Hypertension is one of the leading chronic diseases globally and a major contributor to cardiovascular morbidity and mortality. Despite advances in pharmacological therapy, medication alone remains limited in achieving optimal control. This review synthesizes recent hypertension management guidelines, including those from the European Society of Cardiology (ESC, 2024), American Heart Association/American College of Cardiology (AHA/ACC, 2025), Taiwan Society of Cardiology/Hypertension Society (2022), and Korean Society of Hypertension (KSH, 2018). All guidelines consistently emphasize sodium restriction, weight reduction, regular exercise, moderation of alcohol intake, smoking cessation, and adoption of healthy dietary patterns such as the Dietary Approaches to Stop Hypertension, Mediterranean, or culturally adapted diets. The ESC 2024 guideline elevates lifestyle modification to Class I, Level A, specifying targets for sodium (<2 g/day) and potassium (≥3.5 g/day). The AHA/ACC 2025 guideline provides quantitative estimates, reporting approximately 1/1 mm Hg blood pressure reduction per kilogram of weight loss, and incorporates newer strategies such as glucagon-like peptide-1 receptor agonists and bariatric surgery when lifestyle measures alone are insufficient. Taiwan’s 2022 guideline frames recommendations under the S-ABCDE (sodium restriction, alcohol limitation, body weight reduction, cigarette cessation, diet adaptation, exercise adoption) mnemonic and uniquely includes genetic factors such as ALDH2 polymorphisms. The KSH 2018 guideline emphasizes salt restriction (<6 g/day), maintaining a body mass index <25 kg/m2, and adherence to traditional Korean diets. Lifestyle modification remains the cornerstone of hypertension prevention and management, particularly in primary care. Future directions should focus on integrating these approaches with pharmacotherapy, digital health strategies, and personalized prescriptions.

This study aimed to provide a comprehensive understanding of aging with disability among polio survivors who continue to live with long-term sequelae. Although poliomyelitis has been eradicated in most regions, survivors entering older age face a dual challenge, as age-related decline overlaps with pre-existing impairments, creating a need for integrated management strategies. This narrative review examined the epidemiology, clinical manifestations, and late effects of polio, with particular attention to post-polio syndrome, secondary musculoskeletal disorders, and other systemic conditions. International and Korean studies were compared to highlight similarities and contextual differences. Polio survivors frequently experience accelerated functional decline due to post-polio syndrome, fatigue, pain, musculoskeletal disorders (e.g., arthritis, osteoporosis, fractures), and cardiopulmonary dysfunction. Approximately 64% report major falls, with 35% sustaining fractures, often at vulnerable sites such as the hip or distal femur. Psychological distress, sleep disturbances, metabolic syndrome, and cardiovascular disease are also prevalent, further compounding frailty. In Korea, where most survivors are now over 60 years of age, epidemiological patterns differ from those of Western cohorts; however, systematic investigations remain limited. Polio survivors exemplify the dual burden of aging and long-term disability, underscoring the need to move beyond fragmented, symptom-focused care toward integrated, life course–oriented approaches. Anticipating and managing late effects, strengthening preventive strategies, and ensuring equitable healthcare access are essential for maintaining function, independence, and quality of life. Lessons drawn from polio survivors offer valuable insights for understanding aging with disability more broadly.

Fragility fractures, particularly hip fractures, represent a major public health concern among older adults and are associated with high morbidity, mortality, functional decline, and socioeconomic burden. Cognitive impairment is common in older adults with hip fractures and contributes to increased fracture risk, poor postoperative outcomes, delayed recovery, and higher rates of institutionalization. This review aimed to examine rehabilitation strategies for older adults with hip fractures, with a specific focus on considerations for those with cognitive impairment. Evidence suggests that individuals with mild-to-moderate cognitive impairment can achieve meaningful functional gains through structured, intensive, multidisciplinary rehabilitation programs incorporating progressive resistance training, balance and mobility exercises, and individualized approaches tailored to cognitive and physical abilities. However, the implementation of such programs is often hindered by insufficient staff training and awareness in dementia-specific rehabilitation, limited resources, and the lack of standardized protocols defining eligibility, intensity, and adaptation. Optimizing outcomes requires structured, tailored rehabilitation protocols, enhanced staff education, interprofessional collaboration, and proactive management of delirium and secondary fracture prevention through fracture liaison services, while concurrently addressing systemic barriers such as resource constraints. Integrated, coordinated care across the continuum is essential to maximize recovery, independence, and quality of life in older adults with hip fractures and cognitive impairment.

Pediatric anesthesia presents unique challenges due to children’s distinct physiological and anatomical characteristics, including variations in drug metabolism, airway structure, and respiratory and circulatory regulation. Despite significant advances in patient safety that have reduced anesthesia-related mortality over recent decades, the declining pediatric population has made specialized training and clinical practice increasingly difficult. This narrative review addresses practical aspects of pediatric anesthesia, emphasizing patient monitoring, airway management, and recent clinical advances. Oxygen supply targets in children require careful titration to ensure adequate tissue oxygenation while avoiding oxygen toxicity and its associated complications, such as bronchopulmonary dysplasia and retinopathy of prematurity. Quantitative monitoring of neuromuscular blockade, such as with train-of-four stimulation, is essential to prevent postoperative respiratory complications. Temperature monitoring is equally critical in pediatric surgery because children and neonates are highly susceptible to intraoperative hypothermia. Airway management in infants and young children is complicated by anatomical differences, and while video laryngoscopy offers advantages, evidence for its benefits in neonates remains inconclusive. Extubation strategies must be individualized, taking into account risks such as laryngospasm and airway obstruction, as both deep and awake extubation have demonstrated comparable safety profiles. Emerging modalities, such as transfontanelle ultrasonography, provide real-time cerebral blood flow assessment and enhance perioperative brain monitoring. Regional anesthesia techniques in neonates and infants reduce exposure to general anesthetics and facilitate faster recovery but require meticulous technique and monitoring to ensure safety. Multidisciplinary collaboration and effective communication with parents are essential to achieving optimal outcomes.

Globally, rapid population aging—particularly in Korea—has extended life expectancy but not proportionally extended healthy life expectancy, resulting in longer periods of illness or disability and a higher demand for complex medical and social care. Therefore, prolonging healthy life and improving health-related quality of life have become primary objectives in geriatric medicine and rehabilitation. Geriatric rehabilitation is a critical intervention aimed at optimizing the functioning of older adults and pre-morbidly frail individuals who have lost independence due to acute illness or injury. For many older patients, the goal shifts from complete recovery to achieving a new equilibrium, maximizing autonomy despite greater dependency. Geriatric rehabilitation also targets key geriatric syndromes such as frailty, recognizing it as a dynamic and potentially reversible state that provides a crucial “time window” for intervention. This review summarizes the core principles and structural elements essential for geriatric rehabilitation, emphasizing the implementation challenges within the Korean healthcare system. Unlike the European consensus, which supports structured inpatient and outpatient services with seamless transitions of care guided by Comprehensive Geriatric Assessment, the Korean healthcare system remains fragmented and heavily centered on acute hospitals. This highlights the urgent need for a systematic model to integrate care facilities and strengthen interprofessional collaboration to support community-based “aging in place.” Effective geriatric rehabilitation requires multidisciplinary teams and multifaceted approaches to optimize quality of life, social participation, and independent living. Despite its importance, substantial awareness gaps and policy barriers persist, underscoring an urgent call to action.

Diabetes mellitus is a complex chronic disease with a rapidly increasing global prevalence. For this condition, non-pharmacological lifestyle modification is as important as pharmacological treatment. This review aims to comprehensively examine lifestyle prescriptions for diabetes across multiple domains to integrate current insights and understanding. In medical nutrition therapy, which is central to diabetes treatment and management, excessive carbohydrate intake should be restricted, while individualized consumption of high-quality carbohydrates, protein, and unsaturated fatty acids is recommended. Intake of added sugars and sodium should also be limited. Physical activity should similarly be tailored to the individual, with a combination of aerobic exercise and resistance training recommended. Careful consideration of hypoglycemia risk and diabetes complications is essential. Additional strategies include limitations on uninterrupted sedentary time to less than 30 minutes, maintenance of a healthy body weight, smoking cessation, alcohol abstinence, sleep health improvements, and attention to psychosocial care. In primary care settings, patient-specific assessment, multidisciplinary lifestyle prescriptions, and education to support behavior modification are expected to play a pivotal role in the treatment and management of diabetes.

South Korea is experiencing a rapid demographic transition, with the proportion of older adults projected to exceed 20% by 2025. This unprecedented pace has intensified the demand for healthcare and social support, creating complex challenges in the management of multimorbidity, frailty, and functional dependency. Historically, Korea has relied on a rigid, provider-centered model, with healthcare financed through National Health Insurance and long-term care through long-term care insurance. Although these systems expanded service availability, they also entrenched fragmentation between long-term care hospitals and nursing homes. Recent reforms mark a paradigm shift toward person-centered, integrated care. The Community Care pilot programs (2019–2022) and the Integrated Community Care Support Act (2024) introduced coordinated models that link healthcare, housing, and social services under local government leadership. Evidence from domestic and international studies underscores the risks of prolonged institutionalization and highlights the benefits of integrated approaches, including reduced hospitalizations, improved functional independence, and higher satisfaction among older adults and their families. At the same time, experiences from Korea and Japan suggest that institutional care remains indispensable for individuals with high medical needs or at the end of life, emphasizing the need for balanced strategies. Successful implementation of the 2026 reforms will require redefining the role of institutions, expanding community-based alternatives, developing a professional care manager workforce, achieving interoperability of data systems, and undertaking financing reforms to align incentives. Beyond structural change, embedding a cultural ethos that values dignity, autonomy, and personhood will be essential. Korea’s evolving model not only responds to urgent demographic challenges but also offers lessons for other aging societies.

Generative artificial intelligence (GenAI), including large language models such as GPT-4 and image-generation tools like DALL-E, is rapidly transforming the landscape of medical education. These technologies present promising opportunities for advancing personalized learning, clinical simulation, assessment, curriculum development, and academic writing. Medical schools have begun incorporating GenAI tools to support students’ self-directed study, design virtual patient encounters, automate formative feedback, and streamline content creation. Preliminary evidence suggests improvements in engagement, efficiency, and scalability. However, GenAI integration also introduces substantial challenges. Key concerns include hallucinated or inaccurate content, bias and inequity in artificial intelligence (AI)-generated materials, ethical issues related to plagiarism and authorship, risks to academic integrity, and the potential erosion of empathy and humanistic values in training. Furthermore, most institutions currently lack formal policies, structured training, and clear guidelines for responsible GenAI use. To realize the full potential of GenAI in medical education, educators must adopt a balanced approach that prioritizes accuracy, equity, transparency, and human oversight. Faculty development, AI literacy among learners, ethical frameworks, and investment in infrastructure are essential for sustainable adoption. As the role of AI in medicine expands, medical education must evolve in parallel to prepare future physicians who are not only skilled users of advanced technologies but also compassionate, reflective practitioners.

