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.

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.

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.

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.

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.

Background: The coronavirus disease-2019 (COVID-19) pandemic has led to restrictions on daily living including social distancing. These restrictions had an impact on the individual's healthy lifestyle and health status. We investigated the associated factors with changes of metabolic abnormalities among general population in COVID-19. Methods: The participants were 43,639 people who received health check-ups twice in 2019 and 2021 during COVID-19 pandemic, at 16 health promotion centers. Metabolic abnormalities were identified according to the third report of the cholesterol education program criteria. Multiple logistic regression analysis was performed to confirm the factors related to changes of metabolic abnormalities during COVID-19. Results: Metabolic syndrome and metabolic abnormalities increased overall during the COVID-19 pandemic. This increase was mostly appeared in males. The occurrence of metabolic syndrome during COVID-19 was associated with 50s and older age (odds ratio [OR], 1.130; 95% confidence interval [CI], 1.019-1.254), attempt to quit smoking (OR, 1.467; 95% CI, 1.171-1.839), start smoking (OR, 1.251; 95% CI, 1.110-1.412), decrease in aerobic exercise (OR, 1.328; 95% CI, 1.162-1.517), and increase in strength exercise (OR, 0.704; 95% CI, 0.592-0.838). Conclusions The metabolic syndrome is closely related to smoking experience and lack of exercise during COVID-19.

Abstinence from prolonged psychostimulant use prompts stimulant withdrawal syndrome. Molecular adaptations within the dorsal striatum have been considered the main hallmark of stimulant abstinence. Here we explored striatal miRNA-target interaction and its impact on circulating miRNA marker as well as behavioral dysfunctions in methamphetamine (MA) abstinence. We conducted miRNA sequencing and profiling in the nonhuman primate model of MA abstinence, followed by miRNA qPCR, LC-MS/MS proteomics, immunoassays, and behavior tests in mice. In nonhuman primates, MA abstinence triggered a lasting upregulation of miR-137 in the dorsal striatum but a simultaneous downregulation of circulating miR-137. In mice, aberrant increase in striatal miR-137-dependent inhibition of SYNCRIP essentially mediated the MA abstinence-induced reduction of circulating miR-137. Pathway modeling through experimental deduction illustrated that the MA abstinence-mediated downregulation of circulating miR-137 was caused by reduction of SYNCRIP-dependent miRNA sorting into the exosomes in the dorsal striatum. Furthermore, diminished SYNCRIP in the dorsal striatum was necessary for MA abstinence-induced behavioral bias towards egocentric spatial learning. Taken together, our data revealed circulating miR-137 as a potential blood-based marker that could reflect MA abstinence-dependent changes in striatal miR-137/SYNCRIP axis, and striatal SYNCRIP as a potential therapeutic target for striatum-associated cognitive dysfunction by MA withdrawal syndrome.

Objectives: This study aimed to determine whether self-educational intervention on patient safety via a smartphone application could improve the level of self-efficacy and safety behaviors of patients. In addition the effect of change in self-efficacy on the improvement of safety behaviors after self-educational intervention was investigated. Methods: A one-group pre- and post-test design and convenience sampling were implemented. Self-educational intervention via smartphone application was provided to 94 participants in a tertiary university hospital in South Korea. The smartphone application included learning contents on why the participation of patients is critical in preventing hospital-acquired infections and surgery-related adverse events during hospitalization. Paired t-tests and hierarchical regression analysis were conducted to assess the effect of selfeducational intervention and self-efficacy on the improvement of safety behaviors of patients. Results: After the intervention, the level of self-efficacy and safety behaviors significantly increased from 2.53 to 2.95 and from 2.00 to 2.62, respectively. In the hierarchical regression analysis, the change in self-efficacy accounted for 35.4% of the variance in the improvement of safety behaviors. Conclusions The results of this study demonstrated that self-education on patient safety via a smartphone application was an effective strategy to enhance patients’ self-efficacy and safety behaviors. This process could ultimately enhance patient safety by promoting patient involvement during hospitalization and preventing the occurrence of medical errors.

Purpose: In this study, we examined the impact of reconstruction using tissue expander insertion (TEI) on the risk of radiation dermatitis in patients undergoing postmastectomy radiotherapy (PMRT). Methods: Between August 2015 and March 2019, patients with breast cancer who had received systemic chemotherapy and PMRT were prospectively included. Skin parameters, including melanin, erythema, hydration, sebum, and elasticity, were measured using a multiprobe instrument at 6 time points: before the initiation of radiotherapy (pre-RT), at weeks 1, 3, and 5 during radiotherapy (weeks 1–5), and 1 and 3-month after radiotherapy (post-RT-1m and post-RT-3m). Patient-reported outcomes (PROs) were assessed at each time point.Changes in biophysical parameters and PRO were compared between patients with and without TEI (TEI+ vs. TEI−). Results: Thirty-eight patients, including 18 with TEI+ and 20 with TEI-, were analyzed. The pattern of time-course changes in biophysical parameters and PRO did not differ between TEI+ and TEI− patients. The melanin index was highest at post-RT-1m, while the erythema index was highest at week 5. At post-RT-3m, TEI+ patients presented higher melanin values than TEI- patients, with no statistical significance (coefficient, 47.9 vs. 14.2%; p = 0.07). In all patients, water content decreased throughout the measurement period. At post-RT-3m, TEI+ patients demonstrated a further decrease in water content, while the TEI- group nearly recovered the water content to pre-RT status (coefficient, −17.1, −2.5; p = 0.11). The sebum and elasticity levels were not altered by TEI. Conclusion In patients undergoing PMRT, TEI did not significantly affect the changing patterns of skin biophysical parameters and PRO during radiotherapy.

Purpose: In this study, we examined the impact of reconstruction using tissue expander insertion (TEI) on the risk of radiation dermatitis in patients undergoing postmastectomy radiotherapy (PMRT). Methods: Between August 2015 and March 2019, patients with breast cancer who had received systemic chemotherapy and PMRT were prospectively included. Skin parameters, including melanin, erythema, hydration, sebum, and elasticity, were measured using a multiprobe instrument at 6 time points: before the initiation of radiotherapy (pre-RT), at weeks 1, 3, and 5 during radiotherapy (weeks 1–5), and 1 and 3-month after radiotherapy (post-RT-1m and post-RT-3m). Patient-reported outcomes (PROs) were assessed at each time point.Changes in biophysical parameters and PRO were compared between patients with and without TEI (TEI+ vs. TEI−). Results: Thirty-eight patients, including 18 with TEI+ and 20 with TEI-, were analyzed. The pattern of time-course changes in biophysical parameters and PRO did not differ between TEI+ and TEI− patients. The melanin index was highest at post-RT-1m, while the erythema index was highest at week 5. At post-RT-3m, TEI+ patients presented higher melanin values than TEI- patients, with no statistical significance (coefficient, 47.9 vs. 14.2%; p = 0.07). In all patients, water content decreased throughout the measurement period. At post-RT-3m, TEI+ patients demonstrated a further decrease in water content, while the TEI- group nearly recovered the water content to pre-RT status (coefficient, −17.1, −2.5; p = 0.11). The sebum and elasticity levels were not altered by TEI. Conclusion In patients undergoing PMRT, TEI did not significantly affect the changing patterns of skin biophysical parameters and PRO during radiotherapy.