The aims of this study were to determine the antimicrobial susceptibility profile and genetic diversity of Staphylococcus spp. isolated from dairy cows in Minas Gerais, Brazil, and to assess the relationship among the isolates’ susceptibility profiles and pulsed‐field gel electrophoresis ( PFGE ) genotypes. Seventy‐nine isolates were used, including S. aureus ( n = 71) and coagulase‐negative staphylococci (Co NS ) ( n = 8). Susceptibility to 12 antimicrobial agents was performed. All Staphylococcus spp. were subjected to PFGE . Staphylococcus aureus and Co NS isolates exhibited full susceptibility only to cephalothin. The greatest percentages of resistance among Staphylococcus spp. were observed to penicillins, folate pathway inhibitors, and tetracyclines. Twelve S. aureus and four Co NS were classified as multidrug resistance strains. Percentage of MRSA was also higher among Co NS (75%), compared to S. aureus isolates (2.81%). Adopting 100% of similarity, 34 different genotypes were identified. Association of minimum‐spanning tree ( MST ) analysis with data from municipalities, herds, methicillin‐resistant S. aureus ( MRSA ), and resistance patterns for all isolates did not show any clustering. However, a clustering pattern of bacterial species was observed. Results from this study indicate a high frequency of antimicrobial resistance, especially among Co NS , and a high genetic diversity among Staphylococcus spp. isolated from dairy cows with mastitis in Minas Gerais, Brazil.

Canada has implemented on-farm antimicrobial resistance (AMR) surveillance systems for food-producing animals under the Canadian Integrated Program for Antimicrobial Resistance (CIPARS); however, dairy cattle have not been included in that program yet. The objective of this manuscript was to describe the development and implementation of the Canadian Dairy Network for Antimicrobial Stewardship and Resistance (CaDNetASR). An Expert Panel (EP) of researchers was created to lead the development of the dairy surveillance system. The EP initiated a draft document outlining the essential elements of the surveillance framework. This document was then circulated to a Steering Committee (SC), which provided recommendations used by the EP to finalize the framework. CaDNetASR has the following components: (1) a herd-level antimicrobial use quantification system; (2) annually administered risk factor questionnaires; and (3) methods for herd-level detection of AMR in three sentinel enteric pathogens (generic Escherichia coli, Campylobacter spp., and Salmonella spp.) recovered from pooled fecal samples collected from calves, heifers, cows, and the manure pit. A total of 144 dairy farms were recruited in five Canadian provinces (British-Columbia, Alberta, Ontario, Québec, and Nova-Scotia), with the help of local herd veterinarians and regional field workers, and in September 2019, the surveillance system was launched. 97.1 and 94.4% of samples were positive for E. coli, 63.8, and 49.1% of samples were positive for Campylobacter spp., and 5.0 and 7.7% of samples were positive for Salmonella spp., in 2019 and 2020, respectively. E. coli was equally distributed among all sample types. However, it was more likely that Campylobacter spp. were recovered from heifer and cow samples. On the other hand, it was more common to isolate Salmonella spp. from the manure pit compared to samples from calves, heifers, or cows. CaDNetASR will continue sampling until 2022 after which time this system will be integrated into CIPARS. CaDNetASR will provide online access to farmers and veterinarians interested in visualizing benchmarking metrics regarding AMU practices and their relationship to AMR and animal health in dairy herds. This will provide an opportunity to enhance antimicrobial stewardship practices on dairy farms in Canada.

