[HTML][HTML] Oxidative stress and ischemia/reperfusion injury in kidney transplantation: focus on ferroptosis, mitophagy and new antioxidants

S Granata, V Votrico, F Spadaccino, V Catalano… - Antioxidants, 2022 - mdpi.com
Although there has been technical and pharmacological progress in kidney transplant
medicine, some patients may experience acute post-transplant complications. Among the …

Prediction, prevention, and management of delayed graft function: where are we now?

B Nashan, M Abbud‐Filho, F Citterio - Clinical Transplantation, 2016 - Wiley Online Library
Delayed graft function (DGF) remains a major barrier to improved outcomes after kidney
transplantation. High‐risk transplant recipients can be identified, but no definitive prediction …

Emerging biomarkers of delayed graft function in kidney transplantation

V Mezzolla, P Pontrelli, M Fiorentino, A Stasi… - Transplantation …, 2021 - Elsevier
Abstract Delayed Graft Function (DGF) is one of the most common early complications in
kidney transplantation, associated with poor graft outcomes, prolonged post-operative …

[HTML][HTML] Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods

A Decruyenaere, P Decruyenaere, P Peeters… - BMC medical informatics …, 2015 - Springer
Background Predictive models for delayed graft function (DGF) after kidney transplantation
are usually developed using logistic regression. We want to evaluate the value of machine …

Hyperspectral imaging (HSI) of human kidney allografts

R Sucher, T Wagner, H Köhler, E Sucher… - Annals of …, 2022 - journals.lww.com
Objective: Aim of our study was to test a noninvasive HSI technique as an intraoperative real
time assessment tool for deceased donor kidney quality and function in human kidney …

[HTML][HTML] Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning

S Kawakita, JL Beaumont, V Jucaud, MJ Everly - Scientific reports, 2020 - nature.com
Abstract Machine learning (ML) has shown its potential to improve patient care over the last
decade. In organ transplantation, delayed graft function (DGF) remains a major concern in …

[HTML][HTML] Heparanase: a potential new factor involved in the renal epithelial mesenchymal transition (EMT) induced by ischemia/reperfusion (I/R) injury

V Masola, G Zaza, G Gambaro, M Onisto, G Bellin… - PloS one, 2016 - journals.plos.org
Background Ischemia/reperfusion (I/R) is an important cause of acute renal failure and
delayed graft function, and it may induce chronic renal damage by activating epithelial to …

A machine learning prediction model for immediate graft function after deceased donor kidney transplantation

RM Quinino, F Agena, LGM de Andrade… - …, 2023 - journals.lww.com
Background. After kidney transplantation (KTx), the graft can evolve from excellent
immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with …

Automated en masse machine learning model generation shows comparable performance as classic regression models for predicting delayed graft function in renal …

KY Jen, S Albahra, F Yen, J Sageshima, LX Chen… - …, 2021 - journals.lww.com
Background. Several groups have previously developed logistic regression models for
predicting delayed graft function (DGF). In this study, we used an automated machine …

Prediction models for delayed graft function: external validation on The Dutch Prospective Renal Transplantation Registry

J Kers, H Peters-Sengers… - Nephrology Dialysis …, 2018 - academic.oup.com
Background Delayed graft function (DGF) is a common complication after kidney
transplantation in the era of accepting an equal number of brain-and circulatory-death donor …