Emerging infectious diseases have risen sharply due to population growth, urbanization, travel, trade, and environmental changes, with outbreaks like severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 highlighting the global need for effective surveillance systems. Various infectious disease surveillance systems are applied depending on the surveillance objectives, target populations, and geographical scope. While Korea has a robust surveillance system, challenges remain in integrating data, enhancing coordination, and improving response efficiency. This article reviews the types and roles of infectious disease surveillance systems through a literature review and proposes strategies for improving Korea’s surveillance system by comparing it with those of other countries, including the World Health Organization (WHO). To strengthen Korea’s surveillance framework, a comprehensive strategy should be implemented to interconnect multiple surveillance mechanisms and enhance real-time data sharing. A centralized data platform must integrate these systems, leveraging artificial intelligence and big data analytics for faster outbreak analysis. International collaboration through data-sharing networks with the WHO, European Center for Disease Prevention and Control, and U.S Centers for Disease Control and Prevention is essential, along with standardized reporting formats to improve interoperability.

Emerging infectious diseases have risen sharply due to population growth, urbanization, travel, trade, and environmental changes, with outbreaks like severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 highlighting the global need for effective surveillance systems. Various infectious disease surveillance systems are applied depending on the surveillance objectives, target populations, and geographical scope. While Korea has a robust surveillance system, challenges remain in integrating data, enhancing coordination, and improving response efficiency. This article reviews the types and roles of infectious disease surveillance systems through a literature review and proposes strategies for improving Korea’s surveillance system by comparing it with those of other countries, including the World Health Organization (WHO). To strengthen Korea’s surveillance framework, a comprehensive strategy should be implemented to interconnect multiple surveillance mechanisms and enhance real-time data sharing. A centralized data platform must integrate these systems, leveraging artificial intelligence and big data analytics for faster outbreak analysis. International collaboration through data-sharing networks with the WHO, European Center for Disease Prevention and Control, and U.S Centers for Disease Control and Prevention is essential, along with standardized reporting formats to improve interoperability.

Emerging infectious diseases have risen sharply due to population growth, urbanization, travel, trade, and environmental changes, with outbreaks like severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 highlighting the global need for effective surveillance systems. Various infectious disease surveillance systems are applied depending on the surveillance objectives, target populations, and geographical scope. While Korea has a robust surveillance system, challenges remain in integrating data, enhancing coordination, and improving response efficiency. This article reviews the types and roles of infectious disease surveillance systems through a literature review and proposes strategies for improving Korea’s surveillance system by comparing it with those of other countries, including the World Health Organization (WHO). To strengthen Korea’s surveillance framework, a comprehensive strategy should be implemented to interconnect multiple surveillance mechanisms and enhance real-time data sharing. A centralized data platform must integrate these systems, leveraging artificial intelligence and big data analytics for faster outbreak analysis. International collaboration through data-sharing networks with the WHO, European Center for Disease Prevention and Control, and U.S Centers for Disease Control and Prevention is essential, along with standardized reporting formats to improve interoperability.

Emerging infectious diseases have risen sharply due to population growth, urbanization, travel, trade, and environmental changes, with outbreaks like severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019 highlighting the global need for effective surveillance systems. Various infectious disease surveillance systems are applied depending on the surveillance objectives, target populations, and geographical scope. While Korea has a robust surveillance system, challenges remain in integrating data, enhancing coordination, and improving response efficiency. This article reviews the types and roles of infectious disease surveillance systems through a literature review and proposes strategies for improving Korea’s surveillance system by comparing it with those of other countries, including the World Health Organization (WHO). To strengthen Korea’s surveillance framework, a comprehensive strategy should be implemented to interconnect multiple surveillance mechanisms and enhance real-time data sharing. A centralized data platform must integrate these systems, leveraging artificial intelligence and big data analytics for faster outbreak analysis. International collaboration through data-sharing networks with the WHO, European Center for Disease Prevention and Control, and U.S Centers for Disease Control and Prevention is essential, along with standardized reporting formats to improve interoperability.

