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Background: Nurse navigators are an emerging workforce providing care to people with multiple chronic conditions. The role of the navigators is to identify patients requiring support in negotiating their health care. Purpose: A critical discourse analysis was used to examine qualitative data collected from nurse navigators and consenting navigated patients to identify key indicators of how nurse navigators do their work and where the success of their work is most evident. Discussion: Nurse navigators help patients who have lost trust in the health system to re-engage with their interdisciplinary health care team. This re-engagement is the final step in a journey of addressing unmet needs, essential to hospital avoidance. Conclusion: Nurse navigators provide a continuum of authentic and holistic care. To acknowledge the true value of nurse navigators, their performance indicators need to embrace the value-added care they provide.

The cycloaspeptides are bioactive pentapeptides produced by various filamentous fungi, which have garnered interest from the agricultural industry due to the reported insecticidal activity of the minor metabolite, cycloaspeptide E. Genome sequencing, bioinformatics and heterologous expression confirmed that the cycloaspeptide gene cluster contains a minimal 5-module nonribosomal peptide synthetase (NRPS) and a new type of trans-acting N-methyltransferase (N-MeT). Deletion of the N-MeT encoding gene and subsequent feeding studies determined that two modules of the NRPS preferentially accept and incorporate N-methylated amino acids. This discovery allowed the development of a system with unprecedented control over substrate supply and thus output, both increasing yields of specific metabolites and allowing the production of novel fluorinated analogues. Furthermore, the biosynthetic pathway to ditryptophenaline, another fungal nonribosomal peptide, was shown to be similar, in that methylated phenylalanine is accepted by the ditryptophenaline NRPS. Again, this allowed the directed biosynthesis of a fluorinated analogue, through the feeding of a mutant strain. These discoveries represent a new paradigm for the production of N-methylated cyclic peptides via the selective incorporation of N-methylated free amino acids. © 2018 The Royal Society of Chemistry.

The field of predictive toxicology requires the development of open, public, computable, standardized toxicology vocabularies and ontologies to support the applications required by in silico, in vitro, and in vivo toxicology methods and related analysis and reporting activities. In this article we review ontology developments based on a set of perspectives showing how ontologies are being used in predictive toxicology initiatives and applications. Perspectives on resources and initiatives reviewed include OpenTox, eTOX, Pistoia Alliance, ToxWiz, Virtual Liver, EU-ADR, BEL, ToxML, and Bioclipse. We also review existing ontology developments in neighboring fields that can contribute to establishing an ontological framework for predictive toxicology. A significant set of resources is already available to provide a foundation for an ontological framework for 21st century mechanistic-based toxicology research. Ontologies such as ToxWiz provide a basis for application to toxicology investigations, whereas other ontologies under development in the biological, chemical, and biomedical communities could be incorporated in an extended future framework. OpenTox has provided a semantic web framework for the implementation of such ontologies into software applications and linked data resources. Bioclipse developers have shown the benefit of interoperability obtained through ontology by being able to link their workbench application with remote OpenTox web services. Although these developments are promising, an increased international coordination of efforts is greatly needed to develop a more unified, standardized, and open toxicology ontology framework.

Foreign substances can have a dramatic and unpredictable adverse effect on human health. In the development of new therapeutic agents, it is essential that the potential adverse effects of all candidates be identified as early as possible. The field of predictive toxicology strives to profile the potential for adverse effects of novel chemical substances before they occur, both with traditional in vivo experimental approaches and increasingly through the development of in vitro and computational methods which can supplement and reduce the need for animal testing. To be maximally effective, the field needs access to the largest possible knowledge base of previous toxicology findings, and such results need to be made available in such a fashion so as to be interoperable, comparable, and compatible with standard toolkits. This necessitates the development of open, public, computable, and standardized toxicology vocabularies and ontologies so as to support the applications required by in silico, in vitro, and in vivo toxicology methods and related analysis and reporting activities. Such ontology development will support data management, model building, integrated analysis, validation and reporting, including regulatory reporting and alternative testing submission requirements as required by guidelines such as the REACH legislation, leading to new scientific advances in a mechanistically-based predictive toxicology. Numerous existing ontology and standards initiatives can contribute to the creation of a toxicology ontology supporting the needs of predictive toxicology and risk assessment. Additionally, new ontologies are needed to satisfy practical use cases and scenarios where gaps currently exist. Developing and integrating these resources will require a well-coordinated and sustained effort across numerous stakeholders engaged in a public-private partnership. In this communication, we set out a roadmap for the development of an integrated toxicology ontology, harnessi