In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

This paper introduces the current status of Seoul National University Hospital Dementia Brain Bank (SNUH-DBB), focusing on the concordance rate between clinical diagnoses and postmortem neuropathological diagnoses. We detail SNUH-DBB operations, including protocols for specimen handling, induced pluripotent stem cells (iPSC) and cerebral organoids establishment from postmortem dural fibroblasts, and adult neural progenitor cell cultures. We assessed clinical-neuropathological diagnostic concordance rate. Between 2015 and September 2024, 162 brain specimens were collected via brain donation and autopsy. The median donor age was 73 years (1-94) with a male-to -female ratio of 2:1. The median postmortem interval was 9.5 hours (range: 2.5-65). Common neuropathological diagnoses included pure Lewy body disease (10.6%), Lewy body disease (LBD) with other brain diseases (10.6%), pure Alzheimer's disease-neuropathological change (ADNC) (6.0%), ADNC with other brain diseases (10.7%), vascular brain injury (15.2%), and primary age-related tauopathy (7.3%). APOE genotype distribution was following: ε3/ε3: 62.3%, ε2/ε3:9.6%, ε2/ε4: 3.4%, ε3/ε4: 24.0%, and ε4/ε4: 0.7%. Concordance rates between pathological and clinical diagnoses were: ADNC/AD at 42.4%; LBD at 59.0%; PSP at 100%; ALS at 85.7%; Huntington’s disease 100%. The varying concordance rates across different diseases emphasize the need for improved diagnostic criteria and biomarkers, particularly for AD and LBD. Tissues have been distributed to over 40 national studies. SNUH-DBB provides high-quality brain tissues and cell models for neuroscience research, operating under standardized procedures and international guidelines. It supports translational research in dementia and neurodegenerative diseases, potentially advancing diagnostic and therapeutic strategies.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.

In the 2023 series, we summarized the major clinical research advances in gynecologic oncology based on communications at the conference of Asian Society of Gynecologic Oncology Review Course. The review consisted of 1) Endometrial cancer: immune checkpoint inhibitor, antibody drug conjugates (ADCs), selective inhibitor of nuclear export, CDK4/6 inhibitors WEE1 inhibitor, poly (ADP-ribose) polymerase (PARP) inhibitors. 2) Cervical cancer: surgery in low-risk early-stage cervical cancer, therapy for locally advanced stage and advanced, metastatic, or recurrent setting; and 3) Ovarian cancer: immunotherapy, triplet therapies using immune checkpoint inhibitors along with antiangiogenic agents and PARP inhibitors, and ADCs. In 2023, the field of endometrial cancer treatment witnessed a landmark year, marked by several practice-changing outcomes with immune checkpoint inhibitors and the reliable efficacy of PARP inhibitors and ADCs.

This paper introduces the current status of Seoul National University Hospital Dementia Brain Bank (SNUH-DBB), focusing on the concordance rate between clinical diagnoses and postmortem neuropathological diagnoses. We detail SNUH-DBB operations, including protocols for specimen handling, induced pluripotent stem cells (iPSC) and cerebral organoids establishment from postmortem dural fibroblasts, and adult neural progenitor cell cultures. We assessed clinical-neuropathological diagnostic concordance rate. Between 2015 and September 2024, 162 brain specimens were collected via brain donation and autopsy. The median donor age was 73 years (1-94) with a male-to -female ratio of 2:1. The median postmortem interval was 9.5 hours (range: 2.5-65). Common neuropathological diagnoses included pure Lewy body disease (10.6%), Lewy body disease (LBD) with other brain diseases (10.6%), pure Alzheimer's disease-neuropathological change (ADNC) (6.0%), ADNC with other brain diseases (10.7%), vascular brain injury (15.2%), and primary age-related tauopathy (7.3%). APOE genotype distribution was following: ε3/ε3: 62.3%, ε2/ε3:9.6%, ε2/ε4: 3.4%, ε3/ε4: 24.0%, and ε4/ε4: 0.7%. Concordance rates between pathological and clinical diagnoses were: ADNC/AD at 42.4%; LBD at 59.0%; PSP at 100%; ALS at 85.7%; Huntington’s disease 100%. The varying concordance rates across different diseases emphasize the need for improved diagnostic criteria and biomarkers, particularly for AD and LBD. Tissues have been distributed to over 40 national studies. SNUH-DBB provides high-quality brain tissues and cell models for neuroscience research, operating under standardized procedures and international guidelines. It supports translational research in dementia and neurodegenerative diseases, potentially advancing diagnostic and therapeutic strategies.

