Patients with pneumonia and parapneumonic effusion (PPE) have elevated mortality and a poor prognosis. The aim of this study was to discover novel biomarkers to help distinguish between uncomplicated PPE (UPPE) and complicated PPE (CPPE). Using an iTRAQ-based quantitative proteomics, we identified 766 proteins in pleural effusions from PPE patients. In total, 45 of these proteins were quantified as upregulated proteins in CPPE. Four novel upregulated candidates (BPI, NGAL, AZU1, and calprotectin) were selected and further verified using enzyme-linked immunosorbent assays (ELISAs) on 220 patients with pleural effusions due to different causes. The pleural fluid levels of BPI, NGAL, AZU1, and calprotectin were significantly elevated in patients with CPPE. Among these four biomarkers, BPI had the best diagnostic value for CPPE, with an AUC value of 0.966, a sensitivity of 97%, and a specificity of 91.4%. A logistic regression analysis demonstrated a strong association between BPI levels > 10 ng/ml and CPPE (odds ratio = 341.3). Furthermore, the combination of pleural fluid BPI levels with LDH levels improved the sensitivity and specificity to 100% and 91.4%, respectively. Thus, our findings provided a comprehensive effusion proteome data set for PPE biomarker discovery and revealed novel biomarkers for the diagnosis of CPPE.
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http://dx.doi.org/10.1038/s41598-017-04189-4 | DOI Listing |
Medicine (Baltimore)
January 2025
Cardiovascular Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
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View Article and Find Full Text PDFPLoS One
January 2025
Department of Emergency Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, People's Republic of China.
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View Article and Find Full Text PDFCancer Rep (Hoboken)
January 2025
Department of Biology, College of Sciences, Shiraz University, Shiraz, Iran.
Background: The breakthrough discovery of novel biomarkers with prognostic and diagnostic value enables timely medical intervention for the survival of patients diagnosed with gastric cancer (GC). Typically, in studies focused on biomarker analysis, highly connected nodes (hubs) within the protein-protein interaction network (PPIN) are proposed as potential biomarkers. However, this study revealed an unexpected finding following the clustering of network nodes.
View Article and Find Full Text PDFAging Cell
January 2025
EPITERNA, Epalinges, Switzerland.
In this study, we investigated age-related changes in clinical laboratory data and their association with mortality in dogs from the Golden Retriever Lifetime Study. By analyzing complete blood count (CBC) and biochemistry data from 2'412 Golden Retrievers over 16,678 visits, we observed significant changes during the first 2 years of life and throughout aging. Based on these observations, we developed a biological aging clock using a LASSO model to predict age based on blood markers, achieving an accuracy of R = 0.
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