Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model. Materials and Methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital’s electronic health record from South Korea; IQVIA’s United Kingdom (UK) database for general practitioners; and IQVIA’s United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea. Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%–62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34–2.07 (Korea), 0.13–0.30 (US); hypoparathyroidism, 0.40–1.20 (Korea), 0.59–1.01 (US), 0.00–1.78 (UK); and pheochromocytoma/paraganglioma, 0.95–1.67 (Korea), 0.35–0.77 (US), 0.00–0.49 (UK). Conclusion Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Ancient DNA (aDNA) analysis has developed rapidly since it first emerged in the 1980s, becoming an almost indispensable tool in anthropological and archaeological sciences. Earlier aDNA study was based on the polymerase chain reaction (PCR) technique, with which, unfortunately, modern DNA contamination and other authenticity issues were often incurred. These technical hurdles were soon overcome by application of advancements in the forms of the next generation sequencing (NGS) technique and others. However, since NGS requires money, time, and, in the case of large projects, manpower as well, genetic analysis of some ancient samples considered to be insignificant is commonly delayed or, in the worst cases, neglected entirely. We acknowledge that as a diagnostic tool in aDNA analysis, PCR is less accurate than NGS and more easily affected by modern DNA contamination; but it also has advantages, such as simplicity, time-saving, and greater ease of interpretation, among others. The role of PCR in aDNA analysis, then, should be reconsidered.

Ancient DNA (aDNA) analysis has developed rapidly since it first emerged in the 1980s, becoming an almost indispensable tool in anthropological and archaeological sciences. Earlier aDNA study was based on the polymerase chain reaction (PCR) technique, with which, unfortunately, modern DNA contamination and other authenticity issues were often incurred. These technical hurdles were soon overcome by application of advancements in the forms of the next generation sequencing (NGS) technique and others. However, since NGS requires money, time, and, in the case of large projects, manpower as well, genetic analysis of some ancient samples considered to be insignificant is commonly delayed or, in the worst cases, neglected entirely. We acknowledge that as a diagnostic tool in aDNA analysis, PCR is less accurate than NGS and more easily affected by modern DNA contamination; but it also has advantages, such as simplicity, time-saving, and greater ease of interpretation, among others. The role of PCR in aDNA analysis, then, should be reconsidered.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that lacks hormone receptor and Her2 (ERBB2) expression, leaving chemotherapy as the only treatment option. The urgent need for targeted therapy for TNBC patients has led to the investigation of small interfering RNAs (siRNAs), which can target genes in a sequence-specific manner, unlike other drugs. However, the clinical translation of siRNAs has been hindered by the lack of an effective delivery system, except in the case of liver diseases. The MYC oncogene is commonly overexpressed in TNBC compared to other breast cancer subtypes. In this study, we used siRNA to target MYC in MDA-MB-231, MDA-MB-157, MDA-MB-436 and Hs-578T cells. We designed various symmetric and asymmetric (asiRNAs), screened them for in vitro efficacy, modified them for enhanced nuclease resistance and reduced off-target effects, and conjugated them with cholesterol (ChoL) and docosanoic acid (DCA) as a delivery system. DCA was conjugated to the 3’ end of asiRNA by a cleavable phosphodiester linker for in vivo delivery. Our findings demonstrated that asiRNA-VP and Mod_asiRNA10-6 efficiently downregulated MYC and its downstream targets, including RRM2, RAD51 and PARP1. Moreover, in a tumor xenograft model, asiRNA-VP-DCA effectively knocked down MYC mRNA and protein expression. Remarkably, durable knockdown persisted for at least 46 days postdosing in mouse tumor xenografts, with no visible signs of toxicity, underscoring the safety of DCA-conjugated asiRNAs. In conclusion, this study developed novel asiRNAs, design platforms, validated modification patterns, and in vivo, delivery systems specifically targeting MYC in TNBC.

