Background: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis and lacks clinical biomarkers. Exosomes are extracellular vesicles that facilitate cell–cell communication by distributing macromolecules, such as small RNAs (smRNAs). We assessed the potential of exosome-derived small RNAs (Ex-smRNAs) as PDAC biomarkers. Methods: Peripheral blood was collected from 51 patients with PDAC and 15 control individuals. Exosomes were isolated using an aqueous two-phase system. Ex-smRNAs were analyzed using smRNA sequencing. smRNA-mediated target gene regulation was verified via The Cancer Genome Atlas analysis and in vitro transfection and wound-healing assays using PDAC organoids. Results: The total Ex-smRNA count was substantially reduced in patients with PDAC compared with that in control individuals. The levels of microRNAs (miRNAs) miR-125a-5p, miR-30e-5p, miR-16-2-3p, miR-98-5p, and the let-7 family were significantly suppressed, whereas that of miR-6731-5p was significantly elevated. Let-7c-5p and miR-98-5p were found to interact with the long non-coding RNA OLMALINC to regulate their common target genes, BACH1 and CCND1, thus controlling PDAC proliferation and migration. The expressions of CARS1-AS1 and miR-142-5p were upregulated in treatment-responsive patients.Multivariable Cox regression analyses, adjusting for potential prognostic factors such as sex, Eastern Cooperative Oncology Group performance status, and tumor size and stage, revealed that CARS1-AS1 (adjusted hazard ratio [HR] 0.33; 95% confidence interval [CI], 0.15–0.73; P = 0.0061) and miR-142-5p (adjusted HR 0.79; 95% CI, 0.61–1.01; P = 0.0581) were associated with improved overall survival. Conclusions We identified potential Ex-smRNA biomarkers involved in PDAC progression and prognosis that reflect key molecular alterations in PDAC and may serve as clinically relevant biomarkers for disease monitoring.

This guideline aims to promote the prudent use of antibacterial agents for managing carbapenem-resistant Enterobacterales (CRE) infections in clinical practice in Korea. The general section encompasses recommendations for the management of common CRE infections and diagnostics, whereas each specific section is structured with key questions that are focused on antibacterial agents and disease-specific approaches. This guideline covers both currently available and upcoming antibacterial agents in Korea.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Purpose: This study aimed to identify the effects of nurse’s knowledge and self-efficacy on nursing performance in pediatric intravenous fluid management and provide the primary data necessary for the efficient intravenous injection management of hospitalized children. Methods: This study was a descriptive study design with 141 nurses who perform pediatric intravenous therapy care at eight hospitals in the S, C, D, and S regions. Data were collected from September 1, 2023, to September 30, 2023. Results: Nursing performance of pediatric intravenous injection management was significantly positively correlated with knowledge (r=.44, p<.001) and self-efficacy (r=.19, p=.022). Nurses’ knowledge (β=.42, p<.001) and self-efficacy (β=.22, p=.004) of pediatric intravenous injection management and care were identified as significant predictors of nursing performance thereof, with these two factors explaining 21.9% of the variance. Conclusion This study found that knowledge and self-efficacy of pediatric intravenous injection management are significant predictors of the practice of intravenous care among pediatric nurses. Therefore, considering these factors, education and intervention programs should be developed to enhance pediatric nurses' knowledge and self-efficacy regarding intravenous injection management.

Background: Since the emergence of hypervirulent strains of Clostridioides difficile, the incidence of C. difficile infections (CDI) has increased significantly. Methods: To assess the incidence of CDI in Korea, we conducted a prospective multicentre observational study from October 2020 to October 2021. Additionally, we calculated the incidence of CDI from mass data obtained from the Health Insurance Review and Assessment Service (HIRA) from 2008 to 2020. Results: In the prospective study with active surveillance, 30,212 patients had diarrhoea and 907 patients were diagnosed with CDI over 1,288,571 patient-days and 193,264 admissions in 18 participating hospitals during 3 months of study period; the CDI per 10,000 patientdays was 7.04 and the CDI per 1,000 admission was 4.69. The incidence of CDI was higher in general hospitals than in tertiary hospitals: 6.38 per 10,000 patient-days (range: 3.25–12.05) and 4.18 per 1,000 admissions (range: 1.92–8.59) in 11 tertiary hospitals, vs. 9.45 per 10,000 patient-days (range: 5.68–13.90) and 6.73 per 1,000 admissions (range: 3.18–15.85) in seven general hospitals. With regard to HIRA data, the incidence of CDI in all hospitals has been increasing over the 13-year-period: from 0.3 to 1.8 per 10,000 patient-days, 0.3 to 1.6 per 1,000 admissions, and 6.9 to 56.9 per 100,000 population, respectively. Conclusion The incidence of CDI in Korea has been gradually increasing, and its recent value is as high as that in the United State and Europe. CDI is underestimated, particularly in general hospitals in Korea.

