Background: The taxonomic status of house mice (Mus musculus) on the Korean Peninsula has long been debated due to conflicting morphological classifications and limited genetic evidence. Historically, three subspecies (M. m.molossinus, M. m. utsuryonis, and M. m. yamashinai) have been proposed based on external traits, although the validity of these proposals remains uncertain. Thus, this study aimed to integrate genetic and morphological analyses to clarify the phylogenetic relationships of Korean mice relative to the well-known primary M. musculus subspecies and evaluate the taxonomic distinctiveness. Results: Genetic analysis of mitochondrial DNA (cytb gene) from mice across Korea, including islands, mountains, and agricultural fields, confirmed that these mice belong to the Eurasian M. m. musculus lineage. Morphologically, Korean mice exhibited tail ratios consistent with previously assigned subspecies, suggesting these traits represent intraspecific variation within M. m. musculus. Craniometric analyses revealed distinctive features, such as a shorter, narrower premaxillary tooth-patch width and a longer maxillary tooth-row length, thereby distinguishing these mice from laboratory strains derived from M. m. domesticus. These cranial configurations, visualized via three-dimensional micro-computed tomography scans, further supported the morphological divergence of these mice from other subspecies. Conclusions Our findings indicate that Korean house mice belong to a single subspecific group within M. m.musculus, with observed morphological variations reflecting local adaptation rather than distinct taxonomic divisions.The Korean Peninsula likely served as an ecological bridge, facilitating the spatiotemporal diversification of M. m.musculus across East Eurasia. This study resolves longstanding taxonomic ambiguities and underscores the subspecific status of Korean house mice within M. m. musculus. These insights provide a foundation for understanding the biogeographic history of human commensal species and future biomedical research utilizing wild-derived mouse models.

Immunosenescence, an age-associated decline in immune function, is increasingly recognized as a central determinant of health and disease in older adults. Characterized by thymic involution, loss of naïve T cells, contraction of T cell receptor diversity, accumulation of senescent and exhausted lymphocytes, and a chronic inflammatory state known as inflammaging, immunosenescence compromises both innate and adaptive immune responses. Immunosenescence contributes to the pathogenesis of diverse age-related diseases. In autoimmune and metabolic diseases, premature accumulation of senescent T cells and impaired regulatory T cell function drive chronic inflammation and tissue damage, while in neurodegenerative diseases, microglial aging and sustained neuroinflammation exacerbate neuronal loss. These findings highlight immunosenescence as a unifying mechanism linking aging to systemic and organ-specific pathologies. Advances in biomarker discovery, including phenotypic markers, telomere attrition, and epigenetic signatures, have enabled the quantitative assessment of immune aging, while emerging therapeutic strategies, such as cytokine modulation, mTOR inhibition, senolytics, and epigenetic reprogramming, show promise in restoring immune competence. Here, we summarize recent research on immunosenescence in various diseases, particularly chronic inflammatory, metabolic, and neurodegenerative diseases, and suggest novel strategies for the development of senolytic drugs.

Background and Objectives: Primary prevention (PP) implantable cardioverter-defibrillator (ICD) therapy for ischemic cardiomyopathy (ICM) is underused in Asian countries, including South Korea. Both clinical and hospital factors may influence appropriate ICD use. We evaluated whether determinants of PP ICD implantation differ by hospital implantation volume. Methods: In this prospective, multicenter observational registry (blinded for review), patients eligible for PP ICD were enrolled. Factors associated with ICD implantation—clinical characteristics and hospital-level systems—were examined across 4 large-volume hospitals (≥ 15 implants during the study) and 12 small-volume hospitals (< 15). Multivariable logistic regression identified independent predictors. Results: Among 3,083 ICM patients (2,403 men; median age 70 years), PP ICD implantation rates were 10.8% in large-volume and 5.7% in small-volume hospitals. Across groups, male sex and chronic kidney disease independently predicted ICD implantation. Regarding hospital factors, non-monetary incentives for referral were the sole independent predictor in large-volume centers (odds ratio [OR], 3.55; 95% confidence interval [CI], 2.07–6.10;P < 0.001). In small-volume centers, heart failure conferences (OR, 12.73; 95% CI, 1.72–94.37;P = 0.013), structured education systems (OR, 11.72; 95% CI, 2.45–56.12; P = 0.02), and pacemaker clinics (OR, 11.4; 95% CI, 2.24–58.39; P = 0.003) were independently associated with implantation. Conclusions Clinical predictors of PP ICD use were consistent across hospital volumes, but hospital-level determinants differed. Referral incentives characterized large-volume centers, whereas conferences, education systems, and pacemaker clinics were key in smallvolume centers. Tailored institutional strategies by hospital volume may help close the PP ICD underuse gap and improve evidence-based implementation.