Purpose: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening in patients with atypical squamous cells of undetermined significance (ASCUS)/low-grade squamous intraepithelial lesion (LSIL). Materials and Methods: Between 2010 and 2021, we monitored 1,273 HPV-positive women with ASCUS/LSIL every 6 months for up to 60 months. HPV infections were categorized as persistent (HPV positivity consistently observed post-enrollment), negative (HPV negativity consistently observed post-enrollment), or non-persistent (neither consistently positive nor negative). HPV genotypes were grouped into high-risk (Hr) groups 1 (types 16, 18, 31, 33, 45, 52, and 58) and 2 (types 35, 39, 51, 56, 59, 66, and 68) and a low-risk group. Hr1 was subdivided into types (a) 16 and 18; (b) 31, 33, and 45; and (c) 52 and 58. Cox regression and machine learning (ML) algorithms were used to analyze progression rates. Results: Among 1,273 participants, 17.6% with persistent HPV infections experienced disease progression versus no progression in the HPV-negative group (p < 0.001). Cox analysis revealed the highest hazard ratios (HRs) for Hr1-a (11.6, p < 0.001), followed by Hr1-b (9.26, p < 0.001) and Hr1-c (7.21, p < 0.001). HRs peaked at 12-24 months, with Hr1-a maintaining significance at 24-36 months (10.7, p=0.034). ML analysis identified the final cytology change pattern as the most significant factor, with 14-15 months the optimal time for detecting progression from the first examination. Conclusion In ASCUS/LSIL cases, follow-up strategies should be based on HPV risk types. Annual follow-up was the most effective monitoring for detecting progression/regression.

Purpose: Cystic duct cancers (CDCs) have been classified as extrahepatic bile duct cancers or gallbladder cancers (GBCs); however, it is unclear whether their clinical behavior is similar to that of distal extrahepatic bile duct cancers (DBDCs) or GBCs. Materials and Methods: T category of the CDCs was classified using current T category scheme of the GBCs and DBDCs, and clinicopathological factors were compared among 38 CDCs, 345 GBCs, and 349 DBDCs. We modified Nakata’s classifications (type 1, confined within cystic duct [CD]; combined types 2-4, extension beyond CD) and compared them. Results: No significant overall survival (OS) difference was observed between the patients with CDC, GBC, and DBDC. The T category of GBC staging was more accurate at distinguishing OS in patients with CDC than the DBDC staging. Patients with T3 CDC and GBC showed a significant OS difference when using the T category for GBC staging, while those with T1-T2 CDC and GBC showed no significant difference. In contrast, the T category of DBDC staging did not show any significant OS difference between patients with T1-T2 CDC and DBDC or T3 CDC and DBDC. Patients with type 1 CDC had significantly better OS than those with combined types. Conclusion Unlike GBCs and DBDCs, CDCs exhibit distinct clinicopathological characteristics. The OS is better when the CDC confines within the CD, compared to when it extends beyond it. Therefore, we propose a new T category scheme (T1, confined to CD; T2, invaded beyond CD) for better classifying CDCs.

Purpose: This multicenter, open-label, phase II trial evaluated the efficacy and safety of bortezomib combined with dexamethasone for the treatment of relapsed/refractory cutaneous T-cell lymphoma (CTCL) in previously treated patients across 14 institutions in South Korea. Materials and Methods: Between September 2017 and July 2020, 29 patients with histologically confirmed CTCL received treatment, consisting of eight 4-week cycles of induction therapy followed by maintenance therapy, contingent upon response, for up to one year. The primary endpoint was the proportion of patients achieving an objective global response. Results: Thirteen of the 29 patients (44.8%) achieved an objective global response, including two complete responses. The median progression-free survival (PFS) was 5.8 months, with responders showing a median PFS of 14.0 months. Treatment-emergent adverse events were generally mild, with a low incidence of peripheral neuropathy and hematologic toxicities. Despite the trend toward shorter PFS in patients with higher mutation burdens, genomic profiling before and after treatment showed no significant emergence of new mutations indicative of disease progression. Conclusion This study supports the use of bortezomib and dexamethasone as a viable and safe treatment option for previously treated CTCL, demonstrating substantial efficacy and manageability in adverse effects. Further research with a larger cohort is suggested to validate these findings and explore the prognostic value of mutation profiles.

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.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.