This commentary presents a scientific basis for managing as one chemical class the thousands of chemicals known as PFAS (per-and polyfluoroalkyl substances). The class includes perfluoroalkyl acids, perfluoroalkylether acids, and their precursors; fluoropolymers and perfluoropolyethers; and other PFAS. The basis for the class approach is presented in relation to their physicochemical, environmental, and toxicological properties. Specifically, the high persistence, accumulation potential, and/or hazards (known and potential) of PFAS studied to date warrant treating all PFAS as a single class. Examples are provided of how some PFAS are being regulated and how some businesses are avoiding all PFAS in their products and purchasing decisions. We conclude with options for how governments and industry can apply the class-based approach, emphasizing the importance of eliminating non-essential uses of PFAS, and further developing safer alternatives and methods to remove existing PFAS from the environment.

BackgroundThere is growing interest in using machine learning approaches to priority rank studies and reduce human burden in screening literature when conducting systematic reviews. In addition, identifying addressable questions during the problem formulation phase of systematic review can be challenging, especially for topics having a large literature base. Here, we assess the performance of the SWIFT-Review priority ranking algorithm for identifying studies relevant to a given research question. We also explore the use of SWIFT-Review during problem formulation to identify, categorize, and visualize research areas that are data rich/data poor within a large literature corpus.MethodsTwenty case studies, including 15 public data sets, representing a range of complexity and size, were used to assess the priority ranking performance of SWIFT-Review. For each study, seed sets of manually annotated included and excluded titles and abstracts were used for machine training. The remaining references were then ranked for relevance using an algorithm that considers term frequency and latent Dirichlet allocation (LDA) topic modeling. This ranking was evaluated with respect to (1) the number of studies screened in order to identify 95 % of known relevant studies and (2) the “Work Saved over Sampling” (WSS) performance metric. To assess SWIFT-Review for use in problem formulation, PubMed literature search results for 171 chemicals implicated as EDCs were uploaded into SWIFT-Review (264,588 studies) and categorized based on evidence stream and health outcome. Patterns of search results were surveyed and visualized using a variety of interactive graphics.ResultsCompared with the reported performance of other tools using the same datasets, the SWIFT-Review ranking procedure obtained the highest scores on 11 out of 15 of the public datasets. Overall, these results suggest that using machine learning to triage documents for screening has the potential to save, on average, more than 50 % of the screening effort ordinarily required when using un-ordered document lists. In addition, the tagging and annotation capabilities of SWIFT-Review can be useful during the activities of scoping and problem formulation.ConclusionsText-mining and machine learning software such as SWIFT-Review can be valuable tools to reduce the human screening burden and assist in problem formulation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-016-0263-z) contains supplementary material, which is available to authorized users.

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Food packaging is of high societal value because it conserves and protects food, makes food transportable and conveys information to consumers. It is also relevant for marketing, which is of economic significance. Other types of food contact articles, such as storage containers, processing equipment and filling lines, are also important for food production and food supply. Food contact articles are made up of one or multiple different food contact materials and consist of food contact chemicals. However, food contact chemicals transfer from all types of food contact materials and articles into food and, consequently, are taken up by humans. Here we highlight topics of concern based on scientific findings showing that food contact materials and articles are a relevant exposure pathway for known hazardous substances as well as for a plethora of toxicologically uncharacterized chemicals, both intentionally and non-intentionally added. We describe areas of certainty, like the fact that chemicals migrate from food contact articles into food, and uncertainty, for example unidentified chemicals migrating into food. Current safety assessment of food contact chemicals is ineffective at protecting human health. In addition, society is striving for waste reduction with a focus on food packaging. As a result, solutions are being developed toward reuse, recycling or alternative (non-plastic) materials. However, the critical aspect of chemical safety is often ignored. Developing solutions for improving the safety of food contact chemicals and for tackling the circular economy must include current scientific knowledge. This cannot be done in isolation but must include all relevant experts and stakeholders. Therefore, we provide an overview of areas of concern and related activities that will improve the safety of food contact articles and support a circular economy. Our aim is to initiate a broader discussion involving scientists with relevant expertise but not currently working on food contact materials, and decision makers and influencers addressing single-use food packaging due to environmental concerns. Ultimately, we aim to support science-based decision making in the interest of improving public health. Notably, reducing exposure to hazardous food contact chemicals contributes to the prevention of associated chronic diseases in the human population.