Purpose: This study aimed to determine whether zero echo time magnetic resonance imaging (ZTE-MRI), as an alternative imaging modality, and conventional computed tomography (CT) have similar diagnostic qualities for assessing pediatric osseous pathologies. Materials and Methods: Twenty-six sets of pediatric musculoskeletal CT and MRI scans (15 boys and 11 girls; mean age, 12 ± 4 years; range, 5–23 years) acquired at Seoul National University Children’s Hospital (January 2021 to November 2023) were retrospectively evaluated. CT-like images from ZTE-MRI were generated using grayscale inversion. Two radiologists independently assessed ZTE-MRI image quality (S anat) on a 5-point scale (1 = nondiagnostic, 5 = excellent) and a comparative scale (–2 = CT greater, 0 = same, 2 = ZTE-MRI greater) for lesion delineation (Scomp). The confidence interval of proportions and intraclass correlation coefficient were calculated to assess inter-rater agreement, and Wilcoxon rank-sum test, Mann–Whitney U test, or paired t-test was used to compare image quality or cortical thickness between the modalities. Results: ZTE-MRI demonstrated diagnostic quality (S anat ≥ 3) in 85%–96% of the cases, 89%–96% for cortical delineation, 92%–100% for intramedullary cavity (IMC) delineation, and 92% for lesion delineation. Compared with conventional CT, ZTE-MRI showed comparable diagnostic power (Scomp ≥ –1) in 92%–96% of the cases, with Scomp scores indicating no significant difference in lesion delineation (p = 0.53 in reader 1 and p = 0.25 in reader 2). There was a preference for CT over ZTE-MRI in terms of overall image quality and delineation of the cortex and IMC (p < 0.001). Cortical thickness was not significantly different (p = 0.11) between ZTE-MRI and CT. Conclusion ZTE-MRI demonstrated diagnostic quality comparable to that of CT, particularly in lesion delineation. In addition to the unique information that conventional MRI can provide, ZTE-MRI can provide additional information about osseous structures similar to that provided by CT, which we believe will be valuable in the future.

Purpose: This study aimed to determine whether zero echo time magnetic resonance imaging (ZTE-MRI), as an alternative imaging modality, and conventional computed tomography (CT) have similar diagnostic qualities for assessing pediatric osseous pathologies. Materials and Methods: Twenty-six sets of pediatric musculoskeletal CT and MRI scans (15 boys and 11 girls; mean age, 12 ± 4 years; range, 5–23 years) acquired at Seoul National University Children’s Hospital (January 2021 to November 2023) were retrospectively evaluated. CT-like images from ZTE-MRI were generated using grayscale inversion. Two radiologists independently assessed ZTE-MRI image quality (S anat) on a 5-point scale (1 = nondiagnostic, 5 = excellent) and a comparative scale (–2 = CT greater, 0 = same, 2 = ZTE-MRI greater) for lesion delineation (Scomp). The confidence interval of proportions and intraclass correlation coefficient were calculated to assess inter-rater agreement, and Wilcoxon rank-sum test, Mann–Whitney U test, or paired t-test was used to compare image quality or cortical thickness between the modalities. Results: ZTE-MRI demonstrated diagnostic quality (S anat ≥ 3) in 85%–96% of the cases, 89%–96% for cortical delineation, 92%–100% for intramedullary cavity (IMC) delineation, and 92% for lesion delineation. Compared with conventional CT, ZTE-MRI showed comparable diagnostic power (Scomp ≥ –1) in 92%–96% of the cases, with Scomp scores indicating no significant difference in lesion delineation (p = 0.53 in reader 1 and p = 0.25 in reader 2). There was a preference for CT over ZTE-MRI in terms of overall image quality and delineation of the cortex and IMC (p < 0.001). Cortical thickness was not significantly different (p = 0.11) between ZTE-MRI and CT. Conclusion ZTE-MRI demonstrated diagnostic quality comparable to that of CT, particularly in lesion delineation. In addition to the unique information that conventional MRI can provide, ZTE-MRI can provide additional information about osseous structures similar to that provided by CT, which we believe will be valuable in the future.

