INTRODUCTION:Quantification of antimicrobial resistance (AMR) is beneficial to inform policies and direct prudent antimicrobial use. AIM:This study aimed to assess the current published evidence of AMR from passive and active ad hoc surveillance activities within the Australian dairy cattle industry. METHODS:Following a scoping review framework 373 articles published before January 2023 were retrieved using the keyword search function from two online databases (PubMed® and Web of Science™ Core Collection). The duplicate articles were removed and the title, abstract, and full text of the remaining articles were reviewed following the study objectives and inclusion criteria (location, subject/theme, and data). Data from the remaining articles were extracted, summarised, interpreted and the study quality assessed using the Grades of Recommendations, Assessment, Development, and Evaluation guidelines. RESULTS:A total of 29 articles dating from the 1960 s until 2022 were identified to meet the study criteria (passive: n = 15; active: n = 14). Study characteristics such as sampling type, sampling method, and AMR assessment were all common characteristics from both passive and active surveillance articles, being milk samples, individual sampling, and phenotypic assessment respectively. Passive surveillance articles had a wider range in both the type of bacteria and the number of antimicrobials investigated, while active surveillance articles included a higher number of bacterial isolates and sampling from healthy populations. There was an overall low level of clinical AMR across all articles. Higher prevalence of non-wildtype Escherichia coli, Salmonella spp., and Staphylococcus spp., although limited in data, was suggested for commonly used Australian veterinary antimicrobials for these bacteria. The prevalence of phenotypic AMR varied due to the health and age status of the sampled animals. The articles reviewed in this study suggest the prevalence of AMR genes was higher for commonly used antimicrobials, although genes were not always related to the phenotypic AMR profile. CONCLUSIONS:Published evidence of AMR in the Australian dairy cattle industry is limited as demonstrated by only 29 articles included in this review following selection criteria screening. However, collectively these articles provide insight on industry AMR prevalence. For example, the suggestion of non-wildtype bacteria within the Australian dairy cattle indicating a risk of emerging or increasing industry AMR. Therefore, further surveillance is required to monitor the development of future AMR risk within the industry. Additionally, evidence suggesting that animals varying in health and age differ in prevalence of AMR imply a requirement for further research into animal population demographics to reduce potential bias in data collated in both national and global surveillance activities.
Background Scenario tree modelling is a well-known method used to evaluate the confidence of freedom from infection or to assess the sensitivity of a surveillance system in detecting an infection at a certain design prevalence. It facilitates the use of data from various sources and the inclusion of risk factors into calculations, while still obtaining quantitative estimates of surveillance sensitivity and probability of freedom. Objectives We conducted a scoping review to identify scenario tree models (STMs) applied to assess freedom from infection in veterinary medicine, characterize their use, parameterisation, reporting and potential limitations.Eligibility criteria:We included published scientific articles and grey literature that were a) neither reviews nor expert opinions, b) aimed to assess freedom from infection, provided methods to assess it, or aimed to estimate the sensitivity of a surveillance program for early detection of an infection at a design prevalence, c) targeted infection in animals and d) used scenario tree modelling. The search covered documents published between January 2006 and August 2021. Design Several search methods were used to retrieve scientific articles and grey literature relevant to the subject. The search strategy included searching in scientific databases and/or grey literature repositories, contacting experts across the world that previously worked with STMs and retrieving citations from relevant reviews. Results and discussion Four hundred twenty-four distinct documents were retrieved with our search string. After screening, data was extracted from 99 documents representing 67 projects. Forty different animal diseases were modelled with STMs, the most represented being infections with tuberculous Mycobacterium sp., Avian Influenza A virus and Brucella sp. STMs were mostly used for diseases of cattle, swine and wild mammals. Results showed that STMs were used in a large variety of studies, are very versatile and were used in disparate frameworks. However, we also found that studies are not reported in a standardized way and often lack important information. This makes results hard to interpret, compare and reproduce. Additionally, we identified common assumptions and misconceptions, the most important ones regarding sensitivity and specificity, which could have an impact on the results of the studies using STMs. Conclusion We recommend the elaboration of internationally agreed guidelines about how to report results from STMs in a uniform manner. Such guidelines should include information on the study setting, procedures and analyses, but also on how the results could be interpreted concerning freedom from infection.
