Chronic kidney disease (CKD) in children is a rare but devastating condition. Once a critical amount of nephron mass has been lost, progression of CKD is irreversible and results in end-stage renal disease (ESRD) and need of renal replacement therapy. The time course of childhood CKD is highly variable. While in children suffering from congenital anomalies of the kidneys and the urinary tract, progression of CKD in general is slow, in children with acquired glomerulopathies, disease progression can be accelerated resulting in ESRD within months. However, irrespective of the underlying kidney disease, hypertension and proteinuria are independent risk factors for progression. Thus, in order to prevent progression, the primary objective of treatment should always aim for efficient control of blood pressure and reduction of urinary protein excretion. Blockade of the renin-angiotensin-aldosterone system preserves kidney function not only by lowering blood pressure, but also by reducing proteinuria and exerting additional anti-proteinuric, anti-fibrotic, and anti-inflammatory effects. Besides, intensified blood pressure control, aiming for a target blood pressure below the 50th percentile, may exert additive renoprotective effects. Additionally, other modifiable risk factors, such as anemia, metabolic acidosis, dyslipidemia, and altered bone-mineral homeostasis may also contribute to CKD progression. In conclusion, beyond strict blood pressure control and reduction of urinary protein excretion, identification and treatment of both, renal disease-related and conventional risk factors are mandatory in children with CKD in order to prevent deterioration of kidney function.
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http://dx.doi.org/10.1007/s00431-012-1814-5 | 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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