Perioperative pain management has shifted from standardized, procedure-based protocols toward individualized, patient-centered approaches. Inadequate pain control can result in short-term adverse outcomes, including delayed ambulation, prolonged hospitalization, and increased complications, as well as long-term sequelae such as chronic persistent postsurgical pain. Early models of preemptive and preventive analgesia emphasized pain relief primarily through the use of opioids. Growing concern about opioid-related adverse effects established the basis for multimodal and opioid-sparing strategies. Nevertheless, with the onset of the global opioid crisis, heightened awareness of the risks of opioid overuse has fueled interest in opioid-free techniques. However, evidence does not demonstrate that opioid-free methods are superior to opioid-sparing approaches. This underscores the importance of returning to the central goals of enhanced recovery after surgery: early restoration of function and reduction of complications. Within this framework, personalized pain management has emerged as a practical paradigm that tailors interventions to individual characteristics, including comorbidities, psychological status, pain sensitivity, and recovery objectives. This review outlines the rationale, current practices, and future directions of personalized perioperative pain management and proposes a framework for integrating new strategies into clinical care.

The integration of regional anesthesia (RA) with general anesthesia (GA) has become a central component of multimodal strategies to improve perioperative pain management. This approach not only enhances analgesic efficacy but also reduces opioid requirements and mitigates opioid-related adverse effects. By targeting peripheral or neuraxial nociceptive pathways, RA attenuates the surgical stress response and decreases central sensitization, complementing the systemic actions of GA. The combined application of RA and GA has shown substantial benefits across a wide range of surgical procedures, including abdominal, thoracic, orthopedic, and pediatric operations. Reported advantages include improved hemodynamic stability, enhanced pulmonary function, earlier ambulation, faster gastrointestinal recovery, and greater patient satisfaction. Moreover, recent evidence indicates a positive association between effective postoperative pain control and long-term outcomes, such as reduced incidence of persistent postsurgical pain, better functional independence, and even improved immune function and survival following cancer surgery. The development of sustained-release local anesthetic delivery systems, which provide localized and prolonged analgesia, further extends the benefits of RA-GA integration into the postoperative period. This review summarizes the mechanistic rationale, clinical applications, and future directions of RA-GA combinations in modern surgical care, with special emphasis on their role in enhanced recovery after surgery protocols.

Non‑operative management, particularly the watch and wait (WW) strategy, has emerged as an alternative to total mesorectal excision for selected patients with locally advanced rectal cancer who achieve a clinical complete response (cCR) after neoadjuvant treatment. This narrative review examines oncologic outcomes, functional and quality‑of‑life benefits, diagnostic challenges, and surveillance requirements associated with WW compared to radical surgery. Evidence from randomized trials and international registries indicates that WW provides overall and disease-free survival rates comparable to those of surgery, provided that stringent selection criteria and intensive surveillance are maintained for 3 to 5 years. Local regrowth occurs in 15%–40% of patients—most commonly within 24 months—but salvage surgery is curative in over 90% of cases and restores oncologic equivalence. Nevertheless, distant metastasis is more frequent in patients who experience regrowth, underscoring the importance of early detection and the need for optimized systemic therapy. Accurate determination of cCR remains the primary limitation; digital rectal examination, high‑resolution magnetic resonance imaging, and endoscopy, even when combined, cannot reliably exclude microscopic residual disease. Total neoadjuvant therapy increases cCR rates to 30%–60% and expands the pool of WW candidates, but also intensifies the need for standardized response definitions and surveillance algorithms. WW offers organ preservation and quality‑of‑life improvements without compromising survival in carefully selected patients, provided that multidisciplinary teams ensure rigorous response assessment and lifelong monitoring. Future advances in imaging, molecular biomarkers, and individualized risk stratification are expected to further enhance the safety of WW and expand eligibility to a broader patient population.

Purpose
This study aimed to describe mortality trends in the Republic of Korea in 2022 by analyzing total deaths, crude and age-standardized mortality rates, as well as age- and sex-specific patterns and changes in cause-specific mortality. The analysis updates previous reports with newly available data from 2022.
Methods
A repeated cross-sectional analysis was performed using nationwide death certificate data collected through municipal administrative offices. Deaths occurring in 2022 were aggregated from reports filed over a 16-month period, spanning January 2022 to April 2023. Causes of death were classified according to the World Health Organization’s International Classification of Diseases. Quality assurance was ensured through administrative record linkage across 22 databases and validation using an independent infant mortality survey. Descriptive statistics were employed to summarize the findings.
Results
In 2022, Korea recorded 372,939 deaths (the highest annual total since 1983), corresponding to a crude death rate of 727.6 per 100,000 population. This increase contributed to a net population decline of 123,751. Mortality rates rose across most age groups, with particularly marked increases among those aged 1–9 and those aged 80 or older. Coronavirus disease 2019 (COVID-19) became the third leading cause of death (31,280 deaths; 61.0 per 100,000), driven largely by the Omicron variant and heightened infection rates among older adults. Pancreatic cancer overtook stomach cancer in the mortality rankings. There were sharp increases in deaths attributed to Alzheimer’s disease and diabetes. Although deaths from intentional self-harm declined, suicide remained a significant cause of death among younger individuals.
Conclusion
Korea experienced a record-high mortality rate in 2022, largely due to the impacts of COVID-19 and ongoing population aging. Notable shifts in cause-specific mortality were observed, including increases in deaths from Alzheimer’s disease, diabetes, and pancreatic cancer, underscoring evolving public health challenges.

