Purpose: Preterm birth remains a leading cause of long-term neurodevelopmental impairment, yet early evaluations frequently underestimate subsequent deficits. This study examined longitudinal neurodevelopmental trajectories across gestational age groups and identified predictors of developmental delay. Methods: A retrospective cohort of 532 preterm children, stratified by gestational age, was followed from the neonatal period to early preschool age. Neurodevelopment was assessed using the Korean version of the Bayley Scales of Infant and Toddler Development, Third Edition at 8–12 months (n=481), 13–24 months (n=118), and 25–42 months (n=100). Longitudinal trajectories were analyzed using general linear models, and predictors of developmental delay were identified through multivariable logistic regression. Results: During the first year, motor scores differed significantly across gestational age groups, with extremely preterm infants showing the lowest values. By the third to fourth years of life, cognitive and language scores diverged markedly, with extremely preterm children exhibiting the steepest decline and additional deficits in motor and adaptive behavior domains. Lower gestational age remained an independent predictor of both cognitive and language delay at early preschool age, while no independent predictors were identified for motor, social-emotional, or adaptive behavior outcomes. Conclusion Neurodevelopmental outcomes in preterm children follow dynamic, domain-specific trajectories influenced by gestational age and developmental timing. Motor delays are most evident in infancy, whereas cognitive and language impairments emerge by early preschool age. Gestational age remains a consistent predictor of later delay, emphasizing the need for longitudinal, gestational age–stratified monitoring and early, targeted intervention.

The gut microbiome is not just a bystander of colorectal carcinogenesis but is an active driver of colorectal cancer (CRC). CRC-associated microbiome contributes in the tumorigenesis through chronic inflammation, formation of toxic metabolite and genotoxins, oncogenic signal activation, immune evasion, and barrier disruption—all reinforcing a tumor microenvironment. In contrast, beneficial microbiome supports the barrier-immune-metabolic axis by maintaining mucosal integrity and balanced immune tone. Despite extensive studies of microbiome-based CRC biomarkers, microbiome-based CRC biomarkers have not been yet ready for routine clinical use due to variation across populations and lack of standardization of key steps such as sampling, analysis, cutoffs, and interpretation. Microbiome-based therapies aim to change the overall intestinal ecosystem rather than simply adding or removing single strains. At present, dietary modulation and prebiotics are considered supportive measures, while probiotics or synbiotics are in preclinical stage. Fecal microbiota transplantation (FMT) still faces important challenges in effectiveness, standardization and safety. By its role in reshaping the tumor–host immune environment, FMT is viewed as a potential option for cancer therapy after further development through well-controlled clinical trials with careful safety monitoring.

Rectal cancer, which accounts for approximately 40% of colorectal cancers, remains a major clinical concern. Recent advances in diagnostic imaging, surgical techniques, radiotherapy, and systemic treatment have steadily improved rectal cancer outcomes. Considering this, the Korean Rectal Cancer Multidisciplinary (KRCM) Committee has aimed to provide clinicians and policymakers with up-to-date, evidence-based clinical practice guidelines to support optimal decision-making, reflecting current evidence, the Korean healthcare context, and patient values and preferences. The Clinical Practice Guidelines for Rectal Cancer version 2.0 were developed through multidisciplinary collaboration with related academic societies, building upon and updating the KRCM Clinical Practice Guidelines version 1.0 (titled “Multidisciplinary guidelines for the management of rectal cancer”). These consensus guidelines of the KRCM were established based on a comprehensive literature review, evidence synthesis, with recommendation development guided by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology, and consideration of applicability in real-world clinical practice under the national health insurance system. Each recommendation has been presented with its strength and level of evidence.

Background: Extended-spectrum β-lactamase (ESBL)-producing Escherichia coli is among the most important multidrug-resistant pathogens causing bloodstream infections (BSIs).Cefotaximase (CTX-M) enzymes are the most common and highly diverse ESBL family in E.coli. CTX-M-15 in group CTX-M-1 and CTX-M-14 in group CTX-M-9 are the most extensively disseminated enzymes. Multidrug-resistant E. coli strains complicate empirical therapy and increase healthcare burden globally and in Korea. We investigated the molecular epidemiology, sequence types (STs), and ESBL genotypes of E. coli bloodstream isolates in Korea and identified clinical risk factors for cefotaxime resistance. Methods: We collected all non-duplicated isolates of E. coli and related clinical information from patients with BSIs at eight sentinel hospitals in the Korean Global Antimicrobial Resistance Surveillance System (Kor-GLASS) collection network during 2017–2021. Duplicate isolates were removed to ensure representativeness of the data. Antimicrobial susceptibility was tested using disk diffusion tests, and multilocus sequence typing and betalactamase genotyping were performed. Results: Among 9,232 E. coli blood isolates, resistance rates to cefotaxime and ceftazidime were 36.4% and 11.4%, respectively. Among the clinical factors, age > 65 yrs (adjusted odds ratio [aOR], 1.36), hospital-origin infection (aOR, 2.55), and admission type (intensive care unit [ICU] vs. general ward; aOR, 1.34) were significant cefotaxime resistance risk factors. ST131 was the most prevalent among cefotaxime-resistant E. coli (64.8%, 2,180/3,363), followed by ST1193 (5.3%, N = 177), and ST69 (5.1%, N = 170).ST131, ST648, ST405, and ST410 cefotaxime-resistant E. coli isolates frequently harbored blaCTX-M-15, whereas ST1193 and ST68 showed a high proportion of blaCTX-M-27 carriers, and most ST457 and ST5150 isolates carried blaCTX-M-55. Conclusions Continuous monitoring of ESBL-producing E. coli is required to prevent further dissemination, guide empirical therapy, inform infection control policies, and ensure early detection of multidrug-resistant clones with the potential for widespread transmission.

