UNLABELLED: Although the link between ischemia-reperfusion injury (IRI) and T cell-mediated rejection (TCMR) in kidney transplantation (KT) is well known, the mechanism remains unclear. We investigated essential genes and biological processes involve...
BACKGROUND: The demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft ...
Machine learning can be used to identify risk factors associated with graft rejection after corneal transplantation for keratoconus. The study included all keratoconus eyes that underwent primary corneal transplantation from 1994 to 2021. Data relati...
Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various kidney diseases, including renal fibrosis. However, the role of different regulated cell death (RCD) ...
With the rising demand for liver transplantation (LT), research on acute rejection (AR) has become increasingly diverse, yet no consensus has been reached. This study presents a bibliometric and latent Dirichlet allocation (LDA) topic modeling analys...
Acute allograft rejection in patients undergoing renal transplantation is diagnosed through histopathological analysis of renal graft biopsies, which can be used to quantify elementary lesions. However, quantification of elementary lesions requires c...
BACKGROUND: Chronic rejection forms the leading cause of late graft loss in pediatric kidney transplant recipients. Despite improvement in short-term graft outcomes, chronic rejection impedes comparable progress in long-term graft outcomes.
BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent...
PURPOSE OF REVIEW: The objective of this review is to provide an update on the application of artificial intelligence (AI) for the histological interpretation of kidney transplant biopsies.