Interleukin-17D and Nrf2 mediate initial innate immune cell recruitment and restrict MCMV infection.

Innate immune cells quickly infiltrate the site of pathogen entry and not only stave off infection but also initiate antigen presentation and promote adaptive immunity. The recruitment of innate leukocytes has been well studied in the context of extracellular bacterial and fungal infection but less during viral infections. We have recently shown that the understudied cytokine Interleukin (IL)-17D can mediate neutrophil, natural killer (NK) cell and monocyte infiltration in sterile inflammation and cancer. Herein, we show that early immune cell accumulation at the peritoneal site of infection by mouse cytomegalovirus (MCMV) is mediated by IL-17D. Mice deficient in IL-17D or the transcription factor Nuclear factor (erythroid-derived 2)-like 2 (Nrf2), an inducer of IL-17D, featured an early decreased number of innate immune cells at the point of viral entry and were more susceptible to MCMV infection. Interestingly, we were able to artificially induce innate leukocyte infiltration by applying the Nrf2 activator tert-butylhydroquinone (tBHQ), which rendered mice less susceptible to MCMV infection. Our results implicate the Nrf2/IL-17D axis as a sensor of viral infection and suggest therapeutic benefit in boosting this pathway to promote innate antiviral responses

Systems medicine and infection

By using a systems based approach, mathematical and computational techniques can be used to develop models that describe the important mechanisms involved in infectious diseases. An iterative approach to model development allows new discoveries to continually improve the model, and ultimately increase the accuracy of predictions. SIR models are used to describe epi demics, predicting the extent and spread of disease. Genome-wide genotyping and sequencing technologies can be used to identify the biological mechanisms behind diseases. These tools help to build strategies for disease prevention and treatment, an example being the recent outbreak of Ebola in West Africa where these techniques were deployed. HIV is a complex disease where much is still to be learnt about the virus and the best effective treatment. With basic mathematical modelling techniques, significant discoveries have been made over the last 20 years. With recent technological advances, the computation al resources now available and interdisciplinary cooperation, further breakthroughs are inevitable. In TB, modelling has traditionally been empirical in nature, with clinical data providing the fuel for this top-down approach. Recently, projects have begun to use data derived from laboratory experiments and clinical trials to create mathematical models that describe the mechanisms responsible for the disease. A systems medicine approach to infection modelling helps identify important biological questions that then direct future experiments , the results of which improve the model in an iterative cycle . This means that data from several model systems can be integrated and synthesised to explore complex biological systems .Postprin

Glucose Monitoring During Pregnancy

Self-monitoring of blood glucose in women with mild gestational diabetes has recently been proven to be useful in reducing the rates of fetal overgrowth and gestational weight gain. However, uncertainty remains with respect to the optimal frequency and timing of self-monitoring. A continuous glucose monitoring system may have utility in pregnant women with insulin-treated diabetes, especially for those women with blood sugars that are difficult to control or who experience nocturnal hypoglycemia; however, continuous glucose monitoring systems need additional study as part of larger, randomized trials

Economic evaluation of posaconazole versus fluconazole prophylaxis in patients with graft-versus-host disease (GVHD) in the Netherlands

The objective of this study was to evaluate the cost-effectiveness of posaconazole versus fluconazole for the prevention of invasive fungal infections (IFI) in graft-versus-host disease (GVHD) patients in the Netherlands. A decision analytic model was developed based on a double-blind randomized trial that compared posaconazole with fluconazole antifungal prophylaxis in recipients of allogeneic HSCT with GVHD who were receiving immunosuppressive therapy (Ullmann et al., N Engl J Med 356:335–347, 2007). Clinical events were modeled with chance nodes reflecting probabilities of IFIs, IFI-related death, and death from other causes. Data on life expectancy, quality-of-life, medical resource consumption, and costs were obtained from the literature. The total cost with posaconazole amounted to €9,428 (95% uncertainty interval €7,743–11,388), which is €4,566 (€2,460–6,854) more than those with fluconazole. Posaconazole prophylaxis resulted in 0.17 (0.02–0.36) quality adjusted life year (QALY) gained compared to fluconazole prophylaxis, corresponding to an incremental cost effectiveness ratio (ICER) of €26,225 per QALY gained. A scenario analysis demonstrated that at an increased background IFI risk (from 9% to 15%) the ICER was €13,462 per QALY. Given the underlying data and assumptions, posaconazole prophylaxis is expected to be cost-effective relative to fluconazole in recipients of allogeneic HSCT developing GVHD in the Netherlands. The cost-effectiveness of posaconazole depends on the IFI risk, which can vary by hospital

Validation of a self-efficacy instrument and its relationship to performance of crisis resource management skills

Self-efficacy is thought to be important for resuscitation proficiency in that it influences the development of and access to the associated medical knowledge, procedural skills and crisis resource management (CRM) skills. Since performance assessment of CRM skills is challenging, self-efficacy is often used as a measure of competence in this area. While self-efficacy may influence performance, the true relationship between self-efficacy and performance in this setting has not been delineated. We developed an instrument to measure pediatric residents’ self-efficacy in CRM skills and assessed its content validity, internal structure, and relationship to other variables. After administering the instrument to 125 pediatric residents, critical care fellows and faculty, we performed an exploratory factor analysis within a confirmatory factor analysis as well as a known group comparison. The analyses specified four factors that we defined as: situation awareness, team management, environment management, and decision making. Pediatric residents reported lower self-efficacy than fellows and faculty in each factor. We also examined the correlation between self-efficacy and performance scores for a subset of 30 residents who led video recorded simulated resuscitations and had their performances rated by three observers. We found a significant, positive correlation between residents’ self-efficacy in situation awareness and environment management and their overall performance of CRM skills. Our findings suggest that in a specific context, self-efficacy as a form of self-assessment may be informative with regards to performance

