Background: Studies on whether donor-to-recipient sex match status affects long-term survival after liver transplantation (LT) have yielded contradictory results. This study evaluated whether donor-to-recipient sex match status influenced long-term survival after living donor liver transplantation (LDLT) or deceased donor liver transplantation (DDLT) at a high-volume center. Methods: The study included 6,664 patients who underwent primary LT between January 2000 and December 2022 at our institution. Patients were divided into four groups according to donor-to-recipient sex match status: male-to-male (n=3,427 [51.4%]), male-to-female (n=1,152 [17.3%]), female-to-male (n=1,385 [20.8%]), and female-to-female (n=700 [10.5%]). Results: Regarding clinical characteristics, the four groups differed significantly regarding background liver disease (P<0.001), model for end-stage liver disease score (P<0.001), serum protein induced by vitamin K absence or antagonist II level (P=0.003), presence of concurrent hepatocellular carcinoma (HCC; P<0.001), and type of LT (P=0.003). Overall survival (OS) of all LT recipients did not differ significantly among the groups (P=0.377). Donor-to-recipient sex match status did not affect long-term OS in either LDLT (P=0.176) or DDLT (P=0.220) groups. In addition, sex match status did not significantly influence posttransplant OS among patients who underwent LDLT without HCC (P=0.464), LDLT with HCC (P=0.236), DDLT without HCC (P=0.338), or DDLT with HCC (P=0.818). Conclusions Donor-to-recipient sex match status does not significantly affect posttransplant patient survival or HCC prognosis after LDLT or DDLT.

Background: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence–enabled electrocardiography (AI-ECG) mod‑ els show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients. Methods: In this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area under the receiver operating char‑ acteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, and specificity. Patients were stratified into high- and low-risk groups based on a threshold that achieved 90% sensitivity. A Kaplan–Meier analysis was used to compare clinical outcomes. Results: Among the 2,813 LBBB patients (mean age, 70.7 years; male sex, 43.7%), hypertension and a history of heart failure were common. The AiTiALVSD model showed strong diagnostic performance for LVSD (AUROC, 0.930 [95% CI, 0.924–0.937]; AUPRC, 0.913 [95% CI, 0.902–0.923]; sensitivity, 0.979; specificity, 0.473). During the mean follow-up of 4.1 years, high-risk patients had significantly higher hazards than low-risk patients for all-cause mortality (adjusted hazard ratio [HR], 1.87; 95% CI, 1.53–2.28), implantable cardioverter defibrillator/cardiac resynchronization therapy implantation (adjusted HR, 15.2; 95% CI, 7.51–30.77), and cardiovascular hospitalization (adjusted HR, 1.11; 95% CI, 0.96–1.28). Conclusions AiTiALVSD effectively detects LVSD and stratifies long-term cardiovascular risk in LBBB patients, support‑ ing its clinical utility for early detection and patient management.

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: We developed a simplified variant of the ADV score, the AFP-PIVKAII (AP) score for post-transplant hepatocellular carcinoma (HCC) prognosis, which considers only AFP and PIVKA-II levels excluding morphometric tumor size information from the ADV score. This study investigated the prognostic performance of the AP score in predicting HCC recurrence and overall survival (OS) after living donor liver transplantation (LDLT). Methods: We analyzed 843 patients with HCC who underwent LDLT between 2006 and 2015, assessing HCC recurrence and OS in relation to AP score. Results: The median pretransplant AFP and PIVKA-II levels were 12.8 ng/mL and 27 mAU/mL, respectively. The median and mean AP scores were 2.6 log (range: 0.6–9.2 log) and 2.9±1.1 log, respectively. The 5-year time-dependent area under the receiver operating characteristic curve for the AP score in predicting post-transplant HCC recurrence was 0.672 (p<0.001). HCC recurrence and OS curves along AP score intervals of 1.0 log showed statistical differences in accordance with the AP scores (both p<0.001). Using a Youden index J-derived AP score cutoff of 4.0 log, two-tiered groups (ADV <4.0 log vs. ADV ≥4.0 log) showed statistically significant differences in HCC recurrence and OS (both p<0.001). Harrell’s c-indices for AP score with cutoff of 4.0 log and ADV scores with cutoff of 5.0 log regarding HCC recurrence and OS were similar. Conclusion The AP score functions as an integrated surrogate marker for predicting post-transplant outcomes in patients with HCC undergoing LDLT. It may serve as a simplified alternative to the ADV score, particularly in patients with small HCCs.

