This study aimed to establish infection control strategies for preventing the spread of scabies within a single institution by analyzing the epidemiological characteristics of patients diagnosed with scabies. Methods: This was a retrospective descriptive study. The electronic medical records of 430 patients diagnosed with scabies at the dermatology outpatient department of Uijeongbu St. Mary’s Hospital from January 2020 to December 2022 were used to collect data on their characteristics. The subjects were divided into three groups: patients, family of confirmed patients, and healthcare workers. General and epidemiological characteristics were analyzed. Results: The average age was 60.89 ± 22.39 years. The number of days from symptom onset to diagnosis was unknown in many cases (65.3%), although the average was 63.42 ± 64.18 days. Repeated visits after treatment were observed in 193 patients (67.5%), 38 family members of confirmed patients (55.1%), and 35 healthcare workers (46.7%), showing a statistically significant difference (p = .002). The most common place of residence before the visit was home (56.2%), and the most common suspected origin of contagion was the home (38.3%), and the most common category of the suspected contagious person was family (49.6%). Conclusion: Cases of scabies were disproportionately common in women and older adults. The interval from symptom onset to diagnosis was long, and about half of the cases involved itching but no skin lesions. More than one-thirds of cases did not revisit for a follow-up after 2 weeks. In the overall results, unlike previous studies, factors related to home and family were frequently observed as epidemiological characteristics.

Emerging evidence suggests that systemic inflammation may play a critical role in neurological disorders. Recent studies have shown the connection between inflammatory bowel diseases (IBD) and neurological disorders, revealing a bidirectional relationship through the gut-brain axis.Immunotherapies, such as Treg cells infusion, have been proposed for IBD. However, the role of adaptive immune cells in IBD-induced neuroinflammation remains unclear. In this study, we established an animal model for IBD in mice with severe combined immune-deficient (SCID), an adaptive immune deficiency, to investigate the role of adaptive immune cells in IBD-induced neuroinflammation. Mice were fed 1%, 3%, or 5% dextran sulfate sodium (DSS) for 5 days. We measured body weight, colon length, disease activity index (DAI), and crypt damage. Pro-inflammatory cytokines were measured in the colon, while microglial morphology, neuronal count, and inflammatory cytokines were analyzed in the brain. In the 3% DSS group, colitis symptoms appeared at day 7, with reduced colon length and increased crypt damage showing colitis-like symptoms. By day 21, colon length and crypt damage persisted, while DAI showed recovery. Although colonic inflammation peaked at day 7, no significant increase in inflammatory cytokines or microglial hyperactivation was observed in the brain. By day 21, neuroinflammation was detected, albeit with a slight delay, in the absence of adaptive immune cells. The colitis-induced neuroinflammation model provides insights into the fundamental immune mechanisms of the gut-brain axis and may contribute to developing immune cell therapies for IBD-induced neuroinflammation.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

This study aimed to establish infection control strategies for preventing the spread of scabies within a single institution by analyzing the epidemiological characteristics of patients diagnosed with scabies. Methods: This was a retrospective descriptive study. The electronic medical records of 430 patients diagnosed with scabies at the dermatology outpatient department of Uijeongbu St. Mary’s Hospital from January 2020 to December 2022 were used to collect data on their characteristics. The subjects were divided into three groups: patients, family of confirmed patients, and healthcare workers. General and epidemiological characteristics were analyzed. Results: The average age was 60.89 ± 22.39 years. The number of days from symptom onset to diagnosis was unknown in many cases (65.3%), although the average was 63.42 ± 64.18 days. Repeated visits after treatment were observed in 193 patients (67.5%), 38 family members of confirmed patients (55.1%), and 35 healthcare workers (46.7%), showing a statistically significant difference (p = .002). The most common place of residence before the visit was home (56.2%), and the most common suspected origin of contagion was the home (38.3%), and the most common category of the suspected contagious person was family (49.6%). Conclusion: Cases of scabies were disproportionately common in women and older adults. The interval from symptom onset to diagnosis was long, and about half of the cases involved itching but no skin lesions. More than one-thirds of cases did not revisit for a follow-up after 2 weeks. In the overall results, unlike previous studies, factors related to home and family were frequently observed as epidemiological characteristics.

Objective: To establish local diagnostic reference levels (DRLs) for pediatric neck CT based on age, weight, and water-equivalent diameter (WED) across multiple university hospitals in South Korea. Materials and Methods: This retrospective study analyzed pediatric neck CT examinations from nine university hospitals, involving patients aged 0–18 years. Data were categorized by age, weight, and WED, and radiation dose metrics, including volume CT dose index (CTDIvol) and dose length product, were recorded. Data retrieval and analysis were conducted using a commercially available dose-management system (Radimetrics, Bayer Healthcare). Local DRLs were established following the International Commission on Radiological Protection guidelines, using the 75th percentile as the reference value. Results: A total of 1159 CT examinations were analyzed, including 169 scans from Institution 1, 132 from Institution 2, 126 from Institution 3, 129 from Institution 4, 128 from Institution 5, 105 from Institution 6, 162 from Institution 7, 127 from Institution 8, and 81 from Institution 9. Radiation dose metrics increased with age, weight, and WED, showing significant variability both within and across institutions. For patients weighing less than 10 kg, the DRL for CTDIvol was 5.2 mGy. In the 10–19 kg group, the DRL was 5.8 mGy; in the 20–39 kg group, 7.6 mGy; in the 40–59 kg group, 11.0 mGy; and for patients weighing 60 kg or more, 16.2 mGy. DRLs for CTDIvol by age groups were as follows: 5.3 mGy for infants under 1 year, 5.7 mGy for children aged 1–4 years, 7.6 mGy for ages 5–9 years, 11.2 mGy for ages 10–14 years, and 15.6 mGy for patients 15 years or older. Conclusion Local DRLs for pediatric neck CT were established based on age, weight, and WED across nine university hospitals in South Korea.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.