التفاصيل البيبلوغرافية
| العنوان: |
Identification of a serum and urine extracellular vesicle signature predicting renal outcome after kidney transplant. |
| المؤلفون: |
Burrello, Jacopo, Monticone, Silvia, Burrello, Alessio, Bolis, Sara, Cristalli, Carlotta Pia, Comai, Giorgia, Corradetti, Valeria, Grange, Cristina, Orlando, Giuseppe, Bonafè, Massimiliano, Manna, Gaetano La, Barile, Lucio, Bussolati, Benedetta |
| المصدر: |
Nephrology Dialysis Transplantation; Mar2023, Vol. 38 Issue 3, p764-777, 14p |
| مصطلحات موضوعية: |
KIDNEY transplantation, EXTRACELLULAR vesicles, RECEIVER operating characteristic curves, CELL surface antigens, SUPERVISED learning |
| مستخلص: |
Background A long-standing effort is dedicated towards the identification of biomarkers allowing the prediction of graft outcome after kidney transplant. Extracellular vesicles (EVs) circulating in body fluids represent an attractive candidate, as their cargo mirrors the originating cell and its pathophysiological status. The aim of the study was to investigate EV surface antigens as potential predictors of renal outcome after kidney transplant. Methods We characterized 37 surface antigens by flow cytometry, in serum and urine EVs from 58 patients who were evaluated before, and at 10–14 days, 3 months and 1 year after transplant, for a total of 426 analyzed samples. The outcome was defined according to estimated glomerular filtration rate (eGFR) at 1 year. Results Endothelial cells and platelets markers (CD31, CD41b, CD42a and CD62P) in serum EVs were higher at baseline in patients with persistent kidney dysfunction at 1 year, and progressively decreased after kidney transplant. Conversely, mesenchymal progenitor cell marker (CD1c, CD105, CD133, SSEEA-4) in urine EVs progressively increased after transplant in patients displaying renal recovery at follow-up. These markers correlated with eGFR, creatinine and proteinuria, associated with patient outcome at univariate analysis and were able to predict patient outcome at receiver operating characteristics curves analysis. A specific EV molecular signature obtained by supervised learning correctly classified patients according to 1-year renal outcome. Conclusions An EV-based signature, reflecting the cardiovascular profile of the recipient, and the repairing/regenerative features of the graft, could be introduced as a non-invasive tool for a tailored management of follow-up of patients undergoing kidney transplant. [ABSTRACT FROM AUTHOR] |
|
Copyright of Nephrology Dialysis Transplantation is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder