Effects of short-term treatment with atorvastatin in smokers with asthma - a randomized controlled trial

<b>Background</b> The immune modulating properties of statins may benefit smokers with asthma. We tested the hypothesis that short-term treatment with atorvastatin improves lung function or indices of asthma control in smokers with asthma.<p></p> <b>Methods</b> Seventy one smokers with mild to moderate asthma were recruited to a randomized double-blind parallel group trial comparing treatment with atorvastatin (40 mg per day) versus placebo for 4 weeks. After 4 weeks treatment inhaled beclometasone (400 ug per day) was added to both treatment arms for a further 4 weeks. The primary outcome was morning peak expiratory flow after 4 weeks treatment. Secondary outcome measures included indices of asthma control and airway inflammation.<p></p> <b>Results</b> At 4 weeks, there was no improvement in the atorvastatin group compared to the placebo group in morning peak expiratory flow [-10.67 L/min, 95% CI -38.70 to 17.37, p=0.449], but there was an improvement with atorvastatin in asthma quality of life score [0.52, 95% CI 0.17 to 0.87 p=0.005]. There was no significant improvement with atorvastatin and inhaled beclometasone compared to inhaled beclometasone alone in outcome measures at 8 weeks.<p></p> <b>Conclusions</b> Short-term treatment with atorvastatin does not alter lung function but may improve asthma quality of life in smokers with mild to moderate asthma. Clinicaltrials.gov identifier: NCT0046382

Out of the Conflict Zone: The Case for Community Consent Processes in the Extractive Sector

An examination of contemporary struggles over extractive industry projects shows that they are not adequately captured by current CSR strategies because they are not exclusively disputes about the environment, human rights or health and safety as those subjects are generally understood by companies. Rather, they are better understood as disputes over community control of resources and the right of community members to control the direction of their lives. This Article proposes that extractive industries can tackle the underlying causes of the growing opposition to their projects in the developing world by engaging in consent processes with communities and groups directly affected by projects with a view to obtaining their free prior and informed consent (FPIC). The authors propose that FPIC must be enduring, enforceable, and meaningful in order to take companies and communities out of their current defensive positions. FPIC should instead allow companies and communities to take up proactive positions - with those companies that have the consent of the communities in which they operate obtaining a competitive advantage and those communities that have enforceable agreements with companies obtaining control over the naturalresource- based development process on which their future depends

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers