Objective: Optimism, social support, and spirituality can be important factors related to coronavirus disease-2019 (COVID-19) stress. However, studies investigating the influence and interplay of optimism, social support, and spirituality on COVID-19 simultaneously are still few. This study is aimed to explore the influence of optimism, social support, and spirituality on COVID-19 stress in the Christian church community. Methods: A total 350 participants were included in this study. This study was cross-sectionally conducted by using an online survey on optimism, social support, spirituality, and COVID-19 stress that were measured by the Life Orientation Test-Revised (LOT-R), Multidimensional Scale of Perceived Social Support Scale (MSPSS), Spiritual Well-Being Scale (SWBS), and COVID-19 Stress Scale for Korean People (CSSK). The prediction models for COVID-19 stress were analyzed by using univariate and multiple linear regression. Results: Based on the results of univariate linear regression, subjective feelings on income (p<0.001) and health status (p<0.001), LOTR (p<0.001), MSPSS (p=0.025), and SWBS (p<0.001) scores were significantly associated with COVID-19 stress. The multiple linear regression model with subjective feelings on income and health status and SWSB score was significant (p<0.001) and explained 17.7% of the variance (R2=0.177). Conclusion This study showed that subjective feeling on low income, those who had poor health status, lower optimism, lower perceived social support, and lower spirituality were significantly affected with COVID-19 stress. Especially, the model with subjective feelings on income and health status, and spirituality showed highly significant effects, despite the interaction with associated factors. To cope with unpredictable stressful situations like the COVID-19 pandemic, integrated interventions on psycho-socio-spiritual aspect are warranted.

Background: Dopamine D2 and D3 receptor agonist quinpirole have been tried as one of drug-induced bipolar animal models. An open-field test is used to assess locomotor activity related to anxiety. Not many studies have analyzed the effects of quinpirole dosages on locomotor activity. The purpose of this study was to look at the locomotor activity of quinpirole-injected mice in an open-field test. Methods: The open-field test was used to observe the locomotor activities of 28 mice. Quinpirole was administrated at 0.05-5 mg/kg and normal saline were used as a control. The Mann-Whitney U-test was employed to compare the locomotor activities in the quinpirole and control groups. Results: Quinpirole-induced locomotor activities reduced as time elapsed during the first 30 minutes following the injection in most mice, then increased or fluctuated in the later 30 minutes. As the dosage was increased, there was a stronger initial inhibition, followed by a rapid and further increase in the last 30 minutes. Conclusion This study showed the differential effects of quinpirole-induced locomotor activities depending on dosage, and initial suppression of locomotor activities by quinpirole was observed. Additionally, longitudinal observation of more than 1 hour would be required to look into the biphasic pattern of quinpirole in an animal model.

Background: Several studies support the effectiveness and tolerability of virtual reality (VR) interventions in the psychiatric field. This study aimed to examine changes of cognitive performance in VR-assisted mental health promotion programs and to investigate the difference in performance according to clinical characteristics. Methods: Thirty subjects aged >55 years participated in the study. The clinical characteristics of depression, anxiety, perceived stress, quality of life, and cognition were assessed. Cognitive performance in VR-assisted mental health promotion programs was compared between the clusters classified by clinical characteristics. Results: Cluster analysis classified the subjects into three groups. In Cluster 1, the Module 3 training score was significantly different before and after VR performance. In Cluster 2, significant differences were observed in the Module 1 training score, the Module 2 training score, the Module 2 defense failure score, and the Module 3 training score.In Cluster 3, a significant difference was observed in the Module 3 training score. Conclusion The results of this study suggest that VR performance might differ according to clinical characteristics. A cognitive training strategy using VR has to be differentially established depending on the characteristics of the community population.

Objective: Behavioral assessments that effectively predict sleep-wake states were tried in animal research. This study aimed to examine the prediction power of an infrared locomotion detector on the sleep-wake states in ICR (Institute Cancer Research) mice. We also explored the influence of the durations and ways of data processing on the prediction power. Methods: The locomotor activities of seven male mice in home cages were recorded by infrared detectors. Their sleep-wake states were assessed by video analysis. Using the receiver operating characteristic curve analysis, the cut-off score was determined, then the area under the curve (AUC) values of the infrared motion detector that predicted sleep-wake states were calculated. In order to improve the prediction power, the four ways of data processing on the prediction power were performed by Matlab 2013b. Results: In the initial analysis of raw data, the AUC value was 0.785, but it gradually reached to 0.942 after data summation. The simple data averaging and summation among four different methods showed the maximal AUC value. The 10-minute data summation improved sensitivity (0.889) and specificity (0.901) significantly from the baseline value (sensitivity 0.615; specificity 0.936) (p ＜ 0.001). Conclusion This study suggests that the locomotor activity measured by an infrared motion detector might be useful to predict the sleep-wake states in ICR mice. It also revealed that only simple data summation may improve the predictive power. Using daily locomotor activities measured by an infrared motion detector is expected to facilitate animal research related to sleep-wake states.

