Do gender and year of study affect the ability of the theory of planned behaviour to predict binge-drinking intentions and episodes?

Background: The present study tested the utility of the theory of planned behaviour (TPB), augmented with anticipated regret, as a model to predict binge-drinking intentions and episodes among female and male undergraduates and undergraduates in different years of study. Method: Undergraduate students (N = 180, 54 males, 126 females, 60 per year of study) completed baseline measures of demographic variables, binge-drinking episodes (BDE), TPB constructs and anticipated regret. BDE were assessed one-week later. Results: The TPB accounted for 60% of the variance in female undergraduates' intentions and 54% of the variance in male undergraduates' intentions. The TPB accounted for 57% of the variance in intentions in first-year undergraduates, 63% of the variance in intentions in second-year undergraduates and 68% of the variance in intentions in final-year undergraduates. Follow-up BDE was predicted by intentions and baseline BDE for female undergraduates as well as second- and final-year undergraduates. Baseline BDE predicted male undergraduates’ follow-up BDE and first-year undergraduates’ follow-up BDE. Conclusion: Results show that while the TPB constructs predict undergraduates’ binge-drinking intentions, intentions only predict BDE in female undergraduates, second- and final-year undergraduates. Implications of these findings for interventions to reduce binge drinking are outlined

Vertebroplasty : letter to the editor

In separate letters the authors debate whether the promotion of vertebroplasty in routine care is both premature and potentially dangerous as there are no completed RCTs. <br /

The prognostic value of cardiopulmonary exercise testing in interstitial lung disease:a systematic review

The heterogeneity of interstitial lung disease (ILD) results in prognostic uncertainty concerning end of life discussions and optimal timing for transplantation. Effective prognostic markers and prediction models are needed. Cardio-Pulmonary Exercise Testing (CPET) provides a comprehensive assessment of the physiological changes in the respiratory, cardiovascular, and musculoskeletal systems in a controlled laboratory environment. It has shown promise as a prognostic factor for other chronic respiratory conditions. We sought to evaluate the prognostic value of CPET in predicting outcomes in longitudinal studies of ILD . Medline, Embase and Cochrane systematic review databases were used to identify studies reporting prognostic value of CPET in predicting outcomes in longitudinal studies of ILD. Study quality was assessed using the Quality in Prognosis Study risk of bias tool.Thirteen studies were included that reported the prognostic value of CPET in ILD. All studies reported at least one CPET parameter predicting clinical outcomes in ILD; with survival being the principle outcome assessed. Maximum oxygen consumption, reduced ventilatory efficiency and exercise induced hypoxaemia were all reported to have prognostic value in ILD. Issues with study design (primarily due to inherent problems of retrospective studies, patient selection and presentation of numerous CPET parameters), insufficient adjustment for important confounders and inadequate statistical analyses limits the strength of conclusions that can be drawn at this stage.There is insufficient evidence to confirm the value of CPET in facilitating ‘real-world’ clinical decisions in ILD. Additional prospective studies are required to validate the putative prognostic associations reported in previous studies in carefully phenotyped patient populations. <br/

Are some areas more equal than others? : Socioeconomic inequality in potentially avoidable emergency hospital admissions within English local authority areas

Objectives Reducing health inequalities is an explicit goal of England's health system. Our aim was to compare the performance of English local administrative areas in reducing socioeconomic inequality in emergency hospital admissions for ambulatory care sensitive chronic conditions. Methods We used local authority area as a stable proxy for health and long-term care administrative geography between 2004/5 and 2011/12. We linked inpatient hospital activity, deprivation, primary care, and population data to small area neighbourhoods (typical population 1500) within administrative areas (typical population 250,000). We measured absolute inequality gradients nationally and within each administrative area using neighbourhood-level linear models of the relationship between national deprivation and age-sex-adjusted emergency admission rates. We assessed local equity performance by comparing local inequality against national inequality to identify areas significantly more or less equal than expected; evaluated stability over time; and identified where equity performance was steadily improving or worsening. We then examined associations between change in socioeconomic inequalities and change in within-area deprivation (gentrification). Finally, we used administrative area-level random and fixed effects models to examine the contribution of primary care to inequalities in admissions. Results Data on 316 administrative areas were included in the analysis. Local inequalities were fairly stable between consecutive years, but 32 areas (10%) showed steadily improving or worsening equity. In the 21 improving areas, the gap between most and least deprived fell by 3.9 admissions per 1000 (six times the fall nationally) between 2004/5 and 2011/12, while in the 11 areas worsening, the gap widened by 2.4. There was no indication that measured improvements in local equity were an artefact of gentrification or that changes in primary care supply or quality contributed to changes in inequality. Conclusions Local equity performance in reducing inequality in emergency admissions varies both geographically and over time. Identifying this variation could provide insights into which local delivery strategies are most effective in reducing such inequalities

