Background: and Purpose Although gastrointestinal (GI) surgery can affect drug efficacy, its impact on oral anticoagulants (OACs) remains unclear. This study investigated the impact of GI surgery on the comparative effectiveness and safety of direct oral anticoagulants (DOACs) versus warfarin in atrial fibrillation (AF). Methods: Using a nationwide South Korean claims database, AF patients prescribed OACs between 2015 and 2021 were analyzed. The primary outcome was ischemic stroke, and secondary outcomes were major bleeding, all-cause death, and composite outcome (ischemic stroke, major bleeding, all-cause death). Outcomes were compared by OAC type (DOAC vs. warfarin) and GI surgery status using inverse probability of treatment weighting and a Fine–Gray subdistribution hazard model with time-varying covariates. Results: Among 388,214 AF patients (mean age, 71.9 yr; 42.7% male), 6,907 (1.8%) underwent GI surgery. The effect of GI surgery differed between warfarin and DOACs for ischemic stroke (Pint= 0.018), with increased risk for warfarin (adjusted HR [aHR]: 2.32; 95% confidence interval [CI] 1.17–4.62), but not for DOACs (aHR: 0.97 [95% CI 0.77–1.22]) after surgery. Effects of surgery on major bleeding, all-cause death, and composite outcome did not differ. In post–GI surgery setting, DOACs demonstrated lower ischemic stroke risk (aHR: 0.35 [95% CI 0.17–0.72]) and comparable risks for other outcomes. Exploratory analyses by surgery location suggested a more favorable profile of DOACs after upper GI surgery. Conclusions In AF patients, GI surgery significantly increased ischemic stroke risk among warfarin users, but not among DOAC users. DOACs showed generally favorable profiles after GI surgery and may remain a reasonable anticoagulant, with potentially more favorable profile after upper GI surgery.

Purpose: : This retrospective study clarified the peripheral limb ischemiaincidence and examined the differences in demographic, clinical, and nursing care-related characteristics according to the presence of peripheral limb ischemia in intensive care unit (ICU) patients undergoing extracorporeal membrane oxygenation (ECMO) therapy. Methods: : Participants were 170 patients who received ECMO therapy at a university hospital over a 10-year period (January 2013 to December 2022). Data, including general, clinical, and nursing care characteristics, were collected from electronic medical records. Data analysis was conducted using descriptive statistics, independent t-tests, χ2-tests, and Fisher’s exact tests. Results: : Participants’ mean age was 57.49 years, 125 (73.5%) were male, and the average duration of ECMO therapy was 7.22 days. Among them, 75 (44.1%) patients developed peripheral limb ischemia. Significant differences were found between the groups with and without peripheral limb ischemia in mean arterial pressure (t=6.19, p ‹.001), vasopressor use (χ2=8.54, p =.003), anticoagulant use (χ2=15.01, p ‹.001), use of clotting factors (χ2=5.84, p=.016), presence of abdominal distension (χ2=8.97, p=.003), insertion site bleeding (χ2=8.14, p =.004), and laboratory values including white blood cell count (t=2.65, p =.009), hemoglobin (t=2.71, p =.008), platelet count (t=2.88, p =.004), creatine kinase (t=-4.47, p ‹.001), prothrombin time (t=-4.22, p ‹.001), international normalized ratio (t=-4.29, p ‹.001), and frequency of lower limb monitoring (t=5.57, p ‹.001). Conclusion : This study explored the clinical and nursing care-related characteristics associated with peripheral limb ischemia in ICU patients undergoing ECMO therapy. The findings provide evidence to support future research focusing on the early detection and proactive nursing interventions to prevent peripheral limb ischemia in this high-risk population.

