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- Bravo, L, et al.
(author)
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- 2021
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swepub:Mat__t
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- Niemi, MEK, et al.
(author)
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- 2021
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swepub:Mat__t
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- Tabiri, S, et al.
(author)
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- 2021
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swepub:Mat__t
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- Allgulander, C, et al.
(author)
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A non-inferiority comparison of duloxetine and venlafaxine in the treatment of adult patients with generalized anxiety disorder
- 2008
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In: Journal of psychopharmacology (Oxford, England). - : SAGE Publications. - 0269-8811 .- 1461-7285. ; 22:4, s. 417-425
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Journal article (peer-reviewed)abstract
- The present study is a non-inferiority comparison of duloxetine 60— 120 mg/day and venlafaxine extended-release (XR) 75—225 mg/day for the treatment of adults with generalized anxiety disorder (GAD). The non-inferiority test was a prespecified plan to pool data from two nearly identical 10-week, multicentre, randomized, placebo-controlled, double-blind studies of duloxetine 60-120 mg/day and venlafaxine 75—225 mg/ day for the treatment of GAD. An independent expert consensus panel provided six statistical and clinical criteria for determining non-inferiority between treatments. Response was defined as ≥50% reduction in Hamilton Anxiety Rating Scale (HAMA) total score. In the pooled sample, patients were randomly assigned to duloxetine ( n = 320), venlafaxine XR ( n = 333) or placebo ( n = 331). For the non-inferiority analysis, the per-protocol patients who were treated with duloxetine ( n = 239) or venlafaxine XR ( n = 262) improved significantly more (mean HAMA reductions were −15.4 and −15.2, respectively) than placebo-treated patients ( n = 267; −11.6, P ≤ 0.001, both comparisons). Response rates were 56%, 58% and 40%, respectively. Discontinuation rate because of AEs was significantly higher for duloxetine (13.4%, P ≤ 0.001) and venlafaxine XR (11.4%, P ≤ 0.01) groups compared with placebo (5.4%). Duloxetine 60—120 mg/day met all statistical and clinical criteria for non-inferiority and exhibited a similar tolerability profile compared with venlafaxine XR 75—225 mg/day for the treatment of adults with GAD.
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- O'Leary, CD, et al.
(author)
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Evidence for isovector neutron-proton pairing from high-spin states in N=Z Rb-74
- 2003
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In: Physical Review C (Nuclear Physics). - 0556-2813. ; 67:2: 021301
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Journal article (peer-reviewed)abstract
- High-spin states in the odd-odd N=Z nucleus Rb-74(37)37 were studied using the Ca-40(Ca-40,alphanp) reaction. A previously observed odd-spin T=0 band has been extended to I-pi=(31(+)) and an even-spin T=0 band has been observed for the first time to I-pi=(22(+)); both have a pi(g(9/2))circle timesnu(g(9/2)) structure. A strongly coupled low-spin T=0,K=3 band has been interpreted as being based upon a pi[312]3/2 circle timesnu[312]3/2 configuration. Cranked relativistic Hartree-Bogoliubov calculations, which are corrected for the t=1 np-pair field by restoring isospin symmetry, reproduce the observed spectrum. These new results provide evidence for the existence of an isovector pair field that contains a neutron-proton component with the proper strength for ensuring isospin conservation.
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- Romagnoni, A, et al.
(author)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Journal article (peer-reviewed)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.
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