使用循环DNA和RNA进行无创产前检测:进展、挑战和可能性。

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Mira N Moufarrej, Diana W Bianchi, Gary M Shaw, David K Stevenson, Stephen R Quake
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引用次数: 0

摘要

在过去的十年里,使用循环无细胞DNA测序的产前筛查改变了产科护理,并显著减少了羊水穿刺等遗传疾病侵入性诊断程序的数量。尽管如此,紧急护理仍然是先兆子痫和早产等并发症的唯一选择,这两种最常见的产科综合征。无创产前检测的进展扩大了产科护理中精准医学的范围。在这篇综述中,我们讨论了提供主动、个性化产前护理的进展、挑战和可能性。突出的进展主要集中在无细胞核酸上;然而,我们也回顾了使用来自代谢组学、蛋白质组学、完整细胞和微生物组的信号的研究。我们讨论了提供护理的道德挑战。最后,我们展望了未来的可能性,包括重新定义疾病分类学,从生物标志物相关性转向生物因果关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noninvasive Prenatal Testing Using Circulating DNA and RNA: Advances, Challenges, and Possibilities.

Prenatal screening using sequencing of circulating cell-free DNA has transformed obstetric care over the past decade and significantly reduced the number of invasive diagnostic procedures like amniocentesis for genetic disorders. Nonetheless, emergency care remains the only option for complications like preeclampsia and preterm birth, two of the most prevalent obstetrical syndromes. Advances in noninvasive prenatal testing expand the scope of precision medicine in obstetric care. In this review, we discuss advances, challenges, and possibilities toward the goal of providing proactive, personalized prenatal care. The highlighted advances focus mainly on cell-free nucleic acids; however, we also review research that uses signals from metabolomics, proteomics, intact cells, and the microbiome. We discuss ethical challenges in providing care. Finally, we look to future possibilities, including redefining disease taxonomy and moving from biomarker correlation to biological causation.

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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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