Beginning with the concept of the brain–gut axis, the importance of the interaction between the brain and the gastrointestinal tract has been extended to the microbiome with increasing clinical applications. With the recent development of various techniques for microbiome analysis, the number of relevant preclinical and clinical studies on animals and human subjects has rapidly increased. Various psychotic symptoms affect the intestinal microbiome through the hypothalamus–pituitary–adrenal gland axis. Conversely, the intestinal microbiome regulates the gastrointestinal tract environment and affects psychological factors by means of the microorganisms or their metabolites, either acting directly on the brain or through the synthesis of various neurotransmitters. This review discusses the clinical applicability of the brain–gut–microbiome axis and directions for improving psychological symptoms based on the studies published to date.

Jang SH, et al. J Korean Med Sci. 2018 Jan;33(12):e88. https://doi.org/10.3346/jkms.2018.33.e88

Objective Coronavirus disease 2019 (COVID-19) has psychological effects such as anxiety and depression as well as direct infection in people. The Fear of COVID-19 scale is a scale that can measure anxiety related to COVID-19 in a short time. The purpose of this study was to verify the reliability and validity the Korean version of Fear of COVID-19 scale (KF-COVID-19S).Methods The data of total 186 normal adults and 17 patients were finally used for the statistical analysis. For internal consistency, Cronbach’s α was calculated. For concurrent and discriminant validity, the correlations with the Hospital Anxiety and Depression scale (HADS), Patient Health Questionnaire-15 (PHQ-15), World Health Organization Quality of Life Assessment Instrument Brief Form (WHOQOLBREF) were analyzed. For construct validity, exploratory and confirmatory factor analysis were conducted.Results Cronbach alpha was 0.88. The two-factor model (factor 1: Physical fear, factor 2: Emotional fear) showed significantly positive correlations and appeared to be “good” fitness (CFI=0.906, IFI=0.907, NFI=0.902).Conclusion The KF-COVID-19S can be a useful scale that can measure the physical and emotional fears associated with COVID-19 in a short time. Because the psychiatric patients are a more vulnerable group to the fear, it is thought that the KF-COVID-19S will help to determine the patient’s level of anxiety and make a therapeutic plan for the underlying mental disorder.

Objective The aim of this study was to assess the psychosocial characteristics of the employees working at a university hospital and investigated the factors affecting their quality of life (QOL) under COVID-19.Methods This study enrolled 1,191 healthcare workers from a university hospital, including doctors, nurses, administrative officer and technicians. Besides demographic information, depression, anxiety, somatization, insomnia, resilience, and QOL were assessed.Results The nurses presented significantly higher scores for anxiety, depression and showed significantly higher insomnia scores and significantly lower resilience scores. The occupations showed significant differences in the QOL and sub-groups, including the overall quality of life and general health (F=4.774, p<0.001), psychological domain (F=6.230, p<0.001), and environment domain (F=5.254, p<0.001). There was a positive correlation between the QOL and resilience (r=0.608, p<0.01). However, depression (r=-0.502, p<0.01), anxiety (r=-0.425, p<0.01), somatization (r=-0.364, p<0.01), and insomnia (r=-0.385, p<0.01) showed negative correlations with the QOL. Resilience was the most important factor influencing the QOL.Conclusion The results of this study showed that low resilience adversely affected the QOL and the mental health of the healthcare workers, which consequently had a direct effect on the quality of medical care given to patients.

Objective There are growing interests on suicide risk screening in clinical settings and classifying high-risk groups of suicide with suicidal ideation is crucial for a more effective suicide preventive intervention. Previous statistical techniques were limited because they tried to predict suicide risk using a simple algorithm. Machine learning differs from the traditional statistical techniques in that it generates the most optimal algorithm from various predictors.Methods We aim to analyze the Personality Assessment Inventory (PAI) profiles of child and adolescent patients who received outpatient psychiatric care using machine learning techniques, such as logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGB), to develop and validate a classification model for individuals with high suicide risk.Results We developed prediction models using seven relevant features calculated by Boruta algorithm and subsequently tested all models using the testing dataset. The area under the ROC curve of these models were above 0.9 and the RF model exhibited the best performance.Conclusion Suicide must be assessed based on multiple aspects, and although Personality Assessment Inventory for Adolescent assess an array of domains, further research is needed for predicting high suicide risk groups.

Jeong Seok Seo, Won-Myong Bahk, Young Sup Woo, Young-Min Park, Won Kim, Jong-Hyun Jeong, Se-Hoon Shim, Jung Goo Lee, Seung-Ho Jang, Chan-Mo Yang, Sheng-Min Wang, Myung Hun Jung, Hyung Mo Sung, IL Han Choo, Bo-Hyun Yoon, Sang-Yeol Lee, Duk-In Jon, Kyung Joon Min. Clin Psychopharmacol Neurosci 2021;19:751-72. https://doi.org/10.9758/cpn.2021.19.4.751