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.

Background and Objectives: Some individuals exhibit discrepancies between risk classifications assessed using clinical factors and those obtained by vascular imaging. We aimed to evaluate whether statins provide clinical outcome benefits in patients classified as having low to moderate cardiovascular risk but with carotid plaque. Methods: This was a retrospective propensity score matching study. A total of 12,158 consecutive patients undergoing carotid ultrasound between January 2012 to February 2020 were screened. Individuals with low to moderate cardiovascular risk who were not currently recommended for statin therapy but had carotid plaques were included. Among 1,611 enrolled individuals, 806 (statin group: 403, control group: 403) were analyzed. The primary outcomes were major adverse cardiovascular and cerebrovascular events (MACCEs: cardiovascular death, myocardial infarction, coronary revascularization, and ischemic stroke or transient ischemic attack) and all-cause mortality. Results: During the median follow-up of 6.0 years, the incidence of MACCEs did not differ between the groups (6.1 and 5.7/1,000 person-years in the control and statin groups, respectively; adjusted hazard ratio [HR], 0.95; p=0.90). The incidence of all-cause mortality did not differ (3.9 and 3.9/1,000 person-years, respectively; adjusted HR, 1.02; p=0.97). Kaplan-Meier curves revealed similar rates of MACCEs (log-rank p=0.72) and all-cause mortality (log-rank p=0.99) in the 2 groups. Age and smoking were independent predictors of MACCEs. Subgroups exhibited no differences in clinical outcomes with statin use. Conclusions Benefit of statin therapy was likely to be limited in low to moderate risk patients with carotid plaques. These results could guide physicians in clinical decision-making regarding cardiovascular prevention.

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.

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.

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.

Purpose: When splitting a liver for adult and pediatric graft recipients, the retained left medial section (S4) will undergo ischemic necrosis and the right trisection graft becomes an extended right liver (ERL) graft. We investigated the fates of the retained S4 and its prognostic impact in adult split liver transplantation (SLT) using an ERL graft. Methods: This was a retrospective analysis of 25 adult SLT recipients who received split ERL grafts. Results: The mean model for end-stage liver disease (MELD) score was 27.3 ± 10.9 and graft-recipient weight ratio (GRWR) was 1.98 ± 0.44. The mean donor age was 26.5 ± 7.7 years. The split ERL graft weight was 1,181.5 ± 252.8 g, which resulted in a mean GRWR of 1.98 ± 0.44. Computed tomography of the retained S4 parenchyma revealed small ischemic necrosis in 16 patients (64.0%) and large ischemic necrosis in the remaining 9 patients (36.0%). No S4-associated biliary complications were developed. The mean GRWR was 1.87 ± 0.43 in the 9 patients with large ischemic necrosis and 2.10 ± 0.44 in the 15 cases with small ischemic necrosis (P = 0.283). The retained S4 parenchyma showed gradual atrophy on follow-up imaging studies. The amount of S4 ischemic necrosis was not associated with graft (P = 0.592) or patient (P = 0.243) survival. A MELD score of >30 and pretransplant ventilator support were associated with inferior outcomes. Conclusion The amount of S4 ischemic necrosis is not a prognostic factor in adult SLT recipients, probably due to a sufficiently large GRWR.

Purpose: When splitting a liver for adult and pediatric graft recipients, the retained left medial section (S4) will undergo ischemic necrosis and the right trisection graft becomes an extended right liver (ERL) graft. We investigated the fates of the retained S4 and its prognostic impact in adult split liver transplantation (SLT) using an ERL graft. Methods: This was a retrospective analysis of 25 adult SLT recipients who received split ERL grafts. Results: The mean model for end-stage liver disease (MELD) score was 27.3 ± 10.9 and graft-recipient weight ratio (GRWR) was 1.98 ± 0.44. The mean donor age was 26.5 ± 7.7 years. The split ERL graft weight was 1,181.5 ± 252.8 g, which resulted in a mean GRWR of 1.98 ± 0.44. Computed tomography of the retained S4 parenchyma revealed small ischemic necrosis in 16 patients (64.0%) and large ischemic necrosis in the remaining 9 patients (36.0%). No S4-associated biliary complications were developed. The mean GRWR was 1.87 ± 0.43 in the 9 patients with large ischemic necrosis and 2.10 ± 0.44 in the 15 cases with small ischemic necrosis (P = 0.283). The retained S4 parenchyma showed gradual atrophy on follow-up imaging studies. The amount of S4 ischemic necrosis was not associated with graft (P = 0.592) or patient (P = 0.243) survival. A MELD score of >30 and pretransplant ventilator support were associated with inferior outcomes. Conclusion The amount of S4 ischemic necrosis is not a prognostic factor in adult SLT recipients, probably due to a sufficiently large GRWR.