As a key component of the neurovascular unit, astrocytes play an important role in dementia. They regulate cerebral blood flow, promote myelin regeneration, and maintain central nervous system (CNS) homeostasis. However, glial fibrillary acidic protein (GFAP), a biomarker of reactive astrocytes and an emerging indicator of dementia, has often been overlooked. There is still a lack of systematic reviews on its clinical progress in early diagnosis and prognosis assessment of dementia. This review first explores the pathophysiological mechanisms of astrocytes in dementia and describes the biological characteristics of GFAP. We then summarize the role of GFAP in early diagnosis and prognosis of different dementia types, including Alzheimer's disease (AD), vascular dementia (VaD), and frontotemporal dementia (FTD), with a focus on advances in AD research. We also discuss the latest GFAP detection technologies, such as SIMOA and Lumipulse, and issues like differences between plasma and CSF GFAP measurements. This review aims to provide a comprehensive understanding of GFAP's role in dementia research and clinical practice, offering a theoretical basis for developing more effective diagnostics and therapies.
PURPOSE:This study aimed to identify distinct trajectories of self-rated health (SRH) among frail older adults and examine sociodemographic and health-related factors associated with each trajectory group. METHODS:A secondary analysis was conducted using data from Waves 5-9 of the Korean Longitudinal Study of Aging (KLoSA). Of 10,436 participants, 552 community-dwelling adults aged 65 years or older who met the frailty criteria were included. Group-based trajectory modeling was applied to identify SRH trajectories, and multinomial logistic regression was performed to examine predictors of trajectory group membership, with the high-health declining group serving as the reference. RESULTS:Five SRH trajectories were identified: Low-Declining SRH (Group 1), Increasing SRH (Group 2), Decline-Then-Increasing SRH (Group 3), Moderate-Declining SRH (Group 4), and High-Declining SRH (Group 5; reference). Compared with the reference group, Group 1 exhibited lower levels of weekly physical activity (OR = 0.47), social participation (OR = 0.46), and income (OR = 0.59), as well as a higher prevalence of hypertension, diabetes, and denture use. Group 2, which showed linear improvement, included more women (OR = 0.35) and individuals with lower income (OR = 0.52). Group 3 was less likely to receive the Basic Old-Age Pension (OR = 0.31), suggesting relatively higher socioeconomic status. Group 4 demonstrated lower social participation (OR = 0.54) and higher prevalence of hypertension (OR = 1.69). CONCLUSION:Frail older adults demonstrate heterogeneous SRH trajectories. Social participation, health behaviors, and economic resources are key determinants of SRH patterns. SRH may serve as a practical screening indicator for identifying high-risk groups and informing tailored community-based care strategies.
PURPOSE:The purpose of the study was to explore the self-management experiences of low-income patients with diabetes. METHODS:This qualitative study employed thematic analysis to derive inductive themes from in-depth interviews with 25 low-income patients with diabetes in Korea. Data were collected through face-to-face interviews between February and August 2022, transcribed verbatim, and analyzed using Braun and Clarke's 6-phase framework. RESULTS:The results revealed that participants developed diabetes due to factors including busy and stressful lifestyles, alcohol dependence, and treatments for other illnesses. Unemployment, smoking and drinking, and financial difficulties hindered self-management, resulting in the participants losing motivation. Financial instability made diabetes management more difficult. Their medical and living expenses were covered by government support, which led them to prioritize maintaining their eligibility for this assistance over having a job and income. However, this ultimately had a devastating effect on the management of their disease. CONCLUSIONS:The hardships of life, financial burden of diabetes self-management, and lack of motivation and education on diabetes self-management hinder effective self-management. It is recommended that a program be developed that can help low-income patients with diabetes cope with the disease, provide continuous motivation, and increase their self-efficacy. Educational programs on diabetes management following diagnosis are essential to support long-term self-management and reduce health inequalities in the country.
Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i.e., vertebrae and skull). However, sCTA is not considered mainstream because of the high radiation dose generated by the two-scan protocol. We aimed to solve the overexposure problem by training a U-Net-based CA segmentation model using a low-dose computed tomographic angiography (CTA) image-based dataset with various pre-processing methods to achieve a performance similar to that of sCTA. We optimized a non-local means (NLM) algorithm using the coefficient of variation and contrast-to-noise ratio. In addition, datasets were constructed by predicting the CA mask using a semiautomatic thresholding technique based on region growing method. Then, CTA images of 35 (2052 slices), 4 (248 slices), and 5 patients (594 slices) were used, respectively, for the train, validation, and test sets. To evaluate the performance of the U-Net-based CA segmentation model quantitatively according to the constructed dataset, the average precision (AP), intersection over union (IoU), and F1-score were calculated. For the dataset to which both the optimized NLM algorithm and semiautomatic thresholding technique were applied, the segmentation model showed the most improved performance. In particular, the quantitative evaluation of the low-dose CTA image with the NLM algorithm and the semiautomatic thresholding-based U-Net model calculated AP, IoU, and F1-scores of approximately 0.880, 0.955, and 0.809, respectively, which were most similar to the CA segmentation performance of the sCTA technique. The proposed U-Net model provided CA segmentation results without additional radiation exposure. In addition, the selection and optimization of an appropriate pre-processing methods were identified as essential for achieving higher segmentation performance for the U-Net model.
This study explored factors influencing foot self-care behaviors across adherence in patients with a diabetic foot ulcers history using quantile regression analysis. A descriptive cross-sectional design was employed, and 130 patients receiving outpatient treatment at a tertiary hospital in South Korea were recruited through convenience sampling. Data were collected between August 18, 2022, and November 4, 2022, using a structured questionnaire. Quantile regression revealed that foot care knowledge significantly influenced all quantiles (10%, 25%, 50%, 75%, 90%). Stigma impacted middle and upper quantiles (50%, 75%, 90%), while severe depression was significant in the upper quantiles (75%, 90%). Social support influenced all quantiles except the 10% quantile (25%, 50%, 75%, 90%). The explanatory power for the quantiles ranged from 24.4% to 28.8%. The findings emphasize the importance of considering influencing factors across different levels of foot self-care behaviors when designing tailored interventions. Enhancing foot self-care behaviors is critical for patients with a history of diabetic foot ulcers, and interventions based on these results can serve as effective strategies to promote self-care and prevent further complications.
