85 research outputs found

    Polycyclic aromatic hydrocarbons as skin carcinogens:Comparison of benzo [a]pyrene, dibenzo[def,p]chrysene and three environmental mixtures in the FVB/N mouse

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    The polycyclic aromatic hydrocarbon (PAH), benzo[a]pyrene (BaP), was compared to dibenzo[def,p]chrysene (DBC) and combinations of three environmental PAH mixtures (coal tar, diesel particulate and cigarette smoke condensate) using a two stage, FVB/N mouse skin tumor model. DBC (4 nmol) was most potent, reaching 100% tumor incidence with a shorter latency to tumor formation, less than 20 weeks of 12-O-tetradecanoylphorbol-13-acetate (TPA) promotion compared to all other treatments. Multiplicity was 4 times greater than BaP (400 nmol). Both PAHs produced primarily papillomas followed by squamous cell carcinoma and carcinoma in situ. Diesel particulate extract (1 mg SRM 1650b; mix 1) did not differ from toluene controls and failed to elicit a carcinogenic response. Addition of coal tar extract (1 mg SRM 1597a; mix 2) produced a response similar to BaP. Further addition of 2 mg of cigarette smoke condensate (mix 3) did not alter the response with mix 2. PAH-DNA adducts measured in epidermis 12 h post initiation and analyzed by (32)P post- labeling, did not correlate with tumor incidence. PAH- dependent alteration in transcriptome of skin 12 h post initiation was assessed by microarray. Principal component analysis (sum of all treatments) of the 922 significantly altered genes (p<0.05), showed DBC and BaP to cluster distinct from PAH mixtures and each other. BaP and mixtures up-regulated phase 1 and 2 metabolizing enzymes while DBC did not. The carcinogenicity with DBC and two of the mixtures was much greater than would be predicted based on published Relative Potency Factors (RPFs)

    Development of a prototype clinical decision support tool for osteoporosis disease management: a qualitative study of focus groups

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    <p>Abstract</p> <p>Background</p> <p>Osteoporosis affects over 200 million people worldwide, and represents a significant cost burden. Although guidelines are available for best practice in osteoporosis, evidence indicates that patients are not receiving appropriate diagnostic testing or treatment according to guidelines. The use of clinical decision support systems (CDSSs) may be one solution because they can facilitate knowledge translation by providing high-quality evidence at the point of care. Findings from a systematic review of osteoporosis interventions and consultation with clinical and human factors engineering experts were used to develop a conceptual model of an osteoporosis tool. We conducted a qualitative study of focus groups to better understand physicians' perceptions of CDSSs and to transform the conceptual osteoporosis tool into a functional prototype that can support clinical decision making in osteoporosis disease management at the point of care.</p> <p>Methods</p> <p>The conceptual design of the osteoporosis tool was tested in 4 progressive focus groups with family physicians and general internists. An iterative strategy was used to qualitatively explore the experiences of physicians with CDSSs; and to find out what features, functions, and evidence should be included in a working prototype. Focus groups were conducted using a semi-structured interview guide using an iterative process where results of the first focus group informed changes to the questions for subsequent focus groups and to the conceptual tool design. Transcripts were transcribed verbatim and analyzed using grounded theory methodology.</p> <p>Results</p> <p>Of the 3 broad categories of themes that were identified, major barriers related to the accuracy and feasibility of extracting bone mineral density test results and medications from the risk assessment questionnaire; using an electronic input device such as a Tablet PC in the waiting room; and the importance of including well-balanced information in the patient education component of the osteoporosis tool. Suggestions for modifying the tool included the addition of a percentile graph showing patients' 10-year risk for osteoporosis or fractures, and ensuring that the tool takes no more than 5 minutes to complete.</p> <p>Conclusions</p> <p>Focus group data revealed the facilitators and barriers to using the osteoporosis tool at the point of care so that it can be optimized to aid physicians in their clinical decision making.</p
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