and Relevance: Severe necrosis and mild fibrosis were observed in the liver and forelimb skeletal muscles. Based on histological analysis, we diagnosed iron overload myopathy by secondary hemochromatosis. Secondary hemochromatosis with severe muscle atrophy and myositis is very rare, and this is the first report of iron-overload myopathy in a dog.

and Relevance: Severe necrosis and mild fibrosis were observed in the liver and forelimb skeletal muscles. Based on histological analysis, we diagnosed iron overload myopathy by secondary hemochromatosis. Secondary hemochromatosis with severe muscle atrophy and myositis is very rare, and this is the first report of iron-overload myopathy in a dog.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Background: The positive relationship between triiodothyronine (T3) and steatotic liver disease (SLD) demonstrated only in crosssectional study. We aimed to evaluated whether total T3 (TT3) is associated with the development/resolution of SLD in longitudinal design. Methods: This retrospective, longitudinal, population-based cohort study included 1,665 South Korean euthyroid adults with ≥4 thyroid function test. We explored the impact of mean TT3 during follow-up on development/resolution of either SLD (diagnosed by ultrasound) or modified metabolic dysfunction-associated steatotic liver disease (MASLD) using Cox proportional hazards regression models. Results: During about median 5 years follow-up, 807/1,216 (66.3%) participants among participants without SLD at baseline developed SLD, and 253/318 (79.5%) participants among participants with SLD at baseline SLD resolved fatty liver. Mean TT3 rather than thyroid stimulating hormone or mean free thyroxine was significantly related with development (adjusted hazard ratio [HR], 1.01; 95% confidence interval [CI], 1.00 to 1.02; P=0.002) and resolution (adjusted HR, 0.97; 95% CI, 0.96 to 0.99; P=0.005) of SLD. Compared with low mean TT3 group, high mean TT3 group was positively associated with development of SLD (adjusted HR, 1.20; 95% CI, 1.05 to 1.38; P=0.008) and inversely associated with resolution of SLD (adjusted HR, 0.66; 95% CI, 0.51 to 0.85; P=0.001). The statistical significance remained for development (adjusted HR, 1.29; 95% CI, 1.10 to 1.51; P=0.001) and resolution (adjusted HR, 0.71; 95% CI, 0.54 to 0.94; P=0.018) of modified MASLD. Conclusion In Korean euthyroid adults, TT3 level was associated with development and resolution of either SLD or modified MASLD.

Background: Barriers to treatment with intravenous thrombolysis (IVT) for patients with acute ischemic stroke (AIS) in South Korea remain incompletely characterized. We analyze a nationwide prospective cohort to determine patient-level features associated with delayed presentation and non-treatment of potential IVT-eligible patients. Methods: We identified consecutive patients with AIS from 01/2011 to 08/2023 from a multicenter and prospective acute stroke registry in Korea. Patients were defined as IVT candidates if they presented within 4.5 hours from the last known well, had no lab evidence of coagulopathy, and had National Institute of Health Stroke Scale (NIHSS) ≥ 4. Multivariable generalized linear mixed regression models were used to investigate the associations between their characteristics and the IVT candidates or the use of IVT among the candidates. Results: Among 84,103 AIS patients, 41.0% were female, with a mean age of 69 ± 13 years and presentation NIHSS of 4 [interquartile range, 1–8]. Out of these patients, 13,757 (16.4%) were eligible for IVT, of whom 8,179 (59.5%) received IVT. Female sex (adjusted risk ratio [RR], 0.90; 95% confidence interval [CI], 0.86–0.94) and lower years of education (adjusted RR, 0.90; 95% CI, 0.84–0.97 for 0–3 years, compared to ≥ 13 years) were associated with a decreased likelihood of presenting as eligible for IVT after AIS; meanwhile, young age (adjusted RR, 1.12; 95% CI, 1.01–1.24 for ≤ 44 years, compared to 75–84 years) was associated with an increased likelihood of being an IVT candidate. Among those who were eligible for IVT, only age was significantly associated with the use of IVT (adjusted RR, 1.09; 95% CI, 1.03–1.16 for age 65–74 and adjusted RR, 0.83; 95% CI, 0.76–0.90 for ≥ 85 years, respectively). Conclusion Most patients with AIS present outside IVT eligibility in South Korea, and only 60% of eligible patients were ultimately treated. We identified increased age, female sex and lower education as key features on which to focus interventions for improving IVT utilization.

Background: Prevention of contrast-induced nephropathy (CIN) is crucial in acute myocardial infarction (AMI) patients undergoing coronary interventions. Previous studies suggest that high-dose statins may aid in CIN prevention, yet comparative studies among different statin types using cystatin C (cysC) as a biomarker for CIN are absent. This study evaluated the effectiveness of high-dose rosuvastatin versus atorvastatin in preventing cysC-based CIN (cysC-CIN) in AMI patients. Methods: This multicenter registry included 431 patients (rosuvastatin 20 mg: n = 231, atorvastatin 40 mg: n = 200). The primary endpoint was cysC-CIN incidence within 48 hours post contrast; the secondary endpoints were creatinine-based CIN (cr-CIN) incidence within 72 hours post contrast and post 30 days adverse events. Results: The incidences of cysC-CIN (12.1% vs. 7.5%, P = 0.103) and cr-CIN (6.2% vs. 3.5%, P = 0.103) were higher in the atorvastatin group without significant statistical differences.Multivariable regression analysis, which was adjusted for CIN risk factors and the variables with univariate association, showed no increased odds ratio (OR) (OR, 2.185; 95% confidence interval [CI], 0.899, 5.315; P = 0.085) for cysC-CIN in the atorvastatin group compared to the rosuvastatin group. However, statin-naïve atorvastatin subgroup had significantly increased odds of cysC-CIN compared to the rosuvastatin group (OR, 2.977; 95% CI, 1.057, 8.378; P = 0.039). At post 30 days renal, cardiovascular, and mortality event rates were both low and similar between the two groups. Conclusion No significant difference in cysC-CIN incidence was found between the highdose rosuvastatin and atorvastatin groups in AMI patients and cysC was more sensitive to the early detection of CIN than creatinine.

The femoral artery is the preferred access route for neurointerventions. The transfemoral approach (TFA) offers advantages such as a large diameter and easy access. However, it also entails disadvantages such as patient discomfort and high risk of complications. Following the initial report of coronary angiography using the transradial approach (TRA) in 1989, cardiologists discovered the advantages of TRA over the TFA and gradually replaced it with the TRA. In 1997, Matsumoto et al. used the TRA for cerebral angiography and neurointervention. Thereafter, the adoption of TRA for neurointervention gradually increased and good outcomes were reported. However, despite these developments, the adoption rate of TRA is relatively low. We reviewed the relevant studies to increase the accessibility of TRA for neurointerventionists.

Portal vein preparation for inflow anastomosis is a critical step in liver transplantation. Although portal vein thrombosis is well documented and classified according to Yerdel grading, calcification or sclerosis of the portal vein is rarely reported. Segmental or diffuse calcification of portal vein compromises its structural integrity and may even result in flow obstruction, rendering it unsuitable for reconstruction and necessitating alternative inflow strategies. This case report describes a patient with portal vein tearing around chronic calcification who underwent successful living donor liver transplantation using a cryopreserved iliac vein graft anastomosed to gastric varices.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.