OBJECTIVES: Individuals with disabilities are at greater risk of hospitalization than the general population. We examined 10-year trends in potentially preventable hospitalizations (PPH) in Korea, comparing individuals with and without disabilities and assessing age-specific patterns. METHODS: Using National Health Information Database claims data (2010–2019), we established a fixed cohort of newly registered individuals with disabilities and control subjects statistically matched (1:1.5) at baseline. Annual PPH rates among patients with each condition were calculated and age- and sex-standardized according to Organization for Economic Cooperation and Development Health Care Quality Indicators definitions. Trends and annual percent changes (APCs) were analyzed by disability status and age group (non-older: 30–64; older adults: ≥65 years). RESULTS: Between 2010 and 2019, PPH rates declined significantly in both groups. Among individuals with disabilities, the steepest decline was observed for hypertension (APC, −15.7%; 95% confidence interval [CI], −17.7 to −13.7), whereas congestive heart failure showed the largest reduction among individuals without disabilities (APC, −7.8%; 95% CI, −10.8 to −4.7). Declines were generally greater among non-older adults aged 30–64 years, regardless of disability status. The disparity between disability and non-disability groups narrowed over the decade, primarily due to larger improvements among non-older adults. Older adults with disabilities consistently exhibited the highest PPH rates for most conditions, whereas younger individuals with disabilities had the highest rates for diabetes. CONCLUSIONS PPH rates declined over the decade among both individuals with and without disabilities, particularly for hypertension and among non-older adults. However, older adults with disabilities remain at elevated risk, underscoring the need for targeted strategies to improve access to community-based primary care.

Objectives: The aim of this study was to assess the impact of a multi-component program, including partially hydrolyzed guar gum (PHGG, Sunfiber®) supplementation, on glycemic control, gut health, and nutritional status to support diabetes prevention and management among Korean adults. Methods: A single-arm trial was conducted with 29 adults (aged 20-55 years) with fasting plasma glucose (FPG) ≥ 100 mg/dL. Over a six-week period, participants engaged in a multi-component program that incorporated the supplementation of PHGG (Sunfiber®, 12.5 g/day), weekly nutritional coaching, and the use of continuous glucose monitoring devices. The program’s effectiveness was evaluated by measuring FPG and glycated hemoglobin (HbA1c) levels through blood tests conducted before and after the intervention. Improvements in gut health were gauged using the Korean Gut Quotient Measurement Scales, while enhancements in nutritional status were assessed using the Nutrition Quotient (NQ) and surveys that evaluated improvements in gut health and nutritional status. Results: Participants’ average age was 43.89 years, with approximately 80% being male. Most participants (about 75%) were classified as overweight or obese. After six-weeks, 17 participants who adhered closely to the program (meeting certification criteria) exhibited significant reductions in key blood glucose markers. FPG levels decreased from 113.06 ± 23.16 mg/dL to 106.24 ± 16.33 mg/dL (P < 0.05), and HbA1c levels decreased from 6.08% ± 0.81% to 5.87% ± 0.53% (P < 0.05). The NQ evaluation revealed significant increases in comprehensive nutrition scores, and in the balance and practice domain scores for all participants (P < 0.05). Furthermore, in the gut health survey, approximately 82.1% of all participants reported experiencing positive changes. Conclusion Among adults with elevated FPG levels, a multi-component intervention program that included PHGG (Sunfiber®) supplementation, structured dietary management, and the use of health-monitoring devices showed significant benefits in improving glycemic control, overall nutritional status, and gut health.Trial Registration: Clinical Research Information Service Identifier: KCT0010049.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Vitamin C is a well-known antioxidant with antiviral, anticancer, and anti-inflammatory properties based on its antioxidative function. Aptamin C, a complex of vitamin C with its specific aptamer, has been reported to maintain or even enhance the efficacy of vitamin C while increasing its stability. To investigate in vivo distribution of Aptamin C, Gulo knockout mice, which, like humans, cannot biosynthesize vitamin C, were administered Aptamin C orally for 2 and 4 weeks.The results showed higher vitamin C accumulation in all tissues when administered Aptamin C, especially in the spleen. Next, the activity of natural killer (NK) cells were conducted. CD69, a marker known for activating for NK cells, which had decreased due to vitamin C deficiency, did not recover with vitamin C treatment but showed an increasing with Aptamin C. Furthermore, the expression of CD107a, a cell surface marker that increases during the killing process of target cells, also did not recover with vitamin C but increased with Aptamin C. Based on these results, when cultured with tumor cells to measure the extent of tumor cell death, an increase in tumor cell death was observed. To investigate the signaling mechanisms and related molecules involved in the proliferation and activation of NK cells by Aptamin C showed that Aptamin C treatment led to an increase in intracellular STAT3 activation. In conclusion, Aptamin C has a higher capability to activate NK cells and induce tumor cell death compared to vitamin C and it is mediated through the activation of STAT3.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Purpose: High-dose radiotherapy (RT) for localized prostate cancer requires careful consideration of target position changes and adjacent organs-at-risk (OARs), such as the rectum and bladder. Therefore, daily monitoring of target position and OAR changes is crucial in minimizing interfractional dosimetric uncertainties. For efficient monitoring of the internal condition of patients, we assessed the feasibility of an auto-segmentation of OARs on the daily acquired images, such as megavoltage computed tomography (MVCT), via a commercial artificial intelligence (AI)-based solution in this study. Materials and Methods: We collected MVCT images weekly during the entire course of RT for 100 prostate cancer patients treated with the helical TomoTherapy system. Based on the manually contoured body outline, the bladder including prostate area, and rectal balloon regions for the 100 MVCT images, we trained the commercially available fully convolutional (FC)-DenseNet model and tested its auto-contouring performance. Results: Based on the optimally determined hyperparameters, the FC-DenseNet model successfully auto-contoured all regions of interest showing high dice similarity coefficient (DSC) over 0.8 and a small mean surface distance (MSD) within 1.43 mm in reference to the manually contoured data. With this well-trained AI model, we have efficiently monitored the patient's internal condition through six MVCT scans, analyzing DSC, MSD, centroid, and volume differences. Conclusion We have verified the feasibility of utilizing a commercial AI-based model for auto-segmentation with low-quality daily MVCT images. In the future, we will establish a fast and accurate auto-segmentation and internal organ monitoring system for efficiently determining the time for adaptive replanning.

Purpose: This study is qualitative study using phenomenology approach of Colazzi, to identify meaning and essential structure of the hospitalization with Complex Regional Pain Syndrome (CRPS). Methods: Experiential data were collected through in-depth interviews with 10 patients who had been hospitalized in hospital rehabilitation medical wards. The main question was ‘Could you describe your hospitalization experience with CRPS? Results: Five categories obtained were ‘Experience despair in the swamp of long suffering’, ‘A hospital system that only adherence to treatment procedures’, ‘There are aggravating factors all over the hospital environment’, ‘Finding support to cover the disease’, and ‘Cross the tunnel of pain with the medical staff’. Conclusion Patients with complex pain syndrome experienced extreme pain and sudden pain even after hospitalization, and were more aggravated due to treatment procedures and regulation systems. It is thought that multidisciplinary team approach nursing intervention is necessary to improve this situation.