New Approaches to the Diagnosis of Rejection and Prediction of Tolerance in Liver Transplantation.
New Approaches to the Diagnosis of Rejection and Prediction of Tolerance in Liver Transplantation.
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DOI：
10.1097/tp.0000000000004160
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发表时间：
2022-10-01
期刊：
影响因子：
6.2
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中科院分区：
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肝移植后免疫抑制对预防同种异体移植排斥反应至关重要。然而，长期的药物毒性和相关并发症需要研究免疫抑制最小化和停药方案。这种方案的发展受到目前监测同种异体移植物功能和排斥状态的范例的阻碍。目前诊断排斥反应的标准是根据班夫排斥反应活性指数进行组织病理学评估和肝活检分级。然而，这种方法受到成本、采样可变性和观察者间变化的限制。此外，活检的侵入性增加了患者并发症的风险。结合非侵入性技术可以通过提高对排斥原因、肝脏空间结构和特发性纤维炎症区作用的理解来补充现有的方法。这些技术也有助于量化，并有助于将新兴组学分析与当前评估相结合。另外，新兴的非侵入性方法显示出检测和区分不同类型排斥反应的潜力，同时将不良反应的风险降到最低。虽然生物标记物尚未取代活检，但初步研究表明，与传统方法相比，几种分析物可能在与人工智能结合的情况下，以更高的灵敏度和更早的阶段检测排斥反应。在此，我们提供了最新的努力，优化诊断排斥在肝移植的概述。
Immunosuppression following liver transplantation is essential for preventing allograft rejection. However, long term drug toxicity and associated complications necessitate investigation of immunosuppression minimization and withdrawal protocols. Development of such protocols is hindered by reliance on current paradigms for monitoring allograft function and rejection status. The current standard-of-care for diagnosis of rejection is histopathologic assessment and grading of liver biopsies in accordance with the Banff Rejection Activity Index. However, this method is limited by cost, sampling variability, and inter-observer variation. Moreover, the invasive nature of biopsy increases risk of patient complications. Incorporating non-invasive techniques may supplement existing methods through improved understanding of rejection causes, hepatic spatial architecture, and the role of idiopathic fibro-inflammatory regions. These techniques may also aid in quantification and help integrate emerging -omics analyses with current assessments. Alternatively, emerging non-invasive methods show potential to detect and distinguish between different types of rejection while minimizing risk of adverse advents. Though biomarkers have yet to replace biopsy, preliminary studies suggest that several classes of analytes may be used to detect rejection with greater sensitivity and in earlier stages than traditional methods, possibly when coupled with artificial intelligence. Herein we provide an overview of the latest efforts in the optimizing the diagnosis of rejection in liver transplantation.