This study employed focus group interviews (FGIs) to evaluate the preparedness and training requirements for an emerging infectious disease response system in the Republic of Korea.Based on the FGIs, the critical role of interdepartmental cooperation in responding to emerging infectious diseases was identified, with agencies such as public health centers, police, and fire services playing key roles in scene control, decontamination, and patient transport. Frequent staff turnover and a lack of trained personnel at local government levels were significant challenges, necessitating the development of training materials for unskilled workers. Civil complaints, common during outbreaks, require public officials to be educated on legal frameworks and the management of patients’ rights. The absence of standardized procedures for managing patients, such as bed assignments considering underlying conditions and sample collection, underscores the need for comprehensive guidelines.Interviewees emphasized cross-departmental training, detailed manuals, and legal education to improve infectious disease response capabilities.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

This study employed focus group interviews (FGIs) to evaluate the preparedness and training requirements for an emerging infectious disease response system in the Republic of Korea.Based on the FGIs, the critical role of interdepartmental cooperation in responding to emerging infectious diseases was identified, with agencies such as public health centers, police, and fire services playing key roles in scene control, decontamination, and patient transport. Frequent staff turnover and a lack of trained personnel at local government levels were significant challenges, necessitating the development of training materials for unskilled workers. Civil complaints, common during outbreaks, require public officials to be educated on legal frameworks and the management of patients’ rights. The absence of standardized procedures for managing patients, such as bed assignments considering underlying conditions and sample collection, underscores the need for comprehensive guidelines.Interviewees emphasized cross-departmental training, detailed manuals, and legal education to improve infectious disease response capabilities.

This study employed focus group interviews (FGIs) to evaluate the preparedness and training requirements for an emerging infectious disease response system in the Republic of Korea.Based on the FGIs, the critical role of interdepartmental cooperation in responding to emerging infectious diseases was identified, with agencies such as public health centers, police, and fire services playing key roles in scene control, decontamination, and patient transport. Frequent staff turnover and a lack of trained personnel at local government levels were significant challenges, necessitating the development of training materials for unskilled workers. Civil complaints, common during outbreaks, require public officials to be educated on legal frameworks and the management of patients’ rights. The absence of standardized procedures for managing patients, such as bed assignments considering underlying conditions and sample collection, underscores the need for comprehensive guidelines.Interviewees emphasized cross-departmental training, detailed manuals, and legal education to improve infectious disease response capabilities.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

This study employed focus group interviews (FGIs) to evaluate the preparedness and training requirements for an emerging infectious disease response system in the Republic of Korea.Based on the FGIs, the critical role of interdepartmental cooperation in responding to emerging infectious diseases was identified, with agencies such as public health centers, police, and fire services playing key roles in scene control, decontamination, and patient transport. Frequent staff turnover and a lack of trained personnel at local government levels were significant challenges, necessitating the development of training materials for unskilled workers. Civil complaints, common during outbreaks, require public officials to be educated on legal frameworks and the management of patients’ rights. The absence of standardized procedures for managing patients, such as bed assignments considering underlying conditions and sample collection, underscores the need for comprehensive guidelines.Interviewees emphasized cross-departmental training, detailed manuals, and legal education to improve infectious disease response capabilities.

Purpose: Placing dental implants in areas with low bone density or in conditions where bone healing is suppressed is challenging for clinicians. An experiment using a rodent model was performed with the aim of determining the efficacy of host modulation by increasing the systemic level of cholesterol sulfate (CS) using Irosustat in the context of the bone healing process around dental implants. Methods: In 16 ovariectomised female Sprague-Dawley rats, 2 implant fixtures were placed in the tibial bones (1 fixture on each side). At 1 week after surgery, the high-CS group (n=8) received Irosustat-mixed feed, while the control group (n=8) was fed conventionally. Block specimens were obtained at 5 weeks post-surgery for histologic analysis and the data were evaluated statistically (P<0.05). Results: Unlike the high-CS group, half of the specimens in the control group demonstrated severe bone resorption along with a periosteal reaction in the cortex. The mean percentages of bone-to-implant contact (21.5%) and bone density (28.1%) near the implant surface were significantly higher in the high-CS group than in the control group (P<0.05), as was the number of Haversian canals (by 5.3). Conclusions Host modulation by increasing the CS level may enhance the osseointegration of dental implants placed under conditions of impaired bone healing.

Background: and Purpose The wearing-off (WO) phenomenon is the most common motor complication in advanced Parkinson’s disease (PD), but its identification remains challenging. The 9- and 19-item Wearing-off Questionnaires (WOQ-9 and WOQ-19) are self-assessment tools for motor and nonmotor symptoms that are widely used for WO screening. We produced Korean versions of the WOQ-19 and WOQ-9 (K-WOQ-19 and K-WOQ-9) and investigated their validity and reliability. Methods: We used the translation–back translation method to produce K-WOQ-19 and KWOQ-9, which were self-administered by 124 patients with PD. We conducted in-depth 10-minute interviews for confirming the presence of the WO phenomenon, and then stratified the participants into groups with and without WO. Diagnostic accuracy was assessed by analyzing receiver operating characteristic curves. Concurrent validity was assessed using the Movement Disorder Society–Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and the Hoehn and Yahr stage with Spearman’s rank correlation analysis. Reliability was assessed based on test–retest Cohen’s kappa (κ) values and intraclass correlation coefficients (ICCs). Results: The optimal cutoff scores on the K-WOQ-19 and K-WOQ-9 for WO screening were 4 and 2, respectively. The test–retest ICCs of K-WOQ-19 and K-WOQ-9 were 0.943 and 0.938, respectively. Nineteen of the combined 20 items in K-WOQ-19 and K-WOQ-9 showed moderate-to-substantial agreement (κ=0.412–0.771, p<0.001). The scores on the translated scales were significantly correlated with MDS-UPDRS IV scores. Conclusions K-WOQ-19 and K-WOQ-9 are reliable and valid tools for detecting WO, with optimal cutoff scores of 4 and 2, respectively.

Objective: Although maintenance treatment for mood disorders is important, the treatment discontinuation rate is reported to be high. This study aimed to investigate the dropout rates and associated factors in mood disorders. Methods: The patients in a mood disorder clinic (n = 535) were examined. Demographic and clinical factors, scores of psychometric scales, time to dropout from initial treatment in patients with bipolar disorder (BP) (n = 288) and depressive disorder (DD) (n = 143) were evaluated based on database of the mood disorder clinic. Results: Among the studied patients with BP and DD, 50% showed dropout in 4.05 and 2.17 years, respectively. The mean survival times were 8.90 years in bipolar disorder I (BP-I), 5.19 years in bipolar II disorder, 3.22 years in bipolar disorder not otherwise specified, 4.24 years in major depressive disorder, and 4.03 years in other depressive disorders.In the multivariate Cox proportional hazards regression model in the BP group, diagnosis BP-I was found to be significantly related to the decrease in dropout rate (hazard ratio [HR] = 0.22, p = 0.001); however, increased past suicide attempt number was significantly related to the increase in dropout rate (HR = 1.13, p = 0.017). In the DD group, none of anxiety disorders as comorbidity, increased scores of openness, and extraversion personality were related to the increase in dropout rate. Conclusion Patients with BP, especially BP-I, showed a lower dropout rate as compared to patients with other mood disorders.