Objectives: Currently, patients with pre-exsiting donor-specific antibody (DSA) are prone to antibody-mediated rejection (AMR) after surgery and are at a relatively high risk of postoperative complications and graft failure. The risk of postoperative complications and graft failure is relatively high. This study aims to discuss the clinical outcome of DSA-positive kidney transplantation and analyze the role and safety of preoperative pretreatment in DSA-positive kidney transplantation, providing single-center treatment experience for DSA-positive kidney transplantation.
Methods: We retrospectively analyzed the clinical data of 15 DSA-positive kidney transplants in the Department of Renal Transplantation of First Affiliated Hospital of Zhengzhou University from August 2017 to July 2022. Eight cases were organ donation after citizen's death (DCD) kidney transplant recipients, of which 3 cases in the early stage were not treated with preoperative desensitisation therapy (DCD untreated group, =3), and 5 recipients were treated with preoperative rituximab desensitisation (DCD preprocessing group, =5). The remaining 7 cases were living related donors recipients (LRD) who received preoperative desensitisation treatment with rituximab and plasma exchange (LRD preprocessing group, =7). We observed and recorded the incidence of complications with changes in renal function and DSA levels in the recipients and the survival of the recipients and transplanted kidneys at 1, 3 and 5 years, and to compare the differences in recovery and postoperative complications between 3 groups.
Results: All 15 recipients were positive for preoperative panel reactive antibody (PRA) and DSA and were treated with methylprednisolone+rabbit anti-human thymocyte immunoglobulin induction before kidney transplantation. DCD untreated group all suffered from DSA level rebound, delayed renal graft function (DGF) and rejection reaction after surgery. After the combined treatment, DSA level was reduced and the graft renal function returned to normal. The DCD preprocessing group were all without antibody rebound, 1 recipient developed DGF and the renal function returned to normal after plasmapheresis, and the remaining 4 recipients recovered their renal function to normal within 2 weeks after the operation. In the LRD preprocessing group, 2 cases had antibody rebound and 1 case had rejection, but all of them recovered to normal after treatment, and DSA was maintained at a low level or even disappeared. The incidence of DGF and rejection in the DCD untreated group were significantly higher than that in the DCD preprocessing group and the LRD preprocessing group; and there were no significant difference in the incidence of postoperative haematuria, proteinuria, bacterial and fungal infections, and BK virus infection between the 3 groups (all >0.05). A total of 11 of the 15 recipients were followed up for more than 1 year, 6 for more than 3 years, and 1 for more than 5 years, and the survival rates of both the recipients and the transplanted kidneys were 100%.
Conclusions: Effective preoperative pretreatment with desensitization therapy can effectively prevent antibody rebound in DSA-positive kidney transplantation and reduce perioperative complications.
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http://dx.doi.org/10.11817/j.issn.1672-7347.2023.230144 | DOI Listing |
Sci Data
January 2025
Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, 04103, Germany.
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, Spain.
Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF).
View Article and Find Full Text PDFJ Clin Med
January 2025
Centre of Excellence for Sustainable Living and Working (SustAInLivWork), 51423 Kaunas, Lithuania.
: This study focuses on the critical task of blood vessel segmentation in medical image analysis, essential for diagnosing cardiovascular diseases and enabling effective treatment planning. Although deep learning architectures often produce very high segmentation results in medical images, coronary computed tomography angiography (CTA) images are more challenging than invasive coronary angiography (ICA) images due to noise and the complexity of vessel structures. : Classical architectures for medical images, such as U-Net, achieve only moderate accuracy, with an average Dice score of 0.
View Article and Find Full Text PDFInt J Food Microbiol
January 2025
Beijing Key Laboratory of Optimization Design for Modern Agriculture Equipment, College of Engineering, China Agriculture University, Beijing 100083, China.
Understanding and controlling the dynamic process of aflatoxin B (AFB) accumulation by Aspergillus flavus (A. flavus) remains challenging. In this study, the A.
View Article and Find Full Text PDFClin Radiol
December 2024
Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China. Electronic address:
Aim: To provide a theoretical basis for the study of the pathogenesis of residual dizziness (RD) from the perspective of imaging.
Materials And Methods: The general clinical data of the RD group and healthy control (HC) group were statistically analysed by two independent sample t tests, rank sum tests or chi-square tests. The imaging data of the two groups of people were preprocessed and statistically analysed by using the data processing and analysis for brain imaging (DPABI) software package.
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