The STOP AF trial demonstrated that cryoballoon ablation is a safe and effective alternative to antiarrhythmic medication for the treatment of patients with symptomatic paroxysmal AF, for whom at least one antiarrhythmic drug has failed, with risks within accepted standards for ablation therapy. (A Clinical Study of the Arctic Front Cryoablation Balloon for the Treatment of Paroxysmal Atrial Fibrillation [Stop AF]; NCT00523978).
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.
This series of 4 studies describes the psychometric properties of the Neuropsychology Behavior and Affect Profile, which consists of 5 peer-rated scales (106 items) designed to measure personality change in brain-impaired individuals. Study 1 pertains to item derivation. Study 2 used relatives of 61 Ss identified as demented to determine the test's internal consistency. Results showed moderate levels of internal consistency across the 5 scales, with slightly higher coefficients (.6S-.82) obtained for present (vs. premorbid) emotional status. High test-retest reliability was demonstrated in Study 3 (intraclass correlation coefficients ranged from .92 to .99). Study 4 established discriminant validity; the instrument differentiated 61 demented Ss from 88 normal elderly controls on the basis of present behavioral affective style.
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