The DOI matches its Crossref record; 512 of the 512 references it cites were found in a registry or an index.
Nucleic Acid Tests for Clinical Translation
Abstract
Nucleic acids, including deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), are natural biopolymers composed of nucleotides that store, transmit, and express genetic information. Overexpressed or underexpressed as well as mutated nucleic acids have been implicated in many diseases. Therefore, nucleic acid tests (NATs) are extremely important. Inspired by intracellular DNA replication and RNA transcription, in vitro NATs have been extensively developed to improve the detection specificity, sensitivity, and simplicity. The principles of NATs can be in general classified into three categories: nucleic acid hybridization, thermal-cycle or isothermal amplification, and signal amplification. Driven by pressing needs in clinical diagnosis and prevention of infectious diseases, NATs have evolved to be a rapidly advancing field. During the past ten years, an explosive increase of research interest in both basic research and clinical translation has been witnessed. In this review, we aim to provide comprehensive coverage of the progress to analyze nucleic acids, use nucleic acids as recognition probes, construct detection devices based on nucleic acids, and utilize nucleic acids in clinical diagnosis and other important fields. We also discuss the new frontiers in the field and the challenges to be addressed.
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Cites
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Metadata
- Authors
- Li, M., Yin, F., Song, L., Mao, X., Li, F., Fan, C., Zuo, X., & Xia, Q.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-07-13
- Volume
- 121
- Issue
- 17
- Pages
- 10469-10558
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W3177853972
- Cited by
- 264
- References
- 512
- FWCI
- 3.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, M., Yin, F., Song, L., Mao, X., Li, F., Fan, C., Zuo, X., & Xia, Q. (2021). Nucleic Acid Tests for Clinical Translation. Chemical Reviews. https://doi.org/10.1021/acs.chemrev.1c00241
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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