Mastitis is the most common disease affecting dairy cattle and is associated with substantial milk loss. Somatic cell count (SCC) has been widely used as an indicator of udder inflammation (e.g., subclinical mastitis). More recently, differential somatic cell count (DSCC) has become available as an auxiliary tool for milk quality control, with the potential to indicate different stages of inflammation when combined with SCC. This paper aimed to investigate the association of somatic cell score (SCS) and DSCC with milk yield and milk components at the quarter-level. A convenience sample of 5 dairy herds using an automated milking system (AMS) was selected and visited every other week for milk sample collection. Fat, protein, and lactose content were analyzed at Lactanet (Canadian Network for Dairy Excellence, Sainte-Anne-de-Bellevue, Quebec, Canada) by mid-infrared spectroscopy using a MilkoScan FT6000. Determination of SCC and DSCC was also performed by Lactanet using a CombiFoss 7 DC instrument. Milk yield data was retrieved from the AMS. Given the hierarchical structure of the data, a linear mixed model was built with either milk yield or milk components as the outcomes. The results showed that elevated somatic cell score (SCS), in combination with lower proportions of DSCC, was associated with the highest milk loss for primiparous and multiparous cows. For instance, the estimated milk loss for a quarter with a SCS of 7 and a DSCC of 55% was 1.45 kg/d compared with a quarter with a SCS of 2 and DSCC of 65% in multiparous cows. The association was similar when the outcome was the lactose content. Quarters with elevated SCS and lower DSCC had the lowest lactose percentage. No significant changes in fat content were observed across different SCS levels in multiparous cows, and quarters with higher DSCC had the lowest fat percentage in primiparous and multiparous cows. Protein content tended to be lower in quarters with increased SCS and low DSCC. In quarters from primiparous cows with DSCC levels above 70%, protein content showed slight variation across SCS levels. For multiparous cows, however, protein content remained relatively stable across different SCS and DSCC levels. In conclusion, our findings revealed that the combination of elevated SCS and low DSCC was associated with the most substantial milk loss. These results could be used to optimize udder health management.

• Infectious bovine keratoconjunctivitis (IBK) is a widespread, contagious ocular disease that affects especially dairy breeds. • The mucoadhesive nanoparticle promoted a greater ability of adherence of cloxacillin benzathine to the ocular surface. • The mucoadhesive nanoparticle-based formulation promoted clinical cure with a low number of doses of antibiotics.

Mastitis poses significant economic challenges for dairy farms. Therefore, enhancing the accuracy of diagnostic methods for detecting intramammary infections (IMI) can potentially improve prevention, control and treatment strategies. The somatic cell count (SCC) is a well-established parameter for identifying inflammation resulting from IMI. Given the recent introduction of differential somatic cell count (DSCC) for routine milk sample screening, limited research has been conducted to assess its additional benefits for diagnosing IMI. Therefore, our main objective was to evaluate the diagnostic accuracy of SCC, DSCC and SCC-DSCC combinations in detecting IMI caused by any pathogen or by major pathogens using quarter milk samples. Five dairy herds using automated milking systems were selected using convenience sampling in Québec, Canada. Determination of SCC and DSCC was performed by Lactanet (Ste-Anne de Bellevue, QC, Canada) using a CombiFoss 7 DC instrument. A 5-populations 2-tests Bayesian latent class models was used, with bacteriological culture employed as the imperfect reference test. Posterior estimates for sensitivity (Se), specificity (Sp), and the predictive values for 2 hypotheticals IMI prevalences due to any pathogen or major pathogens were computed. The proportion of quarters positive for any pathogen or major pathogen using milk culture was 31.7% (5,125/16,176) and 5.4% (871/16,176), respectively. For the detection of IMI by any pathogen, using a serial interpretation for the combination of SCC ≥100,000 and DSCC at ≥65% increased the Sp from 0.71 (95BCI: 0.70, 0.72) to 0.84 (95BCI: 0.83, 0.86) compared with SCC alone at the cut-off ≥100,000 cells/mL, although resulting in a slight decrease in Se from 0.49 (95BCI: 0.43, 0.54) to 0.46 (95BCI: 0.42, 0.50). Moreover, for detecting IMI caused by major pathogens, combining SCC at the threshold of ≥100,000 cells/mL and DSCC at the threshold of ≥65% using serial interpretation increased the Sp from 0.68 (95BCI: 0.67, 0.69) to 0.80 (95BCI: 0.79, 0.81) compared with SCC alone at the ≥100,000 cells/mL threshold. Our findings suggest that DSCC could be combined with SCC to provide a modest improvement in Sp with minimal compromise in Se for identifying IMI caused by any or by major pathogens. In addition, DSCC combined with SCC provided a small improvement in Se for detecting any pathogen using the parallel interpretation. However, no improvements in Se were observed when using the combination of SCC and DSCC for detecting major pathogens.