Background: Recent advancements in large language models (LLMs) have accelerated their integration into clinical domains, including laboratory medicine. The performance of LLMs in answering board-level laboratory medicine questions has not been comprehensively evaluated. Given the importance of diagnostic accuracy in this field, rigorous and objective evaluations of LLM capabilities are essential. Methods: We assessed 12 LLMs from OpenAI, Anthropic, and Google using 320 Korean Residency Examination questions (2021–2024) spanning six laboratory medicine subspecialties. Standardized prompts were provided via their application programming interfaces under deterministic settings (temperature = 0). Questions were administered thrice to assess response reproducibility. Outputs were compared with validated answers and analyzed for accuracy, reasoning quality, and error typology. Results: Google’s Gemini 2.0 Pro achieved the highest accuracy (80.0%), followed by OpenAI’s GPT-4.5 (77.2%) and Anthropic’s Claude 3.7 Sonnet (74.1%). Accuracy decreased as the difficulty of questions increased (78.0% for easy vs. 45.1% for challenging). Subspecialty performance varied. Al models underperformed on questions on transfusion medicine (mean accuracy: 38.8%), primarily because of limitations in domain-specific and regional knowledge representations. Incorrect answers primarily resulted from reasoning errors. Reproducibility exceeded 95% for most models; however, some residual non-determinism appeared even with greedy decoding (temperature = 0). Conclusions LLMs demonstrated substantial potential for integration into laboratory medicine, particularly in clinical chemistry and immunology. Performance inconsistencies (particularly for high-difficulty questions) and knowledge gaps (notably for transfusion medicine) highlight the necessity for further development—potentially including domain-specific fine-tuning and retrieval-augmented generation integration—and robust expert oversight before clinical application.

Background: Fusion genes are major drivers of acute leukemia. Conventional diagnostics are limited in detecting the diverse fusions included in recently updated acute leukemia classifications. We evaluated the fusion detection performance of RNA sequencing (RNAseq) compared with that of conventional diagnostics in patients with acute leukemia. Methods: We retrospectively obtained the data of 101 patients with acute leukemia who underwent conventional diagnostics (i.e., karyotyping, FISH, or multiplex reverse transcription PCR) at diagnosis at Samsung Medical Center, Seoul, Korea, between September 2022 and September 2023. Whole RNA-seq was performed using the Illumina Stranded mRNA Prep kit (Illumina, San Diego, CA, USA). The concordance, sensitivity, and specificity of RNA-seq for fusion gene detection were compared with those of conventional diagnostics. Results: RNA-seq helped identify 52 fusion genes in 51 (50.5%) of 101 patients, with detection rates of 40.7%, 70.3%, 37.5%, and 50% in acute myeloid leukemia, B-cell acute lymphoblastic leukemia, T-cell acute lymphoblastic leukemia, and mixed-phenotype acute leukemia, respectively. RNA-seq showed 83.3% sensitivity and 80.8% concordance with conventional diagnostics; it missed eight fusions, likely because of low transcript abundance or enhancer hijacking. RNA-seq also helped clarify three previously unspecified rearrangements and detected 12 fusions (21.4%) in 56 cases that tested negative with conventional diagnostics, including four novel (KMT2A::THAP12 , RUNX1::PRPF19 , MLLT10::UBE2L6, and FUS::ZNF362) and three rare (HNRNPH1::ERG, RUNX1::USP42, and ETV6::NCOA2) fusions. Conclusions This was the first study to evaluate the performance of whole RNA-seq in fusion detection in patients with acute leukemia in Korea. Incorporating RNA-seq into diagnostic workflows may facilitate earlier and more precise therapeutic decisions and improve prognostic assessment in patients with acute leukemia.

