Background: Studies have implicated hypofrontality in the pathogenesis of impaired theory of mind (ToM) and executive function (EF) in major depressive disorder (MDD). These symptoms are usually resistant to treatment. Repetitive transcranial magnetic stimulation (rTMS) has been shown to reverse hypofrontality. Moreover, BDNF is an effective biomarker of antidepressant effects, but there have been very few studies on the correlation between BDNF and rTMS. We aimed to evaluate the efficacy of 20 sessions of a 10 Hz unilateral rTMS intervention over the left dorsolateral prefrontal cortex (DLPFC) in improving ToM and EF in patients with MDD and its correlation with BDNF. Methods: A total of 120 MDD patients were enrolled in this randomized, sham-controlled, double-blind trial. Each participant received 20 sessions of rTMS at 10 Hz frequency through the active or the sham coil over 4 weeks. ToM was assessed with the facial emotion identification test (FEIT) and hinting task (HT). EF was assessed with the Wisconsin card sorting test (WCST). BDNF assessments were carried out at baseline and 2-, 4-, 12-, and 24-week follow-ups. Results: The improvement in the ToM (FEIT, HT) in the active rTMS group was significantly different from that in the sham rTMS group (F = 18.09, p < 0.001; F = 5.02, p = 0.026). There were significant differences in the WCST (categories completed, response errors, response perseverative errors, non-response perseverative errors) after logarithmic transformation at different time points in the active rTMS group (F = 14.71, p < 0.001; F = 5.99, p = 0.046; F = 8.90, p = 0.031; F = 2.31, p = 0.048). However, there was no significant difference in log transformed BDNF concentration between the two groups (t = 0.07 to t = 1.29, p > 0.05). BDNF was negatively correlated with WCST categories completed at the 24th week (r = −0.258, p = 0.046). Conclusions: The results show that rTMS may improve the ToM and EF of patients with MDD and there was no significant correlation with serum BDNF concentration. RTMS can not only be used for treatment of patients with MDD but also has a positive effect on ToM and EF.

Background: Factors related to medication adherence in patients with schizophrenia have always been key to the treatment and rehabilitation of these patients. However, the treatment modes in different countries are not the same, and there is no research on the factors influencing medication adherence under different mental health service modes. Objectives: The purpose of this study was to explore medication adherence and its influencing factors in patients with schizophrenia in the Chinese institutional environment. Methods: We conducted a cross-sectional study of hospitalized persons living with schizophrenia from November 2018 to January 2019. A systematic sampling method was used to select 217 hospitalized persons living with schizophrenia. The Medication Adherence Rating Scale (MARS), Positive and Negative Syndrome Scale (PANSS), General Self-Efficacy Scale (GSES), Schizophrenia Quality of Life Scale (SQLS), and Scale of Social Skills for Psychiatric Inpatients (SSPI) were used to explore medication compliance and its influencing factors in the Chinese institutional environment. Results: The descriptive analysis and ANOVA showed that there were no significant differences in medication adherence when assessed by demographic characteristics such as sex, marital status, and education level (p > 0.05). A correlation analysis showed that there was no significant correlation between medication adherence and mental symptoms (p > 0.05) but that there was a positive correlation with self-efficacy, quality of life, and activities of daily living (p < 0.01). The linear regression analysis showed that self-efficacy, psychosocial factors, symptoms/side effects, and activities of daily living had significant effects on medication adherence (F = 30.210, p < 0.001). Conclusions: Our findings show that the self-efficacy, quality of life, and social function of patients with schizophrenia are important self-factors influencing medication adherence in the Chinese institutional environment.

Objectives: Computer-aided schizophrenia (SZ) detection methods mainly depend on electroencephalogram and brain magnetic resonance images, which both capture physical signals from patients’ brains. These inspection techniques take too much time and affect patients’ compliance and cooperation, while difficult for clinicians to comprehend the principle of detection decisions. This study proposes a novel method using face diagnosis images based on traditional Chinese medicine principles, providing a non-invasive, efficient, and interpretable alternative for SZ detection. Methods: An innovative face diagnosis image analysis method for SZ detection, which learns feature representations based on Vision Transformer (ViT) directly from face diagnosis images. It provides a face features distribution visualization and quantitative importance of each facial region and is proposed to supplement interpretation and to increase efficiency in SZ detection while keeping a high detection accuracy. Results: A benchmarking platform comprising 921 face diagnostic images, 6 benchmark methods, and 4 evaluation metrics was established. The experimental results demonstrate that our method significantly improves SZ detection performance with a 3–10% increase in accuracy scores. Additionally, it is found that facial regions rank in descending order according to importance in SZ detection as eyes, mouth, forehead, cheeks, and nose, which is exactly consistent with the clinical traditional Chinese medicine experience. Conclusions: Our method fully leverages semantic feature representations of first-introduced face diagnosis images in SZ, offering strong interpretability and visualization capabilities. It not only opens a new path for SZ detection but also brings new tools and concepts to the research and application in the field of mental illness.

To identify clinical risk factors for diabetes mellitus (DM) in long-stay schizophrenia (LS-SCZ) patients, explore shared molecular mechanisms of schizophrenia and DM, and validate targeted interventions. Clinical data of LS-SCZ patients were analyzed via multiple logistic regression to identify DM risk factors. Differentially expressed genes (DEGs) from GSE53987/GSE161355 datasets were screened; overlapping DEGs were analyzed for co-expression and KEGG enrichment. Support vector machine (SVM) selected feature genes whose diagnostic efficacy was evaluated by ROC curves. Molecular docking verified Genistein’s binding with feature gene proteins. Risk factors for diabetes in LS-SCZ patients included age ≥50 years, hospitalization >20 years, BRI >5.306, family history of diabetes, and hypertension (all p < 0.05). Molecularly, 27 overlapping DEGs from two datasets were enriched in neuroactive ligand–receptor interaction, HIF-1, and MAPK pathways. SVM identified four feature genes (NPY, MKNK2, IFITM3, and S100A8) with good diagnostic efficacy. Genistein bound strongly to their proteins (binding energies: −8.98 to −6.026 kcal/mol). In conclusion, LS-SCZ patients have high DM risk. Targeting clinical risk factors and using Genistein for feature genes may reduce DM comorbidity.