detect sepsis accurately are needed. In addition, its proper diagnosis could reduce the misuse of antibiotics, which is essential fighting against antimicrobial resistance. This case is a particular issue in farm animals, as antibiotics have been traditionally given massively, but now they are becoming increasingly restricted. When sepsis is suspected in animals, the most frequently used biomarkers are acute phase proteins such as C-reactive protein, serum amyloid A and haptoglobin, but their concentrations can increase in other inflammatory conditions. In human patients, the most promising biomarkers to detect sepsis are currently procalcitonin and presepsin, and there is a wide range of other biomarkers under study. However, there is little information on the application of these biomarkers in veterinary species. This review aims to describe the general concepts of sepsis and the current knowledge about the biomarkers of sepsis in pigs, horses, and cattle and to discuss possible advances in the field.