Short communication: minimum bactericidal concentration of disinfectants evaluated for bovine digital dermatitis-associated Treponema phagedenis-like spirochetes.

The bacterial spirochetes, Treponema spp., are thought to be a major contributor to the etiology of bovine digital dermatitis (DD), a skin disease with worldwide economic impact. Hoofbath strategies are commonly used in an attempt to control and prevent the development of DD and continuing research has been done to develop an optimal hoofbath strategy for this purpose. The aim of this study was to develop a protocol that can be used as part of the screening process for candidate hoofbath disinfectants. This protocol allows an accurate determination of the in vitro minimum inhibitory concentration and minimum bactericidal concentration of a series of disinfectants for Treponema microorganisms. Assays were performed in triplicate for each of the disinfectants at 30-s and 10-min exposure times and exposed to 10 and 20% manure (vol/vol). The results of this study can be used to categorize disinfectants based on the effect of exposure and manure concentration regarding their ability to inhibit Treponema growth. This information can then aid in optimizing strategies for hoofbath-based control of DD development and spread.

Use of neural networks to detect minor and major pathogens that cause bovine mastitis.

The objectives of this research were to test the potential of unsupervised (USNN) and supervised neural network (SNN) models for detecting major and minor mastitis pathogens based on changes in milk parameters. A data set of 4,852 quarter milk samples with records for milk parameters and bacteriological status was used to train and validate the models by classifying milk samples into 3 different bacteriological states: not infected, intramammary infection (IMI) by minor pathogens, and IMI by major pathogens. Sensitivity of the USNN model was 97% for detecting noninfected quarters, 89% for minor pathogen IMI, and 80% for major pathogen IMI. Specificities of USNN models were close to 99% for all bacteriological states. The sensitivity of SNN models was affected by the ratio of infected to noninfected cases in the data set. As the ratio of infected to healthy cases increased from 1:1 to 1:10, detection accuracy for noninfected quarters increased from 82 to 98% but that for minor pathogen IMI decreased from 86 to 44%. The sensitivity for major pathogen IMI was 20% when the ratio was 1:1, but ranged from 20 to 40% when different ratios were tested. The SNN models indicated that somatic cell score and electrical resistance index had the most discriminating power. It was concluded that both USNN and SNN models were able to effectively differentiate between noninfected quarters and those infected by minor mastitis pathogens, and that the USNN model had a better agreement with results obtained from conventional microbiological methods. These types of models can be used in in-line milking systems to detect the infection status of a quarter and provide the farmer with diagnosing options for managing mastitis.

Field observations on the variation of Streptococcus uberis populations in a pasture-based dairy farm.

Microbiological and molecular tools were used to monitor Streptococcus uberis populations on farm tracks and paddocks on a dairy farm during different seasons of a year to identify and profile potential environmental niches of Strep. uberis in a pasture-based dairying system. Farm tracks of high or low cow traffic were sampled every 2 wk for an entire year and Strep. uberis numbers were enumerated from a selective medium. During each season of the year, paddocks were sampled for the presence of Strep. uberis before and after grazing by dairy cows. Farm tracks of high cow traffic generally had greater concentrations of Strep. uberis isolated compared with tracks with less cow traffic, but there was also significant variation in the concentrations of Strep. uberis contamination among seasons, being highest in winter and lowest in summer. The bacterium was detected in paddocks only after cow grazing had occurred, but the bacteria could still be detected in soil for up to 2 wk following grazing in winter. Multilocus sequence typing showed great heterogeneity, with some commonality between farm track and milk isolates, which may help explain cow-to-environment or environment-to-cow transmission of the bacterium in the dairy setting.