Publications by authors named "H S Choe"
At-risk conifer stands growing in hot, arid conditions at low elevations may contain the most climate change-adapted seeds needed for sustainable forestry. This study used a triage framework to identify high-priority survey areas for Pinus ponderosa (Pipo) within a large region, by intersecting an updated range map with a map of seed zones and elevation bands (SZEBs). The framework assesses place-based climate change and potential wildfire risks by rank-order across 740 potential collection units.
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- * Researchers analyzed data from 450 patients, finding that 310 were classified as ultra-low risk (ULR) based on their rapid clinical response and low MAP scores, leading to significantly better outcomes.
- * Patients in the ULR group had higher response rates at day 28 and lower non-relapse mortality at six months, suggesting that careful monitoring can guide safer, more effective GVHD treatment strategies.
Purpose: The purpose of this study was to assess whether an excessively increased medial proximal tibial angle (MPTA) resulted in the deterioration of long-term clinical outcomes after opening wedge high tibial osteotomy (OWHTO) for patients with knee osteoarthritis (OA).
Methods: A total of 69 OA knees that underwent OWHTO, with follow-up for a minimum of 10 years, were retrospectively reviewed. The knee and function scores of the Knee Society Score were assessed separately, and cases with a score decline greater than or equal to the minimal clinically important difference from postoperative 1 to 10 years were defined as showing clinical deterioration.
In the context of a global shortage of glucagon-like peptide-1 (GLP-1) receptor agonists, we assessed the impact of discontinuing dulaglutide on metabolic control in individuals with type 2 diabetes. Our analysis included data from 69 individuals and revealed a significant deterioration in glycemic control following the discontinuation. Specifically, the average hemoglobin A1c level increased from 7.
View Article and Find Full Text PDFAim/introduction: We assess the efficacy of artificial intelligence (AI)-based, fully automated, volumetric body composition metrics in predicting the risk of diabetes.
Materials And Methods: This was a cross-sectional and 10-year retrospective longitudinal study. The cross-sectional analysis included health check-up data of 15,330 subjects with abdominal computed tomography (CT) images between January 1, 2011, and September 30, 2012.