This study aimed to evaluate the differences in the baseline characteristics and patterns of antibiotic usage among hospitals based on their participation in the Korea National Antimicrobial Use Analysis System (KONAS). We obtained claims data from the National Health Insurance for inpatients admitted to all secondary- and tertiary-care hospitals between January 2020 and December 2021 in Korea. 15.9% (58/395) of hospitals were KONAS participants, among which the proportion of hospitals with > 900 beds (31.0% vs.2.6%, P < 0.001) and tertiary care (50.0% vs. 5.2%, P < 0.001) was higher than that among non-participants. The consumption of antibiotics targeting antimicrobial-resistant gram positive bacteria (33.7 vs. 27.1 days of therapy [DOT]/1,000 patient-days, P = 0.019) and antibiotics predominantly used for resistant gram-negative bacteria (4.8 vs. 3.7 DOT/1,000 patient-days, P = 0.034) was higher in KONAS-participating versus -non-participating hospitals. The current KONAS data do not fully represent all secondary- and tertiary-care hospitals in Korea; thus, the KONAS results should be interpreted with caution.

Background: We investigated the impacts of tocolytic agents on maternal and neonatal blood glucose levels in women with gestational diabetes mellitus (GDM) who used tocolytics for preterm labor. Methods: This multi-center, retrospective cohort study included women with GDM who were admitted for preterm labor from twelve hospitals in South Korea. We excluded women with multiple pregnancies, anomalies, overt DM diagnosed before pregnancy or 23 weeks of gestation, and women who received multiple tocolytics. The patients were divided according to the types of tocolytics; atosiban, ritodrine, and nifedipine group. We collected baseline maternal characteristics, pregnancy outcomes, maternal glucose levels during hospitalization, and neonatal glucose levels. We compared the frequency of maternal hyperglycemia and neonatal hypoglycemia among three groups. A multivariate logistic regression analysis was performed to evaluate the contributing factors to the occurrence of maternal hyperglycemia and neonatal hypoglycemia. Results: A total of 128 women were included: 44 (34.4%), 51 (39.8%), and 33 (25.8%) women received atosiban, ritodrine, and nifedipine, respectively. Mean fasting blood glucose (FBG) (112.3, 109.6, and 89.5 mg/dL, P < 0.001) and 2-hour postprandial glucose (PPG2) levels (145.4, 148.3, and 116.5 mg/dL, P = 0.004) were significantly higher in atosiban and ritodrine group than those in nifedipine group. Even after adjusting for covariates including antenatal steroid use, gestational age at admission, and pre-pregnancy body mass index, there was an increased risk of high maternal mean FBG (≥ 95 mg/dL) and PPG2 (≥ 120 mg/dL) levels in the atosiban and ritodrine group than in nifedipine group. The atosiban and ritodrine groups are also at increased risk of neonatal hypoglycemia (< 47 mg/dL) compared to the nifedipine group with the odds ratio of 4.58 and 4.67, respectively (P < 0.05). Conclusion There is an increased risk of maternal hyperglycemia and neonatal hypoglycemia in women with GDM using atosiban and ritodrine tocolytics for preterm labor compared to those using nifedipine.

This guideline aims to promote the prudent use of antibacterial agents for managing carbapenem-resistant Enterobacterales (CRE) infections in clinical practice in Korea. The general section encompasses recommendations for the management of common CRE infections and diagnostics, whereas each specific section is structured with key questions that are focused on antibacterial agents and disease-specific approaches. This guideline covers both currently available and upcoming antibacterial agents in Korea.

Purpose: This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images. Methods: This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS. Results: The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision. Conclusion The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.