Excessive Daytime Sleepiness and Insomnia Symptoms in Adolescents With Major Depressive Disorder: Prevalence, Clinical Correlates, and the Relationship With Psychiatric Medications Use

Objective@#Excessive daytime sleepiness (EDS) and insomnia symptoms are common in patients with major depressive disorder (MDD), which might lead to a poor prognosis and an increased risk of depression relapse. The current study aimed to investigate the prevalence, and sociodemographic and clinical correlates of EDS and insomnia symptoms among adolescents with MDD. @*Methods@#The sample of this cross-sectional study included 297 adolescents (mean age=15.26 years; range=12–18 years; 218 females) with MDD recruited from three general and four psychiatric hospitals in five cities (Hefei, Bengbu, Fuyang, Suzhou, and Ma’anshan) in Anhui Province, China between January and August, 2021. EDS and insomnia symptoms, and clinical severity of depressive symptoms were assessed using Epworth sleepiness scale, Insomnia Severity Index, and Clinical Global Impression-Severity. @*Results@#The prevalence of EDS and insomnia symptoms in adolescents with MDD was 39.7% and 38.0%, respectively. Binary logistic regression analyses showed that EDS symptoms were significantly associated with higher body mass index (odds ratio [OR]=1.097, 95% confidence interval [CI]=1.027–1.172), more severe depressive symptoms (OR=1.313, 95% CI=1.028–1.679), and selective serotonin reuptake inhibitors use (OR=2.078, 95% CI=1.199–3.601). And insomnia symptoms were positively associated with female sex (OR=1.955, 95% CI=1.052–3.633), suicide attempts (OR=1.765, 95% CI=1.037–3.005), more severe depressive symptoms (OR=2.031, 95% CI=1.523–2.709), and negatively associated with antipsychotics use (OR=0.433, 95% CI=0.196–0.952). @*Conclusion@#EDS and insomnia symptoms are common among adolescents with MDD. Considering their negative effects on the clinical prognosis, regular screening and clinical managements should be developed for this patient population.

Prediction of concentration immediately dangerous to life or health of benzene and its derivatives based on quantitative structure-activity relationship / 环境与职业医学

Background With the increasing exposure to hazardous chemicals in the workplace and frequency of occupational injuries and occupational safety accidents, the acquisition of occupational exposure limits of hazardous chemicals is imminent. Objective To obtain more unknown immediately dangerous to life or health (IDLH) concentrations of hazardous chemicals in the workplace by exploring the application of quantitative structure-activity relationship (QSAR) prediction method to IDLH concentrations, and to provide a theoretical basis and technical support for the assessment and prevention of occupational injuries. Methods QSAR was used to correlate the IDLH values of 50 benzene and its derivatives with the molecular structures of target compounds. Firstly, affinity propagation algorithm was applied to cluster sample sets. Secondly, Dragon 2.1 software was used to calculate and pre-screen 537 molecular descriptors. Thirdly, the genetic algorithm was used to select six characteristic molecular descriptors as dependent variables and to construct a multiple linear regression model (MLR) and two nonlinear models using support vector machine (SVM) and artificial neural network (ANN) respectively. Finally, model performance was evaluated by internal and external validation and Williams diagram was drawn to determine the scopes of selected models. Results The ANN model results showed that \begin{document}$ {R}_{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}}