Background/Aims: Cardiovascular disease remains a major cause of mortality in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). This study evaluated the association between subclinical carotid atherosclerosis (SCA) and MASLD or MASLD and increased alcohol intake (MetALD) in asymptomatic individuals. Methods: This cross-sectional study included 56,889 adults undergoing health check-ups in South Korea. Hepatic steatosis was diagnosed by ultrasound, and SCA was defined by carotid plaques or increased intima-media thickness. Liver fibrosis was evaluated using the fibrosis-4 index and elastography. Results: SCA was identified in 13.5%. MASLD and MetALD were significantly associated with SCA in models adjusted for demographic and lifestyle factors (adjusted odds ratio [aOR], 1.26;95% confidence interval [CI], 1.19 to 1.33; aOR, 1.43; 95% CI, 1.30 to 1.58; respectively, p<0.001for both). However, these associations attenuated and lost statistical significance when metabolic risk factors were further adjusted. The risk of SCA increased with greater hepatic steatosis and liver fibrosis severity. In patients with MASLD, aORs were 1.70 (hepatic steatosis index >36),1.23 (fibrosis-4 index ≥1.3), and 1.78 (liver stiffness measurement ≥5.6 kPa), compared to indi-viduals without MASLD. Similar trends were observed in the MetALD group. Additionally, hyper-tension and clustering of ≥3 cardiometabolic risk factors were significantly associated with SCA inthe MASLD group, supporting the role of metabolic burden in SCA development. Conclusions MASLD and MetALD were associated with increased SCA risk, particularly in individuals with hepatic steatosis and fibrosis. These findings suggest that metabolic burden and liver disease severity jointly contribute to subclinical atherosclerosis risk.

Background: Though accumulating evidence suggests an association between childhood trauma and anhedonia, further analysis is needed to consider specific traumatic dimensions, both traits and state anhedonia, and the role of circulating proteins. Therefore, this study investigated the association between different types of childhood traumas and their influence on anhedonia-related symptoms, and to evaluate the influence of anhedonia traits and plasma proteins as mediators. Methods: This study included 170 patients with schizophrenia, bipolar disorder, major depressive disorder, and healthy controls aged 19–65 years. Multiple reaction monitoring was performed to quantify plasma proteins, and 464 proteins were analyzed. The association between childhood trauma dimensions, anhedonic traits, and related symptoms was analyzed with linear regression. A series of mediation analyses was performed to determine whether anhedonic traits and plasma proteins mediated the association between childhood trauma and anhedonia-related symptoms. Results: Childhood emotional neglect was significantly associated with anhedonic traits and anhedonia-related symptoms. Mediation analysis revealed that the indirect effect of anhedonic traits for childhood emotional neglect on anhedonia-related symptoms (effect = 0.037; bias-corrected CI, 0.009 to 0.070) was statistically significant. The indirect effect of plasma TNR5 for anhedonic traits on anhedonia-related symptoms was statistically significant (effect = −0.011; bias-corrected CI, −0.026 to −0.002). Serial mediation analysis revealed that the indirect effect of childhood emotional neglect on anhedonia-related symptoms via anhedonic traits and TNR5 was statistically significant (effect = 0.007; biascorrected CI, 0.001 to 0.017). Conclusion Anhedonic traits and plasma TNR5 protein levels serially mediated the association between childhood emotional neglect and anhedonia-related symptoms.The study highlights the importance of considering both psychopathological traits and biological correlates when investigating the association between childhood trauma and psychopathological symptoms.

