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.

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.

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.

Recent investigations on familial thoracic aortic aneurysm and dissection (TAAD) identified several genetic variants. Meanwhile, intracranial vasculopathy in familial TAAD has been scarcely reported. We report a case of a young man with Marfanoid habitus and familial TAAD carrying MFAP5, c.472C>T variant. He presented with recurrent thunderclap headache and multifocal intracranial vasculopathy, which is predominantly suggestive of reversible cerebral vasoconstriction syndrome. While the role of MFAP5 in vasculopathy requires clarification, we propose its haploinsufficiency may contribute to both TAAD and intracranial stenosis, highlighting a potential risk of cerebrovascular disease in familial TAAD.

Purpose: This study aimed to assess the factors influencing the political participation of nursing students who, as they become future leaders in the nursing field, will need to increase their participation in health policy decisions. Methods: Data were collected using web-based questionnaires answered by 157 nursing students between March 13 and 27, 2023. Those data were analyzed using t-test, a one-way ANOVA, Pearson’s correlation coefficient, and a multiple linear regression. Results: The average score of political participation was 2.77±0.70 out of 5 points. Political participation showed positive correlations between political efficacy (r=.48, p<.001) and nursing professionalism (r=.27, p<.001). Furthermore, sex (female) and political efficacy were identified as influencing factors on political participation among nursing students, which explained it with 24.0% power. Conclusion Providing timely nursing education is necessary for enhancing political efficacy, which could promote political participation among nursing students.