Early response to first-line antipsychotic treatments is strongly associated with positive long-term symptomatic and functional outcome in psychosis. Unfortunately, attempts to identify reliable predictors of treatment response in first-episode psychosis (FEP) patients have not yet been successful. One reason for this could be that FEP patients are highly heterogeneous in terms of symptom expression and underlying disease biological mechanisms, thereby impeding the identification of one-size-fits-all predictors of treatment response. We have used a clustering approach to stratify 325 FEP patients into four clinical subtypes, termed C1A, C1B, C2A and C2B, based on their symptoms assessed using the Positive and Negative Syndrome Scale (PANSS) scale. Compared to C1B, C2A and C2B patients, those from the C1A subtype exhibited the most severe symptoms and were the most at risk of being non-remitters when treated with the second-generation antipsychotic drug amisulpride. Before treatment, C1A patients exhibited higher serum levels of several pro-inflammatory cytokines and inflammation-associated biomarkers therefore validating our stratification approach on external biological measures. Most importantly, in the C1A subtype, but not others, lower serum levels of interleukin (IL)-15, higher serum levels of C-X-C motif chemokine 12 (CXCL12), previous exposure to cytomegalovirus (CMV), use of recreational drugs and being younger were all associated with higher odds of being non-remitters 4 weeks after treatment. The predictive value of this model was good (mean area under the curve (AUC) = 0.73 ± 0.10), and its specificity and sensitivity were 45 ± 0.09% and 83 ± 0.03%, respectively. Further validation and replication of these results in clinical trials would pave the way for the development of a blood-based assisted clinical decision support system in psychosis.

Genetic studies of the familial forms of Parkinson’s disease (PD) have identified a number of causative genes with an established role in its pathogenesis. These genes only explain a fraction of the diagnosed cases. The emergence of Next Generation Sequencing (NGS) expanded the scope of rare variants identification in novel PD related genes. In this study we describe whole exome sequencing (WES) genetic findings of 60 PD patients with 125 variants validated in 51 of these cases. We used strict criteria for variant categorization that generated a list of variants in 20 genes. These variants included loss of function and missense changes in 18 genes that were never previously linked to PD ( NOTCH4 , BCOR, ITM2B , HRH4 , CELSR1 , SNAP91 , FAM174A , BSN , SPG7 , MAGI2 , HEPHL1 , EPRS , PUM1 , CLSTN1 , PLCB3 , CLSTN3 , DNAJB9 and NEFH ) and 2 genes that were previously associated with PD ( EIF4G1 and ATP13A2 ). These genes either play a critical role in neuronal function and/or have mouse models with disease related phenotypes. We highlight NOTCH4 as an interesting candidate in which we identified a deleterious truncating and a splice variant in 2 patients. Our combined molecular approach provides a comprehensive strategy applicable for complex genetic disorders.

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It is an admitted fact that mainstream boosting algorithms like AdaBoost do not perform well to estimate class conditional probabilities. In this paper, we analyze, in the light of this problem, a recent algorithm, unn, which leverages nearest neighbors while minimizing a convex loss. Our contribution is threefold. First, we show that there exists a subclass of surrogate losses, elsewhere called balanced, whose minimization brings simple and statistically efficient estimators for Bayes posteriors. Second, we show explicit convergence rates towards these estimators for unn, for any such surrogate loss, under a Weak Learning Assumption which parallels that of classical boosting results. Third and last, we provide experiments and comparisons on synthetic and real datasets, including the challenging SUN computer vision database. Results clearly display that boosting nearest neighbors may provide highly accurate estimators, sometimes more than a hundred times more accurate than those of other contenders like support vector machines.