Evaluation of risk based microbiological criteria for Campylobacter in broiler carcasses in Belgium using TRiMiCri

Campylobacteriosis is the most frequently reported foodborne zoonosis worldwide. Consumer´s exposure to Campylobacter might be reduced by establishing a microbiological criterion (MC) for Campylobacter on broiler meat. In the present study two possible approaches were evaluated, using the freely available software tool for risk based microbiological criteria TRiMiCri (http://tools.food.dtu.dk/trimicri). The first approach was the traditional one that implies a microbiological limit (ML-MC) and the second one which is based on the relative risk estimate (RRL-MC). The analyses were based on Campylobacter quantitative data collected from 28 Campylobacter positive bathes processed in 6 Belgian broiler slaughterhouses. To evaluate the performance of ML-MC, n=6, different c (0,1,2) and m (100,1 000,10 000) were used. Results showed that more than 90% of Campylobacter positive batches were not complying with strict ML criteria based on the m=100 for all applied combination of c. The RRL approach requires a baseline risk which was estimated based on the Campylobacter baseline data collected in Belgium in 2008. Approximately 60% of evaluated Campylobacter positive batches account for higher risk than the baseline risk. For both approaches, application of less stringent criteria results in lower percentage of NC and higher minimum relative residual risks (MRRR; it refers to the change in risk when all batches are sampled and all NC batches undergo treatment that effectively eliminates Campylobacter so they are replaced by zero risk batches). It was also observed that the number of samples (n) had little effect on risk estimates. Additionally, the results from ML-MC and RRL-MC follow the same curve when plotting percentage of NC against MRRR. However, for RRL-MC the percentage of NC batches and MRRR was lower and higher, respectively. To conclude, obtained results indicate that TRiMiCri is a useful and user friendly tool to make a risk based decision on the choice of the MC

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results