Optimization of cultivar, germination time and extraction for radish sprout extract with high sulforaphene content.

BACKGROUND: Cruciferous vegetable sprout has been highlighted as a promising functional material rich in bioactive compounds called isothiocyanates (ITCs) and it can be grown in very short periods in controlled indoor farms. However, because ITCs content depends on multiple factors such as cultivar, germination time and myrosinase activity, those variables need to be controlled during germination or extraction to produce functional materials enriched in ITCs. Sulforaphene (SFEN), an ITC found primarily in radishes (Raphanus sativus L.), exerts beneficial effects on obesity. However, the optimal germination and extraction conditions for radish sprout (RSP) to increase SFEN content remain unascertained, and the extract's anti-obesity effect has yet to be evaluated. RESULTS: The present study found that the SFEN content was highest in purple radish sprout (PRSP) among the six cultivars investigated. Optimal SFEN content occurred after 2 days of PRSP germination (2 days PRSP). To maximize the dry matter yield, total ITCs and SFEN contents in RSP extract, we found the optimal conditions for extracting PRSP [27.5 °C, 60 min, 1:75.52 solute/solvent (w/v), no ascorbic acid] using response surface methodology. Consistent with high SFEN content, 2 days PRSP extract significantly outperformed 3 days or 4 days PRSP extract in inhibiting lipid accumulation in 3T3-L1 cells. Moreover, 2 days PRSP extract suppressed adipogenesis and lipogenesis-related protein expression. CONCLUSION: Regarding the cultivar, germination time and extraction conditions, optimally produced PRSP extract contains high SFEN content and exerts anti-obesity effects. Thus, we suggest PRSP extract as a potent functional material for obesity prevention. © 2024 Society of Chemical Industry.

Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography.

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.

Optimization of extraction condition for platycodin D from Platycodon grandiflorum root and verification of its biological activity.

Platycosides, major components of Platycodon grandiflorum (PG) extract, have been implicated in a wide range of biological effects. In particular, platycodin D (PD) is a well-known main bioactive compound of Platycosides. Despite the biological significance of PD, optimization of extract condition for PD from PG root has not been well investigated. Here, we established the optimum extraction condition as ethanol concentration of 0%, temperature of 50°C, and extraction time of 11 h to obtain PD-rich P. grandiflorum extract (PGE) by using response surface methodology (RSM) with Box-Behnken design (BBD). The 5.63 mg/g of PD was extracted from the PG root in optimum condition, and this result was close to the predicted PD content. To analyze the biological activity of PGE related to mucin production, we demonstrated the inhibitory effect of PGE on PMA-induced hyperexpression of MUC5AC as well as ERK activation, a signal mediator of MUC5AC expression. Moreover, we showed that PGE had expectorant activity in mice. These results indicated that PGE had sufficient functions as a potential mucoregulator and expectorant for treating diverse airway diseases. Additionally, we confirmed that PGE had antioxidant activity and inhibited LPS-induced proinflammatory cytokines, TNF-α, and IL-6. Taken together, PGE derived from novel optimizing conditions showed various biological effects, suggesting that PGE could be directly applied to the food industry as food material having therapeutic and preventive potential for human airway diseases.

Discovery and optimized extraction of the anti-osteoclastic agent epicatechin-7-O-ß-D-apiofuranoside from Ulmus macrocarpa Hance bark.

Ulmus macrocarpa Hance bark (UmHb) has been used as a traditional herbal medicine in East Asia for bone concern diseases for a long time. To find a suitable solvent, we, in this study, compared the efficacy of UmHb water extract and ethanol extract which can inhibit osteoclast differentiation. Compared with two ethanol extracts (70% and 100% respectively), hydrothermal extracts of UmHb more effectively inhibited receptor activators of nuclear factor κB ligand-induced osteoclast differentiation in murine bone marrow-derived macrophages. We identified for the first time that (2R,3R)-epicatechin-7-O-ß-D-apiofuranoside (E7A) is a specific active compound in UmHb hydrothermal extracts through using LC/MS, HPLC, and NMR techniques. In addition, we confirmed through TRAP assay, pit assay, and PCR assay that E7A is a key compound in inhibiting osteoclast differentiation. The optimized condition to obtain E7A-rich UmHb extract was 100 mL/g, 90 °C, pH 5, and 97 min. At this condition, the content of E7A was 26.05 ± 0.96 mg/g extract. Based on TRAP assay, pit assay, PCR, and western blot, the optimized extract of E7A-rich UmHb demonstrated a greater inhibition of osteoclast differentiation compared to unoptimized. These results suggest that E7A would be a good candidate for the prevention and treatment of osteoporosis-related diseases.

Ultra-low-dose computed tomography with deep learning reconstruction for craniosynostosis at radiation doses comparable to skull radiographs: a pilot study.

BACKGROUND: Craniofacial computed tomography (CT) is the diagnostic investigation of choice for craniosynostosis, but high radiation dose remains a concern. OBJECTIVE: To evaluate the image quality and diagnostic performance of an ultra-low-dose craniofacial CT protocol with deep learning reconstruction for diagnosis of craniosynostosis. MATERIALS AND METHODS: All children who underwent initial craniofacial CT for suspected craniosynostosis between September 2021 and September 2022 were included in the study. The ultra-low-dose craniofacial CT protocol using 70 kVp, model-based iterative reconstruction and deep learning reconstruction techniques was compared with a routine-dose craniofacial CT protocol. Quantitative analysis of the signal-to-noise ratio and noise was performed. The 3-dimensional (D) volume-rendered images were independently evaluated by two radiologists with regard to surface coarseness, step-off artifacts and overall image quality on a 5-point scale. Sutural patency was assessed for each of six sutures. Radiation dose was compared between the two protocols. RESULTS: Among 29 patients (15 routine-dose CT and 14 ultra-low-dose CT), 23 patients had craniosynostosis. The 3-D volume-rendered images of ultra-low-dose CT without deep learning showed decreased image quality compared to routine-dose CT. The 3-D volume-rendered images of ultra-low-dose CT with deep learning reconstruction showed higher noise level, higher surface coarseness but decreased step-off artifacts, comparable signal-to-noise ratio and overall similar image quality compared to the routine-dose CT images. Diagnostic performance for detecting craniosynostosis at the suture level showed no significant difference between ultra-low-dose CT without deep learning reconstruction, ultra-low-dose CT with deep learning reconstruction and routine-dose CT. The estimated effective radiation dose for the ultra-low-dose CT was 0.05 mSv (range, 0.03-0.06 mSv), a 95% reduction in dose over the routine-dose CT at 1.15 mSv (range, 0.54-1.74 mSv). This radiation dose is comparable to 4-view skull radiography (0.05-0.1 mSv) and lower than previously reported effective dose for craniosynostosis protocols (0.08-3.36 mSv). CONCLUSION: In this pilot study, an ultra-low-dose CT protocol using radiation doses at a level similar to skull radiographs showed preserved diagnostic performance for craniosynostosis, but decreased image quality compared to the routine-dose CT protocol. However, by combining the ultra-low-dose CT protocol with deep learning reconstruction, image quality was improved to a level comparable to the routine-dose CT protocol, without sacrificing diagnostic performance for craniosynostosis.

Monitoring Posterior Cerebral Perfusion Changes With Dynamic Susceptibility Contrast-Enhanced Perfusion MRI After Anterior Revascularization Surgery in Pediatric Moyamoya Disease.

OBJECTIVE: To determine whether dynamic susceptibility contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) can be used to evaluate posterior cerebral circulation in pediatric patients with moyamoya disease (MMD) who underwent anterior revascularization. MATERIALS AND METHODS: This study retrospectively included 73 patients with MMD who underwent DSC perfusion MRI (age, 12.2 ± 6.1 years) between January 2016 and December 2020, owing to recent-onset clinical symptoms during the follow-up period after completion of anterior revascularization. DSC perfusion images were analyzed using a dedicated software package (NordicICE; Nordic NeuroLab) for the middle cerebral artery (MCA), posterior cerebral artery (PCA), and posterior border zone between the two regions (PCA-MCA). Patients were divided into two groups; the PCA stenosis group included 30 patients with newly confirmed PCA involvement, while the no PCA stenosis group included 43 patients without PCA involvement. The relationship between DSC perfusion parameters and PCA stenosis, as well as the performance of the parameters in discriminating between groups, were analyzed. RESULTS: In the PCA stenosis group, the mean follow-up duration was 5.3 years after anterior revascularization, and visual disturbances were a common symptom. Normalized cerebral blood volume was increased, and both the normalized time-to-peak (nTTP) and mean transit time values were significantly delayed in the PCA stenosis group compared with those in the no PCA stenosis group in the PCA and PCA-MCA border zones. TTPPCA (odds ratio [OR] = 6.745; 95% confidence interval [CI] = 2.665-17.074; P < 0.001) and CBVPCA-MCA (OR = 1.567; 95% CI = 1.021-2.406; P = 0.040) were independently associated with PCA stenosis. TTPPCA showed the highest receiver operating characteristic curve area in discriminating for PCA stenosis (0.895; 95% CI = 0.803-0.986). CONCLUSION: nTTP can be used to effectively diagnose PCA stenosis. Therefore, DSC perfusion MRI may be a valuable tool for monitoring PCA stenosis in patients with MMD.

Contrast-enhanced ultrasound for the diagnosis of acute pyelonephritis in pediatric patients with urinary tract infection: A feasibility study.

PURPOSE: This study aimed to assess the feasibility of contrast-enhanced ultrasound (CEUS) for the diagnosis of acute pyelonephritis (APN) in pediatric patients with febrile urinary tract infection (UTI). MATERIALS AND METHODS: Between March 2019 and January 2021, study participants with suspected UTI were assessed for APN using ultrasound. Parenchymal echogenicity changes, renal pelvis dilatation, and the presence of a focal suspected lesion were assessed using conventional grayscale ultrasound. The presence and location of a decreased perfusion area were evaluated using color Doppler ultrasound (CDUS) and CEUS. Agreement between each ultrasound examination and a 99mTc‒dimercaptosuccinic acid (DMSA) scan was assessed using the κ value, and the most visible period of the lesion was evaluated using CEUS. RESULTS: This study enrolled 21 participants (median age, 8.0 months; range, 2.0-61.0 months) with isolated urinary tract pathogens. Five increased parenchymal echotextures (11.9%) and 14 renal pelvic dilatations (33.3%) were confirmed, but no focal lesions were detected on the grayscale images. CDUS and CEUS showed decreased local perfusion suggestive of APN in two and five kidneys, respectively. DMSA scan showed substantial agreement with CEUS findings (κ = 0.80, P = 0.010), but other grayscale and CDUS findings did not agree with DMSA scan results (P > 0.05). All lesions were best observed in the late parenchymal phase on CEUS. CONCLUSION: CEUS can reveal renal perfusion defects in pediatric patients with suspected APN without radiation exposure or sedation; therefore, CEUS may be a feasible and valuable diagnostic technique.

Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation.

OBJECTIVE: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. MATERIALS AND METHODS: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. RESULTS: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). CONCLUSION: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

Ultrasound findings of subpial hemorrhage in neonates.

PURPOSE: Subpial hemorrhage (SPH) is a subtype of intracranial hemorrhage characterized by damage to the adjacent brain parenchyma. The aim of this study was to describe the sonographic features of SPH in neonates. METHODS: The cranial ultrasound (US) findings of neonates with SPH confirmed by brain magnetic resonance imaging (MRI) were analyzed retrospectively. Initial and follow-up US and MRI scans were reviewed by two pediatric radiologists who were blinded to both clinical history and outcomes. The US features were compared with the MRI findings. RESULTS: Sixteen patients were included (median gestational age, 38 weeks; range, 26 to 40 weeks; 69% term). SPH was detected most often in the temporal lobe (63%), and multiple SPHs were found in seven of 16 neonates, based on MRI. Acute SPH with an underlying venous infarct (UVI) was detected on US in 15 of 16 patients: small or large fan-shaped hyperechoic lesions (n=7 and 4, respectively) and gyriform hyperechoic lesions (n=4). The sonographic yin-yang sign was observed in three of the four large fan-shaped SPH cases. The accompanying findings on US were intraventricular hemorrhage (four out of six MRI-confirmed cases), and concurrent periventricular venous infarcts (five out of nine MRI-confirmed cases). In five patients, subpial cysts were observed on follow-up US or MRI (n=4 and n=4, respectively). CONCLUSION: Acute SPH with UVI can appear as a peripheral fan-shaped or gyriform hyperechoic lesion on cranial US. SPH can be detected and suspected based on the US features of SPH with the accompanying findings.

Usefulness of two-dimensional shear wave elastography in diagnosing hepatic veno-occlusive disease in pediatric patients undergoing hematopoietic stem cell transplantation.

PURPOSE: This study aimed to evaluate the usefulness of two-dimensional shear wave elastography (2D-SWE) in diagnosing hepatic veno-occlusive disease (VOD) in pediatric patients. METHODS: This study retrospectively included pediatric patients who underwent hematopoietic stem cell transplantation (HSCT) between November 2019 and January 2021. All 34 patients (8.7±5.0 years) were examined using 2D-SWE for an initial diagnosis. A subgroup analysis was performed using the data from follow-up examinations of patients diagnosed with VOD. The characteristics of the initial VOD diagnosis were compared with the longitudinal changes observed in VOD patients who underwent multiple ultrasound examinations. RESULTS: In total, 19 patients were diagnosed with VOD at 17.6±9.4 days after HSCT. All VOD patients showed hepatomegaly, ascites, and gallbladder wall thickening. Liver stiffness was higher in VOD patients than in non-VOD patients (12.4±1.1 vs. 6.3±0.8 kPa, P<0.001). Liver stiffness values above 7.2 kPa showed 84.2% sensitivity and 93.3% specificity in distinguishing VOD from non-VOD (area under the curve, 0.925; 95% confidence interval, 0.780 to 0.987; P<0.001). A subgroup analysis of 11 patients showed a linear decrease in liver stiffness values after VOD diagnosis with treatment (first, second, and third follow-ups; 13.5±1.7, 11.3±1.4, and 9.5±0.8 kPa, respectively), but without statistical significance in the pairwise analysis. CONCLUSION: Liver stiffness measured using 2D-SWE increased in pediatric patients who develop VOD after HSCT. Therefore, liver stiffness can be a predictive and quantitative parameter for diagnosing VOD.

Factors influencing outcomes of esophageal balloon dilatation for anastomosis site stenosis after esophageal atresia surgery.

BACKGROUND: Esophageal balloon dilatation is an effective treatment for anastomotic strictures, but the factors affecting the outcome of dilatation remain unclear. OBJECTIVE: To investigate the predictive factors of esophageal balloon dilatation outcome in children with anastomotic stricture after esophageal atresia repair. MATERIALS AND METHODS: We retrospectively reviewed children with esophageal atresia who underwent esophageal balloon dilatation for postoperative strictures between August 2007 and February 2021. We investigated each child's age, weight and height; type of esophageal atresia surgery; shape, length and level of stricture; esophageal balloon dilatation balloon size; application of mitomycin; number of inflation sessions; and number of esophageal balloon dilatation sessions. The outcome of each esophageal balloon dilatation session was determined as improvement in stricture diameter between pre- and post-esophageal balloon dilatation esophagography. We used uni- and multivariate analyses with generalized estimating equations to evaluate outcome predictors. RESULTS: Overall, 69 children (mean age, 2.3 years; 45 boys) underwent 227 esophageal balloon dilatations. In the univariate analysis, the positive effect of esophageal balloon dilatation decreased with increased age, weight, height, balloon size and number of esophageal balloon dilatation sessions. Additionally, the positive effect was decreased in cervical-level strictures and with the application of mitomycin during esophageal balloon dilatation. In the multivariate analysis, independent prognostic factors of the positive esophageal balloon dilatation effect were age (incidence rate ratio [IRR]: -0.01; 95% confidence interval [CI]: -0.01, -0.002), shape of stricture (IRR: -0.54; 95% CI: -0.91, -0.18) and number of esophageal balloon dilatation sessions (IRR, -0.10; 95% CI: -0.14, -0.18). CONCLUSION: Repeated esophageal balloon dilatation, older age and eccentric stricture shape are associated with poor response to esophageal balloon dilatation in children with anastomotic strictures after esophageal atresia repair.

Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI.

INTRODUCTION: Deep learning-based MRI reconstruction has recently been introduced to improve image quality. This study aimed to evaluate the performance of deep learning reconstruction in pediatric brain MRI. METHODS: A total of 107 consecutive children who underwent 3.0 T brain MRI were included in this study. T2-weighted brain MRI was reconstructed using the three different reconstruction modes: deep learning reconstruction, conventional reconstruction with an intensity filter, and original T2 image without a filter. Two pediatric radiologists independently evaluated the following image quality parameters of three reconstructed images on a 5-point scale: overall image quality, image noisiness, sharpness of gray-white matter differentiation, truncation artifact, motion artifact, cerebrospinal fluid and vascular pulsation artifacts, and lesion conspicuity. The subjective image quality parameters were compared among the three reconstruction modes. Quantitative analysis of the signal uniformity using the coefficient of variation was performed for each reconstruction. RESULTS: The overall image quality, noisiness, and gray-white matter sharpness were significantly better with deep learning reconstruction than with conventional or original reconstruction (all P < 0.001). Deep learning reconstruction had significantly fewer truncation artifacts than the other two reconstructions (all P < 0.001). Motion and pulsation artifacts showed no significant differences among the three reconstruction modes. For 36 lesions in 107 patients, lesion conspicuity was better with deep learning reconstruction than original reconstruction. Deep learning reconstruction showed lower signal variation compared to conventional and original reconstructions. CONCLUSION: Deep learning reconstruction can reduce noise and truncation artifacts and improve lesion conspicuity and overall image quality in pediatric T2-weighted brain MRI.

[Jaundice in Young Children]. / 소아 황달의 영상 평가.

Jaundice in children have various etiologies. Among them, physiological jaundice is a very common disease observed in more than half of full-term neonates. When jaundice persists or develops after 2 weeks of age, the total/direct bilirubin is measured in consideration of the possibility of cholestasis. In case of cholestasis, imaging studies differentiate biliary atresia and other disorders of the extrahepatic bile ducts. In this review, we briefly presented the major differential diseases of cholestasis in children and introduced diagnostic imaging techniques, including normal findings.

Development and evaluation of deep-learning measurement of leg length discrepancy: bilateral iliac crest height difference measurement.

BACKGROUND: Leg length discrepancy (LLD) is a common problem that can cause long-term musculoskeletal problems. However, measuring LLD on radiography is time-consuming and labor intensive, despite being a simple task. OBJECTIVE: To develop and evaluate a deep-learning algorithm for measurement of LLD on radiographs. MATERIALS AND METHODS: In this Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study, radiographs were obtained to develop a deep-learning algorithm. The algorithm developed with two U-Net models measures LLD using the difference between the bilateral iliac crest heights. For performance evaluation of the algorithm, 300 different radiographs were collected and LLD was measured by two radiologists, the algorithm alone and the model-assisting method. Statistical analysis was performed to compare the measurement differences with the measurement results of an experienced radiologist considered as the ground truth. The time spent on each measurement was then compared. RESULTS: Of the 300 cases, the deep-learning model successfully delineated both iliac crests in 284. All human measurements, the deep-learning model and the model-assisting method, showed a significant correlation with ground truth measurements, while Pearson correlation coefficients and interclass correlations (ICCs) decreased in the order listed. (Pearson correlation coefficients ranged from 0.880 to 0.996 and ICCs ranged from 0.914 to 0.997.) The mean absolute errors of the human measurement, deep-learning-assisting model and deep-learning-alone model were 0.7 ± 0.6 mm, 1.1 ± 1.1 mm and 2.3 ± 5.2 mm, respectively. The reading time was 7 h and 12 min on average for human reading, while the deep-learning measurement took 7 min and 26 s. The radiologist took 74 min to complete measurements in the deep-learning mode. CONCLUSION: A deep-learning U-Net model measuring the iliac crest height difference was possible on teleroentgenograms in children. LLD measurements assisted by the deep-learning algorithm saved time and labor while producing comparable results with human measurements.

Optimization of the extraction process of high levels of chlorogenic acid and ginsenosides from short-term hydroponic-cultured ginseng and evaluation of the extract for the prevention of atopic dermatitis.

Background: Short-term hydroponic-cultured ginseng (sHCG), which is 1-year-old ginseng seedlings cultivated for 4 weeks in a hydroponic system, is a functional food item with several biological effects. However, the optimal extraction conditions for sHCG, and the bioactivity of its extracts, have not been evaluated. Methods: Chlorogenic acid (CGA) and ginsenoside contents were evaluated in sHCG, white ginseng (WG), and red ginseng (RG) using high-performance liquid chromatography. Response surface methodology (RSM) was used to optimize the extraction conditions (temperature and ethanol concentration) to maximize the yield of dry matter, CGA, and four ginsenosides (Re, Rg1, Rb1, and Rd) from sHCG. The optimal extraction conditions were applied to pilot-scale production of sHCG extracts. The expression levels of tumor necrosis factor (TNF)-α/interferon (IFN)-γ-induced thymic and activation-regulated chemokines (TARC/CCL17) were measured after treatment with sHCG, WG, and RG extracts, and the effects of their bioactive compounds (CGA and four ginsenosides) on human skin keratinocytes (HaCaTs) were evaluated. Results: CGA and four ginsenosides, which are bioactive compounds of sHCG, significantly inhibited TNF-α/IFN-γ-induced TARC/CCL17 expression. The optimal sHCG extraction conditions predicted by the RSM models were 80 °C and 60% ethanol (v/v). The sHCG extracts produced at the pilot scale under optimal conditions greatly alleviated TNF-α/IFN-γ-induced TARC/CCL17 production compared with WG and RG extracts. Conclusions: Pesticide-free sHCG extracts, which contain high levels of CGA and the ginsenosides Re, Rg1, Rb1, and Rd as bioactive compounds, may have therapeutic potential for atopic diseases.

Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network.

OBJECTIVES: To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children. METHODS: For model development, we utilized transfer learning with a pre-trained model based on adult patients. We used CT images of 31 pediatric patients under 19 years of age (mean age, 9.6 years) who underwent PET-CT from institution #1 for transfer learning. Two radiologists manually labeled the skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs, and central nervous system in each CT slice and used these as references. For external validation, we collected 14 pediatric PET/CT scans from institution #2 (mean age, 9.1 years). The Dice similarity coefficients (DSCs), sensitivities, and precision were compared between the algorithms before and after transfer learning. In addition, we evaluated segmentation performance according to sex, age (≤ 8 vs. > 8 years), and body mass index (BMI, ≤ 20 vs. > 20 kg/m2). RESULTS: The algorithm after transfer learning showed better performance than the algorithm before transfer learning for all body compositions (p < 0.001). The average DSC, sensitivity, and precision of each algorithm before and after transfer learning were 98.23% and 99.28%, 98.16% and 99.28%, and 98.29% and 99.28%, respectively. The segmentation performance of the algorithm was generally not affected by age, sex, or BMI, except for precision in the body muscle compartment. CONCLUSION: The developed model with transfer learning enabled accurate and fully automated segmentation of multiple tissues on whole-body CT scans in children. KEY POINTS: • We utilized transfer learning with a pre-trained segmentation algorithm for adult to develop an algorithm for automated segmentation of pediatric whole-body CT. • This algorithm showed excellent performance and was not affected by sex, age, or body mass index, except for precision in body muscle.

Contrast-enhanced voiding ultrasonography to detect intrarenal reflux in children: comparison with 99mTc-DMSA renal scans.

PURPOSE: This study evaluated the diagnostic performance of contrast-enhanced voiding ultrasonography (CeVUS) for detecting intrarenal reflux (IRR) and the correlation between CeVUS-detected IRR sites and photon defect sites in acute 99mTc-dimercaptosuccinic acid (DMSA) renal scans in pediatric patients. METHODS: Fifty-four kidneys from 27 patients (20 males and seven females; mean age, 5.6±4.1 months) who underwent CeVUS and acute DMSA renal scans for recurrent urinary tract infection (UTIs) or pyelonephritis were included. Pediatric experts compared the results of CeVUS with acute DMSA renal scans. RESULTS: Thirteen renal units (13/54, 24.1%) in 10 patients (nine males and one female; mean age, 6.3±3.7 months; age range, 0 to 13 months) showed vesicoureteral reflux and eight renal units (8/54, 14.8%) demonstrated IRR on CeVUS. Ten renal units in eight patients (six males and two females; mean age, 6.9±1.4 months; age range, 2 to 13 months) showed 19 photon defects on acute DMSA renal scans. Fifty-two renal units (96.3%) showed concordant results, and two renal units (3.7%) showed discordant results between CeVUS and acute DMSA renal scans. IRR accounted for 15/19 (78.9%) photon defects in eight renal units of seven patients using CeVUS. In a per-renal-unit analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CeVUS were 80%, 100%, 100%, 95.7%, and 96.3%, respectively. CONCLUSION: CeVUS showed good performance in detecting IRR, and the IRR sites detected by CeVUS closely correlated with photon defect sites in acute DMSA scans. CeVUS may play an important role in managing patients with recurrent UTIs or pyelonephritis with reduced radiation exposure.

Flavonoid Glycosides from Ulmus macrocarpa Inhibit Osteoclast Differentiation via the Downregulation of NFATc1.

The aim of this study was to isolate and identify chemical components with osteoclast differentiation inhibitory activity from Ulmus macrocarpa Hance bark. Spectroscopic analyses, including nuclear magnetic resonance (NMR) and electronic circular dichroism (ECD), resulted in the unequivocal elucidation of active compounds such as (2S)-naringenin-6-C-ß-d-glucopyranoside (1), (2R)-naringenin-6-C-ß-d-glucopyranoside (2), (2R,3S)-catechin-7-O-ß-d-xylopyranoside (3), (2R,3S)-catechin-7-O-ß-d-apiofuranoside (6), (2R,3R)-taxifolin-6-C-ß-d-glucopyranoside (7), and (2S,3S)-taxifolin-6-C-ß-d-glucopyranoside (8). Mechanistically, the compounds may exhibit osteoclast differentiation inhibitory activity via the downregulation of NFATc1, a master regulator involved in osteoclast formation. This is the first report of their inhibitory activities on the receptor activator of nuclear factor κB ligand (RANKL)-induced osteoclast differentiation in murine bone marrow-derived macrophages. These findings provide further scientific evidence for the rational application of the genus Ulmus for the amelioration or treatment of osteopenic diseases.

Transradial Cerebral Angiography: Is it Feasible and Safe for Children?

PURPOSE: To investigate the feasibility and safety of transradial cerebral angiography (TRCA) in comparison to transfemoral cerebral angiography (TFCA) in the pediatric population. MATERIALS AND METHODS: We retrospectively reviewed pediatric patients who underwent TRCA between January 2019 and July 2020. Patients with TFCA experience were examined. Pre- and post-procedural Doppler ultrasonography was performed to evaluate TRCA complications. To evaluate differences in radiation exposure between TRCA and TFCA, we investigated the radiation dose, dose area product (DAP), fluoroscopy time, and examination time. RESULTS: Thirty-five patients (mean age, 13.8 years; 22 male) underwent TRCA, with 18 (mean age, 13.0 years; 10 male) experiencing TFCA. TRCA was successful in all cases without technical failure. Radiation exposure including radiation dose and DAP were significantly higher in the TRCA group (474.1 mGy and 8299.6 µGy m2) compared with the TFCA group (347.8 mGy and 6342.0 µGy m2). Fluoroscopy time and total examination time were significantly longer (145.1% and 32.6%) in TRCA (15.2 and 38.6 min) group compared with the TFCA group (6.2 and 29.1 min). Among the 26 patients who underwent post-procedural Doppler ultrasonography, five (19.2%) had complications after TRCA. Two (7.7%) patients showed radial artery stenosis, two (7.7%) had hematoma at the puncture site, and one (3.8%) showed thrombotic occlusion. CONCLUSION: Although TRCA is a technically feasible and safe method in pediatric patients, high radiation exposure to TFCA must be considered. Therefore, it should be considered as an alternative method in those with unfavorable clinical situations for performing TFCA. LEVEL OF EVIDENCE: Level 4, Case Series.

Deep Learning-Assisted Diagnosis of Pediatric Skull Fractures on Plain Radiographs.

OBJECTIVE: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. MATERIALS AND METHODS: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). RESULTS: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). CONCLUSION: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.