Purpose: This study aimed to determine whether zero echo time magnetic resonance imaging (ZTE-MRI), as an alternative imaging modality, and conventional computed tomography (CT) have similar diagnostic qualities for assessing pediatric osseous pathologies. Materials and Methods: Twenty-six sets of pediatric musculoskeletal CT and MRI scans (15 boys and 11 girls; mean age, 12 ± 4 years; range, 5–23 years) acquired at Seoul National University Children’s Hospital (January 2021 to November 2023) were retrospectively evaluated. CT-like images from ZTE-MRI were generated using grayscale inversion. Two radiologists independently assessed ZTE-MRI image quality (S anat) on a 5-point scale (1 = nondiagnostic, 5 = excellent) and a comparative scale (–2 = CT greater, 0 = same, 2 = ZTE-MRI greater) for lesion delineation (Scomp). The confidence interval of proportions and intraclass correlation coefficient were calculated to assess inter-rater agreement, and Wilcoxon rank-sum test, Mann–Whitney U test, or paired t-test was used to compare image quality or cortical thickness between the modalities. Results: ZTE-MRI demonstrated diagnostic quality (S anat ≥ 3) in 85%–96% of the cases, 89%–96% for cortical delineation, 92%–100% for intramedullary cavity (IMC) delineation, and 92% for lesion delineation. Compared with conventional CT, ZTE-MRI showed comparable diagnostic power (Scomp ≥ –1) in 92%–96% of the cases, with Scomp scores indicating no significant difference in lesion delineation (p = 0.53 in reader 1 and p = 0.25 in reader 2). There was a preference for CT over ZTE-MRI in terms of overall image quality and delineation of the cortex and IMC (p < 0.001). Cortical thickness was not significantly different (p = 0.11) between ZTE-MRI and CT. Conclusion ZTE-MRI demonstrated diagnostic quality comparable to that of CT, particularly in lesion delineation. In addition to the unique information that conventional MRI can provide, ZTE-MRI can provide additional information about osseous structures similar to that provided by CT, which we believe will be valuable in the future.

Animal models have been used to elucidate the pathophysiology of varying diseases and to provide insight into potential targets for therapeutic intervention. Although alternatives to animal testing have been proposed to help overcome potential drawbacks related to animal experiments and avoid ethical issues, their use remains vital for the testing of new drug candidates and to identify the most effective strategies for therapeutic intervention. Particularly, the study of metabolic diseases requires the use of animal models to monitor whole-body physiology. In line with this, the National Institute of Food and Drug Safety Evaluation (NIFDS) in Korea has established their own animal strains to help evaluate both efficacy and safety during new drug development. The objective of this study was to characterize the response of C57BL/6NKorl mice from the NIFDS compared with that of other mice originating from the USA and Japan in a chemical-induced diabetic condition. Multiple low-dose treatments with streptozotocin were used to generate a type-1 diabetic animal model which is closely linked to the known clinical pathology of this disease. There were no significantly different responses observed between the varying streptozotocin-induced type-1 diabetic models tested in this study. When comparing control and diabetic mice, increases in liver weight and disturbances in serum amino acids levels of diabetic mice were most remarkable. Although the relationship between type-1 diabetes and BCAA has not been elucidated in this study, the results, which reveal a characteristic increase in diabetic mice of all origins are considered worthy of further study.
Amino Acids ; Amino Acids, Branched-Chain ; Animal Experimentation ; Animal Testing Alternatives ; Animals ; Ethics ; Japan ; Korea ; Liver ; Metabolic Diseases ; Metabolomics* ; Mice* ; Models, Animal ; Pathology, Clinical ; Physiology ; Streptozocin

