In the neurovascular unit, the neuronal and vascular systems communicate with each other. O2 and nutrients, reaching endothelial cells (ECs) through the blood stream, spread into neighboring cells, such as neural stem cells, and neurons. The proper function of neural circuits in adults requires sufficient O2 and glucose for their metabolic demands through angiogenesis. In a central nervous system (CNS) injury, such as glioma, Parkinson’s disease, and Alzheimer’s disease, damaged ECs can contribute to tissue hypoxia and to the consequent disruption of neuronal functions and accelerated neurodegeneration. This review discusses the current evidence regarding the contribution of oxygen deprivation to CNS injury, with an emphasis on hypoxia-inducible factor (HIF)-mediated pathways and Notch signaling. Additionally, it focuses on adult neurological functions and angiogenesis, as well as pathological conditions in the CNS. Furthermore, the functional interplay between HIFs and Notch is demonstrated in pathophysiological conditions.

Objective: Financial hardship influences depression risk, however, the pathway of the effect of financial hardship on depression and the role of self-esteem remain unclear. This study examined whether changes in financial hardship affected depression, and whether self-esteem mediated by this relationship. Methods: Data from 99,588 observations of 15,331 individuals were extracted from 10 waves of the Korean Welfare Panel Study. The association between changes in financial hardship and depression was investigated using a generalized estimation equation, and the extent to which these associations were mediated by self-esteem was assessed. Results: The results indicated that changes in financial hardship were associated with depression, with varying magnitude. Experiencing severe financial hardship over two consecutive years (odds ratio [OR]: 3.87, 95% confidence interval [CI]=3.09–4.85) or increased financial hardship over the previous year strongly influenced depression (e.g., OR: 3.88, 95% CI=3.09–4.86 for low financial hardship at t-1 year and high at t year). Self-esteem plays a mediating role in the relationship between changes in financial hardship and depression, where persistent financial hardship is associated with low self-esteem, leading to depression. Conclusion These findings highlighted the importance of monitoring and intervention for financial hardship and psychological problems to help manage depression.

BACKGROUND: Diabetes education, also known as diabetes self-management training or diabetes selfmanagement education, is effective in helping patients with diabetes control their illness and maximize their health. However, there is no established institutional strategy in South Korea because economic evaluations of the benefits and costs of diabetes education have been limited. The purpose of this study is two-fold: (1) describe economic evaluation methodologies, one of the tools available to help choose wisely from a range of alternatives and implement effective resources; and (2) suggest applications of economic evaluation in terms of diabetes education. METHODS: There are three types of commonly used economic evaluations in diabetes education: cost benefit analysis, cost effective analysis, and cost utility analysis. RESULTS: The understanding of the economic value of diabetes education for people with diabetes has a number of uses: to provide empirical evidence to influence policy-making in diabetes education, to offer proof of the benefits of diabetes self-management, to improve awareness of the importance and necessity of diabetes education, to reduce costs of diabetes management, and to enhance healthcare quality. CONCLUSION: Further research is needed to evaluate the economic benefits and costs associated with diabetes education.
Cost-Benefit Analysis ; Education* ; Evaluation Studies as Topic ; Humans ; Korea ; Quality of Health Care ; Self Care

Objectives: Mild cognitive impairment (MCI) is known to have a high rate of progression to Alzheimer’s disease. Early detection and intervention of MCI are of great interest in psychiatric and socioeconomic aspects. There are various screening tools for MCI, but their sensitivity and specificity vary greatly. This study assessed the usefulness of virtual reality (VR) neurocognitive tests as an assessment tool for neurocognitive function deficit in MCI. Methods: Both VR neurocognitive tests and conventional neurocognitive tests, including MiniMental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the Seoul Neuropsychological Screening Battery (SNSB), were conducted, and 21 participants completed the tests. The test results of the MCI and normal groups were compared, and correlation coefficients between the VR neurocognitive tests and SNSB were examined. Results: The mean VR neurocognitive test total score of the MCI participants was significantly lower than that of normal participants (30.0±1.0 vs. 36.9±6.4; p<0.001). There were no significant differences in the SNSB, MMSE, and MoCA scores between the two groups. The VR neurocognitive total score correlated significantly with the MMSE, MoCA, and SNSB total scores (r=0.61, r=0.54, r=0.50, respectively; p<0.05). The scores of the subdomains of VR neurocognitive tests showed significant correlations with those of MMSE, MoCA, and subdomains of SNSB, with VR executive function and visuospatial function scores showing significant correlations with the SNSB executive function (r=0.46; p<0.05) and visuospatial function (r=0.60; p<0.01) scores, respectively. Conclusion This preliminary study suggests that the VR neurocognitive test can be a feasible and realistic tool for assessing the subtle but complex cognitive deficits in MCI, emphasizing spatial reasoning and executive functions.

