ivations and expectancies of use and associated psychosocial functioning. Understanding motives and expectancies can help segregate which users are at higher risk of worse functioning. These findings are timely when designing targeted assessment and treatment strategies, particularly as cannabis is further decriminalized and accessibility increases.We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status.Background Prescription drug misuse and its related risks are considered a worldwide public health issue. Current trends show that the extent of such phenomenon may not be limited to subjects with psychiatric disorders, as it also spreads to dance party and nightclub attendees, who often consume prescription drugs in combination with alcohol and psychoactive substances. This study aims to report the sociodemographic data and the psychiatric and clinical features of a sample of clubbers reporting prescription drugs use. Methods Patients admitted to the psychiatry ward of the Can Misses Hospital in Ibiza were recruited for the study during a span of four consecutive years (2015-2018). The inclusion criteria were age 18-75 years old and the intake of psychoactive substances or more than five alcohol units during the previous 24 h. Substance use habits, psychopathological features, and use of unprescribed pharmaceuticals were investigated. Urine samples were collected and analyzed using gas chromatography/mass sprescription drugs to prevent serious medical and psychiatric consequences.The SARS-CoV-2 (COVID-19) pandemic has contributed to increasing levels of anxiety, depression and other symptoms of stress around the globe. Reasons for this increase are understandable in the context of individual level factors such as self-isolation, lockdown, grief, survivor guilt, and other factors but also broader social and economic factors such as unemployment, insecure employment and resulting poverty, especially as the impacts of 2008 recession are still being felt in many countries further accompanied by social isolation. For those who are actively employed a fear of job and income loss and those who have actually become ill and recovered or those who have lost family and friends to illness, it is not surprising that they are stressed and feeling the psychological impact. Furthermore, multiple uncertainties contribute to this sense of anxiety. These fears and losses are major immediate stresses and undoubtedly can have long-term implications on mental health. Economic uncertainty combined with a sement has a major effect on increasing suicide, especially in middle-aged groups. However, the impact of economic decline through losses of national income (GDP per capita) are substantially greater than those of unemployment and influence suicide throughout the life course, especially at the oldest ages.Objective The primary study aim was to identify long non-coding RNA (lncRNA) abnormalities associated with ultra-high-risk (UHR) for psychosis based on a weighted gene co-expression network analysis. Methods UHR patients were screened by the structured interview for prodromal syndromes (SIPS). We performed a WGCNA analysis on lncRNA and mRNA microarray profiles generated from the peripheral blood samples in 14 treatment-seeking patients with UHR who never received psychiatric medication and 18 demographically matched typically developing controls. Gene Ontology (GO) analysis and canonical correlation analysis were then applied to reveal functions and correlation between lncRNAs and mRNAs. Results The lncRNAs were organized into co-expressed modules by WGCNA, two modules of which were strongly associated with UHR.  https://www.selleckchem.com/products/Perifosine.html  The mRNA networks were constructed and two disease-associated mRNA modules were identified. A functional enrichment analysis showed that mRNAs were highly enriched for immune regulation and inflammation. Moreover, a significant correlation between lncRNAs and mRNAs were verified by a canonical correlation analysis. Conclusion We identified novel lncRNA modules related to UHR. These results contribute to our understanding of the molecular basis of UHR from the perspective of systems biology and provide a theoretical basis for early intervention in the assumed development of schizophrenia.Objectives One of the largest clusters of Covid-19 infections was observed in Italy. The population was forced to home confinement, exposing individuals to increased risk for insomnia, which is, in turn, associated with depression and anxiety. Through a cross-sectional online survey targeting all Italian adult population (≥18 yrs), insomnia prevalence and its interactions with relevant factors were investigated. Methods The survey was distributed from 1st April to 4th May 2020. We collected information on insomnia severity, depression, anxiety, sleep hygiene behaviors, dysfunctional beliefs about sleep, circadian preference, emotion regulation, cognitive flexibility, perceived stress, health habits, self-report of mental disorders, and variables related to individual difference in life changes due to the pandemic's outbreak. Results The final sample comprised 1,989 persons (38.4 ± 12.8 yrs). Prevalence of clinical insomnia was 18.6%. Results from multivariable linear regression showed that insomnia severity was associated with poor sleep hygiene behaviors [β = 0.