Purpose: This study aimed to investigate the influence of uncertainty-related factors on the health behavior of individuals with coronary artery disease (CAD) based on Mishel’s uncertainty in illness theory (UIT). Methods: We conducted a cross-sectional study and path analysis to investigate uncertainty and factors related to health behavior. The study participants were 228 CAD patients who visited the outpatient cardiology department between September 2020 and June 2021. We used SPSS 25.0 and AMOS 25.0 software to analyze the data. Results: The final model demonstrated a good fit with the data. Eleven of the twelve paths were significant. Uncertainty positively affected danger and negatively affected self-efficacy and opportunity. Danger had a positive effect on perceived risk. Opportunity positively affected social support, self-efficacy, perceived benefit and intention, whereas it negatively affected perceived risk. Social support, self-efficacy, perceived benefit and intention had a positive effect on health behavior. We found that perceived benefit and intention had the most significant direct effects, whereas self-efficacy indirectly affected the relationship between uncertainty and health behavior. Conclusion The path model is suitable for predicting the health behavior of CAD patients who experience uncertainty. When patients experience uncertainty, interventions to increase their self-efficacy are required first. Additionally, we need to develop programs that quickly shift to appraisal uncertainty as an opportunity, increase perceived benefits of health behavior, and improve intentions.

Urinary tract infections (UTIs) are one of the most common bacterial infections in febrile children and a common cause of hospitalization, especially in very young children. We examined the clinical characteristics and predictive factors of concomitant bacteremia in pediatric patients with febrile UTI aged ≤24 months. Methods: This retrospective multicenter study reviewed medical data from 2,141 patients from three centers from January 2000 to December 2019. Enrolled cases were classified into the bacteremic UTI and non-bacteremic UTI groups according to the presence of blood culture pathogens. Results: Among 2,141 patients with febrile UTI, 40 (1.9%) had concomitant bacteremia. All patients in the bacterial group were aged ≤6 months. Multivariate analysis revealed that younger age, lower blood lymphocyte counts and serum albumin levels, higher C-reactive protein (CRP) levels, blood urea nitrogen (BUN) levels, and BUN/serum albumin ratio were independent risk factors of concomitant bacteremia. The area under the receiver-operating characteristic curves predicting bacteremia were 0.668 for CRP, 0.673 for lymphocytes, and 0.759 for the BUN/albumin ratio. Conclusion: The present study identified the BUN/albumin ratio and lower blood lymphocyte counts as novel predictive factors for bacteremia in young infants with febrile UTI in addition to the previously identified factors of younger age and higher CRP levels. Our findings could help to identify patients at high risk of bacteremia and benefit decision-making in the management of infants with febrile UTI.

Purpose: We investigated the consistent efficacy and safety of eflapegrastim, a novel long-acting granulocyte-colony stimulating factor (G-CSF), in Koreans and Asians compared with the pooled population of two global phase 3 trials. Materials and Methods: Two phase 3 trials (ADVANCE and RECOVER) evaluated the efficacy and safety of fixed-dose eflapegrastim (13.2 mg/0.6 mL [3.6 mg G-CSF equivalent]) compared to pegfilgrastim (6 mg based on G-CSF) in breast cancer patients who received neoadjuvant or adjuvant docetaxel/cyclophosphamide. The primary objective was to demonstrate non-inferiority of eflapegrastim compared to pegfilgrastim in mean duration of severe neutropenia (DSN) in cycle 1, in Korean and Asian subpopulations. Results: Among a total of 643 patients randomized to eflapegrastim (n=314) or pegfilgrastim (n=329), 54 Asians (29 to eflapegrastim and 25 to pegfilgrastim) including 28 Koreans (14 to both eflapegrastim and pegfilgrastim) were enrolled. The primary endpoint, DSN in cycle 1 in the eflapegrastim arm was non-inferior to the pegfilgrastim arm in Koreans and Asians. The DSN difference between the eflapegrastim and pegfilgrastim arms was consistent across populations: –0.120 days (95% confidence interval [CI], –0.227 to –0.016), –0.288 (95% CI, –0.714 to 0.143), and –0.267 (95% CI, –0.697 to 0.110) for pooled population, Koreans and Asians, respectively. There were few treatment-related adverse events that caused discontinuation of eflapegrastim (1.9%) or pegfilgrastim (1.5%) in total and no notable trends or differences across patient populations. Conclusion This study may suggest that eflapegrastim showed non-inferior efficacy and similar safety compared to pegfilgrastim in Koreans and Asians, consistently with those of pooled population.

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions.However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.