Background: Though accumulating evidence suggests an association between childhood trauma and anhedonia, further analysis is needed to consider specific traumatic dimensions, both traits and state anhedonia, and the role of circulating proteins. Therefore, this study investigated the association between different types of childhood traumas and their influence on anhedonia-related symptoms, and to evaluate the influence of anhedonia traits and plasma proteins as mediators. Methods: This study included 170 patients with schizophrenia, bipolar disorder, major depressive disorder, and healthy controls aged 19–65 years. Multiple reaction monitoring was performed to quantify plasma proteins, and 464 proteins were analyzed. The association between childhood trauma dimensions, anhedonic traits, and related symptoms was analyzed with linear regression. A series of mediation analyses was performed to determine whether anhedonic traits and plasma proteins mediated the association between childhood trauma and anhedonia-related symptoms. Results: Childhood emotional neglect was significantly associated with anhedonic traits and anhedonia-related symptoms. Mediation analysis revealed that the indirect effect of anhedonic traits for childhood emotional neglect on anhedonia-related symptoms (effect = 0.037; bias-corrected CI, 0.009 to 0.070) was statistically significant. The indirect effect of plasma TNR5 for anhedonic traits on anhedonia-related symptoms was statistically significant (effect = −0.011; bias-corrected CI, −0.026 to −0.002). Serial mediation analysis revealed that the indirect effect of childhood emotional neglect on anhedonia-related symptoms via anhedonic traits and TNR5 was statistically significant (effect = 0.007; biascorrected CI, 0.001 to 0.017). Conclusion Anhedonic traits and plasma TNR5 protein levels serially mediated the association between childhood emotional neglect and anhedonia-related symptoms.The study highlights the importance of considering both psychopathological traits and biological correlates when investigating the association between childhood trauma and psychopathological symptoms.

Background: Though accumulating evidence suggests an association between childhood trauma and anhedonia, further analysis is needed to consider specific traumatic dimensions, both traits and state anhedonia, and the role of circulating proteins. Therefore, this study investigated the association between different types of childhood traumas and their influence on anhedonia-related symptoms, and to evaluate the influence of anhedonia traits and plasma proteins as mediators. Methods: This study included 170 patients with schizophrenia, bipolar disorder, major depressive disorder, and healthy controls aged 19–65 years. Multiple reaction monitoring was performed to quantify plasma proteins, and 464 proteins were analyzed. The association between childhood trauma dimensions, anhedonic traits, and related symptoms was analyzed with linear regression. A series of mediation analyses was performed to determine whether anhedonic traits and plasma proteins mediated the association between childhood trauma and anhedonia-related symptoms. Results: Childhood emotional neglect was significantly associated with anhedonic traits and anhedonia-related symptoms. Mediation analysis revealed that the indirect effect of anhedonic traits for childhood emotional neglect on anhedonia-related symptoms (effect = 0.037; bias-corrected CI, 0.009 to 0.070) was statistically significant. The indirect effect of plasma TNR5 for anhedonic traits on anhedonia-related symptoms was statistically significant (effect = −0.011; bias-corrected CI, −0.026 to −0.002). Serial mediation analysis revealed that the indirect effect of childhood emotional neglect on anhedonia-related symptoms via anhedonic traits and TNR5 was statistically significant (effect = 0.007; biascorrected CI, 0.001 to 0.017). Conclusion Anhedonic traits and plasma TNR5 protein levels serially mediated the association between childhood emotional neglect and anhedonia-related symptoms.The study highlights the importance of considering both psychopathological traits and biological correlates when investigating the association between childhood trauma and psychopathological symptoms.

