Recurrent IgA nephropathy (rIgAN) is an important cause of kidney allograft loss. Till now, no proven strategies have been confirmed to prevent/decrease the rIgAN. Here, a systematic review and meta-analysis were performed on the available interventions impacting rIgAN. PubMed, Embase, Web of sciences, ProQuest, and Cochrane library databases along with Google Scholar were searched for articles evaluating the rIgAN after kidney transplantation (up to 23 February 2023). The main inclusion criteria were kidney transplantation because of primary IgAN and articles studying the rate of the rIgAN based on different therapeutic interventions to find their effects on the disease recurrence. Based on our criteria, 11 papers were included in this systematic review, two of which pleased the criteria for the meta-analysis. Meta-analysis showed that the risk of the rIgAN in the steroid-free group was 3.33 times more than that of the steroid-receiving group (Pooled Hazard Ratio = 3.33, 95% CI 0.60 to18.33, Z-value = 1.38, p-value = 0.16). Steroid-free therapy increases the risk of rIgAN in kidney transplant recipients with primary IgAN. High-quality trials with large sample sizes studies are needed to confirm the impact of the steroids on decreasing the rate of the rIgAN.
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http://dx.doi.org/10.1016/j.trim.2023.101878 | DOI Listing |
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Hengyang Key Laboratory of Hemorrhagic Cerebrovascular Disease, Department of Neurosurgery, the Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421000, Hunan, China.
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School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
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Rib pathology is uniquely difficult and time-consuming for radiologists to diagnose. AI can reduce radiologist workload and serve as a tool to improve accurate diagnosis. To date, no reviews have been performed synthesizing identification of rib fracture data on AI and its diagnostic performance on X-ray and CT scans of rib fractures and its comparison to physicians.
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Department of Social Work, the Chinese University of Hong Kong, Hong Kong, China.
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