Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Objective: This study aimed to identify serum biomarkers prospectively associated with remission at 12 weeks in outpatients with depressive disorders receiving stepwise psychopharmacotherapy, according to the main antidepressant used during the treatment period. Methods: This study included 1,024 depressive outpatients initially treated using antidepressant monotherapy, followed by alternating pharmacological strategies during the acute phase (3−12 weeks; 3-week interval). Fourteen serum biomarkers, sociodemographics, and clinical characteristics were evaluated at baseline. Based on the use frequency and mechanism of action, four main antidepressant types were distinguished: escitalopram, other selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), and mirtazapine. A Hamilton Depression Rating Scale score ≤ 7 was take to indicate remission. Results: Lower high-sensitivity C-reactive protein levels were correlated with remission at 12 weeks for all antidepressant types. Lower interleukin (IL)-6 levels and tumor necrosis factor-alpha levels were associated with remission using escitalopram and other SSRIs respectively. Lower IL-1β and leptin levels, predicted remission in association with SSRIs including escitalopram. For SNRIs, remission at 12 weeks was predicted by lower IL-4 and IL-10 levels. For mirtazapine, remission at 12 weeks was associated with lower leptin levels, and higher serotonin and folate levels. Conclusion Baseline serum status, as estimated by nine serum markers, may help clinicians determine the most appropriate antidepressant to achieve remission in the acute phase of depression.

Objective: This study investigated associations of life stressors and serum ghrelin levels with suicidal ideation (SI), and evaluated the potential mediating effect of ghrelin on associations between life stressors and SI in patients with acute coronary syndrome (ACS). Methods: In total, 969 ACS patients recruited from a tertiary university hospital in Korea within 2 weeks of disease onset were evaluated in terms of life stressors (using the List of Threatening Events Questionnaire), serum ghrelin levels, and SI (using the “suicidal thoughts” item of the Montgomery–Asberg Depression Rating Scale). Covariates included sociodemographics, depression, vascular risk factors, and disease severity. After 1 year, 711 patients were re-evaluated in terms of SI; logistic regression was performed with adjustment for covariates. Results: Life stressors were significantly associated with SI at baseline and follow-up. Serum ghrelin showed no such associations, but high levels thereof mediated associations of life stressors with SI; significant interaction terms were also observed after adjustment for covariates. Conclusion By evaluating life stressors and serum ghrelin levels, clinical prediction of SI in the acute and chronic phases of ACS could be improved.

Objective: Obsessive-compulsive symptoms (OCS) and suicidal ideation (SI) are common in patients with acute coronary syndrome (ACS). This study investigated the associations of OCS and serum cortisol levels with SI, and further evaluated the possible modifying effects of cortisol on the associations between OCS and SI in acute and chronic phases of ACS. Methods: In total, 969 ACS patients were recruited from a tertiary university hospital in Korea within 2 weeks of disease onset and evaluated in terms of OCS (using the OCS dimension of the Symptom Checklist-90–Revised), serum cortisol levels, and SI (using the “suicidal thoughts” item of the Montgomery–Åsberg Depression Rating Scale). Covariates included sociodemographics, depression, vascular risk factors, and disease severity. After 1 year, 711 patients were re-evaluated in terms of SI. Logistic regression analysis was performed with adjustment for covariates. Results: Higher OCS was significantly associated with SI both at baseline and follow-up. Serum cortisol showed no such association, but modified the association between OCS and SI. That was the associations were significant only in the higher but not in the lower serum cortisol levels, with significant interaction terms after adjusted for relevant covariates. Conclusion Evaluating OCS and serum cortisol levels at the acute phase could improve the accuracy of clinical predictions of SI both in the acute and chronic phases of ACS.

Fexuprazan (DWP14012), a potassium-competitive acid blocker, is a medical formulation prescribed to inhibit the secretion of gastric acid. The present study encompasses a comparative evaluation of pharmacokinetic (PK) analysis between the previous (reference) and size-reduced (test) formulation of fexuprazan 20 mg in healthy subjects. The study employed a randomized, open-label, single-dose, 2-sequence, 2-period, crossover design with a 7-day wash-out between periods. A total of 24 subjects were enrolled in this randomized study. During each period, the 21 subjects received either the test or reference formulation. Blood samples were collected at multiple time point ranging from 0 (pre-dose) to 48 hours post-dosing for PK analysis. The calculated PK parameters were considered bioequivalent when the 90% confidence intervals (CIs) of the geometric mean ratios (GMRs) were within the bioequivalence limit of 0.8–1.25. Safety and tolerability were included in the evaluation. A total of 20 subjects completed the study. Point estimates (90% CIs) of the GMRs were 1.1014 (0.9892–1.2265) for the maximum plasma concentration and 1.0530 (0.9611–1.1536) for the area under the plasma concentration-time curve from zero to the time of the last quantifiable concentration, between the test and reference formulations.The reference and size-reduced test formulations of fexuprazan were well tolerated with no reports of serious adverse events. In conclusion, size-reduced and previous formulations of fexuprazan 20 mg were bioequivalent with regard to PKs, safety and tolerability.

Objective: This study investigated the effect of TNF-α rs1800629 polymorphism on white matter integrity and memory function in patients with schizophrenia. Methods: Fifty-five participants with schizophrenia were enrolled in this study. They were genotyped for TNF-α rs1800629 polymorphism and underwent diffusion tensor imaging. Memory function was assessed using the Rey–Kim memory test. Participants with schizophrenia were grouped into GG homozygotes and A-allele carriers. Results: Compared to GG homozygotes, A-allele carriers had significantly lower scores for immediate and delayed recall and recognition of verbal memory and showed significantly lower fractional anisotropy in extensive brain regions. Lower total scores in immediate and delayed recall of verbal memory, immediate recall of visual memory, and figure copy of visual memory were significantly correlated with decreased mean fractional anisotropy in the white matter tracts of the corresponding brain regions. Conclusion Our findings suggest that the A-allele, which is associated with higher levels of TNF-α expression, correlates with lower connectivity of the fronto-temporal white matter compared to that in GG homozygotes. Impaired fronto-temporal connectivity may be associated with genetic vulnerability to schizophrenia, leading to verbal and visual memory deficits in patients with schizophrenia.

Objective: To investigate associations between baseline serum serotonin levels and short- and long-term treatment outcomes in outpatients with depressive disorders in a naturalistic one-year prospective study design. Methods: Patients were recruited at a University hospital in South Korea from March 2012 to April 2017. At baseline, blood samples were obtained from 1,094 patients who received initial antidepressant monotherapy (Step 1). After the Step 1 treatment, further treatment steps (at least Steps 2−4) could be administered every 3 weeks during the acute treatment phase (3, 6, 9, and 12 weeks; n = 1,086), and every 3 months during the continuation treatment phase (6, 9, and 12 months; n = 884). In cases showing an insufficient response or intolerable side effects, patients were asked to choose whether to remain at the current step or enter the next treatment step, with alternative strategies including switching, augmentation, combination, and a mixture of these approaches. Remission was defined as a Hamilton Depression Rating Scale score of ≤ 7. Results: The remission group had significantly higher baseline serum serotonin levels among patients who received Step 1 monotherapy in both acute and continuation treatment phases. These associations remained significant after adjustment for relevant covariates. No associations were found with any other treatment steps. Conclusion Baseline serum serotonin levels may be used as a biomarker for predicting short- and long-term treatment outcomes in antidepressant monotherapy-treated patients with depressive disorders in a real-world clinical setting.