Background: Coronavirus disease 2019 (COVID-19) outbreaks occur in hospitals in many parts of the world. In hospital settings, the possibility of airborne transmission needs to be investigated thoroughly. Materials and Methods: There was a nosocomial outbreak of COVID-19 in a hematologic ward in a tertiary hospital, Seoul, Korea. We found 11 patients and guardians with COVID-19 through vigorous contact tracing and closed-circuit television monitoring. We found one patient who probably had acquired COVID-19 through airborne-transmission. We performed airflow investigation with simulation software, whole-genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results: Of the nine individuals with COVID-19 who had been in the hematologic ward, six stayed in one multi-patient room (Room 36), and other three stayed in different rooms (Room 1, 34, 35). Guardian in room 35 was close contact to cases in room 36, and patient in room 34 used the shared bathroom for teeth brushing 40 minutes after index used.Airflow simulation revealed that air was spread from the bathroom to the adjacent room 1 while patient in room 1 did not used the shared bathroom. Airflow was associated with poor ventilation in shared bathroom due to dysfunctioning air-exhaust, grill on the door of shared bathroom and the unintended negative pressure of adjacent room. Conclusion Transmission of SARS-CoV-2 in the hematologic ward occurred rapidly in the multi-patient room and shared bathroom settings. In addition, there was a case of possible airborne transmission due to unexpected airflow.

Background: Coronavirus disease 2019 (COVID-19) outbreaks occur in hospitals in many parts of the world. In hospital settings, the possibility of airborne transmission needs to be investigated thoroughly. Materials and Methods: There was a nosocomial outbreak of COVID-19 in a hematologic ward in a tertiary hospital, Seoul, Korea. We found 11 patients and guardians with COVID-19 through vigorous contact tracing and closed-circuit television monitoring. We found one patient who probably had acquired COVID-19 through airborne-transmission. We performed airflow investigation with simulation software, whole-genome sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Results: Of the nine individuals with COVID-19 who had been in the hematologic ward, six stayed in one multi-patient room (Room 36), and other three stayed in different rooms (Room 1, 34, 35). Guardian in room 35 was close contact to cases in room 36, and patient in room 34 used the shared bathroom for teeth brushing 40 minutes after index used.Airflow simulation revealed that air was spread from the bathroom to the adjacent room 1 while patient in room 1 did not used the shared bathroom. Airflow was associated with poor ventilation in shared bathroom due to dysfunctioning air-exhaust, grill on the door of shared bathroom and the unintended negative pressure of adjacent room. Conclusion Transmission of SARS-CoV-2 in the hematologic ward occurred rapidly in the multi-patient room and shared bathroom settings. In addition, there was a case of possible airborne transmission due to unexpected airflow.

Purpose: Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin–stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients. Materials and Methods: A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study. Results: The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis. Conclusion In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.