Clinical trials

Tuffier's line is commonly used as a landmark for estimating lumbar vertebral levels during spinal anesthesia. However, its accuracy can vary depending on factors such as age, sex, BMI, and posture. This study evaluates the consistency of an alternative landmark, the "ASIS line" (a line connecting both Anterior Superior Iliac Spines), for identifying lumbar levels in the lateral decubitus position. Researchers will mark both the ASIS line and Tuffier's line on participants and use ultrasound to verify the exact vertebral level where each line crosses the spine. The goal is to determine if the ASIS line provides a more consistent and reliable estimation of lumbar levels compared to Tuffier's line.

The goal of this clinical trial is to test whether wearing a continuous glucose monitor (CGM) part-time works as well as wearing it full-time in adults with type 2 diabetes who do not use insulin. The main questions it aims to answer are: * Does wearing CGM part-time (2 weeks per month) control blood sugar as well as wearing it continuously? * How do the two wearing patterns compare for blood sugar levels throughout the day, weight, blood pressure, and patient satisfaction? Researchers will compare part-time CGM use to continuous CGM use to see if part-time use is just as effective for blood sugar control. All participants will: * Wear a CGM device continuously for 3 months to learn how their blood sugar responds to different foods and activities * Then be randomly assigned to wear CGM either part-time (2 weeks each month) or continuously for another 3 months * Replace the CGM sensor on their arm every 2 weeks * Use a smartphone app to track their blood sugar readings * Visit the clinic 3 times for blood tests and check-ups * Receive education on adjusting diet based on their CGM readings * Keep their current diabetes medications unchanged during the study The study will last about 6 months total. Participants will use the FreeStyle Libre 2 CGM device, which is already approved for use in people with diabetes.

The purpose of this study is to build a comprehensive brain and behavioral dataset on anxiety disorders and identify clinical factors related to treatment response and prognosis in patients with anxiety disorders. By conducting an integrated analysis of clinical features (phenotypes), comorbid conditions, virtual reality (VR)-based behavioral data, electroencephalogram (EEG) data, and MRI-based data in patients undergoing treatment for anxiety disorders and healthy controls, this study aims to enhance the understanding of the pathophysiology of anxiety disorders and establish a foundation for precision medicine. Anxiety disorders significantly impair quality of life by inducing irrational fear and apprehension even in everyday situations. Over the past few years, the prevalence of anxiety disorders and associated medical expenses have surged, escalating the societal and economic burden. However, the current diagnostic system, DSM-5, relies on symptom-based approaches, which often lead to heterogeneity within the same diagnostic category. This limitation hinders the accurate prediction of treatment response and the design of optimal therapeutic strategies. To address these issues, the U.S. National Institute of Mental Health (NIMH) proposed the Research Domain Criteria (RDoC). RDoC is a multidimensional framework that integrates behavioral, psychological, and neurological data to facilitate the development of treatment strategies rooted in pathophysiology. Nonetheless, the acquisition of objective and reliable assessment data remains a challenge due to the inherent characteristics of mental disorders. Virtual reality (VR) technology has emerged as a promising solution to overcome these limitations. VR enables the creation of immersive 3D environments that can elicit anxiety-related behaviors and precisely evaluate them in controlled settings. It also allows data collection in scenarios that closely resemble real-life situations while offering advantages such as repeatability and ease of use. Combining behavioral data collected in VR environments with EEG data can provide a more detailed understanding of the neurophysiological representations underlying anxiety behaviors. EEG, as a biomarker reflecting neural activity in real time, facilitates intuitive and effective analyses. Moreover, MRI-based brain imaging plays a critical role in identifying structural and functional connectivity differences between anxiety disorder patients and healthy controls. It offers crucial insights into the biomarkers that predict treatment outcomes and prognosis. By integrating these data, this study seeks to uncover the key factors influencing treatment response and prognosis in patients with anxiety disorders.

This is a multicenter, open-label, prospective, phase 2 study of trastuzumab, bevacizumab, and paclitaxel as second-line treatment for patients with HER2-positive advanced gastric cancer who had progressed on first-line chemotherapy including trastuzumab or anti-HER2 agents.

This study aimed to evaluate the effectiveness of use of Continuous Glucose Monitoring (CGM), combined with personalized dietary education and use of smartphone applications in improving glucose control and weight management.

Building a Microbiome Data Platform and Conducting Clinical Evidence Research in Individuals Infected with the Human Immunodeficiency Virus (HIV) and Hepatitis B virus.

The aim of this study is to collect data on Korean patients with borderline ovarian tumors.