Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Time-of-flight (TOF) magnetic resonance angiography (MRA) is widely used to identify aneurysm in human brain. Various deep learning models have been developed to help TOF-MRA reading in the field. The performance of those TOF-MRA analysis tools, however, faces several limitations in cerebral aneurysm detection. These challenges primarily come from the fact that cerebral aneurysms occupy less than 0.1% of the total TOF-MRA voxel size. This study aims to improve the efficiency of cerebral aneurysm detection by developing a landmark-based semi-supervised deep learning method, a technology that automatically generates landmark boxes in areas with a high probability of cerebral aneurysm occurrence. Methods: We used data from a total of 500 aneurysm-positive and 50 aneurysm-negative subjects. The aneurysm detection model was developed using clustering and a dilated residual network. Results: When the number of landmarks was ten and their size was 36 mm3, the best performance was achieved in our experiment. Although landmark occupies a small portion of the entire image, up to 98.2% of landmarks were cerebral aneurysms. The sensitivity of the model for cerebral aneurysm detection was 83.0%, with a false positive rate of 3.4%. Conclusions This study developed a deep learning model using TOF-MRA image. This model generates the most suitable landmarks for each individual, excluding unnecessary areas for cerebral aneurysm detection, which makes it possible to focus on areas with a high probability of occurrence. This model is expected to enhance the efficiency and accuracy of cerebral aneurysm detection in the field.

Background: Information on the effectiveness of nirmatrelvir/ritonavir against the omicron is limited. The clinical response and viral kinetics to therapy in the real world need to be evaluated. Methods: Mild to moderate coronavirus disease 2019 (COVID-19) patients with risk factors for severe illness were prospectively enrolled as a treatment group with nirmatrelvir/ritonavir therapy versus a control group with supportive care. Serial viral load and culture from the upper respiratory tract were evaluated for seven days, and clinical responses and adverse reactions were evaluated for 28 days. Results: A total of 51 patients were analyzed including 40 in the treatment group and 11 in the control group. Faster symptom resolution during hospitalization (P= 0.048) was observed in the treatment group. Only minor adverse reactions were reported in 27.5% of patients. The viral load on Day 7 was lower in the treatment group (P = 0.002). The viral culture showed a positivity of 67.6% (25/37) vs. 100% (6/6) on Day 1, 0% (0/37) vs. 16.7 (1/6) on Day 5, and 0% (0/16) vs. 50.0% (2/4) on Day 7 in the treatment and control groups, respectively. Conclusions Nirmatrelvir/ritonavir against the omicron was safe and resulted in negative viral culture conversion after Day 5 of treatment with better symptomatic resolution.

Purpose: This study aims to determine nursing students’ orientation toward patient-centered care (PCC) and identify its influencing factors: their personality traits, empathy, and psychological capital (PsyCap). A mediating model was used to test the role of PsyCap in the association between empathy and PCC. Methods: This cross-sectional study included 300 nursing students from seven universities in South Korea. Their orientation toward PCC was measured using the Patient-Practitioner Orientation Scale (PPOS). Moreover, we analyzed the data using independent t-tests, ANOVA, Pearson's r, and SPSS PROCESS macro. Results: The overall mean score on the PPOS was 3.82±0.43, with the Caring and Sharing subscales accounting for 3.95±0.68 and 3.69±0.50, respectively. Nursing students’ satisfaction with their majors, practicums, and motivation for choosing this profession was significantly related to Caring-not Sharing. Nevertheless, PsyCap positively influenced both Caring (β=.24, p<.001) and Sharing (β=.18, p=.002). While empathy failed to significantly impact both Caring (β=.12, p=.087) and Sharing (β=.01, p=.931). PsyCap mediated the association of empathy with Caring (β=.20, p=.004); however, the same was not true for Sharing (β=.07, p=.366). Conclusion The findings indicate that the orientation of Korean nursing students toward PCC is not high. Therefore, to improve the PPOS Caring subscale, nurse educators should formulate better strategies to enhance nursing students’ satisfaction with their major and practicums, empathy, and PsyCap. The importance of sharing health-related information with patients and family caregivers should be emphasized in the nursing curriculum.

Total body irradiation (TBI) is included in the conditioning regimen for allogeneic hematopoietic stem cell transplantation (HSCT), with unique advantages such as uniform distribution over the whole body and decreased exposure to cytotoxic chemotherapeutic agents. For individuals who lack matched sibling or matched unrelated donors, the use of haploidentical donors has been increasing despite challenges such as graft rejection and graft-versus-host disease (GVHD). Although a limited number of studies have been performed to assess the clinical role of TBI in haploidentical HSCT, TBI-based conditioning showed comparable results in terms of survival outcomes, rate of relapse, and GVHD in diverse hematologic malignancies such as leukemia, lymphoma, and multiple myeloma. Advances in supportive care, along with recent technical improvements such as restriction of maximum tolerated dose, appropriate fractionation, and organ shielding, help to overcome diverse adverse events related to TBI. Post-transplantation cyclophosphamide was used in most studies to reduce the risk of GVHD. Additionally, it was found that post-transplantation rituximab may improve outcomes in TBI-based haploidentical HSCT, especially in patients with B-cell lymphoma. Along with the advances of techniques and strategies, the expansion of age restriction would be another important issue for TBI-based haploidentical HSCT considering the current tendency toward increasing age limitation and lack of matched donors. This review article summarizes the current use and future perspectives of TBI in haploidentical HSCT.

Total body irradiation (TBI) is included in the conditioning regimen for allogeneic hematopoietic stem cell transplantation (HSCT), with unique advantages such as uniform distribution over the whole body and decreased exposure to cytotoxic chemotherapeutic agents. For individuals who lack matched sibling or matched unrelated donors, the use of haploidentical donors has been increasing despite challenges such as graft rejection and graft-versus-host disease (GVHD). Although a limited number of studies have been performed to assess the clinical role of TBI in haploidentical HSCT, TBI-based conditioning showed comparable results in terms of survival outcomes, rate of relapse, and GVHD in diverse hematologic malignancies such as leukemia, lymphoma, and multiple myeloma. Advances in supportive care, along with recent technical improvements such as restriction of maximum tolerated dose, appropriate fractionation, and organ shielding, help to overcome diverse adverse events related to TBI. Post-transplantation cyclophosphamide was used in most studies to reduce the risk of GVHD. Additionally, it was found that post-transplantation rituximab may improve outcomes in TBI-based haploidentical HSCT, especially in patients with B-cell lymphoma. Along with the advances of techniques and strategies, the expansion of age restriction would be another important issue for TBI-based haploidentical HSCT considering the current tendency toward increasing age limitation and lack of matched donors. This review article summarizes the current use and future perspectives of TBI in haploidentical HSCT.