Purpose: This study aimed to investigate the relationship between body composition and glaucoma by analyzing the associations between anthropometric and ocular parameters. Methods: A total of 494 eyes from 247 patients were reviewed from a general health examination database at a tertiary hospital. Anthropometric parameters were assessed using a multifrequency bioelectrical impedance device. Mean ocular perfusion pressure (MOPP) was calculated based on systolic and diastolic blood pressures and intraocular pressure (IOP). Retinal thickness and other ocular parameters were analyzed for their association with body composition. Results: A total of 221 eyes from 221 patients, including 104 with glaucoma, were enrolled in the final analysis. The prevalence of sarcopenia was significantly higher in patients with glaucomatous damage than in those without (p = 0.025). Higher IOP showed significant associations with lower MOPP (p < 0.001), higher body mass index (BMI; p = 0.001), and higher waist to hip ratio (p = 0.001). Retinal thickness was not significantly associated with body composition parameters, including BMI and appendicular lean mass adjusted with squared height. Higher MOPP was significantly correlated with lower IOP (p < 0.001), higher BMI (p < 0.001), higher waist to hip ratio (p < 0.001), and higher appendicular lean mass divided by squared height (p = 0.009). Conclusions Skeletal muscle mass and BMI were significantly associated with MOPP. Since low MOPP is a known risk factor for glaucoma, its association with skeletal muscle mass may indicate a relationship between systemic muscle health, ocular blood perfusion, and glaucomatous damage. Further large-scale studies are needed to validate these associations between skeletal muscle mass and glaucoma and explore their clinical implications.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Purpose: Panic disorder (PD) and PD with comorbid agoraphobia (PDA) share similar clinical characteristics but possess distinct symptom structures. However, studies specifically investigating the differences between PD and PDA are rare. Thus, the present study conducted a network analysis to examine the clinical networks of PD and PDA, focusing on panic symptom severity, anxiety sensitivity, anticipatory fear, and avoidance responses. By comparing the differences in network structures between PD and PDA, with the goal of identifying the central and bridge, we suggest clinical implications for the development of targeted interventions. Materials and Methods: A total sample (n=147; 55 male, 92 female) was collected from the psychiatric outpatient clinic of the university hospital. We conducted network analysis to examine crucial nodes in the PD and PDA networks and compared the two networks to investigate disparities and similarities in symptom structure. Results The most influential node within the PD network was Anxiety Sensitivity Index-Revised (ASI-R1; fear of respiratory symptom), whereas Panic Disorder Severity Scale (PDSS5; phobic avoidance of physical sensations) had the highest influence in the PDA network. Additionally, bridge centrality estimates indicated that each of the two nodes met the criteria for “bridge nodes” within their respective networks: ASI-R1 (fear of respiratory symptom) and Albany Panic and Phobic Questionnaire (APPQ3; interoceptive fear) for the PD group, and PDSS5 (phobic avoidance of physical sensation) and APPQ1 (panic frequency) for the PDA group Conclusion: Although the network comparison test did not reveal statistical differences between the two networks, disparities in community structure, as well as central and bridging symptoms, were observed, suggesting the possibility of distinct etiologies and treatment targets for each group. The clinical implications derived from the similarities and differences between PD and PDA networks are discussed.

Background: Epidermal growth factor receptor (EGFR) gene mutation testing is crucial for the administration of tyrosine kinase inhibitors to treat non–small cell lung cancer. In addition to traditional tissue-based tests, liquid biopsies using plasma are increasingly utilized, particularly for detecting T790M mutations. This study compared tissue- and plasma-based EGFR testing methods. Methods: A total of 248 patients were tested for EGFR mutations using tissue and plasma samples from 2018 to 2023 at Pusan National University Yangsan Hospital. Tissue tests were performed using PANAmutyper, and plasma tests were performed using the Cobas EGFR Mutation Test v2. Results: All 248 patients underwent tissue-based EGFR testing, and 245 (98.8%) showed positive results. Of the 408 plasma tests, 237 (58.1%) were positive. For the T790M mutation, tissue biopsies were performed 87 times in 69 patients, and 30 positive cases (38.6%) were detected. Plasma testing for the T790M mutation was conducted 333 times in 207 patients, yielding 62 positive results (18.6%). Of these, 57 (27.5%) were confirmed to have the mutation via plasma testing. Combined tissue and plasma tests for the T790M mutation were positive in nine patients (13.4%), while 17 (25.4%) were positive in tissue only and 12 (17.9%) in plasma only. This mutation was not detected in 28 patients (43.3%). Conclusions Although the tissue- and plasma-based tests showed a sensitivity of 37.3% and 32.8%, respectively, combined testing increased the detection rate to 56.7%. Thus, neither test demonstrated superiority, rather, they were complementary.

