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.

The study aims to examine the 20-year developmental trajectory of medical education at Ewha Womans University College of Medicine (2004–2025). It analyzes educational support documents, self-evaluation reports, and Curriculum Committee meeting minutes to illuminate both the direction and significance of Ewha’s medical education reforms. Key milestones include the formal establishment of the Medical Education Office in 2004 and the subsequent founding of the Department of Medical Education in 2005. Major innovations over this period encompass the expansion of objective structured clinical examinations and the introduction of problem-based learning modules. Additional advancements include the establishment of the Ewha Medical Simulation Center and Learning Resource Center, as well as the reversion to an undergraduate medical college format in 2015. The college has also prioritized faculty development workshops and medical education seminars, implemented the Ewha Social Active Communication program, and introduced team-based learning. Noteworthy initiatives include the enhancement of student research capacity and the launch of a dedicated medical education newsletter. In 2022, the Medical Education Office was reorganized as the Ewha Center for Medical Education, marking a new era of integrated leadership and expanded educational initiatives. Ewha has consistently achieved high accreditation statuses, reflecting ongoing excellence in curriculum development, assessment, and faculty development. This progress demonstrates the dedication and collaboration of both faculty and staff, resulting in a robust educational framework. The institution’s continuous growth serves not only as a testament to past achievements but also as a foundation for future advancements in Ewha’s medical education, with the ultimate aim of cultivating women leaders in Korean healthcare.

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 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
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 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.