Purpose: Neoadjuvant treatment (NAT) is now the standard for borderline resectable pancreatic cancer (RPC) and is being considered for RPC. Early recurrence after curative surgery in RPC is often seen as a treatment failure, prompting considerations for NAT. Our goal was to develop an artificial intelligence (AI)-based predictive model utilizing preoperatively available factors to forecast early recurrences of resected RPC. Methods: This study included 469 patients who underwent surgery for RPC between 2011 and 2019. Clinicopathologic and oncologic data were retrospectively reviewed. Preoperative variables, including laboratory data and imaging findings, were collected. Early recurrence was defined as recurrence occurring within a year after surgery. Deep neural networks were then used to select variables by assessing their importance. A new model predicting early recurrence of RPC was subsequently developed. Results: Of the patients evaluated, 199 (42.4%) experienced early recurrence. The predictive model included 14 preoperative variables: CA 19-9, preoperative pancreatitis, serum albumin, platelet count, lymphocyte count, the American Society of Anesthesiologists physical status classification, tumor size, monocyte count, age, body mass index, CRP, hemoglobin, WBC count, and CEA. The area under the curve for the model was 0.786 in the training set and 0.734 in the test set. Conclusion We developed an AI-based model to predict the early recurrence of RPC using preoperative parameters. By identifying patients at risk of early recurrence, optimal individualized treatments such as NAT can be considered. Future prospective studies are crucial to establish clear indications for NAT in RPC.

Pharmacogenomic variations may significantly influence responses to commonly prescribed stroke medications. Despite accumulating evidence, genetic testing has not yet been widely integrated into stroke care. This review summarizes current evidence and provides practical guidance for clinical implementation. Pharmacogenomic studies and clinical guidelines related to antiplatelet agents, anticoagulants, and statins were reviewed, with particular emphasis on East Asian populations. Substantial evidence supports genotype-guided use of clopidogrel (CYP2C19), warfarin (CYP2C9, VKORC1, CYP4F2), and statins (SLCO1B1, ABCG2). For aspirin, PTGS1/2 and PEAR1 variants have been investigated; however, current data remain insufficient for clinical application. Regarding direct oral anticoagulants (DOACs), candidate genes such as ABCB1 and CES1 demonstrate pharmacokinetic associations, though robust clinical outcome data are lacking. Distinct allele frequencies in East Asians—such as higher prevalence of CYP2C19 and ABCG2 variants—underscore the need for population-specific strategies. Beyond single-gene approaches, polygenic risk scores, pharmacogenomic panels, and integration with multi-omics data and artificial intelligence represent promising directions for personalized therapy. Pharmacogenomic testing can enhance stroke pharmacotherapy, particularly in populations with high frequencies of actionable variants. Broader implementation requires rapid testing platforms, clinician education, tailored clinical guidelines, and real-world validation of aspirin, DOACs, and multi-gene approaches. Future research should expand population-specific studies and integrate pharmacogenomics within the broader framework of precision medicine to ensure equitable clinical benefit.

