Objectives: This study investigated changes in toothbrushing in adolescents in response to public health policy changes during coronavirus disease 2019 (COVID-19) focusing on behavioral trends before, during, and after policy enforcement according to socio-demographic subgroups. Methods: Data from the Korean Youth Risk Behavior Survey (2018–2024) were divided into 3 periods: before (2018–2019), during (2020–2022), and after (2023–2024) the implementation of COVID-19 school restrictions. A total of 354 943 middle and high school students were included. The primary outcome variable was self-reported toothbrushing after lunch at school. Multivariable logistic regression was conducted, adjusting for confounders such as sex, academic performance, school level, school type, handwashing, and oral health status (toothache and gum bleeding). Results: The compliance rate for toothbrushing declined significantly during the restriction period across all groups. It varied by sex, school type, school level, health-related behaviors (drinking, smoking, handwashing), and oral health status. Female students, middle schoolers, students in single-sex schools, non-smokers, and those practicing hand hygiene showed higher compliance. After restrictions were lifted, toothbrushing rates improved but remained lower than pre-pandemic levels. Recovery of the toothbrushing rate was observed in most groups, particularly among female students, high schoolers, and those with positive health-related behaviors. However, academic performance and oral health status were not significantly associated with recovery rate in adjusted analyses. Conclusions This study highlights that adolescents’ health behaviors are highly sensitive to environmental and policy changes. Variations in compliance and recovery rates underscore the need for adolescent-centered policies that account for both compliance and recovery, especially during crises such as pandemics.

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.

Objective: We evaluated the pharmacokinetics, tissue concentrations, and toxicities of belotecan during rotational intraperitoneal pressurized aerosol chemotherapy (RIPAC) in pigs. Methods: We sprayed belotecan in 10% and 30% of doses for intravenous chemotherapy in six pigs (cohort 1, n=3, 0.50 mg/m2 ; cohort 2, n=3, 1.5 mg/m2 ). We evaluated the time-dependent plasma concentrations of belotecan before RIPAC to 120 hours for the pharmacokinetics, tissue concentrations in twelve peritoneal regions, and hepatic and renal functions before RIPAC to 120 hours in the 2 cohorts. Results: Mean values of the peak plasma concentration (Cmax ), the time to Cmax , the time taken for Cmax to drop in half, and the area under the curve from time zero to the time of last quantifiable concentration were 905 and 3,700 ng/mL, 1.42 and 1.50 hours, 3.64 and 5.60 hours, and 2,260 and 17,900 pg·hr/mL in cohorts 1 and 2, respectively. Mean values of tissue concentrations were 1.5 to 15.3 times higher in cohort 1 than in cohort 2 despite the similar ratio of tissue to plasma concentration, and tissue concentrations in the two cohorts were higher in the parietal peritoneum than in the visceral peritoneum. However, hepatic and renal functions were not different before RIPAC to 120 hours in the two cohorts. Conclusion RIPAC using belotecan of 0.5 mg/m2 and 1.5 mg/m2 may be feasible with fewer hepatic and renal toxicities in pigs. Thus, belotecan of 1.5 mg/m2 may be considered as the starting dose for RIPAC in a phase 1 trial.

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.

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 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.

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.