Olanzapine is a second-generation antipsychotic drug associated with the development of type 2 diabetes. However, the development and remission of diabetes with glutamic acid decarboxylase (GAD) antibody (Ab) positivity related to olanzapine treatment have not been reported. Here, we present the case of a 33-year-old man taking olanzapine who was diagnosed with diabetes with GAD Ab positivity complicated by diabetic ketoacidosis (DKA). After discontinuation of the drug and tight glycemic control, his diabetes remitted, and the GAD Ab became negative spontaneously after 18 months. The classification of diabetes is challenging because of overlapping autoimmune and metabolic features. Olanzapine may have contributed to pancreatic β-cell apoptosis, which could potentially trigger autoimmunity and the development of type 1 diabetes-like features (DKA as the initial presentation, a low C-peptide level, and GAD Ab positivity). The rapid amelioration of hyperglycemia and weight reduction may be responsible for the remission of diabetes after the discontinuation of olanzapine.

Purpose: Cancer poses a significant global health challenge, demanding precise genomic testing for individualized treatment strategies. Targeted-panel sequencing (TPS) has improved personalized oncology but often lacks comprehensive coverage of crucial cancer alterations. Whole-genome sequencing (WGS) addresses this gap, offering extensive genomic testing. This study demonstrates the medical potential of WGS. Materials and Methods: This study evaluates target-enhanced WGS (TE-WGS), a clinical-grade WGS method sequencing both cancer and matched normal tissues. Forty-nine patients with various solid cancer types underwent both TE-WGS and TruSight Oncology 500 (TSO500), one of the mainstream TPS approaches. Results: TE-WGS detected all variants reported by TSO500 (100%, 498/498). A high correlation in variant allele fractions was observed between TE-WGS and TSO500 (r=0.978). Notably, 223 variants (44.8%) within the common set were discerned exclusively by TE-WGS in peripheral blood, suggesting their germline origin. Conversely, the remaining subset of 275 variants (55.2%) were not detected in peripheral blood using the TE-WGS, signifying them as bona fide somatic variants. Further, TE-WGS provided accurate copy number profiles, fusion genes, microsatellite instability, and homologous recombination deficiency scores, which were essential for clinical decision-making. Conclusion TE-WGS is a comprehensive approach in personalized oncology, matching TSO500’s key biomarker detection capabilities. It uniquely identifies germline variants and genomic instability markers, offering additional clinical actions. Its adaptability and cost-effectiveness underscore its clinical utility, making TE-WGS a valuable tool in personalized cancer treatment.

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.

Vaccine-induced hypermetabolic lymph nodes have been clinically observed following coronavirus disease 2019 (COVID-19) mRNA vaccination. Specifically, the booster dose of the mRNA vaccines, produced by Pfizer and Moderna, has been linked to a relatively high incidence of lymphadenopathy. We present the case of a kidney transplant recipient who developed an enlarged abdominal mass after receiving a booster dose of the COVID-19 mRNA vaccine. This mass occupied the retroperitoneal space, infiltrated the psoas muscle, and resulted in ureteric compression and hydronephrosis. Percutaneous drainage and analysis of the perirenal fluid revealed the presence of lymphatic fluid. In summary, lymphadenopathy is a recognized adverse reaction to the Pfizer and Moderna vaccines. Patients with compromised immune systems should be informed about the incidence and potential severity of lymphadenopathy following booster vaccination.

Although oxidative stress is the main pathophysiology of the development of diabetic neuropathy, oral administration of antioxidants has given disappointing results. Here, we hypothesized that local delivery of antioxidants would provide protective effects on Schwann cells due to the high concentration of local lesions. We prepared alpha-tocopherol (ATF)-loaded liposomes and tested their skin penetration after sonication. An in vitro study using IMS-32 cells was conducted to determine the level of reactive oxygen species (ROS) scavenging effects of ATF-liposomes. ATF reduced ROS in high-glucose-exposed IMS-32 cells in a dosedependent manner. ATF-liposomes also reduced the ROS level in vitro and ultrasound irradiation enhanced delivery to the dermis in porcine ear skin. This study showed that it is feasible to deliver ATF through the skin and can effectively reduce ROS. This model is worthy of development for clinical use.

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.