Background: Handgrip strength (HGS) is an indicator of late-life health, associated with disability, cardiovascular and all-cause mortality. While exercise helps maintain HGS, diabetes may reduce its benefits. This study investigated the association between exercise and low HGS (LHGS) stratified by diabetes status and sex. Methods: Data from 16,443 participants in the 2017–2019 Korea National Health and Nutrition Examination Survey were analyzed. HGS was measured using a digital dynamometer. Exercise type and frequency were assessed by questionnaire. Three-way and twoway interaction terms were analyzed for exercise, sex and diabetes. Results: Aerobic exercise, resistance exercise, and diabetes were significantly associated with LHGS (P<0.05). A significant interaction between diabetes and aerobic exercise was observed in females (odds ratio [OR] of LHGS=1.704, 95% confidence interval= 1.073–2.707). Among males, both exercise types were associated with lower odds of LHGS regardless of diabetes status, except for aerobic exercise in non-diabetic males. Among males, the ORs of LHGS for aerobic and resistance exercise were 0.479 (0.278– 0.827) and 0.317 (0.165–0.611) with diabetes, 0.757 (0.554–1.035) and 0.536 (0.360–0.798) without diabetes. Among females, the ORs of LHGS for aerobic exercise and resistance exercise were 1.109 (0.716–1.719) and 0.529 (0.224–1.249) with diabetes, 0.676 (0.539–0.848) and 0.795 (0.564–1.121) without diabetes. Conclusions The modifying effect of diabetes on the relationship between exercise and grip strength was observed in females but not in males. Females with diabetes may require tailored exercise guideline to prevent LHGS.

Background and Objectives: Clinical characteristics and outcomes in acute heart failure (AHF) vary by phenotype. We assessed phenotype-specific features, treatment patterns, and outcomes in a nationwide Korean cohort. Methods: The Korean Heart Failure III registry prospectively enrolled 7,351 AHF admissions at 47 hospitals. Among 6,777 patients with available left ventricular ejection fraction (EF), phenotypes were defined as heart failure with reduced EF (HFrEF, ≤40%), mildly reduced EF (HFmrEF,41–49%), or preserved EF (HFpEF, ≥50%). The primary endpoint was a 12-month composite of all-cause death or heart transplantation, evaluated from index admission and, among hospital survivors, from discharge. We used inverse probability weighting (multinomial generalized boosted models with stabilized, trimmed weights) and weighted Cox proportional-hazards models to estimate hazard ratios (HRs). Results: Phenotype distribution was 58.9% HFrEF, 13.6% HFmrEF, and 27.5% HFpEF. Crude 12-month composite rates from index admission were 13.4% (HFrEF), 12.7% (HFmrEF), and 16.8% (HFpEF). After weighting, from index admission, HFmrEF (HR, 0.892; 95% confidence interval [CI], 0.731–1.088) and HFpEF (HR, 1.101; 95% CI, 0.939–1.291) did not differ from HFrEF; from discharge, HFpEF had modestly higher risk (HR, 1.207; 95% CI, 1.008–1.445) whereas HFmrEF did not (HR, 1.039; 95% CI, 0.844–1.279). Hyponatremia and chronic kidney disease were consistent adverse markers, while angiotensin-converting enzyme inhibitor/ angiotensin II receptor blocker use at discharge was protective. Conclusions Across the EF spectrum, phenotypes showed distinct profiles and risk. Postdischarge risk was modestly higher in HFpEF, supporting phenotype-tailored care and systematic discharge optimization in Korean patients with AHF.

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.

Osteoarthritis (OA) involves cartilage degeneration, thereby causing inflammation and pain. Cardiovascular diseases, such as dyslipidemia, are risk factors for OA; however, the mechanism is unclear. We investigated the effect of dyslipidemia on the development of OA. Treatment of cartilage cells with low-density lipoprotein (LDL) enhanced abnormal autophagy but suppressed normal autophagy and reduced the activity of transcription factor EB (TFEB), which is important for the function of lysosomes. Treatment of LDL-exposed chondrocytes with rapamycin, which activates TFEB, restored normal autophagy. Also, LDL enhanced the inflammatory death of chondrocytes, an effect reversed by rapamycin. In an animal model of hyperlipidemia-associated OA, dyslipidemia accelerated the development of OA, an effect reversed by treatment with a statin, an anti-dyslipidemia drug, or rapamycin, which activates TFEB. Dyslipidemia reduced the autophagic flux and induced necroptosis in the cartilage tissue of patients with OA. The levels of triglycerides, LDL, and total cholesterol were increased in patients with OA compared to those without OA. The C-reactive protein level of patients with dyslipidemia was higher than that of those without dyslipidemia after total knee replacement arthroplasty. In conclusion, oxidized LDL, an important risk factor of dyslipidemia, inhibited the activity of TFEB and reduced the autophagic flux, thereby inducing necroptosis in chondrocytes.

