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.

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.

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.

Although major countries, such as South Korea, have developed and disseminated national smoking cessation guidelines, these efforts have been limited to developing individual societies or specialized institution-based recommendations. Therefore, evidence-based clinical guidelines are essential for developing smoking cessation interventions and promoting effective smoking cessation treatments. This guideline targets frontline clinical practitioners involved in a smoking cessation treatment support program implemented in 2015 with the support of the National Health Insurance Service. The Guideline Development Group of 10 multidisciplinary smoking cessation experts employed the Grading of Recommendations Assessment, Development, and Evaluation (GRADE)-ADOLOPMENT approach to review recent domestic and international research and guidelines and to determine evidence levels using the GRADE methodology. The guideline panel formulated six strong recommendations and one conditional recommendation regarding pharmacotherapy choices among general and special populations (mental disorders and chronic obstructive lung disease [COPD]). Strong recommendations favor varenicline rather than a nicotine patch or bupropion, using varenicline even if they are not ready to quit, using extended pharmacotherapy (>12 weeks) rather than standard treatment (8–12 weeks), or using pharmacotherapy for individuals with mental disorders or COPD. The conditional recommendation suggests combining varenicline with a nicotine patch instead of using varenicline alone. Aligned with the Korean Society of Medicine’s clinical guideline development process, this is South Korea’s first domestic smoking cessation treatment guideline that follows standardized guidelines. Primarily focusing on pharmacotherapy, it can serve as a foundation for comprehensive future smoking cessation clinical guidelines, encompassing broader treatment topics beyond medications.