Background and Objectives: Self-monitoring of blood pressure (SMBP) is a reliable method used to assess BP accurately. However, patients do not often know how to respond to the measured BP value. We developed a mobile application-based feed-back algorithm (SMBPApp) for tailored recommendations. In this study, we aim to evaluate whether SMBP-App is superior to SMBP alone in terms of BP reduction and drug adherence improvement in patients with hypertension. Methods: Self-Monitoring of blood pressure and Feed-back using APP in TReatment of UnconTrolled Hypertension (SMART-BP) is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App compared with SMBP alone. Patients with uncomplicated essential hypertension will be randomly assigned to the SMBP-App (90 patients) and SMBP alone (90 patients) groups. In the SMBP group, the patients will perform home BP measurement and receive the standard care, whereas in the SMBP-App group, the patients will receive additional recommendations from the application in response to the obtained BP value. Follow-up visits will be scheduled at 12 and 24 weeks after randomization. The primary endpoint of the study is the mean home systolic BP. The secondary endpoints include the drug adherence, the home diastolic BP, home and office BP. Conclusions SMART-BP is a prospective, randomized, open-label, multicenter trial to evaluate the efficacy of SMBP-App. If we can confirm its efficacy, SMBP-App may be scaled-up to improve the treatment of hypertension.Trial Registration: ClinicalTrials.gov Identifier: NCT04470284

Familial hypercholesterolemia (FH) is the most common monogenic disorder. Due to the marked elevation of cardiovascular risk, the early detection, diagnosis, and proper management of this disorder are critical. Herein, the 2022 Korean guidance on this disease is presented. Clinical features include severely elevated low-density lipoprotein cholesterol (LDL-C) levels, tendon xanthomas, and premature coronary artery disease. Clinical diagnostic criteria include clinical findings, family history, or pathogenic mutations in the LDLR, APOB, or PCSK9. Proper suspicion of individuals with typical characteristics is essential for screening. Cascade screening is known to be the most efficient diagnostic approach. Early initiation of lipid-lowering therapy and the control of other risk factors are important. The first-line pharmacological treatment is statins, followed by ezetimibe, and PCSK9 inhibitors as required. The ideal treatment targets are 50% reduction and < 70 or < 55 mg/dL (in the presence of vascular disease) of LDL-C, although less strict targets are frequently used. Homozygous FH is characterized by untreated LDL-C > 500 mg/dL, xanthoma since childhood, and family history. In children, the diagnosis is made with criteria, including items largely similar to those of adults. In women, lipid-lowering agents need to be discontinued before conception.

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

Objective: Differentiating intracranial aneurysms from normal variants using CT angiography (CTA) or MR angiography (MRA) poses significant challenges. This study aimed to evaluate the efficacy of proton-density MRA (PD-MRA) compared to highresolution time-of-flight MRA (HR-MRA) in diagnosing aneurysms among patients with indeterminate findings on conventional CTA or MRA. Materials and Methods: In this retrospective analysis, we included patients who underwent both PD-MRA and HR-MRA from August 2020 to July 2022 to assess lesions deemed indeterminate on prior conventional CTA or MRA examinations. Three experienced neuroradiologists independently reviewed the lesions using HR-MRA and PD-MRA with reconstructed voxel sizes of 0.253 mm3 or 0.23 mm3 , respectively. A neurointerventionist established the gold standard with digital subtraction angiography.We compared the performance of HR-MRA, PD-MRA (0.253 -mm3 voxel), and PD-MRA (0.23 -mm3 voxel) in diagnosing aneurysms, both per lesion and per patient. The Fleiss kappa statistic was used to calculate inter-reader agreement. Results: The study involved 109 patients (average age 57.4 ± 11.0 years; male:female ratio, 11:98) with 141 indeterminate lesions. Of these, 78 lesions (55.3%) in 69 patients were confirmed as aneurysms by the reference standard. PD-MRA (0.253 -mm3voxel) exhibited significantly higher per-lesion diagnostic performance compared to HR-MRA across all three readers: sensitivity ranged from 87.2%–91.0% versus 66.7%–70.5%; specificity from 93.7%–96.8% versus 58.7%–68.3%; and accuracy from 90.8%–92.9% versus 63.8%–69.5% (P ≤ 0.003). Furthermore, PD-MRA (0.253 -mm3 voxel) demonstrated significantly superior per-patient specificity and accuracy compared to HR-MRA across all evaluators (P ≤ 0.013). The diagnostic accuracy of PD-MRA (0.23 -mm3 voxel) surpassed that of HR-MRA and was comparable to PD-MRA (0.253 -mm3 voxel). The kappa values for inter-reader agreements were significantly higher in PD-MRA (0.820–0.938) than in HR-MRA (0.447–0.510). Conclusion PD-MRA outperformed HR-MRA in diagnostic accuracy and demonstrated almost perfect inter-reader consistency in identifying intracranial aneurysms among patients with lesions initially indeterminate on CTA or MRA.

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.