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Background Hypothesis/Objectives Animals Methods Results Conclusions and Clinical Importance Carbapenem‐resistant Enterobacterales (CRE) are a concern in both human and animal medicine globally. Despite extensive research in humans, limited data exist on CRE in companion animals, with a lack of nationwide prevalence estimates.To assess the occurrence and trends of CRE in cats and dogs across the United States by analyzing 4 years of commercial antimicrobial susceptibility testing (AST) data.Between 2019 and 2022, 477 426 ASTs were conducted on Enterobacterales isolates against imipenem. Isolates were derived from 379 598 dogs and 97 828 cats. Animal origin was not disclosed.In this retrospective study, antimicrobial susceptibility test data from IDEXX Laboratories were analyzed. Analysis included resistance estimations to imipenem stratified by sampling site, an assessment of resistance patterns over time and location, and the application of space‐time cluster analysis to identify potential outbreaks. Antibiograms were produced for carbapenem‐resistant isolates.Susceptibility to imipenem was high, at 98.86%. Temporal analysis indicated stability in susceptibility, with an unexplained reduction in susceptible isolates in June 2019. Spatial analysis identified 2 high‐risk clusters along the Western Coast (relative risk [RR]: 23.26; P < .001) and in Texas (RR: 10.72; P < .001) in that month. Three other clusters were found, in Missouri (RR: 39.55; P = .038), Florida (RR: 4.53; P < .001), and New York (RR: 9.20; P < .001).CRE are present at a low prevalence in dogs and cats across the United States. Variations in prevalence across patient‐level and environmental factors highlight the need for tailored stewardship programs. [ABSTRACT FROM AUTHOR]

Elizabethkingia is a genus of environmental gram-negative bacteria that can cause severe opportunistic infections in humans. The 3 main Elizabethkingia species are E. meningoseptica, the most common cause of disease; [...]

Copyright of Canadian Journal of Veterinary Research / Revue Canadienne de Recherche Vétérinaire is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is a need for wildlife professionals to work together to more effectively protect wildlife health; however, collaboration between people of different professions and institutions can be challenging. Our objective was to examine and compare how the conservation and animal health fields share wildlife health data and information in Ontario, Canada. Fifty-five (50%) of 110 invited participants completed an online questionnaire about their wildlife health communication network over the previous 12 months. The professional communication networks (i.e., ego-networks) of practitioners were compared with respect to data sharing between institutions and professions. Results showed low levels of tie dispersion and high levels of ego-alter similarity. There was more data and information sharing occurring between people of different institutions than between people of different professions based on comparing tie dispersion and ego-alter similarity values for these categories. This study highlights opportunities for improved networking between wildlife conservation and health practitioners in Ontario. [ABSTRACT FROM AUTHOR]

Introduction: Group A rotaviruses are major pathogens in causing severe diarrhea in young children and neonates of many different species of animals worldwide and group A rotavirus sequence data are becoming increasingly available over time. Different methods exist that allow for rotavirus genotyping, but machine learning methods have yet to be explored. Usage of machine learning algorithms such as random forest alongside alignment-based methodology may allow for both efficient and accurate classification of circulating rotavirus genotypes through the dual classification system. Methods: Random forest models were trained on positional features obtained from pairwise and multiple sequence alignment and cross-validated using methods of repeated 10-fold cross-validation thrice and leave one- out cross validation. Models were then validated on unseen data from the testing datasets to observe real-world performance. Results: All models were found to perform strongly in classification of VP7 and VP4 genotypes with high overall accuracy and kappa values during model training (0.975–0.992, 0.970–0.989) and during model testing (0.972–0.996, 0.969–0.996), respectively. Models trained on multiple sequence alignment generally had slightly higher overall accuracy and kappa values than models trained on pairwise sequence alignment method. In contrast, pairwise sequence alignment models were found to be generally faster than multiple sequence alignment models in computational speed when models do not need to be retrained. Models that used repeated 10-fold cross-validation thrice were also found to be much faster in model computational speed than models that used leave-one-out cross validation, with no noticeable difference in overall accuracy and kappa values between the cross-validation methods. Discussion: Overall, random forest models showed strong performance in the classification of both group A rotavirus VP7 and VP4 genotypes. Application of these models as classifiers will allow for rapid and accurate classification of the increasing amounts of rotavirus sequence data that are becoming available. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Veterinary Journal / Revue Vétérinaire Canadienne is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)