Background: Human muscle satellite (stem) cells (MuSCs) are essential for investigating muscle physiology, regeneration, and disease mechanisms. Primary cultures derived directly from human tissues offer a more physiologically relevant model than immortalized cell lines. However, the isolation and characterization of MuSCs from human upper limb tissues are limited. Therefore, this study aimed to establish and characterize a primary culture system for MuSCs obtained from human upper limb muscle tissue. Methods: Human muscle tissues were obtained from upper limb surgical specimens. Muscle samples were mechanically and enzymatically dissociated to isolate muscle-derived cells, which were cultured under standard growth conditions. Cell morphology and proliferation were monitored during the culture period. Myogenic characteristics were assessed by examining the expression of muscle-specific markers including myogenic regulatory factors and structural proteins. Additionally, myogenic differentiation capacity was evaluated by inducing differentiation and analyzing the formation of multinucleated myotubes. Results: Primary MuSCs were isolated from human upper limb tissues and expanded in vitro. The cultured cells exhibited a typical spindle-shaped morphology and demonstrated significant proliferative capacity. Characterization confirmed the expression of myogenic markers, indicating the presence of muscle-derived precursor cells. Following induction of differentiation, the cells formed multinucleated myotube-like structures and expressed muscle proteins associated with differentiation, highlighting their potential for myogenic differentiation. Conclusion This study established a reliable protocol for isolating and culturing MuSCs from human upper limb tissues. Cultured cells displayed typical myogenic characteristics and differentiation capacity, indicating that this model could be a valuable platform for studying human muscle biology and potential therapeutic applications.

Background: Motorcycles are widely used in various transportation sectors, but riders are directly exposed to external risks. Consequently, motorcyclists are more vulnerable to severe injuries. Road speed limits serve as key policy interventions to mitigate this risk. This study aims to evaluate the effects of reducing urban road speed limits on motorcyclist traffic accidents. Methods: In this study, National Emergency Department Information System data from the seven largest cities in South Korea were analyzed by comparing a pre-implementation (April 17, 2018 to April 16, 2019) and post-implementation (April 17, 2021 to April 16, 2022) phase. The Pearson chi-square test was used. Additionally, univariable and multivariable logistic regression analyses were performed to assess the effects of the policy on emergency operations, intensive care unit (ICU) admission, and clinical outcomes. Statistical significance was set at p<0.05. Results: The number of patients decreased from 16,124 to 13,201, along with a reduction in emergency surgeries (n=61) and ICU admissions (n=184); however, unfavorable outcomes increased (n=9). The risk of emergency surgery (adjusted odds ratio [aOR], 1.093; 95% confidence interval [CI], 0.935–1.277) was not statistically significant. However, the risk of ICU admission (aOR, 1.147; 95% CI, 1.015–1.296) and unfavorable outcomes (aOR, 1.502; 95% CI, 1.052–2.145) increased significantly in the post-implementation period. Conclusion Although the number of patients significantly decreased, there was no corresponding improvement in clinical outcomes. Instead of abolishing the policy, revising it would be a more appropriate approach. Therefore, additional public interventions and educational programs are required.

Objectives: This study explored the experiences of medical students enrolled in an elective course titled "Healthcare Innovation and Women's Ventures II" at Ewha Womans University College of Medicine. The research questions were as follows: First, what motivated medical students to participate in the experiential entrepreneurship course? Second, what experiences did the students have during the course? Third, what changes did the students undergo as a result of the course? Methods: Focus group interviews were conducted with six medical students who participated in the experiential entrepreneurship course from February 13 to 23, 2024. Results: The analysis identified three domains, seven categories, and 17 subcategories. In terms of motivations for enrolling in the experiential entrepreneurship course, two categories were identified: "existing interest" and "new exploration." With respect to the experiences gained from the course, three categories emerged: "cognitive experiences," "emotional experiences," and "behavioral experiences." Finally, two categories were identified concerning the changes participants experienced through the course: "changes related to entrepreneurship" and "changes related to career paths." Conclusion Students were motivated to enroll in this course by both their existing interests and their desire to explore new areas. Following the course, they underwent cognitive, emotional, and behavioral changes. Their perceptions of entrepreneurship and career paths were significantly altered.This study is important because it explores the impact of entrepreneurship education in medical schools from the students' perspective.

Background: When suspicious lesions are observed on computer-tomography (CT), invasive tests are needed to confirm lung cancer. Compared with other procedures, bronchoscopy has fewer complications. However, the sensitivity of peripheral lesion through bronchoscopy including washing cytology is low. A new test with higher sensitivity through bronchoscopy is needed. In our previous study, DNA methylation of PCDHGA12 in bronchial washing cytology has a diagnostic value for lung cancer. In this study, combination of PCDHGA12 and CDO1 methylation obtained through bronchial washing cytology was evaluated as a diagnostic tool for lung cancer. Methods: A total of 187 patients who had suspicious lesions in CT were enrolled. PCDHGA12methylation test, CDO1 methylation test, and cytological examination were performed using 3-plex LTE-qMSP test. Results: Sixty-two patients were diagnosed with benign diseases and 125 patients were diagnosed with lung cancer. The sensitivity of PCDHGA12 was 74.4% and the specificity of PCDHGA12 was 91.9% respectively. CDO1 methylation test had a sensitivity of 57.6% and a specificity of 96.8%. The combination of both PCDHGA12 methylation test and CDO1 methylation test showed a sensitivity of 77.6% and a specificity of 90.3%. The sensitivity of lung cancer diagnosis was increased by combining both PCDHGA12 and CDO1 methylation tests. Conclusion Checking DNA methylation of both PCDHGA12 and CDO1 genes using bronchial washing fluid can reduce the invasive procedure to diagnose lung cancer.

Objective: The aim of this study was to analyze the origins of prostatic arteries (PAs) in the Korean population and compare them with those reported in the literature. Materials and Methods: From April 2018 to February 2024, 108 male (mean age ± standard deviation: 71.6 ± 9.7 years) with lower urinary tract symptoms (n = 102) or refractory hematuria (n = 6) underwent prostatic artery embolization (PAE). Computed tomography and angiography images were retrospectively reviewed. The branching pattern of the internal iliac artery (IIA) was classified according to the Yamaki system. The origin of the PA was categorized using the de Assis definition, and the incidence of each type was recorded. A systematic literature review was conducted and the most common types of PA were investigated. Results: PAE was successfully implemented on 211 of the 216 pelvic sidewalls. PA cannulation failed in five sidewalls due to a steno-occlusive state. The most common IIA type was type A, in which the IIA was divided into the superior gluteal artery and gluteal–pudendal trunk (77%). Of 226 PAs analyzed, including 15 in 211 sidewalls exhibiting dual PAs, the most common PA origin was the internal pudendal artery (type IV, 35%), followed by the superior vesical (type I, 25%) and obturator (type III, 21%) arteries. Anterior division of IIA (type II) was less common (10%). Type V (uncommon origins) occurred in 8% of cases, including five distal internal pudendal arteries, four quadfurcations, three inferior gluteal arteries, three trifurcations, two medial femoral circumflex arteries, and two rectal arteries. Two of the five patients with surgically or endovascularly altered anatomy were successfully treated via PAs originating from the medial femoral circumflex arteries. Globally, type I is the most common PA type. Conclusion In the Korean population, the most common IIA pattern and PA origin were types A and IV, respectively.

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.