Background: According to international pediatric urinary tract infection (UTI) guidelines, selecting ampicillin/ sulbactam or amoxicillin/clavulanate is recommended as the first-line treatment for pediatric UTI. In Korea, elevated resistance to ampicillin and ampicillin/sulbactam has resulted in the widespread use of third-generation cephalosporins for treating pediatric UTIs. This study aims to compare the efficacy of piperacillin-tazobactam (TZP) and cefotaxime (CTX) as first-line treatments in hospitalized children with UTIs. Materials and Methods: The study, conducted at Jeju National University Hospital, retrospectively analyzed medical records of children hospitalized for febrile UTIs between 2014 and 2017. UTI diagnosis included unexplained fever, abnormal urinalysis, and the presence of significant uropathogens. Treatment responses, recurrence, and antimicrobial susceptibility were assessed. Results: Out of 323 patients, 220 met the inclusion criteria. Demographics and clinical characteristics were similar between TZP and CTX groups. For children aged ≥3 months, no significant differences were found in treatment responses and recurrence. Extended-spectrum beta-lactamase (ESBL)-positive strains were associated with recurrence in those <3 months. Conclusion In Korea, escalating resistance to empirical antibiotics has led to the adoption of broad-spectrum empirical treatment. TZP emerged as a viable alternative to CTX for hospitalized children aged ≥3 months with UTIs.Consideration of ESBL-positive strains and individualized approaches for those <3 months are crucial.

Despite the widespread impact of the severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019, COVID-19) and vaccination in South Korea, our understanding of kidney diseases following these events remains limited. We aimed to address this gap by investigating the characteristics of glomerular diseases following the COVID-19 infection and vaccination in South Korea. Methods: Data from multiple centers were used to identify de novo glomerulonephritis (GN) cases with suspected onset following COVID-19 infection or vaccination. Retrospective surveys were used to determine the COVID-19–related histories of patients who were initially not implicated. Bayesian structural time series and autoregressive integrated moving average models were used to determine causality. Results: Glomerular diseases occurred shortly after the infection or vaccination. The most prevalent postinfection GN was podocytopathy (42.9%), comprising primary focal segmental glomerulosclerosis and minimal change disease, whereas postvaccination GN mainly included immunoglobulin A nephropathy (IgAN; 57.9%) and Henoch-Schönlein purpura nephritis (HSP; 15.8%). No patient progressed to end-stage kidney disease. Among the patients who were initially not implicated, nine patients with IgAN/HSP were recently vaccinated against COVID-19. The proportion of glomerular diseases changed during the pandemic in South Korea, with an increase in acute interstitial nephritis and a decrease in pauci-immune crescentic GN. Conclusion: This study showed the characteristics of GNs following COVID-19 infection or vaccination in South Korea. Understanding these associations is crucial for developing effective patient management and vaccination strategies. Further investigation is required to fully comprehend COVID-19’s impact on GN.

Background: Multiple endocrine neoplasia type 1 (MEN1) is an autosomal dominant disorder characterized by tumors in multiple endocrine organs, caused by variants in the MEN1 gene. This study analyzed the clinical and genetic features of MEN1 in a Korean cohort, identifying prevalent manifestations and genetic variants, including novel variants. Methods: This multicenter retrospective study reviewed the medical records of 117 MEN1 patients treated at three tertiary centers in Korea between January 2012 and September 2022. Patient demographics, tumor manifestations, outcomes, and MEN1 genetic testing results were collected. Variants were classified using American College of Medical Genetics and Genomics (ACMG) and French Oncogenetics Network of Neuroendocrine Tumors propositions (TENGEN) guidelines. Results: A total of 117 patients were enrolled, including 55 familial cases, with a mean age at diagnosis of 37.4±15.3 years. Primary hyperparathyroidism was identified as the most common presentation (84.6%). The prevalence of gastroenteropancreatic neuroendocrine tumor and pituitary neuroendocrine tumor (PitNET) was 77.8% (n=91) and 56.4% (n=66), respectively. Genetic testing revealed 61 distinct MEN1 variants in 101 patients, with 18 being novel. Four variants were reclassified according to the TENGEN guidelines. Patients with truncating variants (n=72) exhibited a higher prevalence of PitNETs compared to those with non-truncating variants (n=25) (59.7% vs. 36.0%, P=0.040). Conclusion The association between truncating variants and an increased prevalence of PitNETs in MEN1 underscores the importance of genetic characterization in guiding the clinical management of this disease. Our study sheds light on the clinical and genetic characteristics of MEN1 among the Korean population.

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.