Purpose
This study evaluated the feasibility and performance of a deep learning approach utilizing the Korean Medical BERT (KM-BERT) model for the automated classification of underlying causes of death within national mortality statistics. It aimed to assess predictive accuracy throughout the cause-of-death coding workflow and to identify limitations and opportunities for further artificial intelligence (AI) integration.
Methods
We performed a retrospective prediction study using 693,587 death certificates issued in Korea between January 2021 and December 2022. Free-text fields for immediate, antecedent, and contributory causes were concatenated and fine-tuned with KM-BERT. Three classification models were developed: (1) final underlying cause prediction (International Classification of Diseases, 10th Revision [ICD-10] code) from certificate inputs, (2) tentative underlying cause selection based on ICD-10 Volume 2 rules, and (3) classification of individual cause-of-death entries. Models were trained and validated using 2021 data (80% training, 20% validation) and evaluated on 2022 data. Performance metrics included overall accuracy, weighted F1 score, and macro F1 score.
Results
On 306,898 certificates from 2022, the final cause model achieved 62.65% accuracy (F1-weighted, 0.5940; F1-macro, 0.1503). The tentative cause model demonstrated 95.35% accuracy (F1-weighted, 0.9516; F1-macro, 0.4996). The individual entry model yielded 79.51% accuracy (F1-weighted, 0.7741; F1-macro, 0.9250). Error analysis indicated reduced reliability for rare diseases and for specific ICD chapters, which require supplementary administrative data.
Conclusion
Despite strong performance in mapping free-text inputs and selecting tentative underlying causes, there remains a need for improved data quality, administrative record integration, and model refinement. A systematic, long-term approach is essential for the broad adoption of AI in mortality statistics.

Purpose
This study aimed to assess the spatiotemporal associations between air pollution and emergency room visits for cardiovascular and cerebrovascular diseases in South Korea using a graph autoencoder (GAE). A multivariate graph-based approach was used to uncover seasonal and regional variations in pollutant–disease relationships.
Methods
We collected monthly data from 2022 to 2023, including concentrations of 6 air pollutants (SO2, NO2, O3, CO, PM10, and PM2.5) and emergency room visits for 4 disease types: cardiac arrest, myocardial infarction, ischemic stroke, and hemorrhagic stroke. Pearson correlation coefficients were used to construct adjacency matrices, which, along with normalized feature matrices, were used as inputs to the GAE. The model was trained separately for each month and region to estimate the strength of pollutant–disease associations.
Results
The pollutant–disease network structures exhibited clear seasonal variations. In winter, strong associations were observed between O3, NO2, and all disease outcomes. In spring, PM2.5 and PM10 were strongly linked to cardiac and stroke-related visits. These connections weakened during summer but became more pronounced in autumn, especially for NO2 and cardiac arrest. Urban areas displayed denser and stronger associations than non-urban areas.
Conclusion
Our findings underscore the necessity for season- and region-specific air quality management strategies. In winter, focused control of O3 and NO2 is needed in urban areas, while in spring, PM mitigation is required in urban and selected rural regions. Autumn NO2 control may be especially beneficial in non-urban areas. Spatiotemporally tailored interventions could reduce the burden of air pollution-related emergency room visits.

Purpose
This study developed and validated a deep learning model for the automated early detection of androgenetic alopecia (AGA) using trichoscopic images, and evaluated the model’s diagnostic performance in a Korean clinical cohort.
Methods
We conducted a retrospective observational study using 318 trichoscopic scalp images labeled by board-certified dermatologists according to the Basic and Specific (BASP) system, collected at Ewha Womans University Medical Center between July 2018 and January 2024. The images were categorized as BASP 0 (no hair loss) or BASP 1–3 (early-stage hair loss). A ResNet-18 convolutional neural network, pretrained on ImageNet, was fine-tuned for binary classification. Internal validation was performed using stratified 5-fold cross-validation, and external validation was conducted through ensemble soft voting on a separate hold-out test set of 20 images. Model performance was measured by accuracy, precision, recall, F1-score, and area under the curve (AUC), with 95% confidence intervals (CIs) calculated for hold-out accuracy.
Results
Internal validation revealed robust model performance, with 4 out of 5 folds achieving an accuracy above 0.90 and an AUC above 0.93. In external validation on the hold-out test set, the ensemble model achieved an accuracy of 0.90 (95% CI, 0.77–1.03) and an AUC of 0.97, with perfect recall for early-stage hair loss. No missing data were present, and the model demonstrated stable convergence without requiring data augmentation.
Conclusion
This model demonstrated high accuracy and generalizability for detecting early-stage AGA from trichoscopic images, supporting its potential utility as a screening tool in clinical and teledermatology settings.

