Objectives: We aimed to investigate the effect of dexmedetomidine infusion on the amount of opioid that is consumed during the operation, the amount of analgesic that the patient requires after the operation and on pain scores.
Methods: Forty patients who were ASA I-II, between 18-50 years old, and who were scheduled for mastoidectomy operation were included in the study. Patients were randomized into two groups as group Dexmedetomidine (Group D) and group Placebo (Group P). Dexmedetomidine was administered at the rate of 0.5 mcg/kg/hour to the cases in Group D during operation and 9% NaCl was administered at the same rate and volume to the cases in Group P. Patients were connected to a Patient-Controlled Analgesia (PCA) device prepared with tramadol. Patients were followed for 24 hours. Ramsay Sedation Scale, visual analog scale (VAS), non-invasive systolic blood pressure (SBP), diastolic blood pressure (DBP), mean blood pressure (MBP), end-tidal sevoflurane, extubation times, total remifentanil consumption, total demand of PCA, and total tramadol consumption from PCA were recorded.
Results: No difference was determined between groups in demographic level and extubation times. Total remifentanil consumption, additional analgesic requirement, total demand of PCA, total amount of PCA consumption, and mean VAS were higher in the control group. First demand time of PCA was longer in the study group.
Conclusion: Results of our study demonstrated that continuous infusion of dexmedetomidine during the operation could provide postoperative patient comfort without affecting the extubation time while concomitantly decreasing the consumption of tramadol.
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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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