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Kidney transplantation is the treatment of choice for patients with advanced chronic kidney disease (CKD) and end stage renal disease (ESRD). However, acute rejection (AR) is a common complication in kidney transplantation and is associated with reduced graft survival. Current diagnosis of AR relies mainly on clinical monitoring including serum creatinine, proteinuria, and confirmation by histopathologic assessment in the biopsy specimen of graft kidney. Although an early protocol biopsy is indispensable for depicting the severity of pathologic lesions in subclinical acute rejection (subAR), it is not acceptable in some cases and cannot be performed because of its invasive nature. Therefore, we examined the detection of noninvasive biomarkers that are closely related to the pathology of subAR in protocol biopsies three months after kidney transplantation. In this study, the urinary level of microtubule-associated protein 1 light chain 3 (LC3), monocyte chemotactic protein-1 (MCP-1), liver-type fatty acid-binding protein (L-FABP), neutrophil gelatinase-associated lipocalin (NGAL), and human epididymis secretory protein 4 (HE4) were measured three months after kidney transplantation. Urine samples of 80 patients undergoing kidney transplantation between August 2014 to September 2016, were prospectively collected after three months. SubAR was observed in 11 patients (13.8%) in protocol biopsy. The urinary levels of LC3, MCP-1, NGAL, and HE4 were significantly higher in patients with subAR than in those without, while those of L-FABP did not differ between the two groups. Multivariate regression models, receiver-operating characteristics (ROC), and areas under ROC curves (AUC) were used to identify predicted values of subAR. Urinary HE4 levels were able to better identify subAR (AUC = 0.808) than the other four urinary biomarkers. In conclusion, urinary HE4 is increased in kidney transplant recipients of subAR three months after kidney transplantation, suggesting that HE4 has the potential to be used as a novel clinical biomarker for predicting subAR.
Pubmed ID: 31546745
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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