Purpose
Internal ribosome entry site (IRES) elements, present in both viral and cellular messenger RNAs (mRNAs), facilitate cap-independent translation by recruiting ribosomes to internal regions of mRNA. This study aimed to investigate the impact of inserting G-quadruplex and hairpin structures into the 5' untranslated region (UTR) and poly(A) sequences on the translation efficiency of the encephalomyocarditis virus (EMCV) IRES, using an IRES-based RNA platform encoding OX40L, 4-1BBL, and GFP.
Methods
G-quadruplex and hairpin structures, derived from HIV-1 (human immunodeficiency virus type 1) or custom-designed, were synthesized and inserted into the 5' UTR and poly(A) tail regions of EMCV IRES vectors. These constructs were amplified by polymerase chain reaction, ligated into plasmids, and transcribed in vitro. B16 melanoma, TC-1 tumor, and HEK293 cells were transfected with these RNA constructs. Protein expression levels were assessed at 6, 12, and 24 hours post-transfection by flow cytometry and fluorescence microscopy. Statistical analyses employed one-way analysis of variance with the Dunnett test.
Results
The insertion of G-quadruplex and hairpin structures altered RNA secondary structure, significantly reducing protein expression. In the 5' UTR, the G-quadruplex nearly abolished OX40L expression (1.18%±0.41% at 6 hours vs. 18.23%±0.16% for control), while the hairpin structure reduced it (16.29%±1.46% vs. 22.84%±1.17%). In the poly(A) tail region, both structures decreased GFP expression across all cell lines (4.86%±1.35% to 7.27%±0.32% vs. 39.56%±2.07% in B16 cells).
Conclusion
Inserting G-quadruplex and hairpin structures into EMCV IRES UTRs inhibits translation efficiency, suggesting the need for precise RNA structure modeling to enhance IRES-mediated translation.

Purpose
This study compares 3 deep learning models (UNet, TransUNet, and MIST) for left atrium (LA) segmentation of cardiac computed tomography (CT) images from patients with congenital heart disease (CHD). It investigates how architectural variations in the MIST model, such as spatial squeeze-and-excitation attention, impact Dice score and HD95.
Methods
We analyzed 108 publicly available, de-identified CT volumes from the ImageCHD dataset. Volumes underwent resampling, intensity normalization, and data augmentation. UNet, TransUNet, and MIST models were trained using 80% of 97 cases, with the remaining 20% employed for validation. Eleven cases were reserved for testing. Performance was evaluated using the Dice score (measuring overlap accuracy) and HD95 (reflecting boundary accuracy). Statistical comparisons were performed via one-way repeated measures analysis of variance.
Results
MIST achieved the highest mean Dice score (0.74; 95% confidence interval, 0.67–0.81), significantly outperforming TransUNet (0.53; P<0.001) and UNet (0.49; P<0.001). Regarding HD95, TransUNet (9.09 mm) and MIST (5.77 mm) similarly outperformed UNet (27.49 mm; P<0.0001). In ablation experiments, the inclusion of spatial attention did not further enhance the MIST model’s performance, suggesting redundancy with existing attention mechanisms. However, the integration of multi-scale features and refined skip connections consistently improved segmentation accuracy and boundary delineation.
Conclusion
MIST demonstrated superior LA segmentation, highlighting the benefits of its integrated multi-scale features and optimized architecture. Nevertheless, its computational overhead complicates practical clinical deployment. Our findings underscore the value of advanced hybrid models in cardiac imaging, providing improved reliability for CHD evaluation. Future studies should balance segmentation accuracy with feasible clinical implementation.

Purpose
This study developed and evaluated a feature-based ensemble model integrating the synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) methods with a random forest approach to address class imbalance in machine learning for early diabetes detection, aiming to improve predictive performance.
Methods
Using the Scikit-learn diabetes dataset (442 samples, 10 features), we binarized the target variable (diabetes progression) at the 75th percentile and split it 80:20 using stratified sampling. The training set was balanced to a 1:2 minority-to-majority ratio via SMOTE (0.6) and RUS (0.66). A feature-based ensemble model was constructed by training random forest classifiers on 10 two-feature subsets, selected based on feature importance, and combining their outputs using soft voting. Performance was compared against 13 baseline models, using accuracy and area under the curve (AUC) as metrics on the imbalanced test set.
Results
The feature-based ensemble model and balanced random forest both achieved the highest accuracy (0.8764), followed by the fully connected neural network (0.8700). The ensemble model had an excellent AUC (0.9227), while k-nearest neighbors had the lowest accuracy (0.8427). Visualizations confirmed its superior discriminative ability, especially for the minority (high-risk) class, which is a critical factor in medical contexts.
Conclusion
Integrating SMOTE, RUS, and feature-based ensemble learning improved classification performance in imbalanced diabetes datasets by delivering robust accuracy and high recall for the minority class. This approach outperforms traditional resampling techniques and deep learning models, offering a scalable and interpretable solution for early diabetes prediction and potentially other medical applications.