Mechanisms by which sialylated milk oligosaccharides impact bone biology in a gnotobiotic mouse model of infant undernutrition

Undernutrition in children is a pressing global health problem, manifested in part by impaired linear growth (stunting). Current nutritional interventions have been largely ineffective in overcoming stunting, emphasizing the need to obtain better understanding of its underlying causes. Treating Bangladeshi children with severe acute malnutrition with therapeutic foods reduced plasma levels of a biomarker of osteoclastic activity without affecting biomarkers of osteoblastic activity or improving their severe stunting. To characterize interactions among the gut microbiota, human milk oligosaccharides (HMOs), and osteoclast and osteoblast biology, young germ-free mice were colonized with cultured bacterial strains from a 6-mo-old stunted infant and fed a diet mimicking that consumed by the donor population. Adding purified bovine sialylated milk oligosaccharides (S-BMO) with structures similar to those in human milk to this diet increased femoral trabecular bone volume and cortical thickness, reduced osteoclasts and their bone marrow progenitors, and altered regulators of osteoclastogenesis and mediators of Th2 responses. Comparisons of germ-free and colonized mice revealed S-BMO-dependent and microbiota-dependent increases in cecal levels of succinate, increased numbers of small intestinal tuft cells, and evidence for activation of a succinate-induced tuft cell signaling pathway linked to Th2 immune responses. A prominent fucosylated HMO, 2'-fucosyllactose, failed to elicit these changes in bone biology, highlighting the structural specificity of the S-BMO effects. These results underscore the need to further characterize the balance between, and determinants of, osteoclastic and osteoblastic activity in stunted infants/children, and suggest that certain milk oligosaccharides may have therapeutic utility in this setting

Subsea Compression Applications ,

TutorialTutorial 22: The use of Subsea gas compression technology for subsea re-injection and/or gas transport boosting represents a new and exciting application for rotating equipment, which will allow new gas/condensate field production opportunities, enhanced recovery of existing gas/condensate fields and cost effective production from marginal gas fields. This panel session includes short presentations on the benefits of subsea compression, an overview of currently ongoing projects, and recent advances and technologies that are available and/or under development for subsea gas compression. The panel session includes presentations from SIEMENS ENERGY, MAN DIESEL & TURBO, GE OIL & GAS, and DRESSER-RAND and. The respective presentation titles are: 1. Subsea Electrical Distribution – Siemens Energy, 2. HOFIMTM Type Compressors for Subsea Applications – MAN Diesel & Turbo, 3. GE Oil & Gas Experience in Subsea Gas Compression– GE Oil & Gas, and 4. DATUM I Compressor for Subsea Applications: Update on Qualification Efforts- Dresser-Ran

Measurement error in a multi-level analysis of air pollution and health: a simulation study.

BACKGROUND: Spatio-temporal models are increasingly being used to predict exposure to ambient outdoor air pollution at high spatial resolution for inclusion in epidemiological analyses of air pollution and health. Measurement error in these predictions can nevertheless have impacts on health effect estimation. Using statistical simulation we aim to investigate the effects of such error within a multi-level model analysis of long and short-term pollutant exposure and health. METHODS: Our study was based on a theoretical sample of 1000 geographical sites within Greater London. Simulations of "true" site-specific daily mean and 5-year mean NO2 and PM10 concentrations, incorporating both temporal variation and spatial covariance, were informed by an analysis of daily measurements over the period 2009-2013 from fixed location urban background monitors in the London area. In the context of a multi-level single-pollutant Poisson regression analysis of mortality, we investigated scenarios in which we specified: the Pearson correlation between modelled and "true" data and the ratio of their variances (model versus "true") and assumed these parameters were the same spatially and temporally. RESULTS: In general, health effect estimates associated with both long and short-term exposure were biased towards the null with the level of bias increasing to over 60% as the correlation coefficient decreased from 0.9 to 0.5 and the variance ratio increased from 0.5 to 2. However, for a combination of high correlation (0.9) and small variance ratio (0.5) non-trivial bias (> 25%) away from the null was observed. Standard errors of health effect estimates, though unaffected by changes in the correlation coefficient, appeared to be attenuated for variance ratios > 1 but inflated for variance ratios < 1. CONCLUSION: While our findings suggest that in most cases modelling errors result in attenuation of the effect estimate towards the null, in some situations a non-trivial bias away from the null may occur. The magnitude and direction of bias appears to depend on the relationship between modelled and "true" data in terms of their correlation and the ratio of their variances. These factors should be taken into account when assessing the validity of modelled air pollution predictions for use in complex epidemiological models

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments