Imaging and Clinical Findings of Xanthogranulomatous Inflammatory Disease of Various Abdominal and Pelvic Organs: A Pictorial Essay.

Xanthogranulomatous (XG) inflammatory disease is a rare benign disease involving various organs, including the gallbladder, bile duct, pancreas, spleen, stomach, small bowel, colon, appendix, kidney, adrenal gland, urachus, urinary bladder, retroperitoneum, and female genital organs. The imaging features of XG inflammatory disease are nonspecific, usually presenting as a heterogeneous solid or cystic mass. The disease may also extend to adjacent structures. Due to its aggressive nature, it is occasionally misdiagnosed as a malignant neoplasm. Herein, we review the radiological features and clinical manifestations of XG inflammatory diseases in various organs of the abdomen and pelvis.

North Korean CO emissions reconstruction using DMZ ground observations, TROPOMI space-borne data, and the CMAQ air quality model.

Emission uncertainty in North Korea can act as an obstacle when developing air pollution management plans in the country and neighboring countries when the transboundary transport of air pollutants is considered. This study introduces a novel approach for adjusting and reallocating North Korean CO emissions, aiming to complement the limited observational and emissions data on the country's air pollutants. We utilized ground observations from demilitarized zone (DMZ) and vertical column density (VCD) data from a TROPOspheric Monitoring Instrument (TROPOMI), which were combined with the Community Multi-Scale Air Quality (CMAQ) chemistry transport model simulations. The Clean Air Support System (CAPSS) and Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) emissions inventories served as the basis for our initial simulations. A two-step procedure was proposed to adjust both the emission intensity and the spatial distribution of emissions. First, air quality simulations were conducted to explore model sensitivity to changes in North Korean CO emissions with respect to ground concentrations. DMZ observations then constrained these simulations to estimate corresponding emission intensity. Second, the spatial structure of North Korean CO emission sources was reconstructed with the help of TROPOMI CO VCD distributions. Our two-step hybrid method outperformed individual emissions adjustment and spatial reallocation based solely on surface or satellite observations. Validation using ground observations from the Chinese Dandong site near the China-North Korea border revealed significantly improved model simulations when applying the updated CO emissions. The adjusted CO emissions were 10.9 times higher than those derived from the bottom-up emissions used in this study, highlighting the lack of information on North Korean pollutants and emission sources. This approach offers an efficient and practical solution for identifying potential missing emission sources when there is limited on-site information about air quality on emissions.

Mathematical modeling for freshness/spoilage of chicken breast using chemometric analysis.

Chicken meat spoilage is a significant concern for food safety and quality, and this study aims to predict the spoilage point of chicken breast meat through various attributes and metabolites. Chicken meat was stored in anaerobic packaging at 4 °C for 13 days, and various meat quality attributes (pH, drip loss, color, volatile basic nitrogen [VBN], total aerobic bacteria [TAB], and metabolites) were examined. First, the spoiled point (VBN >20 mg/100 g and/or TAB >7 log CFU/g) of the chicken breast meat was determined. Using univariate and multivariate analyses, twenty-four candidate metabolites were identified. A receiver operating characteristic (ROC) analysis was used to validate the obtained binary logistic regression model using nine metabolites (proline, methionine, glutamate, threonine, acetate, uridine 5'-monophosphate, hypoxanthine, glycine, and glutamine). The results showed a high area under the ROC curve value (0.992). Thus, this study confirmed the predictability of spoilage points in chicken breast meat through these nine metabolites.

Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires.

Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM2.5. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM2.5 concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM2.5 concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires affect human health which creates the basis for the development of effective and efficient mitigation efforts.

Adjusting elemental carbon emissions in Northeast Asia using observed surface concentrations of downwind area and simulated contributions.

In this study, we developed a practical approach to augment elemental carbon (EC) emissions to improve the reproducibility of the most recent air quality with photochemical grid modeling in support of source-receptor relationship analysis. We demonstrated the usefulness of this approach with a series of simulations for EC concentrations over Northeast Asia during the 2016 Korea-United States Air Quality study. Considering the difficulty of acquiring EC observational data in foreign countries, our approach takes two steps: (1) augmenting upwind EC emissions based on simulated upwind contributions and observational data at a downwind EC monitor considered as the most representative monitor for upwind influences and (2) adjusting downwind EC emissions based on simulated downwind contributions, including the effects of updated upwind emissions from the first step and observational data at the downwind EC monitors. The emission adjustment approach resulted in EC emissions 2.5 times higher than the original emissions in the modeling domain. The EC concentration in the downwind area was observed to be 1.0 µg m-3 during the study period, while the simulated EC concentration was 0.5 µg m-3 before the emission adjustment. After the adjustment, the normalized mean error of the daily mean EC concentration decreased from 48 % to 22 % at ground monitor locations. We found that the EC simulation results were improved at high altitudes, and the contribution of the upwind areas was greater than that of the downwind areas for EC concentrations downwind with or without emission adjustment. This implies that collaborating with upwind regions is essential to alleviate high EC concentrations in downwind areas. The developed emission adjustment approach can be used for any upwind or downwind area when transboundary air pollution mitigation is needed because it provides better reproducibility of the most recent air quality through modeling with improved emission data.

Comparative Quality Traits, Flavor Compounds, and Metabolite Profile of Korean Native Black Goat Meat.

Although goat meat has several health benefits than other red meats but comsumers reluctant it due to its unpleasant flavor. This study aimed to investigate the odorant of goat meat as well as compare the quality traits of meat regarding sex status. The loin meats [non-castrated male (NCM), castrated male (CM), and female (FM)] were collected and stored at 4°C in a laboratory refrigerator and analyzed on the 1st, 5th, and 8th consecutive days. The moisture content was the lowest and the protein content was highest in FM (p<0.05). Fat and ash content in NCM and FM were similar while lowest in CM. The CIE L* was significantly higher in NCM, but there were no significant differences of CIE a* and CIE b* within groups at initial day. The color intensity increased on the 5th storage day and decreased again after the 8th storage day, except in NCM. NCM displayed the highest thiobarbituric acid reactive substances value (p<0.05), whereas CM displayed a higher pH value than other groups throughout the storage period. Indole levels were the highest in NCM (0.031 mg/kg); however, skatole levels were not significant differences across all treatments (p>0.05). No significant difference was observed in the fatty acid composition between NCM and CM (p>0.05), whereas a significant difference was observed in FM (p<0.05). Most of the water-soluble metabolites showed significant differences between sexes. Overall, sex status effects on quality properties of meat and castration can improve the overall sensory acceptance by reducing goaty flavor of Korean native black goat meat.

Comparison between single and serial computed tomography images in classification of acute appendicitis, acute right-sided diverticulitis, and normal appendix using EfficientNet.

This study aimed to develop a convolutional neural network (CNN) using the EfficientNet algorithm for the automated classification of acute appendicitis, acute diverticulitis, and normal appendix and to evaluate its diagnostic performance. We retrospectively enrolled 715 patients who underwent contrast-enhanced abdominopelvic computed tomography (CT). Of these, 246 patients had acute appendicitis, 254 had acute diverticulitis, and 215 had normal appendix. Training, validation, and test data were obtained from 4,078 CT images (1,959 acute appendicitis, 823 acute diverticulitis, and 1,296 normal appendix cases) using both single and serial (RGB [red, green, blue]) image methods. We augmented the training dataset to avoid training disturbances caused by unbalanced CT datasets. For classification of the normal appendix, the RGB serial image method showed a slightly higher sensitivity (89.66 vs. 87.89%; p = 0.244), accuracy (93.62% vs. 92.35%), and specificity (95.47% vs. 94.43%) than did the single image method. For the classification of acute diverticulitis, the RGB serial image method also yielded a slightly higher sensitivity (83.35 vs. 80.44%; p = 0.019), accuracy (93.48% vs. 92.15%), and specificity (96.04% vs. 95.12%) than the single image method. Moreover, the mean areas under the receiver operating characteristic curve (AUCs) were significantly higher for acute appendicitis (0.951 vs. 0.937; p < 0.0001), acute diverticulitis (0.972 vs. 0.963; p = 0.0025), and normal appendix (0.979 vs. 0.972; p = 0.0101) with the RGB serial image method than those obtained by the single method for each condition. Thus, acute appendicitis, acute diverticulitis, and normal appendix could be accurately distinguished on CT images by our model, particularly when using the RGB serial image method.

Ultrasonography of intrascrotal torsed appendages: size and interval between symptom onset and the ultrasonographic examination according to echogenicity.

PURPOSE: This study investigated the size of torsed appendages and the interval between symptom onset and the ultrasonographic examination according to the echogenicity of the torsed appendages. METHODS: This was a retrospective analysis of 54 cases in 46 patients with torsion of the testicular appendages between December 2008 and July 2021. Eight patients received follow-up ultrasonography 7-48 days after initial ultrasonography. The echogenicity of torsed appendages was classified into three groups: hypoechoic, hyperechoic, or isoechoic. RESULTS: The 54 torsed appendages were hypoechoic (n=40), hyperechoic (n=9), or isoechoic (n=5). The size of the torsed appendages ranged from 4 to 14 mm (8.0±3.1 mm) in hypoechoic torsed appendages and from 2.6 to 5.0 mm (3.7±0.9 mm) in hyperechoic torsed appendages. The interval between symptom onset and the ultrasonographic examination ranged from 0 to 17 days (4.2±4.4 days) in hypoechoic torsed appendages and from 8 to 48 days (29.8±16.0 days) in hyperechoic torsed appendages. The hyperechoic torsed appendages were smaller and had longer intervals between symptom onset and the ultrasonographic examination than the hypoechoic torsed appendages (P<0.05). Three hypoechoic torsed appendages and a single isoechoic torsed appendage on initial ultrasonography became hyperechoic on follow-up ultrasonography. CONCLUSION: The size of the torsed appendages and the interval between symptom onset and the ultrasonographic examination varied according to the echogenicity of the torsed appendages. The hyperechoic torsed appendages were smaller and had longer intervals until the examination than the hypoechoic torsed appendages.

Role of vertical advection and diffusion in long-range PM2.5 transport in Northeast Asia.

This study quantitatively analyzed the role of vertical mixing in long-range transport (LRT) of PM2.5 during its high concentration episode in Northeast Asia toward the end of February 2014. The PM2.5 transport process from an upwind to downwind area was examined using the Community Multi-scale Air Quality (CMAQ) modeling system with its instrumented tool and certain code modifications. We identified serial distinctive roles of vertical advection (ZADV) and diffusion (VDIF) processes. The surface PM2.5 in an upwind area became aloft by VDIF- during daytime-to the planetary boundary layer (PBL) altitude of 1 km or lower. In contrast, ZADV updraft effectively transported PM2.5 vertically to an altitude of 2-3 km above the PBL. Furthermore, we found that the VDIF and ZADV in the upwind area synergistically promoted the vertical mixing of air pollutants up to an altitude of 1 km and higher. The aloft PM2.5 in the upwind area was then transported to the downwind area by horizontal advection (HADV), which was faster than HADV at the surface layer. Additionally, VDIF and ZADV over the downwind area mixed down the aloft PM2.5 on the surface. During this period, the VDIF and ZADV increased the PM2.5 concentrations in the downwind area by up to 15 µg·m-3 (15%) and 101 µg·m-3 (60%), respectively. This study highlights the importance of vertical mixing on long-range PM2.5 transport and warrants more in-depth model analysis with three-dimensional observations to enhance its comprehensive understanding.

[Efforts to Improve the E-Learning Center of the Korean Society of Radiology: Survey on User Experience and Satisfaction]. / 대한영상의학회 이러닝 센터 ë°œì „ì„ 위한 ë ¸ë ¥: 대한영상의학회 회원 설문조사.

Purpose: As part of ongoing efforts to improve the current e-learning center, a survey was conducted regarding user experience and satisfaction to identify areas of improvement. Materials and Methods: Radiologists (n = 454/617) and radiology residents (n = 163/617) of the Korean Society of Radiology were asked to answer a survey via email. The questionnaire asked for basic user information as well as user experiences relating to the e-learning center, such as workplace, frequency of use, overall satisfaction levels, reasons for satisfaction or dissatisfaction, and other suggestions for improvement. Results: Annual members and all members of the e-learning center reported above average satisfaction levels of 67% and 42%, respectively. Approximately 30% of respondents viewed e-learning center lectures more than 5 times a month, with residents having a particularly high usage frequency. There was a high demand for additional lectures covering more diverse specialties (e-learning for annual members only: n = 28/97, e-learning for all members: n = 72/166), a smoother and more convenient searching platform/interface (n = 37/97 and n = 58/166, respectively), and regular content updates. In addition, many of the members suggested the addition of user-friendly functions such as playback speed control, a way to save viewing history, as well as requests for improved system stability. Conclusion: Based on survey results, the educational committee plans to continue its efforts to improve the e-learning center by increasing the quality and quantity of available lectures, and increasing technical support to improve the stability and convenience of the e-learning digital system.