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Textile production methods present a rich set of strategies for developing materials with both form and function encoded at the fiber scale. Beyond simply acting as a static flexible barrier, the ability to incorporate environmentally responsible materials into fabric architectures significantly expands the textile design space by adding on-demand and programmable 3D structural morphing. To this end, liquid crystal elastomers (LCEs) are a promising candidate for enabling these reversible actuation behaviors in fabric-based constructs. Drawing on traditional textile manufacturing techniques and through a detailed exploration of the vast woven textile design space, we have demonstrated programmable and reversible curling, puffing, and in-plane shrinkage behaviors by embedding the functionality of LCE fibers into single and multi-layered woven structures. Predictable shifts in fabric structure directly influence the mechanical properties and the resulting form factor of the actuated textiles, which can in turn be effectively leveraged for the generation of multi-functional devices, enabling new directions for the engineering of flexible stimuli-responsive materials.

Abstract In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.

Introduction Pathogenic variants in STXBP1 cause a spectrum of disorders mainly consisting of developmental and epileptic encephalopathy (DEE), often featuring drug-resistant epilepsy. An increased mortality risk occurs in individuals with drug-resistant epilepsy and DEE, with sudden unexpected death in epilepsy (SUDEP) often the major cause of death. This study aimed to identify the rate and causes of mortality in STXBP1 -related disorders. Methods Through an international call, we analyzed data on individuals with STXBP1 pathogenic variants, who passed away from causes related to their disease. Results We estimated a mortality rate of 3.2% (31/966), based on the STXBP1 Foundation and the STXBP1 Global Connect registry. In total, we analyzed data on 40 individuals (23 males) harboring pathogenic STXBP1 variants, collected from different centers worldwide. They died at a median age of 13 years (range: 11 months—46 years). The most common cause of death was SUDEP (36%), followed by pulmonary infections and respiratory complications (33%). The incidence of SUDEP peaked in mid-childhood, while non-SUDEP causes were more frequent in early childhood or adulthood (p = 0.006). In the most severe phenotypes, death was related to non-SUDEP causes ( p  = 0.018). Conclusion We found a mortality rate in STXBP1 -related disorders similar to other DEEs, with an early age at death and SUDEP as well as pulmonary infections as the main cause of death. These findings assist in prognostic evaluation and genetic counseling for the families. They help to define the mortality risk of STXBP1 -related disorders and implement preventative strategies.

Several multivariate prognostic models have been published to predict outcomes in patients with first episode psychosis (FEP), but it remains unclear whether those predictions generalize to independent populations. Using a subset of demographic and clinical baseline predictors, we aimed to develop and externally validate different models predicting functional outcome after a FEP in the context of a schizophrenia-spectrum disorder (FES), based on a previously published cross-validation and machine learning pipeline. A crossover validation approach was adopted in two large, international cohorts (EUFEST, n = 338, and the PSYSCAN FES cohort, n = 226). Scores on the Global Assessment of Functioning scale (GAF) at 12 month follow-up were dichotomized to differentiate between poor (GAF current < 65) and good outcome (GAF current ≥ 65). Pooled non-linear support vector machine (SVM) classifiers trained on the separate cohorts identified patients with a poor outcome with cross-validated balanced accuracies (BAC) of 65-66%, but BAC dropped substantially when the models were applied to patients from a different FES cohort (BAC = 50–56%). A leave-site-out analysis on the merged sample yielded better performance (BAC = 72%), highlighting the effect of combining data from different study designs to overcome calibration issues and improve model transportability. In conclusion, our results indicate that validation of prediction models in an independent sample is essential in assessing the true value of the model. Future external validation studies, as well as attempts to harmonize data collection across studies, are recommended.