Background: The level of copeptin, a stable fragment of pro-arginine-vasopressin (AVP), correlates with disease severity. It is an established, short-term prognostic marker for patients with heart failure with reduced ejection fraction (HFREF). We aimed to examine the association between copeptin and long-term mortality. We also studied the clinical usefulness of copeptin as a prognostic biomarker by analysing the improvement of net reclassification.
Methods: Copeptin concentrations were measured in a cohort of 195 consecutive patients with HFREF. Disease severity and clinical parameters were determined at baseline, and all-cause mortality was recorded after five-year follow-up.
Results: One hundred and ten patients died during the five-year follow-up (five-year mortality rate: 0.56). Univariate analysis identified copeptin (HR 2.168 [95% CI 1.740-2.700]) as a predictor of mortality. The final, multivariable Cox survival model identified a number of independent predictors of death. These included higher NHYA functional class, previous MI, at least one hospitalisation for worsening HF (within the two years before inclusion into the study), elevated blood urea nitrogen, NT-proBNP-, and copeptin levels, as well as increased red blood cell distribution width, and decreased GFR. The addition of copeptin alone to the baseline predictive model (NT-proBNP only) resulted in a minor (8.21%) improvement, whereas the final, multivariable model showed a significant increase in net reclassification (10.26%, p=0.015).
Conclusions: These data indicate that copeptin is an independent long-term prognostic marker in HFREF, with possible clinical relevance for multimarker risk prediction algorithms.
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http://dx.doi.org/10.1016/j.hlc.2014.10.008 | DOI Listing |
Biomol Biomed
January 2025
Necmettin Erbakan University, Meram Faculty of Medicine, Department of Medical Oncology, Konya, Turkey.
The cysteine-rich epidermal growth factor ligand domain 2 protein (CRELD2) is associated with pathways that regulate epithelial-to-mesenchymal transition, a critical process driving cancer metastasis. This study aimed to determine the prognostic value of CRELD2 status on survival outcomes in triple-negative breast cancer (TNBC). Seventy patients were included in the study.
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West China School of Medicine, Sichuan University, Chengdu, China.
Gastric cancer is an aggressive malignancy characterized by significant clinical heterogeneity arising from complex genetic and environmental interactions. This study employed single-cell RNA sequencing, using the 10 × Genomics platform, to analyze 262,532 cells from gastric cancer samples, identifying 32 distinct clusters and 10 major cell types, including immune cells (e.g.
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December 2024
Université Libre de Bruxelles, Hôpital Universitaire de Bruxelles, Instiut Jules Bordet, Departement of Medical Oncology.
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Recent Findings: Trials like Neo-Combi, Neo-Trio and COMBI-Neo show that pCR is crucial for long-term success in targeted therapy for melanoma, while studies like OpACIN-neo and SWOG S1801 demonstrate that immunotherapy can provide durable benefits even with partial responses. Findings from NADINA and the INMC analysis highlight that immunotherapy achieves higher pathologic response rates and improved survival outcomes, offering broader benefits compared to the pCR-dependent outcomes of targeted therapy.
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January 2025
Hunan Provincial Key Laboratory of Micro & Nano Materials Interface Science, College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
Microelectrodes offer exceptional sensitivity, rapid response, and versatility, making them ideal for real-time detection and monitoring applications. Photoelectrochemical (PEC) sensors have shown great value in many fields due to their high sensitivity, fast response, and ease of operation. Nevertheless, conventional PEC sensing relies on cumbersome external light sources and bulky electrodes, hindering its miniaturization and implantation, thereby limiting its application in real-time disease monitoring.
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Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.
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