Objective: The development of individual subtypes based on biomarkers offers a cost-effective and timely avenue to comprehending individual differences pertaining to mental health, independent from individuals’ subjective insights. Incorporating 2-channel electroencephalography (EEG) and photoplethysmogram (PPG), we sought to establish a subtype classification system with clinical relevance. Methods: One hundred healthy participants and 99 patients with psychiatric disorders were recruited. Classification thresholds were determined using the EEG and PPG data from 2,278 individuals without mental disorders, serving to classify subtypes in our sample of 199 participants. Multivariate analysis of variance was applied to examine psychological distinctions among these subtypes. K-means clustering was employed to verify the classification system. Results: The distribution of subtypes differed between healthy participants and those with psychiatric disorders. Cognitive abilities were contingent upon brain subtypes, while mind subtypes exhibited significant differences in symptom severity, overall health, and cognitive stress. K-means clustering revealed that the results of our theory-based classification and data-driven classification are comparable. The synergistic assessment of both brain and mind subtypes was also explored. Conclusion Our subtype classification system offers a concise means to access individuals’ mental health. The utilization of EEG and PPG signals for subtype classification offers potential for the future of digital mental healthcare.

Objective: To identify the adherence rate to poly (ADP-ribose) polymerase (PARP) inhibitors and identify factors contributing to the deterioration of adherence at our institution. Methods: The adherence rate to PARP inhibitors was calculated using self-reported Adherence to Refills and Medications Scale questionnaires from a cross-sectional survey. Multivariable logistic regression analysis was performed to identify the factors that affected adherence. Results: Of the 131 respondents, 32 (24.4%) showed non-adherence to PARP inhibitors.In the multivariable logistic regression analysis, unemployed or retired status (odds ratio [OR]=4.878; 95% confidence interval [CI]=1.528–15.572; p=0.008), patients receiving niraparib (OR=3.387; 95% CI=1.283–8.940; p=0.014), and a lower score on the quality-oflife assessment (EORTC-QLQ-OV28), which reflects a better quality of life (QOC) with a lower symptom burden (OR=1.056; 95% CI=1.027–1.086; p<0.001) were associated with high adherence to PARP inhibitors. Conclusion Approximately one-fourth of patients with ovarian cancer are non-adherent to PARP inhibitors as maintenance treatment for newly diagnosed advanced ovarian cancer. The occupational status, type of PARP inhibitor, and QOC may affect adherence to PARP inhibitors.

Petechial angiomata are benign vascular skin tumors, that typically present as flat, irregularly shaped spots ranging from bright red to purple in color, measuring approximately 0.2 to 3 mm in diameter. They are primarily found on the trunk and proximal extremities. We report atypical petechial angiomata with acral involvement in a 75-year-old woman. She had a history of bladder cancer that was treated surgically and had been on raloxifene therapy for osteoporosis for approximately 4 years. Histopathologic finding revealed dilated vascular channels and an enlarged capillary lake within the papillary dermis surrounded by tortuous capillaries. Immunohistochemical analysis showed positivity of CD31 in the entire cell lining, but negativity for D2-40. To our knowledge, acral involvement in petechial angiomata has not been reported previously. Although both the nosology and pathophysiology of this feature remain unclear, endothelial dilation related to long-term raloxifene medication may contribute to the development of atypical vascular lesions.

Objective: To identify the adherence rate to poly (ADP-ribose) polymerase (PARP) inhibitors and identify factors contributing to the deterioration of adherence at our institution. Methods: The adherence rate to PARP inhibitors was calculated using self-reported Adherence to Refills and Medications Scale questionnaires from a cross-sectional survey. Multivariable logistic regression analysis was performed to identify the factors that affected adherence. Results: Of the 131 respondents, 32 (24.4%) showed non-adherence to PARP inhibitors.In the multivariable logistic regression analysis, unemployed or retired status (odds ratio [OR]=4.878; 95% confidence interval [CI]=1.528–15.572; p=0.008), patients receiving niraparib (OR=3.387; 95% CI=1.283–8.940; p=0.014), and a lower score on the quality-oflife assessment (EORTC-QLQ-OV28), which reflects a better quality of life (QOC) with a lower symptom burden (OR=1.056; 95% CI=1.027–1.086; p<0.001) were associated with high adherence to PARP inhibitors. Conclusion Approximately one-fourth of patients with ovarian cancer are non-adherent to PARP inhibitors as maintenance treatment for newly diagnosed advanced ovarian cancer. The occupational status, type of PARP inhibitor, and QOC may affect adherence to PARP inhibitors.