Background And Aims: This study aimed to evaluate plasma neuron-specific enolase (NSE) and S100β levels in orthotopic liver transplantation.
Materials And Methods: A total of 56 patients who underwent orthotopic liver transplantation were divided into 3 groups. Healthy donors (group D), end-stage liver failure (ESLF) patients (recipient, group R), and ESLF patients diagnosed with hepatic encephalopathy (HE, group HE). Prognosis, preoperative routine laboratory findings, serum NSE, and S100β in samples obtained preoperation and first and sixth months postoperation were analyzed.
Results: Serum NSE and S100β levels were significantly higher in ESLF patients compared to healthy donors, particularly during the preoperative period. There was a significant decrease in serum NSE and S100β in ESLF patients during the postoperative measurement periods compared to preoperative levels. Serum NSE and S100β levels measured at 3 different time points showed no significant difference between ESLF patients and ESLF patients with HE. However, the recent Model of End-Stage Liver Disease (MELD) and Child-Turcotte-Pugh (CTP) scores showed a significant correlation with serum NSE and S100β in ESLF patients diagnosed with HE. Serum NSE and S100β levels in healthy donors significantly increased within the first month following hepatectomy and decreased in the sixth month following surgery.
Conclusion: Although serum NSE and S100β levels significantly decreased with improved liver function in recipients following liver transplantation, there was no complete recovery within 6 months after surgery. The increase in serum levels of NSE and S100β in donors measured following hepatectomy was detected to remain slightly higher in the sixth postoperative months.
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http://dx.doi.org/10.1016/j.transproceed.2020.04.1818 | DOI Listing |
Resuscitation
December 2024
Department of Critical Care Medicine, Hospital for Sick Children, Department of Paediatrics, University of Toronto, Neurosciences and Mental Health Program, Research Institute Toronto, ON, Canada.
Aim: To evaluate the ability of blood-biomarkers, clinical examination, electrophysiology, or neuroimaging, assessed within 14 days from return of circulation to predict good neurological outcome in children following out- or in-hospital cardiac arrest.
Methods: Medline, EMBASE and Cochrane Trials databases were searched (2010-2023). Sensitivity and false positive rates (FPR) for good neurological outcome (defined as either 'no, mild, moderate disability or minimal change from baseline') in paediatric survivors were calculated for each predictor.
Front Oncol
December 2024
Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China.
BMJ Open
December 2024
The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, Yancheng, Jiangsu, China
Introduction: Prone positioning with head rotation can influence cerebral haemodynamics, potentially affecting cerebral perfusion and oxygenation. Elderly patients with impaired brain perfusion and oxygenation are at an increased risk of developing postoperative delirium (POD). Despite this, few studies have explored whether head orientation during prone positioning contributes to POD in older adults, an aspect often overlooked by clinicians.
View Article and Find Full Text PDFSci Rep
December 2024
School of Civil Engineering, Chongqing Three Gorges University, Chongqing, 404100, China.
Runoff fluctuations under the influence of climate change and human activities present a significant challenge and valuable application in constructing high-accuracy runoff prediction models. This study aims to address this challenge by taking the Wanzhou station in the Three Gorges Reservoir area as a case study to optimize various prediction models. The study first selects artificial neural network (ANN) and support vector machine (SVM) as the base models.
View Article and Find Full Text PDFSci Rep
December 2024
College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Accurate prediction of runoff is of great significance for rational planning and management of regional water resources. However, runoff presents non-stationary characteristics that make it impossible for a single model to fully capture its intrinsic characteristics. Enhancing its precision poses a significant challenge within the area of water resources management research.
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