Workplace policies played a crucial role in the creation of cognitive dissonance. Nurses used different strategies to cope with dissonance such as rationalizing smoking benefits, hiding their smoking behaviour, denial of smoking risks, and failing to engage with smoking cessation interventions. Some nurses expressed more positive aspirations to cope with their dissonance, including a willingness to quit and to embrace smoking cessation interventions with their patients. Implementing smoke-free policies and supportive interventions targeting nurses' cognitive dissonance may assist them to quit smoking and improve their engagement in smoking cessation practices.Implementing smoke-free policies and supportive interventions targeting nurses' cognitive dissonance may assist them to quit smoking and improve their engagement in smoking cessation practices.This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large-scale multiomics analysis and real-world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis-related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI's GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P less then .001), TFRC (P less then .001), TSC1 (P less then .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P less then .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki-67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients' prognosis using proteomic data, gene expression data and real-world data and this prognostic model showed stronger predictive value than other biomarkers (Ki-67, Beta-catenin, etc) in multi-cohorts.Polyarticular juvenile idiopathic arthritis (pJIA) is a pediatric chronic inflammatory arthritis, much like rheumatoid arthritis (RA) in adults. Drug development for pJIA can potentially be expedited by using extrapolation of efficacy from adult RA; however, the lack of understanding of the response and exposure relationship between pJIA and RA to therapeutic interventions has been a major roadblock. To address this, the objective of our analysis was to conduct a systematic response and exposure comparison between pJIA and RA trials for biologic products. Data from registration RA and pJIA clinical trials (parallel or withdrawal design) for infliximab, tocilizumab, golimumab, and adalimumab were utilized. First, exposure was compared between the pJIA trials and RA pivotal trials. Subsequently, the pJIA vs. RA response similarity was assessed by comparing similar individual subcomponents of the American College of Rheumatology (ACR) scores between the two populations. The exposure comparison demonstrated that at the pJIA trial dose, exposure in pediatric patients was similar to or higher than adults for all biologics evaluated except infliximab, where lower exposure was observed in pJIA patients ≤ 35 kg. Response comparison for individual subcomponents indicated that in a majority of the cases, pJIA response was similar or higher as compared with response from RA trials. Overall, this analysis suggests response similarity between pJIA and RA across the biologic products when exposures are matched between the two populations. These analyses provide support for the use of pharmacokinetic exposure-matching for extrapolation of efficacy from adult RA to pediatric pJIA for the products with established mechanism(s) of action. To describe strategies nursing leaders use to promote evidence-based practice implementation at point-of-care using data from health systems in Australia, Canada, England and Sweden. A descriptive, exploratory case-study design based on individual interviews using deductive and inductive thematic analysis and interpretation. Fifty-five nursing leaders from Australia, Canada, England and Sweden were recruited to participate in the study. Data were collected between September 2015 and April 2016. Nursing leaders both in formal managerial roles and enabling roles across four country jurisdictions used similar strategies to promote evidence-based practice implementation. Nursing leaders actively promote evidence-based practice implementation, work to influence evidence-based practice implementation processes and integrate evidence-based practice implementation into everyday policy and practices. The deliberative, conscious strategies nursing leaders used were consistent across country setting, context aaders in promoting evidence-based practice implementation through mediating and adapting modes of activity is necessary to improve patient outcomes and system effectiveness.Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pneumopathy is characterized by a complex clinical picture and heterogeneous pathological lesions, both involving alveolar and vascular components. The severity and distribution of morphological lesions associated with SARS-CoV-2 and how they relate to clinical, laboratory, and radiological data have not yet been studied systematically. The main goals of the present study were to objectively identify pathological phenotypes and factors that, in addition to SARS-CoV-2, may influence their occurrence. Lungs from 26 patients who died from SARS-CoV-2 acute respiratory failure were comprehensively analysed. Robust machine learning techniques were implemented to obtain a global pathological score to distinguish phenotypes with prevalent vascular or alveolar injury. The score was then analysed to assess its possible correlation with clinical, laboratory, radiological, and tissue viral data. Bay K 8644 in vivo Furthermore, an exploratory random forest algorithm was developed to identify the most discriminative clinical characteristics at hospital admission that might predict pathological phenotypes of SARS-CoV-2.