Purpose
Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy.
Methods
ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers.
Results
CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches.
Conclusion
Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Purpose
This study aimed to leverage Shapley additive explanation (SHAP)-based feature engineering to predict appendix cancer. Traditional models often lack transparency, hindering clinical adoption. We propose a framework that integrates SHAP for feature selection, construction, and weighting to enhance accuracy and clinical relevance.
Methods
Data from the Kaggle Appendix Cancer Prediction dataset (260,000 samples, 21 features) were used in this prediction study conducted from January through March 2025, in accordance with TRIPOD-AI guidelines. Preprocessing involved label encoding, SMOTE (synthetic minority over-sampling technique) to address class imbalance, and an 80:20 train-test split. Baseline models (random forest, XGBoost, LightGBM) were compared; LightGBM was selected for its superior performance (accuracy=0.8794). SHAP analysis identified key features and guided 3 engineering steps: selection of the top 15 features, construction of interaction-based features (e.g., chronic severity), and feature weighting based on SHAP values. Performance was evaluated using accuracy, precision, recall, and F1-score.
Results
Four LightGBM model configurations were evaluated: baseline (accuracy=0.8794, F1-score=0.8691), feature selection (accuracy=0.8968, F1-score=0.8860), feature construction (accuracy=0.8980, F1-score=0.8872), and feature weighting (accuracy=0.8986, F1-score=0.8877). SHAP-based engineering yielded performance improvements, with feature weighting achieving the highest precision (0.9940). Key features (e.g., red blood cell count and chronic severity) contributed to predictions while maintaining interpretability.
Conclusion
The SHAP-based framework substantially improved the accuracy and transparency of appendix cancer predictions using LightGBM (F1-score=0.8877). This approach bridges the gap between predictive power and clinical interpretability, offering a scalable model for rare disease prediction. Future validation with real-world data is recommended to ensure generalizability.

Citations

• Uncovering Key Factors of Student Performance in Math: An Explainable Deep Learning Approach Using TIMSS 2019 Data
  Abdelamine Elouafi, Ilyas Tammouch, Souad Eddarouich, Raja Touahni
  Information.2025; 16(6): 480.     CrossRef
• Concurrent high-grade appendiceal mucinous neoplasm and adenocarcinoma: a unique case report and literature review
  Mohammed N AlAli, Jawad S Alnajjar, Mohamed S Essa, Arwa F Alrasheed, Ruba M Alzuhairi, Nouf A Alromaih, Sadiq M Amer, Mohammed Sbaih
  Journal of Surgical Case Reports.2025;[Epub]     CrossRef

This review examines the bidirectional relationship between periodontitis and systemic health conditions, offering an integrated perspective based on current evidence. It synthesizes epidemiological data, biological mechanisms, and clinical implications to support collaborative care strategies recognizing oral health as a key component of overall wellness. Periodontitis affects 7.4% to 11.2% of adults worldwide, and its prevalence increases with age. Beyond its local effects, including gingival inflammation, periodontal pocket formation, and alveolar bone loss, periodontitis is associated with various systemic conditions. Emerging evidence has established links with obesity, diabetes mellitus, cardiovascular disease, chronic kidney disease, inflammatory bowel disease, rheumatoid arthritis, respiratory diseases, adverse pregnancy outcomes, certain malignancies, neurodegenerative diseases, psychological disorders, and autoimmune conditions. These associations are mediated by 3 primary mechanisms: dysbiotic oral biofilms, chronic low-grade systemic inflammation, and the dissemination of periodontal pathogens throughout the body. The pathophysiology involves elevated levels of pro-inflammatory cytokines (including interleukin 6, tumor necrosis factor alpha, and C-reactive protein), impaired immune function, oxidative stress, and molecular mimicry. Periodontal pathogens, particularly Porphyromonas gingivalis, are crucial in initiating and sustaining systemic inflammatory responses. Treatment of periodontitis has demonstrated measurable improvements in numerous systemic conditions, emphasizing the clinical significance of these interconnections. Periodontitis should be understood as more than just a localized oral disease; it significantly contributes to the overall systemic inflammatory burden, with implications for general health. An integrated, multidisciplinary approach to prevention, early detection, and comprehensive treatment is vital for optimal patient outcomes. Healthcare providers should acknowledge oral health as an essential element of systemic well-being.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef
• The Correlations Between Diabetes Mellitus and Oro-Maxillofacial Disorders: A Statistical Perspective
  Ionut Catalin Botezatu, Mihaela Salceanu, Ana Emanuela Botez, Cristina Daniela Dimitriu, Oana Elena Ciurcanu, Claudiu Topoliceanu, Elena-Carmen Cotrutz, Maria-Alexandra Martu
  Dentistry Journal.2025; 13(8): 373.     CrossRef
• Association between periodontal disease and Alzheimer's disease: a scoping review
  Xiaocui Zhang, Xin Huang, Mengdie Chang
  Frontiers in Aging Neuroscience.2025;[Epub]     CrossRef

