Introduction: Irisin is a newly identified 112 amino acid hormone, derived as a product of fibronectin type III domain containing 5 (FNDC5), which is highly related to metabolic activity in skeletal muscle and brown fat. The effects of irisin on cardiovascular functions are unknown.
Purpose: To explore the effects of central and peripheral irisin on cardiovascular functions.
Methods: Irisin was either administrated into 3rd ventricle of rats or intravenously, and its effects on blood pressure and cardiac contractibility measured.
Results: Administration of recombinant human irisin into the 3rd brain ventricle of rats activated neurons in the paraventricular nuclei of the hypothalamus. Central administration of irisin increased blood pressure and cardiac contractibility. Exogenous irisin reversed atenolol-induced inhibition of cardiac contractibility. In contrast, peripheral administration of irisin reduced blood pressure in both control and spontaneously hypertensive rats. Irisin dilated mesenteric artery rings through ATP-sensitive potassium channels.
Conclusion: Our studies indicate that central and peripheral irisin may differentially regulate cardiovascular activities.
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http://dx.doi.org/10.1007/s10557-015-6580-y | DOI Listing |
Circ Genom Precis Med
January 2025
Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Los Angeles. (W.F., N.D.W.).
Background: Lp(a; Lipoprotein[a]) is a predictor of atherosclerotic cardiovascular disease (ASCVD); however, there are few algorithms incorporating Lp(a), especially from real-world settings. We developed an electronic health record (EHR)-based risk prediction algorithm including Lp(a).
Methods: Utilizing a large EHR database, we categorized Lp(a) cut points at 25, 50, and 75 mg/dL and constructed 10-year ASCVD risk prediction models incorporating Lp(a), with external validation in a pooled cohort of 4 US prospective studies.
Hypertension
January 2025
Department of Nephrology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany (S.A.P., I.Q., D. Arifaj, M.K., D. Argov, L.C.R., J.S.).
Background: Ciliary neurotrophic factor (CNTF), mainly known for its neuroprotective properties, belongs to the IL-6 (interleukin-6) cytokine family. In contrast to IL-6, the effects of CNTF on the vasculature have not been explored. Here, we examined the role of CNTF in AngII (angiotensin II)-induced hypertension.
View Article and Find Full Text PDFWounds from gunshots and other explosive devices are a source of loss of substances directly or secondary to a well- conducted debridement. In addition, these types of wounds are by definition contaminated. The major challenge in this context for any surgeon remains coverage.
View Article and Find Full Text PDFJHEP Rep
February 2025
Department of Gastroenterology and Hepatology, Hospital Universitario Ramón y Cajal, Instituto Ramon y Cajal de Investigación Sanitaria (IRYCIS), Universidad de Alcalá, Madrid, Spain.
Background & Aims: Systemic inflammation is a driver of decompensation in cirrhosis with unclear relevance in the compensated stage. We evaluated inflammation and bacterial translocation markers in compensated cirrhosis and their dynamics in relation to the first decompensation.
Methods: This study is nested within the PREDESCI trial, which investigated non-selective beta-blockers for preventing decompensation in compensated cirrhosis and clinically significant portal hypertension (CSPH: hepatic venous pressure gradient ≥10 mmHg).
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
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