Estimated prevalence and sociodemographic correlates of mental disorders in medical students of Hebei Province, China: A cross-sectional study.

BACKGROUND: In China, the identification rate and treatment rate of mental disorders are low, and there are few surveys on the prevalence of mental disorders among college students using diagnostic tools such as Mini-International Neuropsychiatric Interview (MINI), so the prevalence and treatment of mental disorders among college students are unclear. AIM: To estimate prevalence of mental disorders among medical students in Hebei Province, and provide guidance for improving their mental health. METHODS: This was a cross-sectional study based on an Internet-based survey. Three levels of medical students in Hebei Province were randomly selected (by cluster sampling) for screening. Using the information network assessment system, the subjects scanned the 2D code with their mobile phones, clicked to sign the informed consent, and answered a scale. A self-designed general status questionnaire was used to collect information about age, gender, ethnicity, grade, and origin of students. The MINI 5.0. was used to investigate mental disorders. Data analysis was performed with SPSS software. Statistically significant findings were determined using a two-tailed P value of 0.05. RESULTS: A total of 7117 subjects completed the survey between October 11 and November 7, 2021. The estimated prevalence of any mental disorders within 12 mo was 7.4%. Mood disorders were the most common category (4.3%), followed by anxiety disorders (3.9%); 15.0% had been to psychological counseling, while only 5.7% had been to a psychiatric consultation, and only 10% had received drug therapy in the past 12 mo. CONCLUSION: Although the estimated prevalence of mental disorders in medical students is lower than in the general population, the rate of adequate treatment is low. We determined that improving the mental health of medical students is an urgent matter.

Association between peripheral orexin A/B levels and depression with childhood trauma.

BACKGROUND: Orexin dysfunction has previously been demonstrated to be associated with depression. However, no studies reported the different effects of orexin A/B on depression with and without childhood trauma (CT). In this study，we assessed the correlation between expression of orexin A/B and depression severity in major depressive disorder (MDD) patients and healthy controls. METHODS: A total of 97 MDD patients and 51 healthy controls were recruited in this study. According to the total scores of childhood trauma questionnaire (CTQ), the MDD patients were further divided into two subgroups, MDD with CT and MDD without CT. The 17-item Hamilton Depression Scale (HAMD-17), and plasma orexin A and orexin B concentrations were measured in all participants using enzyme-linked immunosorbent assay. RESULTS: Orexin B plasma levels were significantly higher in MDD patients with CT and without CT than that in the healthy control group (P < 0.05), whereas there was no statistical difference between the two depression groups. After adjusting age and BMI for covariates, the LASSO regression revealed significant association between the plasma orexin B levels and the total scores of HAMD (ß = 3.348), CTQ (ß = 2.005). There was no difference in plasma orexin A levels among three groups (P > 0.05). CONCLUSIONS: Although peripheral orexin B levels are associated with the depression, rather than orexin A, CT appear to play a role in the association between orexin B levels and depression. China Clinical Trial Registration Center (Registration No.: ChiCTR2000039692).

An Adaptive Parallel Feature Learning and Hybrid Feature Fusion-Based Deep Learning Approach for Machining Condition Monitoring.

The rapid development of information and communication technologies has facilitated machining condition monitoring toward a data-driven paradigm, of which the Industrial Internet of Things (IIoT) serves as the fundamental basis to acquire data from physical equipment with sensing technologies as well as to learn the relationship between the system condition and the collected condition monitoring data. However, most data-driven methods suffer from using a single-domain space, ignoring the importance of the learned features, and failing to incorporate the handcrafted features assisted by domain knowledge. To solve these limitations, a novel deep learning approach is proposed for machining condition monitoring in the IIoT environment, which consists of three phases, including: 1) the unsupervised parallel feature extraction; 2) adaptive feature importance weighting; and 3) hybrid feature fusion. First, separate sparse autoencoders are utilized to conduct the unsupervised parallel feature extraction, which enables to learn abstract feature representation from multiple domain spaces simultaneously. Then, an attention module is designed for the adaptive feature importance weighting, which can assign higher weights to those critical features accordingly. Moreover, a hybrid feature fusion is deployed to complement the automatic feature learning and further yield better model performance by fusing the handcrafted features assisted by domain knowledge. Finally, a real-life case study and extensive experiments have been conducted to show the effectiveness and superiority of the proposed approach.