Objective: This study aimed to explore the relationship among several indices of circadian rhythms and lipid peroxidation of brain tissue in mice. Methods: After entrainment of 4-week-old mice, one group was disrupted their circadian rhythms for three days and the other group for seven days (n = 10, respectively). After a recovery period, the Y-maze test, the elevated plus maze test, the tail suspension test, and the forced swimming test were conducted. To assess lipid peroxidation in brain tissue, thiobarbituric acid reactive substances were measured in the cortex, hippocampus, and cerebellum. Results: When circadian rhythms were disrupted and adapted back to their original rhythm, the recovery time of the 7-day disruption group (median 3.35 days) was significiantly faster than one of the 3-day disruption group (median 4.87 days). In the group with a 7-day disruption, mice that had recovered their rhythms early had higher malondialdehyde levels in their hippocampus compared to those with delayed recovery. The entrainment of circadian rhythms was negatively correlated with the malondialdehyde level of brain tissue. The behavioral test results showed no differences depending on the disruption durations or recovery patterns of circadian rhythms. Conclusion These results suggest that disruption types, recovery patterns, and the entrainment of circadian rhythms are likely to affect oxidative stress in adolescents or young adult mice. Future study is needed to confirm and specify these results on the effects of circadian rhythms on oxidative stress and age-dependent effects.

Objective: Behavioral assessments that effectively predict sleep-wake states were tried in animal research. This study aimed to examine the prediction power of an infrared locomotion detector on the sleep-wake states in ICR (Institute Cancer Research) mice. We also explored the influence of the durations and ways of data processing on the prediction power. Methods: The locomotor activities of seven male mice in home cages were recorded by infrared detectors. Their sleep-wake states were assessed by video analysis. Using the receiver operating characteristic curve analysis, the cut-off score was determined, then the area under the curve (AUC) values of the infrared motion detector that predicted sleep-wake states were calculated. In order to improve the prediction power, the four ways of data processing on the prediction power were performed by Matlab 2013b. Results: In the initial analysis of raw data, the AUC value was 0.785, but it gradually reached to 0.942 after data summation. The simple data averaging and summation among four different methods showed the maximal AUC value. The 10-minute data summation improved sensitivity (0.889) and specificity (0.901) significantly from the baseline value (sensitivity 0.615; specificity 0.936) (p ＜ 0.001). Conclusion This study suggests that the locomotor activity measured by an infrared motion detector might be useful to predict the sleep-wake states in ICR mice. It also revealed that only simple data summation may improve the predictive power. Using daily locomotor activities measured by an infrared motion detector is expected to facilitate animal research related to sleep-wake states.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: Although maintenance treatment for mood disorders is important, the treatment discontinuation rate is reported to be high. This study aimed to investigate the dropout rates and associated factors in mood disorders. Methods: The patients in a mood disorder clinic (n = 535) were examined. Demographic and clinical factors, scores of psychometric scales, time to dropout from initial treatment in patients with bipolar disorder (BP) (n = 288) and depressive disorder (DD) (n = 143) were evaluated based on database of the mood disorder clinic. Results: Among the studied patients with BP and DD, 50% showed dropout in 4.05 and 2.17 years, respectively. The mean survival times were 8.90 years in bipolar disorder I (BP-I), 5.19 years in bipolar II disorder, 3.22 years in bipolar disorder not otherwise specified, 4.24 years in major depressive disorder, and 4.03 years in other depressive disorders.In the multivariate Cox proportional hazards regression model in the BP group, diagnosis BP-I was found to be significantly related to the decrease in dropout rate (hazard ratio [HR] = 0.22, p = 0.001); however, increased past suicide attempt number was significantly related to the increase in dropout rate (HR = 1.13, p = 0.017). In the DD group, none of anxiety disorders as comorbidity, increased scores of openness, and extraversion personality were related to the increase in dropout rate. Conclusion Patients with BP, especially BP-I, showed a lower dropout rate as compared to patients with other mood disorders.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.