241 research outputs found

    Pfas environmental pollution and antioxidant responses: An overview of the impact on human field

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    Due to their unique properties, perfluorinated substances (PFAS) are widely used in multiple industrial and commercial applications, but they are toxic for animals, humans included. This review presents some available data on the PFAS environmental distribution in the world, and in particular in Europe and in the Veneto region of Italy, where it has become a serious problem for human health. The consumption of contaminated food and drinking water is considered one of the major source of exposure for humans. Worldwide epidemiological studies report the negative effects that PFAS have on human health, due to environmental pollution, including infertility, steroid hormone perturbation, thyroid, liver and kidney disorders, and metabolic disfunctions. In vitro and in vivo researches correlated PFAS exposure to oxidative stress effects (in mammals as well as in other vertebrates of human interest), produced by a PFAS-induced increase of reactive oxygen species formation. The cellular antioxidant defense system is activated by PFAS, but it is only partially able to avoid the oxidative damage to biomolecules

    Requirements for a Dashboard to Support Quality Improvement Teams in Pain Management

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    Pain management is often considered lower priority than many other aspects of health management in hospitals. However, there is potential for Quality Improvement (QI) teams to improve pain management by visualising and exploring pain data sets. Although dashboards are already used by QI teams in hospitals, there is limited evidence of teams accessing visualisations to support their decision making. This study aims to identify the needs of the QI team in a UK Critical Care Unit (CCU) and develop dashboards that visualise longitudinal data on the efficacy of patient pain management to assist the team in making informed decisions to improve pain management within the CCU. This research is based on an analysis of transcripts of interviews with healthcare professionals with a variety of roles in the CCU and their evaluation of probes. We identified two key uses of pain data: direct patient care (focusing on individual patient data) and QI (aggregating data across the CCU and over time); in this paper, we focus on the QI role. We have identified how CCU staff currently interpret information and determine what supplementary information can better inform their decision making and support sensemaking. From these, a set of data visualisations has been proposed, for integration with the hospital electronic health record. These visualisations are being iteratively refined in collaboration with CCU staff and technical staff responsible for maintaining the electronic health record. The paper presents user requirements for QI in pain management and a set of visualisations, including the design rationale behind the various methods proposed for visualising and exploring pain data using dashboards

    Opportunities and barriers for adoption of a decision-support tool for Alzheimer's Disease

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    Clinical decision-support tools (DSTs) represent a valuable resource in healthcare. However, lack of Human Factors considerations and early design research has often limited their successful adoption. To complement previous technically focused work, we studied adoption opportunities of a future DST built on a predictive model of Alzheimer’s Disease (AD) progression. Our aim is two-fold: exploring adoption opportunities for DSTs in AD clinical care, and testing a novel combination of methods to support this process. We focused on understanding current clinical needs and practices, and the potential for such a tool to be integrated into the setting, prior to its development. Our user-centred approach was based on field observations and semi-structured interviews, analysed through workflow analysis, user profiles, and a design-reality gap model. The first two are common practice, whilst the latter provided added value in highlighting specific adoption needs. We identified the likely early adopters of the tool as being both psychiatrists and neurologists based in research-oriented clinical settings. We defined ten key requirements for the translation and adoption of DSTs for AD around IT, user, and contextual factors. Future works can use and build on these requirements to stand a greater chance to get adopted in the clinical setting
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