Maastricht University

About: Maastricht University is a education organization based out in Maastricht, Limburg, Netherlands. It is known for research contribution in the topics: Population & Health care. The organization has 19263 authors who have published 53291 publications receiving 2266866 citations. The organization is also known as: Universiteit Maastricht & UM.

The impact of brief alcohol interventions in primary healthcare: a systematic review of reviews.

Abstract: Aims: The aim of the study was to assess the cumulative evidence on the effectiveness of brief alcohol interventions in primary healthcare in order to highlight key knowledge gaps for further research. Methods: An overview of systematic reviews and meta-analyses of the effectiveness of brief alcohol intervention in primary healthcare published between 2002 and 2012. Findings: Twenty-four systematic reviews met the eligibility criteria (covering a total of 56 randomized controlled trials reported across 80 papers). Across the included studies, it was consistently reported that brief intervention was effective for addressing hazardous and harmful drinking in primary healthcare, particularly in middle-aged, male drinkers. Evidence gaps included: brief intervention effective- ness in key groups (women, older and younger drinkers, minority ethnic groups, dependent/co-morbid drinkers and those living in tran- sitional and developing countries); and the optimum brief intervention length and frequency to maintain longer-term effectiveness. Conclusion: This overview highlights the large volume of primarily positive evidence supporting brief alcohol intervention effects as well as some unanswered questions with regards to the effectiveness of brief alcohol intervention across different cultural settings and in specific population groups, and in respect of the optimum content of brief interventions that might benefit from further research.

Data-analysis strategies for image-based cell profiling

Abstract: Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.

Predictors of attempts to stop smoking and their success in adult general population samples: a systematic review

Abstract: Aims To identify the predictors of attempts to stop smoking and the predictors of quit attempt success in adult general population samples. Methods We performed an electronic search of EMBASE, Pubmed, Web of Science, PsychINFO and the Cochrane Tobacco Addiction Group specialized register for articles that examined, in prospective adult general population samples, predictors of quit attempts and the success of quit attempts. Experts were contacted for knowledge of other relevant studies. Eight studies met the inclusion criteria and results were extracted independently by two researchers. Results There was considerable methodological heterogeneity between studies. Motivational factors dominated the prediction of quit attempts, whereas only cigarette dependence consistently predicted success after an attempt had been made. Social grade also appeared to predict success but was only examined in two studies. None of the other socio-demographic factors consistently predicted making a quit attempt or success. Conclusions Population-level studies from a number of countries show that past quit attempts and measures of motivation to stop are highly predictive of quit attempts, whereas only measures of dependence are consistently predictive of success of those attempts. Gender, age and marital status and educational level are not related consistently to quit attempts or quit success across countries.