Janus kinase (JAK) inhibitors are an important treatment option for ulcerative colitis, providing rapid onset of action, oral administration, and efficacy even after biologic failure. The 3 approved agents—tofacitinib, filgotinib, and upadacitinib—differ in JAK isoform selectivity, leading to clinically meaningful differences in efficacy and safety. Evidence from network meta-analyses, clinical trials, and real-world studies consistently shows that upadacitinib provides the highest efficacy for induction and maintenance of remission, whereas filgotinib demonstrates the most favorable safety profile. The strong efficacy of upadacitinib and tofacitinib is particularly relevant in patients with severe disease, including acute severe ulcerative colitis, and upadacitinib maintains high efficacy regardless of prior advanced therapy exposure. JAK inhibitors also benefit extraintestinal manifestations. Although risks such as herpes zoster, serious infection, thromboembolism, and major cardiovascular events differ among agents, long-term data suggest generally acceptable safety when used appropriately. Intraclass JAK-to-JAK cycling is feasible, with about half of patients achieving steroid-free clinical remission in retrospective cohorts. Based on mechanistic, clinical, and real-world evidence, filgotinib may be a first-line option for patients with lower disease activity or when safety is a priority, whereas upadacitinib or tofacitinib may be preferred in higher disease activity. Strategically selecting agents may improve durability and outcomes.

Background/Aims: Biliary tract cancer (BTC) is a rare malignancy with poor prognosis. We investigated genomic determinants of clinical benefit from gemcitabine, cisplatin, and nab-paclitaxel (GAP) versus gemcitabine and cisplatin (GC) in advanced BTC. Methods: Clinical and genomic data using TruSight Oncology 500 were analyzed from patients treated with GAP (N=198) or GC (N=89) as first-line therapy. Results: With a median follow-up of 33.0 months, GAP modestly improved progression-free survival (PFS) (hazard ratio [HR] 0.764; 95% confidence interval [CI] 0.591–0.989) without significant overall survival (OS) difference compared to GC. Genomic profiling revealed frequent alterations in TP53 (35.2%), KRAS (16.4%), SMAD4 (10.5%), and TNFRSF14 (10.5%), involving RTK/RAS (44.3%), TP53 (41.8%), and PI3K (20.2%) pathways. Single-gene mutations did not predict treatment benefit. However, pathway-level analysis identified PI3K pathway activation as significantly associated with inferior PFS (HR 2.148; 95% CI 1.478–3.124) and OS (HR 2.096; 95% CI 1.413–3.109) in patients receiving GAP, an effect not observed with GC. Importantly, GAP conferred clinical benefit only in patients without PI3K pathway activation, while no survival advantage was seen in those with such alterations (Pinteraction=0.023 for PFS, Pinteraction=0.003 for OS). Similar results were obtained in the independent validation cohort treated with GAP (N=103) or GC (N=64) for BTC. Conclusions Genomic profiling using next-generation sequencing identified PI3K pathway activation as key molecular determinant that differentiates patient outcomes between GAP and GC treatments in advanced BTC.

Background: and Purpose Previous research on patients with acute ischemic stroke (AIS) has shown a 0.5% incidence of major gastrointestinal bleeding (GIB) requiring blood transfusion during hospitalization. The existing literature has insufficiently explored the long-term incidence in this population despite the decremental impact of GIB on stroke outcomes. Methods: We analyzed the data from a cohort of patients with AIS admitted to 14 hospitals as part of a nationwide multicenter prospective stroke registry between 2011 and 2013. These patients were followed up for up to 6 years. The occurrence of major GIB events, defined as GIB necessitating at least two units of blood transfusion, was tracked using the National Health Insurance Service claims data. Results: Among 10,818 patients with AIS (male, 59%; mean age, 68±13 years), 947 (8.8%) experienced 1,224 episodes of major GIB over a median follow-up duration of 3.1 years. Remarkably, 20% of 947 patients experienced multiple episodes of major GIB. The incidence peaked in the first month after AIS, reaching 19.2 per 100 person-years, and gradually decreased to approximately one-sixth of this rate by the 2nd year with subsequent stabilization. Multivariable analysis identified the following predictors of major GIB: anemia, estimated glomerular filtration rate <60 mL/min/1.73 m2 , and a 3-month modified Rankin Scale score of ≥4. Conclusion Patients with AIS are susceptible to major GIB, particularly in the first month after the onset of AIS, with the risk decreasing thereafter. Implementing preventive strategies may be important, especially for patients with anemia and impaired renal function at stroke onset and those with a disabling stroke.