Teaching Feedback to First-year Medical Students: Long-term Skill Retention and Accuracy of Student Self-assessment

Giving and receiving feedback are critical skills and should be taught early in the process of medical education, yet few studies discuss the effect of feedback curricula for first-year medical students. To study short-term and long-term skills and attitudes of first-year medical students after a multidisciplinary feedback curriculum. Prospective pre- vs. post-course evaluation using mixed-methods data analysis. First-year students at a public university medical school. We collected anonymous student feedback to faculty before, immediately after, and 8 months after the curriculum and classified comments by recommendation (reinforcing/corrective) and specificity (global/specific). Students also self-rated their comfort with and quality of feedback. We assessed changes in comments (skills) and self-rated abilities (attitudes) across the three time points. Across the three time points, students’ evaluation contained more corrective specific comments per evaluation [pre-curriculum mean (SD) 0.48 (0.99); post-curriculum 1.20 (1.7); year-end 0.95 (1.5); p = 0.006]. Students reported increased skill and comfort in giving and receiving feedback and at providing constructive feedback (p < 0.001). However, the number of specific comments on year-end evaluations declined [pre 3.35 (2.0); post 3.49 (2.3); year-end 2.8 (2.1)]; p = 0.008], as did students’ self-rated ability to give specific comments. Teaching feedback to early medical students resulted in improved skills of delivering corrective specific feedback and enhanced comfort with feedback. However, students’ overall ability to deliver specific feedback decreased over time

Compassion as a practical and evolved ethic for conservation

© The Author(s) 2015. Published by Oxford University Press on behalf of the American Institute of Biological Sciences. The ethical position underpinning decisionmaking is an important concern for conservation biologists when setting priorities for interventions. The recent debate on how best to protect nature has centered on contrasting intrinsic and aesthetic values against utilitarian and economic values, driven by an inevitable global rise in conservation conflicts. These discussions have primarily been targeted at species and ecosystems for success, without explicitly expressing concern for the intrinsic value and welfare of individual animals. In part, this is because animal welfare has historically been thought of as an impediment to conservation. However, practical implementations of conservation that provide good welfare outcomes for individuals are no longer conceptually challenging; they have become reality. This reality, included under the auspices of "compassionate conservation," reflects an evolved ethic for sharing space with nature and is a major step forward for conservation

Neuroimaging in Dementia

Dementia is a common illness with an incidence that is rising as the aged population increases. There are a number of neurodegenerative diseases that cause dementia, including Alzheimer’s disease, dementia with Lewy bodies, and frontotemporal dementia, which is subdivided into the behavioral variant, the semantic variant, and nonfluent variant. Numerous other neurodegenerative illnesses have an associated dementia, including corticobasal degeneration, Creutzfeldt–Jakob disease, Huntington’s disease, progressive supranuclear palsy, multiple system atrophy, Parkinson’s disease dementia, and amyotrophic lateral sclerosis. Vascular dementia and AIDS dementia are secondary dementias. Diagnostic criteria have relied on a constellation of symptoms, but the definite diagnosis remains a pathologic one. As treatments become available and target specific molecular abnormalities, differentiating amongst the various primary dementias early on becomes essential. The role of imaging in dementia has traditionally been directed at ruling out treatable and reversible etiologies and not to use imaging to better understand the pathophysiology of the different dementias. Different brain imaging techniques allow the examination of the structure, biochemistry, metabolic state, and functional capacity of the brain. All of the major neurodegenerative disorders have relatively specific imaging findings that can be identified. New imaging techniques carry the hope of revolutionizing the diagnosis of neurodegenerative disease so as to obtain a complete molecular, structural, and metabolic characterization, which could be used to improve diagnosis and to stage each patient and follow disease progression and response to treatment. Structural and functional imaging modalities contribute to the diagnosis and understanding of the different dementias

A model of online protection to reduce children's online risk exposure: empirical evidence from Asia

Children are surrounded by a variety of digital media and are exposed to potential risks that come with such easy accessibility. Learning how to be safe online is an important consideration for both children and their caregivers. The present study proposes an integrated model of online safety based on constructs from protection motivation theory and the health belief model, namely perceived severity of (and susceptibility to) risk, online self-efficacy, online privacy concern, and digital literacy. The study comprised a survey conducted among 420 schoolchildren aged 9–16 years. Using partial least squares-structural equation modelling, the results illustrated the presence of a negative effect of ‘perceived severity of online risk’ toward online risks, whereas the effect of ‘digital literacy’ was found to be positive. Children whose perception of online risks was more severe were less exposed to online risks if they had higher ‘online privacy concerns’ than the children with higher ‘digital literacy’ who are more exposed to online risk. Results of the study show that engaging in safe online behaviour requires children to have a high perception regarding severity of online risks as well as knowledge of online privacy concerns. Online risks and opportunities occur in parallel. Consequently, the factors that increase or decrease risk may also increase or decrease the benefits

Automatic Detection of Cyberbullying in Social Media Text

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.Comment: 21 pages, 9 tables, under revie