Blastocystis is one of the most commonly detected genera of protozoan parasites in the human intestines as well as the intestines of many other species such as pigs in several geographical regions worldwide. However, no studies have examined Blastocystis in pigs in Korea. In this study, PCR and nucleotide sequencing were performed to evaluate the genetic diversity and zoonotic potential of Blastocystis using pig fecal samples. We obtained 646 stool samples from groups of piglets, weaners, growers, finishers, and sows in Korea. A total of 390 Blastocystis-positive samples were identified, and the infection rate was 60.4%. The infection rates were significantly related to age and region. The 4 subtypes (STs) of Blastocystis confirmed by phylogenetic analysis were ST1, ST2, ST3, and ST5, indicating the high genetic diversity of Blastocystis in Korean pigs. ST5 was highly distributed in Korean pigs among detected STs in this study. Some sequences were closely related to those of Blastocystis isolated from humans. This is the first study of Blastocystis in pigs in Korea. Based on the results, Blastocystis is prevalent in Korean pigs. Although a small number of samples were obtained in some areas, the clinical development of Blastocystis infection in pigs and potential for human transmission should be further examined.
Blastocystis Infections ; Blastocystis ; Genetic Variation ; Humans ; Intestines ; Korea ; Parasites ; Phylogeny ; Polymerase Chain Reaction ; Prevalence ; Swine

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

Background: Inflammatory bowel disease (IBD), including both Crohn’s disease and ulcerative colitis, are chronic human diseases that are challenging to cure and are often unable to be resolved. The inbred mouse strain C57BL/ 6 N has been used in investigations of IBD as an experimental animal model. The purpose of the current study was to compare the inflammatory responsiveness of C57BL/6NKorl mice, a sub-strain recently established by the National Institute of Food and Drug Safety Evaluation (NIFDS), with those of C57BL/6 N mice from two different sources using a dextran sulfate sodium (DSS)-induced colitis model. Results: Male mice (8 weeks old) were administered DSS (0, 1, 2, or 3%) in drinking water for 7 days. DSS significantly decreased body weight and colon length and increased the colon weight-to-length ratio. Moreover, severe colitisrelated clinical signs including diarrhea and rectal bleeding were observed beginning on day 4 in mice administered DSS at a concentration of 3%. DSS led to edema, epithelial layer disruption, inflammatory cell infiltration, and cytokine induction (tumor necrosis factor-α, interleukin-6, and interleukin-1β) in the colon tissues. However, no significant differences in DSS-promoted abnormal symptoms or their severity were found between the three sub-strains. Conclusions These results indicate that C57BL/6NKorl mice responded to DSS-induced colitis similar to the generally used C57BL6/N mice, thus this newly developed mouse sub-strain provides a useful animal model of IBD.

Objective: To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs. Materials and Methods: Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience. Results: The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988–0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618– 0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001). Conclusion The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

Background: Inflammatory bowel disease (IBD), including both Crohn’s disease and ulcerative colitis, are chronic human diseases that are challenging to cure and are often unable to be resolved. The inbred mouse strain C57BL/ 6 N has been used in investigations of IBD as an experimental animal model. The purpose of the current study was to compare the inflammatory responsiveness of C57BL/6NKorl mice, a sub-strain recently established by the National Institute of Food and Drug Safety Evaluation (NIFDS), with those of C57BL/6 N mice from two different sources using a dextran sulfate sodium (DSS)-induced colitis model. Results: Male mice (8 weeks old) were administered DSS (0, 1, 2, or 3%) in drinking water for 7 days. DSS significantly decreased body weight and colon length and increased the colon weight-to-length ratio. Moreover, severe colitisrelated clinical signs including diarrhea and rectal bleeding were observed beginning on day 4 in mice administered DSS at a concentration of 3%. DSS led to edema, epithelial layer disruption, inflammatory cell infiltration, and cytokine induction (tumor necrosis factor-α, interleukin-6, and interleukin-1β) in the colon tissues. However, no significant differences in DSS-promoted abnormal symptoms or their severity were found between the three sub-strains. Conclusions These results indicate that C57BL/6NKorl mice responded to DSS-induced colitis similar to the generally used C57BL6/N mice, thus this newly developed mouse sub-strain provides a useful animal model of IBD.

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.