Background: Though accumulating evidence suggests an association between childhood trauma and anhedonia, further analysis is needed to consider specific traumatic dimensions, both traits and state anhedonia, and the role of circulating proteins. Therefore, this study investigated the association between different types of childhood traumas and their influence on anhedonia-related symptoms, and to evaluate the influence of anhedonia traits and plasma proteins as mediators. Methods: This study included 170 patients with schizophrenia, bipolar disorder, major depressive disorder, and healthy controls aged 19–65 years. Multiple reaction monitoring was performed to quantify plasma proteins, and 464 proteins were analyzed. The association between childhood trauma dimensions, anhedonic traits, and related symptoms was analyzed with linear regression. A series of mediation analyses was performed to determine whether anhedonic traits and plasma proteins mediated the association between childhood trauma and anhedonia-related symptoms. Results: Childhood emotional neglect was significantly associated with anhedonic traits and anhedonia-related symptoms. Mediation analysis revealed that the indirect effect of anhedonic traits for childhood emotional neglect on anhedonia-related symptoms (effect = 0.037; bias-corrected CI, 0.009 to 0.070) was statistically significant. The indirect effect of plasma TNR5 for anhedonic traits on anhedonia-related symptoms was statistically significant (effect = −0.011; bias-corrected CI, −0.026 to −0.002). Serial mediation analysis revealed that the indirect effect of childhood emotional neglect on anhedonia-related symptoms via anhedonic traits and TNR5 was statistically significant (effect = 0.007; biascorrected CI, 0.001 to 0.017). Conclusion Anhedonic traits and plasma TNR5 protein levels serially mediated the association between childhood emotional neglect and anhedonia-related symptoms.The study highlights the importance of considering both psychopathological traits and biological correlates when investigating the association between childhood trauma and psychopathological symptoms.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Objective: This study aimed to develop and validate the Korean version of the Somatic Symptom Disorder-B Criteria Scale (SSD-12) in outpatients at a psychiatric clinic and assess its diagnostic accuracy. Methods: A total of 207 patients completed SSD-12. For the diagnostic accuracy of SSD-12, the somatic symptom disorder (SSD) section of the structured clinical interview for DSM-5 disorders-research version (SCID-5-RV) was used. The SSD-12 construct and concurrent validity were assessed by examining the correlations with Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), PHQ-15, 5-level EQ-5D version (EQ-5D-5L), and World Health Organization Quality of Life Brief Version (WHOQOL-BREF). Results: The SSD-12 had excellent internal consistency (Cronbach α=0.90). Confirmatory factor analysis revealed good fit indices for a general factor model (comparative fit index [CFI]=0.92, Tucker-Lewis index [TLI]=0.88, root mean square error of approximation [RMSEA]=0.10; 95% confidence interval [CI], 0.08–0.11) and a three-factor model (CFI=0.94, TLI=0.91, RMSEA=0.08; 95% CI, 0.07–0.10). The total SSD-12 score was significantly correlated with anxiety (GAD-7: r=0.53, p<0.001), depression (PHQ-9: r=0.52, p<0.001), physical symptom burden (PHQ-15: r=0.36, p<0.001), and quality of life (EQ-5D-5L: r=-0.40, p<0.001; WHOQOL-BREF: r=-0.51, p<0.001). SSD-12 demonstrated good accuracy (area under the curve=0.75, standard error=0.04; 95% CI, 0.68–0.82) with an optimal cut-off of 29. Conclusion The Korean SSD-12 demonstrates reliability and validity for diagnosing SSD in clinical setting.

Objective: This study was performed to evaluate the efficacy and safety of lurasidone (160 mg/day) compared to quetiapine XR (QXR; 600 mg/day) in the treatment of acutely psychotic patients with schizophrenia. Methods: Patients were randomly assigned to 6 weeks of double-blind treatment with lurasidone 160 mg/day (n=105) or QXR 600 mg/day (n=105). Primary efficacy measure was the change from baseline to week 6 in Positive and Negative Syndrome Scale (PANSS) total score and Clinical Global Impressions severity (CGI-S) score. Adverse events, body measurements, and laboratory parameters were assessed. Results: Lurasidone demonstrated non-inferiority to QXR on the PANSS total score. Adjusted mean±standard error change at week 6 on the PANSS total score was -26.42±2.02 and -27.33±2.01 in the lurasidone and QXR group, respectively. The mean difference score was -0.91 (95% confidence interval -6.35–4.53). The lurasidone group showed a greater reduction in PANSS total and negative subscale on week 1 and a greater reduction in end-point CGI-S score compared to the QXR group. Body weight, body mass index, and waist circumference in the lurasidone group were reduced, with significantly lower mean change compared to QXR. Endpoint changes in glucose, cholesterol, triglycerides, and low-density lipoprotein levels were also significantly lower. The most common adverse drug reactions with lurasidone were akathisia and nausea. Conclusion Lurasidone 160 mg/day was found to be non-inferior to QXR 600 mg/day in the treatment of schizophrenia with comparable efficacy and tolerability. Adverse effects of lurasidone were generally tolerable, and beneficial effects on metabolic parameters can be expected.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.