Background: Post-transplantation cyclophosphamide (PTCy) and anti-thymocyte globulin (ATG) are common pro‑ phylactic strategies for graft-versus-host disease (GVHD) after haploidentical hematopoietic stem cell transplantation (haplo-HSCT). Interleukin (IL)-6 is a surrogate marker for cytokine release syndrome (CRS) and acute GVHD.Method The clinical outcomes and complications of haplo-HSCT with PTCy plus ATG versus PTCy monotherapy were compared according to serum IL-6 levels at Chungnam National University Hospital (Daejeon, South Korea) from Jan‑ uary 2019 to February 2023. Results: Forty patients who underwent haplo-HSCT were analyzed. A significant difference in IL-6 levels was observed between the PTCy plus ATG and PTCy alone groups (7.47 ± 10.55 vs. 117.65 ± 127.67; p = 0.003). More patients in the PTCy plus ATG group had a CRS grade of 0 than in the PTCy alone group (p < 0.001). Serum IL-6 levels were associated with grades II–IV acute GVHD (r = 0.547, p < 0.001). The cumulative incidence (CI) of grades II–IV acute GVHD was significantly higher in the PTCy alone group (67.9% vs. 4.8%; p < 0.001). No significant difference in the CI for chronic GVHD was detected between the PTCy plus ATG and PTCy alone groups (72.1% vs. 82.0%; p = 0.730). The CI of 1-year non-relapse mortality was significantly higher in the PTCy alone group than in the PTCy plus ATG group (42.2% vs. 15.9%; p = 0.022). The 1-year overall survival (OS) was significantly better in the PTCy plus ATG group (75.9% vs. 35.3%; p = 0.011). The 1-year GVHD-free, relapse-free survival rate was 29.4% in the PTCy alone group and 54.0% in the PTCy plus ATG group (p = 0.038). Conclusion Serum IL-6 levels were higher in the PTCy alone group than in the PTCy plus ATG group. The addition of ATG before stem cell infusion affected IL-6 levels and reduced the incidences of CRS and grade II–IV acute GVHD in haplo-HSCT patients. This study suggests that PTCy plus ATG as GVHD prophylaxis in haplo-HSCT is beneficial in terms of clinical outcomes and complications of HSCT.

Background: Post-transplantation cyclophosphamide (PTCy) and anti-thymocyte globulin (ATG) are common pro‑ phylactic strategies for graft-versus-host disease (GVHD) after haploidentical hematopoietic stem cell transplantation (haplo-HSCT). Interleukin (IL)-6 is a surrogate marker for cytokine release syndrome (CRS) and acute GVHD.Method The clinical outcomes and complications of haplo-HSCT with PTCy plus ATG versus PTCy monotherapy were compared according to serum IL-6 levels at Chungnam National University Hospital (Daejeon, South Korea) from Jan‑ uary 2019 to February 2023. Results: Forty patients who underwent haplo-HSCT were analyzed. A significant difference in IL-6 levels was observed between the PTCy plus ATG and PTCy alone groups (7.47 ± 10.55 vs. 117.65 ± 127.67; p = 0.003). More patients in the PTCy plus ATG group had a CRS grade of 0 than in the PTCy alone group (p < 0.001). Serum IL-6 levels were associated with grades II–IV acute GVHD (r = 0.547, p < 0.001). The cumulative incidence (CI) of grades II–IV acute GVHD was significantly higher in the PTCy alone group (67.9% vs. 4.8%; p < 0.001). No significant difference in the CI for chronic GVHD was detected between the PTCy plus ATG and PTCy alone groups (72.1% vs. 82.0%; p = 0.730). The CI of 1-year non-relapse mortality was significantly higher in the PTCy alone group than in the PTCy plus ATG group (42.2% vs. 15.9%; p = 0.022). The 1-year overall survival (OS) was significantly better in the PTCy plus ATG group (75.9% vs. 35.3%; p = 0.011). The 1-year GVHD-free, relapse-free survival rate was 29.4% in the PTCy alone group and 54.0% in the PTCy plus ATG group (p = 0.038). Conclusion Serum IL-6 levels were higher in the PTCy alone group than in the PTCy plus ATG group. The addition of ATG before stem cell infusion affected IL-6 levels and reduced the incidences of CRS and grade II–IV acute GVHD in haplo-HSCT patients. This study suggests that PTCy plus ATG as GVHD prophylaxis in haplo-HSCT is beneficial in terms of clinical outcomes and complications of HSCT.

