Hungkuang University

Research has shown that patients with psychiatric disorders are more susceptible to contracting SARS-CoV-2 and tend to experience worse outcomes from COVID-19. Therefore, vaccination is strongly suggested for these patients to enhance protection. However, there is limited research on how COVID-19 vaccines induce antibody responses, specifically in individuals with psychiatric conditions. Furthermore, the situation has become more complex with the emergence of variants of concern (VOCs), and the effectiveness of antibody responses against these variants is still poorly understood. To determine whether patients with severe mental illness (SMI) mount an adequate immune reaction to COVID-19 vaccination, we studied the immunization effect of two widely used vaccines, ChAdOx1-S (AstraZeneca) and mRNA-1273 (Moderna), in patients with SMI compared to healthy controls. We enrolled 163 patients with SMI (mainly schizophrenia) and 66 healthy controls, all of whom were negative for previous SARS-CoV-2 infection. We analyzed anti-SARS-CoV-2 spike antibody titers against wild-type (WT), Delta, and Omicron variants after full (two-dose) vaccination with either the ChAdOx1-S or mRNA-1273 COVID-19 vaccines. Patients with SMI exhibited significantly lower mean anti-SARS-CoV-2 spike antibody titers. These results were consistent across different vaccine types and VOCs. Vaccination with ChAdOx1-S consistently induced lower spike-binding antibodies against WT, Delta, and Omicron variants compared to the mRNA-1273 vaccine. While the antibody activities against Omicron are well preserved, they are weaker than those against the WT and Delta variants. Our findings explain why patients with SMI are particularly susceptible to SARS-CoV-2 infection, breakthrough infections, and poorer COVID-19 outcomes.

Psychiatric comorbidity is a prevalent phenomenon that imposes a significant burden on patients, healthcare systems, and society. However, current research on psychiatric comorbidity is often limited to single disorders or partial associations. This study aims to utilize network analysis methods to construct a psychiatric comorbidity network and explore the network structural characteristics under different network weight definitions.

Based on the psychiatric outpatient data from Changhua Christian Hospital in Taiwan from January 1, 2016, to June 30, 2024, the ICD-10 diagnostic codes (F00-F99) of all patients that appeared at least three times were extracted. Three different comorbidity networks were constructed using co-occurrence counts, Jaccard index, and partial correlation coefficient estimated by the mixed graphical model (MGM) as the weights of the network edges. Network structure was analyzed using indicators such as degree centrality, modularity, and community detection.

The dataset included 16,954 patients. The comorbidity frequency network showed that mood disorders (F34) and anxiety disorders (F41) had the highest weighted degree centrality. In the Jaccard coefficient network, the weighted degree centrality of developmental disorders (F8x) increased. The MGM network highlighted the central role of substance use disorders (F1x).

Our findings suggested the roles and interrelationships of different disease categories in the comorbidity network. The results provide new perspectives and data support for clinical practice and future research.

The aesthetic appearance of the mouth during smiling significantly influences facial attractiveness, thereby, making smile analysis crucial in orthodontics, craniofacial surgery, and cosmetic dentistry. Accurate and quantitative evaluation of facial smile attractiveness is crucial for surgical planning and outcome assessment in orthognathic surgery (OGS). In this study, a transfer learning (TL) model using a convolutional neural network (CNN) based on three-dimensional (3D) contour line features was employed to assess facial smile attractiveness before and after OGS. A retrospective cohort study involving 135 patients was conducted between 2021 and 2024 to compare facial smile attractiveness before and after OGS. Using the 3dMD™ face system, 3D facial photos were captured in a natural head position with forward-facing eyes, relaxed facial muscles, and habitual dental occlusion, before and at least 6 months after surgery. Subsequently, 3D contour images were extracted from these photos for web-based automatic facial smile attractiveness assessment using TL with CNN model. Postoperatively, facial smile attractiveness significantly improved, with scores increasing from 2.62 to 3.27, representing a 25% enhancement as determined by the constructed machine learning model. The web-based system offered clinicians a user-friendly interface, providing rapid assessment of results and serving as an effective tool for doctor-patient communication. This study marks the first attempt to automatically evaluate facial smile attractiveness before and after surgery in an objective and quantitative manner, using a machine learning model based on the 3D contours feature map.