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Detection of Paratuberculosis in Dairy Herds by Analyzing the Scent of Feces, Alveolar Gas, and Stable Air.

ABSTRACT: Paratuberculosis is an important disease of ruminants caused by Mycobacterium avium ssp. paratuberculosis (MAP). Early detection is crucial for successful infection control, but available diagnostic tests are still dissatisfying. Methods allowing a rapid, economic, and reliable identification of animals or herds affected by MAP are urgently required. This explorative study evaluated the potential of volatile organic compounds (VOCs) to discriminate between cattle with and without MAP infections. Headspaces above fecal samples and alveolar fractions of exhaled breath of 77 cows from eight farms with defined MAP status were analyzed in addition to stable air samples. VOCs were identified by GC-MS and quantified against reference substances. To discriminate MAP-positive from MAP-negative samples, VOC feature selection and random forest classification were performed. Classification models, generated for each biological specimen, were evaluated using repeated cross-validation. The robustness of the results was tested by predicting samples of two different sampling days. For MAP classification, the different biological matrices emitted diagnostically relevant VOCs of a unique but partly overlapping pattern (fecal headspace: 19, alveolar gas: 11, stable air: 4-5). Chemically, relevant compounds belonged to hydrocarbons, ketones, alcohols, furans, and aldehydes. Comparing the different biological specimens, VOC analysis in fecal headspace proved to be most reproducible, discriminatory, and highly predictive.

SUBMITTER: Weber M

PROVIDER: S-EPMC8150929 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundA common approach to the application of epidemiological models is to determine a single (point estimate) parameterisation using the information available in the literature. However, in many cases there is considerable uncertainty about parameter values, reflecting both the incomplete nature of current knowledge and natural variation, for example between farms. Furthermore model outcomes may be highly sensitive to different parameter values. Paratuberculosis is an infection for which many of the key parameter values are poorly understood and highly variable, and for such infections there is a need to develop and apply statistical techniques which make maximal use of available data.ResultsA technique based on Latin hypercube sampling combined with a novel reweighting method was developed which enables parameter uncertainty and variability to be incorporated into a model-based framework for estimation of prevalence. The method was evaluated by applying it to a simulation of paratuberculosis in dairy herds which combines a continuous time stochastic algorithm with model features such as within herd variability in disease development and shedding, which have not been previously explored in paratuberculosis models. Generated sample parameter combinations were assigned a weight, determined by quantifying the model's resultant ability to reproduce prevalence data. Once these weights are generated the model can be used to evaluate other scenarios such as control options. To illustrate the utility of this approach these reweighted model outputs were used to compare standard test and cull control strategies both individually and in combination with simple husbandry practices that aim to reduce infection rates.ConclusionsThe technique developed has been shown to be applicable to a complex model incorporating realistic control options. For models where parameters are not well known or subject to significant variability, the reweighting scheme allowed estimated distributions of parameter values to be combined with additional sources of information, such as that available from prevalence distributions, resulting in outputs which implicitly handle variation and uncertainty. This methodology allows for more robust predictions from modelling approaches by allowing for parameter uncertainty and combining different sources of information, and is thus expected to be useful in application to a large number of disease systems.

Project description:Various measures have been advocated for the control of Johne's disease (caused by Mycobacterium avium subsp. paratuberculosis, MAP) in different countries. Farmers' compliance has been reported to be variable depending on disease prevalence and incentives to participate in control programs. After the prevalence of MAP shedding and risk factors for within-herd spread of MAP were assessed in 17 Swiss cattle herds (10 dairy and 7 beef), general and herd-specific recommendations were given to the farmers to reduce MAP transmission within the herd. Participation in the study and implementation of control measures were voluntary, no financial incentives were provided for the realization of control measures. After a 3-year period of monitored observation including biannual farm visits and discussion of the situation, the implementation of the recommended control measures and their effect on prevalence of MAP shedding were evaluated. Implementation of recommended general and farm-specific control measures was only partially realized. Neither the number of animals tested positive (before or during the study) nor the farmers' knowledge about paratuberculosis were significantly associated with their compliance for the implementation of management changes. The apparent within-herd prevalence remained constant despite limited implementation of control measures, and no particular group of control measures was found to be associated with changes in prevalence. Farmers' compliance for the implementation of control measures to reduce the impact of Johne's disease in infected farms was very limited under Swiss farming conditions in the frame of voluntary participation in a research project. These results indicate that the losses associated with paratuberculosis in Swiss dairy and beef operations are not estimated to be high enough by the farmers to justify important efforts for control measures, and that incentives may be necessary to achieve efficient implementation of such measures.

Dataset Information

Detection of Paratuberculosis in Dairy Herds by Analyzing the Scent of Feces, Alveolar Gas, and Stable Air.

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