Purpose: This study aimed to investigate the relationship between body composition and glaucoma by analyzing the associations between anthropometric and ocular parameters. Methods: A total of 494 eyes from 247 patients were reviewed from a general health examination database at a tertiary hospital. Anthropometric parameters were assessed using a multifrequency bioelectrical impedance device. Mean ocular perfusion pressure (MOPP) was calculated based on systolic and diastolic blood pressures and intraocular pressure (IOP). Retinal thickness and other ocular parameters were analyzed for their association with body composition. Results: A total of 221 eyes from 221 patients, including 104 with glaucoma, were enrolled in the final analysis. The prevalence of sarcopenia was significantly higher in patients with glaucomatous damage than in those without (p = 0.025). Higher IOP showed significant associations with lower MOPP (p < 0.001), higher body mass index (BMI; p = 0.001), and higher waist to hip ratio (p = 0.001). Retinal thickness was not significantly associated with body composition parameters, including BMI and appendicular lean mass adjusted with squared height. Higher MOPP was significantly correlated with lower IOP (p < 0.001), higher BMI (p < 0.001), higher waist to hip ratio (p < 0.001), and higher appendicular lean mass divided by squared height (p = 0.009). Conclusions Skeletal muscle mass and BMI were significantly associated with MOPP. Since low MOPP is a known risk factor for glaucoma, its association with skeletal muscle mass may indicate a relationship between systemic muscle health, ocular blood perfusion, and glaucomatous damage. Further large-scale studies are needed to validate these associations between skeletal muscle mass and glaucoma and explore their clinical implications.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Purpose: Panic disorder (PD) and PD with comorbid agoraphobia (PDA) share similar clinical characteristics but possess distinct symptom structures. However, studies specifically investigating the differences between PD and PDA are rare. Thus, the present study conducted a network analysis to examine the clinical networks of PD and PDA, focusing on panic symptom severity, anxiety sensitivity, anticipatory fear, and avoidance responses. By comparing the differences in network structures between PD and PDA, with the goal of identifying the central and bridge, we suggest clinical implications for the development of targeted interventions. Materials and Methods: A total sample (n=147; 55 male, 92 female) was collected from the psychiatric outpatient clinic of the university hospital. We conducted network analysis to examine crucial nodes in the PD and PDA networks and compared the two networks to investigate disparities and similarities in symptom structure. Results The most influential node within the PD network was Anxiety Sensitivity Index-Revised (ASI-R1; fear of respiratory symptom), whereas Panic Disorder Severity Scale (PDSS5; phobic avoidance of physical sensations) had the highest influence in the PDA network. Additionally, bridge centrality estimates indicated that each of the two nodes met the criteria for “bridge nodes” within their respective networks: ASI-R1 (fear of respiratory symptom) and Albany Panic and Phobic Questionnaire (APPQ3; interoceptive fear) for the PD group, and PDSS5 (phobic avoidance of physical sensation) and APPQ1 (panic frequency) for the PDA group Conclusion: Although the network comparison test did not reveal statistical differences between the two networks, disparities in community structure, as well as central and bridging symptoms, were observed, suggesting the possibility of distinct etiologies and treatment targets for each group. The clinical implications derived from the similarities and differences between PD and PDA networks are discussed.

Purpose: This study aimed to investigate the relationship between body composition and glaucoma by analyzing the associations between anthropometric and ocular parameters. Methods: A total of 494 eyes from 247 patients were reviewed from a general health examination database at a tertiary hospital. Anthropometric parameters were assessed using a multifrequency bioelectrical impedance device. Mean ocular perfusion pressure (MOPP) was calculated based on systolic and diastolic blood pressures and intraocular pressure (IOP). Retinal thickness and other ocular parameters were analyzed for their association with body composition. Results: A total of 221 eyes from 221 patients, including 104 with glaucoma, were enrolled in the final analysis. The prevalence of sarcopenia was significantly higher in patients with glaucomatous damage than in those without (p = 0.025). Higher IOP showed significant associations with lower MOPP (p < 0.001), higher body mass index (BMI; p = 0.001), and higher waist to hip ratio (p = 0.001). Retinal thickness was not significantly associated with body composition parameters, including BMI and appendicular lean mass adjusted with squared height. Higher MOPP was significantly correlated with lower IOP (p < 0.001), higher BMI (p < 0.001), higher waist to hip ratio (p < 0.001), and higher appendicular lean mass divided by squared height (p = 0.009). Conclusions Skeletal muscle mass and BMI were significantly associated with MOPP. Since low MOPP is a known risk factor for glaucoma, its association with skeletal muscle mass may indicate a relationship between systemic muscle health, ocular blood perfusion, and glaucomatous damage. Further large-scale studies are needed to validate these associations between skeletal muscle mass and glaucoma and explore their clinical implications.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Purpose: Panic disorder (PD) and PD with comorbid agoraphobia (PDA) share similar clinical characteristics but possess distinct symptom structures. However, studies specifically investigating the differences between PD and PDA are rare. Thus, the present study conducted a network analysis to examine the clinical networks of PD and PDA, focusing on panic symptom severity, anxiety sensitivity, anticipatory fear, and avoidance responses. By comparing the differences in network structures between PD and PDA, with the goal of identifying the central and bridge, we suggest clinical implications for the development of targeted interventions. Materials and Methods: A total sample (n=147; 55 male, 92 female) was collected from the psychiatric outpatient clinic of the university hospital. We conducted network analysis to examine crucial nodes in the PD and PDA networks and compared the two networks to investigate disparities and similarities in symptom structure. Results The most influential node within the PD network was Anxiety Sensitivity Index-Revised (ASI-R1; fear of respiratory symptom), whereas Panic Disorder Severity Scale (PDSS5; phobic avoidance of physical sensations) had the highest influence in the PDA network. Additionally, bridge centrality estimates indicated that each of the two nodes met the criteria for “bridge nodes” within their respective networks: ASI-R1 (fear of respiratory symptom) and Albany Panic and Phobic Questionnaire (APPQ3; interoceptive fear) for the PD group, and PDSS5 (phobic avoidance of physical sensation) and APPQ1 (panic frequency) for the PDA group Conclusion: Although the network comparison test did not reveal statistical differences between the two networks, disparities in community structure, as well as central and bridging symptoms, were observed, suggesting the possibility of distinct etiologies and treatment targets for each group. The clinical implications derived from the similarities and differences between PD and PDA networks are discussed.