Background and Objectives: Genomic studies have identified several SNP loci associated with schizophrenia in East Asian populations. Environmental factors, particularly urbanization, play a significant role in schizophrenia development. This study aimed to identify schizophrenia susceptibility loci and characterize their biological functions and molecular pathways in Taiwanese urban Han individuals. Materials and Methods: Participants with schizophrenia were recruited from the Taiwan Precision Medicine Initiative at Tri-Service General Hospital. Genotype–phenotype association analysis was performed, with significant variants annotated and analyzed for functional relevance. Results: A total of 137 schizophrenia patients and 26,129 controls were enrolled. Ten significant variants (p < 1 × 10⁻⁵) and 15 expressed genes were identified, including rs1010840 (SOWAHC and RGPD6), rs11083963 (TRPM4), rs11619878 (LINC00355 and LINC01052), rs117010638 (AGBL1 and MIR548AP), rs1170702 (LINC01680 and LINC01720), rs12028521 (KAZN and PRDM2), rs12859097 (DMD), rs1556812 (ATP11A), rs78144262 (LINC00977), and rs9997349 (ENPEP). These variants and associated genes are involved in immune response, blood pressure regulation, muscle function, and the cytoskeleton. Conclusions: Identified variants and associated genes suggest a potential genetic predisposition to schizophrenia in the Taiwanese urban Han population, highlighting the importance of potential comorbidities, considering population-specific genetic and environmental interactions.

Background: Mental health stigma undermines nursing care quality and contributes to health inequities. Purpose: To evaluate changes in students’ attitudes toward people with mental illness and explore their learning experiences after participating in a project-based podcast creation activity. Methods: A one-group pretest–posttest design with qualitative evaluation was used. Thirty-five undergraduate nursing students completed an 18-week course. Attitudes were examined before and after the course by using the Attitudes Toward Mental Illness scale. Written reflections were analyzed using thematic analysis. Results: Attitudes toward people with mental illness positively changed after the course ( t = 7.55, P < .001), with a large effect size (Hedges g = 2.01). Qualitative findings indicated greater empathy, less stereotypical thinking, stronger professional identity in health communication, and personal growth through reflection and collaboration after the course. Conclusions: Project-based podcast production is feasible and supports positive attitudinal changes, empathy development, and professional identity formation among nursing students.

Background: Major depressive disorder (MDD) with suicidality represents a significant public health concern, as suicide ranks among the leading causes of death worldwide. While electroencephalography (EEG) has shown promise in depression diagnosis, its utility in identifying suicidal risk remains underexplored. This study aims to develop and validate a Suicidal Risk Index (SR Index) using EEG biomarkers and machine learning (ML) algorithms for distinguishing between MDD patients with and without suicidality. Methods: In this retrospective observational study, resting-state EEG data were collected using Stress EEG Assessment (SEA) system. SR Index was developed by integrating the PHQ-9 scale with EEG-derived features, including band power, coherence, and fractal dimension, optimized using ML algorithms to enhance accuracy. Results: The study included 268 participants (159 without suicidality, 109 with suicidality). The SR Index demonstrated robust discriminative ability with an AUC of 0.8117 ( p =2.63×10 ^-18 ). At the optimal cutoff value of 8, the model achieved 88.99% sensitivity, 57.23% specificity, 67.86% positive predictive value, and 78.85% negative predictive value, with a balanced accuracy of 73.11%. Conclusions: The SR Index shows promise as an objective tool for identifying suicidality in MDD patients, potentially complementing traditional clinical assessments. This approach may enhance early detection and risk stratification in clinical settings, potentially improving suicide prevention strategies. Highlights: Novel EEG- and ML-based Suicidal Risk Index distinguishes MDD patients with and without suicidality. SR Index achieves 88.99% sensitivity and 73.11% balanced accuracy in identifying suicidal risk.