Renal transplantation.

IF 17.3 3区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computational Visual Media Pub Date : 2002-03-02 DOI:10.1136/bmj.324.7336.530

Peter A Andrews

引用次数: 0

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

肾移植。

自从1954年首例双胞胎肾脏移植成功以来，肾脏移植已经从处于前沿发展成为一项成熟的技术。注册数据显示，目前来自尸体的移植物在移植后1年和10年的存活率分别约为88%和60%，而来自活体供体的移植物的存活率则超过95%和70%(图11)。在过去的10年里，这些比率稳步提高，从尸体和活体供体移植的一年存活率在此期间提高了约5%。1、3移植后患者1年生存率稳定在95%，5年生存率87%。在移植后的中期(1 - 3年)，临床结果很好，很难提高患者或移植物的存活率。近年来排异率有所下降，排异反应已成为早期移植失败的罕见原因。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

16.90

自引率

5.80%

发文量

243

审稿时长

6 weeks

期刊介绍： Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.

期刊最新文献

TrafPS: A shapley-based visual analytics approach to interpret traffic CLIP-Flow: Decoding images encoded in CLIP space CLIP-SP: Vision-language model with adaptive prompting for scene parsing SGformer: Boosting transformers for indoor lighting estimation from a single image Central similarity consistency hashing for asymmetric image retrieval