Objectives: ：This study was aimed to investigate whether 1) sexual dimorphism in subcortical gray matter volumes (GMV) and the length ratio between the second and fourth digits (2D:4D) would be found and 2) 2D:4D would have associated with subcortical GMV in healthy elderly people. Methods: ：Sixty-two females aged 70.3±6.3 (mean±SD) years and 23 males aged 70.4±4.9 years were recruited from the Dementia Clinic in the Pusan National University Hospital. The subjects with the clinical dementia rating scale-sum of boxes (CDR-SB) total score greater than 2.0, any psychiatric or neurological disease, or any pathologic lesion on brain MRI other than micro-angiopathy were excluded. The 2D:4D of the left and right hands were measured 3 times each. Volumetric segmentation of T1-weighted MRI scans was done by Freesurfer software (v7.1.1.1). Results: ：2D:4Ds of males were smaller than those of females significantly on repeated measures ANOVA. The males’ thalamus, putamen, hippocampus in both hemispheres and the right amygdala were larger than females’. These differences were not significant after controlling for age, education and total intracranial volume (ICV). In the females, the left 2D:4D was negatively correlated with the left hippocampal volume. In the males, 2D:4D was positively correlated with the volumes of ipsilateral or contralateral thalamus, hippocampus, amygdala and accumbens. These correlations were not significant after Bonferroni’s correction, except for the right accumbens. Conclusions ：Sexual dimorphism of 2D:4D is preserved in healthy elderly people. There is a significant correlation between the right 2D:4D and GMV of the accumbens in males.

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.

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.

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: Due to the high frequency of depressive symptoms associated with breast cancer, it is crucial to screen for depression in breast cancer patients. While numerous screening tools are available for depression in this population, there is a need for a brief and convenient tool to enhance clinical use. This study aims to investigate the psychometric properties of the Patient Health Questionnaire-9 (PHQ-9) in patients with breast cancer. Methods: Patients with breast cancer (n=327) who visited the Breast Cancer Clinic were included in this study. The reliability of the PHQ-9 was analyzed by Cronbach’s α, and the construct validity of the PHQ-9 was explored by factor analysis. The concurrent validity of the PHQ-9 was evaluated by Pearson correlation analysis with the Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS). Results: The values of Cronbach’s α ranged from 0.800 to 0.879 was acceptable. The exploratory factor analysis revealed that the one-factor model and two-factor model of the PHQ-9 explained 46% and 57% of the variance, respectively. The PHQ-9 were significantly correlated with those of HADS (r=0.702, p<0.001) and PSS (r=0.466, p<0.001). Consequently, the PHQ-9 demonstrated acceptable reliability and validity in breast cancer patients. Conclusion The findings of this study indicate that the PHQ-9 exhibits acceptable reliability and validity in patients with breast cancer. The convenience of this brief self-report questionnaire suggests its potential as a reliable and valid tool for assessing depression in breast cancer clinics.

OBJECTIVES: Recent neuroimaging studies focus on dysfunctions in connectivity between cognitive circuits and emotional circuits: anterior cingulate cortex that connects dorsolateral orbitofrontal cortex and prefrontal cortex to limbic system. Previous studies on pediatric depression using DTI have reported decreased neural connectivity in several brain regions, including the amygdala, anterior cingulate cortex, superior longitudinal fasciculus. We compared the neural connectivity of psychotropic drug naïve adolescent patients with a first onset of major depressive episode with healthy controls using DTI. METHODS: Adolescent psychotropic drug naïve patients(n=26, 10 men, 16 women; age range, 13–18 years) who visited the Korea University Guro Hospital and were diagnosed with first onset major depressive disorder were registered. Healthy controls(n=27, 5 males, 22 females; age range, 12–17 years) were recruited. Psychiatric interviews, complete psychometrics including IQ and HAM-D, MRI including diffusion weighted image acquisition were conducted prior to antidepressant administration to the patients. Fractional anisotropy(FA), radial, mean, and axial diffusivity were estimated using DTI. FMRIB Software Library-Tract Based Spatial Statistics was used for statistical analysis. RESULTS: We did not observe any significant difference in whole brain analysis. However, ROI analysis on right superior longitudinal fasciculus resulted in 3 clusters with significant decrease of FA in patients group. CONCLUSIONS: The patients with adolescent major depressive disorder showed statistically significant FA decrease in the DTI-based structure compared with healthy control. Therefore we suppose DTI can be used as a biomarker in psychotropic drug-naïve adolescent patients with first onset major depressive disorder.
Adolescent ; Amygdala ; Brain ; Depression ; Depressive Disorder, Major ; Diffusion ; Diffusion Tensor Imaging ; Female ; Gyrus Cinguli ; Humans ; Korea ; Limbic System ; Magnetic Resonance Imaging ; Male ; Neuroimaging ; Prefrontal Cortex ; Psychometrics ; White Matter

Hemophagocytic lymphohistiocytosis (HLH) is a rare disorder characterized by fever, pancytopenia, hyperferritinemia, and phagocytosis of hematopoietic cells in bone marrow, liver, or lymph nodes. HLH can occur during the course of systemic lupus erythematosus (SLE), but can also be a presenting manifestation. Because development of pancytopenia occurs in less than 10 percent of SLE cases, investigation for HLH is necessary when otherwise unexplained pancytopenia persists despite adequate treatment. We experienced three cases of secondary HLH associated with SLE. Among the three patients, two patients developed HLH during the clinical course of SLE. The other patient who presented with pancytopenia was first diagnosed with HLH, and later with SLE. In her case, HLH turned out to be a presenting manifestation of SLE. We report on three successfully treated cases, and discuss the prevalence, characteristics, treatments, and prognosis of secondary HLH associated with SLE.
Bone Marrow ; Fever ; Humans ; Liver ; Lupus Erythematosus, Systemic ; Lymph Nodes ; Lymphohistiocytosis, Hemophagocytic* ; Pancytopenia ; Phagocytosis ; Prevalence ; Prognosis

OBJECTIVE: A popular design for the investigation of such effects, including effects of parent-of-origin (imprinting), maternal genotype, and maternal-fetal genotype interactions, is to collect deoxyribonucleic acid (DNA) from affected offspring and their mothers and to compare with an appropriate control sample. We investigate the effects of estimation of maternal, imprinting and interaction effects using multimodal modeling using parents and their offspring with schizophrenia in Korean population. METHODS: We have recruited 27 probands (with schizophrenia) with their parents and siblings whenever possible. We analyzed 20 SNPs of 7 neuronal genes in chromosome 18. We used EMIM analysis program for the estimation of maternal, imprinting and interaction effects using multimodal modeling. RESULTS: Of analyzed 20 single nucleotide polymorphisms (SNPs), significant SNP (rs 2276186) was suggested in EMIM analysis for child genetics effects (p=0.0225438044) and child genetic effects allowing for maternal genetic effects (p=0.0209453210) with very stringent multiple comparison Bonferroni correction. CONCLUSION: Our results are the pilot study for epigenetic study in mental disorder and help to understanding and use of EMIM statistical genetics analysis program with many limitations including small pedigree numbers.
Child ; Chromosomes, Human, Pair 18 ; DNA ; Epigenomics ; Genetics ; Genotype ; Humans ; Linear Models ; Mental Disorders ; Mothers ; Neurons ; Parents ; Pedigree ; Pilot Projects ; Polymorphism, Single Nucleotide ; Schizophrenia ; Siblings