Purpose: Prostate cancer ranks as the second most common cancer in men globally, representing a significant cause of cancer-related mortality. Metastasis, the spread of cancer cells from the primary site to distant organs, remains a major challenge in managing prostate cancer. Pyruvate dehydrogenase kinase 4 (PDK4) is implicated in the regulation of aerobic glycolysis, emerging as a potential player in various cancers. However, its role in prostate cancer remains unclear. This study aims to analyze PDK4 expression in prostate cancer cells and human samples, and to explore the gene's clinical significance. Materials and Methods: PDK4 expression was detected in cell lines and human tissue samples. Migration ability was analyzed using Matrigel-coated invasion chambers. Human samples were obtained from the Kyungpook National University Chilgok Hospital. Results: PDK4 expression was elevated in prostate cancer cell lines compared to normal prostate cells, with particularly high levels in DU145 and LnCap cell lines. PDK4 knockdown in these cell lines suppressed their invasion ability, indicating a potential role of PDK4 in prostate cancer metastasis. Furthermore, our results revealed alterations in epithelial-mesenchymal transition markers and downstream signaling molecules following PDK4 suppression, suggesting its involvement in the modulation of invasion-related pathways. Furthermore, PDK4 expression was increased in prostate cancer tissues, especially in castration-resistant prostate cancer, compared to normal prostate tissues, with PSA and PDK4 expression showing a significantly positive correlation. Conclusions PDK4 expression in prostate cancer is associated with tumor invasion and castration status. Further validation is needed to demonstrate its effectiveness as a therapeutic target.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Objectives: . The recent expansion of eligibility for cochlear implantation (CI) by the U.S. Food and Drug Administration (FDA) to include infants as young as 9 months has reignited debates concerning the clinically appropriate cut-off age for pediatric CI. Our study compared the early postoperative trajectories of receptive and expressive language development in children who received CI before 9 months of age with those who received it between 9 and 12 months. This study involved a unique pediatric cohort with documented etiology, where the timing of CI was based on objective criteria and efforts were made to minimize the influence of parental socioeconomic status. Methods: . A retrospective review of 98 pediatric implantees recruited at a tertiary referral center was conducted. The timing of CI was based on auditory and language criteria focused on the extent of delay corresponding to the bottom 1st percentile of language development among age-matched controls, with patients categorized into very early (CI at <9 months), early (CI at 9–12 months) and delayed (CI at 12–18 months) CI groups. Postoperative receptive/expressive language development was assessed using the Sequenced Language Scale for Infants receptive and expressive standardized scores and percentiles. Results: . Only the very early CI group showed significant improvements in receptive language starting at 3 months post-CI, aligning with normal-hearing peers by 9 months and maintaining this level until age 2 years. During this period (<2 years), all improvements were more pronounced in receptive language than in expressive language. Conclusion . CI before 9 months of age significantly improved receptive language development compared to later CI, with improvements sustained at least up to the age of 2. This study supports the consideration of earlier CI, beyond pediatric Food and Drug Administration labeling criteria (>9 months), in children with profound deafness who have a clear deafness etiology and language development delays (<1st percentile).

Background: Chronic cough is a common symptom encountered by healthcare practitioners.The global prevalence of chronic cough is 9.6%, with a female predominance. The aim of our study is to reveal the sex differences in prevalence and severity of chronic cough in South Korea, stratified by age and etiology. Methods: This study included adult patients with chronic cough who were recruited from 19 respiratory centers in South Korea. Patients completed the cough numeric rating scale (NRS) and COugh Assessment Test (COAT) questionnaire to assess the severity and multidimensional impact of cough. Results: Among the 625 patients, 419 (67.0%) were females, with a male-to-female ratio of 1:2.03. The mean age was 49.4 years, and the median duration of cough was 12 weeks. The mean NRS and COAT scores were 5.5 ± 1.8 and 9.5 ± 3.6, respectively. Female patients were older (45.3 ± 15.4 vs. 51.6 ± 15.2, P < 0.001) and more likely to have asthma/cough variant asthma (CVA) (26.7% vs. 40.8%, P = 0.001) than male patients. There was no difference in the duration or severity of cough between sexes, regardless of the cause. The male-tofemale ratio was lower for upper airway cough syndrome (UACS), asthma/CVA, and gastroesophageal reflux disease (GERD), but not for eosinophilic bronchitis (EB) or unexplained cough. The mean age of female patients was higher in UACS and asthma/CVA, but not in EB, GERD, or unexplained cough. The majority (24.2%) fell within the age category of 50s. The proportion of females with cough increased with age, with a significant rise in the 50s, 60s, and 70–89 age groups. The severity of cough decreased in the 50s, 60s, and 70–89 age groups, with no significant sex differences within the same age group. Conclusion The sex disparities in prevalence and severity of cough varied significantly depending on the age category and etiology. Understanding the specific sex-based difference could enhance comprehension of cough-related pathophysiology and treatment strategies.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.