Extra ventricular neurocytoma (EVN) is a rare brain tumor with histologic features similar with a central neurocytoma, but located outside of the ventricular system. In this study, we present an unusual case of hypothalamic EVN in a 14-year-old patient. The patient underwent subtotal removal and had tumor relapse. The patient was then treated using intensity modulated radiation therapy, and the tumor remained stable for 24 months. This case report may be important in that this is the first pediatric case of EVN located in the hypothalamic region. EVN has similar radiologic features with pilocytic astrocytomas and therefore a hypothalamic EVN may be misdiagnosed as a hypothalamic glioma. Also, the pathologic-radiologic-clinical correlation of EVN located in the hypothalamic area may be different from that of EVNs originating from other usual sites.
Adolescent ; Astrocytoma ; Brain Neoplasms ; Glioma ; Humans ; Hypothalamic Neoplasms ; Neurocytoma* ; Radiotherapy, Adjuvant* ; Recurrence

Extra ventricular neurocytoma (EVN) is a rare brain tumor with histologic features similar with a central neurocytoma, but located outside of the ventricular system. In this study, we present an unusual case of hypothalamic EVN in a 14-year-old patient. The patient underwent subtotal removal and had tumor relapse. The patient was then treated using intensity modulated radiation therapy, and the tumor remained stable for 24 months. This case report may be important in that this is the first pediatric case of EVN located in the hypothalamic region. EVN has similar radiologic features with pilocytic astrocytomas and therefore a hypothalamic EVN may be misdiagnosed as a hypothalamic glioma. Also, the pathologic-radiologic-clinical correlation of EVN located in the hypothalamic area may be different from that of EVNs originating from other usual sites.
Adolescent ; Astrocytoma ; Brain Neoplasms ; Glioma ; Humans ; Hypothalamic Neoplasms ; Neurocytoma* ; Radiotherapy, Adjuvant* ; Recurrence

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.

OBJECTIVE: To investigate the efficacy of adjuvant treatment in patients with high-grade meningioma. METHODS: A retrospective analysis was performed for patients with high-grade meningioma, World Health Organization grade 2 or 3, in a single center between 2003 and 2014. The patients were reviewed according to age at diagnosis, sex, the location of meningioma, degree of tumor resection, histological features, and type of adjuvant treatment. These factors were analyzed by Firth logistic regression analyses. RESULTS: Fifty-three patients with high-grade meningioma were enrolled. Thirty-four patients received adjuvant treatment; conventional radiotherapy or radiosurgery. Clinical follow-up ranged from 13–113 months with a median follow-up of 35.5 months. Gross total removal (GTR), Simpson grade 1 or 2, was achieved in 29 patients and, among them, 13 patients received adjuvant treatment. In the other 24 patients with non-GTR, conventional adjuvant radiotherapy and radiosurgery were performed in 11 and 10 patients, respectively. The other 3 patients did not receive any adjuvant treatment. Radiation-related complications did not occur. Of the 53 patients, 19 patients had suffered from recurrence. The recurrence rate in the adjuvant treatment group was 23.5% (8 out of 34). On the other hand, the rate for the non-adjuvant treatment group was 57.9% (11 out of 19) (odds ratio [OR]=0.208, p=0.017). In the GTR group, the recurrence rate was 7.5% (1 out of 13) for patients with adjuvant treatment and 50% (8 out of 16) for patients without adjuvant treatment (OR=0.121, p=0.04). CONCLUSION: Adjuvant treatment appears to be safe and effective, and could lead to a lower recurrence rate in high-grade meningioma, regardless of the extent of removal. Our results might be used as a reference for making decisions when planning adjuvant treatments for patients with high-grade meningioma after surgery.
Diagnosis ; Follow-Up Studies ; Hand ; Humans ; Logistic Models ; Meningioma* ; Radiosurgery ; Radiotherapy ; Radiotherapy, Adjuvant ; Recurrence ; Retrospective Studies ; World Health Organization

It has been shown that the extracts including eupatilin and quercetin-3-beta-D-glucuronopyranoside had mucoprotective effects on the esophagus and stomach through their antioxidant activities. This study was designed to investigate the anti-inflammatory effect of these flavonoid compounds in an animal model of inflammatory bowel disease induced by 2,4,6-trinitrobenzene sulfonic acid. Experimental colitis was induced by intracolonic administration of 2,4,6-trinitrobenzene sulfonic acid. Extracts including eupatilin or quercetin-3-beta-D-glucuronopyranoside were orally administered to animals 48, 24, and 1 h prior to the induction of colitis and then again 24 h later. The animals were sacrificed 48 h after by 2,4,6-trinitrobenzene sulfonic acid treatment and the macroscopic appearance of the colonic lesions was scored in a blinded manner on a scale of 1 to 10. The inflammatory response to colitis induction was assessed by measuring myeloperoxidase activity, nitric oxide production, tumor necrosis factor-alpha expression, total glutathione levels, and malondialdehyde concentrations in the colon. The results indicated that extracts including eupatilin and extracts including quercetin-3-beta-D-glucuronopyranoside dose-dependently improved the morphology of the lesions induced by 2,4,6-trinitrobenzene sulfonic acid and reduced the ulcer index accordingly. In addition, rats receiving extracts including eupatilin and extracts including quercetin-3-beta-D-glucuronopyranoside showed significantly decreased levels of mucosal myeloperoxidase activity, nitric oxide production, tumor necrosis factor-alpha expression, and malondialdehyde levels, and increased total glutathione levels. Extracts including eupatilin and extracts including quercetin-3-beta-D-glucuronopyranoside ameliorated the inflammatory response and colonic injury in acute colitis by decreasing oxidative stress and neutrophil activation. Extracts including eupatilin and extracts including quercetin-3-beta-D-glucuronopyranoside may inhibit acute colitis.
Animals ; Colitis* ; Colon ; Esophagus ; Flavonoids* ; Glutathione ; Inflammation ; Inflammatory Bowel Diseases ; Malondialdehyde ; Models, Animal ; Neutrophil Activation ; Nitric Oxide ; Oxidative Stress ; Peroxidase ; Quercetin ; Rats* ; Reactive Oxygen Species ; Stomach ; Tumor Necrosis Factor-alpha ; Ulcer

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.