Background: Post-transplantation cyclophosphamide (PTCy) and anti-thymocyte globulin (ATG) are common pro‑ phylactic strategies for graft-versus-host disease (GVHD) after haploidentical hematopoietic stem cell transplantation (haplo-HSCT). Interleukin (IL)-6 is a surrogate marker for cytokine release syndrome (CRS) and acute GVHD.Method The clinical outcomes and complications of haplo-HSCT with PTCy plus ATG versus PTCy monotherapy were compared according to serum IL-6 levels at Chungnam National University Hospital (Daejeon, South Korea) from Jan‑ uary 2019 to February 2023. Results: Forty patients who underwent haplo-HSCT were analyzed. A significant difference in IL-6 levels was observed between the PTCy plus ATG and PTCy alone groups (7.47 ± 10.55 vs. 117.65 ± 127.67; p = 0.003). More patients in the PTCy plus ATG group had a CRS grade of 0 than in the PTCy alone group (p < 0.001). Serum IL-6 levels were associated with grades II–IV acute GVHD (r = 0.547, p < 0.001). The cumulative incidence (CI) of grades II–IV acute GVHD was significantly higher in the PTCy alone group (67.9% vs. 4.8%; p < 0.001). No significant difference in the CI for chronic GVHD was detected between the PTCy plus ATG and PTCy alone groups (72.1% vs. 82.0%; p = 0.730). The CI of 1-year non-relapse mortality was significantly higher in the PTCy alone group than in the PTCy plus ATG group (42.2% vs. 15.9%; p = 0.022). The 1-year overall survival (OS) was significantly better in the PTCy plus ATG group (75.9% vs. 35.3%; p = 0.011). The 1-year GVHD-free, relapse-free survival rate was 29.4% in the PTCy alone group and 54.0% in the PTCy plus ATG group (p = 0.038). Conclusion Serum IL-6 levels were higher in the PTCy alone group than in the PTCy plus ATG group. The addition of ATG before stem cell infusion affected IL-6 levels and reduced the incidences of CRS and grade II–IV acute GVHD in haplo-HSCT patients. This study suggests that PTCy plus ATG as GVHD prophylaxis in haplo-HSCT is beneficial in terms of clinical outcomes and complications of HSCT.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Developmental dysplasia of the hip is a condition characterized by hip joint instability due to acetabular dysplasia in infancy, necessitating precise ultrasound examination. LeggCalvé-Perthes disease is caused by a temporary disruption in blood flow to the femoral head during childhood, progressing through avascular, fragmentation, re-ossification, and residual stages. Slipped capital femoral epiphysis is a condition where the femoral head shifts medially along the epiphyseal line during adolescence due to stress, such as weight-bearing.Differentiating between transient hip synovitis and septic arthritis may require joint fluid aspiration. Osteomyelitis can be associated with soft tissue edema and osteolysis. When multiple lesions are present, it is essential to distinguish between Langerhans cell histiocytosis and metastatic neuroblastoma. This review will introduce imaging techniques and typical findings for these conditions.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Background: This study aimed to investigate the erosive potential of powdered vitamin C on the bovine enamel tooth surface. Methods: The experiment included five groups: Lemona, Vitagran, Korea Eundan, Coca-Cola (positive controls), and artificialsaliva (negative controls). The pH and titratable acidity were measured. Bovine enamel specimens were immersed in the experimental solutions for 15 minutes each day for 7 days. The surface microhardness was measured using the Vickers hardness number before immersion and on the 1st, 3rd, 5th, and 7th days. The surfaces of the bovine enamel specimens were observed by scanning electron microscopy (SEM). Results: The pH of the experimental groups was as follows: Lemona (2.04±0.04) had the lowest pH, followed by Vitagran(2.56±0.01), the positive control group Coca-Cola (2.60±0.03), Korea Eundan (3.14±0.02), and the negative control group artificial saliva (7.06±0.05). Surface microhardness decreased significantly during the immersion period (p＜0.001). The largest surface microhardness reduction value was shown in Lemona (–201.22±20.60), followed by Vitagran (–190.02±14.73), Korea Eundan (–189.27±27.14), Coca-Cola (–99.28±17.21), artificial saliva (–10.99±9.94). According to the SEM findings, the experimental and positive control groups exhibited rough surfaces with micropores, whereas the negative control group exhibited smooth surfaces before specimen immersion. Conclusion Consuming powdered vitamin C at a low pH may degrade the enamel surface. To reduce the erosive effect, it isrecommended to rinse the mouth with water and brush the teeth after an hour.