Myiasis, a parasitic infestation caused by dipteran larvae in living humans or animals, is rarely encountered postmortem. Here, we report a case of myiasis identified during postmortem inspection in a 40-year-old man with a 10-year history of untreated schizophrenia. During the winter between late 2022 and early 2023, the decedent walked outdoors barefoot and sustained frostbite and was advised to undergo limb amputation at a university hospital in March 2024. He died at home in late May 2024. At the scene, the body showed displaceable livor mortis and absence of rigor mortis, suggesting a short postmortem interval. Severe necrosis was present below the knees, and numerous third-instar larvae without postfeeding stage were observed within the necrotic tissue, suggesting myiasis development at the frostbite sites. Species identification via larval sampling was not possible; thus, photographic analysis was attempted. The larva at the most advanced developmental stage was most likely a third-instar Lucilia sericata, the most common species causing myiasis in Korea. Assuming a skin temperature of 34°C under blanket coverage, the estimated post-oviposition interval ranged from 38.9–71.7 hours. Considering the estimated postmortem interval, the oviposition may have occurred antemortem. Since myiasis can indicate neglect or contribute to death, forensic pathologist should assess whether the postmortem interval aligns with the developmental stage of the larvae. For accurate forensic entomological analysis, adequate sampling of larvae is essential during postmortem inspections.

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.

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: 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.

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.