Purpose: Patient-derived cells (PDCs) and patient-derived organoids (PDOs) are complementary preclinical models widely used in translational cancer research. However, their molecular and functional differences have not been systematically characterized. This study established and analyzed paired PDC and PDO models derived from the same gastric cancer ascites to delineate platform-dependent molecular and functional profiles. Materials and Methods: Malignant ascites or pleural fluid obtained from 6 patients with advanced gastric cancer were used to establish paired PDC and PDO models. All pairs underwent comprehensive multi-omics profiling, integrating genomic, transcriptomic, and proteomic data. Phenotypic characterization included morphological, histological, proliferative, and cell cycle analyses. Drug sensitivity assays were performed using 4 chemotherapeutic agents commonly used to treat gastric cancer. Results: The 6 paired PDC and PDO models exhibited distinct morphological characteristics.Whole-genome analyses demonstrated high concordance among primary tumors, PDCs, and PDOs, confirming tumor representation across platforms. Multi-omics profiling identified platform-dependent molecular signatures; PDOs were enriched for extracellular matrix remodeling and stemness, whereas PDCs displayed proliferation- and immune-related signatures. Clinically relevant biomarkers, including HER2 and MET alterations, were concordant with primary tumors. Notably, drug responses differed between platforms and patients, indicating platform-dependent and patient-specific chemosensitivity. Conclusions Paired PDC and PDO models derived from the same patients preserved core patient-specific tumor characteristics while exhibiting distinct molecular and functional profiles. These findings underscore the culture platform as a critical determinant of experimental outcomes and therapeutic responses. Therefore, careful selection of an appropriate preclinical model is essential to accurately address biological questions and optimize precision oncology strategies.

Background: Postoperative liver regeneration is essential for maintaining hepatic function. This study evaluated the rate, determinants, and volumetric patterns of early liver regeneration after hemihepatectomy. Methods: A retrospective review was conducted of 50 patients who underwent right or left hemihepatectomy between April 2019 and March 2025. Liver and spleen volumes (SV) were assessed preoperatively, at postoperative day (POD) 1 week, and at POD 3 months. Early liver regeneration rate (LRR) was defined as the percentage increase in remnant liver volume at POD 1 week relative to the preoperative future liver remnant (FLR), and patients were categorized into low (<50%) and high (≥50%) LRR groups. Clinical, biochemical, and volumetric variables were compared, and predictors of regeneration were identified using multivariable analyses. Regeneration patterns were also examined according to whether the FLR/standard liver volume (SLV) ratio was <50% or ≥50%. Results: FLR/SLV was the strongest independent predictor of rapid early liver regeneration (p<0.001). Remnants with FLR/SLV <50% exhibited rapid and sustained regeneration, whereas those with FLR/SLV ≥50% showed slower regrowth that plateaued after reaching approximately 90% of SLV. SV increased at POD 1 week in all patients; however, only the FLR/SLV ≥50% group showed a reduction by POD 3 months, whereas the <50% group maintained elevated volumes. Conclusions FLR/SLV reliably predicts early postoperative liver regeneration. Smaller remnants regenerate more rapidly, whereas persistent splenic enlargement suggests a sustained portal hemodynamic burden. Combined evaluation of FLR/SLV and SV may enhance perioperative risk assessment and surgical planning.

Purpose: Accurate identification of the bile duct bifurcation site is crucial in pure laparoscopic donor right hepatectomy (PLDRH) for living donor liver transplantation. This study aimed to develop and evaluate a deep learning model to predict the bile duct bifurcation site for surgical precision. Methods: We retrospectively analyzed 55 PLDRH procedures conducted between August 2021 and April 2022. A deep learning model combining UNet with a MiT-B3 encoder was trained on 150 manually annotated frames. We then incorporated expert-reviewed pseudo-labels from an additional 901 frames to refine the model. Performance was evaluated using 5-fold cross-validation and an independent test set. Results: Clinical evaluation showed a 97% accuracy in 5-fold cross-validation and 93.3% accuracy in the independent test set. From the initial to final model, dice similarity coefficient improved from 0.392 to 0.472, intersection over union from 0.279 to 0.339, and sensitivity from 0.487 to 0.643, while specificity remained consistent at 0.993. Conclusion The proposed artificial intelligence (AI) model demonstrated strong clinical performance in predicting the bile duct bifurcation site during PLDRH. Despite modest quantitative scores, the high clinical accuracy highlights the potential of integrating AI for precise donor hepatectomy.

Tuberculosis survivors often face long-term physical, psychological, and social challenges that extend beyond microbiological cure. Post-tuberculosis complications include pulmonary damage, cardiovascular and neurological sequelae, as well as significant impacts on overall wellbeing and livelihoods. Despite increasing awareness, most care continues to focus primarily on lung disease, overlooking the multisystem and social needs of survivors. A holistic, people-centered framework that integrates medical, rehabilitative, and community-based strategies is essential for restoring health and facilitating societal participation among tuberculosis survivors.

Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.

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.

Background: This study aimed to identify the clinical characteristics of multidrug-resistant/ rifampicin-resistant tuberculosis (MDR/RR-TB) in the Republic of Korea. Methods: Data of notified people with tuberculosis between July 2018 and December 2021 were retrieved from the Korea Tuberculosis Cohort database. MDR/RR-TB was further categorized according to isoniazid susceptibility as follows: multidrug-resistant tuberculosis (MDR-TB), rifampicin-monoresistant tuberculosis (RMR-TB), and RR-TB if susceptibility to isoniazid was unknown. Multivariable logistic regression analysis was conducted to identify the factors associated with MDR/RR-TB. Results: Between 2018 and 2021, the proportion of MDR/RR-TB cases among all TB cases and TB cases with known drug susceptibility test results was 2.1% (502/24,447). The proportions of MDR/RR-TB and MDR-TB cases among TB cases with known drug susceptibility test results were 3.3% (502/15,071) and 1.9% (292/15,071), respectively. Among all cases of rifampicin resistance, 31.7% (159/502) were RMR-TB and 10.2% (51/502) were RR-TB. Multivariable logistic regression analyses revealed that younger age, foreigners, and prior tuberculosis history were significantly associated with MDR/ RR-TB. Conclusion Rapid identification of rifampicin resistance targeting the high-risk populations, such as younger generations, foreign-born individuals, and previously treated patients are necessary for patient-centered care.

Background: s/Aims: While large-for-size syndrome is uncommon in liver transplantation (LT), it can result in fatal outcome. To prevent such fatality, we manufactured 3D-printed intra-abdominal cavity replicas to provide intuitive understanding of the sizes of the graft and the patient’s abdomen in patients with small body size between July 2020 and February 2022. Methods: Clinical outcomes were compared between patients using our 3D model during LT, and patients who underwent LT without 3D model by using 1 : 5 ratio propensity score-matched analysis. Results: After matching, a total of 20 patients using 3D-printed abdominal cavity model and 100 patients of the control group were included in this study. There were no significant differences in 30-day postoperative complication (50.0% vs. 64.0%, p = 0.356) and the incidence of large-for-size syndrome (0% vs. 7%, p = 0.599). Overall survival of the 3D-printed group was similar to that of the control group (p = 0.665), but graft survival was significantly superior in the 3D-printed group, compared to the control group (p = 0.034). Conclusions Since it showed better graft survival, as well as low cost and short production time, our 3D-printing protocol can be a feasible option for patients with small abdominal cavity to prevent large-for-size syndrome after LT.

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.