Purpose Of Review: As the induction and maintenance of donor-specific tolerance is a central aim in solid organ transplantation, it is essential that clinicians are able to identify and monitor tolerance accurately and reliably. This review highlights recent advances in defining sets of biomarkers in noninvasive samples that may guide minimization and withdrawal of immunosuppression in tolerant recipients.
Recent Findings: Recent studies in liver and kidney transplant recipients have identified distinct biomarker profiles that are associated with operational tolerance. Although there is some heterogeneity in the findings of these studies, these have suggested novel cellular mechanisms for the development of tolerance.
Summary: Multiple platforms such as microarray gene expression analysis, flow cytometry, and immune cell functional assays have been used to discover and validate composite sets of biomarkers, which identify recipients with operational tolerance both in liver and kidney transplantation. These studies suggest that distinct cellular and molecular mechanisms lead to the development of tolerance in different transplanted organs. These putative biomarker profiles now need to be validated prospectively in trials of immunosuppression withdrawal and in novel approaches to induce transplant tolerance.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/MOT.0b013e3283636fd5 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
[Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training].
Probl Endokrinol (Mosk)
January 2024
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.
View Article and Find Full Text PDFLung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Understanding the genetic factors of these disparities is critical for developing targeted treatment therapies. This study aims to identify both patient-specific and cohort-specific biomarker genes that contribute to lung cancer health disparities among African American males (AAMs), European American males (EAMs), African American females (AAFs), and European American females (EAFs).
View Article and Find Full Text PDFSERS profiling of blood serum filtrate components from patients with type II diabetes using 100 kDa filtration devices.
RSC Adv
January 2025
Département de Chimie, Faculté des Sciences et de Génie, Université Laval Québec QC G1V 0A6 Canada.
Blood carries some of the most valuable biomarkers for disease screening as it interacts with various tissues and organs in the body. Human blood serum is a reservoir of high molecular weight fraction (HMWF) and low molecular weight fraction (LMWF) proteins. The LMWF proteins are considered disease marker proteins and are often suppressed by HMWF proteins during analysis.
View Article and Find Full Text PDFFrom isolation to detection, advancing insights into endothelial matrix-bound vesicles.
Extracell Vesicle
December 2024
Department of Chemical Engineering, University of Puerto Rico-Mayaguez, Route 108, Mayaguez, Puerto Rico, USA.
Matrix-bound vesicles (MBVs), an integral part of the extracellular matrix (ECM), are emerging as pivotal factors in ECM-driven molecular signaling. This study is the first to report the isolation of MBVs from porcine arterial endothelial cell basement membranes (A-MBVs) and thyroid cartilage (C-MBVs), the latter serving as a negative control due to its minimal vascular characteristics. Using Transmission Electron Microscopy (TEM), Nano-Tracking Analysis (NTA), Electrochemical Impedance Spectroscopy (EIS), and Atomic Force Microscopy (AFM), we orthogonally characterized the isolated MBVs.
View Article and Find Full Text PDFA Bayesian Joint Model of Multiple Nonlinear Longitudinal and Competing Risks Outcomes for Dynamic Prediction in Multiple Myeloma: Joint Estimation and Corrected Two-Stage Approaches.
Stat Med
February 2025
Hoffmann-La Roche Ltd, Basel, Switzerland.
Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomarkers, such as serum concentration of the paraprotein secreted by plasma cells (M-protein). Therefore, the time-dependent behavior of M-protein and the transition across lines of therapy (LoT), which may be a consequence of disease progression, should be accounted for in statistical models to predict relevant clinical outcomes.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!