Purpose
This study aimed to identify the types of human rights violations and the associated psychological trauma experienced by North Korean defectors. It also examined the impact of trauma on the defectors’ interpersonal relationships, employment, and overall quality of life, while evaluating existing psychological support policies to suggest potential improvements.
Methods
A multidisciplinary research team conducted an observational survey and in-depth interviews with approximately 300 North Korean defectors residing in South Korea from June to September 2017. Standardized measurement tools, including the Post-Traumatic Stress Disorder (PTSD) Checklist (PCL-5), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Scale-7 (GAD-7), and Short Form-8 Health Survey (SF-8), were employed. Statistical analyses consisted of frequency analysis, cross-tabulation, factor analysis, and logistic regression.
Results
The findings revealed a high prevalence of human rights violations, such as public executions (82%), forced self-criticism (82.3%), and severe starvation or illness (62.7%). Additionally, there were elevated rates of PTSD (56%), severe depression (28.3%), anxiety (25%), and insomnia (23.3%). Defectors who resided in China before entering South Korea reported significantly worse mental health outcomes and a lower quality of life. Moreover, trauma was strongly and negatively correlated with social adjustment, interpersonal relationships, employment stability, and overall well-being.
Conclusion
An urgent revision of existing policies is needed to incorporate specialized, trauma-informed care infrastructures within medical institutions. Furthermore, broad societal education to reduce stigma and enhance integration efforts is essential to effectively support the psychological well-being and social integration of North Korean defectors.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef

Heart failure (HF) represents a significant global health burden characterized by high morbidity, mortality, and healthcare utilization. Traditional in-person care models face considerable limitations in providing continuous monitoring and timely interventions for HF patients. Telemedicine—defined as the remote delivery of healthcare via information and communication technologies—has emerged as a promising solution to these challenges. This review examines the evolution, current applications, clinical evidence, limitations, and future directions of telemedicine in HF management. Evidence from randomized controlled trials and meta-analyses indicates that telemedicine interventions can improve guideline-directed medical therapy implementation, reduce hospitalization rates, improve patient engagement, and potentially decrease mortality among HF patients. Remote monitoring systems that track vital signs, symptoms, and medication adherence allow for the early detection of clinical deterioration, enabling timely interventions before decompensation occurs. Despite these benefits, telemedicine implementation faces several barriers, including technological limitations, reimbursement issues, digital literacy gaps, and challenges in integrating workflows. Future directions include developing standardized guidelines, designing patient-centered technologies, and establishing hybrid care models that combine virtual and in-person approaches. As healthcare systems worldwide seek more efficient and effective strategies for managing the growing population of individuals with HF, telemedicine offers a solution that may significantly improve patient outcomes and quality of life.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef

Purpose
This study aimed to investigate whether proteins present in the molting membranes of third-stage (L3) Anisakis larvae could serve as potential risk factors for allergic reactions.
Methods
Third-stage larvae (L3) of Anisakis spp. were primarily collected from mackerels and cultured in vitro to yield both molting membranes and fourth-stage (L4) larvae. Major soluble proteins in the molting membranes were identified using SDS-PAGE (sodium dodecyl sulfate–polyacrylamide gel electrophoresis). Crude antigens extracted from L3, L4, and the molting membranes were subsequently evaluated by western blotting using sera from Anisakis-infected rabbits and patients with eosinophilia.
Results
Antigens derived from the molting membranes reacted with sera from Anisakis-infected rabbits as well as with sera from 7 patients with eosinophilia of unknown origin. These findings suggest that unidentified proteins in the molting membranes of Anisakis L3 may contribute to early allergic reactions, particularly in patients sensitized by specific molecular components.
Conclusion
Our results indicate that proteins present in the molting membranes of third-stage Anisakis spp. larvae may be associated with allergic responses. Further studies are required to confirm the correlation between these membranes and Anisakis-induced allergies.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef

Recent advancements in tuberculosis treatment research emphasize innovative strategies that enhance treatment efficacy, reduce adverse effects, and adhere to patient-centered care principles. As tuberculosis remains a significant global health challenge, integrating new and repurposed drugs presents promising avenues for more effective management, particularly against drug-resistant strains. Recently, the spectrum concept in tuberculosis infection and disease has emerged, underscoring the need for research aimed at developing treatment plans specific to each stage of the disease. The application of precision medicine to tailor treatments to individual patient profiles is crucial for addressing the diverse and complex nature of tuberculosis infections. Such personalized approaches are essential for optimizing therapeutic outcomes and improving patient adherence—both of which are vital for global tuberculosis eradication efforts. The role of tuberculosis cohort studies is also emphasized, as they provide critical data to support the development of these tailored treatment plans and deepen our understanding of disease progression and treatment response. To advance these innovations, a robust tuberculosis policy framework is required to foster the integration of research findings into practice, ensuring that treatment innovations are effectively translated into improved health outcomes worldwide.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef

Purpose
The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy.
Methods
We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software.
Results
In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis.
Conclusion
This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Purpose
This study aimed to analyze dementia-related death statistics in Korea between 2013 and 2023.
Methods
The analysis utilized microdata from Statistics Korea’s cause-of-death statistics. Among all recorded deaths, those related to dementia were extracted and analyzed using the underlying cause-of-death codes from the International Classification of Diseases, 10th revision.
Results
The number of dementia-related deaths increased from 8,688 in 2013 to 14,402 in 2023. The crude death rate rose from 17.2 per 100,000 in 2013 to 28.2 per 100,000 in 2023, although the age-standardized death rate declined from 9.7 to 8.7 over the same period. The dementia death rate is 2.1 times higher in women than in men, and mortality among individuals aged 85 and older exceeds 976 per 100,000. By specific cause, Alzheimer’s disease accounted for 77.1% of all dementia deaths, and by place, the majority occurred in hospitals (76.2%), followed by residential institutions including nursing homes (15.3%) in 2023.
Conclusion
The rising mortality associated with dementia, especially Alzheimer’s disease, highlights a growing public health concern in Korea. These findings support the need for enhanced prevention efforts, improved quality of care, and targeted policies addressing the complexities of dementia management. It is anticipated that this empirical analysis will contribute to reducing the social burden.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef

The Mycobacterium avium complex (MAC), comprising M. avium and M. intracellulare, constitutes the predominant cause of nontuberculous mycobacterial pulmonary disease (NTM-PD) in Korea, followed by the M. abscessus complex. Its global prevalence is increasing, as shown by a marked rise in Korea from 11.4 to 56.7 per 100,000 individuals between 2010 and 2021, surpassing the incidence of tuberculosis. Among the older adult population (aged ≥65 years), the prevalence escalated from 41.9 to 163.1 per 100,000, accounting for 47.6% of cases by 2021. Treatment should be individualized based on prognostic indicators, including cavitary disease, low body mass index, and positive sputum smears for acid-fast bacilli. Current therapeutic guidelines recommend a 3-drug regimen—consisting of a macrolide, rifampin, and ethambutol—administered for a minimum of 12 months following culture conversion. Nevertheless, treatment success rates are only roughly 60%, and over 30% of patients experience recurrence. This is often attributable to reinfection rather than relapse. Antimicrobial susceptibility testing for clarithromycin and amikacin is essential, as resistance significantly worsens prognosis. Ethambutol plays a crucial role in preventing the development of macrolide resistance, whereas the inclusion of rifampin remains a subject of ongoing debate. Emerging therapeutic strategies suggest daily dosing for milder cases, increased azithromycin dosing, and the substitution of rifampin with clofazimine in severe presentations. Surgical resection achieves a notable sputum conversion rate of approximately 93% in eligible candidates. For refractory MAC-PD, adjunctive therapy with amikacin is advised, coupled with strategies to reduce environmental exposure. Despite advancements in therapeutic approaches, patient outcomes remain suboptimal, highlighting the urgent need for novel interventions.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef

Lung cancer remains a leading cause of cancer-related mortality worldwide. Low-dose computed tomography (LDCT) screening has demonstrated efficacy in reducing lung cancer mortality by enabling early detection. In several countries, including Korea, LDCT-based screening for high-risk populations has been incorporated into national healthcare policies. However, in regions with a high tuberculosis (TB) burden, the effectiveness of LDCT screening for lung cancer may be influenced by TB-related pulmonary changes. Studies indicate that the screen-positive rate in TB-endemic areas differs from that in low-TB prevalence regions. A critical challenge is the differentiation between lung cancer lesions and TB-related abnormalities, which can contribute to false-positive findings and increase the likelihood of unnecessary invasive procedures. Additionally, structural lung damage from prior TB infections can alter LDCT interpretation, potentially reducing diagnostic accuracy. Nontuberculous mycobacterial infections further complicate this issue, as their radiologic features frequently overlap with those of TB and lung cancer, necessitating additional microbiologic confirmation. Future research incorporating artificial intelligence and biomarkers may enhance diagnostic precision and facilitate a more personalized approach to lung cancer screening in TB-endemic settings.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef

Chronic obstructive pulmonary disease (COPD) is a leading cause of respiratory morbidity and mortality, most often linked to smoking. However, growing evidence indicates that previous tuberculosis (TB) infection is also a critical risk factor for COPD. This review aimed at providing a comprehensive perspective on TB-COPD, covering its epidemiologic significance, pathogenesis, clinical characteristics, and current management approaches. Tuberculosis-associated chronic obstructive pulmonary disease (TB-COPD) is characterized by persistent inflammatory responses, altered immune pathways, and extensive structural lung damage—manifested as cavitation, fibrosis, and airway remodeling. Multiple epidemiologic studies have shown that individuals with a history of TB have a significantly higher likelihood of developing COPD and experiencing worse outcomes, such as increased breathlessness and frequent exacerbations. Key pathogenic mechanisms include elevated matrix metalloproteinase activity and excessive neutrophil-driven inflammation, which lead to alveolar destruction, fibrotic scarring, and the development of bronchiectasis. Treatment generally follows current COPD guidelines, advocating the use of long-acting bronchodilators and the selective application of inhaled corticosteroids. Studies have demonstrated that indacaterol significantly improves lung function and respiratory symptoms, while long-acting muscarinic antagonists have shown survival benefits.

Citations

• Recent advances in pulmonary tuberculosis, the application of deep learning to medical topics, and highlights from this issue of Ewha Medical Journal
  Hae-Sun Chung
  Ewha Medical Journal.2025; 48(2): e16.     CrossRef
• History of Pulmonary Tuberculosis Accelerates Early Onset and Severity of COPD: Evidence from a Multicenter Study in Romania
  Ramona Cioboata, Silviu Gabriel Vlasceanu, Denisa Maria Mitroi, Ovidiu Mircea Zlatian, Mara Amalia Balteanu, Gabriela Marina Andrei, Viorel Biciusca, Mihai Olteanu
  Journal of Clinical Medicine.2025; 14(17): 5980.     CrossRef