Purpose: Psychiatric conditions are common in children and adolescents with diabetes and can hinder disease management. In this study, we examined whether mental health status at diagnosis predicts glycemic control at 1 year. Methods: We included 57 patients aged 6–18 years diagnosed with type 1 or type 2 diabetes between 2019 and 2023 at Seoul National University Bundang Hospital. Mental health was assessed within 3 months of diagnosis using the Eating Disorder Inventory-2, Children’s Depression Inventory, and Child Behavior Checklist (CBCL) for ages 6–18. Poor glycemic control was defined as glycated hemoglobin >6.5% at 1 year. Associations between screening results and glycemic control were analyzed using Fisher exact test and multivariate logistic regression. Results: Of the 57 patients, 32 (56.1%) had type 1 diabetes, and the mean age at diagnosis was 12.9±3.1 years; 31 (54.4%) were male. Poor glycemic control at 1 year was observed in 16 patients (28.1%). Although individual subscale positivity was not significantly associated with glycemic control, borderline somatic complaints on the CBCL were significantly associated with poor control (p=0.022). In multivariate analysis, having 2 or more positive CBCL subscales showed a trend toward association with poor glycemic control (adjusted odds ratio=21.47, p=0.054). Conclusion Early psychological screening, especially for somatic symptoms or multiple psychological problems, may help identify those at risk for poor glycemic control in pediatric diabetes. These findings underscore the importance of early detection and intervention in optimizing diabetes management.

Purpose: We aimed to compare broad-spectrum antibiotic use between hospice and nonhospice patients with cancer at the end of life using nationwide data from Korea. Methods: In this retrospective cohort study, we analyzed the Korean National Health Insurance Service data of adult patients with cancer who died between 2018 and 2021. Hospice users were defined as patients who received inpatient, home-based, or consultation-based hospice care before death. We applied propensity score matching (1:2) to balance the baseline characteristics of the hospice and non-hospice groups. Broad-spectrum antibiotic use, including anti-pseudomonal penicillins, anti-pseudomonal cephalosporins, carbapenems, and glycopeptides, was assessed during the last 3 months of life using prescription proportions and days of therapy per 1,000 patient-days. Results: After matching, 38,102 hospice and 75,736 non-hospice users were analyzed. During the last 3 months of life, 74.6% of hospiceand 79.0% of non-hospice users received at least one broad-spectrum antibiotic (P＜0.001).The proportion of patients receiving broad-spectrum antibiotics was generally lower amonghospice users across all time intervals (P＜0.001), and the number of days of therapy wasalso lower, with the largest differences observed during the final week and last 3 days of life.Subgroup analyses showed the highest antibiotic exposure among patients with hematologic and pancreatobiliary cancers, particularly in the non-hospice group. Conclusion Hospice involvement was associated with lower use and reduced exposure to broad-spectrum antibiotics among patients with cancer near the end of life. These findings support the alignment of end-of-life treatment decisions with the comfort-oriented goals of hospice care.

Purpose: This study aimed to develop and evaluate the effectiveness of the Trauma-nursing Education and Skill Support (TESS) program based on the ADDIE model (Analysis, Design, Development, Implementation, Evaluation model). The program was designed to enhance trauma nurses’ clinical competencies, including trauma-related knowledge, self-efficacy, and problem-solving ability, through the integration of theoretical education and simulation-based practice. Methods: A quasi-experimental study using a non-equivalent control group pretest–posttest design was conducted. Participants included 108 trauma nurses from regional trauma centers, military trauma centers, and emergency care facilities, who were assigned to an experimental group (n=52) or a control group (n=56). The TESS program consisted of a 2-day, 14-hour blended-learning course that included eight lecture sessions and four simulation-based practice stations. Data were collected at baseline, immediately after the intervention, and at 6 months using validated instruments measuring trauma-related knowledge, self-efficacy, and problem-solving ability. Two-way repeated-measures analysis of variance was used for data analysis. Results: The experimental group demonstrated significant improvements in trauma-related knowledge, self-efficacy, and problem-solving ability compared with baseline (all p<.001). These improvements were sustained at 6 months, although trauma-related knowledge scores showed a slight decline compared with immediate posttest levels. Between-group analyses confirmed significant group-by-time interaction effects for all outcomes: trauma-related knowledge (η2=0.12, p<.001), self-efficacy (η2=0.09, p=.002), and problem-solving ability (η2=0.08, p=.003). Conclusion The TESS program effectively enhanced trauma nurses’ trauma-related knowledge, self-efficacy, and problem-solving ability, with effects sustained for up to 6 months. Incorporating blended learning and simulation-based training into standardized trauma nursing education may strengthen clinical competencies and ultimately contribute to improved patient outcomes.