Individuals with Asia type DEL blood group, the RhD-variant that classified as serologically RhD-negative, do not produce anti-D even when exposed to the D-antigen. Therefore, it is considered safe to transfuse RhD-positive blood products to them. However, such transfusions are still rare in medical institutions, with only two cases reported in Korea. Here, we present cases of two additional patients based on our experience. A 60-year-old female patient undergoing extra corporeal membrane oxygenation (ECMO) for myocarditis presented with severe anemia.The patient was serologic RhD-negative. Due to the lack of RhD-negative RBC inventory for emergency transfusion, RhD-positive blood was transfused. After confirming the patient’s RHD genotype as Asia type DEL, the planned RhD-positive blood transfusion was continued. A total of 13 units of RhD-positive RBCs and 26 units of single donor platelets (SDPs) were transfused over 25 days. Throughout this period, all unexpected antibody tests were negative. The second patient, a 50-year-old male diagnosed with myelodysplastic syndrome (MDS), was serologic RhD-negative, and the RHD genotyping confirmed Asia type DEL. During the hospitalization period, a total of 113 units of RhD-positive SDPs and 10 units of fresh frozen plasma (FFP) were transfused over 64 days, and all unexpected antibody tests were negative. These two cases suggest the transfusion of RhD-positive blood products to patients with Asia type DEL is safe.

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020‒December 31, 2020 and January 20, 2020‒January 31, 2021) and testing data (January 1, 2021‒February 28, 2021 and February 1, 2021‒February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values’ comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020‒December 31, 2020 and January 20, 2020‒January 31, 2021) and testing data (January 1, 2021‒February 28, 2021 and February 1, 2021‒February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values’ comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

Objective: To examine the effect of mobile neurofeedback training on the clinical symptoms, attention abilities, and execution functions of children with attention deficit hyperactivity disorder (ADHD). Methods: The participants were 74 children with ADHD aged 8−15 years who visited the Department of Child and Adolescent Psychiatry at Seoul National University Children’s Hospital. The participants were randomly assigned to the mobile neurofeedback (n = 35) or control (sham; n = 39) group. Neurofeedback training was administered using a mobile app (equipped with a headset with a 2-channel electroencephalogram [EEG] sensor) for 30 min/day, 3 days/week, for 3 months. Children with ADHD were individually administered various neuropsychological tests, including the continuous performance test, Children’s Color Trails Test-1 and 2, and Stroop Color and Word Tests. The effects of mobile neurofeedback were evaluated at baseline and at 3 and 6 months after treatment initiation. Results: Following treatment, both mobile neurofeedback-only and sham-only groups showed significant improvements in attention and response inhibition. In the visual continuous performance test, omission errors decreased to the normal range in the mobile neurofeedback-only group after training, suggesting that mobile neurofeedback effectively reduced inattention in children with ADHD. In the advanced test of attention, auditory response times decreased in the mobile neurofeedback + medication group after training, but increased in the sham+medication group. Overall, there were no significant between-group differences in other performance outcomes. Conclusion Mobile neurofeedback may have potential as an additional therapeutic option alongside medication for children with ADHD.