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 Korean Endocrine Hormone Reference Standard Data Center (KEHRS DC) has created reference standards (RSs) for endocrine hormones since 2020. This study is the first of its kind, wherein the KEHRS DC established RSs for serum Cpeptide levels in a healthy Korean population. Methods: Healthy Korean adults were recruited from May 2021 to September 2023. After excluding participants according to our criteria, serum samples were collected; each participant could then choose between fasting glucose only or fasting glucose plus an oral glucose tolerance test (OGTT). If their sample showed high glucose (≥100 mg/dL) or hemoglobin A1c (HbA1c) (≥5.70%), their C-peptide levels were excluded from analyzing the RSs. Results: A total of 1,532 participants were recruited; however, only the data of 1,050 participants were analyzed after excluding those whose samples showed hyperglycemia or high HbA1c. Post-30-minute OGTT data from 342 subjects and post-120-minute OGTT data from 351 subjects were used. The means±2 standard deviations and expanded uncertainties of fasting, post-30-minute and 120-minute OGTT C-peptide levels were 1.26±0.82 and 0.34–3.18, 4.74±3.57 and 1.14–8.33, and 4.85±3.58 and 1.25–8.34 ng/mL, respectively. Serum C-peptide levels correlated with obesity, serum glucose levels, and HbA1c levels. Conclusion The RSs for serum C-peptide levels established in this study are expected to be useful in both clinical and related fields.

Background/Aims: We investigated how interactions between humans and computer-aided detection (CADe) systems are influenced by the user’s experience and polyp characteristics. Methods: We developed a CADe system using YOLOv4, trained on 16,996 polyp images from 1,914 patients and 1,800 synthesized sessile serrated lesion (SSL) images. The performance of polyp detection with CADe assistance was evaluated using a computerized test module. Eighteen participants were grouped by colonoscopy experience (nurses, fellows, and experts). The value added by CADe based on the histopathology and detection difficulty of polyps were analyzed. Results: The area under the curve for CADe was 0.87 (95% confidence interval [CI], 0.83 to 0.91). CADe assistance increased overall polyp detection accuracy from 69.7% to 77.7% (odds ratio [OR], 1.88; 95% CI, 1.69 to 2.09). However, accuracy decreased when CADe inaccurately detected a polyp (OR, 0.72; 95% CI, 0.58 to 0.87). The impact of CADe assistance was most and least prominent in the nurses (OR, 1.97; 95% CI, 1.71 to 2.27) and the experts (OR, 1.42; 95% CI, 1.15 to 1.74), respectively. Participants demonstrated better sensitivity with CADe assistance, achieving 81.7% for adenomas and 92.4% for easy-to-detect polyps, surpassing the standalone CADe performance of 79.7% and 89.8%, respectively. For SSLs and difficult-to-detect polyps, participants' sensitivities with CADe assistance (66.5% and 71.5%, respectively) were below those of standalone CADe (81.1% and 74.4%). Compared to the other two groups (56.1% and 61.7%), the expert group showed sensitivity closest to that of standalone CADe in detecting SSLs (79.7% vs 81.1%, respectively). Conclusions CADe assistance boosts polyp detection significantly, but its effectiveness depends on the user’s experience, particularly for challenging lesions.

Background: This study implemented an external quality assessment (EQA) of serum protein electrophoresis (SPEP) and immunofixation electrophoresis/ immunotyping (IFE/IT) tests and aimed to present domestic guidelines regarding the interpretation report. Methods: We conducted the EQA of SPEP and IFE/IT tests similar to the proficiency testing (PT) program of the Korean Association of External Quality Assessment (KEQAS). We prepared four test samples by pooling residual serum specimens, according to the SPEP pattern, and the existence and isotype of monoclonal proteins. Each test sample was aliquoted and sent to 29 clinical laboratories, each laboratory conducted SPEP and IFE/IT tests and returned quantitative values and interpretation reports. Results: Variations in the quantitative values (g/dL) of each fraction and ratios (%) of each fraction to total protein were observed. The differences between the electrophoresis methods or manufacturers were not statistically significant. Of the four EQA samples, two samples had a monoclonal protein, and the presence and absence of monoclonal protein and isotypes were consistent in all participating institutions. However, there were statistically significant differences in the numerical values and ratios of monoclonal proteins between institutions. Conclusions This study examined the possibility of SPEP and IFE/IT tests being included in the PT program of the KEQAS, and we identified what should be supplemented for future assessments. Furthermore, we have presented the guidelines regarding SPEP and IFE/IT tests in Korea for the first time, and further studies are required to establish the EQA programs and standardized guidelines.

Objective: The aim of this study was to investigate the association between the use of statins and the occurrence of delirium in a large cohort of patients in the intensive care unit (ICU), considering disease severity and statin properties. Methods: We obtained clinical and demographical information from 3,604 patients admitted to the ICU from January 2013 to April 2020. This included information on daily statin use and delirium state, as assessed by the Confusion Assessment Method for ICU. We used inverse probability of treatment weighting and categorized the patients into four groups based on the Acute Physiology and Chronic Health Evaluation II score (group 1: 0−10 - mild; group 2: 11−20 -mild to moderate; group 3: 21−30 - moderate to severe; group 4: ＞ 30 - severe). We analyzed the association between the use of statin and the occurrence of delirium in each group, while taking into account the properties of statins. Results: Comparisons between statin and non-statin patient groups revealed that only in group 2, patients who were administered statin showed significantly higher occurrence of delirium (p = 0.004, odds ratio [OR] = 1.58) compared to the patients who did not receive statin. Regardless of whether statins were lipophilic (p = 0.036, OR = 1.47) or hydrophilic (p = 0.032, OR = 1.84), the occurrence of delirium was higher only in patients from group 2. Conclusion The use of statins may be associated with the increases in the risk of delirium occurrence in patients with mild to moderate disease severity, irrespective of statin properties.

Background/Aims: Smoking is considered a risk factor for the development of nonalcoholic fatty liver disease (NAFLD). However, the association of a weight change after a change in smoking status and the risk of NAFLD remains undetermined. Methods: This study used the Korean National Health Insurance Service-National Sample Cohort. Based on the first (2009 to 2010) and second (2011 to 2012) health examination periods, 139,180 adults aged at least 40 years were divided into nonsmoking, smoking cessation, smoking relapse, and sustained smoking groups. NAFLD was operationally defined using the fatty liver index. The adjusted odds ratio (aOR) and 95% confidence interval (CI) were calculated using multivariable-adjusted logistic regression. Results: Compared to nonsmoking with no body mass index (BMI) change, the risk of NAFLD was significantly increased among subjects with BMI gain and nonsmoking (aOR, 4.07; 95% CI, 3.77 to 4.39), smoking cessation (aOR, 5.52; 95% CI, 4.12 to 7.40), smoking relapse (aOR, 7.51; 95% CI, 4.81 to 11.72), and sustained smoking (aOR, 6.65; 95% CI, 5.33 to 8.29), whereas the risk of NAFLD was reduced among participants with BMI loss in all smoking status groups. In addition, smoking cessation (aOR, 1.76; 95% CI, 1.35 to 2.29) and sustained smoking (aOR, 1.64; 95% CI, 1.39 to 1.94) were associated with higher risk of NAFLD among participants with no BMI change.The liver enzyme levels were higher among participants with smoking cessation and BMI gain. Conclusions Monitoring and management of weight change after a change in smoking status may be a promising approach to reducing NAFLD.