This paper introduces the current status of Seoul National University Hospital Dementia Brain Bank (SNUH-DBB), focusing on the concordance rate between clinical diagnoses and postmortem neuropathological diagnoses. We detail SNUH-DBB operations, including protocols for specimen handling, induced pluripotent stem cells (iPSC) and cerebral organoids establishment from postmortem dural fibroblasts, and adult neural progenitor cell cultures. We assessed clinical-neuropathological diagnostic concordance rate. Between 2015 and September 2024, 162 brain specimens were collected via brain donation and autopsy. The median donor age was 73 years (1-94) with a male-to -female ratio of 2:1. The median postmortem interval was 9.5 hours (range: 2.5-65). Common neuropathological diagnoses included pure Lewy body disease (10.6%), Lewy body disease (LBD) with other brain diseases (10.6%), pure Alzheimer's disease-neuropathological change (ADNC) (6.0%), ADNC with other brain diseases (10.7%), vascular brain injury (15.2%), and primary age-related tauopathy (7.3%). APOE genotype distribution was following: ε3/ε3: 62.3%, ε2/ε3:9.6%, ε2/ε4: 3.4%, ε3/ε4: 24.0%, and ε4/ε4: 0.7%. Concordance rates between pathological and clinical diagnoses were: ADNC/AD at 42.4%; LBD at 59.0%; PSP at 100%; ALS at 85.7%; Huntington’s disease 100%. The varying concordance rates across different diseases emphasize the need for improved diagnostic criteria and biomarkers, particularly for AD and LBD. Tissues have been distributed to over 40 national studies. SNUH-DBB provides high-quality brain tissues and cell models for neuroscience research, operating under standardized procedures and international guidelines. It supports translational research in dementia and neurodegenerative diseases, potentially advancing diagnostic and therapeutic strategies.

In the 2023 series, we summarized the major clinical research advances in gynecologic oncology based on communications at the conference of Asian Society of Gynecologic Oncology Review Course. The review consisted of 1) Endometrial cancer: immune checkpoint inhibitor, antibody drug conjugates (ADCs), selective inhibitor of nuclear export, CDK4/6 inhibitors WEE1 inhibitor, poly (ADP-ribose) polymerase (PARP) inhibitors. 2) Cervical cancer: surgery in low-risk early-stage cervical cancer, therapy for locally advanced stage and advanced, metastatic, or recurrent setting; and 3) Ovarian cancer: immunotherapy, triplet therapies using immune checkpoint inhibitors along with antiangiogenic agents and PARP inhibitors, and ADCs. In 2023, the field of endometrial cancer treatment witnessed a landmark year, marked by several practice-changing outcomes with immune checkpoint inhibitors and the reliable efficacy of PARP inhibitors and ADCs.

Objective: The rapid societal changes have underscored the importance of effective stress detection and management. Chronic mental stress significantly contributes to both physical and psychological illnesses. However, many individuals often remain unaware of their stress levels until they face physical health issues, highlighting the necessity for regular stress monitoring. This study aimed to investigate the effectiveness of vocal biomarkers in detecting stress levels among healthy Korean employees and to contribute to digital healthcare solutions. Methods: We conducted a multi-center clinical study by collecting voice recordings from 115 healthy Korean employees under both relaxed and stress-induced conditions. Stress was induced using the socially evaluated cold pressor test. The Emphasized Channel Attention, Propagation and Aggregation in Time delay neural network (ECAPA-TDNN) deep learning architecture, renowned for its advanced capabilities in analyzing person-specific voice features, was employed to develop stress prediction scores. Results: The proposed model achieved a 70% accuracy rate in detecting stress. This performance underscores the potential of vocal biomarkers as a convenient and effective tool for individuals to self-monitor and manage their stress levels within digital healthcare frameworks. Conclusion The findings emphasize the promise of voice-based mental stress assessments within the Korean population and the importance of continued research on vocal biomarkers across diverse linguistic demographics.