In the search for mollicutes in wild birds, six Mycoplasma strains were isolated from tracheal swabs taken from four different species of seabirds. Four strains originated from three Yellow-legged gulls (Larus michahellis) and a Cory's shearwater (Calonectris borealis) from Spain, one from a South African Kelp gull (Larus dominicanus), and one from an Italian Black-headed gull (Chroicocephalus ridibundus). These Mycoplasma strains presented 99 % 16S rRNA gene sequence similarity values with Mycoplasma (M.) gallisepticum. Phylogenetic analyses of marker genes (16S rRNA gene and rpoB) confirmed the close relationship of the strains to M. gallisepticum and M. tullyi. The seabirds' strains grew well in modified Hayflick medium, and colonies showed typical fried egg morphology. They produced acid from glucose and mannose but did not hydrolyze arginine or urea. Transmission electron microscopy revealed a cell morphology characteristic of mycoplasmas, presenting spherical to flask-shaped cells with an attachment organelle. Gliding motility was also observed. Furthermore, serological tests, MALDI-ToF mass spectrometry and genomic studies demonstrated that the strains were different to any known Mycoplasma species, for which the name Mycoplasma bradburyae sp. nov. is proposed; the type strain is T158T (DSM 110708 = NCTC 14398).
In the first wave of the COVID-19 pandemic (April 2020), SARS-CoV-2 was detected in farmed minks and genomic sequencing was performed on mink farms and farm personnel. Here, we describe the outbreak and use sequence data with Bayesian phylodynamic methods to explore SARS-CoV-2 transmission in minks and humans on farms. High number of farm infections (68/126) in minks and farm workers (>50% of farms) were detected, with limited community spread. Three of five initial introductions of SARS-CoV-2 led to subsequent spread between mink farms until November 2020. Viruses belonging to the largest cluster acquired an amino acid substitution in the receptor binding domain of the Spike protein (position 486), evolved faster and spread longer and more widely. Movement of people and distance between farms were statistically significant predictors of virus dispersal between farms. Our study provides novel insights into SARS-CoV-2 transmission between mink farms and highlights the importance of combining genetic information with epidemiological information when investigating outbreaks at the animal-human interface.
Animals like mink, cats and dogs are susceptible to SARS-CoV-2 infection. In the Netherlands, 69 out of 127 mink farms were infected with SARS-CoV-2 between April and November 2020 and all mink on infected farms were culled after SARS-CoV-2 infection to prevent further spread of the virus. On some farms, (feral) cats and dogs were present. This study provides insight into the prevalence of SARS-CoV-2-positive cats and dogs in 10 infected mink farms and their possible role in transmission of the virus. Throat and rectal swabs of 101 cats (12 domestic and 89 feral cats) and 13 dogs of 10 farms were tested for SARS-CoV-2 using PCR. Serological assays were performed on serum samples from 62 adult cats and all 13 dogs. Whole Genome Sequencing was performed on one cat sample. Cat-to-mink transmission parameters were estimated using data from all 10 farms. This study shows evidence of SARS-CoV-2 infection in 12 feral cats and 2 dogs. Eleven cats (18%) and two dogs (15%) tested serologically positive. Three feral cats (3%) and one dog (8%) tested PCR-positive. The sequence generated from the cat throat swab clustered with mink sequences from the same farm. The calculated rate of mink-to-cat transmission showed that cats on average had a chance of 12% (95%CI 10%-18%) of becoming infected by mink, assuming no cat-to-cat transmission. As only feral cats were infected it is most likely that infections in cats were initiated by mink, not by humans. Whether both dogs were infected by mink or humans remains inconclusive. This study presents one of the first reports of interspecies transmission of SARS-CoV-2 that does not involve humans, namely mink-to-cat transmission, which should also be considered as a potential risk for spread of SARS-CoV-2.
